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"""simple docstring"""
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , _a=None , _a=None , **_a ) -> List[str]:
"""simple docstring"""
super().__init__(*_a , **_a )
SCREAMING_SNAKE_CASE__ : str = eval_examples
SCREAMING_SNAKE_CASE__ : Optional[Any] = post_process_function
def _a ( self , _a=None , _a=None , _a=None , _a = "eval" ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.eval_dataset if eval_dataset is None else eval_dataset
SCREAMING_SNAKE_CASE__ : Dict = self.get_eval_dataloader(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE__ : Optional[int] = self.compute_metrics
SCREAMING_SNAKE_CASE__ : int = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
SCREAMING_SNAKE_CASE__ : Dict = time.time()
try:
SCREAMING_SNAKE_CASE__ : Optional[int] = eval_loop(
_a , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_a , metric_key_prefix=_a , )
finally:
SCREAMING_SNAKE_CASE__ : Optional[int] = compute_metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = self.args.eval_batch_size * self.args.world_size
if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_a , _a , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
SCREAMING_SNAKE_CASE__ : List[Any] = self.post_process_function(_a , _a , output.predictions )
SCREAMING_SNAKE_CASE__ : int = self.compute_metrics(_a )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'''{metric_key_prefix}_''' ):
SCREAMING_SNAKE_CASE__ : int = metrics.pop(_a )
metrics.update(output.metrics )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(_a )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
SCREAMING_SNAKE_CASE__ : List[str] = self.callback_handler.on_evaluate(self.args , self.state , self.control , _a )
return metrics
def _a ( self , _a , _a , _a=None , _a = "test" ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_test_dataloader(_a )
# Temporarily disable metric computation, we will do it in the loop here.
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.compute_metrics
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
SCREAMING_SNAKE_CASE__ : Tuple = time.time()
try:
SCREAMING_SNAKE_CASE__ : Any = eval_loop(
_a , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=_a , metric_key_prefix=_a , )
finally:
SCREAMING_SNAKE_CASE__ : Optional[Any] = compute_metrics
SCREAMING_SNAKE_CASE__ : str = self.args.eval_batch_size * self.args.world_size
if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
output.metrics.update(
speed_metrics(
_a , _a , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.post_process_function(_a , _a , output.predictions , """predict""" )
SCREAMING_SNAKE_CASE__ : int = self.compute_metrics(_a )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f'''{metric_key_prefix}_''' ):
SCREAMING_SNAKE_CASE__ : str = metrics.pop(_a )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=_a )
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : List[Any] = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : str = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE__ : Optional[int] = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
SCREAMING_SNAKE_CASE__ : int = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE__ : List[str] = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE__ : List[Any] = ParquetDatasetReader(__lowerCAmelCase , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
SCREAMING_SNAKE_CASE__ : Optional[int] = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase , split=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
if issubclass(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = parquet_path
elif issubclass(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = [parquet_path]
SCREAMING_SNAKE_CASE__ : List[str] = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : List[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
SCREAMING_SNAKE_CASE__ : str = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_dataset(__lowerCAmelCase , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=("train",) ) -> Dict:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
for split in splits:
SCREAMING_SNAKE_CASE__ : List[Any] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : List[Any] = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : int = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
SCREAMING_SNAKE_CASE__ : str = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=__lowerCAmelCase , keep_in_memory=__lowerCAmelCase ).read()
_check_parquet_datasetdict(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
SCREAMING_SNAKE_CASE__ : List[Any] = features.copy() if features else default_expected_features
SCREAMING_SNAKE_CASE__ : Tuple = (
Features({feature: Value(__lowerCAmelCase ) for feature, dtype in features.items()} ) if features is not None else None
)
SCREAMING_SNAKE_CASE__ : int = ParquetDatasetReader({"""train""": parquet_path} , features=__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_datasetdict(__lowerCAmelCase , __lowerCAmelCase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
if split:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {split: parquet_path}
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """train"""
SCREAMING_SNAKE_CASE__ : str = {"""train""": parquet_path, """test""": parquet_path}
SCREAMING_SNAKE_CASE__ : Any = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : str = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = ParquetDatasetReader(__lowerCAmelCase , cache_dir=__lowerCAmelCase ).read()
_check_parquet_datasetdict(__lowerCAmelCase , __lowerCAmelCase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] = ParquetDatasetWriter(__lowerCAmelCase , tmp_path / """foo.parquet""" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE__ : Tuple = pq.ParquetFile(tmp_path / """foo.parquet""" )
SCREAMING_SNAKE_CASE__ : List[str] = pf.read()
assert dataset.data.table == output_table
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Tuple = str(shared_datadir / """test_image_rgb.jpg""" )
SCREAMING_SNAKE_CASE__ : List[str] = {"""image""": [image_path]}
SCREAMING_SNAKE_CASE__ : str = Features({"""image""": Image()} )
SCREAMING_SNAKE_CASE__ : List[Any] = Dataset.from_dict(__lowerCAmelCase , features=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = ParquetDatasetWriter(__lowerCAmelCase , tmp_path / """foo.parquet""" )
assert writer.write() > 0
SCREAMING_SNAKE_CASE__ : int = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
SCREAMING_SNAKE_CASE__ : str = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=__lowerCAmelCase ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
assert get_writer_batch_size(__lowerCAmelCase ) == expected
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowercase ( __lowerCAmelCase = 100 ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : str = 2
for i in range(2 , max_n + 1 ):
SCREAMING_SNAKE_CASE__ : Any = pre_numerator
SCREAMING_SNAKE_CASE__ : List[Any] = 2 * i // 3 if i % 3 == 0 else 1
SCREAMING_SNAKE_CASE__ : List[Any] = cur_numerator
SCREAMING_SNAKE_CASE__ : str = e_cont * pre_numerator + temp
return sum_digits(__lowerCAmelCase )
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import math
import sys
import cva
import numpy as np
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = math.sqrt(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ** 2) * 0.5 )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ : str = kernel_size // 2
return img[x - half : x + half + 1, y - half : y + half + 1]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> np.ndarray:
# Creates a gaussian kernel of given dimension.
SCREAMING_SNAKE_CASE__ : int = np.zeros((kernel_size, kernel_size) )
for i in range(0 , __lowerCAmelCase ):
for j in range(0 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = math.sqrt(
abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
return vec_gaussian(__lowerCAmelCase , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , ) -> np.ndarray:
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.zeros(img.shape )
SCREAMING_SNAKE_CASE__ : Any = get_gauss_kernel(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = img.shape
for i in range(kernel_size // 2 , size_x - kernel_size // 2 ):
for j in range(kernel_size // 2 , size_y - kernel_size // 2 ):
SCREAMING_SNAKE_CASE__ : str = get_slice(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = img_s - img_s[kernel_size // 2, kernel_size // 2]
SCREAMING_SNAKE_CASE__ : Optional[int] = vec_gaussian(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.multiply(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.multiply(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = np.sum(__lowerCAmelCase ) / np.sum(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = val
return imga
def _lowercase ( __lowerCAmelCase ) -> tuple:
SCREAMING_SNAKE_CASE__ : List[Any] = args[1] if args[1:] else """../image_data/lena.jpg"""
SCREAMING_SNAKE_CASE__ : Any = float(args[2] ) if args[2:] else 1.0
SCREAMING_SNAKE_CASE__ : Any = float(args[3] ) if args[3:] else 1.0
if args[4:]:
SCREAMING_SNAKE_CASE__ : int = int(args[4] )
SCREAMING_SNAKE_CASE__ : Tuple = kernel_size + abs(kernel_size % 2 - 1 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 5
return filename, spatial_variance, intensity_variance, kernel_size
if __name__ == "__main__":
a ,a ,a ,a :int = parse_args(sys.argv)
a :List[Any] = cva.imread(filename, 0)
cva.imshow("input image", img)
a :int = img / 255
a :Tuple = out.astype("float32")
a :Dict = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
a :Any = out * 255
a :Any = np.uinta(out)
cva.imshow("output image", out)
cva.waitKey(0)
cva.destroyAllWindows()
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
a :Optional[int] = re.compile(r"\b(a|an|the)\b", re.UNICODE)
a :List[str] = None
def _lowercase ( ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
parser.add_argument("""data_file""" , metavar="""data.json""" , help="""Input data JSON file.""" )
parser.add_argument("""pred_file""" , metavar="""pred.json""" , help="""Model predictions.""" )
parser.add_argument(
"""--out-file""" , """-o""" , metavar="""eval.json""" , help="""Write accuracy metrics to file (default is stdout).""" )
parser.add_argument(
"""--na-prob-file""" , """-n""" , metavar="""na_prob.json""" , help="""Model estimates of probability of no answer.""" )
parser.add_argument(
"""--na-prob-thresh""" , """-t""" , type=__lowerCAmelCase , default=1.0 , help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" , )
parser.add_argument(
"""--out-image-dir""" , """-p""" , metavar="""out_images""" , default=__lowerCAmelCase , help="""Save precision-recall curves to directory.""" )
parser.add_argument("""--verbose""" , """-v""" , action="""store_true""" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def _lowercase ( __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE__ : Optional[int] = bool(qa["""answers"""]["""text"""] )
return qid_to_has_ans
def _lowercase ( __lowerCAmelCase ) -> str:
def remove_articles(__lowerCAmelCase ):
return ARTICLES_REGEX.sub(""" """ , __lowerCAmelCase )
def white_space_fix(__lowerCAmelCase ):
return " ".join(text.split() )
def remove_punc(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__lowerCAmelCase ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__lowerCAmelCase ) ) ) )
def _lowercase ( __lowerCAmelCase ) -> Any:
if not s:
return []
return normalize_answer(__lowerCAmelCase ).split()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
return int(normalize_answer(__lowerCAmelCase ) == normalize_answer(__lowerCAmelCase ) )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : List[Any] = get_tokens(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = get_tokens(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = collections.Counter(__lowerCAmelCase ) & collections.Counter(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = sum(common.values() )
if len(__lowerCAmelCase ) == 0 or len(__lowerCAmelCase ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1.0 * num_same / len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = 1.0 * num_same / len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (2 * precision * recall) / (precision + recall)
return fa
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = {}
SCREAMING_SNAKE_CASE__ : List[Any] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = qa["""id"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [t for t in qa["""answers"""]["""text"""] if normalize_answer(__lowerCAmelCase )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
SCREAMING_SNAKE_CASE__ : List[Any] = [""""""]
if qid not in preds:
print(F'''Missing prediction for {qid}''' )
continue
SCREAMING_SNAKE_CASE__ : List[Any] = preds[qid]
# Take max over all gold answers
SCREAMING_SNAKE_CASE__ : Any = max(compute_exact(__lowerCAmelCase , __lowerCAmelCase ) for a in gold_answers )
SCREAMING_SNAKE_CASE__ : str = max(compute_fa(__lowerCAmelCase , __lowerCAmelCase ) for a in gold_answers )
return exact_scores, fa_scores
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : str = {}
for qid, s in scores.items():
SCREAMING_SNAKE_CASE__ : Any = na_probs[qid] > na_prob_thresh
if pred_na:
SCREAMING_SNAKE_CASE__ : List[str] = float(not qid_to_has_ans[qid] )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = s
return new_scores
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> Union[str, Any]:
if not qid_list:
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores.values() ) / total),
("""f1""", 100.0 * sum(fa_scores.values() ) / total),
("""total""", total),
] )
else:
SCREAMING_SNAKE_CASE__ : str = len(__lowerCAmelCase )
return collections.OrderedDict(
[
("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
("""total""", total),
] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
for k in new_eval:
SCREAMING_SNAKE_CASE__ : Optional[int] = new_eval[k]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
plt.step(__lowerCAmelCase , __lowerCAmelCase , color="""b""" , alpha=0.2 , where="""post""" )
plt.fill_between(__lowerCAmelCase , __lowerCAmelCase , step="""post""" , alpha=0.2 , color="""b""" )
plt.xlabel("""Recall""" )
plt.ylabel("""Precision""" )
plt.xlim([0.0, 1.05] )
plt.ylim([0.0, 1.05] )
plt.title(__lowerCAmelCase )
plt.savefig(__lowerCAmelCase )
plt.clf()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : na_probs[k] )
SCREAMING_SNAKE_CASE__ : List[Any] = 0.0
SCREAMING_SNAKE_CASE__ : List[Any] = 1.0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0.0
SCREAMING_SNAKE_CASE__ : List[str] = [1.0]
SCREAMING_SNAKE_CASE__ : List[str] = [0.0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0.0
for i, qid in enumerate(__lowerCAmelCase ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
SCREAMING_SNAKE_CASE__ : Optional[Any] = true_pos / float(i + 1 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = true_pos / float(__lowerCAmelCase )
if i == len(__lowerCAmelCase ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__lowerCAmelCase )
recalls.append(__lowerCAmelCase )
if out_image:
plot_pr_curve(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return {"ap": 100.0 * avg_prec}
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
if out_image_dir and not os.path.exists(__lowerCAmelCase ):
os.makedirs(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
SCREAMING_SNAKE_CASE__ : Union[str, Any] = make_precision_recall_eval(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , out_image=os.path.join(__lowerCAmelCase , """pr_exact.png""" ) , title="""Precision-Recall curve for Exact Match score""" , )
SCREAMING_SNAKE_CASE__ : Tuple = make_precision_recall_eval(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , out_image=os.path.join(__lowerCAmelCase , """pr_f1.png""" ) , title="""Precision-Recall curve for F1 score""" , )
SCREAMING_SNAKE_CASE__ : Any = {k: float(__lowerCAmelCase ) for k, v in qid_to_has_ans.items()}
SCREAMING_SNAKE_CASE__ : Tuple = make_precision_recall_eval(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , out_image=os.path.join(__lowerCAmelCase , """pr_oracle.png""" ) , title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" , )
merge_eval(__lowerCAmelCase , __lowerCAmelCase , """pr_exact""" )
merge_eval(__lowerCAmelCase , __lowerCAmelCase , """pr_f1""" )
merge_eval(__lowerCAmelCase , __lowerCAmelCase , """pr_oracle""" )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
if not qid_list:
return
SCREAMING_SNAKE_CASE__ : Optional[Any] = [na_probs[k] for k in qid_list]
SCREAMING_SNAKE_CASE__ : Any = np.ones_like(__lowerCAmelCase ) / float(len(__lowerCAmelCase ) )
plt.hist(__lowerCAmelCase , weights=__lowerCAmelCase , bins=20 , range=(0.0, 1.0) )
plt.xlabel("""Model probability of no-answer""" )
plt.ylabel("""Proportion of dataset""" )
plt.title(F'''Histogram of no-answer probability: {name}''' )
plt.savefig(os.path.join(__lowerCAmelCase , F'''na_prob_hist_{name}.png''' ) )
plt.clf()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
SCREAMING_SNAKE_CASE__ : Any = num_no_ans
SCREAMING_SNAKE_CASE__ : int = cur_score
SCREAMING_SNAKE_CASE__ : List[Any] = 0.0
SCREAMING_SNAKE_CASE__ : Any = sorted(__lowerCAmelCase , key=lambda __lowerCAmelCase : na_probs[k] )
for i, qid in enumerate(__lowerCAmelCase ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
SCREAMING_SNAKE_CASE__ : Any = scores[qid]
else:
if preds[qid]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0
cur_score += diff
if cur_score > best_score:
SCREAMING_SNAKE_CASE__ : List[str] = cur_score
SCREAMING_SNAKE_CASE__ : Dict = na_probs[qid]
return 100.0 * best_score / len(__lowerCAmelCase ), best_thresh
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = find_best_thresh(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = find_best_thresh(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = best_exact
SCREAMING_SNAKE_CASE__ : Any = exact_thresh
SCREAMING_SNAKE_CASE__ : Optional[Any] = best_fa
SCREAMING_SNAKE_CASE__ : List[str] = fa_thresh
def _lowercase ( ) -> List[Any]:
with open(OPTS.data_file ) as f:
SCREAMING_SNAKE_CASE__ : int = json.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = dataset_json["""data"""]
with open(OPTS.pred_file ) as f:
SCREAMING_SNAKE_CASE__ : Dict = json.load(__lowerCAmelCase )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
SCREAMING_SNAKE_CASE__ : int = json.load(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : Tuple = {k: 0.0 for k in preds}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = make_qid_to_has_ans(__lowerCAmelCase ) # maps qid to True/False
SCREAMING_SNAKE_CASE__ : Any = [k for k, v in qid_to_has_ans.items() if v]
SCREAMING_SNAKE_CASE__ : int = [k for k, v in qid_to_has_ans.items() if not v]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_raw_scores(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = apply_no_ans_threshold(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE__ : str = apply_no_ans_threshold(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , OPTS.na_prob_thresh )
SCREAMING_SNAKE_CASE__ : List[Any] = make_eval_dict(__lowerCAmelCase , __lowerCAmelCase )
if has_ans_qids:
SCREAMING_SNAKE_CASE__ : int = make_eval_dict(__lowerCAmelCase , __lowerCAmelCase , qid_list=__lowerCAmelCase )
merge_eval(__lowerCAmelCase , __lowerCAmelCase , """HasAns""" )
if no_ans_qids:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = make_eval_dict(__lowerCAmelCase , __lowerCAmelCase , qid_list=__lowerCAmelCase )
merge_eval(__lowerCAmelCase , __lowerCAmelCase , """NoAns""" )
if OPTS.na_prob_file:
find_all_best_thresh(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , OPTS.out_image_dir )
histogram_na_prob(__lowerCAmelCase , __lowerCAmelCase , OPTS.out_image_dir , """hasAns""" )
histogram_na_prob(__lowerCAmelCase , __lowerCAmelCase , OPTS.out_image_dir , """noAns""" )
if OPTS.out_file:
with open(OPTS.out_file , """w""" ) as f:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
else:
print(json.dumps(__lowerCAmelCase , indent=2 ) )
if __name__ == "__main__":
a :Dict = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
return round(float(moles / volume ) * nfactor )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
return round(float((moles * 0.0_821 * temperature) / (volume) ) )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
return round(float((moles * 0.0_821 * temperature) / (pressure) ) )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
return round(float((pressure * volume) / (0.0_821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Any = logging.get_logger(__name__)
a :str = {
"edbeeching/decision-transformer-gym-hopper-medium": (
"https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """decision_transformer"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :str = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , _a=17 , _a=4 , _a=128 , _a=4_096 , _a=True , _a=1 , _a=1_024 , _a=3 , _a=1 , _a=None , _a="relu" , _a=0.1 , _a=0.1 , _a=0.1 , _a=1E-5 , _a=0.02 , _a=True , _a=True , _a=50_256 , _a=50_256 , _a=False , _a=False , **_a , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dim
SCREAMING_SNAKE_CASE__ : Dict = act_dim
SCREAMING_SNAKE_CASE__ : Dict = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_ep_len
SCREAMING_SNAKE_CASE__ : Tuple = action_tanh
SCREAMING_SNAKE_CASE__ : List[str] = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = n_positions
SCREAMING_SNAKE_CASE__ : Optional[int] = n_layer
SCREAMING_SNAKE_CASE__ : str = n_head
SCREAMING_SNAKE_CASE__ : Union[str, Any] = n_inner
SCREAMING_SNAKE_CASE__ : List[Any] = activation_function
SCREAMING_SNAKE_CASE__ : Optional[Any] = resid_pdrop
SCREAMING_SNAKE_CASE__ : List[str] = embd_pdrop
SCREAMING_SNAKE_CASE__ : Union[str, Any] = attn_pdrop
SCREAMING_SNAKE_CASE__ : Tuple = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = scale_attn_weights
SCREAMING_SNAKE_CASE__ : Tuple = use_cache
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scale_attn_by_inverse_layer_idx
SCREAMING_SNAKE_CASE__ : int = reorder_and_upcast_attn
SCREAMING_SNAKE_CASE__ : Tuple = bos_token_id
SCREAMING_SNAKE_CASE__ : Dict = eos_token_id
super().__init__(bos_token_id=_a , eos_token_id=_a , **_a )
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
SCREAMING_SNAKE_CASE__ : Any = mf_knapsack(i - 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : Any = max(
mf_knapsack(i - 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) , mf_knapsack(i - 1 , __lowerCAmelCase , __lowerCAmelCase , j - wt[i - 1] ) + val[i - 1] , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = val
return f[i][j]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
SCREAMING_SNAKE_CASE__ : int = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][w_]
return dp[n][w_], dp
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
if not (isinstance(__lowerCAmelCase , (list, tuple) ) and isinstance(__lowerCAmelCase , (list, tuple) )):
raise ValueError(
"""Both the weights and values vectors must be either lists or tuples""" )
SCREAMING_SNAKE_CASE__ : Tuple = len(__lowerCAmelCase )
if num_items != len(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = (
"""The number of weights must be the same as the number of values.\n"""
F'''But got {num_items} weights and {len(__lowerCAmelCase )} values'''
)
raise ValueError(__lowerCAmelCase )
for i in range(__lowerCAmelCase ):
if not isinstance(wt[i] , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Any = (
"""All weights must be integers but got weight of """
F'''type {type(wt[i] )} at index {i}'''
)
raise TypeError(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = knapsack(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : set = set()
_construct_solution(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
return optimal_val, example_optional_set
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
# for the current item i at a maximum weight j to be part of an optimal subset,
# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
# where i - 1 means considering only the previous items at the given maximum weight
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(__lowerCAmelCase , __lowerCAmelCase , i - 1 , __lowerCAmelCase , __lowerCAmelCase )
else:
optimal_set.add(__lowerCAmelCase )
_construct_solution(__lowerCAmelCase , __lowerCAmelCase , i - 1 , j - wt[i - 1] , __lowerCAmelCase )
if __name__ == "__main__":
a :List[Any] = [3, 2, 4, 4]
a :int = [4, 3, 2, 3]
a :List[str] = 4
a :List[str] = 6
a :Dict = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
a ,a :Optional[Any] = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
a ,a :Optional[int] = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("optimal_value = ", optimal_solution)
print("An optimal subset corresponding to the optimal value", optimal_subset)
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if n == 1 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
SCREAMING_SNAKE_CASE__ : int = 2
while digits < n:
index += 1
SCREAMING_SNAKE_CASE__ : Any = len(str(fibonacci(__lowerCAmelCase ) ) )
return index
def _lowercase ( __lowerCAmelCase = 1000 ) -> int:
return fibonacci_digits_index(__lowerCAmelCase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a :Union[str, Any] = 10
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
for i in range(__lowerCAmelCase , __lowerCAmelCase ):
if array[i] == target:
return i
return -1
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
while left <= right:
if right - left < precision:
return lin_search(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = (left + right) // 3 + 1
SCREAMING_SNAKE_CASE__ : int = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
SCREAMING_SNAKE_CASE__ : List[Any] = one_third - 1
elif array[two_third] < target:
SCREAMING_SNAKE_CASE__ : str = two_third + 1
else:
SCREAMING_SNAKE_CASE__ : List[Any] = one_third + 1
SCREAMING_SNAKE_CASE__ : Optional[int] = two_third - 1
else:
return -1
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
if left < right:
if right - left < precision:
return lin_search(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = (left + right) // 3 + 1
SCREAMING_SNAKE_CASE__ : Optional[int] = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(__lowerCAmelCase , one_third - 1 , __lowerCAmelCase , __lowerCAmelCase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , __lowerCAmelCase , __lowerCAmelCase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a :Union[str, Any] = input("Enter numbers separated by comma:\n").strip()
a :Tuple = [int(item.strip()) for item in user_input.split(",")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
a :Union[str, Any] = int(input("Enter the number to be found in the list:\n").strip())
a :Dict = ite_ternary_search(collection, target)
a :str = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'Iterative search: {target} found at positions: {resulta}')
print(f'Recursive search: {target} found at positions: {resulta}')
else:
print("Not found")
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
a :Optional[Any] = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = ["""pixel_values"""]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BILINEAR , _a = True , _a = None , _a = True , _a = 1 / 255 , _a = True , _a = None , _a = None , **_a , ) -> None:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = size if size is not None else {"""shortest_edge""": 256}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_size_dict(_a , default_to_square=_a )
SCREAMING_SNAKE_CASE__ : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
SCREAMING_SNAKE_CASE__ : Dict = get_size_dict(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_resize
SCREAMING_SNAKE_CASE__ : int = size
SCREAMING_SNAKE_CASE__ : Dict = resample
SCREAMING_SNAKE_CASE__ : Optional[Any] = do_center_crop
SCREAMING_SNAKE_CASE__ : Any = crop_size
SCREAMING_SNAKE_CASE__ : Tuple = do_rescale
SCREAMING_SNAKE_CASE__ : List[Any] = rescale_factor
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize
SCREAMING_SNAKE_CASE__ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _a ( self , _a , _a , _a = PILImageResampling.BICUBIC , _a = None , **_a , ) -> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_resize_output_image_size(_a , size=size["""shortest_edge"""] , default_to_square=_a )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def _a ( self , _a , _a , _a = None , **_a , ) -> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = get_size_dict(_a )
return center_crop(_a , size=(size["""height"""], size["""width"""]) , data_format=_a , **_a )
def _a ( self , _a , _a , _a = None , **_a ) -> np.ndarray:
"""simple docstring"""
return rescale(_a , scale=_a , data_format=_a , **_a )
def _a ( self , _a , _a , _a , _a = None , **_a , ) -> np.ndarray:
"""simple docstring"""
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def _a ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE__ : int = size if size is not None else self.size
SCREAMING_SNAKE_CASE__ : List[str] = get_size_dict(_a , default_to_square=_a )
SCREAMING_SNAKE_CASE__ : List[str] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE__ : str = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE__ : List[Any] = get_size_dict(_a )
SCREAMING_SNAKE_CASE__ : int = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ : str = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ : Tuple = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE__ : Tuple = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE__ : Optional[int] = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE__ : Union[str, Any] = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE__ : int = [self.center_crop(image=_a , size=_a ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
SCREAMING_SNAKE_CASE__ : Optional[int] = [to_channel_dimension_format(_a , _a ) for image in images]
SCREAMING_SNAKE_CASE__ : List[str] = {"""pixel_values""": images}
return BatchFeature(data=_a , tensor_type=_a )
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
a :Dict = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def _lowercase ( ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Dict = Github(os.environ["""GITHUB_TOKEN"""] )
SCREAMING_SNAKE_CASE__ : str = g.get_repo("""huggingface/transformers""" )
SCREAMING_SNAKE_CASE__ : List[str] = repo.get_issues(state="""open""" )
for issue in open_issues:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __lowerCAmelCase : i.created_at , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = comments[0] if len(__lowerCAmelCase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
from functools import lru_cache
from math import ceil
a :Optional[int] = 100
a :Optional[int] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
a :int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def _lowercase ( __lowerCAmelCase ) -> set[int]:
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
SCREAMING_SNAKE_CASE__ : set[int] = set()
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : int
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def _lowercase ( __lowerCAmelCase = 5000 ) -> int | None:
for number_to_partition in range(1 , __lowerCAmelCase ):
if len(partition(__lowerCAmelCase ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _lowercase ( ) -> Optional[Any]:
raise RuntimeError("""CUDA out of memory.""" )
class __a (nn.Module):
'''simple docstring'''
def __init__( self ) -> str:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = nn.Linear(3 , 4 )
SCREAMING_SNAKE_CASE__ : Any = nn.BatchNormad(4 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = nn.Linear(4 , 5 )
def _a ( self , _a ) -> Any:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(_a ) ) )
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(_a ):
nonlocal batch_sizes
batch_sizes.append(_a )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(_a , [128, 64, 32, 16, 8] )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(_a , _a ):
nonlocal batch_sizes
batch_sizes.append(_a )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = mock_training_loop_function("""hello""" )
self.assertListEqual(_a , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, """hello"""] )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(_a ):
pass
with self.assertRaises(_a ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _a ( self ) -> Tuple:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(_a ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(_a ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def _a ( self ) -> int:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(_a , _a , _a ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(_a ) as cm:
mock_training_loop_function(128 , """hello""" , """world""" )
self.assertIn("""Batch size was passed into `f`""" , cm.exception.args[0] )
self.assertIn("""`f(arg1='hello', arg2='world')""" , cm.exception.args[0] )
def _a ( self ) -> List[Any]:
"""simple docstring"""
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(_a ):
raise ValueError("""Oops, we had an error!""" )
with self.assertRaises(_a ) as cm:
mock_training_loop_function()
self.assertIn("""Oops, we had an error!""" , cm.exception.args[0] )
@require_cuda
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = torch.cuda.memory_allocated()
SCREAMING_SNAKE_CASE__ : int = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , _a )
SCREAMING_SNAKE_CASE__ : Any = release_memory(_a )
self.assertEqual(torch.cuda.memory_allocated() , _a )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
SCREAMING_SNAKE_CASE__ : str = 0.01
with locka.acquire():
with pytest.raises(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : str = time.time()
locka.acquire(__lowerCAmelCase )
assert time.time() - _start > timeout
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : int = """a""" * 1000 + """.lock"""
SCREAMING_SNAKE_CASE__ : Any = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(__lowerCAmelCase )
assert len(os.path.basename(locka._lock_file ) ) <= 255
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(__lowerCAmelCase ):
locka.acquire(0 )
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Dict = []
for line in lines:
SCREAMING_SNAKE_CASE__ : Dict = re.sub(r"""#.*""" , """""" , __lowerCAmelCase ) # remove comments
if line:
filtered_lines.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = """\n""".join(__lowerCAmelCase )
# Make a hash from all this code
SCREAMING_SNAKE_CASE__ : Any = full_str.encode("""utf-8""" )
return shaaaa(__lowerCAmelCase ).hexdigest()
# get importable module names and hash for caching
a :Any = {
"csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
a :Optional[int] = {
".csv": ("csv", {}),
".tsv": ("csv", {"sep": "\t"}),
".json": ("json", {}),
".jsonl": ("json", {}),
".parquet": ("parquet", {}),
".arrow": ("arrow", {}),
".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
a :int = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
a :Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")
_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
SCREAMING_SNAKE_CASE__ : List[str] = i + 1
else:
SCREAMING_SNAKE_CASE__ : Any = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f'{two_pointer([2, 7, 11, 15], 9) = }')
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
from math import factorial
def _lowercase ( __lowerCAmelCase = 20 ) -> int:
SCREAMING_SNAKE_CASE__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
SCREAMING_SNAKE_CASE__ : Dict = n // 2
return int(factorial(__lowerCAmelCase ) / (factorial(__lowerCAmelCase ) * factorial(n - k )) )
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution(20))
else:
try:
a :List[str] = int(sys.argv[1])
print(solution(n))
except ValueError:
print("Invalid entry - please enter a number.")
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
a :List[Any] = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
@require_sentencepiece
@require_tokenizers
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = SpeechTaTokenizer
_SCREAMING_SNAKE_CASE :Optional[int] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
def _a ( self ) -> Dict:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : Tuple = SpeechTaTokenizer(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken("""<mask>""" , lstrip=_a , rstrip=_a )
SCREAMING_SNAKE_CASE__ : Tuple = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """this is a test"""
SCREAMING_SNAKE_CASE__ : List[Any] = """this is a test"""
return input_text, output_text
def _a ( self , _a , _a=False , _a=20 , _a=5 ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.get_input_output_texts(_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : str = tokenizer.decode(_a , clean_up_tokenization_spaces=_a )
return text, ids
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = """<pad>"""
SCREAMING_SNAKE_CASE__ : List[str] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(vocab_keys[-4] , """œ""" )
self.assertEqual(vocab_keys[-2] , """<mask>""" )
self.assertEqual(vocab_keys[-1] , """<ctc_blank>""" )
self.assertEqual(len(_a ) , 81 )
def _a ( self ) -> Tuple:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE__ : int = tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : str = len(_a )
self.assertNotEqual(_a , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
SCREAMING_SNAKE_CASE__ : List[str] = ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.add_tokens(_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = len(_a )
self.assertNotEqual(_a , 0 )
self.assertEqual(_a , _a )
self.assertEqual(_a , len(_a ) )
self.assertEqual(_a , all_size + len(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l""" , add_special_tokens=_a )
self.assertGreaterEqual(len(_a ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
SCREAMING_SNAKE_CASE__ : Tuple = {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.add_special_tokens(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = len(_a )
self.assertNotEqual(_a , 0 )
self.assertEqual(_a , _a )
self.assertEqual(_a , len(_a ) )
self.assertEqual(_a , all_size_a + len(_a ) )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.encode(
""">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l""" , add_special_tokens=_a )
self.assertGreaterEqual(len(_a ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def _a ( self ) -> List[str]:
"""simple docstring"""
pass
def _a ( self ) -> Dict:
"""simple docstring"""
pass
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = tokenizer.tokenize("""This is a test""" )
# fmt: off
self.assertListEqual(_a , [SPIECE_UNDERLINE, """T""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """a""", SPIECE_UNDERLINE, """t""", """e""", """s""", """t"""] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_a , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """92000""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
SCREAMING_SNAKE_CASE__ : str = tokenizer.convert_tokens_to_ids(_a )
# fmt: off
self.assertListEqual(_a , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [SPIECE_UNDERLINE, """I""", SPIECE_UNDERLINE, """w""", """a""", """s""", SPIECE_UNDERLINE, """b""", """o""", """r""", """n""", SPIECE_UNDERLINE, """i""", """n""", SPIECE_UNDERLINE, """<unk>""", """,""", SPIECE_UNDERLINE, """a""", """n""", """d""", SPIECE_UNDERLINE, """t""", """h""", """i""", """s""", SPIECE_UNDERLINE, """i""", """s""", SPIECE_UNDERLINE, """f""", """a""", """l""", """s""", """é""", """."""] )
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [
"""Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
"""general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
"""Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained """
"""models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
"""BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
"""conditioning on both left and right context in all layers.""",
"""The quick brown fox jumps over the lazy dog.""",
]
# fmt: off
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"""attention_mask""": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="""microsoft/speecht5_asr""" , revision="""c5ef64c71905caeccde0e4462ef3f9077224c524""" , sequences=_a , )
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a :int = {
"configuration_blip_2": [
"BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Blip2Config",
"Blip2QFormerConfig",
"Blip2VisionConfig",
],
"processing_blip_2": ["Blip2Processor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Tuple = [
"BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Blip2Model",
"Blip2QFormerModel",
"Blip2PreTrainedModel",
"Blip2ForConditionalGeneration",
"Blip2VisionModel",
]
if TYPE_CHECKING:
from .configuration_blip_a import (
BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlipaConfig,
BlipaQFormerConfig,
BlipaVisionConfig,
)
from .processing_blip_a import BlipaProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip_a import (
BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipaForConditionalGeneration,
BlipaModel,
BlipaPreTrainedModel,
BlipaQFormerModel,
BlipaVisionModel,
)
else:
import sys
a :Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
a :Optional[Any] = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",
"NVFUSER",
"AOT_NVFUSER",
"AOT_CUDAGRAPHS",
"OFI",
"FX2TRT",
"ONNXRT",
"IPEX",
]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
while ask_again:
SCREAMING_SNAKE_CASE__ : Tuple = input(__lowerCAmelCase )
try:
if default is not None and len(__lowerCAmelCase ) == 0:
return default
return convert_value(__lowerCAmelCase ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=[] , __lowerCAmelCase=None , __lowerCAmelCase=0 ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BulletMenu(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = menu.run(default_choice=__lowerCAmelCase )
return convert_value(__lowerCAmelCase ) if convert_value is not None else result
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int = int(__lowerCAmelCase )
return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : List[Any] = int(__lowerCAmelCase )
return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )
def _lowercase ( __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[str] = int(__lowerCAmelCase )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = int(__lowerCAmelCase )
return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(__lowerCAmelCase )
return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )
def _lowercase ( __lowerCAmelCase ) -> str:
return {"yes": True, "no": False}[value.lower()]
class __a (argparse.RawDescriptionHelpFormatter):
'''simple docstring'''
def _a ( self , _a , _a , _a , _a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = super()._format_usage(_a , _a , _a , _a )
SCREAMING_SNAKE_CASE__ : Any = usage.replace("""<command> [<args>] """ , """""" )
return usage
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :int = ["""image_processor""", """tokenizer"""]
_SCREAMING_SNAKE_CASE :str = """CLIPImageProcessor"""
_SCREAMING_SNAKE_CASE :Union[str, Any] = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , _a=None , _a=None , **_a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , _a , )
SCREAMING_SNAKE_CASE__ : Any = kwargs.pop("""feature_extractor""" )
SCREAMING_SNAKE_CASE__ : Dict = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(_a , _a )
def __call__( self , _a=None , _a=None , _a=None , **_a ) -> Dict:
"""simple docstring"""
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
SCREAMING_SNAKE_CASE__ : Dict = self.tokenizer(_a , return_tensors=_a , **_a )
if images is not None:
SCREAMING_SNAKE_CASE__ : List[Any] = self.image_processor(_a , return_tensors=_a , **_a )
if text is not None and images is not None:
SCREAMING_SNAKE_CASE__ : List[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_a ) , tensor_type=_a )
def _a ( self , *_a , **_a ) -> Optional[Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*_a , **_a )
def _a ( self , *_a , **_a ) -> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*_a , **_a )
@property
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _a ( self ) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _a , )
return self.image_processor_class
@property
def _a ( self ) -> Any:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _a , )
return self.image_processor
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a :str = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Tuple = [
"FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
"FocalNetForImageClassification",
"FocalNetForMaskedImageModeling",
"FocalNetBackbone",
"FocalNetModel",
"FocalNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
a :Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowerCAmelCase )
if number < 0:
return False
SCREAMING_SNAKE_CASE__ : Optional[Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
a :List[str] = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a = 101 ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = length
def __len__( self ) -> Tuple:
"""simple docstring"""
return self.length
def __getitem__( self , _a ) -> int:
"""simple docstring"""
return i
class __a :
'''simple docstring'''
def __call__( self , _a ) -> Optional[Any]:
"""simple docstring"""
return {"input_ids": torch.tensor(_a ), "labels": torch.tensor(_a )}
class __a (nn.Module):
'''simple docstring'''
def __init__( self ) -> Optional[int]:
"""simple docstring"""
super().__init__()
# Add some (unused) params otherwise DDP will complain.
SCREAMING_SNAKE_CASE__ : int = nn.Linear(120 , 80 )
def _a ( self , _a , _a=None ) -> Any:
"""simple docstring"""
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class __a (UpperCamelCase_):
'''simple docstring'''
@require_torch_neuroncore
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = f'''--nproc_per_node=2
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE__ : Dict = f'''--output_dir {output_dir}'''.split()
SCREAMING_SNAKE_CASE__ : str = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(_a , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class __a (UpperCamelCase_):
'''simple docstring'''
@require_torch_multi_gpu
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = f'''--nproc_per_node={torch.cuda.device_count()}
--master_port={get_torch_dist_unique_port()}
{self.test_file_dir}/test_trainer_distributed.py
'''.split()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_auto_remove_tmp_dir()
SCREAMING_SNAKE_CASE__ : Optional[int] = f'''--output_dir {output_dir}'''.split()
SCREAMING_SNAKE_CASE__ : int = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(_a , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
a :Tuple = HfArgumentParser((TrainingArguments,))
a :List[Any] = parser.parse_args_into_dataclasses()[0]
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '
f'distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
a :Union[str, Any] = DummyDataset(dataset_length)
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Tuple = list(range(len(__lowerCAmelCase ) ) )
SCREAMING_SNAKE_CASE__ : Tuple = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"""Predictions and/or labels do not match expected results:\n - predictions: """
F'''{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}''' )
return {"success": success}
a :Tuple = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
a :Optional[Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a :Union[str, Any] = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a :Tuple = 2
a :str = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
a :Union[str, Any] = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
a :List[str] = None
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a :List[str] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
"AltCLIPTextConfig",
"AltCLIPVisionConfig",
],
"processing_altclip": ["AltCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = [
"ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"AltCLIPPreTrainedModel",
"AltCLIPModel",
"AltCLIPTextModel",
"AltCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
a :str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
a :Any = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1_000,
"block_out_channels": [32, 64],
"attention_head_dim": 8,
"down_block_types": [
"ResnetDownsampleBlock2D",
"AttnDownBlock2D",
],
"up_block_types": [
"AttnUpBlock2D",
"ResnetUpsampleBlock2D",
],
"resnet_time_scale_shift": "scale_shift",
"upsample_type": "resnet",
"downsample_type": "resnet",
}
a :Any = {
"sample_size": 64,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 3,
"num_class_embeds": 1_000,
"block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
"attention_head_dim": 64,
"down_block_types": [
"ResnetDownsampleBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
],
"up_block_types": [
"AttnUpBlock2D",
"AttnUpBlock2D",
"AttnUpBlock2D",
"ResnetUpsampleBlock2D",
],
"resnet_time_scale_shift": "scale_shift",
"upsample_type": "resnet",
"downsample_type": "resnet",
}
a :Dict = {
"sample_size": 256,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": None,
"block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
"attention_head_dim": 64,
"down_block_types": [
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
],
"up_block_types": [
"AttnUpBlock2D",
"AttnUpBlock2D",
"AttnUpBlock2D",
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D",
],
"resnet_time_scale_shift": "default",
"upsample_type": "resnet",
"downsample_type": "resnet",
}
a :Union[str, Any] = {
"num_train_timesteps": 40,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
a :List[str] = {
"num_train_timesteps": 201,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
a :int = {
"num_train_timesteps": 151,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
def _lowercase ( __lowerCAmelCase ) -> str:
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("""boolean value expected""" )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any = checkpoint[F'''{old_prefix}.in_layers.0.weight''']
SCREAMING_SNAKE_CASE__ : str = checkpoint[F'''{old_prefix}.in_layers.0.bias''']
SCREAMING_SNAKE_CASE__ : Optional[Any] = checkpoint[F'''{old_prefix}.in_layers.2.weight''']
SCREAMING_SNAKE_CASE__ : List[Any] = checkpoint[F'''{old_prefix}.in_layers.2.bias''']
SCREAMING_SNAKE_CASE__ : Optional[int] = checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
SCREAMING_SNAKE_CASE__ : str = checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
SCREAMING_SNAKE_CASE__ : str = checkpoint[F'''{old_prefix}.out_layers.0.weight''']
SCREAMING_SNAKE_CASE__ : Optional[Any] = checkpoint[F'''{old_prefix}.out_layers.0.bias''']
SCREAMING_SNAKE_CASE__ : Tuple = checkpoint[F'''{old_prefix}.out_layers.3.weight''']
SCREAMING_SNAKE_CASE__ : Tuple = checkpoint[F'''{old_prefix}.out_layers.3.bias''']
if has_skip:
SCREAMING_SNAKE_CASE__ : Tuple = checkpoint[F'''{old_prefix}.skip_connection.weight''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = checkpoint[F'''{old_prefix}.skip_connection.bias''']
return new_checkpoint
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None ) -> Tuple:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3 , dim=0 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3 , dim=0 )
SCREAMING_SNAKE_CASE__ : Dict = checkpoint[F'''{old_prefix}.norm.weight''']
SCREAMING_SNAKE_CASE__ : Any = checkpoint[F'''{old_prefix}.norm.bias''']
SCREAMING_SNAKE_CASE__ : str = weight_q.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Tuple = bias_q.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Dict = weight_k.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : int = bias_k.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : str = weight_v.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Dict = bias_v.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
)
SCREAMING_SNAKE_CASE__ : Dict = checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : Dict = {}
SCREAMING_SNAKE_CASE__ : Any = checkpoint["""time_embed.0.weight"""]
SCREAMING_SNAKE_CASE__ : str = checkpoint["""time_embed.0.bias"""]
SCREAMING_SNAKE_CASE__ : int = checkpoint["""time_embed.2.weight"""]
SCREAMING_SNAKE_CASE__ : List[Any] = checkpoint["""time_embed.2.bias"""]
if unet_config["num_class_embeds"] is not None:
SCREAMING_SNAKE_CASE__ : Dict = checkpoint["""label_emb.weight"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = checkpoint["""input_blocks.0.0.weight"""]
SCREAMING_SNAKE_CASE__ : Dict = checkpoint["""input_blocks.0.0.bias"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = unet_config["""down_block_types"""]
SCREAMING_SNAKE_CASE__ : List[str] = unet_config["""layers_per_block"""]
SCREAMING_SNAKE_CASE__ : Optional[int] = unet_config["""attention_head_dim"""]
SCREAMING_SNAKE_CASE__ : str = unet_config["""block_out_channels"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
SCREAMING_SNAKE_CASE__ : Any = channels_list[0]
for i, layer_type in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = channels_list[i]
SCREAMING_SNAKE_CASE__ : List[Any] = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Any = F'''down_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ : Tuple = F'''input_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ : str = True if j == 0 and downsample_block_has_skip else False
SCREAMING_SNAKE_CASE__ : Any = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = F'''down_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''input_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ : int = True if j == 0 and downsample_block_has_skip else False
SCREAMING_SNAKE_CASE__ : Optional[int] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = F'''down_blocks.{i}.attentions.{j}'''
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''input_blocks.{current_layer}.1'''
SCREAMING_SNAKE_CASE__ : List[str] = convert_attention(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
current_layer += 1
if i != len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : str = F'''down_blocks.{i}.downsamplers.0'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = F'''input_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ : List[Any] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
current_layer += 1
SCREAMING_SNAKE_CASE__ : Tuple = current_channels
# hardcoded the mid-block for now
SCREAMING_SNAKE_CASE__ : Optional[Any] = """mid_block.resnets.0"""
SCREAMING_SNAKE_CASE__ : List[str] = """middle_block.0"""
SCREAMING_SNAKE_CASE__ : Any = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = """mid_block.attentions.0"""
SCREAMING_SNAKE_CASE__ : Tuple = """middle_block.1"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_attention(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = """mid_block.resnets.1"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """middle_block.2"""
SCREAMING_SNAKE_CASE__ : Optional[int] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Union[str, Any] = unet_config["""up_block_types"""]
for i, layer_type in enumerate(__lowerCAmelCase ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = F'''up_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ : str = F'''output_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ : Tuple = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
current_layer += 1
if i != len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : Any = F'''up_blocks.{i}.upsamplers.0'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = F'''output_blocks.{current_layer-1}.1'''
SCREAMING_SNAKE_CASE__ : Any = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
SCREAMING_SNAKE_CASE__ : Dict = F'''up_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ : Dict = F'''output_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ : int = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , has_skip=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = F'''up_blocks.{i}.attentions.{j}'''
SCREAMING_SNAKE_CASE__ : List[str] = F'''output_blocks.{current_layer}.1'''
SCREAMING_SNAKE_CASE__ : str = convert_attention(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
current_layer += 1
if i != len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = F'''up_blocks.{i}.upsamplers.0'''
SCREAMING_SNAKE_CASE__ : List[str] = F'''output_blocks.{current_layer-1}.2'''
SCREAMING_SNAKE_CASE__ : Optional[int] = convert_resnet(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = checkpoint["""out.0.weight"""]
SCREAMING_SNAKE_CASE__ : Dict = checkpoint["""out.0.bias"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = checkpoint["""out.2.weight"""]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = checkpoint["""out.2.bias"""]
return new_checkpoint
if __name__ == "__main__":
a :List[str] = argparse.ArgumentParser()
parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
)
parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")
a :Union[str, Any] = parser.parse_args()
a :int = strabool(args.class_cond)
a :int = os.path.basename(args.unet_path)
print(f'Checkpoint: {ckpt_name}')
# Get U-Net config
if "imagenet64" in ckpt_name:
a :Any = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
a :int = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
a :Optional[int] = TEST_UNET_CONFIG
else:
raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')
if not args.class_cond:
a :Any = None
a :Optional[Any] = con_pt_to_diffuser(args.unet_path, unet_config)
a :Dict = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
a :Optional[int] = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
a :int = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
a :List[str] = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(f'Checkpoint type {ckpt_name} is not currently supported.')
a :List[Any] = CMStochasticIterativeScheduler(**scheduler_config)
a :str = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import argparse
a :Any = "docs/source/_static/js/custom.js"
def _lowercase ( __lowerCAmelCase ) -> List[str]:
with open(__lowerCAmelCase , encoding="""utf-8""" , newline="""\n""" ) as f:
SCREAMING_SNAKE_CASE__ : int = f.readlines()
SCREAMING_SNAKE_CASE__ : int = 0
# First let's put the right version
while not lines[index].startswith("""const stableVersion =""" ):
index += 1
SCREAMING_SNAKE_CASE__ : List[Any] = F'''const stableVersion = "v{version}"\n'''
# Then update the dictionary
while not lines[index].startswith("""const versionMapping = {""" ):
index += 1
# We go until the end
while not lines[index].startswith("""}""" ):
index += 1
# We add the new version at the end
lines[index - 1] += F''' "v{version}": "v{version}",\n'''
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(__lowerCAmelCase )
if __name__ == "__main__":
a :List[Any] = argparse.ArgumentParser()
parser.add_argument("--version", help="Release version.")
a :Optional[int] = parser.parse_args()
update_custom_js(args.version)
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a :str = logging.get_logger(__name__)
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Dict = DPTConfig(embedding_type="""hybrid""" )
if "large" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1024
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 4096
SCREAMING_SNAKE_CASE__ : str = 24
SCREAMING_SNAKE_CASE__ : Any = 16
SCREAMING_SNAKE_CASE__ : str = [5, 11, 17, 23]
SCREAMING_SNAKE_CASE__ : Optional[int] = [256, 512, 1024, 1024]
SCREAMING_SNAKE_CASE__ : int = (1, 384, 384)
if "nyu" or "midas" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = 768
SCREAMING_SNAKE_CASE__ : List[str] = [1, 1, 1, 0.5]
SCREAMING_SNAKE_CASE__ : Optional[int] = [256, 512, 768, 768]
SCREAMING_SNAKE_CASE__ : List[str] = 150
SCREAMING_SNAKE_CASE__ : Any = 16
SCREAMING_SNAKE_CASE__ : Optional[int] = (1, 384, 384)
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """project"""
if "ade" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : str = True
SCREAMING_SNAKE_CASE__ : List[Any] = 768
SCREAMING_SNAKE_CASE__ : int = [1, 1, 1, 0.5]
SCREAMING_SNAKE_CASE__ : List[Any] = 150
SCREAMING_SNAKE_CASE__ : List[str] = 16
SCREAMING_SNAKE_CASE__ : Optional[Any] = """huggingface/label-files"""
SCREAMING_SNAKE_CASE__ : List[Any] = """ade20k-id2label.json"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.load(open(cached_download(hf_hub_url(__lowerCAmelCase , __lowerCAmelCase , repo_type="""dataset""" ) ) , """r""" ) )
SCREAMING_SNAKE_CASE__ : str = {int(__lowerCAmelCase ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = idalabel
SCREAMING_SNAKE_CASE__ : Optional[int] = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Tuple = [1, 150, 480, 480]
return config, expected_shape
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : List[Any] = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Any:
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""patch_embed""" , """""" )
if "pos_embed" in name:
SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""proj""" , """projection""" )
if "blocks" in name:
SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name and "backbone" not in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name and "backbone" not in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
SCREAMING_SNAKE_CASE__ : Any = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
SCREAMING_SNAKE_CASE__ : Tuple = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace(F'''refinenet{layer_idx}''' , F'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
SCREAMING_SNAKE_CASE__ : str = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
SCREAMING_SNAKE_CASE__ : str = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
SCREAMING_SNAKE_CASE__ : Any = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""auxlayer""" , """auxiliary_head.head""" )
if "backbone" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""backbone""" , """backbone.bit.encoder""" )
if ".." in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""..""" , """.""" )
if "stem.conv" in name:
SCREAMING_SNAKE_CASE__ : str = name.replace("""stem.conv""" , """bit.embedder.convolution""" )
if "blocks" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""blocks""" , """layers""" )
if "convolution" in name and "backbone" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""convolution""" , """conv""" )
if "layer" in name and "backbone" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""layer""" , """layers""" )
if "backbone.bit.encoder.bit" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""backbone.bit.encoder.bit""" , """backbone.bit""" )
if "embedder.conv" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""embedder.conv""" , """embedder.convolution""" )
if "backbone.bit.encoder.stem.norm" in name:
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""backbone.bit.encoder.stem.norm""" , """backbone.bit.embedder.norm""" )
return name
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE__ : Optional[Any] = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
SCREAMING_SNAKE_CASE__ : Dict = state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : str = in_proj_weight[: config.hidden_size, :]
SCREAMING_SNAKE_CASE__ : int = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE__ : List[str] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE__ : Optional[Any] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE__ : Any = in_proj_bias[-config.hidden_size :]
def _lowercase ( ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Any = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE__ : str = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw )
return im
@torch.no_grad()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = get_dpt_config(__lowerCAmelCase )
# load original state_dict from URL
# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(__lowerCAmelCase )
# rename keys
for key in state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ : Optional[int] = state_dict.pop(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = val
# read in qkv matrices
read_in_q_k_v(__lowerCAmelCase , __lowerCAmelCase )
# load HuggingFace model
SCREAMING_SNAKE_CASE__ : str = DPTForSemanticSegmentation(__lowerCAmelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(__lowerCAmelCase )
model.load_state_dict(__lowerCAmelCase )
model.eval()
# Check outputs on an image
SCREAMING_SNAKE_CASE__ : List[Any] = 480 if """ade""" in checkpoint_url else 384
SCREAMING_SNAKE_CASE__ : str = DPTImageProcessor(size=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = prepare_img()
SCREAMING_SNAKE_CASE__ : Dict = image_processor(__lowerCAmelCase , return_tensors="""pt""" )
# forward pass
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(**__lowerCAmelCase ).logits if """ade""" in checkpoint_url else model(**__lowerCAmelCase ).predicted_depth
if show_prediction:
SCREAMING_SNAKE_CASE__ : List[Any] = (
torch.nn.functional.interpolate(
outputs.unsqueeze(1 ) , size=(image.size[1], image.size[0]) , mode="""bicubic""" , align_corners=__lowerCAmelCase , )
.squeeze()
.cpu()
.numpy()
)
Image.fromarray((prediction / prediction.max()) * 255 ).show()
if pytorch_dump_folder_path is not None:
Path(__lowerCAmelCase ).mkdir(exist_ok=__lowerCAmelCase )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__lowerCAmelCase )
if push_to_hub:
model.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas""" )
if __name__ == "__main__":
a :str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
type=str,
help="URL of the original DPT checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=False,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
parser.add_argument(
"--model_name",
default="dpt-large",
type=str,
help="Name of the model, in case you're pushing to the hub.",
)
parser.add_argument(
"--show_prediction",
action="store_true",
)
a :int = parser.parse_args()
convert_dpt_checkpoint(
args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
)
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = int(__lowerCAmelCase )
# Initialize Result
SCREAMING_SNAKE_CASE__ : str = []
# Traverse through all denomination
for denomination in reversed(__lowerCAmelCase ):
# Find denominations
while int(__lowerCAmelCase ) >= int(__lowerCAmelCase ):
total_value -= int(__lowerCAmelCase )
answer.append(__lowerCAmelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
a :Any = []
a :List[str] = "0"
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
a :Optional[int] = int(input("Enter the number of denominations you want to add: ").strip())
for i in range(0, n):
denominations.append(int(input(f'Denomination {i}: ').strip()))
a :Any = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
a :Union[str, Any] = [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
a :Any = input("Enter the change you want to make: ").strip()
if int(value) == 0 or int(value) < 0:
print("The total value cannot be zero or negative.")
else:
print(f'Following is minimal change for {value}: ')
a :List[Any] = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a :int = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) -> None:
"""simple docstring"""
warnings.warn(
"""The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use SegformerImageProcessor instead.""" , _a , )
super().__init__(*_a , **_a )
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
a :Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
def _lowercase ( __lowerCAmelCase ) -> Any:
warnings.warn(
"""The preprocess method is deprecated and will be removed in a future version. Please"""
""" use VaeImageProcessor.preprocess instead""" , __lowerCAmelCase , )
if isinstance(__lowerCAmelCase , torch.Tensor ):
return image
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = [image]
if isinstance(image[0] , PIL.Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = image[0].size
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
SCREAMING_SNAKE_CASE__ : int = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
SCREAMING_SNAKE_CASE__ : Dict = np.concatenate(__lowerCAmelCase , axis=0 )
SCREAMING_SNAKE_CASE__ : Tuple = np.array(__lowerCAmelCase ).astype(np.floataa ) / 255.0
SCREAMING_SNAKE_CASE__ : List[Any] = image.transpose(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE__ : Dict = 2.0 * image - 1.0
SCREAMING_SNAKE_CASE__ : Dict = torch.from_numpy(__lowerCAmelCase )
elif isinstance(image[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.cat(__lowerCAmelCase , dim=0 )
return image
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
if isinstance(__lowerCAmelCase , torch.Tensor ):
return mask
elif isinstance(__lowerCAmelCase , PIL.Image.Image ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = mask[0].size
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
SCREAMING_SNAKE_CASE__ : Dict = [np.array(m.convert("""L""" ).resize((w, h) , resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
SCREAMING_SNAKE_CASE__ : int = np.concatenate(__lowerCAmelCase , axis=0 )
SCREAMING_SNAKE_CASE__ : List[Any] = mask.astype(np.floataa ) / 255.0
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 1
SCREAMING_SNAKE_CASE__ : List[str] = torch.from_numpy(__lowerCAmelCase )
elif isinstance(mask[0] , torch.Tensor ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cat(__lowerCAmelCase , dim=0 )
return mask
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :UNetaDModel
_SCREAMING_SNAKE_CASE :RePaintScheduler
def __init__( self , _a , _a ) -> Any:
"""simple docstring"""
super().__init__()
self.register_modules(unet=_a , scheduler=_a )
@torch.no_grad()
def __call__( self , _a , _a , _a = 250 , _a = 0.0 , _a = 10 , _a = 10 , _a = None , _a = "pil" , _a = True , ) -> Union[ImagePipelineOutput, Tuple]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = image
SCREAMING_SNAKE_CASE__ : List[Any] = _preprocess_image(_a )
SCREAMING_SNAKE_CASE__ : int = original_image.to(device=self.device , dtype=self.unet.dtype )
SCREAMING_SNAKE_CASE__ : Optional[int] = _preprocess_mask(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = mask_image.to(device=self.device , dtype=self.unet.dtype )
SCREAMING_SNAKE_CASE__ : Optional[int] = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(_a , _a ) and len(_a ) != batch_size:
raise ValueError(
f'''You have passed a list of generators of length {len(_a )}, but requested an effective batch'''
f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )
SCREAMING_SNAKE_CASE__ : int = original_image.shape
SCREAMING_SNAKE_CASE__ : List[str] = randn_tensor(_a , generator=_a , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_a , _a , _a , self.device )
SCREAMING_SNAKE_CASE__ : Dict = eta
SCREAMING_SNAKE_CASE__ : Dict = self.scheduler.timesteps[0] + 1
SCREAMING_SNAKE_CASE__ : Union[str, Any] = generator[0] if isinstance(_a , _a ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
SCREAMING_SNAKE_CASE__ : Tuple = self.unet(_a , _a ).sample
# compute previous image: x_t -> x_t-1
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler.step(_a , _a , _a , _a , _a , _a ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler.undo_step(_a , _a , _a )
SCREAMING_SNAKE_CASE__ : int = t
SCREAMING_SNAKE_CASE__ : int = (image / 2 + 0.5).clamp(0 , 1 )
SCREAMING_SNAKE_CASE__ : Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE__ : Tuple = self.numpy_to_pil(_a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_a )
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ConditionalDetrImageProcessor
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=7 , _a=3 , _a=30 , _a=400 , _a=True , _a=None , _a=True , _a=[0.5, 0.5, 0.5] , _a=[0.5, 0.5, 0.5] , _a=True , _a=1 / 255 , _a=True , ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1_333}
SCREAMING_SNAKE_CASE__ : int = parent
SCREAMING_SNAKE_CASE__ : str = batch_size
SCREAMING_SNAKE_CASE__ : List[str] = num_channels
SCREAMING_SNAKE_CASE__ : List[str] = min_resolution
SCREAMING_SNAKE_CASE__ : List[str] = max_resolution
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_resize
SCREAMING_SNAKE_CASE__ : Optional[int] = size
SCREAMING_SNAKE_CASE__ : Dict = do_normalize
SCREAMING_SNAKE_CASE__ : Dict = image_mean
SCREAMING_SNAKE_CASE__ : List[Any] = image_std
SCREAMING_SNAKE_CASE__ : Optional[int] = do_rescale
SCREAMING_SNAKE_CASE__ : Optional[Any] = rescale_factor
SCREAMING_SNAKE_CASE__ : Dict = do_pad
def _a ( self ) -> Tuple:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def _a ( self , _a , _a=False ) -> List[Any]:
"""simple docstring"""
if not batched:
SCREAMING_SNAKE_CASE__ : Dict = image_inputs[0]
if isinstance(_a , Image.Image ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = image.size
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = image.shape[1], image.shape[2]
if w < h:
SCREAMING_SNAKE_CASE__ : Any = int(self.size["""shortest_edge"""] * h / w )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.size["""shortest_edge"""]
elif w > h:
SCREAMING_SNAKE_CASE__ : Any = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ : List[str] = int(self.size["""shortest_edge"""] * w / h )
else:
SCREAMING_SNAKE_CASE__ : str = self.size["""shortest_edge"""]
SCREAMING_SNAKE_CASE__ : str = self.size["""shortest_edge"""]
else:
SCREAMING_SNAKE_CASE__ : Dict = []
for image in image_inputs:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
SCREAMING_SNAKE_CASE__ : str = max(_a , key=lambda _a : item[0] )[0]
SCREAMING_SNAKE_CASE__ : Any = max(_a , key=lambda _a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = ConditionalDetrImageProcessor if is_vision_available() else None
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ConditionalDetrImageProcessingTester(self )
@property
def _a ( self ) -> Optional[int]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_a , """image_mean""" ) )
self.assertTrue(hasattr(_a , """image_std""" ) )
self.assertTrue(hasattr(_a , """do_normalize""" ) )
self.assertTrue(hasattr(_a , """do_resize""" ) )
self.assertTrue(hasattr(_a , """size""" ) )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1_333} )
self.assertEqual(image_processor.do_pad , _a )
SCREAMING_SNAKE_CASE__ : List[str] = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=_a )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , _a )
def _a ( self ) -> List[str]:
"""simple docstring"""
pass
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a )
for image in image_inputs:
self.assertIsInstance(_a , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE__ : Dict = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.image_processor_tester.get_expected_values(_a , batched=_a )
SCREAMING_SNAKE_CASE__ : Tuple = image_processing(_a , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE__ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , numpify=_a )
for image in image_inputs:
self.assertIsInstance(_a , np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE__ : str = prepare_image_inputs(self.image_processor_tester , equal_resolution=_a , torchify=_a )
for image in image_inputs:
self.assertIsInstance(_a , torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE__ : str = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.image_processor_tester.get_expected_values(_a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_processing(_a , return_tensors="""pt""" ).pixel_values
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = self.image_processor_tester.get_expected_values(_a , batched=_a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.loads(f.read() )
SCREAMING_SNAKE_CASE__ : Dict = {"""image_id""": 39_769, """annotations""": target}
# encode them
SCREAMING_SNAKE_CASE__ : Optional[Any] = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50""" )
SCREAMING_SNAKE_CASE__ : str = image_processing(images=_a , annotations=_a , return_tensors="""pt""" )
# verify pixel values
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["""pixel_values"""].shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _a , atol=1E-4 ) )
# verify area
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _a ) )
# verify boxes
SCREAMING_SNAKE_CASE__ : int = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _a )
SCREAMING_SNAKE_CASE__ : Any = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _a , atol=1E-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _a ) )
# verify is_crowd
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _a ) )
# verify class_labels
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _a ) )
# verify orig_size
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _a ) )
# verify size
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _a ) )
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : int = json.loads(f.read() )
SCREAMING_SNAKE_CASE__ : List[str] = {"""file_name""": """000000039769.png""", """image_id""": 39_769, """segments_info""": target}
SCREAMING_SNAKE_CASE__ : Optional[int] = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
SCREAMING_SNAKE_CASE__ : str = ConditionalDetrImageProcessor(format="""coco_panoptic""" )
SCREAMING_SNAKE_CASE__ : Dict = image_processing(images=_a , annotations=_a , masks_path=_a , return_tensors="""pt""" )
# verify pixel values
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding["""pixel_values"""].shape , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _a , atol=1E-4 ) )
# verify area
SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _a ) )
# verify boxes
SCREAMING_SNAKE_CASE__ : Tuple = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _a , atol=1E-3 ) )
# verify image_id
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _a ) )
# verify is_crowd
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _a ) )
# verify class_labels
SCREAMING_SNAKE_CASE__ : Any = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _a ) )
# verify masks
SCREAMING_SNAKE_CASE__ : Dict = 822_873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , _a )
# verify orig_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _a ) )
# verify size
SCREAMING_SNAKE_CASE__ : int = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _a ) )
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DEISMultistepScheduler,)
_SCREAMING_SNAKE_CASE :List[Any] = (("""num_inference_steps""", 25),)
def _a ( self , **_a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
}
config.update(**_a )
return config
def _a ( self , _a=0 , **_a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs.pop("""num_inference_steps""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE__ : str = 0.1 * sample
SCREAMING_SNAKE_CASE__ : str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_scheduler_config(**_a )
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
scheduler.set_timesteps(_a )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE__ : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_a )
SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class.from_pretrained(_a )
new_scheduler.set_timesteps(_a )
# copy over dummy past residuals
SCREAMING_SNAKE_CASE__ : str = dummy_past_residuals[: new_scheduler.config.solver_order]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = sample, sample
for t in range(_a , time_step + scheduler.config.solver_order + 1 ):
SCREAMING_SNAKE_CASE__ : str = scheduler.step(_a , _a , _a , **_a ).prev_sample
SCREAMING_SNAKE_CASE__ : Any = new_scheduler.step(_a , _a , _a , **_a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _a ( self ) -> Tuple:
"""simple docstring"""
pass
def _a ( self , _a=0 , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE__ : int = kwargs.pop("""num_inference_steps""" , _a )
SCREAMING_SNAKE_CASE__ : Any = self.dummy_sample
SCREAMING_SNAKE_CASE__ : Any = 0.1 * sample
SCREAMING_SNAKE_CASE__ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
scheduler.set_timesteps(_a )
# copy over dummy past residuals (must be after setting timesteps)
SCREAMING_SNAKE_CASE__ : Tuple = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_a )
SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class.from_pretrained(_a )
# copy over dummy past residuals
new_scheduler.set_timesteps(_a )
# copy over dummy past residual (must be after setting timesteps)
SCREAMING_SNAKE_CASE__ : Any = dummy_past_residuals[: new_scheduler.config.solver_order]
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample
SCREAMING_SNAKE_CASE__ : str = new_scheduler.step(_a , _a , _a , **_a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def _a ( self , _a=None , **_a ) -> Union[str, Any]:
"""simple docstring"""
if scheduler is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Any = self.get_scheduler_config(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config(**_a )
SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : str = 10
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_sample_deter
scheduler.set_timesteps(_a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : str = model(_a , _a )
SCREAMING_SNAKE_CASE__ : Tuple = scheduler.step(_a , _a , _a ).prev_sample
return sample
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = dict(self.forward_default_kwargs )
SCREAMING_SNAKE_CASE__ : List[str] = kwargs.pop("""num_inference_steps""" , _a )
for scheduler_class in self.scheduler_classes:
SCREAMING_SNAKE_CASE__ : List[str] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_sample
SCREAMING_SNAKE_CASE__ : Optional[int] = 0.1 * sample
if num_inference_steps is not None and hasattr(_a , """set_timesteps""" ):
scheduler.set_timesteps(_a )
elif num_inference_steps is not None and not hasattr(_a , """set_timesteps""" ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
SCREAMING_SNAKE_CASE__ : Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10]
SCREAMING_SNAKE_CASE__ : List[Any] = dummy_past_residuals[: scheduler.config.solver_order]
SCREAMING_SNAKE_CASE__ : Optional[int] = scheduler.timesteps[5]
SCREAMING_SNAKE_CASE__ : Any = scheduler.timesteps[6]
SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler.step(_a , _a , _a , **_a ).prev_sample
SCREAMING_SNAKE_CASE__ : str = scheduler.step(_a , _a , _a , **_a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = DEISMultistepScheduler(**self.get_scheduler_config() )
SCREAMING_SNAKE_CASE__ : Tuple = self.full_loop(scheduler=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
SCREAMING_SNAKE_CASE__ : Dict = DPMSolverSinglestepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : Tuple = DPMSolverMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : Tuple = UniPCMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : str = DEISMultistepScheduler.from_config(scheduler.config )
SCREAMING_SNAKE_CASE__ : str = self.full_loop(scheduler=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
def _a ( self ) -> List[str]:
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> Dict:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , algorithm_type="""deis""" , solver_order=_a , solver_type=_a , )
def _a ( self ) -> Any:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.full_loop(
solver_order=_a , solver_type=_a , prediction_type=_a , algorithm_type=_a , )
assert not torch.isnan(_a ).any(), "Samples have nan numbers"
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(lower_order_final=_a )
self.check_over_configs(lower_order_final=_a )
def _a ( self ) -> str:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
self.check_over_forward(num_inference_steps=_a , time_step=0 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.full_loop()
SCREAMING_SNAKE_CASE__ : Dict = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.23_916 ) < 1E-3
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.full_loop(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = torch.mean(torch.abs(_a ) )
assert abs(result_mean.item() - 0.091 ) < 1E-3
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config(thresholding=_a , dynamic_thresholding_ratio=0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 10
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter.half()
scheduler.set_timesteps(_a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : List[Any] = model(_a , _a )
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a ).prev_sample
assert sample.dtype == torch.floataa
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Union[str, Any] = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[str] = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :str = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
a :int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a :Union[str, Any] = logging.get_logger(__name__)
a :Dict = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
a :str = {
"tokenizer_file": {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
},
}
a :Dict = {
"gpt-neox-20b": 2_048,
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""input_ids""", """attention_mask"""]
def __init__( self , _a=None , _a=None , _a=None , _a="<|endoftext|>" , _a="<|endoftext|>" , _a="<|endoftext|>" , _a=False , **_a , ) -> List[Any]:
"""simple docstring"""
super().__init__(
_a , _a , tokenizer_file=_a , unk_token=_a , bos_token=_a , eos_token=_a , add_prefix_space=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Tuple = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , _a ) != add_prefix_space:
SCREAMING_SNAKE_CASE__ : Dict = getattr(_a , pre_tok_state.pop("""type""" ) )
SCREAMING_SNAKE_CASE__ : Tuple = add_prefix_space
SCREAMING_SNAKE_CASE__ : Optional[Any] = pre_tok_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = add_prefix_space
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
def _a ( self , _a ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_a , add_special_tokens=_a ) + [self.eos_token_id] )
if len(_a ) > self.model_max_length:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_ids[-self.model_max_length :]
return input_ids
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def _lowercase ( __lowerCAmelCase = 100_0000 , __lowerCAmelCase = 10 ) -> int:
SCREAMING_SNAKE_CASE__ : defaultdict = defaultdict(__lowerCAmelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
SCREAMING_SNAKE_CASE__ : List[Any] = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(__lowerCAmelCase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import numpy as np
class __a :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = (0, 0)
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
SCREAMING_SNAKE_CASE__ : int = 0
def __eq__( self , _a ) -> str:
"""simple docstring"""
return self.position == cell.position
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
print(self.position )
class __a :
'''simple docstring'''
def __init__( self , _a=(5, 5) ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = np.zeros(_a )
SCREAMING_SNAKE_CASE__ : List[str] = world_size[0]
SCREAMING_SNAKE_CASE__ : Any = world_size[1]
def _a ( self ) -> Dict:
"""simple docstring"""
print(self.w )
def _a ( self , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [
(-1, -1),
(-1, 0),
(-1, 1),
(0, -1),
(0, 1),
(1, -1),
(1, 0),
(1, 1),
]
SCREAMING_SNAKE_CASE__ : int = cell.position[0]
SCREAMING_SNAKE_CASE__ : List[Any] = cell.position[1]
SCREAMING_SNAKE_CASE__ : Any = []
for n in neughbour_cord:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = current_x + n[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = current_y + n[1]
if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
SCREAMING_SNAKE_CASE__ : Optional[int] = Cell()
SCREAMING_SNAKE_CASE__ : Dict = (x, y)
SCREAMING_SNAKE_CASE__ : int = cell
neighbours.append(_a )
return neighbours
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : Any = []
SCREAMING_SNAKE_CASE__ : Dict = []
_open.append(__lowerCAmelCase )
while _open:
SCREAMING_SNAKE_CASE__ : Any = np.argmin([n.f for n in _open] )
SCREAMING_SNAKE_CASE__ : str = _open[min_f]
_closed.append(_open.pop(__lowerCAmelCase ) )
if current == goal:
break
for n in world.get_neigbours(__lowerCAmelCase ):
for c in _closed:
if c == n:
continue
SCREAMING_SNAKE_CASE__ : Optional[Any] = current.g + 1
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = n.position
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = goal.position
SCREAMING_SNAKE_CASE__ : Tuple = (ya - ya) ** 2 + (xa - xa) ** 2
SCREAMING_SNAKE_CASE__ : Any = n.h + n.g
for c in _open:
if c == n and c.f < n.f:
continue
_open.append(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
while current.parent is not None:
path.append(current.position )
SCREAMING_SNAKE_CASE__ : Tuple = current.parent
path.append(current.position )
return path[::-1]
if __name__ == "__main__":
a :Optional[Any] = Gridworld()
# Start position and goal
a :str = Cell()
a :Union[str, Any] = (0, 0)
a :Dict = Cell()
a :str = (4, 4)
print(f'path from {start.position} to {goal.position}')
a :List[str] = astar(world, start, goal)
# Just for visual reasons.
for i in s:
a :str = 1
print(world.w)
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowercase ( __lowerCAmelCase = "isbn/0140328726" ) -> dict:
SCREAMING_SNAKE_CASE__ : Optional[int] = olid.strip().strip("""/""" ) # Remove leading/trailing whitespace & slashes
if new_olid.count("""/""" ) != 1:
SCREAMING_SNAKE_CASE__ : Optional[int] = F'''{olid} is not a valid Open Library olid'''
raise ValueError(__lowerCAmelCase )
return requests.get(F'''https://openlibrary.org/{new_olid}.json''' ).json()
def _lowercase ( __lowerCAmelCase ) -> dict:
SCREAMING_SNAKE_CASE__ : Any = {
"""title""": """Title""",
"""publish_date""": """Publish date""",
"""authors""": """Authors""",
"""number_of_pages""": """Number of pages:""",
"""first_sentence""": """First sentence""",
"""isbn_10""": """ISBN (10)""",
"""isbn_13""": """ISBN (13)""",
}
SCREAMING_SNAKE_CASE__ : Optional[int] = {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
get_openlibrary_data(author["""key"""] )["""name"""] for author in data["""Authors"""]
]
SCREAMING_SNAKE_CASE__ : Dict = data["""First sentence"""]["""value"""]
for key, value in data.items():
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """, """.join(__lowerCAmelCase )
return data
if __name__ == "__main__":
import doctest
doctest.testmod()
while True:
a :Any = input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
if isbn.lower() in ("", "q", "quit", "exit", "stop"):
break
if len(isbn) not in (10, 13) or not isbn.isdigit():
print(f'Sorry, {isbn} is not a valid ISBN. Please, input a valid ISBN.')
continue
print(f'\nSearching Open Library for ISBN: {isbn}...\n')
try:
a :Optional[Any] = summarize_book(get_openlibrary_data(f'isbn/{isbn}'))
print("\n".join(f'{key}: {value}' for key, value in book_summary.items()))
except JSONDecodeError: # Workaround for requests.exceptions.RequestException:
print(f'Sorry, there are no results for ISBN: {isbn}.')
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
a :Dict = list[list[int]]
# assigning initial values to the grid
a :Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
a :Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> bool:
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def _lowercase ( __lowerCAmelCase ) -> tuple[int, int] | None:
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def _lowercase ( __lowerCAmelCase ) -> Matrix | None:
if location := find_empty_location(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[Any] = digit
if sudoku(__lowerCAmelCase ) is not None:
return grid
SCREAMING_SNAKE_CASE__ : str = 0
return None
def _lowercase ( __lowerCAmelCase ) -> None:
for row in grid:
for cell in row:
print(__lowerCAmelCase , end=""" """ )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("\nExample grid:\n" + "=" * 20)
print_solution(example_grid)
print("\nExample grid solution:")
a :Any = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("Cannot find a solution.")
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class __a :
'''simple docstring'''
def __init__( self , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : str = 13
SCREAMING_SNAKE_CASE__ : Dict = 7
SCREAMING_SNAKE_CASE__ : Optional[int] = 30
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.seq_length + self.mem_len
SCREAMING_SNAKE_CASE__ : Tuple = 15
SCREAMING_SNAKE_CASE__ : Any = True
SCREAMING_SNAKE_CASE__ : List[str] = True
SCREAMING_SNAKE_CASE__ : Tuple = 99
SCREAMING_SNAKE_CASE__ : Optional[int] = [10, 50, 80]
SCREAMING_SNAKE_CASE__ : Optional[Any] = 32
SCREAMING_SNAKE_CASE__ : int = 32
SCREAMING_SNAKE_CASE__ : Dict = 4
SCREAMING_SNAKE_CASE__ : Tuple = 8
SCREAMING_SNAKE_CASE__ : Dict = 128
SCREAMING_SNAKE_CASE__ : str = 2
SCREAMING_SNAKE_CASE__ : str = 2
SCREAMING_SNAKE_CASE__ : str = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 0
SCREAMING_SNAKE_CASE__ : Dict = 3
SCREAMING_SNAKE_CASE__ : List[str] = self.vocab_size - 1
SCREAMING_SNAKE_CASE__ : Tuple = 0.01
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def _a ( self ) -> List[Any]:
"""simple docstring"""
random.seed(self.seed )
tf.random.set_seed(self.seed )
def _a ( self , _a , _a , _a , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = TFTransfoXLModel(_a )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a ).to_tuple()
SCREAMING_SNAKE_CASE__ : Dict = {"""input_ids""": input_ids_a, """mems""": mems_a}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _a ( self , _a , _a , _a , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = TFTransfoXLLMHeadModel(_a )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = model(_a ).to_tuple()
SCREAMING_SNAKE_CASE__ : Dict = {"""input_ids""": input_ids_a, """labels""": lm_labels}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = model(_a ).to_tuple()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = model([input_ids_a, mems_a] ).to_tuple()
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = model(_a ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _a ( self , _a , _a , _a , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = TFTransfoXLForSequenceClassification(_a )
SCREAMING_SNAKE_CASE__ : List[str] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.prepare_config_and_inputs()
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[str] = {"""input_ids""": input_ids_a}
return config, inputs_dict
@require_tf
class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = () if is_tf_available() else ()
_SCREAMING_SNAKE_CASE :Tuple = (
{
"""feature-extraction""": TFTransfoXLModel,
"""text-classification""": TFTransfoXLForSequenceClassification,
"""text-generation""": TFTransfoXLLMHeadModel,
"""zero-shot""": TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
_SCREAMING_SNAKE_CASE :List[str] = False
_SCREAMING_SNAKE_CASE :Any = False
_SCREAMING_SNAKE_CASE :Dict = False
_SCREAMING_SNAKE_CASE :int = False
def _a ( self , _a , _a , _a , _a , _a ) -> Optional[int]:
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = TFTransfoXLModelTester(self )
SCREAMING_SNAKE_CASE__ : Optional[Any] = ConfigTester(self , config_class=_a , d_embed=37 )
def _a ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
self.model_tester.set_seed()
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
self.model_tester.set_seed()
SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*_a )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE__ : Optional[int] = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : str = model_class(_a )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
SCREAMING_SNAKE_CASE__ : List[Any] = model.get_output_embeddings()
assert isinstance(_a , tf.keras.layers.Layer )
SCREAMING_SNAKE_CASE__ : Any = model.get_bias()
assert name is None
else:
SCREAMING_SNAKE_CASE__ : Any = model.get_output_embeddings()
assert x is None
SCREAMING_SNAKE_CASE__ : List[Any] = model.get_bias()
assert name is None
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@slow
def _a ( self ) -> List[str]:
"""simple docstring"""
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE__ : Tuple = TFTransfoXLModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss.""" )
def _a ( self ) -> int:
"""simple docstring"""
pass
@require_tf
class __a (unittest.TestCase):
'''simple docstring'''
@unittest.skip("""Skip test until #12651 is resolved.""" )
@slow
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103""" )
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
SCREAMING_SNAKE_CASE__ : int = [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(_a , max_length=200 , do_sample=_a )
self.assertListEqual(output_ids[0].numpy().tolist() , _a )
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> set[str]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = set(__lowerCAmelCase ), [start]
while stack:
SCREAMING_SNAKE_CASE__ : int = stack.pop()
explored.add(__lowerCAmelCase )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(__lowerCAmelCase )
return explored
a :Any = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Optional[int] = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE__ : List[Any] = get_edges(__lowerCAmelCase )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = edges.pop()
chosen_vertices.add(__lowerCAmelCase )
chosen_vertices.add(__lowerCAmelCase )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(__lowerCAmelCase )
return chosen_vertices
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : List[Any] = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = """"""
_SCREAMING_SNAKE_CASE :Optional[int] = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , _a = None , _a = None , **_a , ) -> int:
"""simple docstring"""
super().__init__(self , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = repo_info
SCREAMING_SNAKE_CASE__ : Tuple = token
SCREAMING_SNAKE_CASE__ : str = None
def _a ( self ) -> Optional[int]:
"""simple docstring"""
if self.dir_cache is None:
SCREAMING_SNAKE_CASE__ : Tuple = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(_a ): {"""name""": str(_a ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def _a ( self , _a , _a = "rb" , **_a , ) -> Dict:
"""simple docstring"""
if not isinstance(self.repo_info , _a ):
raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}''' )
SCREAMING_SNAKE_CASE__ : Any = hf_hub_url(self.repo_info.id , _a , revision=self.repo_info.sha )
return fsspec.open(
_a , mode=_a , headers=get_authentication_headers_for_url(_a , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def _a ( self , _a , **_a ) -> List[str]:
"""simple docstring"""
self._get_dirs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self._strip_protocol(_a )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(_a )
def _a ( self , _a , _a=False , **_a ) -> Optional[int]:
"""simple docstring"""
self._get_dirs()
SCREAMING_SNAKE_CASE__ : List[Any] = PurePosixPath(path.strip("""/""" ) )
SCREAMING_SNAKE_CASE__ : Tuple = {}
for p, f in self.dir_cache.items():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = PurePosixPath(p.strip("""/""" ) )
SCREAMING_SNAKE_CASE__ : Tuple = p.parent
if root == path:
SCREAMING_SNAKE_CASE__ : str = f
SCREAMING_SNAKE_CASE__ : Tuple = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = AutoTokenizer.from_pretrained("""google/mt5-small""" )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer("""Hello there""" , return_tensors="""np""" ).input_ids
SCREAMING_SNAKE_CASE__ : Dict = tokenizer("""Hi I am""" , return_tensors="""np""" ).input_ids
SCREAMING_SNAKE_CASE__ : str = shift_tokens_right(_a , model.config.pad_token_id , model.config.decoder_start_token_id )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a , decoder_input_ids=_a ).logits
SCREAMING_SNAKE_CASE__ : Union[str, Any] = optax.softmax_cross_entropy(_a , onehot(_a , logits.shape[-1] ) ).mean()
SCREAMING_SNAKE_CASE__ : List[str] = -(labels.shape[-1] * loss.item())
SCREAMING_SNAKE_CASE__ : str = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = RoCBertTokenizer
_SCREAMING_SNAKE_CASE :Tuple = None
_SCREAMING_SNAKE_CASE :Dict = False
_SCREAMING_SNAKE_CASE :int = True
_SCREAMING_SNAKE_CASE :Any = filter_non_english
def _a ( self ) -> Optional[int]:
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ : Any = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
SCREAMING_SNAKE_CASE__ : List[Any] = {}
SCREAMING_SNAKE_CASE__ : List[Any] = {}
for i, value in enumerate(_a ):
SCREAMING_SNAKE_CASE__ : List[Any] = i
SCREAMING_SNAKE_CASE__ : str = i
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_shape_file"""] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_pronunciation_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
with open(self.word_shape_file , """w""" , encoding="""utf-8""" ) as word_shape_writer:
json.dump(_a , _a , ensure_ascii=_a )
with open(self.word_pronunciation_file , """w""" , encoding="""utf-8""" ) as word_pronunciation_writer:
json.dump(_a , _a , ensure_ascii=_a )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.tokenize("""你好[SEP]你是谁""" )
self.assertListEqual(_a , ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(_a ) , [5, 6, 2, 5, 7, 8] )
self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(_a ) , [5, 6, 2, 5, 7, 8] )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = RoCBertBasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = RoCBertBasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = RoCBertBasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = RoCBertBasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = RoCBertBasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = RoCBertBasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=_a , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = {}
for i, token in enumerate(_a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = i
SCREAMING_SNAKE_CASE__ : List[str] = RoCBertWordpieceTokenizer(vocab=_a , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def _a ( self ) -> Any:
"""simple docstring"""
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def _a ( self ) -> Tuple:
"""simple docstring"""
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def _a ( self ) -> Any:
"""simple docstring"""
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(_a ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
if self.test_rust_tokenizer:
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_rust_tokenizer()
self.assertListEqual(
[rust_tokenizer.tokenize(_a ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : List[Any] = self.rust_tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : Dict = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_r.encode_plus(
_a , return_attention_mask=_a , return_token_type_ids=_a , return_offsets_mapping=_a , add_special_tokens=_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer_r.do_lower_case if hasattr(_a , """do_lower_case""" ) else False
SCREAMING_SNAKE_CASE__ : Optional[int] = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""的""", """人""", """有"""]
SCREAMING_SNAKE_CASE__ : Dict = """""".join(_a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : Tuple = self.tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : str = self.rust_tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer_p.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer_r.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer_r.convert_ids_to_tokens(_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer_p.convert_ids_to_tokens(_a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(_a , _a )
self.assertListEqual(_a , _a )
SCREAMING_SNAKE_CASE__ : Any = False
SCREAMING_SNAKE_CASE__ : Tuple = self.rust_tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.tokenizer_class.from_pretrained(_a , **_a )
SCREAMING_SNAKE_CASE__ : str = tokenizer_r.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_p.encode(_a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer_r.convert_ids_to_tokens(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer_p.convert_ids_to_tokens(_a )
# it is expected that only the first Chinese character is not preceded by "##".
SCREAMING_SNAKE_CASE__ : Tuple = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(_a )
]
self.assertListEqual(_a , _a )
self.assertListEqual(_a , _a )
@slow
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.encode("""你好""" , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.encode("""你是谁""" , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenizer.build_inputs_with_special_tokens(_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == [1] + text + [2]
assert encoded_pair == [1] + text + [2] + text_a + [2]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizers(do_lower_case=_a )
for tokenizer in tokenizers:
with self.subTest(f'''{tokenizer.__class__.__name__}''' ):
SCREAMING_SNAKE_CASE__ : Any = """你好，你是谁"""
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer.tokenize(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.convert_tokens_to_shape_ids(_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.convert_tokens_to_pronunciation_ids(_a )
SCREAMING_SNAKE_CASE__ : int = tokenizer.prepare_for_model(
_a , _a , _a , add_special_tokens=_a )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.encode_plus(_a , add_special_tokens=_a )
self.assertEqual(_a , _a )
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging
a :List[Any] = logging.get_logger(__name__)
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = ["""pixel_values"""]
def __init__( self , _a = True , _a = 1 / 255 , _a = True , _a = 8 , **_a , ) -> None:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : str = do_rescale
SCREAMING_SNAKE_CASE__ : List[Any] = rescale_factor
SCREAMING_SNAKE_CASE__ : Tuple = do_pad
SCREAMING_SNAKE_CASE__ : Optional[int] = pad_size
def _a ( self , _a , _a , _a = None , **_a ) -> np.ndarray:
"""simple docstring"""
return rescale(_a , scale=_a , data_format=_a , **_a )
def _a ( self , _a , _a , _a = None ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = get_image_size(_a )
SCREAMING_SNAKE_CASE__ : int = (old_height // size + 1) * size - old_height
SCREAMING_SNAKE_CASE__ : Any = (old_width // size + 1) * size - old_width
return pad(_a , ((0, pad_height), (0, pad_width)) , mode="""symmetric""" , data_format=_a )
def _a ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE__ : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE__ : List[str] = do_pad if do_pad is not None else self.do_pad
SCREAMING_SNAKE_CASE__ : Dict = pad_size if pad_size is not None else self.pad_size
SCREAMING_SNAKE_CASE__ : List[Any] = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE__ : Dict = [to_numpy_array(_a ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.rescale(image=_a , scale=_a ) for image in images]
if do_pad:
SCREAMING_SNAKE_CASE__ : Dict = [self.pad(_a , size=_a ) for image in images]
SCREAMING_SNAKE_CASE__ : Any = [to_channel_dimension_format(_a , _a ) for image in images]
SCREAMING_SNAKE_CASE__ : int = {"""pixel_values""": images}
return BatchFeature(data=_a , tensor_type=_a )
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Tuple = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = 4
SCREAMING_SNAKE_CASE__ : Optional[int] = 48
SCREAMING_SNAKE_CASE__ : str = """pixelshuffle_aux"""
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = [6, 6, 6, 6]
SCREAMING_SNAKE_CASE__ : List[str] = 60
SCREAMING_SNAKE_CASE__ : Optional[int] = [6, 6, 6, 6]
SCREAMING_SNAKE_CASE__ : int = """pixelshuffledirect"""
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = 4
SCREAMING_SNAKE_CASE__ : Tuple = """nearest+conv"""
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : str = 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = 126
SCREAMING_SNAKE_CASE__ : Dict = 7
SCREAMING_SNAKE_CASE__ : List[str] = 255.0
SCREAMING_SNAKE_CASE__ : List[Any] = """"""
return config
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
if "patch_embed.proj" in name and "layers" not in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""layers""" , """encoder.stages""" )
if "residual_group.blocks" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""residual_group.blocks""" , """layers""" )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE__ : int = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE__ : List[Any] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE__ : Dict = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "q_bias" in name:
SCREAMING_SNAKE_CASE__ : str = name.replace("""q_bias""" , """query.bias""" )
if "k_bias" in name:
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""k_bias""" , """key.bias""" )
if "v_bias" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""v_bias""" , """value.bias""" )
if "cpb_mlp" in name:
SCREAMING_SNAKE_CASE__ : Any = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""patch_embed.proj""" , """patch_embed.projection""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE__ : str = """layernorm.bias"""
if "conv_first" in name:
SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""conv_first""" , """first_convolution""" )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = name.replace("""conv_last""" , """final_convolution""" )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""" )
if "upsample.0" in name:
SCREAMING_SNAKE_CASE__ : Tuple = name.replace("""upsample.0""" , """upsample.convolution_0""" )
if "upsample.2" in name:
SCREAMING_SNAKE_CASE__ : Optional[int] = name.replace("""upsample.2""" , """upsample.convolution_1""" )
SCREAMING_SNAKE_CASE__ : List[str] = """upsample.""" + name
elif config.upsampler == "pixelshuffledirect":
SCREAMING_SNAKE_CASE__ : List[str] = name.replace("""upsample.0.weight""" , """upsample.conv.weight""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = name.replace("""upsample.0.bias""" , """upsample.conv.bias""" )
else:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = """swin2sr.""" + name
return name
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = orig_state_dict.pop(__lowerCAmelCase )
if "qkv" in key:
SCREAMING_SNAKE_CASE__ : str = key.split(""".""" )
SCREAMING_SNAKE_CASE__ : Dict = int(key_split[1] )
SCREAMING_SNAKE_CASE__ : Tuple = int(key_split[4] )
SCREAMING_SNAKE_CASE__ : Any = config.embed_dim
if "weight" in key:
SCREAMING_SNAKE_CASE__ : Any = val[:dim, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE__ : List[str] = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE__ : List[Any] = val[:dim]
SCREAMING_SNAKE_CASE__ : Any = val[dim : dim * 2]
SCREAMING_SNAKE_CASE__ : Dict = val[-dim:]
pass
else:
SCREAMING_SNAKE_CASE__ : List[str] = val
return orig_state_dict
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] = get_config(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = SwinaSRForImageSuperResolution(__lowerCAmelCase )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = torch.hub.load_state_dict_from_url(__lowerCAmelCase , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_state_dict(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
if len(__lowerCAmelCase ) > 0:
raise ValueError("""Missing keys when converting: {}""".format(__lowerCAmelCase ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(F'''Unexpected key {key} in state_dict''' )
# verify values
SCREAMING_SNAKE_CASE__ : Any = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Image.open(requests.get(__lowerCAmelCase , stream=__lowerCAmelCase ).raw ).convert("""RGB""" )
SCREAMING_SNAKE_CASE__ : Any = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE__ : str = 126 if """Jpeg""" in checkpoint_url else 256
SCREAMING_SNAKE_CASE__ : Tuple = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = transforms(__lowerCAmelCase ).unsqueeze(0 )
if config.num_channels == 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = pixel_values[:, 0, :, :].unsqueeze(1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(__lowerCAmelCase )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Any = torch.Size([1, 3, 512, 512] )
SCREAMING_SNAKE_CASE__ : Any = torch.tensor(
[[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Dict = torch.Size([1, 3, 1024, 1024] )
SCREAMING_SNAKE_CASE__ : Any = torch.tensor(
[[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
SCREAMING_SNAKE_CASE__ : str = torch.Size([1, 3, 1024, 1024] )
SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor(
[[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Size([1, 3, 512, 512] )
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor(
[[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.Size([1, 3, 1024, 1024] )
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(
[[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]] )
assert (
outputs.reconstruction.shape == expected_shape
), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , __lowerCAmelCase , atol=1E-3 )
print("""Looks ok!""" )
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
"""swin2SR-classical-sr-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
"""swin2SR-classical-sr-x4-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
"""swin2SR-compressed-sr-x4-48"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
"""swin2SR-lightweight-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
"""swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
),
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__lowerCAmelCase )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(__lowerCAmelCase )
if push_to_hub:
model.push_to_hub(F'''caidas/{model_name}''' )
processor.push_to_hub(F'''caidas/{model_name}''' )
if __name__ == "__main__":
a :int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth",
type=str,
help="URL of the original Swin2SR checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the converted model to the hub.")
a :List[Any] = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None ) -> None:
if start is None:
SCREAMING_SNAKE_CASE__ : Tuple = 0
if end is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase ) - 1
if start >= end:
return
SCREAMING_SNAKE_CASE__ : Optional[int] = (start + end) // 2
slowsort(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
slowsort(__lowerCAmelCase , mid + 1 , __lowerCAmelCase )
if sequence[end] < sequence[mid]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = sequence[mid], sequence[end]
slowsort(__lowerCAmelCase , __lowerCAmelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] = filter(lambda __lowerCAmelCase : p.requires_grad , model.parameters() )
SCREAMING_SNAKE_CASE__ : str = sum([np.prod(p.size() ) for p in model_parameters] )
return params
a :Optional[int] = logging.getLogger(__name__)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
if metric == "rouge2":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """{val_avg_rouge2:.4f}-{step_count}"""
elif metric == "bleu":
SCREAMING_SNAKE_CASE__ : int = """{val_avg_bleu:.4f}-{step_count}"""
elif metric == "em":
SCREAMING_SNAKE_CASE__ : str = """{val_avg_em:.4f}-{step_count}"""
elif metric == "loss":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """{val_avg_loss:.4f}-{step_count}"""
else:
raise NotImplementedError(
F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
""" function.""" )
SCREAMING_SNAKE_CASE__ : Any = ModelCheckpoint(
dirpath=__lowerCAmelCase , filename=__lowerCAmelCase , monitor=F'''val_{metric}''' , mode="""max""" , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Any:
return EarlyStopping(
monitor=F'''val_{metric}''' , mode="""min""" if """loss""" in metric else """max""" , patience=__lowerCAmelCase , verbose=__lowerCAmelCase , )
class __a (pl.Callback):
'''simple docstring'''
def _a ( self , _a , _a ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {f'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(_a )
@rank_zero_only
def _a ( self , _a , _a , _a , _a=True ) -> None:
"""simple docstring"""
logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''' )
SCREAMING_SNAKE_CASE__ : List[Any] = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]} )
# Log results
SCREAMING_SNAKE_CASE__ : Optional[Any] = Path(pl_module.hparams.output_dir )
if type_path == "test":
SCREAMING_SNAKE_CASE__ : Tuple = od / """test_results.txt"""
SCREAMING_SNAKE_CASE__ : Any = od / """test_generations.txt"""
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
SCREAMING_SNAKE_CASE__ : List[Any] = od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
results_file.parent.mkdir(exist_ok=_a )
generations_file.parent.mkdir(exist_ok=_a )
with open(_a , """a+""" ) as writer:
for key in sorted(_a ):
if key in ["log", "progress_bar", "preds"]:
continue
SCREAMING_SNAKE_CASE__ : Optional[int] = metrics[key]
if isinstance(_a , torch.Tensor ):
SCREAMING_SNAKE_CASE__ : Any = val.item()
SCREAMING_SNAKE_CASE__ : Tuple = f'''{key}: {val:.6f}\n'''
writer.write(_a )
if not save_generations:
return
if "preds" in metrics:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """\n""".join(metrics["""preds"""] )
generations_file.open("""w+""" ).write(_a )
@rank_zero_only
def _a ( self , _a , _a ) -> Tuple:
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE__ : int = pl_module.model.model.num_parameters()
except AttributeError:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pl_module.model.num_parameters()
SCREAMING_SNAKE_CASE__ : Optional[Any] = count_trainable_parameters(_a )
# mp stands for million parameters
trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6} )
@rank_zero_only
def _a ( self , _a , _a ) -> str:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(_a , _a , """test""" )
@rank_zero_only
def _a ( self , _a , _a ) -> Union[str, Any]:
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a :List[str] = logging.getLogger()
def _lowercase ( ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser()
parser.add_argument("""-f""" )
SCREAMING_SNAKE_CASE__ : str = parser.parse_args()
return args.f
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.StreamHandler(sys.stdout )
logger.addHandler(_a )
def _a ( self , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = get_gpu_count()
if n_gpu > 1:
pass
# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
# cmd = [sys.executable] + distributed_args + args
# execute_subprocess_async(cmd, env=self.get_env())
# XXX: test the results - need to save them first into .json file
else:
args.insert(0 , """run_glue_deebert.py""" )
with patch.object(_a , """argv""" , _a ):
SCREAMING_SNAKE_CASE__ : Dict = run_glue_deebert.main()
for value in result.values():
self.assertGreaterEqual(_a , 0.666 )
@slow
@require_torch_non_multi_gpu
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """
--model_type roberta
--model_name_or_path roberta-base
--task_name MRPC
--do_train
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--max_seq_length 128
--per_gpu_eval_batch_size=1
--per_gpu_train_batch_size=8
--learning_rate 2e-4
--num_train_epochs 3
--overwrite_output_dir
--seed 42
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--save_steps 0
--overwrite_cache
--eval_after_first_stage
""".split()
self.run_and_check(_a )
SCREAMING_SNAKE_CASE__ : int = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--eval_each_highway
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_a )
SCREAMING_SNAKE_CASE__ : List[str] = """
--model_type roberta
--model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--task_name MRPC
--do_eval
--do_lower_case
--data_dir ./tests/fixtures/tests_samples/MRPC/
--output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
--plot_data_dir ./examples/deebert/results/
--max_seq_length 128
--early_exit_entropy 0.1
--eval_highway
--overwrite_cache
--per_gpu_eval_batch_size=1
""".split()
self.run_and_check(_a )
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "col_2": "b"},
{"col_1": 1, "col_2": "c"},
{"col_1": 0, "col_2": "d"},
]
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
return Dataset.from_dict(_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self._create_example_records()
SCREAMING_SNAKE_CASE__ : Tuple = Dataset.from_list(_a )
self.assertListEqual(dset.column_names , ["""col_1""", """col_2"""] )
for i, r in enumerate(_a ):
self.assertDictEqual(_a , example_records[i] )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self._create_example_records()
SCREAMING_SNAKE_CASE__ : Tuple = Dataset.from_list(_a )
SCREAMING_SNAKE_CASE__ : Any = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]} )
self.assertEqual(dset.info , dset_from_dict.info )
def _a ( self ) -> Dict: # checks what happens with missing columns
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = [{"""col_1""": 1}, {"""col_2""": """x"""}]
SCREAMING_SNAKE_CASE__ : Optional[int] = Dataset.from_list(_a )
self.assertDictEqual(dset[0] , {"""col_1""": 1} )
self.assertDictEqual(dset[1] , {"""col_1""": None} ) # NB: first record is used for columns
def _a ( self ) -> str: # checks if the type can be inferred from the second record
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [{"""col_1""": []}, {"""col_1""": [1, 2]}]
SCREAMING_SNAKE_CASE__ : List[str] = Dataset.from_list(_a )
self.assertEqual(dset.info.features["""col_1"""] , Sequence(Value("""int64""" ) ) )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dataset.from_list([] )
self.assertEqual(len(_a ) , 0 )
self.assertListEqual(dset.column_names , [] )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
if index == r:
for j in range(__lowerCAmelCase ):
print(data[j] , end=""" """ )
print(""" """ )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
SCREAMING_SNAKE_CASE__ : Optional[int] = arr[i]
combination_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , index + 1 , __lowerCAmelCase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# A temporary array to store all combination one by one
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , 0 , __lowerCAmelCase , 0 )
if __name__ == "__main__":
# Driver code to check the function above
a :str = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a :Optional[Any] = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a :Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """bert-generation"""
def __init__( self , _a=50_358 , _a=1_024 , _a=24 , _a=16 , _a=4_096 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=0.02 , _a=1E-1_2 , _a=0 , _a=2 , _a=1 , _a="absolute" , _a=True , **_a , ) -> List[str]:
"""simple docstring"""
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
SCREAMING_SNAKE_CASE__ : Dict = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : int = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : List[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Union[str, Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Tuple = initializer_range
SCREAMING_SNAKE_CASE__ : Dict = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Dict = position_embedding_type
SCREAMING_SNAKE_CASE__ : Any = use_cache
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
import math
import unittest
def _lowercase ( __lowerCAmelCase ) -> bool:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Any:
"""simple docstring"""
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
with self.assertRaises(_a ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) , """Zero doesn't have any positive factors, primes must have exactly two.""" , )
self.assertFalse(
is_prime(1 ) , """One only has 1 positive factor, primes must have exactly two.""" , )
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
return "\n".join(
F'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
SCREAMING_SNAKE_CASE__ : Any = sorted(string.lower() )
return len(__lowerCAmelCase ) == len(set(__lowerCAmelCase ) )
if __name__ == "__main__":
a :str = input("Enter a string ").strip()
a :List[Any] = is_isogram(input_str)
print(f'{input_str} is {"an" if isogram else "not an"} isogram.')
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
a :Any = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :bool = field(default=UpperCamelCase_ , metadata={"""help""": """Whether tp freeze the encoder."""})
_SCREAMING_SNAKE_CASE :bool = field(default=UpperCamelCase_ , metadata={"""help""": """Whether to freeze the embeddings."""})
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default="""summarization""" , metadata={"""help""": """Task name, summarization (or summarization_{dataset} for pegasus) or translation"""} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total sequence length for target text after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_42 , metadata={
"""help""": (
"""The maximum total sequence length for validation target text after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded. """
"""This argument is also used to override the ``max_length`` param of ``model.generate``, which is used """
"""during ``evaluate`` and ``predict``."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_42 , metadata={
"""help""": (
"""The maximum total sequence length for test target text after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(default=-1 , metadata={"""help""": """# training examples. -1 means use all."""})
_SCREAMING_SNAKE_CASE :Optional[int] = field(default=-1 , metadata={"""help""": """# validation examples. -1 means use all."""})
_SCREAMING_SNAKE_CASE :Optional[int] = field(default=-1 , metadata={"""help""": """# test examples. -1 means use all."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(default=UpperCamelCase_ , metadata={"""help""": """Source language id for translation."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(default=UpperCamelCase_ , metadata={"""help""": """Target language id for translation."""})
_SCREAMING_SNAKE_CASE :Optional[int] = field(default=UpperCamelCase_ , metadata={"""help""": """# num_beams to use for evaluation."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."""} , )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
logger.info(F'''***** {split} metrics *****''' )
for key in sorted(metrics.keys() ):
logger.info(F''' {key} = {metrics[key]}''' )
save_json(__lowerCAmelCase , os.path.join(__lowerCAmelCase , F'''{split}_results.json''' ) )
def _lowercase ( ) -> Dict:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = parser.parse_args_into_dataclasses()
check_output_dir(__lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , __lowerCAmelCase )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE__ : Dict = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
assert hasattr(__lowerCAmelCase , __lowerCAmelCase ), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
setattr(__lowerCAmelCase , __lowerCAmelCase , getattr(__lowerCAmelCase , __lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : int = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
SCREAMING_SNAKE_CASE__ : int = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(__lowerCAmelCase , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(__lowerCAmelCase )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
SCREAMING_SNAKE_CASE__ : List[str] = SeqaSeqDataset
# Get datasets
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
dataset_class(
__lowerCAmelCase , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
SCREAMING_SNAKE_CASE__ : int = (
dataset_class(
__lowerCAmelCase , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
SCREAMING_SNAKE_CASE__ : Any = (
dataset_class(
__lowerCAmelCase , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Tuple = (
build_compute_metrics_fn(data_args.task , __lowerCAmelCase ) if training_args.predict_with_generate else None
)
SCREAMING_SNAKE_CASE__ : Dict = SeqaSeqTrainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , data_args=__lowerCAmelCase , train_dataset=__lowerCAmelCase , eval_dataset=__lowerCAmelCase , data_collator=SeqaSeqDataCollator(
__lowerCAmelCase , __lowerCAmelCase , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , )
SCREAMING_SNAKE_CASE__ : Optional[int] = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
SCREAMING_SNAKE_CASE__ : Any = train_result.metrics
SCREAMING_SNAKE_CASE__ : List[Any] = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , __lowerCAmelCase , training_args.output_dir )
all_metrics.update(__lowerCAmelCase )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : List[Any] = trainer.evaluate(metric_key_prefix="""val""" )
SCREAMING_SNAKE_CASE__ : List[str] = data_args.n_val
SCREAMING_SNAKE_CASE__ : Dict = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , __lowerCAmelCase , training_args.output_dir )
all_metrics.update(__lowerCAmelCase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ : int = trainer.predict(test_dataset=__lowerCAmelCase , metric_key_prefix="""test""" )
SCREAMING_SNAKE_CASE__ : List[Any] = test_output.metrics
SCREAMING_SNAKE_CASE__ : int = data_args.n_test
if trainer.is_world_process_zero():
SCREAMING_SNAKE_CASE__ : Optional[Any] = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , __lowerCAmelCase , training_args.output_dir )
all_metrics.update(__lowerCAmelCase )
if training_args.predict_with_generate:
SCREAMING_SNAKE_CASE__ : int = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=__lowerCAmelCase , clean_up_tokenization_spaces=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = lmap(str.strip , __lowerCAmelCase )
write_txt_file(__lowerCAmelCase , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(__lowerCAmelCase , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
a :List[str] = True
except ImportError:
a :Tuple = False
try:
from torch.hub import _get_torch_home
a :Optional[int] = _get_torch_home()
except ImportError:
a :List[Any] = os.path.expanduser(
os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
)
a :Optional[Any] = os.path.join(torch_cache_home, "transformers")
a :Union[str, Any] = "https://cdn.huggingface.co"
a :str = "https://s3.amazonaws.com/models.huggingface.co/bert"
a :str = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
a :Optional[Any] = os.path.join(PATH, "config.yaml")
a :Any = os.path.join(PATH, "attributes.txt")
a :Optional[Any] = os.path.join(PATH, "objects.txt")
a :Tuple = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
a :Optional[Any] = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
a :Any = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
a :Optional[int] = "pytorch_model.bin"
a :Dict = "config.yaml"
def _lowercase ( __lowerCAmelCase=OBJECTS , __lowerCAmelCase=ATTRIBUTES ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[int] = []
with open(__lowerCAmelCase ) as f:
for object in f.readlines():
vg_classes.append(object.split(""",""" )[0].lower().strip() )
SCREAMING_SNAKE_CASE__ : Optional[int] = []
with open(__lowerCAmelCase ) as f:
for object in f.readlines():
vg_attrs.append(object.split(""",""" )[0].lower().strip() )
return vg_classes, vg_attrs
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : str = OrderedDict()
with open(__lowerCAmelCase , """rb""" ) as f:
SCREAMING_SNAKE_CASE__ : int = pkl.load(__lowerCAmelCase )["""model"""]
for k in copy.deepcopy(list(ckp.keys() ) ):
SCREAMING_SNAKE_CASE__ : List[Any] = ckp.pop(__lowerCAmelCase )
if isinstance(__lowerCAmelCase , np.ndarray ):
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor(__lowerCAmelCase )
else:
assert isinstance(__lowerCAmelCase , torch.tensor ), type(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = v
return r
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = {}
def __init__( self , _a , _a = "root" , _a=0 ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = level
SCREAMING_SNAKE_CASE__ : Tuple = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
SCREAMING_SNAKE_CASE__ : Optional[Any] = copy.deepcopy(_a )
SCREAMING_SNAKE_CASE__ : List[str] = copy.deepcopy(_a )
if isinstance(_a , _a ):
SCREAMING_SNAKE_CASE__ : Tuple = Config(_a , name=_a , level=level + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = v
setattr(self , _a , _a )
SCREAMING_SNAKE_CASE__ : Tuple = d
def __repr__( self ) -> Optional[int]:
"""simple docstring"""
return str(list((self._pointer.keys()) ) )
def __setattr__( self , _a , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = val
SCREAMING_SNAKE_CASE__ : str = val
SCREAMING_SNAKE_CASE__ : str = key.split(""".""" )
SCREAMING_SNAKE_CASE__ : str = len(_a ) - 1
SCREAMING_SNAKE_CASE__ : List[str] = self._pointer
if len(_a ) > 1:
for i, l in enumerate(_a ):
if hasattr(self , _a ) and isinstance(getattr(self , _a ) , _a ):
setattr(getattr(self , _a ) , """.""".join(levels[i:] ) , _a )
if l == last_level:
SCREAMING_SNAKE_CASE__ : Any = val
else:
SCREAMING_SNAKE_CASE__ : str = pointer[l]
def _a ( self ) -> List[str]:
"""simple docstring"""
return self._pointer
def _a ( self , _a , _a ) -> Dict:
"""simple docstring"""
with open(f'''{file_name}''' , """w""" ) as stream:
dump(_a , _a )
def _a ( self , _a , _a ) -> int:
"""simple docstring"""
with open(f'''{file_name}''' , """w""" ) as stream:
json.dump(_a , _a )
@staticmethod
def _a ( _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a ) as stream:
SCREAMING_SNAKE_CASE__ : int = load(_a , Loader=_a )
return data
def __str__( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = """ """
if self._name != "root":
SCREAMING_SNAKE_CASE__ : List[Any] = f'''{t * (self._level-1)}{self._name}:\n'''
else:
SCREAMING_SNAKE_CASE__ : Tuple = """"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self._level
for i, (k, v) in enumerate(self._pointer.items() ):
if isinstance(_a , _a ):
r += f'''{t * (self._level)}{v}\n'''
self._level += 1
else:
r += f'''{t * (self._level)}{k}: {v} ({type(_a ).__name__})\n'''
SCREAMING_SNAKE_CASE__ : str = level
return r[:-1]
@classmethod
def _a ( cls , _a , **_a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = cls.get_config_dict(_a , **_a )
return cls(_a )
@classmethod
def _a ( cls , _a , **_a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = kwargs.pop("""cache_dir""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs.pop("""force_download""" , _a )
SCREAMING_SNAKE_CASE__ : Any = kwargs.pop("""resume_download""" , _a )
SCREAMING_SNAKE_CASE__ : List[str] = kwargs.pop("""proxies""" , _a )
SCREAMING_SNAKE_CASE__ : str = kwargs.pop("""local_files_only""" , _a )
if os.path.isdir(_a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(_a , _a )
elif os.path.isfile(_a ) or is_remote_url(_a ):
SCREAMING_SNAKE_CASE__ : List[str] = pretrained_model_name_or_path
else:
SCREAMING_SNAKE_CASE__ : str = hf_bucket_url(_a , filename=_a , use_cdn=_a )
try:
# Load from URL or cache if already cached
SCREAMING_SNAKE_CASE__ : Any = cached_path(
_a , cache_dir=_a , force_download=_a , proxies=_a , resume_download=_a , local_files_only=_a , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
SCREAMING_SNAKE_CASE__ : List[str] = Config.load_yaml(_a )
except EnvironmentError:
SCREAMING_SNAKE_CASE__ : Tuple = """Can't load config for"""
raise EnvironmentError(_a )
if resolved_config_file == config_file:
print("""loading configuration file from path""" )
else:
print("""loading configuration file cache""" )
return Config.load_yaml(_a ), kwargs
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int = torch.load("""dump.pt""" , map_location=in_tensor.device )
SCREAMING_SNAKE_CASE__ : Dict = in_tensor.numpy()
SCREAMING_SNAKE_CASE__ : Any = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(__lowerCAmelCase , __lowerCAmelCase , rtol=0.01 , atol=0.1 ), (
F'''{sum([1 for x in np.isclose(__lowerCAmelCase , __lowerCAmelCase , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %'''
" element-wise mismatch"
)
raise Exception("""tensors are all good""" )
# Hugging face functions below
def _lowercase ( __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Optional[Any] = urlparse(__lowerCAmelCase )
return parsed.scheme in ("http", "https")
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=True ) -> str:
SCREAMING_SNAKE_CASE__ : List[str] = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
SCREAMING_SNAKE_CASE__ : Tuple = """/""" not in model_id
if legacy_format:
return F'''{endpoint}/{model_id}-{filename}'''
else:
return F'''{endpoint}/{model_id}/{filename}'''
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=0 , __lowerCAmelCase=None , ) -> Tuple:
SCREAMING_SNAKE_CASE__ : List[str] = """python/{}""".format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
ua += "; " + "; ".join("""{}/{}""".format(__lowerCAmelCase , __lowerCAmelCase ) for k, v in user_agent.items() )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
ua += "; " + user_agent
SCREAMING_SNAKE_CASE__ : List[str] = {"""user-agent""": ua}
if resume_size > 0:
SCREAMING_SNAKE_CASE__ : Dict = """bytes=%d-""" % (resume_size,)
SCREAMING_SNAKE_CASE__ : Optional[int] = requests.get(__lowerCAmelCase , stream=__lowerCAmelCase , proxies=__lowerCAmelCase , headers=__lowerCAmelCase )
if response.status_code == 416: # Range not satisfiable
return
SCREAMING_SNAKE_CASE__ : Optional[int] = response.headers.get("""Content-Length""" )
SCREAMING_SNAKE_CASE__ : Any = resume_size + int(__lowerCAmelCase ) if content_length is not None else None
SCREAMING_SNAKE_CASE__ : int = tqdm(
unit="""B""" , unit_scale=__lowerCAmelCase , total=__lowerCAmelCase , initial=__lowerCAmelCase , desc="""Downloading""" , )
for chunk in response.iter_content(chunk_size=1024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(__lowerCAmelCase ) )
temp_file.write(__lowerCAmelCase )
progress.close()
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=10 , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=False , ) -> Union[str, Any]:
if cache_dir is None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = TRANSFORMERS_CACHE
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[Any] = str(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = None
if not local_files_only:
try:
SCREAMING_SNAKE_CASE__ : Dict = requests.head(__lowerCAmelCase , allow_redirects=__lowerCAmelCase , proxies=__lowerCAmelCase , timeout=__lowerCAmelCase )
if response.status_code == 200:
SCREAMING_SNAKE_CASE__ : List[str] = response.headers.get("""ETag""" )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
SCREAMING_SNAKE_CASE__ : Tuple = url_to_filename(__lowerCAmelCase , __lowerCAmelCase )
# get cache path to put the file
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(__lowerCAmelCase ):
return cache_path
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
file
for file in fnmatch.filter(os.listdir(__lowerCAmelCase ) , filename + """.*""" )
if not file.endswith(""".json""" ) and not file.endswith(""".lock""" )
]
if len(__lowerCAmelCase ) > 0:
return os.path.join(__lowerCAmelCase , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
"""Cannot find the requested files in the cached path and outgoing traffic has been"""
""" disabled. To enable model look-ups and downloads online, set 'local_files_only'"""
""" to False.""" )
return None
# From now on, etag is not None.
if os.path.exists(__lowerCAmelCase ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
SCREAMING_SNAKE_CASE__ : List[Any] = cache_path + """.lock"""
with FileLock(__lowerCAmelCase ):
# If the download just completed while the lock was activated.
if os.path.exists(__lowerCAmelCase ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
SCREAMING_SNAKE_CASE__ : Dict = cache_path + """.incomplete"""
@contextmanager
def _resumable_file_manager():
with open(__lowerCAmelCase , """a+b""" ) as f:
yield f
SCREAMING_SNAKE_CASE__ : Tuple = _resumable_file_manager
if os.path.exists(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = os.stat(__lowerCAmelCase ).st_size
else:
SCREAMING_SNAKE_CASE__ : str = 0
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = partial(tempfile.NamedTemporaryFile , dir=__lowerCAmelCase , delete=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
"""%s not found in cache or force_download set to True, downloading to %s""" , __lowerCAmelCase , temp_file.name , )
http_get(
__lowerCAmelCase , __lowerCAmelCase , proxies=__lowerCAmelCase , resume_size=__lowerCAmelCase , user_agent=__lowerCAmelCase , )
os.replace(temp_file.name , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = {"""url""": url, """etag""": etag}
SCREAMING_SNAKE_CASE__ : Optional[Any] = cache_path + """.json"""
with open(__lowerCAmelCase , """w""" ) as meta_file:
json.dump(__lowerCAmelCase , __lowerCAmelCase )
return cache_path
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = url.encode("""utf-8""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = shaaaa(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = url_hash.hexdigest()
if etag:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = etag.encode("""utf-8""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = shaaaa(__lowerCAmelCase )
filename += "." + etag_hash.hexdigest()
if url.endswith(""".h5""" ):
filename += ".h5"
return filename
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=False , ) -> List[str]:
if cache_dir is None:
SCREAMING_SNAKE_CASE__ : str = TRANSFORMERS_CACHE
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = str(__lowerCAmelCase )
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = str(__lowerCAmelCase )
if is_remote_url(__lowerCAmelCase ):
# URL, so get it from the cache (downloading if necessary)
SCREAMING_SNAKE_CASE__ : str = get_from_cache(
__lowerCAmelCase , cache_dir=__lowerCAmelCase , force_download=__lowerCAmelCase , proxies=__lowerCAmelCase , resume_download=__lowerCAmelCase , user_agent=__lowerCAmelCase , local_files_only=__lowerCAmelCase , )
elif os.path.exists(__lowerCAmelCase ):
# File, and it exists.
SCREAMING_SNAKE_CASE__ : Optional[Any] = url_or_filename
elif urlparse(__lowerCAmelCase ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError("""file {} not found""".format(__lowerCAmelCase ) )
else:
# Something unknown
raise ValueError("""unable to parse {} as a URL or as a local path""".format(__lowerCAmelCase ) )
if extract_compressed_file:
if not is_zipfile(__lowerCAmelCase ) and not tarfile.is_tarfile(__lowerCAmelCase ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = os.path.split(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = output_file.replace(""".""" , """-""" ) + """-extracted"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.isdir(__lowerCAmelCase ) and os.listdir(__lowerCAmelCase ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
SCREAMING_SNAKE_CASE__ : Tuple = output_path + """.lock"""
with FileLock(__lowerCAmelCase ):
shutil.rmtree(__lowerCAmelCase , ignore_errors=__lowerCAmelCase )
os.makedirs(__lowerCAmelCase )
if is_zipfile(__lowerCAmelCase ):
with ZipFile(__lowerCAmelCase , """r""" ) as zip_file:
zip_file.extractall(__lowerCAmelCase )
zip_file.close()
elif tarfile.is_tarfile(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Tuple = tarfile.open(__lowerCAmelCase )
tar_file.extractall(__lowerCAmelCase )
tar_file.close()
else:
raise EnvironmentError("""Archive format of {} could not be identified""".format(__lowerCAmelCase ) )
return output_path_extracted
return output_path
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase="," ) -> Optional[Any]:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
if os.path.isfile(__lowerCAmelCase ):
with open(__lowerCAmelCase ) as f:
SCREAMING_SNAKE_CASE__ : Optional[Any] = eval(f.read() )
else:
SCREAMING_SNAKE_CASE__ : str = requests.get(__lowerCAmelCase )
try:
SCREAMING_SNAKE_CASE__ : Dict = requests.json()
except Exception:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = req.content.decode()
assert data is not None, "could not connect"
try:
SCREAMING_SNAKE_CASE__ : Dict = eval(__lowerCAmelCase )
except Exception:
SCREAMING_SNAKE_CASE__ : Dict = data.split("""\n""" )
req.close()
return data
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = requests.get(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = np.array(Image.open(BytesIO(response.content ) ) )
return img
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : str = url.split("""/""" )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(__lowerCAmelCase )
with open(__lowerCAmelCase , """rb""" ) as stream:
SCREAMING_SNAKE_CASE__ : List[str] = pkl.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = weights.pop("""model""" )
SCREAMING_SNAKE_CASE__ : Any = {}
for k, v in model.items():
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.from_numpy(__lowerCAmelCase )
if "running_var" in k:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([0] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = k.replace("""running_var""" , """num_batches_tracked""" )
SCREAMING_SNAKE_CASE__ : int = zero
return new
def _lowercase ( ) -> Optional[Any]:
print(F'''{os.path.abspath(os.path.join(__lowerCAmelCase , os.pardir ) )}/demo.ipynb''' )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase="RGB" ) -> Union[str, Any]:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
if os.path.isfile(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cva.imread(__lowerCAmelCase )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = get_image_from_url(__lowerCAmelCase )
assert img is not None, F'''could not connect to: {im}'''
SCREAMING_SNAKE_CASE__ : Dict = cva.cvtColor(__lowerCAmelCase , cva.COLOR_BGR2RGB )
if input_format == "RGB":
SCREAMING_SNAKE_CASE__ : Optional[Any] = img[:, :, ::-1]
return img
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=1 ) -> str:
return (images[i : i + batch] for i in range(0 , len(__lowerCAmelCase ) , __lowerCAmelCase ))
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = """ylacombe/bark-small"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = """en_speaker_1"""
SCREAMING_SNAKE_CASE__ : Optional[int] = """This is a test string"""
SCREAMING_SNAKE_CASE__ : str = """speaker_embeddings_path.json"""
SCREAMING_SNAKE_CASE__ : List[str] = """speaker_embeddings"""
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
return AutoTokenizer.from_pretrained(self.checkpoint , **_a )
def _a ( self ) -> int:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Optional[int] = BarkProcessor(tokenizer=_a )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Optional[Any] = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : str = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="""(BOS)""" , eos_token="""(EOS)""" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 35
SCREAMING_SNAKE_CASE__ : Any = 2
SCREAMING_SNAKE_CASE__ : Tuple = 8
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""semantic_prompt""": np.ones(_a ),
"""coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
"""fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
SCREAMING_SNAKE_CASE__ : Tuple = processor(text=self.input_string , voice_preset=_a )
SCREAMING_SNAKE_CASE__ : Any = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_a , np.array([] ) ).tolist() )
# test loading voice preset from npz file
SCREAMING_SNAKE_CASE__ : str = os.path.join(self.tmpdirname , """file.npz""" )
np.savez(_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = processor(text=self.input_string , voice_preset=_a )
SCREAMING_SNAKE_CASE__ : Tuple = inputs["""history_prompt"""]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_a , np.array([] ) ).tolist() )
# test loading voice preset from the hub
SCREAMING_SNAKE_CASE__ : List[Any] = processor(text=self.input_string , voice_preset=self.voice_preset )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[Any] = BarkProcessor(tokenizer=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = processor(text=self.input_string )
SCREAMING_SNAKE_CASE__ : Dict = tokenizer(
self.input_string , padding="""max_length""" , max_length=256 , add_special_tokens=_a , return_attention_mask=_a , return_token_type_ids=_a , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
a :Optional[int] = logging.get_logger(__name__)
a :Optional[int] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
a :Dict = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
a :Dict = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :List[Any] = PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE :List[str] = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :List[Any] = DistilBertTokenizer
def __init__( self , _a=None , _a=None , _a=True , _a="[UNK]" , _a="[SEP]" , _a="[PAD]" , _a="[CLS]" , _a="[MASK]" , _a=True , _a=None , **_a , ) -> Optional[int]:
"""simple docstring"""
super().__init__(
_a , tokenizer_file=_a , do_lower_case=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , tokenize_chinese_chars=_a , strip_accents=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , _a ) != do_lower_case
or normalizer_state.get("""strip_accents""" , _a ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , _a ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE__ : int = getattr(_a , normalizer_state.pop("""type""" ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = do_lower_case
SCREAMING_SNAKE_CASE__ : Optional[Any] = strip_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = tokenize_chinese_chars
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalizer_class(**_a )
SCREAMING_SNAKE_CASE__ : List[Any] = do_lower_case
def _a ( self , _a , _a=None ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self._tokenizer.model.save(_a , name=_a )
return tuple(_a )
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = inspect.getfile(accelerate.test_utils )
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_script.py"""] )
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_distributed_data_loop.py"""] )
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_ops.py"""] )
@require_multi_gpu
def _a ( self ) -> Tuple:
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE__ : str = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
@require_multi_gpu
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices.''' )
SCREAMING_SNAKE_CASE__ : Dict = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
print(f'''Command: {cmd}''' )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
@require_multi_gpu
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(_a , env=os.environ.copy() )
@require_multi_gpu
def _a ( self ) -> Any:
"""simple docstring"""
print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only''' )
SCREAMING_SNAKE_CASE__ : List[str] = ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="""0,1""" ):
execute_subprocess_async(_a , env=os.environ.copy() )
if __name__ == "__main__":
a :Tuple = Accelerator()
a :Optional[int] = (accelerator.state.process_index + 2, 10)
a :Any = torch.randint(0, 10, shape).to(accelerator.device)
a :List[Any] = ""
a :List[str] = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
a :List[str] = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
a :Union[str, Any] = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> list[int]:
SCREAMING_SNAKE_CASE__ : Optional[int] = [True] * limit
SCREAMING_SNAKE_CASE__ : int = False
SCREAMING_SNAKE_CASE__ : Any = False
SCREAMING_SNAKE_CASE__ : int = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
SCREAMING_SNAKE_CASE__ : Dict = i * 2
while index < limit:
SCREAMING_SNAKE_CASE__ : int = False
SCREAMING_SNAKE_CASE__ : List[str] = index + i
SCREAMING_SNAKE_CASE__ : Any = [2]
for i in range(3 , __lowerCAmelCase , 2 ):
if is_prime[i]:
primes.append(__lowerCAmelCase )
return primes
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] = prime_sieve(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 0
for i in range(len(__lowerCAmelCase ) ):
for j in range(i + length , len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : Tuple = sum(primes[i:j] )
if sol >= ceiling:
break
if sol in primes:
SCREAMING_SNAKE_CASE__ : Any = j - i
SCREAMING_SNAKE_CASE__ : Dict = sol
return largest
if __name__ == "__main__":
print(f'{solution() = }')
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """ClapFeatureExtractor"""
_SCREAMING_SNAKE_CASE :List[Any] = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , _a , _a ) -> List[str]:
"""simple docstring"""
super().__init__(_a , _a )
def __call__( self , _a=None , _a=None , _a=None , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = kwargs.pop("""sampling_rate""" , _a )
if text is None and audios is None:
raise ValueError("""You have to specify either text or audios. Both cannot be none.""" )
if text is not None:
SCREAMING_SNAKE_CASE__ : int = self.tokenizer(_a , return_tensors=_a , **_a )
if audios is not None:
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extractor(
_a , sampling_rate=_a , return_tensors=_a , **_a )
if text is not None and audios is not None:
SCREAMING_SNAKE_CASE__ : Dict = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_a ) , tensor_type=_a )
def _a ( self , *_a , **_a ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*_a , **_a )
def _a ( self , *_a , **_a ) -> Tuple:
"""simple docstring"""
return self.tokenizer.decode(*_a , **_a )
@property
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.tokenizer.model_input_names
SCREAMING_SNAKE_CASE__ : List[str] = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a :str = logging.get_logger(__name__)
a :Any = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """convbert"""
def __init__( self , _a=30_522 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=2 , _a=0.02 , _a=1E-1_2 , _a=1 , _a=0 , _a=2 , _a=768 , _a=2 , _a=9 , _a=1 , _a=None , **_a , ) -> Dict:
"""simple docstring"""
super().__init__(
pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_size
SCREAMING_SNAKE_CASE__ : Dict = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE__ : int = intermediate_size
SCREAMING_SNAKE_CASE__ : str = hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : int = max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[str] = type_vocab_size
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embedding_size
SCREAMING_SNAKE_CASE__ : str = head_ratio
SCREAMING_SNAKE_CASE__ : Union[str, Any] = conv_kernel_size
SCREAMING_SNAKE_CASE__ : int = num_groups
SCREAMING_SNAKE_CASE__ : Union[str, Any] = classifier_dropout
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE__ : List[Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> bool:
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def _lowercase ( __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[int] = credit_card_number
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
SCREAMING_SNAKE_CASE__ : Dict = len(__lowerCAmelCase ) - 2
for i in range(__lowerCAmelCase , -1 , -2 ):
# double the value of every second digit
SCREAMING_SNAKE_CASE__ : Tuple = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
SCREAMING_SNAKE_CASE__ : Optional[Any] = cc_number[:i] + str(__lowerCAmelCase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(__lowerCAmelCase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def _lowercase ( __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : int = F'''{credit_card_number} is an invalid credit card number because'''
if not credit_card_number.isdigit():
print(F'''{error_message} it has nonnumerical characters.''' )
return False
if not 13 <= len(__lowerCAmelCase ) <= 16:
print(F'''{error_message} of its length.''' )
return False
if not validate_initial_digits(__lowerCAmelCase ):
print(F'''{error_message} of its first two digits.''' )
return False
if not luhn_validation(__lowerCAmelCase ):
print(F'''{error_message} it fails the Luhn check.''' )
return False
print(F'''{credit_card_number} is a valid credit card number.''' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("4111111111111111")
validate_credit_card_number("32323")
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
a :Dict = "tiny-wmt19-en-ru"
# Build
# borrowed from a test
a :Tuple = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
a :List[str] = dict(zip(vocab, range(len(vocab))))
a :List[str] = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
with tempfile.TemporaryDirectory() as tmpdirname:
a :Optional[int] = Path(tmpdirname)
a :Dict = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
a :int = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
a :List[Any] = build_dir / VOCAB_FILES_NAMES["merges_file"]
with open(src_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, "w") as fp:
fp.write("\n".join(merges))
a :Optional[Any] = FSMTTokenizer(
langs=["en", "ru"],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
a :Dict = FSMTConfig(
langs=["ru", "en"],
src_vocab_size=1_000,
tgt_vocab_size=1_000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
a :Any = FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')
# Test
a :str = tokenizer(["Making tiny model"], return_tensors="pt")
a :Union[str, Any] = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f'Generated {mname_tiny}')
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
a :List[Any] = None
a :Optional[int] = logging.get_logger(__name__)
a :Union[str, Any] = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
a :Dict = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
a :int = "▁"
# Segments (not really needed)
a :Dict = 0
a :Optional[int] = 1
a :Tuple = 2
a :List[str] = 3
a :Optional[Any] = 4
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = """left"""
_SCREAMING_SNAKE_CASE :Optional[Any] = XLNetTokenizer
def __init__( self , _a=None , _a=None , _a=False , _a=True , _a=False , _a="<s>" , _a="</s>" , _a="<unk>" , _a="<sep>" , _a="<pad>" , _a="<cls>" , _a="<mask>" , _a=["<eop>", "<eod>"] , **_a , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
super().__init__(
vocab_file=_a , tokenizer_file=_a , do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 3
SCREAMING_SNAKE_CASE__ : Optional[int] = do_lower_case
SCREAMING_SNAKE_CASE__ : List[str] = remove_space
SCREAMING_SNAKE_CASE__ : int = keep_accents
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_file
SCREAMING_SNAKE_CASE__ : Tuple = False if not self.vocab_file else True
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = (EulerDiscreteScheduler,)
_SCREAMING_SNAKE_CASE :Tuple = 10
def _a ( self , **_a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {
"""num_train_timesteps""": 1_100,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
}
config.update(**_a )
return config
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : str = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : int = self.dummy_model()
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE__ : Optional[Any] = sample.to(_a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.scale_model_input(_a , _a )
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
SCREAMING_SNAKE_CASE__ : Dict = scheduler.step(_a , _a , _a , generator=_a )
SCREAMING_SNAKE_CASE__ : str = output.prev_sample
SCREAMING_SNAKE_CASE__ : List[str] = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 10.0_807 ) < 1E-2
assert abs(result_mean.item() - 0.0_131 ) < 1E-3
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps )
SCREAMING_SNAKE_CASE__ : int = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
SCREAMING_SNAKE_CASE__ : Tuple = sample.to(_a )
for i, t in enumerate(scheduler.timesteps ):
SCREAMING_SNAKE_CASE__ : Tuple = scheduler.scale_model_input(_a , _a )
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = scheduler.step(_a , _a , _a , generator=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = output.prev_sample
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 0.0_002 ) < 1E-2
assert abs(result_mean.item() - 2.2_6_7_6E-0_6 ) < 1E-3
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Tuple = scheduler_class(**_a )
scheduler.set_timesteps(self.num_inference_steps , device=_a )
SCREAMING_SNAKE_CASE__ : Dict = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = self.dummy_model()
SCREAMING_SNAKE_CASE__ : int = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
SCREAMING_SNAKE_CASE__ : Dict = sample.to(_a )
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE__ : str = scheduler.scale_model_input(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = output.prev_sample
SCREAMING_SNAKE_CASE__ : Dict = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Tuple = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 10.0_807 ) < 1E-2
assert abs(result_mean.item() - 0.0_131 ) < 1E-3
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler_class(**_a , use_karras_sigmas=_a )
scheduler.set_timesteps(self.num_inference_steps , device=_a )
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
SCREAMING_SNAKE_CASE__ : Optional[int] = sample.to(_a )
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE__ : List[Any] = scheduler.scale_model_input(_a , _a )
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a )
SCREAMING_SNAKE_CASE__ : List[str] = output.prev_sample
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Dict = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 124.52_299_499_511_719 ) < 1E-2
assert abs(result_mean.item() - 0.16_213_932_633_399_963 ) < 1E-3
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase ) + 1
SCREAMING_SNAKE_CASE__ : int = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
SCREAMING_SNAKE_CASE__ : Dict = [[0 for i in range(__lowerCAmelCase )] for j in range(__lowerCAmelCase )]
# since string of zero length match pattern of zero length
SCREAMING_SNAKE_CASE__ : Dict = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : int = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __lowerCAmelCase ):
for j in range(1 , __lowerCAmelCase ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
SCREAMING_SNAKE_CASE__ : Any = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
SCREAMING_SNAKE_CASE__ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
SCREAMING_SNAKE_CASE__ : List[Any] = dp[i - 1][j]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = 0
else:
SCREAMING_SNAKE_CASE__ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
a :Any = "aab"
a :Optional[Any] = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(f'{input_string} matches the given pattern {pattern}')
else:
print(f'{input_string} does not match with the given pattern {pattern}')
| 12 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class __a :
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=30 , _a=2 , _a=3 , _a=True , _a=True , _a=32 , _a=2 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=3 , _a=None , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = parent
SCREAMING_SNAKE_CASE__ : Optional[Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = image_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = patch_size
SCREAMING_SNAKE_CASE__ : str = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = is_training
SCREAMING_SNAKE_CASE__ : int = use_labels
SCREAMING_SNAKE_CASE__ : str = hidden_size
SCREAMING_SNAKE_CASE__ : Optional[int] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE__ : int = intermediate_size
SCREAMING_SNAKE_CASE__ : Dict = hidden_act
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[int] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Dict = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Tuple = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
SCREAMING_SNAKE_CASE__ : Dict = (image_size // patch_size) ** 2
SCREAMING_SNAKE_CASE__ : Dict = num_patches + 1
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ : str = self.get_config()
return config, pixel_values, labels
def _a ( self ) -> Dict:
"""simple docstring"""
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_a , initializer_range=self.initializer_range , )
def _a ( self , _a , _a , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = TFViTModel(config=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a , training=_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
SCREAMING_SNAKE_CASE__ : Tuple = self.image_size // 2
SCREAMING_SNAKE_CASE__ : List[Any] = pixel_values[:, :, :image_size, :image_size]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , interpolate_pos_encoding=_a , training=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def _a ( self , _a , _a , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.type_sequence_label_size
SCREAMING_SNAKE_CASE__ : List[str] = TFViTForImageClassification(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , labels=_a , training=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.image_size // 2
SCREAMING_SNAKE_CASE__ : Optional[Any] = pixel_values[:, :, :image_size, :image_size]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , interpolate_pos_encoding=_a , training=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1
SCREAMING_SNAKE_CASE__ : Tuple = TFViTForImageClassification(_a )
SCREAMING_SNAKE_CASE__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[str] = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE :Dict = (
{"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE :int = False
_SCREAMING_SNAKE_CASE :List[str] = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = TFViTModelTester(self )
SCREAMING_SNAKE_CASE__ : Tuple = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def _a ( self ) -> List[str]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def _a ( self ) -> List[str]:
"""simple docstring"""
pass
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
SCREAMING_SNAKE_CASE__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , tf.keras.layers.Layer ) )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Dict = model_class(_a )
SCREAMING_SNAKE_CASE__ : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE__ : Tuple = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(_a )
def _lowercase ( ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
@cached_property
def _a ( self ) -> str:
"""simple docstring"""
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.default_image_processor
SCREAMING_SNAKE_CASE__ : int = prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[int] = image_processor(images=_a , return_tensors="""tf""" )
# forward pass
SCREAMING_SNAKE_CASE__ : Tuple = model(**_a )
# verify the logits
SCREAMING_SNAKE_CASE__ : Dict = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , _a )
SCREAMING_SNAKE_CASE__ : Dict = tf.constant([-0.2_744, 0.8_215, -0.0_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , _a , atol=1E-4 )
| 12 |
"""simple docstring"""
from math import sqrt
def _lowercase ( __lowerCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(__lowerCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def _lowercase ( __lowerCAmelCase = 1_0001 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = 0
SCREAMING_SNAKE_CASE__ : Tuple = 1
while count != nth and number < 3:
number += 1
if is_prime(__lowerCAmelCase ):
count += 1
while count != nth:
number += 2
if is_prime(__lowerCAmelCase ):
count += 1
return number
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
a :Optional[Any] = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase_)
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , *_a , **_a ) -> List[Any]:
"""simple docstring"""
super().__init__(*_a , **_a )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _a ( self , _a=None , _a=None , _a=None ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {}
SCREAMING_SNAKE_CASE__ : int = {}
if prompt is not None:
SCREAMING_SNAKE_CASE__ : Optional[int] = prompt
if generate_kwargs is not None:
SCREAMING_SNAKE_CASE__ : Tuple = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
SCREAMING_SNAKE_CASE__ : Tuple = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
SCREAMING_SNAKE_CASE__ : Any = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _a , **_a ) -> Union[str, Any]:
"""simple docstring"""
return super().__call__(_a , **_a )
def _a ( self , _a , _a=None ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = load_image(_a )
if prompt is not None:
if not isinstance(_a , _a ):
raise ValueError(
f'''Received an invalid text input, got - {type(_a )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model.config.model_type
if model_type == "git":
SCREAMING_SNAKE_CASE__ : int = self.image_processor(images=_a , return_tensors=self.framework )
SCREAMING_SNAKE_CASE__ : Tuple = self.tokenizer(text=_a , add_special_tokens=_a ).input_ids
SCREAMING_SNAKE_CASE__ : Tuple = [self.tokenizer.cls_token_id] + input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor(_a ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
SCREAMING_SNAKE_CASE__ : int = self.image_processor(images=_a , header_text=_a , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.image_processor(images=_a , return_tensors=self.framework )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.tokenizer(_a , return_tensors=self.framework )
model_inputs.update(_a )
else:
raise ValueError(f'''Model type {model_type} does not support conditional text generation''' )
else:
SCREAMING_SNAKE_CASE__ : int = self.image_processor(images=_a , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
SCREAMING_SNAKE_CASE__ : Dict = None
return model_inputs
def _a ( self , _a , _a=None ) -> List[str]:
"""simple docstring"""
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , _a )
and all(x is None for x in model_inputs["""input_ids"""] )
):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if generate_kwargs is None:
SCREAMING_SNAKE_CASE__ : Dict = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
SCREAMING_SNAKE_CASE__ : Dict = model_inputs.pop(self.model.main_input_name )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.model.generate(_a , **_a , **_a )
return model_outputs
def _a ( self , _a ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
for output_ids in model_outputs:
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""generated_text""": self.tokenizer.decode(
_a , skip_special_tokens=_a , )
}
records.append(_a )
return records
| 12 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = value
SCREAMING_SNAKE_CASE__ : List[Any] = weight
def __repr__( self ) -> List[Any]:
"""simple docstring"""
return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value
def _a ( self ) -> int:
"""simple docstring"""
return self.name
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.weight
def _a ( self ) -> Dict:
"""simple docstring"""
return self.value / self.weight
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
SCREAMING_SNAKE_CASE__ : Any = []
for i in range(len(__lowerCAmelCase ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Optional[Any] = sorted(__lowerCAmelCase , key=__lowerCAmelCase , reverse=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = 0.0, 0.0
for i in range(len(__lowerCAmelCase ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def _lowercase ( ) -> List[str]:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
a :Optional[int] = TypeVar("KEY")
a :Dict = TypeVar("VAL")
@dataclass(frozen=UpperCamelCase_ , slots=UpperCamelCase_)
class __a (Generic[KEY, VAL]):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :KEY
_SCREAMING_SNAKE_CASE :VAL
class __a (_Item):
'''simple docstring'''
def __init__( self ) -> None:
"""simple docstring"""
super().__init__(_a , _a )
def __bool__( self ) -> bool:
"""simple docstring"""
return False
a :Dict = _DeletedItem()
class __a (MutableMapping[KEY, VAL]):
'''simple docstring'''
def __init__( self , _a = 8 , _a = 0.75 ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = initial_block_size
SCREAMING_SNAKE_CASE__ : list[_Item | None] = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
SCREAMING_SNAKE_CASE__ : Optional[int] = capacity_factor
SCREAMING_SNAKE_CASE__ : Any = 0
def _a ( self , _a ) -> int:
"""simple docstring"""
return hash(_a ) % len(self._buckets )
def _a ( self , _a ) -> int:
"""simple docstring"""
return (ind + 1) % len(self._buckets )
def _a ( self , _a , _a , _a ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self._buckets[ind]
if not stored:
SCREAMING_SNAKE_CASE__ : str = _Item(_a , _a )
self._len += 1
return True
elif stored.key == key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = _Item(_a , _a )
return True
else:
return False
def _a ( self ) -> bool:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(_a )
def _a ( self ) -> bool:
"""simple docstring"""
if len(self._buckets ) <= self._initial_block_size:
return False
SCREAMING_SNAKE_CASE__ : List[Any] = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self._buckets
SCREAMING_SNAKE_CASE__ : Tuple = [None] * new_size
SCREAMING_SNAKE_CASE__ : Any = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def _a ( self ) -> None:
"""simple docstring"""
self._resize(len(self._buckets ) * 2 )
def _a ( self ) -> None:
"""simple docstring"""
self._resize(len(self._buckets ) // 2 )
def _a ( self , _a ) -> Iterator[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self._get_bucket_index(_a )
for _ in range(len(self._buckets ) ):
yield ind
SCREAMING_SNAKE_CASE__ : List[str] = self._get_next_ind(_a )
def _a ( self , _a , _a ) -> None:
"""simple docstring"""
for ind in self._iterate_buckets(_a ):
if self._try_set(_a , _a , _a ):
break
def __setitem__( self , _a , _a ) -> None:
"""simple docstring"""
if self._is_full():
self._size_up()
self._add_item(_a , _a )
def __delitem__( self , _a ) -> None:
"""simple docstring"""
for ind in self._iterate_buckets(_a ):
SCREAMING_SNAKE_CASE__ : Dict = self._buckets[ind]
if item is None:
raise KeyError(_a )
if item is _deleted:
continue
if item.key == key:
SCREAMING_SNAKE_CASE__ : List[Any] = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self , _a ) -> VAL:
"""simple docstring"""
for ind in self._iterate_buckets(_a ):
SCREAMING_SNAKE_CASE__ : Optional[Any] = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(_a )
def __len__( self ) -> int:
"""simple docstring"""
return self._len
def __iter__( self ) -> Iterator[KEY]:
"""simple docstring"""
yield from (item.key for item in self._buckets if item)
def __repr__( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = """ ,""".join(
f'''{item.key}: {item.val}''' for item in self._buckets if item )
return f'''HashMap({val_string})'''
| 12 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_nllb import NllbTokenizer
else:
a :Optional[int] = None
a :Optional[Any] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
a :Union[str, Any] = {
"vocab_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/nllb-200-distilled-600M": (
"https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"
),
},
}
a :Any = {
"facebook/nllb-large-en-ro": 1_024,
"facebook/nllb-200-distilled-600M": 1_024,
}
# fmt: off
a :Tuple = ["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :int = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :Tuple = NllbTokenizer
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=None , _a=None , _a=None , _a=False , **_a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = legacy_behaviour
super().__init__(
vocab_file=_a , tokenizer_file=_a , bos_token=_a , eos_token=_a , sep_token=_a , cls_token=_a , unk_token=_a , pad_token=_a , mask_token=_a , src_lang=_a , tgt_lang=_a , additional_special_tokens=_a , legacy_behaviour=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_file
SCREAMING_SNAKE_CASE__ : str = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
SCREAMING_SNAKE_CASE__ : List[str] = {
lang_code: self.convert_tokens_to_ids(_a ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
SCREAMING_SNAKE_CASE__ : Dict = src_lang if src_lang is not None else """eng_Latn"""
SCREAMING_SNAKE_CASE__ : List[str] = self.convert_tokens_to_ids(self._src_lang )
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _a ( self , _a , _a , _a , _a , **_a ) -> Tuple:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Dict = src_lang
SCREAMING_SNAKE_CASE__ : Dict = self(_a , add_special_tokens=_a , return_tensors=_a , **_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_tokens_to_ids(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tgt_lang_id
return inputs
def _a ( self , _a , _a = "eng_Latn" , _a = None , _a = "fra_Latn" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = src_lang
SCREAMING_SNAKE_CASE__ : Dict = tgt_lang
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> str:
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : str = []
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Dict = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : int = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.convert_tokens_to_ids(_a )
if self.legacy_behaviour:
SCREAMING_SNAKE_CASE__ : List[Any] = []
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.eos_token_id, self.cur_lang_code]
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_code]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = [self.eos_token_id]
SCREAMING_SNAKE_CASE__ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.convert_ids_to_tokens(self.suffix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ):
copyfile(self.vocab_file , _a )
return (out_vocab_file,)
| 12 | 1 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a :Any = logging.get_logger(__name__)
a :str = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = """data2vec-audio"""
def __init__( self , _a=32 , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=0.1 , _a=0.0 , _a=0.1 , _a=0.1 , _a=0.02 , _a=1E-5 , _a="gelu" , _a=(512, 512, 512, 512, 512, 512, 512) , _a=(5, 2, 2, 2, 2, 2, 2) , _a=(10, 3, 3, 3, 3, 2, 2) , _a=False , _a=16 , _a=19 , _a=5 , _a=0.05 , _a=10 , _a=2 , _a=0.0 , _a=10 , _a=0 , _a="sum" , _a=False , _a=False , _a=256 , _a=(512, 512, 512, 512, 1_500) , _a=(5, 3, 3, 1, 1) , _a=(1, 2, 3, 1, 1) , _a=512 , _a=0 , _a=1 , _a=2 , _a=False , _a=3 , _a=2 , _a=3 , _a=None , **_a , ) -> int:
"""simple docstring"""
super().__init__(**_a , pad_token_id=_a , bos_token_id=_a , eos_token_id=_a )
SCREAMING_SNAKE_CASE__ : int = hidden_size
SCREAMING_SNAKE_CASE__ : Any = feat_extract_activation
SCREAMING_SNAKE_CASE__ : Dict = list(_a )
SCREAMING_SNAKE_CASE__ : int = list(_a )
SCREAMING_SNAKE_CASE__ : str = list(_a )
SCREAMING_SNAKE_CASE__ : str = conv_bias
SCREAMING_SNAKE_CASE__ : Optional[int] = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE__ : List[str] = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE__ : List[str] = conv_pos_kernel_size
SCREAMING_SNAKE_CASE__ : Tuple = len(self.conv_dim )
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : int = intermediate_size
SCREAMING_SNAKE_CASE__ : List[str] = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout
SCREAMING_SNAKE_CASE__ : Optional[Any] = attention_dropout
SCREAMING_SNAKE_CASE__ : Union[str, Any] = activation_dropout
SCREAMING_SNAKE_CASE__ : int = feat_proj_dropout
SCREAMING_SNAKE_CASE__ : List[Any] = final_dropout
SCREAMING_SNAKE_CASE__ : Tuple = layerdrop
SCREAMING_SNAKE_CASE__ : Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE__ : int = mask_time_prob
SCREAMING_SNAKE_CASE__ : Any = mask_time_length
SCREAMING_SNAKE_CASE__ : List[str] = mask_time_min_masks
SCREAMING_SNAKE_CASE__ : List[Any] = mask_feature_prob
SCREAMING_SNAKE_CASE__ : Tuple = mask_feature_length
SCREAMING_SNAKE_CASE__ : Union[str, Any] = mask_feature_min_masks
# ctc loss
SCREAMING_SNAKE_CASE__ : Dict = ctc_loss_reduction
SCREAMING_SNAKE_CASE__ : List[Any] = ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE__ : int = add_adapter
SCREAMING_SNAKE_CASE__ : List[str] = adapter_kernel_size
SCREAMING_SNAKE_CASE__ : List[Any] = adapter_stride
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_adapter_layers
SCREAMING_SNAKE_CASE__ : Dict = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE__ : List[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE__ : Tuple = list(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = list(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = list(_a )
SCREAMING_SNAKE_CASE__ : Dict = xvector_output_dim
@property
def _a ( self ) -> Any:
"""simple docstring"""
return math.prod(self.conv_stride )
| 12 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0 ) -> Any:
# Format the message.
if name is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
else:
SCREAMING_SNAKE_CASE__ : str = """.""" * max(0 , spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
SCREAMING_SNAKE_CASE__ : Dict = fmt.format(__lowerCAmelCase )
# Print and recurse (if needed).
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
if msg is not None:
print(__lowerCAmelCase )
for k in val.keys():
recursive_print(__lowerCAmelCase , val[k] , spaces + 2 )
elif isinstance(__lowerCAmelCase , torch.Tensor ):
print(__lowerCAmelCase , """:""" , val.size() )
else:
print(__lowerCAmelCase , """:""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
SCREAMING_SNAKE_CASE__ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
SCREAMING_SNAKE_CASE__ : int = (num_heads, hidden_size, num_splits) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : List[str] = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = param.transpose(0 , 2 )
SCREAMING_SNAKE_CASE__ : List[Any] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
SCREAMING_SNAKE_CASE__ : List[str] = (num_heads, num_splits, hidden_size) + input_shape[1:]
SCREAMING_SNAKE_CASE__ : Dict = param.view(*__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = param.transpose(0 , 1 ).contiguous()
SCREAMING_SNAKE_CASE__ : Any = param.view(*__lowerCAmelCase )
return param
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
# The converted output model.
SCREAMING_SNAKE_CASE__ : List[str] = {}
# old versions did not store training args
SCREAMING_SNAKE_CASE__ : List[str] = input_state_dict.get("""args""" , __lowerCAmelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.padded_vocab_size
SCREAMING_SNAKE_CASE__ : Optional[int] = ds_args.max_position_embeddings
SCREAMING_SNAKE_CASE__ : List[Any] = ds_args.hidden_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = ds_args.num_layers
SCREAMING_SNAKE_CASE__ : Dict = ds_args.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
SCREAMING_SNAKE_CASE__ : List[str] = config.n_head
# The hidden_size per head.
SCREAMING_SNAKE_CASE__ : str = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = input_state_dict["""checkpoint_version"""]
else:
SCREAMING_SNAKE_CASE__ : Tuple = 0.0
# The model.
SCREAMING_SNAKE_CASE__ : Any = input_state_dict["""model"""]
# The language model.
SCREAMING_SNAKE_CASE__ : Any = model["""language_model"""]
# The embeddings.
SCREAMING_SNAKE_CASE__ : str = lm["""embedding"""]
# The word embeddings.
SCREAMING_SNAKE_CASE__ : int = embeddings["""word_embeddings"""]["""weight"""]
# Truncate the embedding table to vocab_size rows.
SCREAMING_SNAKE_CASE__ : Any = word_embeddings[: config.vocab_size, :]
SCREAMING_SNAKE_CASE__ : Optional[int] = word_embeddings
# The position embeddings.
SCREAMING_SNAKE_CASE__ : Any = embeddings["""position_embeddings"""]["""weight"""]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
SCREAMING_SNAKE_CASE__ : Tuple = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match''' )
# Store the position embeddings.
SCREAMING_SNAKE_CASE__ : List[Any] = pos_embeddings
# The transformer.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]
# The regex to extract layer names.
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""attention.dense""": """.attn.c_proj.""",
"""self_attention.dense""": """.attn.c_proj.""",
"""mlp.dense_h_to_4h""": """.mlp.c_fc.""",
"""mlp.dense_4h_to_h""": """.mlp.c_proj.""",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
SCREAMING_SNAKE_CASE__ : str = layer_re.match(__lowerCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
SCREAMING_SNAKE_CASE__ : Dict = int(m.group(1 ) )
# The name of the operation.
SCREAMING_SNAKE_CASE__ : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
SCREAMING_SNAKE_CASE__ : str = m.group(3 )
# The name of the layer.
SCREAMING_SNAKE_CASE__ : List[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
SCREAMING_SNAKE_CASE__ : Dict = """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
SCREAMING_SNAKE_CASE__ : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
SCREAMING_SNAKE_CASE__ : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[str] = masked_bias
SCREAMING_SNAKE_CASE__ : List[str] = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
SCREAMING_SNAKE_CASE__ : str = out_val.transpose(0 , 1 ).contiguous()
# Store.
SCREAMING_SNAKE_CASE__ : Dict = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : Any = fix_query_key_value_ordering(__lowerCAmelCase , __lowerCAmelCase , 3 , __lowerCAmelCase , __lowerCAmelCase )
# Store. No change of shape.
SCREAMING_SNAKE_CASE__ : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
SCREAMING_SNAKE_CASE__ : str = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
SCREAMING_SNAKE_CASE__ : int = megatron_to_transformers[op_name]
SCREAMING_SNAKE_CASE__ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = transformer["""final_layernorm.weight"""]
SCREAMING_SNAKE_CASE__ : str = transformer["""final_layernorm.bias"""]
# For LM head, transformers' wants the matrix to weight embeddings.
SCREAMING_SNAKE_CASE__ : Tuple = word_embeddings
# It should be done!
return output_state_dict
def _lowercase ( ) -> List[Any]:
# Create the argument parser.
SCREAMING_SNAKE_CASE__ : str = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__lowerCAmelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__lowerCAmelCase , help="""An optional config json file describing the pre-trained model.""" , )
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
# Extract the basename.
SCREAMING_SNAKE_CASE__ : Optional[int] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}''' )
if args.path_to_checkpoint.endswith(""".zip""" ):
with zipfile.ZipFile(args.path_to_checkpoint , """r""" ) as checkpoint:
with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt""" ) as pytorch_dict:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.load(__lowerCAmelCase , map_location="""cpu""" )
else:
SCREAMING_SNAKE_CASE__ : str = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
SCREAMING_SNAKE_CASE__ : int = input_state_dict.get("""args""" , __lowerCAmelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
SCREAMING_SNAKE_CASE__ : Dict = """gelu_fast"""
elif ds_args.openai_gelu:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu_new"""
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = """gelu"""
else:
# in the very early days this used to be "gelu_new"
SCREAMING_SNAKE_CASE__ : Any = """gelu_new"""
# Spell out all parameters in case the defaults change.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = GPTaConfig(
vocab_size=5_0257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__lowerCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1E-5 , initializer_range=0.02 , summary_type="""cls_index""" , summary_use_proj=__lowerCAmelCase , summary_activation=__lowerCAmelCase , summary_proj_to_labels=__lowerCAmelCase , summary_first_dropout=0.1 , scale_attn_weights=__lowerCAmelCase , use_cache=__lowerCAmelCase , bos_token_id=5_0256 , eos_token_id=5_0256 , )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = GPTaConfig.from_json_file(args.config_file )
SCREAMING_SNAKE_CASE__ : Tuple = ["""GPT2LMHeadModel"""]
# Convert.
print("""Converting""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = convert_megatron_checkpoint(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__lowerCAmelCase , __lowerCAmelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
SCREAMING_SNAKE_CASE__ : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
SCREAMING_SNAKE_CASE__ : Any = """gpt2"""
elif tokenizer_type == "PretrainedFromHF":
SCREAMING_SNAKE_CASE__ : Any = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """gpt2"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = type(__lowerCAmelCase ).__name__
SCREAMING_SNAKE_CASE__ : Dict = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__lowerCAmelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__lowerCAmelCase )
# Store the state_dict to file.
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 12 | 1 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=False ) -> List[Any]:
try:
SCREAMING_SNAKE_CASE__ : Dict = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
SCREAMING_SNAKE_CASE__ : List[Any] = default
else:
# KEY is set, convert it to True or False.
try:
SCREAMING_SNAKE_CASE__ : Tuple = strtobool(__lowerCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'''If set, {key} must be yes or no.''' )
return _value
a :List[str] = parse_flag_from_env("RUN_SLOW", default=False)
def _lowercase ( __lowerCAmelCase ) -> Any:
return unittest.skip("""Test was skipped""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> str:
return unittest.skipUnless(_run_slow_tests , """test is slow""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
return unittest.skipUnless(not torch.cuda.is_available() , """test requires only a CPU""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
return unittest.skipUnless(torch.cuda.is_available() , """test requires a GPU""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
return unittest.skipUnless(is_xpu_available() , """test requires a XPU""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Tuple:
return unittest.skipUnless(is_mps_available() , """test requires a `mps` backend support in `torch`""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , """test requires the Hugging Face suite""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Any:
return unittest.skipUnless(is_bnb_available() , """test requires the bitsandbytes library""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> int:
return unittest.skipUnless(is_tpu_available() , """test requires TPU""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
return unittest.skipUnless(torch.cuda.device_count() == 1 , """test requires a GPU""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> int:
return unittest.skipUnless(torch.xpu.device_count() == 1 , """test requires a XPU""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
return unittest.skipUnless(torch.cuda.device_count() > 1 , """test requires multiple GPUs""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Any:
return unittest.skipUnless(torch.xpu.device_count() > 1 , """test requires multiple XPUs""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> int:
return unittest.skipUnless(is_safetensors_available() , """test requires safetensors""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Any:
return unittest.skipUnless(is_deepspeed_available() , """test requires DeepSpeed""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Optional[Any]:
return unittest.skipUnless(is_torch_version(""">=""" , """1.12.0""" ) , """test requires torch version >= 1.12.0""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase=None , __lowerCAmelCase=None ) -> Dict:
if test_case is None:
return partial(__lowerCAmelCase , version=__lowerCAmelCase )
return unittest.skipUnless(is_torch_version(""">=""" , __lowerCAmelCase ) , F'''test requires torch version >= {version}''' )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Dict:
return unittest.skipUnless(is_tensorboard_available() , """test requires Tensorboard""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> List[str]:
return unittest.skipUnless(is_wandb_available() , """test requires wandb""" )(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> List[Any]:
return unittest.skipUnless(is_comet_ml_available() , """test requires comet_ml""" )(__lowerCAmelCase )
a :Optional[Any] = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
return unittest.skipUnless(
_atleast_one_tracker_available , """test requires at least one tracker to be available and for `comet_ml` to not be installed""" , )(__lowerCAmelCase )
class __a (unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
@classmethod
def _a ( cls ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = tempfile.mkdtemp()
@classmethod
def _a ( cls ) -> Optional[int]:
"""simple docstring"""
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def _a ( self ) -> Dict:
"""simple docstring"""
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("""**/*""" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(_a )
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Dict:
"""simple docstring"""
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = mocks if isinstance(_a , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def _lowercase ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[int] = AcceleratorState()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tensor[None].clone().to(state.device )
SCREAMING_SNAKE_CASE__ : Optional[Any] = gather(__lowerCAmelCase ).cpu()
SCREAMING_SNAKE_CASE__ : List[Any] = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , __lowerCAmelCase ):
return False
return True
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = returncode
SCREAMING_SNAKE_CASE__ : List[Any] = stdout
SCREAMING_SNAKE_CASE__ : Tuple = stderr
async def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> str:
while True:
SCREAMING_SNAKE_CASE__ : Optional[int] = await stream.readline()
if line:
callback(__lowerCAmelCase )
else:
break
async def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False ) -> _RunOutput:
if echo:
print("""\nRunning: """ , """ """.join(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : Any = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=__lowerCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=__lowerCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = []
def tee(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase="" ):
SCREAMING_SNAKE_CASE__ : int = line.decode("""utf-8""" ).rstrip()
sink.append(__lowerCAmelCase )
if not quiet:
print(__lowerCAmelCase , __lowerCAmelCase , file=__lowerCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stdout , label="""stdout:""" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda __lowerCAmelCase : tee(__lowerCAmelCase , __lowerCAmelCase , sys.stderr , label="""stderr:""" ) ) ),
] , timeout=__lowerCAmelCase , )
return _RunOutput(await p.wait() , __lowerCAmelCase , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=180 , __lowerCAmelCase=False , __lowerCAmelCase=True ) -> _RunOutput:
SCREAMING_SNAKE_CASE__ : Optional[int] = asyncio.get_event_loop()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = loop.run_until_complete(
_stream_subprocess(__lowerCAmelCase , env=__lowerCAmelCase , stdin=__lowerCAmelCase , timeout=__lowerCAmelCase , quiet=__lowerCAmelCase , echo=__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : Any = """ """.join(__lowerCAmelCase )
if result.returncode > 0:
SCREAMING_SNAKE_CASE__ : int = """\n""".join(result.stderr )
raise RuntimeError(
F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
F'''The combined stderr from workers follows:\n{stderr}''' )
return result
class __a (UpperCamelCase_):
'''simple docstring'''
pass
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=False ) -> str:
try:
SCREAMING_SNAKE_CASE__ : Any = subprocess.check_output(__lowerCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(__lowerCAmelCase , """decode""" ):
SCREAMING_SNAKE_CASE__ : Tuple = output.decode("""utf-8""" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F'''Command `{' '.join(__lowerCAmelCase )}` failed with the following error:\n\n{e.output.decode()}''' ) from e
| 12 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class __a (UpperCamelCase_):
'''simple docstring'''
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : str = re.compile(r"""(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = input_file.read()
SCREAMING_SNAKE_CASE__ : str = regexp.search(_a )
return match
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
with open(_a , encoding="""utf-8""" ) as input_file:
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(r"""#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()""" , re.DOTALL )
SCREAMING_SNAKE_CASE__ : List[Any] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
SCREAMING_SNAKE_CASE__ : Dict = regexp.finditer(_a )
SCREAMING_SNAKE_CASE__ : int = [match for match in matches if match is not None and match.group(1 ) is not None]
return matches[0] if matches else None
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_a ) ):
raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}''' )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Path("""./datasets""" )
SCREAMING_SNAKE_CASE__ : List[str] = list(dataset_paths.absolute().glob("""**/*.py""" ) )
for dataset in dataset_files:
if self._no_print_statements(str(_a ) ):
raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.''' )
| 12 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : int = F'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
with open(__lowerCAmelCase , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : int = f.readlines()
SCREAMING_SNAKE_CASE__ : List[Any] = F'''class {class_name}('''
SCREAMING_SNAKE_CASE__ : List[Any] = F'''{4 * ' '}def {test_name}('''
SCREAMING_SNAKE_CASE__ : List[str] = F'''{8 * ' '}{correct_line.split()[0]}'''
SCREAMING_SNAKE_CASE__ : Dict = F'''{16 * ' '}{correct_line.split()[0]}'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = False
SCREAMING_SNAKE_CASE__ : str = False
SCREAMING_SNAKE_CASE__ : Any = False
SCREAMING_SNAKE_CASE__ : List[Any] = False
SCREAMING_SNAKE_CASE__ : List[str] = 0
SCREAMING_SNAKE_CASE__ : str = 0
SCREAMING_SNAKE_CASE__ : str = []
for line in lines:
if line.startswith(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = True
elif in_class and line.startswith(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : str = True
elif in_class and in_func and (line.startswith(__lowerCAmelCase ) or line.startswith(__lowerCAmelCase )):
SCREAMING_SNAKE_CASE__ : Tuple = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
SCREAMING_SNAKE_CASE__ : int = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
SCREAMING_SNAKE_CASE__ : Dict = True
if in_class and in_func and in_line and insert_line:
new_lines.append(F'''{spaces * ' '}{correct_line}''' )
SCREAMING_SNAKE_CASE__ : List[Any] = False
else:
new_lines.append(__lowerCAmelCase )
with open(__lowerCAmelCase , """w""" ) as f:
for line in new_lines:
f.write(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=None ) -> Dict:
if fail is not None:
with open(__lowerCAmelCase , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : Any = {l.strip() for l in f.readlines()}
else:
SCREAMING_SNAKE_CASE__ : str = None
with open(__lowerCAmelCase , """r""" ) as f:
SCREAMING_SNAKE_CASE__ : Any = f.readlines()
SCREAMING_SNAKE_CASE__ : Tuple = defaultdict(__lowerCAmelCase )
for line in correct_lines:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if __name__ == "__main__":
a :int = argparse.ArgumentParser()
parser.add_argument("--correct_filename", help="filename of tests with expected result")
parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
a :List[Any] = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 12 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class __a :
'''simple docstring'''
def __init__( self , _a , _a=99 , _a=13 , _a=7 , _a=9 , _a=True , _a=True , _a=False , _a=32 , _a=5 , _a=4 , _a=37 , _a=8 , _a=0.1 , _a=0.002 , _a=1 , _a=0 , _a=0 , _a=None , _a=None , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = parent
SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size
SCREAMING_SNAKE_CASE__ : Tuple = encoder_seq_length
SCREAMING_SNAKE_CASE__ : str = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE__ : Optional[int] = self.decoder_seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Dict = use_attention_mask
SCREAMING_SNAKE_CASE__ : List[str] = use_labels
SCREAMING_SNAKE_CASE__ : str = vocab_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Any = num_attention_heads
SCREAMING_SNAKE_CASE__ : Any = d_ff
SCREAMING_SNAKE_CASE__ : Any = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : List[str] = initializer_factor
SCREAMING_SNAKE_CASE__ : List[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : Any = decoder_start_token_id
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ : str = decoder_layers
def _a ( self ) -> Tuple:
"""simple docstring"""
return TaConfig.from_pretrained("""google/umt5-base""" )
def _a ( self , _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Any:
"""simple docstring"""
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : int = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : str = torch.ones(config.num_hidden_layers , config.num_attention_heads , device=_a )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : List[str] = torch.ones(config.num_decoder_layers , config.num_attention_heads , device=_a )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.ones(
config.num_decoder_layers , config.num_attention_heads , device=_a )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.encoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for NllbMoe the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
SCREAMING_SNAKE_CASE__ : Tuple = input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = decoder_input_ids.clamp(self.pad_token_id + 1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config()
SCREAMING_SNAKE_CASE__ : List[str] = config.num_attention_heads
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_inputs_dict(_a , _a , _a )
return config, input_dict
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self ) -> List[str]:
"""simple docstring"""
return TaConfig(
vocab_size=166 , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self ) -> List[Any]:
"""simple docstring"""
return TaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , d_ff=self.d_ff , d_kv=self.hidden_size // self.num_attention_heads , num_layers=self.num_hidden_layers , num_decoder_layers=self.decoder_layers , num_heads=self.num_attention_heads , relative_attention_num_buckets=self.relative_attention_num_buckets , dropout_rate=self.dropout_rate , initializer_factor=self.initializer_factor , eos_token_id=self.eos_token_id , bos_token_id=self.pad_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = UMTaModel(config=_a )
model.to(_a )
model.eval()
SCREAMING_SNAKE_CASE__ : Dict = model(
input_ids=_a , decoder_input_ids=_a , attention_mask=_a , decoder_attention_mask=_a , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = result.last_hidden_state
SCREAMING_SNAKE_CASE__ : Dict = result.past_key_values
SCREAMING_SNAKE_CASE__ : Any = result.encoder_last_hidden_state
self.parent.assertEqual(encoder_output.size() , (self.batch_size, self.encoder_seq_length, self.hidden_size) )
self.parent.assertEqual(decoder_output.size() , (self.batch_size, self.decoder_seq_length, self.hidden_size) )
# There should be `num_layers` key value embeddings stored in decoder_past
self.parent.assertEqual(len(_a ) , config.num_layers )
# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
self.parent.assertEqual(len(decoder_past[0] ) , 4 )
def _a ( self , _a , _a , _a , _a , _a , _a , ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).get_decoder().to(_a ).eval()
# first forward pass
SCREAMING_SNAKE_CASE__ : str = model(_a , use_cache=_a )
SCREAMING_SNAKE_CASE__ : str = model(_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a , use_cache=_a )
self.parent.assertTrue(len(_a ) == len(_a ) )
self.parent.assertTrue(len(_a ) == len(_a ) + 1 )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE__ : List[Any] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# append to next input_ids and
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : Tuple = model(_a , past_key_values=_a )["""last_hidden_state"""]
# select random slice
SCREAMING_SNAKE_CASE__ : List[str] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE__ : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE__ : List[Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_a , _a , atol=1E-3 ) )
def _a ( self , _a , _a , ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = UMTaModel(config=_a ).to(_a ).half().eval()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(**_a )["""last_hidden_state"""]
self.parent.assertFalse(torch.isnan(_a ).any().item() )
@require_torch
class __a (UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = (
(UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
)
_SCREAMING_SNAKE_CASE :Optional[int] = (UMTaForConditionalGeneration,) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE :List[str] = (
{
"""conversational""": UMTaForConditionalGeneration,
"""feature-extraction""": UMTaModel,
"""summarization""": UMTaForConditionalGeneration,
"""text2text-generation""": UMTaForConditionalGeneration,
"""translation""": UMTaForConditionalGeneration,
"""question-answering""": UMTaForQuestionAnswering,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = True
_SCREAMING_SNAKE_CASE :Tuple = False
_SCREAMING_SNAKE_CASE :Optional[Any] = False
_SCREAMING_SNAKE_CASE :List[Any] = True
_SCREAMING_SNAKE_CASE :List[str] = True
# The small UMT5 model needs higher percentages for CPU/MP tests
_SCREAMING_SNAKE_CASE :Union[str, Any] = [0.8, 0.9]
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = UMTaModelTester(self )
@unittest.skip("""Test has a segmentation fault on torch 1.8.0""" )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : Dict = UMTaModel(config_and_inputs[0] ).to(_a )
with tempfile.TemporaryDirectory() as tmpdirname:
torch.onnx.export(
_a , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]) , f'''{tmpdirname}/t5_test.onnx''' , export_params=_a , opset_version=9 , input_names=["""input_ids""", """decoder_input_ids"""] , )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model_fpaa_forward(*_a )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = ["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = config_and_inputs[0]
SCREAMING_SNAKE_CASE__ : Tuple = UMTaForConditionalGeneration(_a ).eval()
model.to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = {
"""head_mask""": torch.zeros(config.num_layers , config.num_heads , device=_a ),
"""decoder_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
"""cross_attn_head_mask""": torch.zeros(config.num_decoder_layers , config.num_heads , device=_a ),
}
for attn_name, (name, mask) in zip(_a , head_masking.items() ):
SCREAMING_SNAKE_CASE__ : List[str] = {name: mask}
# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
if name == "head_mask":
SCREAMING_SNAKE_CASE__ : str = torch.ones(
config.num_decoder_layers , config.num_heads , device=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.generate(
config_and_inputs[1]["""input_ids"""] , num_beams=1 , max_length=3 , output_attentions=_a , return_dict_in_generate=_a , **_a , )
# We check the state of decoder_attentions and cross_attentions just from the last step
SCREAMING_SNAKE_CASE__ : List[str] = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
self.assertEqual(sum([w.sum().item() for w in attn_weights] ) , 0.0 )
@unittest.skip("""Does not work on the tiny model as we keep hitting edge cases.""" )
def _a ( self ) -> Dict:
"""simple docstring"""
pass
@require_torch
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
@unittest.skip(
"""Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged""" )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = UMTaForConditionalGeneration.from_pretrained("""google/umt5-small""" , return_dict=_a ).to(_a )
SCREAMING_SNAKE_CASE__ : str = AutoTokenizer.from_pretrained("""google/umt5-small""" , use_fast=_a , legacy=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [
"""Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
"""No se como puedo <extra_id_0>.""",
"""This is the reason why we <extra_id_0> them.""",
"""The <extra_id_0> walks in <extra_id_1>, seats""",
"""A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
]
SCREAMING_SNAKE_CASE__ : Tuple = tokenizer(_a , return_tensors="""pt""" , padding=_a ).input_ids
# fmt: off
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor(
[
[ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
[ 517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
[ 320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
] )
# fmt: on
torch.testing.assert_allclose(_a , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.generate(input_ids.to(_a ) )
SCREAMING_SNAKE_CASE__ : int = [
"""<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
"""<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
"""<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
]
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.batch_decode(_a )
self.assertEqual(_a , _a )
| 12 | 1 |
"""simple docstring"""
from functools import lru_cache
def _lowercase ( __lowerCAmelCase ) -> set:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 2
SCREAMING_SNAKE_CASE__ : int = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(__lowerCAmelCase )
if n > 1:
factors.add(__lowerCAmelCase )
return factors
@lru_cache
def _lowercase ( __lowerCAmelCase ) -> int:
return len(unique_prime_factors(__lowerCAmelCase ) )
def _lowercase ( __lowerCAmelCase ) -> bool:
return len(set(__lowerCAmelCase ) ) in (0, 1)
def _lowercase ( __lowerCAmelCase ) -> list:
SCREAMING_SNAKE_CASE__ : List[Any] = 2
while True:
# Increment each value of a generated range
SCREAMING_SNAKE_CASE__ : Tuple = [base + i for i in range(__lowerCAmelCase )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
SCREAMING_SNAKE_CASE__ : str = [upf_len(__lowerCAmelCase ) for x in group]
checker.append(__lowerCAmelCase )
# If all numbers in the list are equal, return the group variable.
if equality(__lowerCAmelCase ):
return group
# Increment our base variable by 1
base += 1
def _lowercase ( __lowerCAmelCase = 4 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = run(__lowerCAmelCase )
return results[0] if len(__lowerCAmelCase ) else None
if __name__ == "__main__":
print(solution())
| 12 |
"""simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a = None , _a = None , _a = False , **_a , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(features=_a , cache_dir=_a , keep_in_memory=_a , **_a )
SCREAMING_SNAKE_CASE__ : List[Any] = Sql(
cache_dir=_a , features=_a , sql=_a , con=_a , **_a , )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : Optional[int] = None
self.builder.download_and_prepare(
download_config=_a , download_mode=_a , verification_mode=_a , base_path=_a , )
# Build dataset for splits
SCREAMING_SNAKE_CASE__ : str = self.builder.as_dataset(
split="""train""" , verification_mode=_a , in_memory=self.keep_in_memory )
return dataset
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , _a = None , _a = None , **_a , ) -> Any:
"""simple docstring"""
if num_proc is not None and num_proc <= 0:
raise ValueError(f'''num_proc {num_proc} must be an integer > 0.''' )
SCREAMING_SNAKE_CASE__ : int = dataset
SCREAMING_SNAKE_CASE__ : Any = name
SCREAMING_SNAKE_CASE__ : Optional[Any] = con
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
SCREAMING_SNAKE_CASE__ : int = num_proc
SCREAMING_SNAKE_CASE__ : int = to_sql_kwargs
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.to_sql_kwargs.pop("""sql""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""con""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = self.to_sql_kwargs.pop("""index""" , _a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._write(index=_a , **self.to_sql_kwargs )
return written
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[int] = args
SCREAMING_SNAKE_CASE__ : List[str] = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
SCREAMING_SNAKE_CASE__ : Any = query_table(
table=self.dataset.data , key=slice(_a , offset + self.batch_size ) , indices=self.dataset._indices , )
SCREAMING_SNAKE_CASE__ : Optional[int] = batch.to_pandas()
SCREAMING_SNAKE_CASE__ : List[Any] = df.to_sql(self.name , self.con , index=_a , **_a )
return num_rows or len(_a )
def _a ( self , _a , **_a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _a , _a )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 12 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[Any] = StableDiffusionDiffEditPipeline
_SCREAMING_SNAKE_CASE :Optional[Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
_SCREAMING_SNAKE_CASE :List[Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
_SCREAMING_SNAKE_CASE :Tuple = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_SCREAMING_SNAKE_CASE :Optional[int] = frozenset([])
def _a ( self ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_a , )
SCREAMING_SNAKE_CASE__ : int = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_a , set_alpha_to_one=_a , )
SCREAMING_SNAKE_CASE__ : Optional[int] = DDIMInverseScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_a , set_alpha_to_zero=_a , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="""gelu""" , projection_dim=512 , )
SCREAMING_SNAKE_CASE__ : str = CLIPTextModel(_a )
SCREAMING_SNAKE_CASE__ : int = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE__ : int = {
"""unet""": unet,
"""scheduler""": scheduler,
"""inverse_scheduler""": inverse_scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self , _a , _a=0 ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 16, 16) , rng=random.Random(_a ) ).to(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(_a ) ).to(_a )
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""prompt""": """a dog and a newt""",
"""mask_image""": mask,
"""image_latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self , _a , _a=0 ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = floats_tensor((1, 3, 32, 32) , rng=random.Random(_a ) ).to(_a )
SCREAMING_SNAKE_CASE__ : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Optional[Any] = Image.fromarray(np.uinta(_a ) ).convert("""RGB""" )
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Tuple = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : Any = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : Any = {
"""image""": image,
"""source_prompt""": """a cat and a frog""",
"""target_prompt""": """a dog and a newt""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""num_maps_per_mask""": 2,
"""mask_encode_strength""": 1.0,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self , _a , _a=0 ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(_a ) ).to(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = Image.fromarray(np.uinta(_a ) ).convert("""RGB""" )
if str(_a ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : List[str] = torch.manual_seed(_a )
else:
SCREAMING_SNAKE_CASE__ : List[str] = torch.Generator(device=_a ).manual_seed(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = {
"""image""": image,
"""prompt""": """a cat and a frog""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""inpaint_strength""": 1.0,
"""guidance_scale""": 6.0,
"""decode_latents""": True,
"""output_type""": """numpy""",
}
return inputs
def _a ( self ) -> Tuple:
"""simple docstring"""
if not hasattr(self.pipeline_class , """_optional_components""" ):
return
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : List[str] = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(_a , _a , _a )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : Dict = pipe(**_a )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.pipeline_class.from_pretrained(_a )
pipe_loaded.to(_a )
pipe_loaded.set_progress_bar_config(disable=_a )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_a , _a ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_inputs(_a )
SCREAMING_SNAKE_CASE__ : List[str] = pipe_loaded(**_a )[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.abs(output - output_loaded ).max()
self.assertLess(_a , 1E-4 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = """cpu"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_mask_inputs(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe.generate_mask(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.array([0] * 9 )
SCREAMING_SNAKE_CASE__ : Any = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_a , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = """cpu"""
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_dummy_inversion_inputs(_a )
SCREAMING_SNAKE_CASE__ : Any = pipe.invert(**_a ).images
SCREAMING_SNAKE_CASE__ : str = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_a , 1E-3 )
def _a ( self ) -> List[str]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = """cpu"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : int = {"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = DPMSolverMultistepScheduler(**_a )
SCREAMING_SNAKE_CASE__ : List[Any] = DPMSolverMultistepInverseScheduler(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.pipeline_class(**_a )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_inversion_inputs(_a )
SCREAMING_SNAKE_CASE__ : Any = pipe.invert(**_a ).images
SCREAMING_SNAKE_CASE__ : Optional[int] = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
SCREAMING_SNAKE_CASE__ : Any = np.array(
[0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_a , 1E-3 )
@require_torch_gpu
@slow
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _a ( cls ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png""" )
SCREAMING_SNAKE_CASE__ : Dict = raw_image.convert("""RGB""" ).resize((768, 768) )
SCREAMING_SNAKE_CASE__ : int = raw_image
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_a , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Optional[Any] = DDIMScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE__ : List[str] = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """a bowl of fruit"""
SCREAMING_SNAKE_CASE__ : List[Any] = """a bowl of pears"""
SCREAMING_SNAKE_CASE__ : int = pipe.generate_mask(
image=self.raw_image , source_prompt=_a , target_prompt=_a , generator=_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe.invert(
prompt=_a , image=self.raw_image , inpaint_strength=0.7 , generator=_a ).latents
SCREAMING_SNAKE_CASE__ : int = pipe(
prompt=_a , mask_image=_a , image_latents=_a , generator=_a , negative_prompt=_a , inpaint_strength=0.7 , output_type="""numpy""" , ).images[0]
SCREAMING_SNAKE_CASE__ : List[str] = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = StableDiffusionDiffEditPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2-1""" , safety_checker=_a , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : List[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE__ : List[Any] = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """a bowl of fruit"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """a bowl of pears"""
SCREAMING_SNAKE_CASE__ : str = pipe.generate_mask(
image=self.raw_image , source_prompt=_a , target_prompt=_a , generator=_a , )
SCREAMING_SNAKE_CASE__ : Tuple = pipe.invert(
prompt=_a , image=self.raw_image , inpaint_strength=0.7 , generator=_a , num_inference_steps=25 , ).latents
SCREAMING_SNAKE_CASE__ : int = pipe(
prompt=_a , mask_image=_a , image_latents=_a , generator=_a , negative_prompt=_a , inpaint_strength=0.7 , num_inference_steps=25 , output_type="""numpy""" , ).images[0]
SCREAMING_SNAKE_CASE__ : Dict = (
np.array(
load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/diffedit/pears.png""" ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 12 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE__ : List[Any] = 1
SCREAMING_SNAKE_CASE__ : int = 1
while repunit:
SCREAMING_SNAKE_CASE__ : str = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _lowercase ( __lowerCAmelCase = 100_0000 ) -> int:
SCREAMING_SNAKE_CASE__ : Dict = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(__lowerCAmelCase ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a :Union[str, Any] = logging.get_logger(__name__)
a :Dict = {
"google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
"google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = """mobilenet_v1"""
def __init__( self , _a=3 , _a=224 , _a=1.0 , _a=8 , _a="relu6" , _a=True , _a=0.999 , _a=0.02 , _a=0.001 , **_a , ) -> Dict:
"""simple docstring"""
super().__init__(**_a )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
SCREAMING_SNAKE_CASE__ : List[Any] = num_channels
SCREAMING_SNAKE_CASE__ : List[Any] = image_size
SCREAMING_SNAKE_CASE__ : Tuple = depth_multiplier
SCREAMING_SNAKE_CASE__ : Optional[int] = min_depth
SCREAMING_SNAKE_CASE__ : Tuple = hidden_act
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf_padding
SCREAMING_SNAKE_CASE__ : int = classifier_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_eps
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = version.parse("""1.11""")
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def _a ( self ) -> float:
"""simple docstring"""
return 1E-4
| 12 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
a :Union[str, Any] = logging.getLogger(__name__)
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""})
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
_SCREAMING_SNAKE_CASE :Optional[int] = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class __a :
'''simple docstring'''
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""})
_SCREAMING_SNAKE_CASE :str = field(
default=UpperCamelCase_ , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Train language if it is different from the evaluation language."""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""})
_SCREAMING_SNAKE_CASE :Optional[str] = field(
default=UpperCamelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
_SCREAMING_SNAKE_CASE :Optional[bool] = field(
default=UpperCamelCase_ , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
_SCREAMING_SNAKE_CASE :str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
_SCREAMING_SNAKE_CASE :bool = field(
default=UpperCamelCase_ , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def _lowercase ( ) -> Union[str, Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
SCREAMING_SNAKE_CASE__ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_xnli""" , __lowerCAmelCase )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : List[Any] = training_args.get_process_log_level()
logger.setLevel(__lowerCAmelCase )
datasets.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.set_verbosity(__lowerCAmelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE__ : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE__ : Optional[int] = load_dataset(
"""xnli""" , model_args.language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE__ : str = load_dataset(
"""xnli""" , model_args.train_language , split="""train""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = train_dataset.features["""label"""].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""validation""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.features["""label"""].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE__ : int = load_dataset(
"""xnli""" , model_args.language , split="""test""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.features["""label"""].names
# Labels
SCREAMING_SNAKE_CASE__ : Any = len(__lowerCAmelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE__ : Any = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=__lowerCAmelCase , idalabel={str(__lowerCAmelCase ): label for i, label in enumerate(__lowerCAmelCase )} , labelaid={label: i for i, label in enumerate(__lowerCAmelCase )} , finetuning_task="""xnli""" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE__ : str = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=__lowerCAmelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : str = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE__ : Optional[Any] = False
def preprocess_function(__lowerCAmelCase ):
# Tokenize the texts
return tokenizer(
examples["""premise"""] , examples["""hypothesis"""] , padding=__lowerCAmelCase , max_length=data_args.max_seq_length , truncation=__lowerCAmelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(len(__lowerCAmelCase ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE__ : str = train_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = train_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on train dataset""" , )
# Log a few random samples from the training set:
for index in random.sample(range(len(__lowerCAmelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE__ : Any = min(len(__lowerCAmelCase ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = eval_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : List[str] = eval_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on validation dataset""" , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE__ : int = min(len(__lowerCAmelCase ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE__ : List[Any] = predict_dataset.select(range(__lowerCAmelCase ) )
with training_args.main_process_first(desc="""prediction dataset map pre-processing""" ):
SCREAMING_SNAKE_CASE__ : Tuple = predict_dataset.map(
__lowerCAmelCase , batched=__lowerCAmelCase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on prediction dataset""" , )
# Get the metric function
SCREAMING_SNAKE_CASE__ : Optional[Any] = evaluate.load("""xnli""" )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Dict = p.predictions[0] if isinstance(p.predictions , __lowerCAmelCase ) else p.predictions
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.argmax(__lowerCAmelCase , axis=1 )
return metric.compute(predictions=__lowerCAmelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE__ : List[Any] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE__ : int = DataCollatorWithPadding(__lowerCAmelCase , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Trainer(
model=__lowerCAmelCase , args=__lowerCAmelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=__lowerCAmelCase , tokenizer=__lowerCAmelCase , data_collator=__lowerCAmelCase , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE__ : Dict = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = last_checkpoint
SCREAMING_SNAKE_CASE__ : str = trainer.train(resume_from_checkpoint=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = train_result.metrics
SCREAMING_SNAKE_CASE__ : Optional[int] = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : Dict = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , __lowerCAmelCase )
trainer.save_metrics("""train""" , __lowerCAmelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
SCREAMING_SNAKE_CASE__ : Any = trainer.evaluate(eval_dataset=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""eval""" , __lowerCAmelCase )
trainer.save_metrics("""eval""" , __lowerCAmelCase )
# Prediction
if training_args.do_predict:
logger.info("""*** Predict ***""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = trainer.predict(__lowerCAmelCase , metric_key_prefix="""predict""" )
SCREAMING_SNAKE_CASE__ : List[str] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : int = min(__lowerCAmelCase , len(__lowerCAmelCase ) )
trainer.log_metrics("""predict""" , __lowerCAmelCase )
trainer.save_metrics("""predict""" , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = np.argmax(__lowerCAmelCase , axis=1 )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(training_args.output_dir , """predictions.txt""" )
if trainer.is_world_process_zero():
with open(__lowerCAmelCase , """w""" ) as writer:
writer.write("""index\tprediction\n""" )
for index, item in enumerate(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : Optional[int] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 12 | 1 |
"""simple docstring"""
a :str = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
a :str = [{"type": "code", "content": INSTALL_CONTENT}]
a :Union[str, Any] = {
"{processor_class}": "FakeProcessorClass",
"{model_class}": "FakeModelClass",
"{object_class}": "FakeObjectClass",
}
| 12 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
a :str = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
a :int = {
# fairseq:
"wmt19-ru-en": {"length_penalty": 1.1},
"wmt19-en-ru": {"length_penalty": 1.15},
"wmt19-en-de": {"length_penalty": 1.0},
"wmt19-de-en": {"length_penalty": 1.1},
# allenai:
"wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
"wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
"wmt16-en-de-12-1": {"length_penalty": 0.8},
"wmt19-de-en-6-6-base": {"length_penalty": 0.6},
"wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}
# this remaps the different models to their organization names
a :Dict = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
a :List[Any] = "facebook"
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
a :str = "allenai"
def _lowercase ( __lowerCAmelCase ) -> Any:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
SCREAMING_SNAKE_CASE__ : str = dict((re.sub(r"""@@$""" , """""" , __lowerCAmelCase ), v) if k.endswith("""@@""" ) else (re.sub(r"""$""" , """</w>""" , __lowerCAmelCase ), v) for k, v in d.items() )
SCREAMING_SNAKE_CASE__ : Tuple = """<s> <pad> </s> <unk>""".split()
# restore the special tokens
for k in keep_keys:
del da[F'''{k}</w>''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d[k] # restore
return da
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
# prep
assert os.path.exists(__lowerCAmelCase )
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
print(F'''Writing results to {pytorch_dump_folder_path}''' )
# handle various types of models
SCREAMING_SNAKE_CASE__ : Optional[Any] = basename(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
SCREAMING_SNAKE_CASE__ : Optional[int] = cls.hub_models()
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
SCREAMING_SNAKE_CASE__ : Optional[Any] = """."""
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F'''using checkpoint {checkpoint_file}''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hub_utils.from_pretrained(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , archive_map=__lowerCAmelCase , **__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = vars(chkpt["""args"""]["""model"""] )
SCREAMING_SNAKE_CASE__ : Any = args["""source_lang"""]
SCREAMING_SNAKE_CASE__ : Any = args["""target_lang"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = dirname(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = basename(__lowerCAmelCase )
# dicts
SCREAMING_SNAKE_CASE__ : Optional[Any] = os.path.join(__lowerCAmelCase , F'''dict.{src_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Any = os.path.join(__lowerCAmelCase , F'''dict.{tgt_lang}.txt''' )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = rewrite_dict_keys(src_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , """vocab-src.json""" )
print(F'''Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
SCREAMING_SNAKE_CASE__ : Optional[Any] = True
for k in src_vocab.keys():
if not k.islower():
SCREAMING_SNAKE_CASE__ : Tuple = False
break
SCREAMING_SNAKE_CASE__ : Optional[Any] = Dictionary.load(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = rewrite_dict_keys(tgt_dict.indices )
SCREAMING_SNAKE_CASE__ : Optional[Any] = len(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , """vocab-tgt.json""" )
print(F'''Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# merges_file (bpecodes)
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(__lowerCAmelCase , VOCAB_FILES_NAMES["""merges_file"""] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
SCREAMING_SNAKE_CASE__ : Union[str, Any] = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
if os.path.exists(__lowerCAmelCase ):
break
with open(__lowerCAmelCase , encoding="""utf-8""" ) as fin:
SCREAMING_SNAKE_CASE__ : Any = fin.read()
SCREAMING_SNAKE_CASE__ : Tuple = re.sub(r""" \d+$""" , """""" , __lowerCAmelCase , 0 , re.M ) # remove frequency number
print(F'''Generating {merges_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as fout:
fout.write(__lowerCAmelCase )
# model config
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(__lowerCAmelCase , """config.json""" )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F'''need to extend tokenizer to support bpe={args['bpe']}'''
assert args["tokenizer"] == "moses", F'''need to extend tokenizer to support bpe={args['tokenizer']}'''
SCREAMING_SNAKE_CASE__ : str = {
"""architectures""": ["""FSMTForConditionalGeneration"""],
"""model_type""": """fsmt""",
"""activation_dropout""": args["""activation_dropout"""],
"""activation_function""": """relu""",
"""attention_dropout""": args["""attention_dropout"""],
"""d_model""": args["""decoder_embed_dim"""],
"""dropout""": args["""dropout"""],
"""init_std""": 0.02,
"""max_position_embeddings""": args["""max_source_positions"""],
"""num_hidden_layers""": args["""encoder_layers"""],
"""src_vocab_size""": src_vocab_size,
"""tgt_vocab_size""": tgt_vocab_size,
"""langs""": [src_lang, tgt_lang],
"""encoder_attention_heads""": args["""encoder_attention_heads"""],
"""encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
"""encoder_layerdrop""": args["""encoder_layerdrop"""],
"""encoder_layers""": args["""encoder_layers"""],
"""decoder_attention_heads""": args["""decoder_attention_heads"""],
"""decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
"""decoder_layerdrop""": args["""decoder_layerdrop"""],
"""decoder_layers""": args["""decoder_layers"""],
"""bos_token_id""": 0,
"""pad_token_id""": 1,
"""eos_token_id""": 2,
"""is_encoder_decoder""": True,
"""scale_embedding""": not args["""no_scale_embedding"""],
"""tie_word_embeddings""": args["""share_all_embeddings"""],
}
# good hparam defaults to start with
SCREAMING_SNAKE_CASE__ : Tuple = 5
SCREAMING_SNAKE_CASE__ : str = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
SCREAMING_SNAKE_CASE__ : Tuple = best_score_hparams[model_dir]["""length_penalty"""]
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = 1.0
print(F'''Generating {fsmt_model_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# tokenizer config
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Tuple = {
"""langs""": [src_lang, tgt_lang],
"""model_max_length""": 1024,
"""do_lower_case""": do_lower_case,
}
print(F'''Generating {fsmt_tokenizer_config_file}''' )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(__lowerCAmelCase , ensure_ascii=__lowerCAmelCase , indent=__lowerCAmelCase ) )
# model
SCREAMING_SNAKE_CASE__ : Dict = chkpt["""models"""][0]
SCREAMING_SNAKE_CASE__ : int = model.state_dict()
# rename keys to start with 'model.'
SCREAMING_SNAKE_CASE__ : Tuple = OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
SCREAMING_SNAKE_CASE__ : str = [
"""model.model""",
"""model.encoder.version""",
"""model.decoder.version""",
"""model.encoder_embed_tokens.weight""",
"""model.decoder_embed_tokens.weight""",
"""model.encoder.embed_positions._float_tensor""",
"""model.decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
model_state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = FSMTConfig.from_pretrained(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FSMTForConditionalGeneration(__lowerCAmelCase )
# check that it loads ok
model_new.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
# save
SCREAMING_SNAKE_CASE__ : int = os.path.join(__lowerCAmelCase , __lowerCAmelCase )
print(F'''Generating {pytorch_weights_dump_path}''' )
torch.save(__lowerCAmelCase , __lowerCAmelCase )
print("""Conversion is done!""" )
print("""\nLast step is to upload the files to s3""" )
print(F'''cd {data_root}''' )
print(F'''transformers-cli upload {model_dir}''' )
if __name__ == "__main__":
a :Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fsmt_checkpoint_path",
default=None,
type=str,
required=True,
help=(
"Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
" bpecodes, etc."
),
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
a :List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)
| 12 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
a :Tuple = "platform"
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , ) -> Any:
if attention_mask is None:
SCREAMING_SNAKE_CASE__ : str = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ : List[Any] = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
SCREAMING_SNAKE_CASE__ : Any = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ : Tuple = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ : Any = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class __a :
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=False , _a=99 , _a=16 , _a=2 , _a=4 , _a=4 , _a="gelu" , _a=0.1 , _a=0.1 , _a=32 , _a=2 , _a=1 , _a=0 , _a=0.02 , ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = parent
SCREAMING_SNAKE_CASE__ : List[Any] = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Tuple = is_training
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : Dict = vocab_size
SCREAMING_SNAKE_CASE__ : Any = hidden_size
SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Tuple = intermediate_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : str = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Optional[Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = pad_token_id
SCREAMING_SNAKE_CASE__ : str = bos_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] = initializer_range
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Any = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
SCREAMING_SNAKE_CASE__ : List[Any] = shift_tokens_right(_a , 1 , 2 )
SCREAMING_SNAKE_CASE__ : Tuple = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_a , )
SCREAMING_SNAKE_CASE__ : Tuple = prepare_blenderbot_inputs_dict(_a , _a , _a )
return config, inputs_dict
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.prepare_config_and_inputs()
return config, inputs_dict
def _a ( self , _a , _a , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = 20
SCREAMING_SNAKE_CASE__ : List[str] = model_class_name(_a )
SCREAMING_SNAKE_CASE__ : Any = model.encode(inputs_dict["""input_ids"""] )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
SCREAMING_SNAKE_CASE__ : Optional[Any] = model.init_cache(decoder_input_ids.shape[0] , _a , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE__ : List[Any] = model.decode(
decoder_input_ids[:, :-1] , _a , decoder_attention_mask=_a , past_key_values=_a , decoder_position_ids=_a , )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ : str = model.decode(
decoder_input_ids[:, -1:] , _a , decoder_attention_mask=_a , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_a , )
SCREAMING_SNAKE_CASE__ : int = model.decode(_a , _a )
SCREAMING_SNAKE_CASE__ : Any = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
def _a ( self , _a , _a , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 20
SCREAMING_SNAKE_CASE__ : List[str] = model_class_name(_a )
SCREAMING_SNAKE_CASE__ : str = model.encode(inputs_dict["""input_ids"""] )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
SCREAMING_SNAKE_CASE__ : List[Any] = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
SCREAMING_SNAKE_CASE__ : Any = model.init_cache(decoder_input_ids.shape[0] , _a , _a )
SCREAMING_SNAKE_CASE__ : List[Any] = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
SCREAMING_SNAKE_CASE__ : Any = model.decode(
decoder_input_ids[:, :-1] , _a , decoder_attention_mask=_a , past_key_values=_a , decoder_position_ids=_a , )
SCREAMING_SNAKE_CASE__ : int = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = model.decode(
decoder_input_ids[:, -1:] , _a , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_a , decoder_position_ids=_a , )
SCREAMING_SNAKE_CASE__ : List[str] = model.decode(_a , _a , decoder_attention_mask=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f'''Max diff is {diff}''' )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = 99
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
SCREAMING_SNAKE_CASE__ : List[Any] = input_ids.shape[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self._get_config_and_data()
SCREAMING_SNAKE_CASE__ : Tuple = FlaxBlenderbotSmallForConditionalGeneration(_a )
SCREAMING_SNAKE_CASE__ : List[str] = lm_model(input_ids=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxBlenderbotSmallForConditionalGeneration(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE__ : Any = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE__ : Dict = lm_model(input_ids=_a , decoder_input_ids=_a )
SCREAMING_SNAKE_CASE__ : Any = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , _a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
SCREAMING_SNAKE_CASE__ : Any = shift_tokens_right(_a , 1 , 2 )
SCREAMING_SNAKE_CASE__ : Dict = np.equal(_a , 1 ).astype(np.floataa ).sum()
SCREAMING_SNAKE_CASE__ : Any = np.equal(_a , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(_a , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[int] = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
_SCREAMING_SNAKE_CASE :Union[str, Any] = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = FlaxBlenderbotSmallModelTester(self )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_a , _a , _a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_a , _a , _a )
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = self._prepare_for_class(_a , _a )
SCREAMING_SNAKE_CASE__ : str = model_class(_a )
@jax.jit
def encode_jitted(_a , _a=None , **_a ):
return model.encode(input_ids=_a , attention_mask=_a )
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE__ : str = encode_jitted(**_a ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE__ : Dict = encode_jitted(**_a ).to_tuple()
self.assertEqual(len(_a ) , len(_a ) )
for jitted_output, output in zip(_a , _a ):
self.assertEqual(jitted_output.shape , output.shape )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE__ : List[Any] = model_class(_a )
SCREAMING_SNAKE_CASE__ : Dict = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
SCREAMING_SNAKE_CASE__ : Any = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(_a , _a , _a ):
return model.decode(
decoder_input_ids=_a , decoder_attention_mask=_a , encoder_outputs=_a , )
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE__ : List[str] = decode_jitted(**_a ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE__ : Optional[int] = decode_jitted(**_a ).to_tuple()
self.assertEqual(len(_a ) , len(_a ) )
for jitted_output, output in zip(_a , _a ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def _a ( self ) -> Any:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : int = model_class_name.from_pretrained("""facebook/blenderbot_small-90M""" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
SCREAMING_SNAKE_CASE__ : Optional[int] = np.ones((1, 1) ) * model.config.eos_token_id
SCREAMING_SNAKE_CASE__ : List[Any] = model(_a )
self.assertIsNotNone(_a )
| 12 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Tuple = (DDPMScheduler,)
def _a ( self , **_a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""num_train_timesteps""": 1_000,
"""beta_start""": 0.0_001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""variance_type""": """fixed_small""",
"""clip_sample""": True,
}
config.update(**_a )
return config
def _a ( self ) -> str:
"""simple docstring"""
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_a )
def _a ( self ) -> str:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=_a , beta_end=_a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_a )
def _a ( self ) -> Any:
"""simple docstring"""
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=_a )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=_a )
def _a ( self ) -> int:
"""simple docstring"""
self.check_over_configs(thresholding=_a )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=_a , prediction_type=_a , sample_max_value=_a , )
def _a ( self ) -> str:
"""simple docstring"""
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=_a )
def _a ( self ) -> str:
"""simple docstring"""
for t in [0, 500, 999]:
self.check_over_forward(time_step=_a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00_979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : int = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Any = len(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : str = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : Optional[Any] = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : int = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : str = pred_prev_sample
SCREAMING_SNAKE_CASE__ : str = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : Any = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 258.9_606 ) < 1E-2
assert abs(result_mean.item() - 0.3_372 ) < 1E-3
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Tuple = self.get_scheduler_config(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_model()
SCREAMING_SNAKE_CASE__ : List[str] = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.manual_seed(0 )
for t in reversed(range(_a ) ):
# 1. predict noise residual
SCREAMING_SNAKE_CASE__ : int = model(_a , _a )
# 2. predict previous mean of sample x_t-1
SCREAMING_SNAKE_CASE__ : List[str] = scheduler.step(_a , _a , _a , generator=_a ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
SCREAMING_SNAKE_CASE__ : Tuple = pred_prev_sample
SCREAMING_SNAKE_CASE__ : Any = torch.sum(torch.abs(_a ) )
SCREAMING_SNAKE_CASE__ : int = torch.mean(torch.abs(_a ) )
assert abs(result_sum.item() - 202.0_296 ) < 1E-2
assert abs(result_mean.item() - 0.2_631 ) < 1E-3
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Dict = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler.timesteps
for i, timestep in enumerate(_a ):
if i == len(_a ) - 1:
SCREAMING_SNAKE_CASE__ : Optional[Any] = -1
else:
SCREAMING_SNAKE_CASE__ : Tuple = timesteps[i + 1]
SCREAMING_SNAKE_CASE__ : int = scheduler.previous_timestep(_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = prev_t.item()
self.assertEqual(_a , _a )
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : int = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(_a , msg="""`custom_timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : List[str] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : int = [100, 87, 50, 1, 0]
SCREAMING_SNAKE_CASE__ : List[str] = len(_a )
with self.assertRaises(_a , msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=_a , timesteps=_a )
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = scheduler_class(**_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
_a , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=_a )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase ) -> list:
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
SCREAMING_SNAKE_CASE__ : str = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : str = unsorted[j - 1], unsorted[j]
SCREAMING_SNAKE_CASE__ : Dict = True
for j in range(__lowerCAmelCase ):
if unsorted[j] > unsorted[j + 1]:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = unsorted[j + 1], unsorted[j]
SCREAMING_SNAKE_CASE__ : Any = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
a :List[Any] = input("Enter numbers separated by a comma:\n").strip()
a :Optional[int] = [int(item) for item in user_input.split(",")]
print(f'{cocktail_shaker_sort(unsorted) = }')
| 12 |
"""simple docstring"""
import os
a :List[str] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : Dict = 0
while index < len(__lowerCAmelCase ) - 1:
SCREAMING_SNAKE_CASE__ : List[Any] = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE__ : Dict = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Optional[int] = """"""
SCREAMING_SNAKE_CASE__ : int = num // 1000
numerals += m_count * "M"
num %= 1000
SCREAMING_SNAKE_CASE__ : List[str] = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
SCREAMING_SNAKE_CASE__ : List[Any] = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _lowercase ( __lowerCAmelCase = "/p089_roman.txt" ) -> int:
SCREAMING_SNAKE_CASE__ : int = 0
with open(os.path.dirname(__lowerCAmelCase ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE__ : str = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = line.strip()
SCREAMING_SNAKE_CASE__ : Dict = parse_roman_numerals(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : str = generate_roman_numerals(__lowerCAmelCase )
savings += len(__lowerCAmelCase ) - len(__lowerCAmelCase )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 12 | 1 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
a :str = "\\n Text data.\n Second line of data."
a :List[Any] = "file"
@pytest.fixture(scope="""session""" )
def _lowercase ( __lowerCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ : Optional[Any] = tmp_path_factory.mktemp("""data""" ) / (FILE_PATH + """.zstd""")
SCREAMING_SNAKE_CASE__ : Optional[Any] = bytes(__lowerCAmelCase , """utf-8""" )
with zstd.open(__lowerCAmelCase , """wb""" ) as f:
f.write(__lowerCAmelCase )
return path
@pytest.fixture
def _lowercase ( __lowerCAmelCase ) -> int:
with open(os.path.join(tmpfs.local_root_dir , __lowerCAmelCase ) , """w""" ) as f:
f.write(__lowerCAmelCase )
return FILE_PATH
@pytest.mark.parametrize("""compression_format""" , ["""gzip""", """xz""", """zstd"""] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Dict = {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
SCREAMING_SNAKE_CASE__ : Dict = input_paths[compression_format]
SCREAMING_SNAKE_CASE__ : Dict = tmp_path / """cache"""
SCREAMING_SNAKE_CASE__ : Any = DownloadConfig(cache_dir=__lowerCAmelCase , extract_compressed_file=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = cached_path(__lowerCAmelCase , download_config=__lowerCAmelCase )
with open(__lowerCAmelCase ) as f:
SCREAMING_SNAKE_CASE__ : Optional[int] = f.read()
with open(__lowerCAmelCase ) as f:
SCREAMING_SNAKE_CASE__ : Dict = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("""default_extracted""" , [True, False] )
@pytest.mark.parametrize("""default_cache_dir""" , [True, False] )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """custom_cache"""
SCREAMING_SNAKE_CASE__ : Dict = """custom_extracted_dir"""
SCREAMING_SNAKE_CASE__ : List[Any] = tmp_path / """custom_extracted_path"""
if default_extracted:
SCREAMING_SNAKE_CASE__ : List[Any] = ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
else:
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR""" , __lowerCAmelCase )
monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH""" , str(__lowerCAmelCase ) )
SCREAMING_SNAKE_CASE__ : List[str] = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
SCREAMING_SNAKE_CASE__ : Optional[int] = xz_file
SCREAMING_SNAKE_CASE__ : str = (
DownloadConfig(extract_compressed_file=__lowerCAmelCase )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=__lowerCAmelCase )
)
SCREAMING_SNAKE_CASE__ : List[str] = cached_path(__lowerCAmelCase , download_config=__lowerCAmelCase )
assert Path(__lowerCAmelCase ).parent.parts[-2:] == expected
def _lowercase ( __lowerCAmelCase ) -> Any:
# absolute path
SCREAMING_SNAKE_CASE__ : Optional[int] = str(Path(__lowerCAmelCase ).resolve() )
assert cached_path(__lowerCAmelCase ) == text_file
# relative path
SCREAMING_SNAKE_CASE__ : List[str] = str(Path(__lowerCAmelCase ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(__lowerCAmelCase ) == text_file
def _lowercase ( __lowerCAmelCase ) -> Union[str, Any]:
# absolute path
SCREAMING_SNAKE_CASE__ : Optional[Any] = str(tmp_path.resolve() / """__missing_file__.txt""" )
with pytest.raises(__lowerCAmelCase ):
cached_path(__lowerCAmelCase )
# relative path
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """./__missing_file__.txt"""
with pytest.raises(__lowerCAmelCase ):
cached_path(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Any:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_from_cache(F'''tmp://{tmpfs_file}''' )
with open(__lowerCAmelCase ) as f:
SCREAMING_SNAKE_CASE__ : Optional[int] = f.read()
assert output_file_content == FILE_CONTENT
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __lowerCAmelCase )
def _lowercase ( ) -> List[Any]:
with pytest.raises(__lowerCAmelCase ):
cached_path("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(__lowerCAmelCase ):
http_get("""https://huggingface.co""" , temp_file=__lowerCAmelCase )
with pytest.raises(__lowerCAmelCase ):
http_head("""https://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : str = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(__lowerCAmelCase ):
ftp_get("""ftp://huggingface.co""" , temp_file=__lowerCAmelCase )
with pytest.raises(__lowerCAmelCase ):
ftp_head("""ftp://huggingface.co""" )
@patch("""datasets.config.HF_DATASETS_OFFLINE""" , __lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.html"""
with pytest.raises(__lowerCAmelCase ):
fsspec_get("""s3://huggingface.co""" , temp_file=__lowerCAmelCase )
with pytest.raises(__lowerCAmelCase ):
fsspec_head("""s3://huggingface.co""" )
| 12 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""" )
SCREAMING_SNAKE_CASE__ : Any = tf.convert_to_tensor(
[[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
SCREAMING_SNAKE_CASE__ : Optional[int] = model(_a )["""last_hidden_state"""]
SCREAMING_SNAKE_CASE__ : List[str] = tf.TensorShape((1, 10, 768) )
self.assertEqual(output.shape , _a )
# compare the actual values for a slice.
SCREAMING_SNAKE_CASE__ : Optional[int] = tf.convert_to_tensor(
[[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
a :Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class __a (UpperCamelCase_):
'''simple docstring'''
def __init__( self , _a , _a , _a , _a , _a , _a , _a , _a , _a , ) -> List[Any]:
"""simple docstring"""
super().__init__()
if hasattr(scheduler.config , """steps_offset""" ) and scheduler.config.steps_offset != 1:
SCREAMING_SNAKE_CASE__ : Dict = (
f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
"""to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
""" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
""" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
""" file"""
)
deprecate("""steps_offset!=1""" , """1.0.0""" , _a , standard_warn=_a )
SCREAMING_SNAKE_CASE__ : str = dict(scheduler.config )
SCREAMING_SNAKE_CASE__ : Any = 1
SCREAMING_SNAKE_CASE__ : List[Any] = FrozenDict(_a )
if hasattr(scheduler.config , """skip_prk_steps""" ) and scheduler.config.skip_prk_steps is False:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (
f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
""" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
""" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
""" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
""" Hub, it would be very nice if you could open a Pull request for the"""
""" `scheduler/scheduler_config.json` file"""
)
deprecate("""skip_prk_steps not set""" , """1.0.0""" , _a , standard_warn=_a )
SCREAMING_SNAKE_CASE__ : Dict = dict(scheduler.config )
SCREAMING_SNAKE_CASE__ : int = True
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FrozenDict(_a )
if safety_checker is None:
logger.warning(
f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""" )
self.register_modules(
segmentation_model=_a , segmentation_processor=_a , vae=_a , text_encoder=_a , tokenizer=_a , unet=_a , scheduler=_a , safety_checker=_a , feature_extractor=_a , )
def _a ( self , _a = "auto" ) -> Optional[int]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
self.enable_attention_slicing(_a )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.device("""cuda""" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(_a , _a )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _a ( self ) -> Optional[int]:
"""simple docstring"""
if self.device != torch.device("""meta""" ) or not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_a , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self , _a , _a , _a , _a = 512 , _a = 512 , _a = 50 , _a = 7.5 , _a = None , _a = 1 , _a = 0.0 , _a = None , _a = None , _a = "pil" , _a = True , _a = None , _a = 1 , **_a , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.segmentation_processor(
text=[text] , images=[image] , padding="""max_length""" , return_tensors="""pt""" ).to(self.device )
SCREAMING_SNAKE_CASE__ : List[str] = self.segmentation_model(**_a )
SCREAMING_SNAKE_CASE__ : Dict = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
SCREAMING_SNAKE_CASE__ : Any = self.numpy_to_pil(_a )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
SCREAMING_SNAKE_CASE__ : Dict = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=_a , image=_a , mask_image=_a , height=_a , width=_a , num_inference_steps=_a , guidance_scale=_a , negative_prompt=_a , num_images_per_prompt=_a , eta=_a , generator=_a , latents=_a , output_type=_a , return_dict=_a , callback=_a , callback_steps=_a , )
| 12 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a :List[Any] = logging.get_logger(__name__)
a :Optional[int] = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = """focalnet"""
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1E-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1E-5 , _a=32 , _a=None , _a=None , **_a , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = image_size
SCREAMING_SNAKE_CASE__ : str = patch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE__ : Union[str, Any] = embed_dim
SCREAMING_SNAKE_CASE__ : List[str] = use_conv_embed
SCREAMING_SNAKE_CASE__ : List[str] = hidden_sizes
SCREAMING_SNAKE_CASE__ : Optional[int] = depths
SCREAMING_SNAKE_CASE__ : Any = focal_levels
SCREAMING_SNAKE_CASE__ : Optional[Any] = focal_windows
SCREAMING_SNAKE_CASE__ : Any = hidden_act
SCREAMING_SNAKE_CASE__ : Tuple = mlp_ratio
SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = drop_path_rate
SCREAMING_SNAKE_CASE__ : str = use_layerscale
SCREAMING_SNAKE_CASE__ : int = layerscale_value
SCREAMING_SNAKE_CASE__ : Optional[int] = use_post_layernorm
SCREAMING_SNAKE_CASE__ : Any = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE__ : Union[str, Any] = normalize_modulator
SCREAMING_SNAKE_CASE__ : str = initializer_range
SCREAMING_SNAKE_CASE__ : Any = layer_norm_eps
SCREAMING_SNAKE_CASE__ : Any = encoder_stride
SCREAMING_SNAKE_CASE__ : Optional[int] = ["""stem"""] + [f'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Dict = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 12 | 1 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a :Optional[Any] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a :Dict = "\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n"
a :Any = "\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n"
a :int = "\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n 'meteor': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric('meteor')\n >>> predictions = [\"It is a guide to action which ensures that the military always obeys the commands of the party\"]\n >>> references = [\"It is a guide to action that ensures that the military will forever heed Party commands\"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results[\"meteor\"], 4))\n 0.6944\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class __a (datasets.Metric):
'''simple docstring'''
def _a ( self ) -> str:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"""] , reference_urls=[
"""https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score""",
"""https://en.wikipedia.org/wiki/METEOR""",
] , )
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
import nltk
nltk.download("""wordnet""" )
if NLTK_VERSION >= version.Version("""3.6.5""" ):
nltk.download("""punkt""" )
if NLTK_VERSION >= version.Version("""3.6.6""" ):
nltk.download("""omw-1.4""" )
def _a ( self , _a , _a , _a=0.9 , _a=3 , _a=0.5 ) -> List[str]:
"""simple docstring"""
if NLTK_VERSION >= version.Version("""3.6.5""" ):
SCREAMING_SNAKE_CASE__ : Optional[int] = [
meteor_score.single_meteor_score(
word_tokenize(_a ) , word_tokenize(_a ) , alpha=_a , beta=_a , gamma=_a )
for ref, pred in zip(_a , _a )
]
else:
SCREAMING_SNAKE_CASE__ : Dict = [
meteor_score.single_meteor_score(_a , _a , alpha=_a , beta=_a , gamma=_a )
for ref, pred in zip(_a , _a )
]
return {"meteor": np.mean(_a )}
| 12 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class __a (unittest.TestCase):
'''simple docstring'''
def __init__( self , _a , _a=13 , _a=7 , _a=True , _a=True , _a=True , _a=True , _a=99 , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=512 , _a=16 , _a=2 , _a=0.02 , _a=4 , ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = parent
SCREAMING_SNAKE_CASE__ : Tuple = batch_size
SCREAMING_SNAKE_CASE__ : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE__ : Optional[int] = is_training
SCREAMING_SNAKE_CASE__ : Optional[Any] = use_attention_mask
SCREAMING_SNAKE_CASE__ : Tuple = use_token_type_ids
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels
SCREAMING_SNAKE_CASE__ : int = vocab_size
SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ : List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads
SCREAMING_SNAKE_CASE__ : Dict = intermediate_size
SCREAMING_SNAKE_CASE__ : int = hidden_act
SCREAMING_SNAKE_CASE__ : Dict = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE__ : Dict = type_vocab_size
SCREAMING_SNAKE_CASE__ : Any = type_sequence_label_size
SCREAMING_SNAKE_CASE__ : int = initializer_range
SCREAMING_SNAKE_CASE__ : Optional[Any] = num_choices
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE__ : int = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ : Tuple = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ : Optional[int] = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_a , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def _a ( self ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[str] = config_and_inputs
SCREAMING_SNAKE_CASE__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_flax
class __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = True
_SCREAMING_SNAKE_CASE :Optional[Any] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerModelTester(self )
@slow
def _a ( self ) -> int:
"""simple docstring"""
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE__ : Tuple = model_class_name.from_pretrained("""junnyu/roformer_chinese_small""" , from_pt=_a )
SCREAMING_SNAKE_CASE__ : Tuple = model(np.ones((1, 1) ) )
self.assertIsNotNone(_a )
@require_flax
class __a (unittest.TestCase):
'''simple docstring'''
@slow
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
SCREAMING_SNAKE_CASE__ : Tuple = jnp.array([[0, 1, 2, 3, 4, 5]] )
SCREAMING_SNAKE_CASE__ : str = model(_a )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = 50_000
SCREAMING_SNAKE_CASE__ : Optional[Any] = (1, 6, vocab_size)
self.assertEqual(output.shape , _a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = jnp.array(
[[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , _a , atol=1E-4 ) )
| 12 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
@register_to_config
def __init__( self , _a = 768 , ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
SCREAMING_SNAKE_CASE__ : str = nn.Parameter(torch.zeros(1 , _a ) )
SCREAMING_SNAKE_CASE__ : List[str] = nn.Parameter(torch.ones(1 , _a ) )
def _a ( self , _a = None , _a = None , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = nn.Parameter(self.mean.to(_a ).to(_a ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = nn.Parameter(self.std.to(_a ).to(_a ) )
return self
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = (embeds - self.mean) * 1.0 / self.std
return embeds
def _a ( self , _a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = (embeds * self.std) + self.mean
return embeds
| 12 |
"""simple docstring"""
a :List[str] = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 0
while place < len(__lowerCAmelCase ):
if (place + 1 < len(__lowerCAmelCase )) and (vals[roman[place]] < vals[roman[place + 1]]):
total += vals[roman[place + 1]] - vals[roman[place]]
place += 2
else:
total += vals[roman[place]]
place += 1
return total
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Any = []
for arabic, roman in ROMAN:
((SCREAMING_SNAKE_CASE__) , (SCREAMING_SNAKE_CASE__)) : List[str] = divmod(__lowerCAmelCase , __lowerCAmelCase )
result.append(roman * factor )
if number == 0:
break
return "".join(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a :Optional[int] = logging.get_logger(__name__)
a :Optional[Any] = {"vocab_file": "sentencepiece.bpe.model"}
a :Union[str, Any] = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
}
}
a :Any = {
"camembert-base": 512,
}
a :Any = "▁"
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :str = ["""input_ids""", """attention_mask"""]
def __init__( self , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a=["<s>NOTUSED", "</s>NOTUSED"] , _a = None , **_a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
SCREAMING_SNAKE_CASE__ : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_a ) )
SCREAMING_SNAKE_CASE__ : int = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
SCREAMING_SNAKE_CASE__ : Any = {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
SCREAMING_SNAKE_CASE__ : List[str] = len(self.fairseq_tokens_to_ids )
SCREAMING_SNAKE_CASE__ : List[str] = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
SCREAMING_SNAKE_CASE__ : str = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cls_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self , _a , _a = None , _a = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _a ( self ) -> Union[str, Any]:
"""simple docstring"""
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self , _a ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_a , out_type=_a )
def _a ( self , _a ) -> Dict:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(_a ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(_a )
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = []
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """"""
SCREAMING_SNAKE_CASE__ : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
SCREAMING_SNAKE_CASE__ : Union[str, Any] = True
SCREAMING_SNAKE_CASE__ : str = []
else:
current_sub_tokens.append(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def __getstate__( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : List[Any] = None
return state
def __setstate__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ : List[str] = {}
SCREAMING_SNAKE_CASE__ : Tuple = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ : str = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 12 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a :Any = {
"configuration_roberta_prelayernorm": [
"ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
"RobertaPreLayerNormConfig",
"RobertaPreLayerNormOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Union[str, Any] = [
"ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"RobertaPreLayerNormForCausalLM",
"RobertaPreLayerNormForMaskedLM",
"RobertaPreLayerNormForMultipleChoice",
"RobertaPreLayerNormForQuestionAnswering",
"RobertaPreLayerNormForSequenceClassification",
"RobertaPreLayerNormForTokenClassification",
"RobertaPreLayerNormModel",
"RobertaPreLayerNormPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Optional[Any] = [
"TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRobertaPreLayerNormForCausalLM",
"TFRobertaPreLayerNormForMaskedLM",
"TFRobertaPreLayerNormForMultipleChoice",
"TFRobertaPreLayerNormForQuestionAnswering",
"TFRobertaPreLayerNormForSequenceClassification",
"TFRobertaPreLayerNormForTokenClassification",
"TFRobertaPreLayerNormMainLayer",
"TFRobertaPreLayerNormModel",
"TFRobertaPreLayerNormPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :List[Any] = [
"FlaxRobertaPreLayerNormForCausalLM",
"FlaxRobertaPreLayerNormForMaskedLM",
"FlaxRobertaPreLayerNormForMultipleChoice",
"FlaxRobertaPreLayerNormForQuestionAnswering",
"FlaxRobertaPreLayerNormForSequenceClassification",
"FlaxRobertaPreLayerNormForTokenClassification",
"FlaxRobertaPreLayerNormModel",
"FlaxRobertaPreLayerNormPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a :Optional[int] = {
"configuration_mgp_str": ["MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP", "MgpstrConfig"],
"processing_mgp_str": ["MgpstrProcessor"],
"tokenization_mgp_str": ["MgpstrTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a :Any = [
"MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST",
"MgpstrModel",
"MgpstrPreTrainedModel",
"MgpstrForSceneTextRecognition",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
a :Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 12 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AlignProcessor, EfficientNetImageProcessor
@require_vision
class __a (unittest.TestCase):
'''simple docstring'''
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Dict = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"""image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
SCREAMING_SNAKE_CASE__ : Tuple = os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(_a , _a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> List[Any]:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def _a ( self , **_a ) -> Any:
"""simple docstring"""
return EfficientNetImageProcessor.from_pretrained(self.tmpdirname , **_a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
SCREAMING_SNAKE_CASE__ : Optional[int] = [Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a ( self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : str = AlignProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
SCREAMING_SNAKE_CASE__ : int = AlignProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE__ : Any = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
SCREAMING_SNAKE_CASE__ : Dict = self.get_image_processor(do_normalize=_a , padding_value=1.0 )
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_a , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def _a ( self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : str = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : List[str] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Any = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[Any] = image_processor(_a , return_tensors="""np""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(images=_a , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Any = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = processor(text=_a )
SCREAMING_SNAKE_CASE__ : Any = tokenizer(_a , padding="""max_length""" , max_length=64 )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : int = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : Any = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def _a ( self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Dict = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Tuple = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
SCREAMING_SNAKE_CASE__ : List[Any] = processor.batch_decode(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def _a ( self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_image_processor()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ : Dict = AlignProcessor(tokenizer=_a , image_processor=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = """lower newer"""
SCREAMING_SNAKE_CASE__ : List[str] = self.prepare_image_inputs()
SCREAMING_SNAKE_CASE__ : List[str] = processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 12 | 1 |
"""simple docstring"""
def _lowercase ( ) -> list[list[int]]:
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
a :str = generate_large_matrix()
a :List[str] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, 7, 6], [-1, -2, -3]],
grid,
)
def _lowercase ( __lowerCAmelCase ) -> None:
assert all(row == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for row in grid )
assert all(list(__lowerCAmelCase ) == sorted(__lowerCAmelCase , reverse=__lowerCAmelCase ) for col in zip(*__lowerCAmelCase ) )
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : Any = 0
SCREAMING_SNAKE_CASE__ : List[str] = len(__lowerCAmelCase ) - 1
# Edge cases such as no values or all numbers are negative.
if not array or array[0] < 0:
return 0
while right + 1 > left:
SCREAMING_SNAKE_CASE__ : Optional[Any] = (left + right) // 2
SCREAMING_SNAKE_CASE__ : int = array[mid]
# Num must be negative and the index must be greater than or equal to 0.
if num < 0 and array[mid - 1] >= 0:
return mid
if num >= 0:
SCREAMING_SNAKE_CASE__ : int = mid + 1
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = mid - 1
# No negative numbers so return the last index of the array + 1 which is the length.
return len(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : List[Any] = 0
SCREAMING_SNAKE_CASE__ : Tuple = len(grid[0] )
for i in range(len(__lowerCAmelCase ) ):
SCREAMING_SNAKE_CASE__ : Dict = find_negative_index(grid[i][:bound] )
total += bound
return (len(__lowerCAmelCase ) * len(grid[0] )) - total
def _lowercase ( __lowerCAmelCase ) -> int:
return len([number for row in grid for number in row if number < 0] )
def _lowercase ( __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : str = 0
for row in grid:
for i, number in enumerate(__lowerCAmelCase ):
if number < 0:
total += len(__lowerCAmelCase ) - i
break
return total
def _lowercase ( ) -> None:
from timeit import timeit
print("""Running benchmarks""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
"""from __main__ import count_negatives_binary_search, """
"""count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
)
for func in (
"count_negatives_binary_search", # took 0.7727 seconds
"count_negatives_brute_force_with_break", # took 4.6505 seconds
"count_negatives_brute_force", # took 12.8160 seconds
):
SCREAMING_SNAKE_CASE__ : Dict = timeit(F'''{func}(grid=grid)''' , setup=__lowerCAmelCase , number=500 )
print(F'''{func}() took {time:0.4f} seconds''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 12 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
a :Optional[Any] = logging.get_logger(__name__)
a :Union[str, Any] = {
"t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
"t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
"t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
"t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
"t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = """t5"""
_SCREAMING_SNAKE_CASE :List[str] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Any = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
def __init__( self , _a=32_128 , _a=512 , _a=64 , _a=2_048 , _a=6 , _a=None , _a=8 , _a=32 , _a=128 , _a=0.1 , _a=1E-6 , _a=1.0 , _a="relu" , _a=True , _a=True , _a=0 , _a=1 , **_a , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = vocab_size
SCREAMING_SNAKE_CASE__ : Tuple = d_model
SCREAMING_SNAKE_CASE__ : int = d_kv
SCREAMING_SNAKE_CASE__ : Union[str, Any] = d_ff
SCREAMING_SNAKE_CASE__ : Union[str, Any] = num_layers
SCREAMING_SNAKE_CASE__ : int = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE__ : Tuple = num_heads
SCREAMING_SNAKE_CASE__ : Dict = relative_attention_num_buckets
SCREAMING_SNAKE_CASE__ : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE__ : Union[str, Any] = dropout_rate
SCREAMING_SNAKE_CASE__ : Union[str, Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE__ : Tuple = feed_forward_proj
SCREAMING_SNAKE_CASE__ : str = use_cache
SCREAMING_SNAKE_CASE__ : List[str] = self.feed_forward_proj.split("""-""" )
SCREAMING_SNAKE_CASE__ : Dict = act_info[-1]
SCREAMING_SNAKE_CASE__ : str = act_info[0] == """gated"""
if len(_a ) > 1 and act_info[0] != "gated" or len(_a ) > 2:
raise ValueError(
f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE__ : List[Any] = """gelu_new"""
super().__init__(
pad_token_id=_a , eos_token_id=_a , is_encoder_decoder=_a , **_a , )
class __a (UpperCamelCase_):
'''simple docstring'''
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = """past_encoder_sequence + sequence"""
SCREAMING_SNAKE_CASE__ : Optional[int] = {0: """batch"""}
SCREAMING_SNAKE_CASE__ : Tuple = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : str = {0: """batch""", 1: """decoder_sequence"""}
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_a , direction="""inputs""" )
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 12 | 1 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer
a :str = logging.get_logger(__name__)
a :Optional[int] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
a :Optional[int] = {
"vocab_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-ctx_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
a :Union[str, Any] = {
"vocab_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-question_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-question_encoder-multiset-base": (
"https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
),
},
}
a :str = {
"vocab_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"facebook/dpr-reader-single-nq-base": (
"https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
),
"facebook/dpr-reader-multiset-base": (
"https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
),
},
}
a :str = {
"facebook/dpr-ctx_encoder-single-nq-base": 512,
"facebook/dpr-ctx_encoder-multiset-base": 512,
}
a :Tuple = {
"facebook/dpr-question_encoder-single-nq-base": 512,
"facebook/dpr-question_encoder-multiset-base": 512,
}
a :Optional[int] = {
"facebook/dpr-reader-single-nq-base": 512,
"facebook/dpr-reader-multiset-base": 512,
}
a :int = {
"facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
a :Dict = {
"facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
"facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
a :Union[str, Any] = {
"facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
"facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :str = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Dict = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :int = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE :int = DPRContextEncoderTokenizer
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :str = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Dict = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :List[Any] = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE :Tuple = DPRQuestionEncoderTokenizer
a :int = collections.namedtuple(
"DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)
a :Optional[Any] = collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])
a :List[str] = r"\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `'tf'`: Return TensorFlow `tf.constant` objects.\n - `'pt'`: Return PyTorch `torch.Tensor` objects.\n - `'np'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer's default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Return:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n "
@add_start_docstrings(UpperCamelCase_)
class __a :
'''simple docstring'''
def __call__( self , _a , _a = None , _a = None , _a = False , _a = False , _a = None , _a = None , _a = None , **_a , ) -> BatchEncoding:
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
_a , padding=_a , truncation=_a , max_length=_a , return_tensors=_a , return_attention_mask=_a , **_a , )
elif titles is None or texts is None:
SCREAMING_SNAKE_CASE__ : str = titles if texts is None else texts
return super().__call__(
_a , _a , padding=_a , truncation=_a , max_length=_a , return_tensors=_a , return_attention_mask=_a , **_a , )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = titles if not isinstance(_a , _a ) else [titles]
SCREAMING_SNAKE_CASE__ : Any = texts if not isinstance(_a , _a ) else [texts]
SCREAMING_SNAKE_CASE__ : int = len(_a )
SCREAMING_SNAKE_CASE__ : int = questions if not isinstance(_a , _a ) else [questions] * n_passages
assert len(_a ) == len(
_a ), f'''There should be as many titles than texts but got {len(_a )} titles and {len(_a )} texts.'''
SCREAMING_SNAKE_CASE__ : Optional[int] = super().__call__(_a , _a , padding=_a , truncation=_a )["""input_ids"""]
SCREAMING_SNAKE_CASE__ : Optional[Any] = super().__call__(_a , add_special_tokens=_a , padding=_a , truncation=_a )["""input_ids"""]
SCREAMING_SNAKE_CASE__ : List[str] = {
"""input_ids""": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(_a , _a )
]
}
if return_attention_mask is not False:
SCREAMING_SNAKE_CASE__ : str = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
SCREAMING_SNAKE_CASE__ : str = attention_mask
return self.pad(_a , padding=_a , max_length=_a , return_tensors=_a )
def _a ( self , _a , _a , _a = 16 , _a = 64 , _a = 4 , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = reader_input["""input_ids"""]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = reader_output[:3]
SCREAMING_SNAKE_CASE__ : Dict = len(_a )
SCREAMING_SNAKE_CASE__ : str = sorted(range(_a ) , reverse=_a , key=relevance_logits.__getitem__ )
SCREAMING_SNAKE_CASE__ : List[DPRReaderOutput] = []
for doc_id in sorted_docs:
SCREAMING_SNAKE_CASE__ : Any = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
SCREAMING_SNAKE_CASE__ : List[Any] = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
SCREAMING_SNAKE_CASE__ : int = sequence_ids.index(self.pad_token_id )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = len(_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=_a , top_spans=_a , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=_a , start_index=_a , end_index=_a , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(_a ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _a ( self , _a , _a , _a , _a , ) -> List[DPRSpanPrediction]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
for start_index, start_score in enumerate(_a ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
SCREAMING_SNAKE_CASE__ : str = sorted(_a , key=lambda _a : x[1] , reverse=_a )
SCREAMING_SNAKE_CASE__ : Optional[int] = []
for (start_index, end_index), score in scores:
assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
SCREAMING_SNAKE_CASE__ : Optional[int] = end_index - start_index + 1
assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(_a ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(UpperCamelCase_)
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = READER_PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Optional[int] = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :List[Any] = READER_PRETRAINED_INIT_CONFIGURATION
_SCREAMING_SNAKE_CASE :Tuple = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :List[Any] = DPRReaderTokenizer
| 12 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
a :Optional[Any] = [8, 5, 9, 7]
a :List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a :int = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class __a :
'''simple docstring'''
def __init__( self , _a , _a , _a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = claim_vector
SCREAMING_SNAKE_CASE__ : Any = allocated_resources_table
SCREAMING_SNAKE_CASE__ : Any = maximum_claim_table
def _a ( self ) -> list[int]:
"""simple docstring"""
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _a ( self ) -> list[int]:
"""simple docstring"""
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _a ( self ) -> list[list[int]]:
"""simple docstring"""
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(_a ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _a ( self ) -> dict[int, list[int]]:
"""simple docstring"""
return {self.__need().index(_a ): i for i in self.__need()}
def _a ( self , **_a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__need()
SCREAMING_SNAKE_CASE__ : Any = self.__allocated_resources_table
SCREAMING_SNAKE_CASE__ : Dict = self.__available_resources()
SCREAMING_SNAKE_CASE__ : Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("""_""" * 50 + """\n""" )
while need_list:
SCREAMING_SNAKE_CASE__ : List[str] = False
for each_need in need_list:
SCREAMING_SNAKE_CASE__ : Dict = True
for index, need in enumerate(_a ):
if need > available_resources[index]:
SCREAMING_SNAKE_CASE__ : Optional[int] = False
break
if execution:
SCREAMING_SNAKE_CASE__ : Any = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
SCREAMING_SNAKE_CASE__ : Tuple = original_need_index
print(f'''Process {process_number + 1} is executing.''' )
# remove the process run from stack
need_list.remove(_a )
# update available/freed resources stack
SCREAMING_SNAKE_CASE__ : Dict = np.array(_a ) + np.array(
alloc_resources_table[process_number] )
print(
"""Updated available resource stack for processes: """
+ """ """.join([str(_a ) for x in available_resources] ) )
break
if safe:
print("""The process is in a safe state.\n""" )
else:
print("""System in unsafe state. Aborting...\n""" )
break
def _a ( self ) -> Any:
"""simple docstring"""
print(""" """ * 9 + """Allocated Resource Table""" )
for item in self.__allocated_resources_table:
print(
f'''P{self.__allocated_resources_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(""" """ * 9 + """System Resource Table""" )
for item in self.__maximum_claim_table:
print(
f'''P{self.__maximum_claim_table.index(_a ) + 1}'''
+ """ """.join(f'''{it:>8}''' for it in item )
+ """\n""" )
print(
"""Current Usage by Active Processes: """
+ """ """.join(str(_a ) for x in self.__claim_vector ) )
print(
"""Initial Available Resources: """
+ """ """.join(str(_a ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 12 | 1 |
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