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"""simple docstring"""
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
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
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
lowercase = False
if is_vision_available():
from PIL import Image
from transformers import PixaStructImageProcessor
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=7 , snake_case=3 , snake_case=18 , snake_case=30 , snake_case=400 , snake_case=None , snake_case=True , snake_case=True , snake_case=None , ) -> Any:
_UpperCAmelCase = size if size is not None else {'height': 20, 'width': 20}
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = size
_UpperCAmelCase = do_normalize
_UpperCAmelCase = do_convert_rgb
_UpperCAmelCase = [512, 1024, 2048, 4096]
_UpperCAmelCase = patch_size if patch_size is not None else {'height': 16, 'width': 16}
def lowerCamelCase_ ( self ) -> Optional[int]:
return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
_UpperCAmelCase = Image.open(requests.get(snake_case , stream=snake_case ).raw ).convert('RGB' )
return raw_image
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''', )
@require_torch
@require_vision
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = PixaStructImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = PixaStructImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> Dict:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(snake_case , 'do_convert_rgb' ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.image_processor_tester.prepare_dummy_image()
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
_UpperCAmelCase = 2048
_UpperCAmelCase = image_processor(snake_case , return_tensors='pt' , max_patches=snake_case )
self.assertTrue(torch.allclose(inputs.flattened_patches.mean() , torch.tensor(0.0606 ) , atol=1E-3 , rtol=1E-3 ) )
def lowerCamelCase_ ( self ) -> List[str]:
# Initialize image_processor
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , Image.Image )
# Test not batched input
_UpperCAmelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase = image_processor(
snake_case , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
# Initialize image_processor
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , Image.Image )
# Test not batched input
_UpperCAmelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
_UpperCAmelCase = True
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
with self.assertRaises(snake_case ):
_UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=snake_case ).flattened_patches
_UpperCAmelCase = 'Hello'
_UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=snake_case , header_text=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase = image_processor(
snake_case , return_tensors='pt' , max_patches=snake_case , header_text=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
# Initialize image_processor
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case , numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , np.ndarray )
_UpperCAmelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase = image_processor(
snake_case , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
def lowerCamelCase_ ( self ) -> int:
# Initialize image_processor
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case , torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , torch.Tensor )
# Test not batched input
_UpperCAmelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* self.image_processor_tester.num_channels
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase = image_processor(
snake_case , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
@unittest.skipIf(
not is_torch_greater_or_equal_than_1_11, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''', )
@require_torch
@require_vision
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = PixaStructImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = PixaStructImageProcessingTester(self , num_channels=4 )
_UpperCAmelCase = 3
@property
def lowerCamelCase_ ( self ) -> int:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(snake_case , 'do_convert_rgb' ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
# Initialize image_processor
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , Image.Image )
# Test not batched input
_UpperCAmelCase = (
(self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
* (self.image_processor_tester.num_channels - 1)
) + 2
for max_patch in self.image_processor_tester.max_patches:
# Test not batched input
_UpperCAmelCase = image_processor(
image_inputs[0] , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (1, max_patch, expected_hidden_dim) , )
# Test batched
_UpperCAmelCase = image_processor(
snake_case , return_tensors='pt' , max_patches=snake_case ).flattened_patches
self.assertEqual(
encoded_images.shape , (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim) , )
| 24 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def UpperCAmelCase ( A : Tuple , A : List[str]=False ):
'''simple docstring'''
try:
_UpperCAmelCase = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase = strtobool(A )
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
lowercase = parse_flag_from_env('''RUN_SLOW''', default=False)
lowercase = parse_flag_from_env('''RUN_REMOTE''', default=False)
lowercase = parse_flag_from_env('''RUN_LOCAL''', default=True)
lowercase = parse_flag_from_env('''RUN_PACKAGED''', default=True)
# Compression
lowercase = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
lowercase = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
lowercase = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')
# Audio
lowercase = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)
# Beam
lowercase = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
reason='''test requires apache-beam and a compatible dill version''',
)
# Dill-cloudpickle compatibility
lowercase = pytest.mark.skipif(
config.DILL_VERSION <= version.parse('''0.3.2'''),
reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)
# Windows
lowercase = pytest.mark.skipif(
sys.platform == '''win32''',
reason='''test should not be run on Windows''',
)
def UpperCAmelCase ( A : Optional[Any] ):
'''simple docstring'''
try:
import faiss # noqa
except ImportError:
_UpperCAmelCase = unittest.skip('test requires faiss' )(A )
return test_case
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
try:
import regex # noqa
except ImportError:
_UpperCAmelCase = unittest.skip('test requires regex' )(A )
return test_case
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
try:
import elasticsearch # noqa
except ImportError:
_UpperCAmelCase = unittest.skip('test requires elasticsearch' )(A )
return test_case
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
try:
import sqlalchemy # noqa
except ImportError:
_UpperCAmelCase = unittest.skip('test requires sqlalchemy' )(A )
return test_case
def UpperCAmelCase ( A : str ):
'''simple docstring'''
if not config.TORCH_AVAILABLE:
_UpperCAmelCase = unittest.skip('test requires PyTorch' )(A )
return test_case
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
if not config.TF_AVAILABLE:
_UpperCAmelCase = unittest.skip('test requires TensorFlow' )(A )
return test_case
def UpperCAmelCase ( A : Optional[Any] ):
'''simple docstring'''
if not config.JAX_AVAILABLE:
_UpperCAmelCase = unittest.skip('test requires JAX' )(A )
return test_case
def UpperCAmelCase ( A : str ):
'''simple docstring'''
if not config.PIL_AVAILABLE:
_UpperCAmelCase = unittest.skip('test requires Pillow' )(A )
return test_case
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
try:
import transformers # noqa F401
except ImportError:
return unittest.skip('test requires transformers' )(A )
else:
return test_case
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip('test requires tiktoken' )(A )
else:
return test_case
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
try:
import spacy # noqa F401
except ImportError:
return unittest.skip('test requires spacy' )(A )
else:
return test_case
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
def _require_spacy_model(A : Dict ):
try:
import spacy # noqa F401
spacy.load(A )
except ImportError:
return unittest.skip('test requires spacy' )(A )
except OSError:
return unittest.skip('test requires spacy model \'{}\''.format(A ) )(A )
else:
return test_case
return _require_spacy_model
def UpperCAmelCase ( A : str ):
'''simple docstring'''
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip('test requires pyspark' )(A )
else:
return test_case
def UpperCAmelCase ( A : Optional[int] ):
'''simple docstring'''
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip('test requires joblibspark' )(A )
else:
return test_case
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
if not _run_slow_tests or _run_slow_tests == 0:
_UpperCAmelCase = unittest.skip('test is slow' )(A )
return test_case
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
if not _run_local_tests or _run_local_tests == 0:
_UpperCAmelCase = unittest.skip('test is local' )(A )
return test_case
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
if not _run_packaged_tests or _run_packaged_tests == 0:
_UpperCAmelCase = unittest.skip('test is packaged' )(A )
return test_case
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
if not _run_remote_tests or _run_remote_tests == 0:
_UpperCAmelCase = unittest.skip('test requires remote' )(A )
return test_case
def UpperCAmelCase ( *A : List[Any] ):
'''simple docstring'''
def decorate(cls : Tuple ):
for name, fn in cls.__dict__.items():
if callable(A ) and name.startswith('test' ):
for decorator in decorators:
_UpperCAmelCase = decorator(A )
setattr(cls , A , A )
return cls
return decorate
class lowercase__ ( A ):
'''simple docstring'''
pass
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 2
@contextmanager
def UpperCAmelCase ( A : Optional[Any]=OfflineSimulationMode.CONNECTION_FAILS , A : Optional[int]=1e-16 ):
'''simple docstring'''
_UpperCAmelCase = requests.Session().request
def timeout_request(A : str , A : Tuple , A : List[Any] , **A : List[str] ):
# Change the url to an invalid url so that the connection hangs
_UpperCAmelCase = 'https://10.255.255.1'
if kwargs.get('timeout' ) is None:
raise RequestWouldHangIndefinitelyError(
f'Tried a call to {url} in offline mode with no timeout set. Please set a timeout.' )
_UpperCAmelCase = timeout
try:
return online_request(A , A , **A )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
_UpperCAmelCase = url
_UpperCAmelCase = e.args[0]
_UpperCAmelCase = (max_retry_error.args[0].replace('10.255.255.1' , f'OfflineMock[{url}]' ),)
_UpperCAmelCase = (max_retry_error,)
raise
def raise_connection_error(A : Dict , A : int , **A : Optional[int] ):
raise requests.ConnectionError('Offline mode is enabled.' , request=A )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch('requests.Session.send' , A ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch('requests.Session.request' , A ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch('datasets.config.HF_DATASETS_OFFLINE' , A ):
yield
else:
raise ValueError('Please use a value from the OfflineSimulationMode enum.' )
@contextmanager
def UpperCAmelCase ( *A : List[str] , **A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = str(Path().resolve() )
with tempfile.TemporaryDirectory(*A , **A ) as tmp_dir:
try:
os.chdir(A )
yield
finally:
os.chdir(A )
@contextmanager
def UpperCAmelCase ( ):
'''simple docstring'''
import gc
gc.collect()
_UpperCAmelCase = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def UpperCAmelCase ( ):
'''simple docstring'''
import gc
gc.collect()
_UpperCAmelCase = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def UpperCAmelCase ( A : Optional[Any] , A : str ):
'''simple docstring'''
return deepcopy(A ).integers(0 , 100 , 10 ).tolist() == deepcopy(A ).integers(0 , 100 , 10 ).tolist()
def UpperCAmelCase ( A : int ):
'''simple docstring'''
import decorator
from requests.exceptions import HTTPError
def _wrapper(A : str , *A : Dict , **A : List[str] ):
try:
return func(*A , **A )
except HTTPError as err:
if str(A ).startswith('500' ) or str(A ).startswith('502' ):
pytest.xfail(str(A ) )
raise err
return decorator.decorator(_wrapper , A )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = returncode
_UpperCAmelCase = stdout
_UpperCAmelCase = stderr
async def UpperCAmelCase ( A : List[str] , A : int ):
'''simple docstring'''
while True:
_UpperCAmelCase = await stream.readline()
if line:
callback(A )
else:
break
async def UpperCAmelCase ( A : str , A : Tuple=None , A : List[str]=None , A : Dict=None , A : int=False , A : Dict=False ):
'''simple docstring'''
if echo:
print('\nRunning: ' , ' '.join(A ) )
_UpperCAmelCase = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=A , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=A , )
# 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)
_UpperCAmelCase = []
_UpperCAmelCase = []
def tee(A : Optional[int] , A : Optional[Any] , A : Optional[int] , A : Optional[Any]="" ):
_UpperCAmelCase = line.decode('utf-8' ).rstrip()
sink.append(A )
if not quiet:
print(A , A , file=A )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout , lambda A : tee(A , A , sys.stdout , label='stdout:' ) ),
_read_stream(p.stderr , lambda A : tee(A , A , sys.stderr , label='stderr:' ) ),
] , timeout=A , )
return _RunOutput(await p.wait() , A , A )
def UpperCAmelCase ( A : Tuple , A : Any=None , A : Dict=None , A : Optional[int]=180 , A : str=False , A : Dict=True ):
'''simple docstring'''
_UpperCAmelCase = asyncio.get_event_loop()
_UpperCAmelCase = loop.run_until_complete(
_stream_subprocess(A , env=A , stdin=A , timeout=A , quiet=A , echo=A ) )
_UpperCAmelCase = ' '.join(A )
if result.returncode > 0:
_UpperCAmelCase = '\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}' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(f'\'{cmd_str}\' produced no output.' )
return result
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.environ.get('PYTEST_XDIST_WORKER' , 'gw0' )
_UpperCAmelCase = re.sub(r'^gw' , '' , A , 0 , re.M )
return int(A )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 2_9500
_UpperCAmelCase = pytest_xdist_worker_id()
return port + uniq_delta
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase ( A : Optional[int] , A : List[str] , A : int ):
'''simple docstring'''
if gpta_config_file == "":
_UpperCAmelCase = GPTaConfig()
else:
_UpperCAmelCase = GPTaConfig.from_json_file(A )
_UpperCAmelCase = GPTaModel(A )
# Load weights from numpy
load_tf_weights_in_gpta(A , A , A )
# Save pytorch-model
_UpperCAmelCase = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
_UpperCAmelCase = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(f'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , A )
print(f'Save configuration file to {pytorch_config_dump_path}' )
with open(A , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--gpt2_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--gpt2_config_file''',
default='''''',
type=str,
help=(
'''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
'''This specifies the model architecture.'''
),
)
lowercase = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)
| 24 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24 | 1 |
"""simple docstring"""
lowercase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
lowercase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def UpperCAmelCase ( A : dict[int, list[int]] , A : int , A : list[bool] ):
'''simple docstring'''
_UpperCAmelCase = True
_UpperCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(A , A , A )
order.append(A )
return order
def UpperCAmelCase ( A : dict[int, list[int]] , A : int , A : list[bool] ):
'''simple docstring'''
_UpperCAmelCase = True
_UpperCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(A , A , A )
return component
def UpperCAmelCase ( A : dict[int, list[int]] ):
'''simple docstring'''
_UpperCAmelCase = len(A ) * [False]
_UpperCAmelCase = {vert: [] for vert in range(len(A ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(A )
_UpperCAmelCase = []
for i, was_visited in enumerate(A ):
if not was_visited:
order += topology_sort(A , A , A )
_UpperCAmelCase = []
_UpperCAmelCase = len(A ) * [False]
for i in range(len(A ) ):
_UpperCAmelCase = order[len(A ) - i - 1]
if not visited[vert]:
_UpperCAmelCase = find_components(A , A , A )
components_list.append(A )
return components_list
| 24 |
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=7 , snake_case=False , snake_case=True , snake_case=False , snake_case=False , snake_case=19 , snake_case=32 , snake_case=5 , snake_case=4 , snake_case=37 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=16 , snake_case=2 , snake_case=0.02 , snake_case=3 , snake_case=4 , snake_case=None , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_input_mask
_UpperCAmelCase = use_token_type_ids
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = type_sequence_label_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_choices
_UpperCAmelCase = scope
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = None
if self.use_input_mask:
_UpperCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = EsmConfig(
vocab_size=33 , hidden_size=self.hidden_size , pad_token_id=1 , 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 , initializer_range=self.initializer_range , is_folding_model=snake_case , esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False} , )
return config
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = EsmForProteinFolding(config=snake_case ).float()
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , attention_mask=snake_case )
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = model(snake_case )
self.parent.assertEqual(result.positions.shape , (8, self.batch_size, self.seq_length, 14, 3) )
self.parent.assertEqual(result.angles.shape , (8, self.batch_size, self.seq_length, 7, 2) )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) = config_and_inputs
_UpperCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = False
_UpperCAmelCase = (EsmForProteinFolding,) if is_torch_available() else ()
_UpperCAmelCase = ()
_UpperCAmelCase = {} if is_torch_available() else {}
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = EsmFoldModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Dict:
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
@unittest.skip('Does not support attention outputs' )
def lowerCamelCase_ ( self ) -> Tuple:
pass
@unittest.skip
def lowerCamelCase_ ( self ) -> List[str]:
pass
@unittest.skip('Esm does not support embedding resizing' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
@unittest.skip('Esm does not support embedding resizing' )
def lowerCamelCase_ ( self ) -> Tuple:
pass
@unittest.skip('ESMFold does not support passing input embeds!' )
def lowerCamelCase_ ( self ) -> Tuple:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowerCamelCase_ ( self ) -> Optional[int]:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowerCamelCase_ ( self ) -> Optional[Any]:
pass
@unittest.skip('ESMFold does not support head pruning.' )
def lowerCamelCase_ ( self ) -> List[Any]:
pass
@unittest.skip('ESMFold does not output hidden states in the normal way.' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
@unittest.skip('ESMfold does not output hidden states in the normal way.' )
def lowerCamelCase_ ( self ) -> List[str]:
pass
@unittest.skip('ESMFold only has one output format.' )
def lowerCamelCase_ ( self ) -> List[str]:
pass
@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality' )
def lowerCamelCase_ ( self ) -> Optional[Any]:
pass
@unittest.skip('ESMFold does not support input chunking.' )
def lowerCamelCase_ ( self ) -> Optional[Any]:
pass
@unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def lowerCamelCase_ ( self ) -> List[str]:
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def lowerCamelCase_ ( self ) -> Any:
pass
@unittest.skip('ESMFold doesn\'t support torchscript compilation.' )
def lowerCamelCase_ ( self ) -> List[Any]:
pass
@unittest.skip('ESMFold doesn\'t support data parallel.' )
def lowerCamelCase_ ( self ) -> Optional[Any]:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
@require_torch
class lowercase__ ( A ):
'''simple docstring'''
@slow
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = EsmForProteinFolding.from_pretrained('facebook/esmfold_v1' ).float()
model.eval()
_UpperCAmelCase = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
_UpperCAmelCase = model(snake_case )['positions']
_UpperCAmelCase = torch.tensor([2.5828, 0.7993, -10.9334] , dtype=torch.floataa )
self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0] , snake_case , atol=1E-4 ) )
| 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
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 DonutImageProcessor
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=7 , snake_case=3 , snake_case=18 , snake_case=30 , snake_case=400 , snake_case=True , snake_case=None , snake_case=True , snake_case=False , snake_case=True , snake_case=True , snake_case=[0.5, 0.5, 0.5] , snake_case=[0.5, 0.5, 0.5] , ) -> List[str]:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = image_size
_UpperCAmelCase = min_resolution
_UpperCAmelCase = max_resolution
_UpperCAmelCase = do_resize
_UpperCAmelCase = size if size is not None else {'height': 18, 'width': 20}
_UpperCAmelCase = do_thumbnail
_UpperCAmelCase = do_align_axis
_UpperCAmelCase = do_pad
_UpperCAmelCase = do_normalize
_UpperCAmelCase = image_mean
_UpperCAmelCase = image_std
def lowerCamelCase_ ( self ) -> int:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DonutImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = DonutImageProcessingTester(self )
@property
def lowerCamelCase_ ( self ) -> Any:
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(snake_case , 'do_resize' ) )
self.assertTrue(hasattr(snake_case , 'size' ) )
self.assertTrue(hasattr(snake_case , 'do_thumbnail' ) )
self.assertTrue(hasattr(snake_case , 'do_align_long_axis' ) )
self.assertTrue(hasattr(snake_case , 'do_pad' ) )
self.assertTrue(hasattr(snake_case , 'do_normalize' ) )
self.assertTrue(hasattr(snake_case , 'image_mean' ) )
self.assertTrue(hasattr(snake_case , 'image_std' ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'height': 18, 'width': 20} )
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'height': 42, 'width': 42} )
# Previous config had dimensions in (width, height) order
_UpperCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'height': 84, 'width': 42} )
def lowerCamelCase_ ( self ) -> Any:
pass
@is_flaky()
def lowerCamelCase_ ( self ) -> Optional[Any]:
# Initialize image_processing
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , Image.Image )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def lowerCamelCase_ ( self ) -> List[Any]:
# Initialize image_processing
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case , numpify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , np.ndarray )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
@is_flaky()
def lowerCamelCase_ ( self ) -> Dict:
# Initialize image_processing
_UpperCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=snake_case , torchify=snake_case )
for image in image_inputs:
self.assertIsInstance(snake_case , torch.Tensor )
# Test not batched input
_UpperCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
# Test batched
_UpperCAmelCase = image_processing(snake_case , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['height'],
self.image_processor_tester.size['width'],
) , )
| 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowercase = logging.getLogger(__name__)
lowercase = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowercase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = field(
default=A, metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(A )}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''The input training data file (a text file).'''} )
_UpperCAmelCase = field(
default=A, metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
_UpperCAmelCase = field(default=A, metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
_UpperCAmelCase = field(
default=0.15, metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
_UpperCAmelCase = field(
default=1 / 6, metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
}, )
_UpperCAmelCase = field(
default=5, metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
_UpperCAmelCase = field(
default=-1, metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase ( A : DataTrainingArguments , A : PreTrainedTokenizer , A : bool = False , A : Optional[str] = None , ):
'''simple docstring'''
def _dataset(A : List[Any] , A : Union[str, Any]=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask' )
return LineByLineWithRefDataset(
tokenizer=A , file_path=A , block_size=args.block_size , ref_path=A , )
return LineByLineTextDataset(tokenizer=A , file_path=A , block_size=args.block_size )
else:
return TextDataset(
tokenizer=A , file_path=A , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=A , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(A ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
'or remove the --do_eval argument.' )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
' --overwrite_output_dir to overcome.' )
# 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.local_rank != -1 ) , training_args.fpaa , )
# 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()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , A )
# 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.
if model_args.config_name:
_UpperCAmelCase = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_UpperCAmelCase = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
_UpperCAmelCase = CONFIG_MAPPING[model_args.model_type]()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.tokenizer_name:
_UpperCAmelCase = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
_UpperCAmelCase = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
' script, save it,and load it from here, using --tokenizer_name' )
if model_args.model_name_or_path:
_UpperCAmelCase = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=A , cache_dir=model_args.cache_dir , )
else:
logger.info('Training new model from scratch' )
_UpperCAmelCase = AutoModelWithLMHead.from_config(A )
model.resize_token_embeddings(len(A ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
'--mlm flag (masked language modeling).' )
if data_args.block_size <= 0:
_UpperCAmelCase = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
_UpperCAmelCase = min(data_args.block_size , tokenizer.max_len )
# Get datasets
_UpperCAmelCase = (
get_dataset(A , tokenizer=A , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
_UpperCAmelCase = (
get_dataset(A , tokenizer=A , evaluate=A , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
_UpperCAmelCase = DataCollatorForPermutationLanguageModeling(
tokenizer=A , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
_UpperCAmelCase = DataCollatorForWholeWordMask(
tokenizer=A , mlm_probability=data_args.mlm_probability )
else:
_UpperCAmelCase = DataCollatorForLanguageModeling(
tokenizer=A , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=A , args=A , data_collator=A , train_dataset=A , eval_dataset=A , prediction_loss_only=A , )
# Training
if training_args.do_train:
_UpperCAmelCase = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=A )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCAmelCase = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate()
_UpperCAmelCase = math.exp(eval_output['eval_loss'] )
_UpperCAmelCase = {'perplexity': perplexity}
_UpperCAmelCase = os.path.join(training_args.output_dir , 'eval_results_lm.txt' )
if trainer.is_world_master():
with open(A , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , A , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
results.update(A )
return results
def UpperCAmelCase ( A : int ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : list[int] , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = 0
_UpperCAmelCase = sum(A )
create_state_space_tree(A , A , A , A , A , A )
return result
def UpperCAmelCase ( A : list[int] , A : int , A : int , A : list[int] , A : list[list[int]] , A : int , ):
'''simple docstring'''
if sum(A ) > max_sum or (remaining_nums_sum + sum(A )) < max_sum:
return
if sum(A ) == max_sum:
result.append(A )
return
for index in range(A , len(A ) ):
create_state_space_tree(
A , A , index + 1 , [*path, nums[index]] , A , remaining_nums_sum - nums[index] , )
lowercase = [3, 34, 4, 12, 5, 2]
lowercase = 9
lowercase = generate_sum_of_subsets_soln(nums, max_sum)
print(*result)
| 24 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
import argparse
import os
import re
lowercase = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
lowercase = re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict''')
# re pattern that matches identifiers in mappings
lowercase = re.compile(r'''\s*\(\s*"(\S[^"]+)"''')
def UpperCAmelCase ( A : List[str] , A : bool = False ):
'''simple docstring'''
with open(A , 'r' , encoding='utf-8' ) as f:
_UpperCAmelCase = f.read()
_UpperCAmelCase = content.split('\n' )
_UpperCAmelCase = []
_UpperCAmelCase = 0
while line_idx < len(A ):
if _re_intro_mapping.search(lines[line_idx] ) is not None:
_UpperCAmelCase = len(re.search(r'^(\s*)\S' , lines[line_idx] ).groups()[0] ) + 8
# Start of a new mapping!
while not lines[line_idx].startswith(' ' * indent + '(' ):
new_lines.append(lines[line_idx] )
line_idx += 1
_UpperCAmelCase = []
while lines[line_idx].strip() != "]":
# Blocks either fit in one line or not
if lines[line_idx].strip() == "(":
_UpperCAmelCase = line_idx
while not lines[line_idx].startswith(' ' * indent + ')' ):
line_idx += 1
blocks.append('\n'.join(lines[start_idx : line_idx + 1] ) )
else:
blocks.append(lines[line_idx] )
line_idx += 1
# Sort blocks by their identifiers
_UpperCAmelCase = sorted(A , key=lambda A : _re_identifier.search(A ).groups()[0] )
new_lines += blocks
else:
new_lines.append(lines[line_idx] )
line_idx += 1
if overwrite:
with open(A , 'w' , encoding='utf-8' ) as f:
f.write('\n'.join(A ) )
elif "\n".join(A ) != content:
return True
def UpperCAmelCase ( A : bool = False ):
'''simple docstring'''
_UpperCAmelCase = [os.path.join(A , A ) for f in os.listdir(A ) if f.endswith('.py' )]
_UpperCAmelCase = [sort_auto_mapping(A , overwrite=A ) for fname in fnames]
if not overwrite and any(A ):
_UpperCAmelCase = [f for f, d in zip(A , A ) if d]
raise ValueError(
f'The following files have auto mappings that need sorting: {", ".join(A )}. Run `make style` to fix'
' this.' )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--check_only''', action='''store_true''', help='''Whether to only check or fix style.''')
lowercase = parser.parse_args()
sort_all_auto_mappings(not args.check_only)
| 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24 | 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
lowercase = {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''TimmBackbone''']
if TYPE_CHECKING:
from .configuration_timm_backbone import TimmBackboneConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timm_backbone import TimmBackbone
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : str ):
'''simple docstring'''
return " ".join(
''.join(word[::-1] ) if len(A ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24 | 1 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , **snake_case ) -> List[str]:
requires_backends(self , ['bs4'] )
super().__init__(**snake_case )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_UpperCAmelCase = parent.find_all(child.name , recursive=snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(snake_case ) else next(i for i, s in enumerate(snake_case , 1 ) if s is child ) )
_UpperCAmelCase = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = BeautifulSoup(snake_case , 'html.parser' )
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
for element in html_code.descendants:
if type(snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_UpperCAmelCase = html.unescape(snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(snake_case )
_UpperCAmelCase , _UpperCAmelCase = self.xpath_soup(snake_case )
stringaxtag_seq.append(snake_case )
stringaxsubs_seq.append(snake_case )
if len(snake_case ) != len(snake_case ):
raise ValueError('Number of doc strings and xtags does not correspond' )
if len(snake_case ) != len(snake_case ):
raise ValueError('Number of doc strings and xsubs does not correspond' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowerCamelCase_ ( self , snake_case , snake_case ) -> List[str]:
_UpperCAmelCase = ''
for tagname, subs in zip(snake_case , snake_case ):
xpath += f'/{tagname}'
if subs != 0:
xpath += f'[{subs}]'
return xpath
def __call__( self , snake_case ) -> BatchFeature:
_UpperCAmelCase = False
# Check that strings has a valid type
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = True
elif isinstance(snake_case , (list, tuple) ):
if len(snake_case ) == 0 or isinstance(html_strings[0] , snake_case ):
_UpperCAmelCase = True
if not valid_strings:
raise ValueError(
'HTML strings must of type `str`, `List[str]` (batch of examples), '
f'but is of type {type(snake_case )}.' )
_UpperCAmelCase = bool(isinstance(snake_case , (list, tuple) ) and (isinstance(html_strings[0] , snake_case )) )
if not is_batched:
_UpperCAmelCase = [html_strings]
# Get nodes + xpaths
_UpperCAmelCase = []
_UpperCAmelCase = []
for html_string in html_strings:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.get_three_from_single(snake_case )
nodes.append(snake_case )
_UpperCAmelCase = []
for node, tag_list, sub_list in zip(snake_case , snake_case , snake_case ):
_UpperCAmelCase = self.construct_xpath(snake_case , snake_case )
xpath_strings.append(snake_case )
xpaths.append(snake_case )
# return as Dict
_UpperCAmelCase = {'nodes': nodes, 'xpaths': xpaths}
_UpperCAmelCase = BatchFeature(data=snake_case , tensor_type=snake_case )
return encoded_inputs
| 24 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = SwinConfig()
_UpperCAmelCase = swin_name.split('_' )
_UpperCAmelCase = name_split[1]
_UpperCAmelCase = int(name_split[4] )
_UpperCAmelCase = int(name_split[3][-1] )
if model_size == "tiny":
_UpperCAmelCase = 96
_UpperCAmelCase = (2, 2, 6, 2)
_UpperCAmelCase = (3, 6, 12, 24)
elif model_size == "small":
_UpperCAmelCase = 96
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (3, 6, 12, 24)
elif model_size == "base":
_UpperCAmelCase = 128
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (4, 8, 16, 32)
else:
_UpperCAmelCase = 192
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (6, 12, 24, 48)
if "in22k" in swin_name:
_UpperCAmelCase = 2_1841
else:
_UpperCAmelCase = 1000
_UpperCAmelCase = 'huggingface/label-files'
_UpperCAmelCase = 'imagenet-1k-id2label.json'
_UpperCAmelCase = json.load(open(hf_hub_download(A , A , repo_type='dataset' ) , 'r' ) )
_UpperCAmelCase = {int(A ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
_UpperCAmelCase = img_size
_UpperCAmelCase = num_classes
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
return config
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
if "patch_embed.proj" in name:
_UpperCAmelCase = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
_UpperCAmelCase = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
_UpperCAmelCase = 'encoder.' + name
if "attn.proj" in name:
_UpperCAmelCase = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
_UpperCAmelCase = name.replace('attn' , 'attention.self' )
if "norm1" in name:
_UpperCAmelCase = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
_UpperCAmelCase = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
_UpperCAmelCase = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
_UpperCAmelCase = name.replace('mlp.fc2' , 'output.dense' )
if name == "norm.weight":
_UpperCAmelCase = 'layernorm.weight'
if name == "norm.bias":
_UpperCAmelCase = 'layernorm.bias'
if "head" in name:
_UpperCAmelCase = name.replace('head' , 'classifier' )
else:
_UpperCAmelCase = 'swin.' + name
return name
def UpperCAmelCase ( A : Dict , A : Tuple ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
_UpperCAmelCase = orig_state_dict.pop(A )
if "mask" in key:
continue
elif "qkv" in key:
_UpperCAmelCase = key.split('.' )
_UpperCAmelCase = int(key_split[1] )
_UpperCAmelCase = int(key_split[3] )
_UpperCAmelCase = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_UpperCAmelCase = val[:dim, :]
_UpperCAmelCase = val[
dim : dim * 2, :
]
_UpperCAmelCase = val[-dim:, :]
else:
_UpperCAmelCase = val[
:dim
]
_UpperCAmelCase = val[
dim : dim * 2
]
_UpperCAmelCase = val[
-dim:
]
else:
_UpperCAmelCase = val
return orig_state_dict
def UpperCAmelCase ( A : Optional[Any] , A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = timm.create_model(A , pretrained=A )
timm_model.eval()
_UpperCAmelCase = get_swin_config(A )
_UpperCAmelCase = SwinForImageClassification(A )
model.eval()
_UpperCAmelCase = convert_state_dict(timm_model.state_dict() , A )
model.load_state_dict(A )
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' , '-' ) ) )
_UpperCAmelCase = Image.open(requests.get(A , stream=A ).raw )
_UpperCAmelCase = image_processor(images=A , return_tensors='pt' )
_UpperCAmelCase = timm_model(inputs['pixel_values'] )
_UpperCAmelCase = model(**A ).logits
assert torch.allclose(A , A , atol=1e-3 )
print(f'Saving model {swin_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(A )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swin_name''',
default='''swin_tiny_patch4_window7_224''',
type=str,
help='''Name of the Swin timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
lowercase = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( A : Any , A : int , A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = RemBertConfig.from_json_file(A )
print('Building PyTorch model from configuration: {}'.format(str(A ) ) )
_UpperCAmelCase = RemBertModel(A )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(A , A , A )
# Save pytorch-model
print('Save PyTorch model to {}'.format(A ) )
torch.save(model.state_dict() , A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--rembert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained RemBERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
lowercase = {
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
'''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
'''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
'''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
'''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
'''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
'''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''ctc_proj''',
'''mask_emb''': '''masked_spec_embed''',
}
lowercase = [
'''ctc_proj''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCAmelCase ( A : Optional[int] , A : Dict , A : List[Any] , A : Union[str, Any] , A : List[Any] , A : List[str] ):
'''simple docstring'''
for attribute in key.split('.' ):
if is_finetuned:
if attribute in ["quantizer", "project_q", "project_hid"]:
# those layers are only relevant for pretraining and should be dropped
return
if attribute == "ctc_proj":
# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
_UpperCAmelCase = 'lm_head'
_UpperCAmelCase = getattr(A , A )
if weight_type is not None:
_UpperCAmelCase = getattr(A , A ).shape
else:
_UpperCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}'
)
if weight_type == "weight":
_UpperCAmelCase = value
elif weight_type == "weight_g":
_UpperCAmelCase = value
elif weight_type == "weight_v":
_UpperCAmelCase = value
elif weight_type == "bias":
_UpperCAmelCase = value
else:
_UpperCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def UpperCAmelCase ( A : str , A : Dict , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = fairseq_model.state_dict()
_UpperCAmelCase = hf_model.unispeech.feature_extractor
for name, value in fairseq_dict.items():
_UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == 'group' , )
_UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_UpperCAmelCase = 'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
_UpperCAmelCase = True
if "*" in mapped_key:
_UpperCAmelCase = name.split(A )[0].split('.' )[-2]
_UpperCAmelCase = mapped_key.replace('*' , A )
if "weight_g" in name:
_UpperCAmelCase = 'weight_g'
elif "weight_v" in name:
_UpperCAmelCase = 'weight_v'
elif "bias" in name:
_UpperCAmelCase = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_UpperCAmelCase = 'weight'
else:
_UpperCAmelCase = None
set_recursively(A , A , A , A , A , A )
continue
if not is_used:
unused_weights.append(A )
logger.warning(f'Unused weights: {unused_weights}' )
def UpperCAmelCase ( A : Union[str, Any] , A : List[Any] , A : Union[str, Any] , A : Optional[int] , A : int ):
'''simple docstring'''
_UpperCAmelCase = full_name.split('conv_layers.' )[-1]
_UpperCAmelCase = name.split('.' )
_UpperCAmelCase = int(items[0] )
_UpperCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
_UpperCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
_UpperCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
_UpperCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
_UpperCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(A )
@torch.no_grad()
def UpperCAmelCase ( A : str , A : List[str] , A : Any=None , A : Any=None , A : Dict=True ):
'''simple docstring'''
if config_path is not None:
_UpperCAmelCase = UniSpeechConfig.from_pretrained(A )
else:
_UpperCAmelCase = UniSpeechConfig()
if is_finetuned:
if dict_path:
_UpperCAmelCase = Dictionary.load_from_json(A )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_UpperCAmelCase = target_dict.pad_index
_UpperCAmelCase = target_dict.bos_index
_UpperCAmelCase = target_dict.eos_index
_UpperCAmelCase = len(target_dict.symbols )
_UpperCAmelCase = os.path.join(A , 'vocab.json' )
if not os.path.isdir(A ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(A ) )
return
os.makedirs(A , exist_ok=A )
_UpperCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_UpperCAmelCase = 42
_UpperCAmelCase = 43
with open(A , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(A , A )
_UpperCAmelCase = WavaVecaPhonemeCTCTokenizer(
A , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='|' , do_lower_case=A , )
_UpperCAmelCase = True if config.feat_extract_norm == 'layer' else False
_UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=A , return_attention_mask=A , )
_UpperCAmelCase = WavaVecaProcessor(feature_extractor=A , tokenizer=A )
processor.save_pretrained(A )
_UpperCAmelCase = UniSpeechForCTC(A )
else:
_UpperCAmelCase = UniSpeechForPreTraining(A )
if is_finetuned:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] ), 'w2v_path': checkpoint_path} )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
_UpperCAmelCase = model[0].eval()
recursively_load_weights(A , A , A )
hf_unispeech.save_pretrained(A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
)
lowercase = parser.parse_args()
convert_unispeech_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 24 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def UpperCAmelCase ( A : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*A )
if __name__ == "__main__":
lowercase = 4
lowercase = 2
lowercase = generate_all_combinations(n, k)
print_all_state(total_list)
| 24 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
_UpperCAmelCase = sd_pipe.to(snake_case )
sd_pipe.set_progress_bar_config(disable=snake_case )
sd_pipe.set_scheduler('sample_euler' )
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = sd_pipe([prompt] , generator=snake_case , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
_UpperCAmelCase = sd_pipe.to(snake_case )
sd_pipe.set_progress_bar_config(disable=snake_case )
sd_pipe.set_scheduler('sample_euler' )
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = sd_pipe([prompt] , generator=snake_case , guidance_scale=9.0 , num_inference_steps=20 , output_type='np' )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
_UpperCAmelCase = sd_pipe.to(snake_case )
sd_pipe.set_progress_bar_config(disable=snake_case )
sd_pipe.set_scheduler('sample_dpmpp_2m' )
_UpperCAmelCase = 'A painting of a squirrel eating a burger'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = sd_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , num_inference_steps=15 , output_type='np' , use_karras_sigmas=snake_case , )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 24 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24 | 1 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints
import yaml
lowercase = NewType('''DataClass''', Any)
lowercase = NewType('''DataClassType''', Any)
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
if isinstance(A , A ):
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 ArgumentTypeError(
f'Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).' )
def UpperCAmelCase ( A : list ):
'''simple docstring'''
_UpperCAmelCase = {str(A ): choice for choice in choices}
return lambda A : str_to_choice.get(A , A )
def UpperCAmelCase ( *,
A : Union[str, List[str]] = None , A : str = None , A : Any = dataclasses.MISSING , A : Callable[[], Any] = dataclasses.MISSING , A : dict = None , **A : List[str] , ):
'''simple docstring'''
if metadata is None:
# Important, don't use as default param in function signature because dict is mutable and shared across function calls
_UpperCAmelCase = {}
if aliases is not None:
_UpperCAmelCase = aliases
if help is not None:
_UpperCAmelCase = help
return dataclasses.field(metadata=A , default=A , default_factory=A , **A )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = 42
def __init__( self , snake_case , **snake_case ) -> Optional[int]:
# To make the default appear when using --help
if "formatter_class" not in kwargs:
_UpperCAmelCase = ArgumentDefaultsHelpFormatter
super().__init__(**snake_case )
if dataclasses.is_dataclass(snake_case ):
_UpperCAmelCase = [dataclass_types]
_UpperCAmelCase = list(snake_case )
for dtype in self.dataclass_types:
self._add_dataclass_arguments(snake_case )
@staticmethod
def lowerCamelCase_ ( snake_case , snake_case ) -> Union[str, Any]:
_UpperCAmelCase = f'--{field.name}'
_UpperCAmelCase = field.metadata.copy()
# field.metadata is not used at all by Data Classes,
# it is provided as a third-party extension mechanism.
if isinstance(field.type , snake_case ):
raise RuntimeError(
'Unresolved type detected, which should have been done with the help of '
'`typing.get_type_hints` method by default' )
_UpperCAmelCase = kwargs.pop('aliases' , [] )
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = [aliases]
_UpperCAmelCase = getattr(field.type , '__origin__' , field.type )
if origin_type is Union or (hasattr(snake_case , 'UnionType' ) and isinstance(snake_case , types.UnionType )):
if str not in field.type.__args__ and (
len(field.type.__args__ ) != 2 or type(snake_case ) not in field.type.__args__
):
raise ValueError(
'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
' the argument parser only supports one type per argument.'
f' Problem encountered in field \'{field.name}\'.' )
if type(snake_case ) not in field.type.__args__:
# filter `str` in Union
_UpperCAmelCase = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
_UpperCAmelCase = getattr(field.type , '__origin__' , field.type )
elif bool not in field.type.__args__:
# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
_UpperCAmelCase = (
field.type.__args__[0] if isinstance(snake_case , field.type.__args__[1] ) else field.type.__args__[1]
)
_UpperCAmelCase = getattr(field.type , '__origin__' , field.type )
# A variable to store kwargs for a boolean field, if needed
# so that we can init a `no_*` complement argument (see below)
_UpperCAmelCase = {}
if origin_type is Literal or (isinstance(field.type , snake_case ) and issubclass(field.type , snake_case )):
if origin_type is Literal:
_UpperCAmelCase = field.type.__args__
else:
_UpperCAmelCase = [x.value for x in field.type]
_UpperCAmelCase = make_choice_type_function(kwargs['choices'] )
if field.default is not dataclasses.MISSING:
_UpperCAmelCase = field.default
else:
_UpperCAmelCase = True
elif field.type is bool or field.type == Optional[bool]:
# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
_UpperCAmelCase = copy(snake_case )
# Hack because type=bool in argparse does not behave as we want.
_UpperCAmelCase = string_to_bool
if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
# Default value is False if we have no default when of type bool.
_UpperCAmelCase = False if field.default is dataclasses.MISSING else field.default
# This is the value that will get picked if we don't include --field_name in any way
_UpperCAmelCase = default
# This tells argparse we accept 0 or 1 value after --field_name
_UpperCAmelCase = '?'
# This is the value that will get picked if we do --field_name (without value)
_UpperCAmelCase = True
elif isclass(snake_case ) and issubclass(snake_case , snake_case ):
_UpperCAmelCase = field.type.__args__[0]
_UpperCAmelCase = '+'
if field.default_factory is not dataclasses.MISSING:
_UpperCAmelCase = field.default_factory()
elif field.default is dataclasses.MISSING:
_UpperCAmelCase = True
else:
_UpperCAmelCase = field.type
if field.default is not dataclasses.MISSING:
_UpperCAmelCase = field.default
elif field.default_factory is not dataclasses.MISSING:
_UpperCAmelCase = field.default_factory()
else:
_UpperCAmelCase = True
parser.add_argument(snake_case , *snake_case , **snake_case )
# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
# Order is important for arguments with the same destination!
# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
# here and we do not need those changes/additional keys.
if field.default is True and (field.type is bool or field.type == Optional[bool]):
_UpperCAmelCase = False
parser.add_argument(f'--no_{field.name}' , action='store_false' , dest=field.name , **snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Tuple:
if hasattr(snake_case , '_argument_group_name' ):
_UpperCAmelCase = self.add_argument_group(dtype._argument_group_name )
else:
_UpperCAmelCase = self
try:
_UpperCAmelCase = get_type_hints(snake_case )
except NameError:
raise RuntimeError(
f'Type resolution failed for {dtype}. Try declaring the class in global scope or '
'removing line of `from __future__ import annotations` which opts in Postponed '
'Evaluation of Annotations (PEP 563)' )
except TypeError as ex:
# Remove this block when we drop Python 3.9 support
if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(snake_case ):
_UpperCAmelCase = '.'.join(map(snake_case , sys.version_info[:3] ) )
raise RuntimeError(
f'Type resolution failed for {dtype} on Python {python_version}. Try removing '
'line of `from __future__ import annotations` which opts in union types as '
'`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
'support Python versions that lower than 3.10, you need to use '
'`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
'`X | None`.' ) from ex
raise
for field in dataclasses.fields(snake_case ):
if not field.init:
continue
_UpperCAmelCase = type_hints[field.name]
self._parse_dataclass_field(snake_case , snake_case )
def lowerCamelCase_ ( self , snake_case=None , snake_case=False , snake_case=True , snake_case=None , snake_case=None , ) -> Tuple[DataClass, ...]:
if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
_UpperCAmelCase = []
if args_filename:
args_files.append(Path(snake_case ) )
elif look_for_args_file and len(sys.argv ):
args_files.append(Path(sys.argv[0] ).with_suffix('.args' ) )
# args files specified via command line flag should overwrite default args files so we add them last
if args_file_flag:
# Create special parser just to extract the args_file_flag values
_UpperCAmelCase = ArgumentParser()
args_file_parser.add_argument(snake_case , type=snake_case , action='append' )
# Use only remaining args for further parsing (remove the args_file_flag)
_UpperCAmelCase , _UpperCAmelCase = args_file_parser.parse_known_args(args=snake_case )
_UpperCAmelCase = vars(snake_case ).get(args_file_flag.lstrip('-' ) , snake_case )
if cmd_args_file_paths:
args_files.extend([Path(snake_case ) for p in cmd_args_file_paths] )
_UpperCAmelCase = []
for args_file in args_files:
if args_file.exists():
file_args += args_file.read_text().split()
# in case of duplicate arguments the last one has precedence
# args specified via the command line should overwrite args from files, so we add them last
_UpperCAmelCase = file_args + args if args is not None else file_args + sys.argv[1:]
_UpperCAmelCase , _UpperCAmelCase = self.parse_known_args(args=snake_case )
_UpperCAmelCase = []
for dtype in self.dataclass_types:
_UpperCAmelCase = {f.name for f in dataclasses.fields(snake_case ) if f.init}
_UpperCAmelCase = {k: v for k, v in vars(snake_case ).items() if k in keys}
for k in keys:
delattr(snake_case , snake_case )
_UpperCAmelCase = dtype(**snake_case )
outputs.append(snake_case )
if len(namespace.__dict__ ) > 0:
# additional namespace.
outputs.append(snake_case )
if return_remaining_strings:
return (*outputs, remaining_args)
else:
if remaining_args:
raise ValueError(f'Some specified arguments are not used by the HfArgumentParser: {remaining_args}' )
return (*outputs,)
def lowerCamelCase_ ( self , snake_case , snake_case = False ) -> Tuple[DataClass, ...]:
_UpperCAmelCase = set(args.keys() )
_UpperCAmelCase = []
for dtype in self.dataclass_types:
_UpperCAmelCase = {f.name for f in dataclasses.fields(snake_case ) if f.init}
_UpperCAmelCase = {k: v for k, v in args.items() if k in keys}
unused_keys.difference_update(inputs.keys() )
_UpperCAmelCase = dtype(**snake_case )
outputs.append(snake_case )
if not allow_extra_keys and unused_keys:
raise ValueError(f'Some keys are not used by the HfArgumentParser: {sorted(snake_case )}' )
return tuple(snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case = False ) -> Tuple[DataClass, ...]:
with open(Path(snake_case ) , encoding='utf-8' ) as open_json_file:
_UpperCAmelCase = json.loads(open_json_file.read() )
_UpperCAmelCase = self.parse_dict(snake_case , allow_extra_keys=snake_case )
return tuple(snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case = False ) -> Tuple[DataClass, ...]:
_UpperCAmelCase = self.parse_dict(yaml.safe_load(Path(snake_case ).read_text() ) , allow_extra_keys=snake_case )
return tuple(snake_case )
| 24 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24 | 1 |
"""simple docstring"""
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowercase = datasets.utils.logging.get_logger(__name__)
class lowercase__ ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstring'''
_UpperCAmelCase = None
_UpperCAmelCase = None
class lowercase__ ( folder_based_builder.FolderBasedBuilder ):
'''simple docstring'''
_UpperCAmelCase = datasets.Audio()
_UpperCAmelCase = '''audio'''
_UpperCAmelCase = AudioFolderConfig
_UpperCAmelCase = 42 # definition at the bottom of the script
_UpperCAmelCase = AudioClassification(audio_column='''audio''', label_column='''label''' )
lowercase = [
'''.aiff''',
'''.au''',
'''.avr''',
'''.caf''',
'''.flac''',
'''.htk''',
'''.svx''',
'''.mat4''',
'''.mat5''',
'''.mpc2k''',
'''.ogg''',
'''.paf''',
'''.pvf''',
'''.raw''',
'''.rf64''',
'''.sd2''',
'''.sds''',
'''.ircam''',
'''.voc''',
'''.w64''',
'''.wav''',
'''.nist''',
'''.wavex''',
'''.wve''',
'''.xi''',
'''.mp3''',
'''.opus''',
]
lowercase = AUDIO_EXTENSIONS
| 24 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24 | 1 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'''pipelines_utils''',
'''0.22.0''',
'''Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.''',
standard_warn=False,
stacklevel=3,
)
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24 | 1 |
"""simple docstring"""
from random import randint, random
def UpperCAmelCase ( A : int , A : int , A : int , A : bool = False , A : bool = False , A : int = 5 , ):
'''simple docstring'''
_UpperCAmelCase = [[-1] * number_of_cells] # Create a highway without any car
_UpperCAmelCase = 0
_UpperCAmelCase = max(A , 0 )
while i < number_of_cells:
_UpperCAmelCase = (
randint(0 , A ) if random_speed else initial_speed
) # Place the cars
i += (
randint(1 , max_speed * 2 ) if random_frequency else frequency
) # Arbitrary number, may need tuning
return highway
def UpperCAmelCase ( A : list , A : int ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = highway_now[car_index + 1 :]
for cell in range(len(A ) ): # May need a better name for this
if cells[cell] != -1: # If the cell is not empty then
return distance # we have the distance we wanted
distance += 1
# Here if the car is near the end of the highway
return distance + get_distance(A , -1 )
def UpperCAmelCase ( A : list , A : float , A : int ):
'''simple docstring'''
_UpperCAmelCase = len(A )
# Beforce calculations, the highway is empty
_UpperCAmelCase = [-1] * number_of_cells
for car_index in range(A ):
if highway_now[car_index] != -1:
# Add 1 to the current speed of the car and cap the speed
_UpperCAmelCase = min(highway_now[car_index] + 1 , A )
# Number of empty cell before the next car
_UpperCAmelCase = get_distance(A , A ) - 1
# We can't have the car causing an accident
_UpperCAmelCase = min(next_highway[car_index] , A )
if random() < probability:
# Randomly, a driver will slow down
_UpperCAmelCase = max(next_highway[car_index] - 1 , 0 )
return next_highway
def UpperCAmelCase ( A : list , A : int , A : float , A : int ):
'''simple docstring'''
_UpperCAmelCase = len(highway[0] )
for i in range(A ):
_UpperCAmelCase = update(highway[i] , A , A )
_UpperCAmelCase = [-1] * number_of_cells
for car_index in range(A ):
_UpperCAmelCase = next_speeds_calculated[car_index]
if speed != -1:
# Change the position based on the speed (with % to create the loop)
_UpperCAmelCase = (car_index + speed) % number_of_cells
# Commit the change of position
_UpperCAmelCase = speed
highway.append(A )
return highway
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = 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
_UpperCAmelCase = 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 UpperCAmelCase ( A : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = GPTaTokenizer
_UpperCAmelCase = GPTaTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = {'''add_prefix_space''': True}
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Any:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCAmelCase = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
_UpperCAmelCase = dict(zip(snake_case , range(len(snake_case ) ) ) )
_UpperCAmelCase = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
_UpperCAmelCase = {'unk_token': '<unk>'}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(snake_case ) )
def lowerCamelCase_ ( self , **snake_case ) -> Dict:
kwargs.update(self.special_tokens_map )
return GPTaTokenizer.from_pretrained(self.tmpdirname , **snake_case )
def lowerCamelCase_ ( self , **snake_case ) -> Union[str, Any]:
kwargs.update(self.special_tokens_map )
return GPTaTokenizerFast.from_pretrained(self.tmpdirname , **snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Optional[Any]:
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = 'lower newer'
return input_text, output_text
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = GPTaTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
_UpperCAmelCase = 'lower newer'
_UpperCAmelCase = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
_UpperCAmelCase = tokenizer.tokenize(snake_case , add_prefix_space=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> int:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer(add_prefix_space=snake_case )
_UpperCAmelCase = 'lower newer'
# Testing tokenization
_UpperCAmelCase = tokenizer.tokenize(snake_case , add_prefix_space=snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
# Testing conversion to ids without special tokens
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case , add_prefix_space=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
# Testing conversion to ids with special tokens
_UpperCAmelCase = self.get_rust_tokenizer(add_prefix_space=snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_prefix_space=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
# Testing the unknown token
_UpperCAmelCase = tokens + [rust_tokenizer.unk_token]
_UpperCAmelCase = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(snake_case ) , snake_case )
def lowerCamelCase_ ( self , *snake_case , **snake_case ) -> List[str]:
# It's very difficult to mix/test pretokenization with byte-level
# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
pass
def lowerCamelCase_ ( self , snake_case=15 ) -> List[Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
_UpperCAmelCase = self.rust_tokenizer_class.from_pretrained(snake_case , **snake_case )
# Simple input
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input 1', 'This is a simple input 2']
_UpperCAmelCase = ('This is a simple input', 'This is a pair')
_UpperCAmelCase = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(snake_case , tokenizer_r.encode , snake_case , max_length=snake_case , padding='max_length' )
# Simple input
self.assertRaises(snake_case , tokenizer_r.encode_plus , snake_case , max_length=snake_case , padding='max_length' )
# Simple input
self.assertRaises(
snake_case , tokenizer_r.batch_encode_plus , snake_case , max_length=snake_case , padding='max_length' , )
# Pair input
self.assertRaises(snake_case , tokenizer_r.encode , snake_case , max_length=snake_case , padding='max_length' )
# Pair input
self.assertRaises(snake_case , tokenizer_r.encode_plus , snake_case , max_length=snake_case , padding='max_length' )
# Pair input
self.assertRaises(
snake_case , tokenizer_r.batch_encode_plus , snake_case , max_length=snake_case , padding='max_length' , )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = GPTaTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input looooooooong', 'This is a simple input']
_UpperCAmelCase = ('This is a simple input', 'This is a pair')
_UpperCAmelCase = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
_UpperCAmelCase = tokenizer.pad_token_id
_UpperCAmelCase = tokenizer(snake_case , padding='max_length' , max_length=30 , return_tensors='np' )
_UpperCAmelCase = tokenizer(snake_case , padding=snake_case , truncate=snake_case , return_tensors='np' )
_UpperCAmelCase = tokenizer(*snake_case , padding='max_length' , max_length=60 , return_tensors='np' )
_UpperCAmelCase = tokenizer(snake_case , padding=snake_case , truncate=snake_case , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = '$$$'
_UpperCAmelCase = GPTaTokenizer.from_pretrained(self.tmpdirname , bos_token=snake_case , add_bos_token=snake_case )
_UpperCAmelCase = 'This is a simple input'
_UpperCAmelCase = ['This is a simple input 1', 'This is a simple input 2']
_UpperCAmelCase = tokenizer.bos_token_id
_UpperCAmelCase = tokenizer(snake_case )
_UpperCAmelCase = tokenizer(snake_case )
self.assertEqual(out_s.input_ids[0] , snake_case )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
_UpperCAmelCase = tokenizer.decode(out_s.input_ids )
_UpperCAmelCase = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , snake_case )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
def lowerCamelCase_ ( self ) -> Dict:
pass
def lowerCamelCase_ ( self ) -> Union[str, Any]:
# TODO: change to self.get_tokenizers() when the fast version is implemented
_UpperCAmelCase = [self.get_tokenizer(do_lower_case=snake_case , add_bos_token=snake_case )]
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
_UpperCAmelCase = 'Encode this.'
_UpperCAmelCase = 'This one too please.'
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
encoded_sequence += tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = tokenizer.encode_plus(
snake_case , snake_case , add_special_tokens=snake_case , return_special_tokens_mask=snake_case , )
_UpperCAmelCase = encoded_sequence_dict['input_ids']
_UpperCAmelCase = encoded_sequence_dict['special_tokens_mask']
self.assertEqual(len(snake_case ) , len(snake_case ) )
_UpperCAmelCase = [
(x if not special_tokens_mask[i] else None) for i, x in enumerate(snake_case )
]
_UpperCAmelCase = [x for x in filtered_sequence if x is not None]
self.assertEqual(snake_case , snake_case )
@require_tokenizers
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> str:
# More context:
# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439
_UpperCAmelCase = AutoTokenizer.from_pretrained('facebook/opt-350m' , from_slow=snake_case )
_UpperCAmelCase = 'A photo of a cat'
_UpperCAmelCase = tokenizer.encode(
snake_case , )
self.assertEqual(snake_case , [2, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained('test_opt' )
_UpperCAmelCase = AutoTokenizer.from_pretrained('./test_opt' )
_UpperCAmelCase = tokenizer.encode(
snake_case , )
self.assertEqual(snake_case , [2, 250, 1345, 9, 10, 4758] )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = AutoTokenizer.from_pretrained('facebook/opt-350m' , use_slow=snake_case )
_UpperCAmelCase = 'A photo of a cat'
_UpperCAmelCase = tokenizer.encode(
snake_case , )
# Same as above
self.assertEqual(snake_case , [2, 250, 1345, 9, 10, 4758] )
@unittest.skip('This test is failing because of a bug in the fast tokenizer' )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = AutoTokenizer.from_pretrained('facebook/opt-350m' , from_slow=snake_case )
_UpperCAmelCase = 'bos'
_UpperCAmelCase = tokenizer.get_vocab()['bos']
_UpperCAmelCase = 'A photo of a cat'
_UpperCAmelCase = tokenizer.encode(
snake_case , )
# We changed the bos token
self.assertEqual(snake_case , [31957, 250, 1345, 9, 10, 4758] )
tokenizer.save_pretrained('./tok' )
_UpperCAmelCase = AutoTokenizer.from_pretrained('./tok' )
self.assertTrue(tokenizer.is_fast )
_UpperCAmelCase = tokenizer.encode(
snake_case , )
self.assertEqual(snake_case , [31957, 250, 1345, 9, 10, 4758] )
| 24 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowercase = ''''''
lowercase = ''''''
lowercase = ''''''
lowercase = 1 # (0 is vertical, 1 is horizontal)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_dataset(A , A )
print('Processing...' )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = update_image_and_anno(A , A , A )
for index, image in enumerate(A ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_UpperCAmelCase = random_chars(32 )
_UpperCAmelCase = paths[index].split(os.sep )[-1].rsplit('.' , 1 )[0]
_UpperCAmelCase = f'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
cva.imwrite(f'/{file_root}.jpg' , A , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'Success {index+1}/{len(A )} with {file_name}' )
_UpperCAmelCase = []
for anno in new_annos[index]:
_UpperCAmelCase = f'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
annos_list.append(A )
with open(f'/{file_root}.txt' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def UpperCAmelCase ( A : str , A : str ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
for label_file in glob.glob(os.path.join(A , '*.txt' ) ):
_UpperCAmelCase = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(A ) as in_file:
_UpperCAmelCase = in_file.readlines()
_UpperCAmelCase = os.path.join(A , f'{label_name}.jpg' )
_UpperCAmelCase = []
for obj_list in obj_lists:
_UpperCAmelCase = obj_list.rstrip('\n' ).split(' ' )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(A )
labels.append(A )
return img_paths, labels
def UpperCAmelCase ( A : list , A : list , A : int = 1 ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = []
for idx in range(len(A ) ):
_UpperCAmelCase = []
_UpperCAmelCase = img_list[idx]
path_list.append(A )
_UpperCAmelCase = anno_list[idx]
_UpperCAmelCase = cva.imread(A )
if flip_type == 1:
_UpperCAmelCase = cva.flip(A , A )
for bbox in img_annos:
_UpperCAmelCase = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
_UpperCAmelCase = cva.flip(A , A )
for bbox in img_annos:
_UpperCAmelCase = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(A )
new_imgs_list.append(A )
return new_imgs_list, new_annos_lists, path_list
def UpperCAmelCase ( A : int = 32 ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
_UpperCAmelCase = ascii_lowercase + digits
return "".join(random.choice(A ) for _ in range(A ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 24 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'hidden_sizes' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_attention_heads' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_encoder_blocks' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=4 , snake_case=[2, 2, 2, 2] , snake_case=[8, 4, 2, 1] , snake_case=[16, 32, 64, 128] , snake_case=[1, 4, 8, 16] , snake_case=[1, 2, 4, 8] , snake_case=True , snake_case=True , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=0.02 , snake_case=3 , snake_case=None , ) -> List[str]:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = num_encoder_blocks
_UpperCAmelCase = sr_ratios
_UpperCAmelCase = depths
_UpperCAmelCase = hidden_sizes
_UpperCAmelCase = downsampling_rates
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = num_labels
_UpperCAmelCase = scope
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[Any]:
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> str:
_UpperCAmelCase = SegformerModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = _UpperCAmelCase = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Union[str, Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = SegformerForSemanticSegmentation(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Tuple:
_UpperCAmelCase = 1
_UpperCAmelCase = SegformerForSemanticSegmentation(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(snake_case )
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertGreater(result.loss , 0.0 )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
_UpperCAmelCase = (
{
'''feature-extraction''': SegformerModel,
'''image-classification''': SegformerForImageClassification,
'''image-segmentation''': SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = SegformerModelTester(self )
_UpperCAmelCase = SegformerConfigTester(self , config_class=snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*snake_case )
@unittest.skip('SegFormer does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
@unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods' )
def lowerCamelCase_ ( self ) -> str:
pass
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
for model_class in self.all_model_classes:
_UpperCAmelCase = True
_UpperCAmelCase = False
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.attentions
_UpperCAmelCase = sum(self.model_tester.depths )
self.assertEqual(len(snake_case ) , snake_case )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case ) , snake_case )
# verify the first attentions (first block, first layer)
_UpperCAmelCase = (self.model_tester.image_size // 4) ** 2
_UpperCAmelCase = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
_UpperCAmelCase = (self.model_tester.image_size // 32) ** 2
_UpperCAmelCase = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
_UpperCAmelCase = len(snake_case )
# Check attention is always last and order is fine
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
self.assertEqual(out_len + 1 , len(snake_case ) )
_UpperCAmelCase = outputs.attentions
self.assertEqual(len(snake_case ) , snake_case )
# verify the first attentions (first block, first layer)
_UpperCAmelCase = (self.model_tester.image_size // 4) ** 2
_UpperCAmelCase = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def lowerCamelCase_ ( self ) -> int:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = self.model_tester.num_encoder_blocks
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.model_tester.is_training:
return
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase = True
for model_class in self.all_model_classes:
if model_class in get_values(snake_case ):
continue
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.train()
_UpperCAmelCase = self._prepare_for_class(snake_case , snake_case , return_labels=snake_case )
_UpperCAmelCase = model(**snake_case ).loss
loss.backward()
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> int:
pass
@slow
def lowerCamelCase_ ( self ) -> Union[str, Any]:
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = SegformerModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCamelCase_ ( self ) -> Union[str, Any]:
# only resize + normalize
_UpperCAmelCase = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=snake_case , align=snake_case , do_random_crop=snake_case )
_UpperCAmelCase = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512' ).to(
snake_case )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' )
_UpperCAmelCase = encoded_inputs.pixel_values.to(snake_case )
with torch.no_grad():
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , snake_case , atol=1E-4 ) )
@slow
def lowerCamelCase_ ( self ) -> int:
# only resize + normalize
_UpperCAmelCase = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=snake_case , align=snake_case , do_random_crop=snake_case )
_UpperCAmelCase = SegformerForSemanticSegmentation.from_pretrained(
'nvidia/segformer-b1-finetuned-cityscapes-1024-1024' ).to(snake_case )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' )
_UpperCAmelCase = encoded_inputs.pixel_values.to(snake_case )
with torch.no_grad():
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , snake_case , atol=1E-1 ) )
@slow
def lowerCamelCase_ ( self ) -> Any:
# only resize + normalize
_UpperCAmelCase = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=snake_case , align=snake_case , do_random_crop=snake_case )
_UpperCAmelCase = SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512' ).to(
snake_case )
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' )
_UpperCAmelCase = encoded_inputs.pixel_values.to(snake_case )
with torch.no_grad():
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = outputs.logits.detach().cpu()
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=snake_case , target_sizes=[(500, 300)] )
_UpperCAmelCase = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , snake_case )
_UpperCAmelCase = image_processor.post_process_semantic_segmentation(outputs=snake_case )
_UpperCAmelCase = torch.Size((128, 128) )
self.assertEqual(segmentation[0].shape , snake_case )
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase = '''src/diffusers'''
lowercase = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
lowercase = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
lowercase = spec.loader.load_module()
def UpperCAmelCase ( A : Any , A : Tuple ):
'''simple docstring'''
return line.startswith(A ) or len(A ) <= 1 or re.search(r'^\s*\)(\s*->.*:|:)\s*$' , A ) is not None
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
_UpperCAmelCase = object_name.split('.' )
_UpperCAmelCase = 0
# First let's find the module where our object lives.
_UpperCAmelCase = parts[i]
while i < len(A ) and not os.path.isfile(os.path.join(A , f'{module}.py' ) ):
i += 1
if i < len(A ):
_UpperCAmelCase = os.path.join(A , parts[i] )
if i >= len(A ):
raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.' )
with open(os.path.join(A , f'{module}.py' ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
_UpperCAmelCase = f.readlines()
# Now let's find the class / func in the code!
_UpperCAmelCase = ''
_UpperCAmelCase = 0
for name in parts[i + 1 :]:
while (
line_index < len(A ) and re.search(rf'^{indent}(class|def)\s+{name}(\(|\:)' , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(A ):
raise ValueError(f' {object_name} does not match any function or class in {module}.' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
_UpperCAmelCase = line_index
while line_index < len(A ) and _should_continue(lines[line_index] , A ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_UpperCAmelCase = lines[start_index:line_index]
return "".join(A )
lowercase = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
lowercase = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
lowercase = re.compile(r'''<FILL\s+[^>]*>''')
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
_UpperCAmelCase = code.split('\n' )
_UpperCAmelCase = 0
while idx < len(A ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(A ):
return re.search(r'^(\s*)\S' , lines[idx] ).groups()[0]
return ""
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
_UpperCAmelCase = len(get_indent(A ) ) > 0
if has_indent:
_UpperCAmelCase = f'class Bla:\n{code}'
_UpperCAmelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=A )
_UpperCAmelCase = black.format_str(A , mode=A )
_UpperCAmelCase , _UpperCAmelCase = style_docstrings_in_code(A )
return result[len('class Bla:\n' ) :] if has_indent else result
def UpperCAmelCase ( A : int , A : int=False ):
'''simple docstring'''
with open(A , 'r' , encoding='utf-8' , newline='\n' ) as f:
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = []
_UpperCAmelCase = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(A ):
_UpperCAmelCase = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = search.groups()
_UpperCAmelCase = find_code_in_diffusers(A )
_UpperCAmelCase = get_indent(A )
_UpperCAmelCase = line_index + 1 if indent == theoretical_indent else line_index + 2
_UpperCAmelCase = theoretical_indent
_UpperCAmelCase = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
_UpperCAmelCase = True
while line_index < len(A ) and should_continue:
line_index += 1
if line_index >= len(A ):
break
_UpperCAmelCase = lines[line_index]
_UpperCAmelCase = _should_continue(A , A ) and re.search(f'^{indent}# End copy' , A ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_UpperCAmelCase = lines[start_index:line_index]
_UpperCAmelCase = ''.join(A )
# Remove any nested `Copied from` comments to avoid circular copies
_UpperCAmelCase = [line for line in theoretical_code.split('\n' ) if _re_copy_warning.search(A ) is None]
_UpperCAmelCase = '\n'.join(A )
# Before comparing, use the `replace_pattern` on the original code.
if len(A ) > 0:
_UpperCAmelCase = replace_pattern.replace('with' , '' ).split(',' )
_UpperCAmelCase = [_re_replace_pattern.search(A ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = pattern.groups()
_UpperCAmelCase = re.sub(A , A , A )
if option.strip() == "all-casing":
_UpperCAmelCase = re.sub(obja.lower() , obja.lower() , A )
_UpperCAmelCase = re.sub(obja.upper() , obja.upper() , A )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
_UpperCAmelCase = blackify(lines[start_index - 1] + theoretical_code )
_UpperCAmelCase = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
_UpperCAmelCase = lines[:start_index] + [theoretical_code] + lines[line_index:]
_UpperCAmelCase = start_index + 1
if overwrite and len(A ) > 0:
# Warn the user a file has been modified.
print(f'Detected changes, rewriting {filename}.' )
with open(A , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(A )
return diffs
def UpperCAmelCase ( A : bool = False ):
'''simple docstring'''
_UpperCAmelCase = glob.glob(os.path.join(A , '**/*.py' ) , recursive=A )
_UpperCAmelCase = []
for filename in all_files:
_UpperCAmelCase = is_copy_consistent(A , A )
diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
if not overwrite and len(A ) > 0:
_UpperCAmelCase = '\n'.join(A )
raise Exception(
'Found the following copy inconsistencies:\n'
+ diff
+ '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.' )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowercase = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 24 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''tokenization_rag''': ['''RagTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''RagModel''',
'''RagPreTrainedModel''',
'''RagSequenceForGeneration''',
'''RagTokenForGeneration''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TFRagModel''',
'''TFRagPreTrainedModel''',
'''TFRagSequenceForGeneration''',
'''TFRagTokenForGeneration''',
]
if TYPE_CHECKING:
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
from .tokenization_rag import RagTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rag import (
TFRagModel,
TFRagPreTrainedModel,
TFRagSequenceForGeneration,
TFRagTokenForGeneration,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 |
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case = None , snake_case = None , snake_case = True , snake_case = None , snake_case = False , snake_case = None , snake_case = True , snake_case = "arrow" , **snake_case , ) -> Optional[int]:
super().__init__(
split=snake_case , features=snake_case , cache_dir=snake_case , keep_in_memory=snake_case , streaming=snake_case , **snake_case , )
_UpperCAmelCase = load_from_cache_file
_UpperCAmelCase = file_format
_UpperCAmelCase = Spark(
df=snake_case , features=snake_case , cache_dir=snake_case , working_dir=snake_case , **snake_case , )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
_UpperCAmelCase = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=snake_case , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24 | 1 |
"""simple docstring"""
import os
def UpperCAmelCase ( A : str = "matrix.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(A ) , A ) ) as in_file:
_UpperCAmelCase = in_file.read()
_UpperCAmelCase = [[int(A ) for cell in row.split(',' )] for row in data.strip().splitlines()]
_UpperCAmelCase = [[0 for cell in row] for row in grid]
_UpperCAmelCase = len(grid[0] )
_UpperCAmelCase = [[0 for i in range(A )] for j in range(A )]
_UpperCAmelCase = grid[0][0]
for i in range(1 , A ):
_UpperCAmelCase = grid[0][i] + dp[0][i - 1]
for i in range(1 , A ):
_UpperCAmelCase = grid[i][0] + dp[i - 1][0]
for i in range(1 , A ):
for j in range(1 , A ):
_UpperCAmelCase = grid[i][j] + min(dp[i - 1][j] , dp[i][j - 1] )
return dp[-1][-1]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
import sys
lowercase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 1
for digit in s:
product *= int(A )
return product
def UpperCAmelCase ( A : str = N ):
'''simple docstring'''
_UpperCAmelCase = -sys.maxsize - 1
_UpperCAmelCase = n[:13]
_UpperCAmelCase = 13
while cur_index < len(A ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
_UpperCAmelCase = substr[1:] + n[cur_index]
cur_index += 1
else:
_UpperCAmelCase = max(A , str_eval(A ) )
_UpperCAmelCase = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = ['''input_features''', '''attention_mask''']
def __init__( self , snake_case=80 , snake_case=16000 , snake_case=80 , snake_case=0.0 , snake_case=True , snake_case=True , snake_case=True , **snake_case , ) -> Tuple:
super().__init__(feature_size=snake_case , sampling_rate=snake_case , padding_value=snake_case , **snake_case )
_UpperCAmelCase = num_mel_bins
_UpperCAmelCase = do_ceptral_normalize
_UpperCAmelCase = normalize_means
_UpperCAmelCase = normalize_vars
_UpperCAmelCase = True
def lowerCamelCase_ ( self , snake_case , ) -> np.ndarray:
_UpperCAmelCase = waveform * (2**15) # Kaldi compliance: 16-bit signed integers
_UpperCAmelCase = torch.from_numpy(snake_case ).unsqueeze(0 )
_UpperCAmelCase = ta_kaldi.fbank(snake_case , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def lowerCamelCase_ ( snake_case , snake_case , snake_case = True , snake_case = True , snake_case = 0.0 , ) -> np.ndarray:
# make sure we normalize float32 arrays
if normalize_means:
_UpperCAmelCase = x[:input_length].mean(axis=0 )
_UpperCAmelCase = np.subtract(snake_case , snake_case )
if normalize_vars:
_UpperCAmelCase = x[:input_length].std(axis=0 )
_UpperCAmelCase = np.divide(snake_case , snake_case )
if input_length < x.shape[0]:
_UpperCAmelCase = padding_value
# make sure array is in float32
_UpperCAmelCase = x.astype(np.floataa )
return x
def lowerCamelCase_ ( self , snake_case , snake_case = None ) -> List[np.ndarray]:
_UpperCAmelCase = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(snake_case , snake_case , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(snake_case , snake_case )
]
def __call__( self , snake_case , snake_case = False , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = None , snake_case = None , **snake_case , ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
f' {self.sampling_rate} and not {sampling_rate}.' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
_UpperCAmelCase = isinstance(snake_case , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(f'Only mono-channel audio is supported for input to {self}' )
_UpperCAmelCase = is_batched_numpy or (
isinstance(snake_case , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase = [np.asarray(snake_case , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(snake_case , np.ndarray ):
_UpperCAmelCase = np.asarray(snake_case , dtype=np.floataa )
elif isinstance(snake_case , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase = [raw_speech]
# extract fbank features
_UpperCAmelCase = [self._extract_fbank_features(snake_case ) for waveform in raw_speech]
# convert into correct format for padding
_UpperCAmelCase = BatchFeature({'input_features': features} )
_UpperCAmelCase = self.pad(
snake_case , padding=snake_case , max_length=snake_case , truncation=snake_case , pad_to_multiple_of=snake_case , return_attention_mask=snake_case , **snake_case , )
# make sure list is in array format
_UpperCAmelCase = padded_inputs.get('input_features' )
if isinstance(input_features[0] , snake_case ):
_UpperCAmelCase = [np.asarray(snake_case , dtype=np.floataa ) for feature in input_features]
_UpperCAmelCase = padded_inputs.get('attention_mask' )
if attention_mask is not None:
_UpperCAmelCase = [np.asarray(snake_case , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
_UpperCAmelCase = (
np.array(snake_case , dtype=np.intaa )
if self._get_padding_strategies(snake_case , max_length=snake_case ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_UpperCAmelCase = self.normalize(
padded_inputs['input_features'] , attention_mask=snake_case )
if return_tensors is not None:
_UpperCAmelCase = padded_inputs.convert_to_tensors(snake_case )
return padded_inputs
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : list[float] , A : str ):
'''simple docstring'''
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(A ):
print(f'{i}\t\t{d}' )
def UpperCAmelCase ( A : list[dict[str, int]] , A : list[float] , A : int ):
'''simple docstring'''
for j in range(A ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = (graph[j][k] for k in ['src', 'dst', 'weight'])
if distance[u] != float('inf' ) and distance[u] + w < distance[v]:
return True
return False
def UpperCAmelCase ( A : list[dict[str, int]] , A : int , A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = [float('inf' )] * vertex_count
_UpperCAmelCase = 0.0
for _ in range(vertex_count - 1 ):
for j in range(A ):
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = (graph[j][k] for k in ['src', 'dst', 'weight'])
if distance[u] != float('inf' ) and distance[u] + w < distance[v]:
_UpperCAmelCase = distance[u] + w
_UpperCAmelCase = check_negative_cycle(A , A , A )
if negative_cycle_exists:
raise Exception('Negative cycle found' )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase = int(input('''Enter number of vertices: ''').strip())
lowercase = int(input('''Enter number of edges: ''').strip())
lowercase = [{} for _ in range(E)]
for i in range(E):
print('''Edge ''', i + 1)
lowercase , lowercase , lowercase = (
int(x)
for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
)
lowercase = {'''src''': src, '''dst''': dest, '''weight''': weight}
lowercase = int(input('''\nEnter shortest path source:''').strip())
lowercase = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 24 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = VQModel
_UpperCAmelCase = '''sample'''
@property
def lowerCamelCase_ ( self , snake_case=(32, 32) ) -> Optional[int]:
_UpperCAmelCase = 4
_UpperCAmelCase = 3
_UpperCAmelCase = floats_tensor((batch_size, num_channels) + sizes ).to(snake_case )
return {"sample": image}
@property
def lowerCamelCase_ ( self ) -> Any:
return (3, 32, 32)
@property
def lowerCamelCase_ ( self ) -> int:
return (3, 32, 32)
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'block_out_channels': [32, 64],
'in_channels': 3,
'out_channels': 3,
'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
'latent_channels': 3,
}
_UpperCAmelCase = self.dummy_input
return init_dict, inputs_dict
def lowerCamelCase_ ( self ) -> Any:
pass
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase , _UpperCAmelCase = VQModel.from_pretrained('fusing/vqgan-dummy' , output_loading_info=snake_case )
self.assertIsNotNone(snake_case )
self.assertEqual(len(loading_info['missing_keys'] ) , 0 )
model.to(snake_case )
_UpperCAmelCase = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = VQModel.from_pretrained('fusing/vqgan-dummy' )
model.to(snake_case ).eval()
torch.manual_seed(0 )
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0 )
_UpperCAmelCase = torch.randn(1 , model.config.in_channels , model.config.sample_size , model.config.sample_size )
_UpperCAmelCase = image.to(snake_case )
with torch.no_grad():
_UpperCAmelCase = model(snake_case ).sample
_UpperCAmelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
_UpperCAmelCase = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143] )
# fmt: on
self.assertTrue(torch.allclose(snake_case , snake_case , atol=1E-3 ) )
| 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : list[int] , A : list[int] ):
'''simple docstring'''
_UpperCAmelCase = len(A )
print('The following activities are selected:' )
# The first activity is always selected
_UpperCAmelCase = 0
print(A , end=',' )
# Consider rest of the activities
for j in range(A ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(A , end=',' )
_UpperCAmelCase = j
if __name__ == "__main__":
import doctest
doctest.testmod()
lowercase = [1, 3, 0, 5, 8, 5]
lowercase = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( A : Tuple , A : List[Any] , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = BertConfig.from_json_file(A )
print(f'Building PyTorch model from configuration: {config}' )
_UpperCAmelCase = BertForPreTraining(A )
# Load weights from tf checkpoint
load_tf_weights_in_bert(A , A , A )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--bert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained BERT model. \n'''
'''This specifies the model architecture.'''
),
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
lowercase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24 | 1 |
"""simple docstring"""
from math import factorial
def UpperCAmelCase ( A : int = 100 ):
'''simple docstring'''
return sum(map(A , str(factorial(A ) ) ) )
if __name__ == "__main__":
print(solution(int(input('''Enter the Number: ''').strip())))
| 24 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = StableDiffusionSAGPipeline
_UpperCAmelCase = TEXT_TO_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Tuple:
torch.manual_seed(0 )
_UpperCAmelCase = 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 , )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=snake_case , set_alpha_to_one=snake_case , )
torch.manual_seed(0 )
_UpperCAmelCase = 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 , )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
_UpperCAmelCase = CLIPTextModel(snake_case )
_UpperCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
_UpperCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Tuple:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'prompt': '.',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 1.0,
'sag_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> str:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' )
_UpperCAmelCase = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = '.'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = sag_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='np' )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
_UpperCAmelCase = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = '.'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = sag_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='np' )
_UpperCAmelCase = output.images
_UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
_UpperCAmelCase = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base' )
_UpperCAmelCase = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = '.'
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = sag_pipe(
[prompt] , width=768 , height=512 , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='np' , )
_UpperCAmelCase = output.images
assert image.shape == (1, 512, 768, 3)
| 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[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],
]
# Define blinker example
lowercase = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]
def UpperCAmelCase ( A : list[list[int]] ):
'''simple docstring'''
_UpperCAmelCase = []
for i in range(len(A ) ):
_UpperCAmelCase = []
for j in range(len(cells[i] ) ):
# Get the number of live neighbours
_UpperCAmelCase = 0
if i > 0 and j > 0:
neighbour_count += cells[i - 1][j - 1]
if i > 0:
neighbour_count += cells[i - 1][j]
if i > 0 and j < len(cells[i] ) - 1:
neighbour_count += cells[i - 1][j + 1]
if j > 0:
neighbour_count += cells[i][j - 1]
if j < len(cells[i] ) - 1:
neighbour_count += cells[i][j + 1]
if i < len(A ) - 1 and j > 0:
neighbour_count += cells[i + 1][j - 1]
if i < len(A ) - 1:
neighbour_count += cells[i + 1][j]
if i < len(A ) - 1 and j < len(cells[i] ) - 1:
neighbour_count += cells[i + 1][j + 1]
# Rules of the game of life (excerpt from Wikipedia):
# 1. Any live cell with two or three live neighbours survives.
# 2. Any dead cell with three live neighbours becomes a live cell.
# 3. All other live cells die in the next generation.
# Similarly, all other dead cells stay dead.
_UpperCAmelCase = cells[i][j] == 1
if (
(alive and 2 <= neighbour_count <= 3)
or not alive
and neighbour_count == 3
):
next_generation_row.append(1 )
else:
next_generation_row.append(0 )
next_generation.append(A )
return next_generation
def UpperCAmelCase ( A : list[list[int]] , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
for _ in range(A ):
# Create output image
_UpperCAmelCase = Image.new('RGB' , (len(cells[0] ), len(A )) )
_UpperCAmelCase = img.load()
# Save cells to image
for x in range(len(A ) ):
for y in range(len(cells[0] ) ):
_UpperCAmelCase = 255 - cells[y][x] * 255
_UpperCAmelCase = (colour, colour, colour)
# Save image
images.append(A )
_UpperCAmelCase = new_generation(A )
return images
if __name__ == "__main__":
lowercase = generate_images(GLIDER, 16)
images[0].save('''out.gif''', save_all=True, append_images=images[1:])
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def UpperCAmelCase ( A : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*A )
if __name__ == "__main__":
lowercase = 4
lowercase = 2
lowercase = generate_all_combinations(n, k)
print_all_state(total_list)
| 24 | 1 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowercase = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
lowercase = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
lowercase = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
lowercase = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
lowercase = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Any:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' ) ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/openai/human-eval' , codebase_urls=['https://github.com/openai/human-eval'] , reference_urls=['https://github.com/openai/human-eval'] , license=_LICENSE , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=[1, 10, 100] , snake_case=4 , snake_case=3.0 ) -> str:
if os.getenv('HF_ALLOW_CODE_EVAL' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('This metric is currently not supported on Windows.' )
with ThreadPoolExecutor(max_workers=snake_case ) as executor:
_UpperCAmelCase = []
_UpperCAmelCase = Counter()
_UpperCAmelCase = 0
_UpperCAmelCase = defaultdict(snake_case )
for task_id, (candidates, test_case) in enumerate(zip(snake_case , snake_case ) ):
for candidate in candidates:
_UpperCAmelCase = candidate + '\n' + test_case
_UpperCAmelCase = (test_program, timeout, task_id, completion_id[task_id])
_UpperCAmelCase = executor.submit(snake_case , *snake_case )
futures.append(snake_case )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(snake_case ):
_UpperCAmelCase = future.result()
results[result["task_id"]].append((result['completion_id'], result) )
_UpperCAmelCase , _UpperCAmelCase = [], []
for result in results.values():
result.sort()
_UpperCAmelCase = [r[1]['passed'] for r in result]
total.append(len(snake_case ) )
correct.append(sum(snake_case ) )
_UpperCAmelCase = np.array(snake_case )
_UpperCAmelCase = np.array(snake_case )
_UpperCAmelCase = k
_UpperCAmelCase = {f'pass@{k}': estimate_pass_at_k(snake_case , snake_case , snake_case ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def UpperCAmelCase ( A : int , A : List[str] , A : str ):
'''simple docstring'''
def estimator(A : int , A : int , A : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(A , A ):
_UpperCAmelCase = itertools.repeat(A , len(A ) )
else:
assert len(A ) == len(A )
_UpperCAmelCase = iter(A )
return np.array([estimator(int(A ) , int(A ) , A ) for n, c in zip(A , A )] )
| 24 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24 | 1 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCAmelCase ( A : Namespace ):
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
lowercase = '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''
class lowercase__ ( A ):
'''simple docstring'''
@staticmethod
def lowerCamelCase_ ( snake_case ) -> str:
_UpperCAmelCase = parser.add_parser(
'convert' , help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.' , )
train_parser.add_argument('--model_type' , type=snake_case , required=snake_case , help='Model\'s type.' )
train_parser.add_argument(
'--tf_checkpoint' , type=snake_case , required=snake_case , help='TensorFlow checkpoint path or folder.' )
train_parser.add_argument(
'--pytorch_dump_output' , type=snake_case , required=snake_case , help='Path to the PyTorch saved model output.' )
train_parser.add_argument('--config' , type=snake_case , default='' , help='Configuration file path or folder.' )
train_parser.add_argument(
'--finetuning_task_name' , type=snake_case , default=snake_case , help='Optional fine-tuning task name if the TF model was a finetuned model.' , )
train_parser.set_defaults(func=snake_case )
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , *snake_case , ) -> str:
_UpperCAmelCase = logging.get_logger('transformers-cli/converting' )
self._logger.info(f'Loading model {model_type}' )
_UpperCAmelCase = model_type
_UpperCAmelCase = tf_checkpoint
_UpperCAmelCase = pytorch_dump_output
_UpperCAmelCase = config
_UpperCAmelCase = finetuning_task_name
def lowerCamelCase_ ( self ) -> Dict:
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(snake_case )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case )
if "ckpt" in self._tf_checkpoint.lower():
_UpperCAmelCase = self._tf_checkpoint
_UpperCAmelCase = ''
else:
_UpperCAmelCase = self._tf_checkpoint
_UpperCAmelCase = ''
convert_transfo_xl_checkpoint_to_pytorch(
snake_case , self._config , self._pytorch_dump_output , snake_case )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(snake_case )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )
| 24 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowercase__ ( nn.Module ):
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = jnp.floataa
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , snake_case ) -> Optional[Any]:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = hidden_states.shape
_UpperCAmelCase = jax.image.resize(
snake_case , shape=(batch, height * 2, width * 2, channels) , method='nearest' , )
_UpperCAmelCase = self.conv(snake_case )
return hidden_states
class lowercase__ ( nn.Module ):
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = jnp.floataa
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , snake_case ) -> Tuple:
# pad = ((0, 0), (0, 1), (0, 1), (0, 0)) # pad height and width dim
# hidden_states = jnp.pad(hidden_states, pad_width=pad)
_UpperCAmelCase = self.conv(snake_case )
return hidden_states
class lowercase__ ( nn.Module ):
'''simple docstring'''
_UpperCAmelCase = 42
_UpperCAmelCase = None
_UpperCAmelCase = 0.0
_UpperCAmelCase = None
_UpperCAmelCase = jnp.floataa
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = self.in_channels if self.out_channels is None else self.out_channels
_UpperCAmelCase = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_UpperCAmelCase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase = nn.Dense(snake_case , dtype=self.dtype )
_UpperCAmelCase = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
_UpperCAmelCase = nn.Dropout(self.dropout_prob )
_UpperCAmelCase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
_UpperCAmelCase = self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut
_UpperCAmelCase = None
if use_nin_shortcut:
_UpperCAmelCase = nn.Conv(
snake_case , kernel_size=(1, 1) , strides=(1, 1) , padding='VALID' , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case=True ) -> Union[str, Any]:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = self.norma(snake_case )
_UpperCAmelCase = nn.swish(snake_case )
_UpperCAmelCase = self.conva(snake_case )
_UpperCAmelCase = self.time_emb_proj(nn.swish(snake_case ) )
_UpperCAmelCase = jnp.expand_dims(jnp.expand_dims(snake_case , 1 ) , 1 )
_UpperCAmelCase = hidden_states + temb
_UpperCAmelCase = self.norma(snake_case )
_UpperCAmelCase = nn.swish(snake_case )
_UpperCAmelCase = self.dropout(snake_case , snake_case )
_UpperCAmelCase = self.conva(snake_case )
if self.conv_shortcut is not None:
_UpperCAmelCase = self.conv_shortcut(snake_case )
return hidden_states + residual
| 24 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : Optional[Any] , A : Optional[int] , A : Dict , A : Dict , A : Optional[Any] , A : Tuple ):
'''simple docstring'''
if index == r:
for j in range(A ):
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
_UpperCAmelCase = arr[i]
combination_util(A , A , A , index + 1 , A , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(A , A , A , A , A , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def UpperCAmelCase ( A : Any , A : str , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(A , A , A , 0 , A , 0 )
if __name__ == "__main__":
# Driver code to check the function above
lowercase = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( A : str , A : Dict , A : Tuple , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = FunnelConfig.from_json_file(A )
print(f'Building PyTorch model from configuration: {config}' )
_UpperCAmelCase = FunnelBaseModel(A ) if base_model else FunnelModel(A )
# Load weights from tf checkpoint
load_tf_weights_in_funnel(A , A , A )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
)
lowercase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
)
| 24 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = 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
_UpperCAmelCase = 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 UpperCAmelCase ( A : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SqueezeBertConfig''',
'''SqueezeBertOnnxConfig''',
],
'''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''SqueezeBertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SqueezeBertForMaskedLM''',
'''SqueezeBertForMultipleChoice''',
'''SqueezeBertForQuestionAnswering''',
'''SqueezeBertForSequenceClassification''',
'''SqueezeBertForTokenClassification''',
'''SqueezeBertModel''',
'''SqueezeBertModule''',
'''SqueezeBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_squeezebert import (
SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
SqueezeBertConfig,
SqueezeBertOnnxConfig,
)
from .tokenization_squeezebert import SqueezeBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_squeezebert import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
SqueezeBertModule,
SqueezeBertPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = IFPipeline
_UpperCAmelCase = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
_UpperCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {'''latents'''}
def lowerCamelCase_ ( self ) -> int:
return self._get_dummy_components()
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> int:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'prompt': 'A painting of a squirrel eating a burger',
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> Any:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != 'cuda' , reason='float16 requires CUDA' )
def lowerCamelCase_ ( self ) -> Optional[int]:
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def lowerCamelCase_ ( self ) -> Any:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def lowerCamelCase_ ( self ) -> Any:
self._test_save_load_local()
def lowerCamelCase_ ( self ) -> str:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> List[str]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> Dict:
# if
_UpperCAmelCase = IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' , variant='fp16' , torch_dtype=torch.floataa )
_UpperCAmelCase = IFSuperResolutionPipeline.from_pretrained(
'DeepFloyd/IF-II-L-v1.0' , variant='fp16' , torch_dtype=torch.floataa , text_encoder=snake_case , tokenizer=snake_case )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to('cuda' )
_UpperCAmelCase , _UpperCAmelCase = pipe_a.encode_prompt('anime turtle' , device='cuda' )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
_UpperCAmelCase = None
_UpperCAmelCase = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(snake_case , snake_case , snake_case , snake_case )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
_UpperCAmelCase = IFImgaImgPipeline(**pipe_a.components )
_UpperCAmelCase = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(snake_case , snake_case , snake_case , snake_case )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
_UpperCAmelCase = IFInpaintingPipeline(**pipe_a.components )
_UpperCAmelCase = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(snake_case , snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case ) -> Union[str, Any]:
# pipeline 1
_start_torch_memory_measurement()
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase = pipe_a(
prompt_embeds=snake_case , negative_prompt_embeds=snake_case , num_inference_steps=2 , generator=snake_case , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (64, 64, 3)
_UpperCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy' )
assert_mean_pixel_difference(snake_case , snake_case )
# pipeline 2
_start_torch_memory_measurement()
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = pipe_a(
prompt_embeds=snake_case , negative_prompt_embeds=snake_case , image=snake_case , generator=snake_case , num_inference_steps=2 , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
_UpperCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy' )
assert_mean_pixel_difference(snake_case , snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case ) -> List[Any]:
# pipeline 1
_start_torch_memory_measurement()
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase = pipe_a(
prompt_embeds=snake_case , negative_prompt_embeds=snake_case , image=snake_case , num_inference_steps=2 , generator=snake_case , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (64, 64, 3)
_UpperCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy' )
assert_mean_pixel_difference(snake_case , snake_case )
# pipeline 2
_start_torch_memory_measurement()
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = pipe_a(
prompt_embeds=snake_case , negative_prompt_embeds=snake_case , image=snake_case , original_image=snake_case , generator=snake_case , num_inference_steps=2 , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
_UpperCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy' )
assert_mean_pixel_difference(snake_case , snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case ) -> Optional[Any]:
# pipeline 1
_start_torch_memory_measurement()
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(snake_case )
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase = pipe_a(
prompt_embeds=snake_case , negative_prompt_embeds=snake_case , image=snake_case , mask_image=snake_case , num_inference_steps=2 , generator=snake_case , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (64, 64, 3)
_UpperCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy' )
assert_mean_pixel_difference(snake_case , snake_case )
# pipeline 2
_start_torch_memory_measurement()
_UpperCAmelCase = torch.Generator(device='cpu' ).manual_seed(0 )
_UpperCAmelCase = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(snake_case )
_UpperCAmelCase = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(snake_case )
_UpperCAmelCase = pipe_a(
prompt_embeds=snake_case , negative_prompt_embeds=snake_case , image=snake_case , mask_image=snake_case , original_image=snake_case , generator=snake_case , num_inference_steps=2 , output_type='np' , )
_UpperCAmelCase = output.images[0]
assert image.shape == (256, 256, 3)
_UpperCAmelCase = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy' )
assert_mean_pixel_difference(snake_case , snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 24 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24 | 1 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCAmelCase ( A : Union[str, Any] ):
'''simple docstring'''
return getitem, k
def UpperCAmelCase ( A : int , A : Optional[Any] ):
'''simple docstring'''
return setitem, k, v
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
return delitem, k
def UpperCAmelCase ( A : Optional[int] , A : Tuple , *A : List[Any] ):
'''simple docstring'''
try:
return fun(A , *A ), None
except Exception as e:
return None, e
lowercase = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
lowercase = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
lowercase = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
lowercase = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
lowercase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowercase = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
'operations' , (
pytest.param(_add_items , id='add items' ),
pytest.param(_overwrite_items , id='overwrite items' ),
pytest.param(_delete_items , id='delete items' ),
pytest.param(_access_absent_items , id='access absent items' ),
pytest.param(_add_with_resize_up , id='add with resize up' ),
pytest.param(_add_with_resize_down , id='add with resize down' ),
) , )
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = HashMap(initial_block_size=4 )
_UpperCAmelCase = {}
for _, (fun, *args) in enumerate(A ):
_UpperCAmelCase , _UpperCAmelCase = _run_operation(A , A , *A )
_UpperCAmelCase , _UpperCAmelCase = _run_operation(A , A , *A )
assert my_res == py_res
assert str(A ) == str(A )
assert set(A ) == set(A )
assert len(A ) == len(A )
assert set(my.items() ) == set(py.items() )
def UpperCAmelCase ( ):
'''simple docstring'''
def is_public(A : str ) -> bool:
return not name.startswith('_' )
_UpperCAmelCase = {name for name in dir({} ) if is_public(A )}
_UpperCAmelCase = {name for name in dir(HashMap() ) if is_public(A )}
assert dict_public_names > hash_public_names
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = '| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
_UpperCAmelCase = dict(zip(snake_case , range(len(snake_case ) ) ) )
_UpperCAmelCase = {
'unk_token': '<unk>',
'bos_token': '<s>',
'eos_token': '</s>',
}
_UpperCAmelCase = {
'feature_size': 1,
'padding_value': 0.0,
'sampling_rate': 16000,
'return_attention_mask': False,
'do_normalize': True,
}
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_UpperCAmelCase = os.path.join(self.tmpdirname , snake_case )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(snake_case ) + '\n' )
with open(self.feature_extraction_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(snake_case ) + '\n' )
# load decoder from hub
_UpperCAmelCase = 'hf-internal-testing/ngram-beam-search-decoder'
def lowerCamelCase_ ( self , **snake_case ) -> Tuple:
_UpperCAmelCase = self.add_kwargs_tokens_map.copy()
kwargs.update(snake_case )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **snake_case )
def lowerCamelCase_ ( self , **snake_case ) -> int:
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **snake_case )
def lowerCamelCase_ ( self , **snake_case ) -> int:
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **snake_case )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
shutil.rmtree(self.tmpdirname )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , snake_case )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , snake_case )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['xx'] )
with self.assertRaisesRegex(snake_case , 'include' ):
WavaVecaProcessorWithLM(
tokenizer=snake_case , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
_UpperCAmelCase = floats_list((3, 1000) )
_UpperCAmelCase = feature_extractor(snake_case , return_tensors='np' )
_UpperCAmelCase = processor(snake_case , return_tensors='np' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
_UpperCAmelCase = 'This is a test string'
_UpperCAmelCase = processor(text=snake_case )
_UpperCAmelCase = tokenizer(snake_case )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowerCamelCase_ ( self , snake_case=(2, 10, 16) , snake_case=77 ) -> Optional[Any]:
np.random.seed(snake_case )
return np.random.rand(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
_UpperCAmelCase = self._get_dummy_logits(shape=(10, 16) , seed=13 )
_UpperCAmelCase = processor.decode(snake_case )
_UpperCAmelCase = decoder.decode_beams(snake_case )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('</s> <s> </s>' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['fork'], ['spawn']] )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
_UpperCAmelCase = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
_UpperCAmelCase = processor.batch_decode(snake_case )
else:
with get_context(snake_case ).Pool() as pool:
_UpperCAmelCase = processor.batch_decode(snake_case , snake_case )
_UpperCAmelCase = list(snake_case )
with get_context('fork' ).Pool() as p:
_UpperCAmelCase = decoder.decode_beams_batch(snake_case , snake_case )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(snake_case , decoded_processor.text )
self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>'] , decoded_processor.text )
self.assertListEqual(snake_case , decoded_processor.logit_score )
self.assertListEqual(snake_case , decoded_processor.lm_score )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
_UpperCAmelCase = self._get_dummy_logits()
_UpperCAmelCase = 15
_UpperCAmelCase = -20.0
_UpperCAmelCase = -4.0
_UpperCAmelCase = processor.batch_decode(
snake_case , beam_width=snake_case , beam_prune_logp=snake_case , token_min_logp=snake_case , )
_UpperCAmelCase = decoded_processor_out.text
_UpperCAmelCase = list(snake_case )
with get_context('fork' ).Pool() as pool:
_UpperCAmelCase = decoder.decode_beams_batch(
snake_case , snake_case , beam_width=snake_case , beam_prune_logp=snake_case , token_min_logp=snake_case , )
_UpperCAmelCase = [d[0][0] for d in decoded_decoder_out]
_UpperCAmelCase = [d[0][2] for d in decoded_decoder_out]
_UpperCAmelCase = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(snake_case , snake_case )
self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>'] , snake_case )
self.assertTrue(np.array_equal(snake_case , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] , snake_case , atol=1E-3 ) )
self.assertTrue(np.array_equal(snake_case , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] , snake_case , atol=1E-3 ) )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
_UpperCAmelCase = self._get_dummy_logits()
_UpperCAmelCase = 2.0
_UpperCAmelCase = 5.0
_UpperCAmelCase = -20.0
_UpperCAmelCase = True
_UpperCAmelCase = processor.batch_decode(
snake_case , alpha=snake_case , beta=snake_case , unk_score_offset=snake_case , lm_score_boundary=snake_case , )
_UpperCAmelCase = decoded_processor_out.text
_UpperCAmelCase = list(snake_case )
decoder.reset_params(
alpha=snake_case , beta=snake_case , unk_score_offset=snake_case , lm_score_boundary=snake_case , )
with get_context('fork' ).Pool() as pool:
_UpperCAmelCase = decoder.decode_beams_batch(
snake_case , snake_case , )
_UpperCAmelCase = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(snake_case , snake_case )
self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'] , snake_case )
_UpperCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , snake_case )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
_UpperCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
_UpperCAmelCase = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
_UpperCAmelCase = os.listdir(snake_case )
_UpperCAmelCase = ['alphabet.json', 'language_model']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = snapshot_download('hf-internal-testing/processor_with_lm' )
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained(snake_case )
_UpperCAmelCase = processor.decoder.model_container[processor.decoder._model_key]
_UpperCAmelCase = Path(language_model._kenlm_model.path.decode('utf-8' ) ).parent.parent.absolute()
_UpperCAmelCase = os.listdir(snake_case )
_UpperCAmelCase = os.listdir(snake_case )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
_UpperCAmelCase = AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm' )
_UpperCAmelCase = floats_list((3, 1000) )
_UpperCAmelCase = processor_wavaveca(snake_case , return_tensors='np' )
_UpperCAmelCase = processor_auto(snake_case , return_tensors='np' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
_UpperCAmelCase = self._get_dummy_logits()
_UpperCAmelCase = processor_wavaveca.batch_decode(snake_case )
_UpperCAmelCase = processor_auto.batch_decode(snake_case )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = self.get_feature_extractor()
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_decoder()
_UpperCAmelCase = WavaVecaProcessorWithLM(tokenizer=snake_case , feature_extractor=snake_case , decoder=snake_case )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='`processor` and `feature_extractor` model input names do not match' , )
@staticmethod
def lowerCamelCase_ ( snake_case , snake_case ) -> Dict:
_UpperCAmelCase = [d[key] for d in offsets]
return retrieved_list
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
_UpperCAmelCase = self._get_dummy_logits()[0]
_UpperCAmelCase = processor.decode(snake_case , output_word_offsets=snake_case )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(snake_case , snake_case ) )
self.assertEqual(' '.join(self.get_from_offsets(outputs['word_offsets'] , 'word' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'] , 'end_offset' ) , [1, 3, 5] )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm' )
_UpperCAmelCase = self._get_dummy_logits()
_UpperCAmelCase = processor.batch_decode(snake_case , output_word_offsets=snake_case )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('text' in outputs )
self.assertTrue('word_offsets' in outputs )
self.assertTrue(isinstance(snake_case , snake_case ) )
self.assertListEqual(
[' '.join(self.get_from_offsets(snake_case , 'word' ) ) for o in outputs['word_offsets']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'word' ) , ['<s>', '<s>', '</s>'] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'start_offset' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['word_offsets'][0] , 'end_offset' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def lowerCamelCase_ ( self ) -> List[str]:
import torch
_UpperCAmelCase = load_dataset('common_voice' , 'en' , split='train' , streaming=snake_case )
_UpperCAmelCase = ds.cast_column('audio' , datasets.Audio(sampling_rate=16000 ) )
_UpperCAmelCase = iter(snake_case )
_UpperCAmelCase = next(snake_case )
_UpperCAmelCase = AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
_UpperCAmelCase = WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
_UpperCAmelCase = processor(sample['audio']['array'] , return_tensors='pt' ).input_values
with torch.no_grad():
_UpperCAmelCase = model(snake_case ).logits.cpu().numpy()
_UpperCAmelCase = processor.decode(logits[0] , output_word_offsets=snake_case )
_UpperCAmelCase = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
_UpperCAmelCase = [
{
'start_time': d['start_offset'] * time_offset,
'end_time': d['end_offset'] * time_offset,
'word': d['word'],
}
for d in output['word_offsets']
]
_UpperCAmelCase = 'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'
# output words
self.assertEqual(' '.join(self.get_from_offsets(snake_case , 'word' ) ) , snake_case )
self.assertEqual(' '.join(self.get_from_offsets(snake_case , 'word' ) ) , output.text )
# output times
_UpperCAmelCase = torch.tensor(self.get_from_offsets(snake_case , 'start_time' ) )
_UpperCAmelCase = torch.tensor(self.get_from_offsets(snake_case , 'end_time' ) )
# fmt: off
_UpperCAmelCase = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
_UpperCAmelCase = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(snake_case , snake_case , atol=0.01 ) )
self.assertTrue(torch.allclose(snake_case , snake_case , atol=0.01 ) )
| 24 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24 | 1 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
lowercase = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['''memory_attention''', '''encoder_attn'''],
['''attention''', '''attn'''],
['''/''', '''.'''],
['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
['''.LayerNorm.beta''', '''_layer_norm.bias'''],
['''r.layer_''', '''r.layers.'''],
['''output_proj''', '''out_proj'''],
['''ffn.dense_1.''', '''fc2.'''],
['''ffn.dense.''', '''fc1.'''],
['''ffn_layer_norm''', '''final_layer_norm'''],
['''kernel''', '''weight'''],
['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
['''embeddings.weights''', '''shared.weight'''],
]
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
for pegasus_name, hf_name in PATTERNS:
_UpperCAmelCase = k.replace(A , A )
return k
def UpperCAmelCase ( A : dict , A : dict ):
'''simple docstring'''
_UpperCAmelCase = DEFAULTS.copy()
cfg_kwargs.update(A )
_UpperCAmelCase = PegasusConfig(**A )
_UpperCAmelCase = PegasusForConditionalGeneration(A )
_UpperCAmelCase = torch_model.model.state_dict()
_UpperCAmelCase = {}
for k, v in tf_weights.items():
_UpperCAmelCase = rename_state_dict_key(A )
if new_k not in sd:
raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})' )
if "dense" in k or "proj" in new_k:
_UpperCAmelCase = v.T
_UpperCAmelCase = torch.tensor(A , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
# make sure embedding.padding_idx is respected
_UpperCAmelCase = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
_UpperCAmelCase = mapping['shared.weight']
_UpperCAmelCase = mapping['shared.weight']
_UpperCAmelCase = {k: torch.zeros_like(A ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**A )
_UpperCAmelCase , _UpperCAmelCase = torch_model.model.load_state_dict(A , strict=A )
_UpperCAmelCase = [
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], f'no matches found for the following tf keys {extra}'
return torch_model
def UpperCAmelCase ( A : Optional[Any]="./ckpt/aeslc/model.ckpt-32000" ):
'''simple docstring'''
_UpperCAmelCase = tf.train.list_variables(A )
_UpperCAmelCase = {}
_UpperCAmelCase = ['Adafactor', 'global_step']
for name, shape in tqdm(A , desc='converting tf checkpoint to dict' ):
_UpperCAmelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
_UpperCAmelCase = tf.train.load_variable(A , A )
_UpperCAmelCase = array
return tf_weights
def UpperCAmelCase ( A : str , A : str ):
'''simple docstring'''
_UpperCAmelCase = Path(A ).parent.name
_UpperCAmelCase = task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
_UpperCAmelCase = PegasusTokenizer.from_pretrained('sshleifer/pegasus' , model_max_length=A )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(A )
# convert model
_UpperCAmelCase = get_tf_weights_as_numpy(A )
_UpperCAmelCase = task_specific_params[f'summarization_{dataset}']
if dataset == "large":
_UpperCAmelCase = task_specific_params
_UpperCAmelCase = convert_pegasus(A , A )
torch_model.save_pretrained(A )
_UpperCAmelCase = torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(A , Path(A ) / 'pytorch_model.bin' )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
lowercase = parser.parse_args()
if args.save_dir is None:
lowercase = Path(args.tf_ckpt_path).parent.name
lowercase = os.path.join('''pegasus''', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 24 |
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = GPTSanJapaneseTokenizer
_UpperCAmelCase = False
_UpperCAmelCase = {'''do_clean_text''': False, '''add_prefix_space''': False}
def lowerCamelCase_ ( self ) -> List[str]:
super().setUp()
# fmt: off
_UpperCAmelCase = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
_UpperCAmelCase = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
_UpperCAmelCase = {'unk_token': '<unk>'}
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_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.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(snake_case ) )
def lowerCamelCase_ ( self , **snake_case ) -> Tuple:
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **snake_case )
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
_UpperCAmelCase = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
_UpperCAmelCase = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
_UpperCAmelCase , _UpperCAmelCase = self.get_input_output_texts(snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = tokenizer.decode(snake_case , clean_up_tokenization_spaces=snake_case )
return text, ids
def lowerCamelCase_ ( self ) -> int:
pass # TODO add if relevant
def lowerCamelCase_ ( self ) -> Dict:
pass # TODO add if relevant
def lowerCamelCase_ ( self ) -> List[Any]:
pass # TODO add if relevant
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = self.get_tokenizer()
# Testing tokenization
_UpperCAmelCase = 'こんにちは、世界。 こんばんは、㔺界。'
_UpperCAmelCase = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
_UpperCAmelCase = tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
# Testing conversion to ids without special tokens
_UpperCAmelCase = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(snake_case )
self.assertListEqual(snake_case , snake_case )
# Testing conversion to ids with special tokens
_UpperCAmelCase = tokens + [tokenizer.unk_token]
_UpperCAmelCase = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(snake_case )
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.get_tokenizer()
# Testing tokenization
_UpperCAmelCase = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
_UpperCAmelCase = 'こんにちは、、、、世界。こんばんは、、、、世界。'
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = tokenizer.decode(snake_case )
self.assertEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
_UpperCAmelCase = 'こんにちは、世界。'
_UpperCAmelCase = 'こんばんは、㔺界。😀'
_UpperCAmelCase = 'こんにちは、世界。こんばんは、世界。😀'
_UpperCAmelCase = tokenizer.encode(prefix_text + input_text )
_UpperCAmelCase = tokenizer.encode('' , prefix_text=prefix_text + input_text )
_UpperCAmelCase = tokenizer.encode(snake_case , prefix_text=snake_case )
_UpperCAmelCase = tokenizer.decode(snake_case )
_UpperCAmelCase = tokenizer.decode(snake_case )
_UpperCAmelCase = tokenizer.decode(snake_case )
self.assertEqual(snake_case , snake_case )
self.assertEqual(snake_case , snake_case )
self.assertEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
_UpperCAmelCase = 'こんにちは、世界。'
_UpperCAmelCase = 'こんばんは、㔺界。😀'
_UpperCAmelCase = len(tokenizer.encode(snake_case ) ) - 2
_UpperCAmelCase = len(tokenizer.encode(snake_case ) ) - 2
_UpperCAmelCase = [1] + [0] * (len_prefix + len_text + 1)
_UpperCAmelCase = [1] * (len_prefix + len_text + 1) + [0]
_UpperCAmelCase = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
_UpperCAmelCase = tokenizer(prefix_text + input_text ).token_type_ids
_UpperCAmelCase = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
_UpperCAmelCase = tokenizer(snake_case , prefix_text=snake_case ).token_type_ids
self.assertListEqual(snake_case , snake_case )
self.assertListEqual(snake_case , snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
_UpperCAmelCase = tokenizer.encode('あンいワ' )
_UpperCAmelCase = tokenizer.encode('' , prefix_text='あンいワ' )
_UpperCAmelCase = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(snake_case ) , tokenizer.decode(snake_case ) )
self.assertEqual(tokenizer.decode(snake_case ) , tokenizer.decode(snake_case ) )
self.assertNotEqual(snake_case , snake_case )
self.assertNotEqual(snake_case , snake_case )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
_UpperCAmelCase = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
_UpperCAmelCase = tokenizer(snake_case , padding=snake_case )
_UpperCAmelCase = tokenizer.batch_encode_plus(snake_case , padding=snake_case )
# fmt: off
_UpperCAmelCase = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
_UpperCAmelCase = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
_UpperCAmelCase = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , snake_case )
self.assertListEqual(x_token.token_type_ids , snake_case )
self.assertListEqual(x_token.attention_mask , snake_case )
self.assertListEqual(x_token_a.input_ids , snake_case )
self.assertListEqual(x_token_a.token_type_ids , snake_case )
self.assertListEqual(x_token_a.attention_mask , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
# Intentionally convert some words to accommodate character fluctuations unique to Japanese
pass
def lowerCamelCase_ ( self ) -> List[str]:
# tokenizer has no padding token
pass
| 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24 | 1 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
lowercase = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
}
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
with open(os.path.dirname(A ) + '/grid.txt' ) as f:
_UpperCAmelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(A ) for x in f.readline().split()] )
_UpperCAmelCase = 0
# right
for i in range(20 ):
for j in range(17 ):
_UpperCAmelCase = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
_UpperCAmelCase = temp
# down
for i in range(17 ):
for j in range(20 ):
_UpperCAmelCase = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
_UpperCAmelCase = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
_UpperCAmelCase = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
_UpperCAmelCase = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
_UpperCAmelCase = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
_UpperCAmelCase = temp
return maximum
if __name__ == "__main__":
print(solution())
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
lowercase = range(2, 20 + 1)
lowercase = [10**k for k in range(ks[-1] + 1)]
lowercase = {}
def UpperCAmelCase ( A : int , A : Tuple , A : List[str] , A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = sum(a_i[j] for j in range(A , len(A ) ) )
_UpperCAmelCase = sum(a_i[j] * base[j] for j in range(min(len(A ) , A ) ) )
_UpperCAmelCase , _UpperCAmelCase = 0, 0
_UpperCAmelCase = n - i
_UpperCAmelCase = memo.get(A )
if sub_memo is not None:
_UpperCAmelCase = sub_memo.get(A )
if jumps is not None and len(A ) > 0:
# find and make the largest jump without going over
_UpperCAmelCase = -1
for _k in range(len(A ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
_UpperCAmelCase = _k
break
if max_jump >= 0:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = jumps[max_jump]
# since the difference between jumps is cached, add c
_UpperCAmelCase = diff + c
for j in range(min(A , len(A ) ) ):
_UpperCAmelCase , _UpperCAmelCase = divmod(A , 10 )
if new_c > 0:
add(A , A , A )
else:
_UpperCAmelCase = []
else:
_UpperCAmelCase = {c: []}
_UpperCAmelCase = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
_UpperCAmelCase , _UpperCAmelCase = next_term(A , k - 1 , i + dn , A )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
_UpperCAmelCase , _UpperCAmelCase = compute(A , A , i + dn , A )
diff += _diff
dn += terms_jumped
_UpperCAmelCase = sub_memo[c]
# keep jumps sorted by # of terms skipped
_UpperCAmelCase = 0
while j < len(A ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(A , (diff, dn, k) )
return (diff, dn)
def UpperCAmelCase ( A : Optional[int] , A : Any , A : Tuple , A : Optional[int] ):
'''simple docstring'''
if i >= n:
return 0, i
if k > len(A ):
a_i.extend([0 for _ in range(k - len(A ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
_UpperCAmelCase = i
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = 0, 0, 0
for j in range(len(A ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
_UpperCAmelCase = ds_c + ds_b
diff += addend
_UpperCAmelCase = 0
for j in range(A ):
_UpperCAmelCase = a_i[j] + addend
_UpperCAmelCase , _UpperCAmelCase = divmod(A , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(A , A , A )
return diff, i - start_i
def UpperCAmelCase ( A : Tuple , A : Dict , A : List[Any] ):
'''simple docstring'''
for j in range(A , len(A ) ):
_UpperCAmelCase = digits[j] + addend
if s >= 10:
_UpperCAmelCase , _UpperCAmelCase = divmod(A , 10 )
_UpperCAmelCase = addend // 10 + quotient
else:
_UpperCAmelCase = s
_UpperCAmelCase = addend // 10
if addend == 0:
break
while addend > 0:
_UpperCAmelCase , _UpperCAmelCase = divmod(A , 10 )
digits.append(A )
def UpperCAmelCase ( A : int = 10**15 ):
'''simple docstring'''
_UpperCAmelCase = [1]
_UpperCAmelCase = 1
_UpperCAmelCase = 0
while True:
_UpperCAmelCase , _UpperCAmelCase = next_term(A , 20 , i + dn , A )
dn += terms_jumped
if dn == n - i:
break
_UpperCAmelCase = 0
for j in range(len(A ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : list[list[int]] , A : int , A : int , A : set ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = len(A ), len(grid[0] )
if (
min(A , A ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
_UpperCAmelCase = 0
count += depth_first_search(A , row + 1 , A , A )
count += depth_first_search(A , row - 1 , A , A )
count += depth_first_search(A , A , col + 1 , A )
count += depth_first_search(A , A , col - 1 , A )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask
import transformers
from transformers import (
AutoConfig,
AutoModelForTokenClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowercase = logging.getLogger(__name__)
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_UpperCAmelCase = field(
default='''NER''', metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_UpperCAmelCase = field(default=A, metadata={'''help''': '''Set this flag to use fast tokenization.'''} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, )
@dataclass
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = field(
metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}, )
_UpperCAmelCase = 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.'''
)
}, )
_UpperCAmelCase = field(
default=A, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
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.
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
' --overwrite_output_dir to overcome.' )
_UpperCAmelCase = import_module('tasks' )
try:
_UpperCAmelCase = getattr(A , model_args.task_type )
_UpperCAmelCase = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}' )
# 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.local_rank != -1 ) , training_args.fpaa , )
# 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()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('Training/evaluation parameters %s' , A )
# Set seed
set_seed(training_args.seed )
# Prepare CONLL-2003 task
_UpperCAmelCase = token_classification_task.get_labels(data_args.labels )
_UpperCAmelCase = dict(enumerate(A ) )
_UpperCAmelCase = len(A )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_UpperCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=A , idalabel=A , labelaid={label: i for i, label in enumerate(A )} , cache_dir=model_args.cache_dir , )
_UpperCAmelCase = 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 , use_fast=model_args.use_fast , )
_UpperCAmelCase = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=A , cache_dir=model_args.cache_dir , )
# Get datasets
_UpperCAmelCase = (
TokenClassificationDataset(
token_classification_task=A , data_dir=data_args.data_dir , tokenizer=A , labels=A , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
_UpperCAmelCase = (
TokenClassificationDataset(
token_classification_task=A , data_dir=data_args.data_dir , tokenizer=A , labels=A , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def align_predictions(A : np.ndarray , A : np.ndarray ) -> Tuple[List[int], List[int]]:
_UpperCAmelCase = np.argmax(A , axis=2 )
_UpperCAmelCase , _UpperCAmelCase = preds.shape
_UpperCAmelCase = [[] for _ in range(A )]
_UpperCAmelCase = [[] for _ in range(A )]
for i in range(A ):
for j in range(A ):
if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
out_label_list[i].append(label_map[label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
return preds_list, out_label_list
def compute_metrics(A : EvalPrediction ) -> Dict:
_UpperCAmelCase , _UpperCAmelCase = align_predictions(p.predictions , p.label_ids )
return {
"accuracy_score": accuracy_score(A , A ),
"precision": precision_score(A , A ),
"recall": recall_score(A , A ),
"f1": fa_score(A , A ),
}
# Data collator
_UpperCAmelCase = DataCollatorWithPadding(A , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
_UpperCAmelCase = Trainer(
model=A , args=A , train_dataset=A , eval_dataset=A , compute_metrics=A , data_collator=A , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_process_zero():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
_UpperCAmelCase = {}
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_UpperCAmelCase = trainer.evaluate()
_UpperCAmelCase = os.path.join(training_args.output_dir , 'eval_results.txt' )
if trainer.is_world_process_zero():
with open(A , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key, value in result.items():
logger.info(' %s = %s' , A , A )
writer.write('%s = %s\n' % (key, value) )
results.update(A )
# Predict
if training_args.do_predict:
_UpperCAmelCase = TokenClassificationDataset(
token_classification_task=A , data_dir=data_args.data_dir , tokenizer=A , labels=A , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = trainer.predict(A )
_UpperCAmelCase , _UpperCAmelCase = align_predictions(A , A )
_UpperCAmelCase = os.path.join(training_args.output_dir , 'test_results.txt' )
if trainer.is_world_process_zero():
with open(A , 'w' ) as writer:
for key, value in metrics.items():
logger.info(' %s = %s' , A , A )
writer.write('%s = %s\n' % (key, value) )
# Save predictions
_UpperCAmelCase = os.path.join(training_args.output_dir , 'test_predictions.txt' )
if trainer.is_world_process_zero():
with open(A , 'w' ) as writer:
with open(os.path.join(data_args.data_dir , 'test.txt' ) , 'r' ) as f:
token_classification_task.write_predictions_to_file(A , A , A )
return results
def UpperCAmelCase ( A : str ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : List[str] ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priority of each operator
_UpperCAmelCase = len(A ) if (len(A ) > 7) else 7
# Print table header for output
print(
'Symbol'.center(8 ) , 'Stack'.center(A ) , 'Postfix'.center(A ) , sep=' | ' , )
print('-' * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(A ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(A ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(A ) == 0:
stack.append(A ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(A ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(A ) # push x to stack
print(
x.center(8 ) , (''.join(A )).ljust(A ) , (''.join(A )).ljust(A ) , sep=' | ' , ) # Output in tabular format
while len(A ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
' '.center(8 ) , (''.join(A )).ljust(A ) , (''.join(A )).ljust(A ) , sep=' | ' , ) # Output in tabular format
return "".join(A ) # return Postfix as str
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = list(infix[::-1] ) # reverse the infix equation
for i in range(len(A ) ):
if infix[i] == "(":
_UpperCAmelCase = ')' # change "(" to ")"
elif infix[i] == ")":
_UpperCAmelCase = '(' # change ")" to "("
return (infix_2_postfix(''.join(A ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
lowercase = input('''\nEnter an Infix Equation = ''') # Input an Infix equation
lowercase = ''''''.join(Infix.split()) # Remove spaces from the input
print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')
| 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24 | 1 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24 | 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 json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def UpperCAmelCase ( A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = botoa.client('iam' )
_UpperCAmelCase = {
'Version': '2012-10-17',
'Statement': [
{'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=A , AssumeRolePolicyDocument=json.dumps(A , indent=2 ) )
_UpperCAmelCase = {
'Version': '2012-10-17',
'Statement': [
{
'Effect': 'Allow',
'Action': [
'sagemaker:*',
'ecr:GetDownloadUrlForLayer',
'ecr:BatchGetImage',
'ecr:BatchCheckLayerAvailability',
'ecr:GetAuthorizationToken',
'cloudwatch:PutMetricData',
'cloudwatch:GetMetricData',
'cloudwatch:GetMetricStatistics',
'cloudwatch:ListMetrics',
'logs:CreateLogGroup',
'logs:CreateLogStream',
'logs:DescribeLogStreams',
'logs:PutLogEvents',
'logs:GetLogEvents',
's3:CreateBucket',
's3:ListBucket',
's3:GetBucketLocation',
's3:GetObject',
's3:PutObject',
],
'Resource': '*',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=A , PolicyName=f'{role_name}_policy_permission' , PolicyDocument=json.dumps(A , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f'role {role_name} already exists. Using existing one' )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = botoa.client('iam' )
return iam_client.get_role(RoleName=A )["Role"]["Arn"]
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = _ask_options(
'How do you want to authorize?' , ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '] , A , )
_UpperCAmelCase = None
if credentials_configuration == 0:
_UpperCAmelCase = _ask_field('Enter your AWS Profile name: [default] ' , default='default' )
_UpperCAmelCase = aws_profile
else:
print(
'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
'`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`' )
_UpperCAmelCase = _ask_field('AWS Access Key ID: ' )
_UpperCAmelCase = aws_access_key_id
_UpperCAmelCase = _ask_field('AWS Secret Access Key: ' )
_UpperCAmelCase = aws_secret_access_key
_UpperCAmelCase = _ask_field('Enter your AWS Region: [us-east-1]' , default='us-east-1' )
_UpperCAmelCase = aws_region
_UpperCAmelCase = _ask_options(
'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?' , ['Provide IAM Role name', 'Create new IAM role using credentials'] , A , )
if role_management == 0:
_UpperCAmelCase = _ask_field('Enter your IAM role name: ' )
else:
_UpperCAmelCase = 'accelerate_sagemaker_execution_role'
print(f'Accelerate will create an iam role "{iam_role_name}" using the provided credentials' )
_create_iam_role_for_sagemaker(A )
_UpperCAmelCase = _ask_field(
'Do you want to use custom Docker image? [yes/NO]: ' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
_UpperCAmelCase = None
if is_custom_docker_image:
_UpperCAmelCase = _ask_field('Enter your Docker image: ' , lambda A : str(A ).lower() )
_UpperCAmelCase = _ask_field(
'Do you want to provide SageMaker input channels with data locations? [yes/NO]: ' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
_UpperCAmelCase = None
if is_sagemaker_inputs_enabled:
_UpperCAmelCase = _ask_field(
'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ' , lambda A : str(A ).lower() , )
_UpperCAmelCase = _ask_field(
'Do you want to enable SageMaker metrics? [yes/NO]: ' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
_UpperCAmelCase = None
if is_sagemaker_metrics_enabled:
_UpperCAmelCase = _ask_field(
'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ' , lambda A : str(A ).lower() , )
_UpperCAmelCase = _ask_options(
'What is the distributed mode?' , ['No distributed training', 'Data parallelism'] , _convert_sagemaker_distributed_mode , )
_UpperCAmelCase = {}
_UpperCAmelCase = _ask_field(
'Do you wish to optimize your script with torch dynamo?[yes/NO]:' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
if use_dynamo:
_UpperCAmelCase = 'dynamo_'
_UpperCAmelCase = _ask_options(
'Which dynamo backend would you like to use?' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
_UpperCAmelCase = _ask_field(
'Do you want to customize the defaults sent to torch.compile? [yes/NO]: ' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
if use_custom_options:
_UpperCAmelCase = _ask_options(
'Which mode do you want to use?' , A , lambda A : TORCH_DYNAMO_MODES[int(A )] , default='default' , )
_UpperCAmelCase = _ask_field(
'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
_UpperCAmelCase = _ask_field(
'Do you want to enable dynamic shape tracing? [yes/NO]: ' , _convert_yes_no_to_bool , default=A , error_message='Please enter yes or no.' , )
_UpperCAmelCase = 'Which EC2 instance type you want to use for your training?'
if distributed_type != SageMakerDistributedType.NO:
_UpperCAmelCase = _ask_options(
A , A , lambda A : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(A )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
_UpperCAmelCase = _ask_field(A , lambda A : str(A ).lower() , default='ml.p3.2xlarge' )
_UpperCAmelCase = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
_UpperCAmelCase = _ask_field(
'How many machines do you want use? [1]: ' , A , default=1 , )
_UpperCAmelCase = _ask_options(
'Do you wish to use FP16 or BF16 (mixed precision)?' , ['no', 'fp16', 'bf16', 'fp8'] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.' )
return SageMakerConfig(
image_uri=A , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=A , use_cpu=A , dynamo_config=A , eca_instance_type=A , profile=A , region=A , iam_role_name=A , mixed_precision=A , num_machines=A , sagemaker_inputs_file=A , sagemaker_metrics_file=A , )
| 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
lowercase = logging.get_logger(__name__)
@add_end_docstrings(
A, R'''
top_k (`int`, defaults to 5):
The number of predictions to return.
targets (`str` or `List[str]`, *optional*):
When passed, the model will limit the scores to the passed targets instead of looking up in the whole
vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
token will be used (with a warning, and that might be slower).
''', )
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> np.ndarray:
if self.framework == "tf":
_UpperCAmelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
_UpperCAmelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case )
else:
raise ValueError('Unsupported framework' )
return masked_index
def lowerCamelCase_ ( self , snake_case ) -> np.ndarray:
_UpperCAmelCase = self.get_masked_index(snake_case )
_UpperCAmelCase = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
'fill-mask' , self.model.base_model_prefix , f'No mask_token ({self.tokenizer.mask_token}) found on the input' , )
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
if isinstance(snake_case , snake_case ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input['input_ids'][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case=None , **snake_case ) -> Dict[str, GenericTensor]:
if return_tensors is None:
_UpperCAmelCase = self.framework
_UpperCAmelCase = self.tokenizer(snake_case , return_tensors=snake_case )
self.ensure_exactly_one_mask_token(snake_case )
return model_inputs
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
_UpperCAmelCase = self.model(**snake_case )
_UpperCAmelCase = model_inputs['input_ids']
return model_outputs
def lowerCamelCase_ ( self , snake_case , snake_case=5 , snake_case=None ) -> Optional[Any]:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
_UpperCAmelCase = target_ids.shape[0]
_UpperCAmelCase = model_outputs['input_ids'][0]
_UpperCAmelCase = model_outputs['logits']
if self.framework == "tf":
_UpperCAmelCase = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
_UpperCAmelCase = outputs.numpy()
_UpperCAmelCase = outputs[0, masked_index, :]
_UpperCAmelCase = stable_softmax(snake_case , axis=-1 )
if target_ids is not None:
_UpperCAmelCase = tf.gather_nd(tf.squeeze(snake_case , 0 ) , target_ids.reshape(-1 , 1 ) )
_UpperCAmelCase = tf.expand_dims(snake_case , 0 )
_UpperCAmelCase = tf.math.top_k(snake_case , k=snake_case )
_UpperCAmelCase , _UpperCAmelCase = topk.values.numpy(), topk.indices.numpy()
else:
_UpperCAmelCase = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=snake_case ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
_UpperCAmelCase = outputs[0, masked_index, :]
_UpperCAmelCase = logits.softmax(dim=-1 )
if target_ids is not None:
_UpperCAmelCase = probs[..., target_ids]
_UpperCAmelCase , _UpperCAmelCase = probs.topk(snake_case )
_UpperCAmelCase = []
_UpperCAmelCase = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
_UpperCAmelCase = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
_UpperCAmelCase = input_ids.numpy().copy()
if target_ids is not None:
_UpperCAmelCase = target_ids[p].tolist()
_UpperCAmelCase = p
# Filter padding out:
_UpperCAmelCase = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
_UpperCAmelCase = self.tokenizer.decode(snake_case , skip_special_tokens=snake_case )
_UpperCAmelCase = {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p] ), 'sequence': sequence}
row.append(snake_case )
result.append(snake_case )
if single_mask:
return result[0]
return result
def lowerCamelCase_ ( self , snake_case , snake_case=None ) -> Dict:
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = [targets]
try:
_UpperCAmelCase = self.tokenizer.get_vocab()
except Exception:
_UpperCAmelCase = {}
_UpperCAmelCase = []
for target in targets:
_UpperCAmelCase = vocab.get(snake_case , snake_case )
if id_ is None:
_UpperCAmelCase = self.tokenizer(
snake_case , add_special_tokens=snake_case , return_attention_mask=snake_case , return_token_type_ids=snake_case , max_length=1 , truncation=snake_case , )['input_ids']
if len(snake_case ) == 0:
logger.warning(
f'The specified target token `{target}` does not exist in the model vocabulary. '
'We cannot replace it with anything meaningful, ignoring it' )
continue
_UpperCAmelCase = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
f'The specified target token `{target}` does not exist in the model vocabulary. '
f'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`.' )
target_ids.append(id_ )
_UpperCAmelCase = list(set(snake_case ) )
if len(snake_case ) == 0:
raise ValueError('At least one target must be provided when passed.' )
_UpperCAmelCase = np.array(snake_case )
return target_ids
def lowerCamelCase_ ( self , snake_case=None , snake_case=None ) -> Optional[int]:
_UpperCAmelCase = {}
if targets is not None:
_UpperCAmelCase = self.get_target_ids(snake_case , snake_case )
_UpperCAmelCase = target_ids
if top_k is not None:
_UpperCAmelCase = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
'fill-mask' , self.model.base_model_prefix , 'The tokenizer does not define a `mask_token`.' )
return {}, {}, postprocess_params
def __call__( self , snake_case , *snake_case , **snake_case ) -> int:
_UpperCAmelCase = super().__call__(snake_case , **snake_case )
if isinstance(snake_case , snake_case ) and len(snake_case ) == 1:
return outputs[0]
return outputs
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24 | 1 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = ['''vqvae''']
def __init__( self , snake_case , snake_case , snake_case , snake_case , ) -> List[Any]:
super().__init__()
self.register_modules(unet=snake_case , scheduler=snake_case , mel=snake_case , vqvae=snake_case )
def lowerCamelCase_ ( self ) -> int:
return 50 if isinstance(self.scheduler , snake_case ) else 1000
@torch.no_grad()
def __call__( self , snake_case = 1 , snake_case = None , snake_case = None , snake_case = 0 , snake_case = 0 , snake_case = None , snake_case = None , snake_case = 0 , snake_case = 0 , snake_case = None , snake_case = 0 , snake_case = None , snake_case = None , snake_case=True , ) -> Union[
Union[AudioPipelineOutput, ImagePipelineOutput],
Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
]:
_UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(snake_case )
_UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
_UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
_UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=snake_case , device=self.device , )
_UpperCAmelCase = noise
_UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(snake_case , snake_case )
_UpperCAmelCase = self.mel.audio_slice_to_image(snake_case )
_UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='uint8' ).reshape(
(input_image.height, input_image.width) )
_UpperCAmelCase = (input_image / 255) * 2 - 1
_UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
_UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(snake_case , 0 ) ).latent_dist.sample(
generator=snake_case )[0]
_UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
_UpperCAmelCase = self.scheduler.add_noise(snake_case , snake_case , self.scheduler.timesteps[start_step - 1] )
_UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
_UpperCAmelCase = int(mask_start_secs * pixels_per_second )
_UpperCAmelCase = int(mask_end_secs * pixels_per_second )
_UpperCAmelCase = self.scheduler.add_noise(snake_case , snake_case , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , snake_case ):
_UpperCAmelCase = self.unet(snake_case , snake_case , snake_case )['sample']
else:
_UpperCAmelCase = self.unet(snake_case , snake_case )['sample']
if isinstance(self.scheduler , snake_case ):
_UpperCAmelCase = self.scheduler.step(
model_output=snake_case , timestep=snake_case , sample=snake_case , eta=snake_case , generator=snake_case , )['prev_sample']
else:
_UpperCAmelCase = self.scheduler.step(
model_output=snake_case , timestep=snake_case , sample=snake_case , generator=snake_case , )['prev_sample']
if mask is not None:
if mask_start > 0:
_UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
_UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
_UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
_UpperCAmelCase = self.vqvae.decode(snake_case )['sample']
_UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
_UpperCAmelCase = (images * 255).round().astype('uint8' )
_UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(snake_case , mode='RGB' ).convert('L' ) for _ in images) )
_UpperCAmelCase = [self.mel.image_to_audio(snake_case ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(snake_case )[:, np.newaxis, :] ) , **ImagePipelineOutput(snake_case ) )
@torch.no_grad()
def lowerCamelCase_ ( self , snake_case , snake_case = 50 ) -> np.ndarray:
assert isinstance(self.scheduler , snake_case )
self.scheduler.set_timesteps(snake_case )
_UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='uint8' ).reshape((1, image.height, image.width) ) for image in images] )
_UpperCAmelCase = (sample / 255) * 2 - 1
_UpperCAmelCase = torch.Tensor(snake_case ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
_UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
_UpperCAmelCase = self.scheduler.alphas_cumprod[t]
_UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
_UpperCAmelCase = 1 - alpha_prod_t
_UpperCAmelCase = self.unet(snake_case , snake_case )['sample']
_UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
_UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
_UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def lowerCamelCase_ ( snake_case , snake_case , snake_case ) -> torch.Tensor:
_UpperCAmelCase = acos(torch.dot(torch.flatten(snake_case ) , torch.flatten(snake_case ) ) / torch.norm(snake_case ) / torch.norm(snake_case ) )
return sin((1 - alpha) * theta ) * xa / sin(snake_case ) + sin(alpha * theta ) * xa / sin(snake_case )
| 24 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def UpperCAmelCase ( A : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*A )
if __name__ == "__main__":
lowercase = 4
lowercase = 2
lowercase = generate_all_combinations(n, k)
print_all_state(total_list)
| 24 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
lowercase = {
'''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
# See all Nat models at https://huggingface.co/models?filter=nat
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''nat'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=4 , snake_case=3 , snake_case=64 , snake_case=[3, 4, 6, 5] , snake_case=[2, 4, 8, 16] , snake_case=7 , snake_case=3.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=0.02 , snake_case=1E-5 , snake_case=0.0 , snake_case=None , snake_case=None , **snake_case , ) -> List[str]:
super().__init__(**snake_case )
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = kernel_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
| 24 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24 | 1 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowercase = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_std''': True,
'''scale''': 0.1,
'''eta''': 0.0,
'''t_grad_cutoff''': 2,
'''device''': '''cpu''',
}
if __name__ == "__main__":
lowercase = '''hopper-medium-v2'''
lowercase = gym.make(env_name)
lowercase = ValueGuidedRLPipeline.from_pretrained(
'''bglick13/hopper-medium-v2-value-function-hor32''',
env=env,
)
env.seed(0)
lowercase = env.reset()
lowercase = 0
lowercase = 0
lowercase = 10_00
lowercase = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
lowercase = pipeline(obs, planning_horizon=32)
# execute action in environment
lowercase , lowercase , lowercase , lowercase = env.step(denorm_actions)
lowercase = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F'''Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'''
F''' {total_score}'''
)
# save observations for rendering
rollout.append(next_observation.copy())
lowercase = next_observation
except KeyboardInterrupt:
pass
print(F'''Total reward: {total_reward}''')
| 24 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''marian'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self , snake_case=58101 , snake_case=None , snake_case=1024 , snake_case=12 , snake_case=4096 , snake_case=16 , snake_case=12 , snake_case=4096 , snake_case=16 , snake_case=0.0 , snake_case=0.0 , snake_case=True , snake_case=True , snake_case="gelu" , snake_case=1024 , snake_case=0.1 , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=58100 , snake_case=False , snake_case=58100 , snake_case=0 , snake_case=0 , snake_case=True , **snake_case , ) -> Dict:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = decoder_vocab_size or vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = d_model
_UpperCAmelCase = encoder_ffn_dim
_UpperCAmelCase = encoder_layers
_UpperCAmelCase = encoder_attention_heads
_UpperCAmelCase = decoder_ffn_dim
_UpperCAmelCase = decoder_layers
_UpperCAmelCase = decoder_attention_heads
_UpperCAmelCase = dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = activation_dropout
_UpperCAmelCase = activation_function
_UpperCAmelCase = init_std
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = decoder_layerdrop
_UpperCAmelCase = use_cache
_UpperCAmelCase = encoder_layers
_UpperCAmelCase = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCAmelCase = share_encoder_decoder_embeddings
super().__init__(
pad_token_id=snake_case , eos_token_id=snake_case , is_encoder_decoder=snake_case , decoder_start_token_id=snake_case , forced_eos_token_id=snake_case , **snake_case , )
class lowercase__ ( A ):
'''simple docstring'''
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
_UpperCAmelCase = {0: 'batch'}
_UpperCAmelCase = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'decoder_sequence'}
_UpperCAmelCase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(snake_case , direction='inputs' )
elif self.task == "causal-lm":
# TODO: figure this case out.
_UpperCAmelCase = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
] )
if self.use_past:
_UpperCAmelCase , _UpperCAmelCase = self.num_layers
for i in range(snake_case ):
_UpperCAmelCase = {0: 'batch', 2: 'past_sequence + sequence'}
_UpperCAmelCase = {0: 'batch', 2: 'past_sequence + sequence'}
else:
_UpperCAmelCase = OrderedDict(
[
('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase = super().outputs
else:
_UpperCAmelCase = super(snake_case , self ).outputs
if self.use_past:
_UpperCAmelCase , _UpperCAmelCase = self.num_layers
for i in range(snake_case ):
_UpperCAmelCase = {0: 'batch', 2: 'past_sequence + sequence'}
_UpperCAmelCase = {0: 'batch', 2: 'past_sequence + sequence'}
return common_outputs
def lowerCamelCase_ ( self , snake_case , snake_case = -1 , snake_case = -1 , snake_case = False , snake_case = None , ) -> Mapping[str, Any]:
_UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case , snake_case , snake_case , snake_case , snake_case )
# Generate decoder inputs
_UpperCAmelCase = seq_length if not self.use_past else 1
_UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case , snake_case , snake_case , snake_case , snake_case )
_UpperCAmelCase = {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
_UpperCAmelCase = dict(**snake_case , **snake_case )
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_UpperCAmelCase , _UpperCAmelCase = common_inputs['input_ids'].shape
_UpperCAmelCase = common_inputs['decoder_input_ids'].shape[1]
_UpperCAmelCase , _UpperCAmelCase = self.num_attention_heads
_UpperCAmelCase = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_UpperCAmelCase = decoder_seq_length + 3
_UpperCAmelCase = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
_UpperCAmelCase = torch.cat(
[common_inputs['decoder_attention_mask'], torch.ones(snake_case , snake_case )] , dim=1 )
_UpperCAmelCase = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
_UpperCAmelCase , _UpperCAmelCase = self.num_layers
_UpperCAmelCase = min(snake_case , snake_case )
_UpperCAmelCase = max(snake_case , snake_case ) - min_num_layers
_UpperCAmelCase = 'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'
for _ in range(snake_case ):
common_inputs["past_key_values"].append(
(
torch.zeros(snake_case ),
torch.zeros(snake_case ),
torch.zeros(snake_case ),
torch.zeros(snake_case ),
) )
# TODO: test this.
_UpperCAmelCase = encoder_shape if remaining_side_name == 'encoder' else decoder_shape
for _ in range(snake_case , snake_case ):
common_inputs["past_key_values"].append((torch.zeros(snake_case ), torch.zeros(snake_case )) )
return common_inputs
def lowerCamelCase_ ( self , snake_case , snake_case = -1 , snake_case = -1 , snake_case = False , snake_case = None , ) -> Mapping[str, Any]:
_UpperCAmelCase = self._generate_dummy_inputs_for_encoder_and_decoder(
snake_case , snake_case , snake_case , snake_case , snake_case )
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_UpperCAmelCase , _UpperCAmelCase = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_UpperCAmelCase = seqlen + 2
_UpperCAmelCase , _UpperCAmelCase = self.num_layers
_UpperCAmelCase , _UpperCAmelCase = self.num_attention_heads
_UpperCAmelCase = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
_UpperCAmelCase = common_inputs['attention_mask'].dtype
_UpperCAmelCase = torch.cat(
[common_inputs['attention_mask'], torch.ones(snake_case , snake_case , dtype=snake_case )] , dim=1 )
_UpperCAmelCase = [
(torch.zeros(snake_case ), torch.zeros(snake_case )) for _ in range(snake_case )
]
return common_inputs
def lowerCamelCase_ ( self , snake_case , snake_case = -1 , snake_case = -1 , snake_case = False , snake_case = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
_UpperCAmelCase = tokenizer.num_special_tokens_to_add(snake_case )
_UpperCAmelCase = compute_effective_axis_dimension(
snake_case , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=snake_case )
# Generate dummy inputs according to compute batch and sequence
_UpperCAmelCase = [' '.join([tokenizer.unk_token] ) * seq_length] * batch_size
_UpperCAmelCase = dict(tokenizer(snake_case , return_tensors=snake_case ) )
return common_inputs
def lowerCamelCase_ ( self , snake_case , snake_case = -1 , snake_case = -1 , snake_case = False , snake_case = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
snake_case , batch_size=snake_case , seq_length=snake_case , is_pair=snake_case , framework=snake_case )
else:
_UpperCAmelCase = self._generate_dummy_inputs_for_causal_lm(
snake_case , batch_size=snake_case , seq_length=snake_case , is_pair=snake_case , framework=snake_case )
return common_inputs
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case ) -> str:
if self.task in ["default", "seq2seq-lm"]:
_UpperCAmelCase = super()._flatten_past_key_values_(snake_case , snake_case , snake_case , snake_case )
else:
_UpperCAmelCase = super(snake_case , self )._flatten_past_key_values_(
snake_case , snake_case , snake_case , snake_case )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24 | 1 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowercase = (7_20, 12_80) # Height, Width
lowercase = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowercase = 1 / 1_00
lowercase = ''''''
lowercase = ''''''
lowercase = ''''''
lowercase = 2_50
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_dataset(A , A )
for index in range(A ):
_UpperCAmelCase = random.sample(range(len(A ) ) , 4 )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = update_image_and_anno(
A , A , A , A , A , filter_scale=A , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_UpperCAmelCase = random_chars(32 )
_UpperCAmelCase = path.split(os.sep )[-1].rsplit('.' , 1 )[0]
_UpperCAmelCase = f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
cva.imwrite(f'{file_root}.jpg' , A , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}' )
_UpperCAmelCase = []
for anno in new_annos:
_UpperCAmelCase = anno[3] - anno[1]
_UpperCAmelCase = anno[4] - anno[2]
_UpperCAmelCase = anno[1] + width / 2
_UpperCAmelCase = anno[2] + height / 2
_UpperCAmelCase = f'{anno[0]} {x_center} {y_center} {width} {height}'
annos_list.append(A )
with open(f'{file_root}.txt' , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def UpperCAmelCase ( A : str , A : str ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = []
for label_file in glob.glob(os.path.join(A , '*.txt' ) ):
_UpperCAmelCase = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(A ) as in_file:
_UpperCAmelCase = in_file.readlines()
_UpperCAmelCase = os.path.join(A , f'{label_name}.jpg' )
_UpperCAmelCase = []
for obj_list in obj_lists:
_UpperCAmelCase = obj_list.rstrip('\n' ).split(' ' )
_UpperCAmelCase = float(obj[1] ) - float(obj[3] ) / 2
_UpperCAmelCase = float(obj[2] ) - float(obj[4] ) / 2
_UpperCAmelCase = float(obj[1] ) + float(obj[3] ) / 2
_UpperCAmelCase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(A )
labels.append(A )
return img_paths, labels
def UpperCAmelCase ( A : list , A : list , A : list[int] , A : tuple[int, int] , A : tuple[float, float] , A : float = 0.0 , ):
'''simple docstring'''
_UpperCAmelCase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_UpperCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_UpperCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_UpperCAmelCase = int(scale_x * output_size[1] )
_UpperCAmelCase = int(scale_y * output_size[0] )
_UpperCAmelCase = []
_UpperCAmelCase = []
for i, index in enumerate(A ):
_UpperCAmelCase = all_img_list[index]
path_list.append(A )
_UpperCAmelCase = all_annos[index]
_UpperCAmelCase = cva.imread(A )
if i == 0: # top-left
_UpperCAmelCase = cva.resize(A , (divid_point_x, divid_point_y) )
_UpperCAmelCase = img
for bbox in img_annos:
_UpperCAmelCase = bbox[1] * scale_x
_UpperCAmelCase = bbox[2] * scale_y
_UpperCAmelCase = bbox[3] * scale_x
_UpperCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_UpperCAmelCase = cva.resize(A , (output_size[1] - divid_point_x, divid_point_y) )
_UpperCAmelCase = img
for bbox in img_annos:
_UpperCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_UpperCAmelCase = bbox[2] * scale_y
_UpperCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_UpperCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_UpperCAmelCase = cva.resize(A , (divid_point_x, output_size[0] - divid_point_y) )
_UpperCAmelCase = img
for bbox in img_annos:
_UpperCAmelCase = bbox[1] * scale_x
_UpperCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_UpperCAmelCase = bbox[3] * scale_x
_UpperCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_UpperCAmelCase = cva.resize(
A , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_UpperCAmelCase = img
for bbox in img_annos:
_UpperCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_UpperCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_UpperCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_UpperCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_UpperCAmelCase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def UpperCAmelCase ( A : int ):
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
_UpperCAmelCase = ascii_lowercase + digits
return "".join(random.choice(A ) for _ in range(A ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24 | 1 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def UpperCAmelCase ( A : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*A )
if __name__ == "__main__":
lowercase = 4
lowercase = 2
lowercase = generate_all_combinations(n, k)
print_all_state(total_list)
| 24 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = 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
_UpperCAmelCase = 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 UpperCAmelCase ( A : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 24 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def UpperCAmelCase ( A : float , A : float ):
'''simple docstring'''
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative' )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=2 , snake_case=True , snake_case=False , snake_case=10 , snake_case=3 , snake_case=32 * 4 , snake_case=32 * 6 , snake_case=4 , snake_case=32 , ) -> Optional[Any]:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = is_training
_UpperCAmelCase = use_auxiliary_loss
_UpperCAmelCase = num_queries
_UpperCAmelCase = num_channels
_UpperCAmelCase = min_size
_UpperCAmelCase = max_size
_UpperCAmelCase = num_labels
_UpperCAmelCase = mask_feature_size
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
snake_case )
_UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=snake_case )
_UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=snake_case ) > 0.5
).float()
_UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=snake_case ) > 0.5).long()
_UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowerCamelCase_ ( self ) -> Dict:
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = output.encoder_hidden_states
_UpperCAmelCase = output.pixel_decoder_hidden_states
_UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(snake_case ) , config.decoder_config.decoder_layers )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case=False ) -> int:
with torch.no_grad():
_UpperCAmelCase = MaskFormerModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(pixel_values=snake_case , pixel_mask=snake_case )
_UpperCAmelCase = model(snake_case , output_hidden_states=snake_case )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(snake_case , snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case ) -> str:
_UpperCAmelCase = MaskFormerForInstanceSegmentation(config=snake_case )
model.to(snake_case )
model.eval()
def comm_check_on_output(snake_case ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
_UpperCAmelCase = model(pixel_values=snake_case , pixel_mask=snake_case )
_UpperCAmelCase = model(snake_case )
comm_check_on_output(snake_case )
_UpperCAmelCase = model(
pixel_values=snake_case , pixel_mask=snake_case , mask_labels=snake_case , class_labels=snake_case )
comm_check_on_output(snake_case )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = MaskFormerModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case , **snake_case , output_hidden_states=snake_case )
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*snake_case )
@unittest.skip(reason='MaskFormer does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='MaskFormer does not have a get_input_embeddings method' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='MaskFormer is not a generative model' )
def lowerCamelCase_ ( self ) -> Any:
pass
@unittest.skip(reason='MaskFormer does not use token embeddings' )
def lowerCamelCase_ ( self ) -> Tuple:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowerCamelCase_ ( self ) -> List[Any]:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[Any]:
for model_name in ["facebook/maskformer-swin-small-coco"]:
_UpperCAmelCase = MaskFormerModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = (self.model_tester.min_size,) * 2
_UpperCAmelCase = {
'pixel_values': torch.randn((2, 3, *size) , device=snake_case ),
'mask_labels': torch.randn((2, 10, *size) , device=snake_case ),
'class_labels': torch.zeros(2 , 10 , device=snake_case ).long(),
}
_UpperCAmelCase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(snake_case )
_UpperCAmelCase = model(**snake_case )
self.assertTrue(outputs.loss is not None )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(snake_case , **snake_case , output_hidden_states=snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case ).to(snake_case )
_UpperCAmelCase = model(**snake_case , output_attentions=snake_case )
self.assertTrue(outputs.attentions is not None )
def lowerCamelCase_ ( self ) -> Any:
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.train()
_UpperCAmelCase = model(snake_case , mask_labels=snake_case , class_labels=snake_case ).loss
loss.backward()
def lowerCamelCase_ ( self ) -> Dict:
# only MaskFormerForInstanceSegmentation has the loss
_UpperCAmelCase = self.all_model_classes[1]
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = True
_UpperCAmelCase = True
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.train()
_UpperCAmelCase = model(snake_case , mask_labels=snake_case , class_labels=snake_case )
_UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
_UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
_UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=snake_case )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
lowercase = 1E-4
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return (
MaskFormerImageProcessor.from_pretrained('facebook/maskformer-swin-small-coco' )
if is_vision_available()
else None
)
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = MaskFormerModel.from_pretrained('facebook/maskformer-swin-small-coco' ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case , return_tensors='pt' ).to(snake_case )
_UpperCAmelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case , (1, 3, 800, 1088) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
_UpperCAmelCase = torch.tensor(
[[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]] ).to(snake_case )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , snake_case , atol=snake_case ) )
_UpperCAmelCase = torch.tensor(
[[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]] ).to(snake_case )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , snake_case , atol=snake_case ) )
_UpperCAmelCase = torch.tensor(
[[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]] ).to(snake_case )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , snake_case , atol=snake_case ) )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco' )
.to(snake_case )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case , return_tensors='pt' ).to(snake_case )
_UpperCAmelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case , (1, 3, 800, 1088) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [
[-1.3737124, -1.7724937, -1.9364233],
[-1.5977281, -1.9867939, -2.1523695],
[-1.5795398, -1.9269832, -2.093942],
]
_UpperCAmelCase = torch.tensor(snake_case ).to(snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case , atol=snake_case ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[
[1.65_12E00, -5.25_72E00, -3.35_19E00],
[3.61_69E-02, -5.90_25E00, -2.93_13E00],
[1.07_66E-04, -7.76_30E00, -5.12_63E00],
] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case , atol=snake_case ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-resnet101-coco-stuff' )
.to(snake_case )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(snake_case , return_tensors='pt' ).to(snake_case )
_UpperCAmelCase = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(snake_case , (1, 3, 800, 1088) )
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# masks_queries_logits
_UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
_UpperCAmelCase = [[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
_UpperCAmelCase = torch.tensor(snake_case ).to(snake_case )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , snake_case , atol=snake_case ) )
# class_queries_logits
_UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
_UpperCAmelCase = torch.tensor(
[[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , snake_case , atol=snake_case ) )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = (
MaskFormerForInstanceSegmentation.from_pretrained('facebook/maskformer-swin-small-coco' )
.to(snake_case )
.eval()
)
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='pt' , )
_UpperCAmelCase = inputs['pixel_values'].to(snake_case )
_UpperCAmelCase = [el.to(snake_case ) for el in inputs['mask_labels']]
_UpperCAmelCase = [el.to(snake_case ) for el in inputs['class_labels']]
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
self.assertTrue(outputs.loss is not None )
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
lowercase = (
'''4S 3H 2C 7S 5H''',
'''9D 8H 2C 6S 7H''',
'''2D 6D 9D TH 7D''',
'''TC 8C 2S JH 6C''',
'''JH 8S TH AH QH''',
'''TS KS 5S 9S AC''',
'''KD 6S 9D TH AD''',
'''KS 8D 4D 9S 4S''', # pair
'''8C 4S KH JS 4D''', # pair
'''QH 8H KD JH 8S''', # pair
'''KC 4H KS 2H 8D''', # pair
'''KD 4S KC 3H 8S''', # pair
'''AH 8S AS KC JH''', # pair
'''3H 4C 4H 3S 2H''', # 2 pairs
'''5S 5D 2C KH KH''', # 2 pairs
'''3C KH 5D 5S KH''', # 2 pairs
'''AS 3C KH AD KH''', # 2 pairs
'''7C 7S 3S 7H 5S''', # 3 of a kind
'''7C 7S KH 2H 7H''', # 3 of a kind
'''AC KH QH AH AS''', # 3 of a kind
'''2H 4D 3C AS 5S''', # straight (low ace)
'''3C 5C 4C 2C 6H''', # straight
'''6S 8S 7S 5H 9H''', # straight
'''JS QS 9H TS KH''', # straight
'''QC KH TS JS AH''', # straight (high ace)
'''8C 9C 5C 3C TC''', # flush
'''3S 8S 9S 5S KS''', # flush
'''4C 5C 9C 8C KC''', # flush
'''JH 8H AH KH QH''', # flush
'''3D 2H 3H 2C 2D''', # full house
'''2H 2C 3S 3H 3D''', # full house
'''KH KC 3S 3H 3D''', # full house
'''JC 6H JS JD JH''', # 4 of a kind
'''JC 7H JS JD JH''', # 4 of a kind
'''JC KH JS JD JH''', # 4 of a kind
'''2S AS 4S 5S 3S''', # straight flush (low ace)
'''2D 6D 3D 4D 5D''', # straight flush
'''5C 6C 3C 7C 4C''', # straight flush
'''JH 9H TH KH QH''', # straight flush
'''JH AH TH KH QH''', # royal flush (high ace straight flush)
)
lowercase = (
('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)
lowercase = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', True),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', False),
('''AS 3S 4S 8S 2S''', True),
)
lowercase = (
('''2H 3H 4H 5H 6H''', True),
('''AS AH 2H AD AC''', False),
('''2H 3H 5H 6H 7H''', False),
('''KS AS TS QS JS''', True),
('''8H 9H QS JS TH''', True),
)
lowercase = (
('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 14]),
('''2H 5D 3C AS 5S''', False, [14, 5, 5, 3, 2]),
('''JH QD KC AS TS''', False, [14, 13, 12, 11, 10]),
('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)
lowercase = (
('''JH AH TH KH QH''', 0),
('''JH 9H TH KH QH''', 0),
('''JC KH JS JD JH''', 7),
('''KH KC 3S 3H 3D''', 6),
('''8C 9C 5C 3C TC''', 0),
('''JS QS 9H TS KH''', 0),
('''7C 7S KH 2H 7H''', 3),
('''3C KH 5D 5S KH''', 2),
('''QH 8H KD JH 8S''', 1),
('''2D 6D 9D TH 7D''', 0),
)
lowercase = (
('''JH AH TH KH QH''', 23),
('''JH 9H TH KH QH''', 22),
('''JC KH JS JD JH''', 21),
('''KH KC 3S 3H 3D''', 20),
('''8C 9C 5C 3C TC''', 19),
('''JS QS 9H TS KH''', 18),
('''7C 7S KH 2H 7H''', 17),
('''3C KH 5D 5S KH''', 16),
('''QH 8H KD JH 8S''', 15),
('''2D 6D 9D TH 7D''', 14),
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = randrange(len(A ) ), randrange(len(A ) )
_UpperCAmelCase = ['Loss', 'Tie', 'Win'][(play >= oppo) + (play > oppo)]
_UpperCAmelCase , _UpperCAmelCase = SORTED_HANDS[play], SORTED_HANDS[oppo]
return hand, other, expected
def UpperCAmelCase ( A : int = 100 ):
'''simple docstring'''
return (generate_random_hand() for _ in range(A ))
@pytest.mark.parametrize('hand, expected' , A )
def UpperCAmelCase ( A : Tuple , A : str ):
'''simple docstring'''
assert PokerHand(A )._is_flush() == expected
@pytest.mark.parametrize('hand, expected' , A )
def UpperCAmelCase ( A : Optional[Any] , A : Optional[int] ):
'''simple docstring'''
assert PokerHand(A )._is_straight() == expected
@pytest.mark.parametrize('hand, expected, card_values' , A )
def UpperCAmelCase ( A : Optional[int] , A : Dict , A : Optional[int] ):
'''simple docstring'''
_UpperCAmelCase = PokerHand(A )
assert player._is_five_high_straight() == expected
assert player._card_values == card_values
@pytest.mark.parametrize('hand, expected' , A )
def UpperCAmelCase ( A : Tuple , A : str ):
'''simple docstring'''
assert PokerHand(A )._is_same_kind() == expected
@pytest.mark.parametrize('hand, expected' , A )
def UpperCAmelCase ( A : Optional[int] , A : Tuple ):
'''simple docstring'''
assert PokerHand(A )._hand_type == expected
@pytest.mark.parametrize('hand, other, expected' , A )
def UpperCAmelCase ( A : str , A : List[str] , A : Any ):
'''simple docstring'''
assert PokerHand(A ).compare_with(PokerHand(A ) ) == expected
@pytest.mark.parametrize('hand, other, expected' , generate_random_hands() )
def UpperCAmelCase ( A : str , A : Dict , A : Tuple ):
'''simple docstring'''
assert PokerHand(A ).compare_with(PokerHand(A ) ) == expected
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = [PokerHand(A ) for hand in SORTED_HANDS]
_UpperCAmelCase = poker_hands.copy()
shuffle(A )
_UpperCAmelCase = chain(sorted(A ) )
for index, hand in enumerate(A ):
assert hand == poker_hands[index]
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = [PokerHand('2D AC 3H 4H 5S' ), PokerHand('2S 3H 4H 5S 6C' )]
pokerhands.sort(reverse=A )
assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = PokerHand('2C 4S AS 3D 5C' )
_UpperCAmelCase = True
_UpperCAmelCase = [5, 4, 3, 2, 14]
for _ in range(10 ):
assert pokerhand._is_five_high_straight() == expected
assert pokerhand._card_values == expected_card_values
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = os.path.abspath(os.path.dirname(A ) )
_UpperCAmelCase = os.path.join(A , 'poker_hands.txt' )
with open(A ) as file_hand:
for line in file_hand:
_UpperCAmelCase = line[:14].strip()
_UpperCAmelCase = line[15:].strip()
_UpperCAmelCase , _UpperCAmelCase = PokerHand(A ), PokerHand(A )
_UpperCAmelCase = player.compare_with(A )
if output == "Win":
answer += 1
assert answer == 376
| 24 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCAmelCase ( ):
'''simple docstring'''
print(sum_of_series(1 , 1 , 10 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
import random
def UpperCAmelCase ( A : Optional[Any] , A : Any , A : str ):
'''simple docstring'''
_UpperCAmelCase = a[left_index]
_UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , A ):
if a[j] < pivot:
_UpperCAmelCase , _UpperCAmelCase = a[i], a[j]
i += 1
_UpperCAmelCase , _UpperCAmelCase = a[i - 1], a[left_index]
return i - 1
def UpperCAmelCase ( A : Dict , A : str , A : str ):
'''simple docstring'''
if left < right:
_UpperCAmelCase = random.randint(A , right - 1 )
_UpperCAmelCase , _UpperCAmelCase = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
_UpperCAmelCase = partition(A , A , A )
quick_sort_random(
A , A , A ) # recursive quicksort to the left of the pivot point
quick_sort_random(
A , pivot_index + 1 , A ) # recursive quicksort to the right of the pivot point
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = input('Enter numbers separated by a comma:\n' ).strip()
_UpperCAmelCase = [int(A ) for item in user_input.split(',' )]
quick_sort_random(A , 0 , len(A ) )
print(A )
if __name__ == "__main__":
main()
| 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowercase = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.dirname(os.path.realpath(A ) )
_UpperCAmelCase = os.path.join(A , 'triangle.txt' )
with open(A ) as f:
_UpperCAmelCase = f.readlines()
_UpperCAmelCase = []
for line in triangle:
_UpperCAmelCase = []
for number in line.strip().split(' ' ):
numbers_from_line.append(int(A ) )
a.append(A )
for i in range(1 , len(A ) ):
for j in range(len(a[i] ) ):
_UpperCAmelCase = a[i - 1][j] if j != len(a[i - 1] ) else 0
_UpperCAmelCase = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(A , A )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import math
def UpperCAmelCase ( A : list , A : int ):
'''simple docstring'''
_UpperCAmelCase = len(A )
_UpperCAmelCase = int(math.floor(math.sqrt(A ) ) )
_UpperCAmelCase = 0
while arr[min(A , A ) - 1] < x:
_UpperCAmelCase = step
step += int(math.floor(math.sqrt(A ) ) )
if prev >= n:
return -1
while arr[prev] < x:
_UpperCAmelCase = prev + 1
if prev == min(A , A ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
lowercase = input('''Enter numbers separated by a comma:\n''').strip()
lowercase = [int(item) for item in user_input.split(''',''')]
lowercase = int(input('''Enter the number to be searched:\n'''))
lowercase = jump_search(arr, x)
if res == -1:
print('''Number not found!''')
else:
print(F'''Number {x} is at index {res}''')
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=None , snake_case=None ) -> List[str]:
_UpperCAmelCase = data
_UpperCAmelCase = previous
_UpperCAmelCase = next_node
def __str__( self ) -> str:
return f'{self.data}'
def lowerCamelCase_ ( self ) -> int:
return self.data
def lowerCamelCase_ ( self ) -> Optional[Any]:
return self.next
def lowerCamelCase_ ( self ) -> Any:
return self.previous
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case ) -> Tuple:
_UpperCAmelCase = head
def __iter__( self ) -> str:
return self
def lowerCamelCase_ ( self ) -> Any:
if not self.current:
raise StopIteration
else:
_UpperCAmelCase = self.current.get_data()
_UpperCAmelCase = self.current.get_next()
return value
class lowercase__ :
'''simple docstring'''
def __init__( self ) -> Union[str, Any]:
_UpperCAmelCase = None # First node in list
_UpperCAmelCase = None # Last node in list
def __str__( self ) -> Optional[Any]:
_UpperCAmelCase = self.head
_UpperCAmelCase = []
while current is not None:
nodes.append(current.get_data() )
_UpperCAmelCase = current.get_next()
return " ".join(str(snake_case ) for node in nodes )
def __contains__( self , snake_case ) -> List[str]:
_UpperCAmelCase = self.head
while current:
if current.get_data() == value:
return True
_UpperCAmelCase = current.get_next()
return False
def __iter__( self ) -> Tuple:
return LinkedListIterator(self.head )
def lowerCamelCase_ ( self ) -> Tuple:
if self.head:
return self.head.get_data()
return None
def lowerCamelCase_ ( self ) -> Union[str, Any]:
if self.tail:
return self.tail.get_data()
return None
def lowerCamelCase_ ( self , snake_case ) -> None:
if self.head is None:
_UpperCAmelCase = node
_UpperCAmelCase = node
else:
self.insert_before_node(self.head , snake_case )
def lowerCamelCase_ ( self , snake_case ) -> None:
if self.head is None:
self.set_head(snake_case )
else:
self.insert_after_node(self.tail , snake_case )
def lowerCamelCase_ ( self , snake_case ) -> None:
_UpperCAmelCase = Node(snake_case )
if self.head is None:
self.set_head(snake_case )
else:
self.set_tail(snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> None:
_UpperCAmelCase = node
_UpperCAmelCase = node.previous
if node.get_previous() is None:
_UpperCAmelCase = node_to_insert
else:
_UpperCAmelCase = node_to_insert
_UpperCAmelCase = node_to_insert
def lowerCamelCase_ ( self , snake_case , snake_case ) -> None:
_UpperCAmelCase = node
_UpperCAmelCase = node.next
if node.get_next() is None:
_UpperCAmelCase = node_to_insert
else:
_UpperCAmelCase = node_to_insert
_UpperCAmelCase = node_to_insert
def lowerCamelCase_ ( self , snake_case , snake_case ) -> None:
_UpperCAmelCase = 1
_UpperCAmelCase = Node(snake_case )
_UpperCAmelCase = self.head
while node:
if current_position == position:
self.insert_before_node(snake_case , snake_case )
return
current_position += 1
_UpperCAmelCase = node.next
self.insert_after_node(self.tail , snake_case )
def lowerCamelCase_ ( self , snake_case ) -> Node:
_UpperCAmelCase = self.head
while node:
if node.get_data() == item:
return node
_UpperCAmelCase = node.get_next()
raise Exception('Node not found' )
def lowerCamelCase_ ( self , snake_case ) -> Tuple:
if (node := self.get_node(snake_case )) is not None:
if node == self.head:
_UpperCAmelCase = self.head.get_next()
if node == self.tail:
_UpperCAmelCase = self.tail.get_previous()
self.remove_node_pointers(snake_case )
@staticmethod
def lowerCamelCase_ ( snake_case ) -> None:
if node.get_next():
_UpperCAmelCase = node.previous
if node.get_previous():
_UpperCAmelCase = node.next
_UpperCAmelCase = None
_UpperCAmelCase = None
def lowerCamelCase_ ( self ) -> Any:
return self.head is None
def UpperCAmelCase ( ):
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase = 1
_UpperCAmelCase = 1
while repunit:
_UpperCAmelCase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def UpperCAmelCase ( A : int = 100_0000 ):
'''simple docstring'''
_UpperCAmelCase = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(A ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24 | 1 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
return x + 2
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = 'x = 3'
_UpperCAmelCase = {}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
assert result == 3
self.assertDictEqual(snake_case , {'x': 3} )
_UpperCAmelCase = 'x = y'
_UpperCAmelCase = {'y': 5}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(snake_case , {'x': 5, 'y': 5} )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = 'y = add_two(x)'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {'add_two': add_two} , state=snake_case )
assert result == 5
self.assertDictEqual(snake_case , {'x': 3, 'y': 5} )
# Won't work without the tool
with CaptureStdout() as out:
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
assert result is None
assert "tried to execute add_two" in out.out
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = 'x = 3'
_UpperCAmelCase = {}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
assert result == 3
self.assertDictEqual(snake_case , {'x': 3} )
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'test_dict = {\'x\': x, \'y\': add_two(x)}'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {'add_two': add_two} , state=snake_case )
self.assertDictEqual(snake_case , {'x': 3, 'y': 5} )
self.assertDictEqual(snake_case , {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = 'x = 3\ny = 5'
_UpperCAmelCase = {}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(snake_case , {'x': 3, 'y': 5} )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = 'text = f\'This is x: {x}.\''
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(snake_case , {'x': 3, 'text': 'This is x: 3.'} )
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = 'if x <= 3:\n y = 2\nelse:\n y = 5'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(snake_case , {'x': 3, 'y': 2} )
_UpperCAmelCase = {'x': 8}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(snake_case , {'x': 8, 'y': 5} )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = 'test_list = [x, add_two(x)]'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {'add_two': add_two} , state=snake_case )
self.assertListEqual(snake_case , [3, 5] )
self.assertDictEqual(snake_case , {'x': 3, 'test_list': [3, 5]} )
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = 'y = x'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {} , state=snake_case )
assert result == 3
self.assertDictEqual(snake_case , {'x': 3, 'y': 3} )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = 'test_list = [x, add_two(x)]\ntest_list[1]'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {'add_two': add_two} , state=snake_case )
assert result == 5
self.assertDictEqual(snake_case , {'x': 3, 'test_list': [3, 5]} )
_UpperCAmelCase = 'test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'
_UpperCAmelCase = {'x': 3}
_UpperCAmelCase = evaluate(snake_case , {'add_two': add_two} , state=snake_case )
assert result == 5
self.assertDictEqual(snake_case , {'x': 3, 'test_dict': {'x': 3, 'y': 5}} )
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = 'x = 0\nfor i in range(3):\n x = i'
_UpperCAmelCase = {}
_UpperCAmelCase = evaluate(snake_case , {'range': range} , state=snake_case )
assert result == 2
self.assertDictEqual(snake_case , {'x': 2, 'i': 2} )
| 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24 | 1 |
"""simple docstring"""
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = int(A )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = t // 3600, (t // 60) % 60, t % 60
return f'{h}:{m:02d}:{s:02d}' if h != 0 else f'{m:02d}:{s:02d}'
def UpperCAmelCase ( A : Any , A : List[Any] , A : List[Any] , A : Union[str, Any] , A : Optional[Any]=300 ):
'''simple docstring'''
return f'\n <div>\n {prefix}\n <progress value=\'{value}\' max=\'{total}\' style=\'width:{width}px; height:20px; vertical-align: middle;\'></progress>\n {label}\n </div>\n '
def UpperCAmelCase ( A : Any ):
'''simple docstring'''
_UpperCAmelCase = '<table border="1" class="dataframe">\n'
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f' <th>{i}</th>\n'
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
_UpperCAmelCase = f'{elt:.6f}' if isinstance(A , A ) else str(A )
html_code += f' <td>{elt}</td>\n'
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class lowercase__ :
'''simple docstring'''
_UpperCAmelCase = 5
_UpperCAmelCase = 0.2
def __init__( self , snake_case , snake_case = None , snake_case = True , snake_case = None , snake_case = 300 , ) -> Any:
_UpperCAmelCase = total
_UpperCAmelCase = '' if prefix is None else prefix
_UpperCAmelCase = leave
_UpperCAmelCase = parent
_UpperCAmelCase = width
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = None
def lowerCamelCase_ ( self , snake_case , snake_case = False , snake_case = None ) -> Tuple:
_UpperCAmelCase = value
if comment is not None:
_UpperCAmelCase = comment
if self.last_value is None:
_UpperCAmelCase = _UpperCAmelCase = time.time()
_UpperCAmelCase = _UpperCAmelCase = value
_UpperCAmelCase = _UpperCAmelCase = None
_UpperCAmelCase = self.warmup
_UpperCAmelCase = 1
self.update_bar(snake_case )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
_UpperCAmelCase = time.time()
_UpperCAmelCase = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
_UpperCAmelCase = self.elapsed_time / (value - self.start_value)
else:
_UpperCAmelCase = None
if value >= self.total:
_UpperCAmelCase = self.total
_UpperCAmelCase = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
_UpperCAmelCase = self.average_time_per_item * (self.total - value)
self.update_bar(snake_case )
_UpperCAmelCase = value
_UpperCAmelCase = current_time
if self.average_time_per_item is None:
_UpperCAmelCase = 1
else:
_UpperCAmelCase = max(int(self.update_every / self.average_time_per_item ) , 1 )
def lowerCamelCase_ ( self , snake_case , snake_case=None ) -> int:
_UpperCAmelCase = ' ' * (len(str(self.total ) ) - len(str(snake_case ) )) + str(snake_case )
if self.elapsed_time is None:
_UpperCAmelCase = f'[{spaced_value}/{self.total} : < :'
elif self.predicted_remaining is None:
_UpperCAmelCase = f'[{spaced_value}/{self.total} {format_time(self.elapsed_time )}'
else:
_UpperCAmelCase = (
f'[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <'
f' {format_time(self.predicted_remaining )}'
)
self.label += f', {1/self.average_time_per_item:.2f} it/s'
self.label += "]" if self.comment is None or len(self.comment ) == 0 else f', {self.comment}]'
self.display()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
_UpperCAmelCase = disp.display(disp.HTML(self.html_code ) , display_id=snake_case )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML('' ) )
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case=None ) -> Union[str, Any]:
super().__init__(snake_case )
_UpperCAmelCase = None if column_names is None else [column_names]
_UpperCAmelCase = None
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
_UpperCAmelCase = disp.display(disp.HTML(self.html_code ) , display_id=snake_case )
else:
self.output.update(disp.HTML(self.html_code ) )
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
if self.inner_table is None:
_UpperCAmelCase = [list(values.keys() ), list(values.values() )]
else:
_UpperCAmelCase = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(snake_case )
_UpperCAmelCase = columns
self.inner_table.append([values[c] for c in columns] )
def lowerCamelCase_ ( self , snake_case , snake_case=None , snake_case=300 ) -> Dict:
_UpperCAmelCase = NotebookProgressBar(snake_case , prefix=snake_case , parent=self , width=snake_case )
return self.child_bar
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = None
self.display()
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase = None
_UpperCAmelCase = None
_UpperCAmelCase = False
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , **snake_case ) -> int:
_UpperCAmelCase = 'Epoch' if args.evaluation_strategy == IntervalStrategy.EPOCH else 'Step'
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = [self.first_column] + ['Training Loss']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('Validation Loss' )
_UpperCAmelCase = NotebookTrainingTracker(state.max_steps , snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , **snake_case ) -> Union[str, Any]:
_UpperCAmelCase = int(state.epoch ) if int(state.epoch ) == state.epoch else f'{state.epoch:.2f}'
self.training_tracker.update(
state.global_step + 1 , comment=f'Epoch {epoch}/{state.num_train_epochs}' , force_update=self._force_next_update , )
_UpperCAmelCase = False
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case=None , **snake_case ) -> Dict:
if not has_length(snake_case ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
_UpperCAmelCase = self.training_tracker.add_child(len(snake_case ) )
else:
_UpperCAmelCase = NotebookProgressBar(len(snake_case ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , **snake_case ) -> List[str]:
if self.prediction_bar is not None:
self.prediction_bar.close()
_UpperCAmelCase = None
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case=None , **snake_case ) -> Union[str, Any]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
_UpperCAmelCase = {'Training Loss': logs['loss']}
# First column is necessarily Step sine we're not in epoch eval strategy
_UpperCAmelCase = state.global_step
self.training_tracker.write_line(snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case=None , **snake_case ) -> Tuple:
if self.training_tracker is not None:
_UpperCAmelCase = {'Training Loss': 'No log', 'Validation Loss': 'No log'}
for log in reversed(state.log_history ):
if "loss" in log:
_UpperCAmelCase = log['loss']
break
if self.first_column == "Epoch":
_UpperCAmelCase = int(state.epoch )
else:
_UpperCAmelCase = state.global_step
_UpperCAmelCase = 'eval'
for k in metrics:
if k.endswith('_loss' ):
_UpperCAmelCase = re.sub(r'\_loss$' , '' , snake_case )
_UpperCAmelCase = metrics.pop('total_flos' , snake_case )
_UpperCAmelCase = metrics.pop('epoch' , snake_case )
_UpperCAmelCase = metrics.pop(f'{metric_key_prefix}_runtime' , snake_case )
_UpperCAmelCase = metrics.pop(f'{metric_key_prefix}_samples_per_second' , snake_case )
_UpperCAmelCase = metrics.pop(f'{metric_key_prefix}_steps_per_second' , snake_case )
_UpperCAmelCase = metrics.pop(f'{metric_key_prefix}_jit_compilation_time' , snake_case )
for k, v in metrics.items():
if k == f'{metric_key_prefix}_loss':
_UpperCAmelCase = v
else:
_UpperCAmelCase = k.split('_' )
_UpperCAmelCase = ' '.join([part.capitalize() for part in splits[1:]] )
_UpperCAmelCase = v
self.training_tracker.write_line(snake_case )
self.training_tracker.remove_child()
_UpperCAmelCase = None
# Evaluation takes a long time so we should force the next update.
_UpperCAmelCase = True
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , **snake_case ) -> Any:
self.training_tracker.update(
state.global_step , comment=f'Epoch {int(state.epoch )}/{state.num_train_epochs}' , force_update=snake_case )
_UpperCAmelCase = None
| 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24 | 1 |
"""simple docstring"""
import random
def UpperCAmelCase ( A : list , A : List[Any] ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = [], [], []
for element in data:
if element < pivot:
less.append(A )
elif element > pivot:
greater.append(A )
else:
equal.append(A )
return less, equal, greater
def UpperCAmelCase ( A : list , A : int ):
'''simple docstring'''
if index >= len(A ) or index < 0:
return None
_UpperCAmelCase = items[random.randint(0 , len(A ) - 1 )]
_UpperCAmelCase = 0
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = _partition(A , A )
_UpperCAmelCase = len(A )
_UpperCAmelCase = len(A )
# index is the pivot
if m <= index < m + count:
return pivot
# must be in smaller
elif m > index:
return quick_select(A , A )
# must be in larger
else:
return quick_select(A , index - (m + count) )
| 24 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = BarthezTokenizer
_UpperCAmelCase = BarthezTokenizerFast
_UpperCAmelCase = True
_UpperCAmelCase = True
def lowerCamelCase_ ( self ) -> Optional[int]:
super().setUp()
_UpperCAmelCase = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=snake_case )
_UpperCAmelCase = tokenizer
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case ) , snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(snake_case ) , 101122 )
def lowerCamelCase_ ( self ) -> List[Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 101122 )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_UpperCAmelCase = [0, 57, 3018, 70307, 91, 2]
_UpperCAmelCase = self.tokenizer(
snake_case , max_length=len(snake_case ) , padding=snake_case , truncation=snake_case , return_tensors='pt' )
self.assertIsInstance(snake_case , snake_case )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
_UpperCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
if not self.test_rust_tokenizer:
return
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = 'I was born in 92000, and this is falsé.'
_UpperCAmelCase = tokenizer.tokenize(snake_case )
_UpperCAmelCase = rust_tokenizer.tokenize(snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = tokenizer.encode(snake_case , add_special_tokens=snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case , add_special_tokens=snake_case )
self.assertListEqual(snake_case , snake_case )
_UpperCAmelCase = self.get_rust_tokenizer()
_UpperCAmelCase = tokenizer.encode(snake_case )
_UpperCAmelCase = rust_tokenizer.encode(snake_case )
self.assertListEqual(snake_case , snake_case )
@slow
def lowerCamelCase_ ( self ) -> Optional[int]:
# fmt: off
_UpperCAmelCase = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
_UpperCAmelCase = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=snake_case , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=snake_case , )
| 24 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''gpt_neo'''
_UpperCAmelCase = ['''past_key_values''']
_UpperCAmelCase = {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self , snake_case=50257 , snake_case=2048 , snake_case=2048 , snake_case=24 , snake_case=[[["global", "local"], 12]] , snake_case=16 , snake_case=None , snake_case=256 , snake_case="gelu_new" , snake_case=0.0 , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case=1E-5 , snake_case=0.02 , snake_case=True , snake_case=50256 , snake_case=50256 , **snake_case , ) -> Any:
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_layers
_UpperCAmelCase = num_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = window_size
_UpperCAmelCase = activation_function
_UpperCAmelCase = resid_dropout
_UpperCAmelCase = embed_dropout
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = classifier_dropout
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = use_cache
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = attention_types
_UpperCAmelCase = self.expand_attention_types_params(snake_case )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.attention_layers)` == `config.num_layers` '
f'but is `len(config.attention_layers) = {len(self.attention_layers )}`, '
f'`config.num_layers = {self.num_layers}`. '
'`config.attention_layers` is prepared using `config.attention_types`. '
'Please verify the value of `config.attention_types` argument.' )
super().__init__(bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
@staticmethod
def lowerCamelCase_ ( snake_case ) -> Optional[int]:
_UpperCAmelCase = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def UpperCAmelCase ( A : Optional[int] , A : int , A : Any , A : Optional[int] ):
'''simple docstring'''
import torch
_UpperCAmelCase = input.size()
_UpperCAmelCase = len(A )
_UpperCAmelCase = shape[dimension]
_UpperCAmelCase = torch.arange(0 , A , A )
_UpperCAmelCase = torch.div(sizedim - size , A , rounding_mode='floor' ) + 1
_UpperCAmelCase = torch.arange(A ) + low_indices[:min_length][:, None]
_UpperCAmelCase = [slice(A )] * rank
_UpperCAmelCase = indices
_UpperCAmelCase = input[s]
_UpperCAmelCase = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(A )
def UpperCAmelCase ( A : Dict , A : Dict ):
'''simple docstring'''
import torch
_UpperCAmelCase = torch.arange(1 , A )
_UpperCAmelCase = torch.remainder(A , A )
_UpperCAmelCase = remainders == 0
_UpperCAmelCase = candidates[divisor_indices]
_UpperCAmelCase = torch.max(A )
return largest_divisor, torch.div(A , A , rounding_mode='floor' )
class lowercase__ ( A ):
'''simple docstring'''
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
_UpperCAmelCase = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
self.fill_with_past_key_values_(snake_case , direction='inputs' )
_UpperCAmelCase = {0: 'batch', 1: 'past_sequence + sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def lowerCamelCase_ ( self ) -> int:
return self._config.num_heads
def lowerCamelCase_ ( self , snake_case , snake_case = -1 , snake_case = -1 , snake_case = False , snake_case = None , ) -> Mapping[str, Any]:
_UpperCAmelCase = super(snake_case , self ).generate_dummy_inputs(
snake_case , batch_size=snake_case , seq_length=snake_case , is_pair=snake_case , framework=snake_case )
# We need to order the input in the way they appears in the forward()
_UpperCAmelCase = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
_UpperCAmelCase , _UpperCAmelCase = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
_UpperCAmelCase = seqlen + 2
_UpperCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
_UpperCAmelCase = [
(torch.zeros(snake_case ), torch.zeros(snake_case )) for _ in range(self.num_layers )
]
_UpperCAmelCase = common_inputs['attention_mask']
if self.use_past:
_UpperCAmelCase = ordered_inputs['attention_mask'].dtype
_UpperCAmelCase = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(snake_case , snake_case , dtype=snake_case )] , dim=1 )
return ordered_inputs
@property
def lowerCamelCase_ ( self ) -> int:
return 13
| 24 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase__ ( A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = DiTPipeline
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
_UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
_UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> str:
torch.manual_seed(0 )
_UpperCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=snake_case , activation_fn='gelu-approximate' , num_embeds_ada_norm=1000 , norm_type='ada_norm_zero' , norm_elementwise_affine=snake_case , )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = DDIMScheduler()
_UpperCAmelCase = {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
return components
def lowerCamelCase_ ( self , snake_case , snake_case=0 ) -> Optional[Any]:
if str(snake_case ).startswith('mps' ):
_UpperCAmelCase = torch.manual_seed(snake_case )
else:
_UpperCAmelCase = torch.Generator(device=snake_case ).manual_seed(snake_case )
_UpperCAmelCase = {
'class_labels': [1],
'generator': generator,
'num_inference_steps': 2,
'output_type': 'numpy',
}
return inputs
def lowerCamelCase_ ( self ) -> List[Any]:
_UpperCAmelCase = 'cpu'
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case )
pipe.to(snake_case )
pipe.set_progress_bar_config(disable=snake_case )
_UpperCAmelCase = self.get_dummy_inputs(snake_case )
_UpperCAmelCase = pipe(**snake_case ).images
_UpperCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_UpperCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case , 1E-3 )
def lowerCamelCase_ ( self ) -> Any:
self._test_inference_batch_single_identical(relax_max_difference=snake_case , expected_max_diff=1E-3 )
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@require_torch_gpu
@slow
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella', 'white shark', 'white wolf']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=40 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
f'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy' )
assert np.abs((expected_image - image).max() ) < 1E-2
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to('cuda' )
_UpperCAmelCase = ['vase', 'umbrella']
_UpperCAmelCase = pipe.get_label_ids(snake_case )
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = pipe(snake_case , generator=snake_case , num_inference_steps=25 , output_type='np' ).images
for word, image in zip(snake_case , snake_case ):
_UpperCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
f'/dit/{word}_512.npy' )
assert np.abs((expected_image - image).max() ) < 1E-1
| 24 | 1 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowercase__ ( tf.keras.optimizers.schedules.LearningRateSchedule ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = None , ) -> List[Any]:
super().__init__()
_UpperCAmelCase = initial_learning_rate
_UpperCAmelCase = warmup_steps
_UpperCAmelCase = power
_UpperCAmelCase = decay_schedule_fn
_UpperCAmelCase = name
def __call__( self , snake_case ) -> Dict:
with tf.name_scope(self.name or 'WarmUp' ) as name:
# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
_UpperCAmelCase = tf.cast(snake_case , tf.floataa )
_UpperCAmelCase = tf.cast(self.warmup_steps , tf.floataa )
_UpperCAmelCase = global_step_float / warmup_steps_float
_UpperCAmelCase = self.initial_learning_rate * tf.math.pow(snake_case , self.power )
return tf.cond(
global_step_float < warmup_steps_float , lambda: warmup_learning_rate , lambda: self.decay_schedule_fn(step - self.warmup_steps ) , name=snake_case , )
def lowerCamelCase_ ( self ) -> Optional[Any]:
return {
"initial_learning_rate": self.initial_learning_rate,
"decay_schedule_fn": self.decay_schedule_fn,
"warmup_steps": self.warmup_steps,
"power": self.power,
"name": self.name,
}
def UpperCAmelCase ( A : float , A : int , A : int , A : float = 0.0 , A : float = 0.9 , A : float = 0.999 , A : float = 1e-8 , A : Optional[float] = None , A : Optional[float] = None , A : float = 0.0 , A : float = 1.0 , A : Optional[List[str]] = None , ):
'''simple docstring'''
_UpperCAmelCase = tf.keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=A , decay_steps=num_train_steps - num_warmup_steps , end_learning_rate=init_lr * min_lr_ratio , power=A , )
if num_warmup_steps:
_UpperCAmelCase = WarmUp(
initial_learning_rate=A , decay_schedule_fn=A , warmup_steps=A , )
if weight_decay_rate > 0.0:
_UpperCAmelCase = AdamWeightDecay(
learning_rate=A , weight_decay_rate=A , beta_a=A , beta_a=A , epsilon=A , clipnorm=A , global_clipnorm=A , exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] , include_in_weight_decay=A , )
else:
_UpperCAmelCase = tf.keras.optimizers.Adam(
learning_rate=A , beta_a=A , beta_a=A , epsilon=A , clipnorm=A , global_clipnorm=A , )
# We return the optimizer and the LR scheduler in order to better track the
# evolution of the LR independently of the optimizer.
return optimizer, lr_schedule
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case = 0.001 , snake_case = 0.9 , snake_case = 0.999 , snake_case = 1E-7 , snake_case = False , snake_case = 0.0 , snake_case = None , snake_case = None , snake_case = "AdamWeightDecay" , **snake_case , ) -> Any:
super().__init__(snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , **snake_case )
_UpperCAmelCase = weight_decay_rate
_UpperCAmelCase = include_in_weight_decay
_UpperCAmelCase = exclude_from_weight_decay
@classmethod
def lowerCamelCase_ ( cls , snake_case ) -> List[Any]:
_UpperCAmelCase = {'WarmUp': WarmUp}
return super(snake_case , cls ).from_config(snake_case , custom_objects=snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> int:
super(snake_case , self )._prepare_local(snake_case , snake_case , snake_case )
_UpperCAmelCase = tf.constant(
self.weight_decay_rate , name='adam_weight_decay_rate' )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = self._do_use_weight_decay(var.name )
if do_decay:
return var.assign_sub(
learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate'] , use_locking=self._use_locking , )
return tf.no_op()
def lowerCamelCase_ ( self , snake_case , snake_case=None , **snake_case ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = list(zip(*snake_case ) )
return super(snake_case , self ).apply_gradients(zip(snake_case , snake_case ) , name=snake_case , **snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Tuple:
if apply_state is None:
return self._decayed_lr_t[var_dtype], {}
_UpperCAmelCase = apply_state or {}
_UpperCAmelCase = apply_state.get((var_device, var_dtype) )
if coefficients is None:
_UpperCAmelCase = self._fallback_apply_state(snake_case , snake_case )
_UpperCAmelCase = coefficients
return coefficients["lr_t"], {"apply_state": apply_state}
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None ) -> int:
_UpperCAmelCase , _UpperCAmelCase = self._get_lr(var.device , var.dtype.base_dtype , snake_case )
_UpperCAmelCase = self._decay_weights_op(snake_case , snake_case , snake_case )
with tf.control_dependencies([decay] ):
return super(snake_case , self )._resource_apply_dense(snake_case , snake_case , **snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case=None ) -> List[str]:
_UpperCAmelCase , _UpperCAmelCase = self._get_lr(var.device , var.dtype.base_dtype , snake_case )
_UpperCAmelCase = self._decay_weights_op(snake_case , snake_case , snake_case )
with tf.control_dependencies([decay] ):
return super(snake_case , self )._resource_apply_sparse(snake_case , snake_case , snake_case , **snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = super().get_config()
config.update({'weight_decay_rate': self.weight_decay_rate} )
return config
def lowerCamelCase_ ( self , snake_case ) -> str:
if self.weight_decay_rate == 0:
return False
if self._include_in_weight_decay:
for r in self._include_in_weight_decay:
if re.search(snake_case , snake_case ) is not None:
return True
if self._exclude_from_weight_decay:
for r in self._exclude_from_weight_decay:
if re.search(snake_case , snake_case ) is not None:
return False
return True
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self ) -> Optional[int]:
_UpperCAmelCase = []
_UpperCAmelCase = None
@property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
if self._accum_steps is None:
_UpperCAmelCase = tf.Variable(
tf.constant(0 , dtype=tf.intaa ) , trainable=snake_case , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
return self._accum_steps.value()
@property
def lowerCamelCase_ ( self ) -> Optional[int]:
if not self._gradients:
raise ValueError('The accumulator should be called first to initialize the gradients' )
return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]
def __call__( self , snake_case ) -> List[Any]:
if not self._gradients:
_UpperCAmelCase = self.step # Create the step variable.
self._gradients.extend(
[
tf.Variable(
tf.zeros_like(snake_case ) , trainable=snake_case , synchronization=tf.VariableSynchronization.ON_READ , aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA , )
if gradient is not None
else gradient
for gradient in gradients
] )
if len(snake_case ) != len(self._gradients ):
raise ValueError(f'Expected {len(self._gradients )} gradients, but got {len(snake_case )}' )
for accum_gradient, gradient in zip(self._gradients , snake_case ):
if accum_gradient is not None and gradient is not None:
accum_gradient.assign_add(snake_case )
self._accum_steps.assign_add(1 )
def lowerCamelCase_ ( self ) -> Tuple:
if not self._gradients:
return
self._accum_steps.assign(0 )
for gradient in self._gradients:
if gradient is not None:
gradient.assign(tf.zeros_like(snake_case ) )
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
_UpperCAmelCase = 0
while n > 0:
res += n % 10
n //= 10
return res
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = abs(A )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def UpperCAmelCase ( A : int ):
'''simple docstring'''
return sum(int(A ) for c in str(abs(A ) ) )
def UpperCAmelCase ( ):
'''simple docstring'''
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(A : Callable , A : int ) -> None:
_UpperCAmelCase = f'{func.__name__}({value})'
_UpperCAmelCase = timeit(f'__main__.{call}' , setup='import __main__' )
print(f'{call:56} = {func(A )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(A , A )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 24 | 1 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = []
create_all_state(1 , A , A , [] , A )
return result
def UpperCAmelCase ( A : int , A : int , A : int , A : list[int] , A : list[list[int]] , ):
'''simple docstring'''
if level == 0:
total_list.append(current_list[:] )
return
for i in range(A , total_number - level + 2 ):
current_list.append(A )
create_all_state(i + 1 , A , level - 1 , A , A )
current_list.pop()
def UpperCAmelCase ( A : list[list[int]] ):
'''simple docstring'''
for i in total_list:
print(*A )
if __name__ == "__main__":
lowercase = 4
lowercase = 2
lowercase = generate_all_combinations(n, k)
print_all_state(total_list)
| 24 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''google/umt5-small''': '''https://huggingface.co/google/umt5-small/resolve/main/config.json''',
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''umt5'''
_UpperCAmelCase = ['''past_key_values''']
def __init__( self , snake_case=250112 , snake_case=512 , snake_case=64 , snake_case=1024 , snake_case=8 , snake_case=None , snake_case=6 , snake_case=32 , snake_case=128 , snake_case=0.1 , snake_case=1E-6 , snake_case=1.0 , snake_case="gated-gelu" , snake_case=True , snake_case=True , snake_case="T5Tokenizer" , snake_case=True , snake_case=0 , snake_case=1 , snake_case=0 , **snake_case , ) -> List[Any]:
super().__init__(
is_encoder_decoder=snake_case , tokenizer_class=snake_case , tie_word_embeddings=snake_case , pad_token_id=snake_case , eos_token_id=snake_case , decoder_start_token_id=snake_case , **snake_case , )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = d_model
_UpperCAmelCase = d_kv
_UpperCAmelCase = d_ff
_UpperCAmelCase = num_layers
_UpperCAmelCase = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
_UpperCAmelCase = num_heads
_UpperCAmelCase = relative_attention_num_buckets
_UpperCAmelCase = relative_attention_max_distance
_UpperCAmelCase = dropout_rate
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_factor
_UpperCAmelCase = feed_forward_proj
_UpperCAmelCase = use_cache
_UpperCAmelCase = self.feed_forward_proj.split('-' )
_UpperCAmelCase = act_info[-1]
_UpperCAmelCase = act_info[0] == 'gated'
if len(snake_case ) > 1 and act_info[0] != "gated" or len(snake_case ) > 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\'' )
if feed_forward_proj == "gated-gelu":
_UpperCAmelCase = 'gelu_new'
@property
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return self.d_model
@property
def lowerCamelCase_ ( self ) -> Tuple:
return self.num_heads
@property
def lowerCamelCase_ ( self ) -> Optional[int]:
return self.num_layers
class lowercase__ ( A ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
_UpperCAmelCase = {
'input_ids': {0: 'batch', 1: 'encoder_sequence'},
'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
}
if self.use_past:
_UpperCAmelCase = 'past_encoder_sequence + sequence'
_UpperCAmelCase = {0: 'batch'}
_UpperCAmelCase = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'decoder_sequence'}
_UpperCAmelCase = {0: 'batch', 1: 'decoder_sequence'}
if self.use_past:
self.fill_with_past_key_values_(snake_case , direction='inputs' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def lowerCamelCase_ ( self ) -> int:
return 13
@property
def lowerCamelCase_ ( self ) -> float:
return 5E-4
| 24 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
lowercase = logging.getLogger()
def UpperCAmelCase ( A : Path , A : list ):
'''simple docstring'''
_UpperCAmelCase = '\n'.join(A )
Path(A ).open('w' ).writelines(A )
lowercase = '''patrickvonplaten/t5-tiny-random'''
lowercase = '''sshleifer/bart-tiny-random'''
lowercase = '''sshleifer/tiny-mbart'''
lowercase = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self , snake_case ) -> str:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
_dump_articles(snake_case , snake_case )
_UpperCAmelCase = str(Path(self.get_auto_remove_tmp_dir() ) / 'scores.json' )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n '.split()
with patch.object(snake_case , 'argv' , snake_case ):
run_generate()
assert Path(snake_case ).exists()
# os.remove(Path(output_file_name))
def lowerCamelCase_ ( self ) -> str:
self.run_eval_tester(snake_case )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> List[Any]:
self.run_eval_tester(snake_case )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def lowerCamelCase_ ( self , snake_case ) -> Dict:
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() ) / 'utest_input.source'
_UpperCAmelCase = input_file_name.parent / 'utest_output.txt'
assert not output_file_name.exists()
_UpperCAmelCase = {
'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
'de': [
'Maschinelles Lernen ist großartig, oder?',
'Ich esse gerne Bananen',
'Morgen ist wieder ein toller Tag!',
],
}
_UpperCAmelCase = Path(self.get_auto_remove_tmp_dir() )
_UpperCAmelCase = str(tmp_dir / 'scores.json' )
_UpperCAmelCase = str(tmp_dir / 'val.target' )
_dump_articles(snake_case , text['en'] )
_dump_articles(snake_case , text['de'] )
_UpperCAmelCase = 'translation_en_to_de' if model == T5_TINY else 'summarization'
_UpperCAmelCase = f'\n run_eval_search.py\n {model}\n {str(snake_case )}\n {str(snake_case )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n '.split()
testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0'] )
with patch.object(snake_case , 'argv' , snake_case ):
with CaptureStdout() as cs:
run_search()
_UpperCAmelCase = [' num_beams | length_penalty', model, 'Best score args']
_UpperCAmelCase = ['Info']
if "translation" in task:
expected_strings.append('bleu' )
else:
expected_strings.extend(snake_case )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(snake_case ).exists()
os.remove(Path(snake_case ) )
| 24 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def UpperCAmelCase ( A : Union[str, Any] , A : Dict , A : int , A : Union[str, Any] , A : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase = StableDiffusionPipeline.from_pretrained(A , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
_UpperCAmelCase = load_file(A )
_UpperCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
_UpperCAmelCase = key.split('.' )[0].split(LORA_PREFIX_TEXT_ENCODER + '_' )[-1].split('_' )
_UpperCAmelCase = pipeline.text_encoder
else:
_UpperCAmelCase = key.split('.' )[0].split(LORA_PREFIX_UNET + '_' )[-1].split('_' )
_UpperCAmelCase = pipeline.unet
# find the target layer
_UpperCAmelCase = layer_infos.pop(0 )
while len(A ) > -1:
try:
_UpperCAmelCase = curr_layer.__getattr__(A )
if len(A ) > 0:
_UpperCAmelCase = layer_infos.pop(0 )
elif len(A ) == 0:
break
except Exception:
if len(A ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
_UpperCAmelCase = layer_infos.pop(0 )
_UpperCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace('lora_down' , 'lora_up' ) )
pair_keys.append(A )
else:
pair_keys.append(A )
pair_keys.append(key.replace('lora_up' , 'lora_down' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
_UpperCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
_UpperCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(A , A ).unsqueeze(2 ).unsqueeze(3 )
else:
_UpperCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
_UpperCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(A , A )
# update visited list
for item in pair_keys:
visited.append(A )
return pipeline
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
)
parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
)
parser.add_argument(
'''--lora_prefix_text_encoder''',
default='''lora_te''',
type=str,
help='''The prefix of text encoder weight in safetensors''',
)
parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
parser.add_argument(
'''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
)
parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
lowercase = parser.parse_args()
lowercase = args.base_model_path
lowercase = args.checkpoint_path
lowercase = args.dump_path
lowercase = args.lora_prefix_unet
lowercase = args.lora_prefix_text_encoder
lowercase = args.alpha
lowercase = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
lowercase = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 24 |
"""simple docstring"""
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
lowercase = logging.get_logger(__name__)
lowercase = TypeVar('''DatasetType''', Dataset, IterableDataset)
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[List[float]] = None , A : Optional[int] = None , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ):
'''simple docstring'''
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.' )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
else:
return _interleave_iterable_datasets(
A , A , A , info=A , split=A , stopping_strategy=A )
def UpperCAmelCase ( A : List[DatasetType] , A : Optional[DatasetInfo] = None , A : Optional[NamedSplit] = None , A : int = 0 , ):
'''simple docstring'''
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(A ):
if not isinstance(A , (Dataset, IterableDataset) ):
if isinstance(A , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
'is an empty dataset dictionary.' )
raise ValueError(
f'Dataset at position {i} has at least one split: {list(A )}\n'
f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A ) )}\']' )
raise ValueError(
f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A ).__name__}.' )
if i == 0:
_UpperCAmelCase , _UpperCAmelCase = (
(Dataset, IterableDataset) if isinstance(A , A ) else (IterableDataset, Dataset)
)
elif not isinstance(A , A ):
raise ValueError(
f'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.' )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(A , info=A , split=A , axis=A )
else:
return _concatenate_iterable_datasets(A , info=A , split=A , axis=A )
| 24 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowercase = 5_00_00
lowercase = 50_00
lowercase , lowercase = os.path.split(__file__)
lowercase = os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))
@get_duration
def UpperCAmelCase ( A : datasets.Dataset , A : Optional[Any] ):
'''simple docstring'''
for i in range(A ):
_UpperCAmelCase = dataset[i]
@get_duration
def UpperCAmelCase ( A : datasets.Dataset , A : Optional[Any] , A : List[Any] ):
'''simple docstring'''
for i in range(0 , len(A ) , A ):
_UpperCAmelCase = dataset[i : i + batch_size]
@get_duration
def UpperCAmelCase ( A : datasets.Dataset , A : List[Any] , A : Dict ):
'''simple docstring'''
with dataset.formatted_as(type=A ):
for i in range(A ):
_UpperCAmelCase = dataset[i]
@get_duration
def UpperCAmelCase ( A : datasets.Dataset , A : Union[str, Any] , A : int , A : Optional[Any] ):
'''simple docstring'''
with dataset.formatted_as(type=A ):
for i in range(0 , A , A ):
_UpperCAmelCase = dataset[i : i + batch_size]
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = {'num examples': SPEED_TEST_N_EXAMPLES}
_UpperCAmelCase = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted, {'type': 'pandas', 'length': SMALL_TEST}),
(read_formatted, {'type': 'torch', 'length': SMALL_TEST}),
(read_formatted, {'type': 'tensorflow', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
]
_UpperCAmelCase = [
(read, {'length': SMALL_TEST}),
(read, {'length': SPEED_TEST_N_EXAMPLES}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 10}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 100}),
(read_batch, {'length': SPEED_TEST_N_EXAMPLES, 'batch_size': 1000}),
(read_formatted, {'type': 'numpy', 'length': SMALL_TEST}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 10}),
(read_formatted_batch, {'type': 'numpy', 'length': SMALL_TEST, 'batch_size': 1000}),
]
with tempfile.TemporaryDirectory() as tmp_dir:
print('generating dataset' )
_UpperCAmelCase = datasets.Features(
{'list': datasets.Sequence(datasets.Value('float32' ) ), 'numbers': datasets.Value('float32' )} )
_UpperCAmelCase = generate_example_dataset(
os.path.join(A , 'dataset.arrow' ) , A , num_examples=A , seq_shapes={'list': (100,)} , )
print('first set of iterations' )
for func, kwargs in functions:
print(func.__name__ , str(A ) )
_UpperCAmelCase = func(A , **A )
print('shuffling dataset' )
_UpperCAmelCase = dataset.shuffle()
print('Second set of iterations (after shuffling' )
for func, kwargs in functions_shuffled:
print('shuffled ' , func.__name__ , str(A ) )
_UpperCAmelCase = func(
A , **A )
with open(A , 'wb' ) as f:
f.write(json.dumps(A ).encode('utf-8' ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_iterating()
| 24 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
_UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = TextaTextGenerationPipeline(model=snake_case , tokenizer=snake_case )
return generator, ["Something to write", "Something else"]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Dict:
_UpperCAmelCase = generator('Something there' )
self.assertEqual(snake_case , [{'generated_text': ANY(snake_case )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['generated_text'].startswith('Something there' ) )
_UpperCAmelCase = generator(['This is great !', 'Something else'] , num_return_sequences=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
_UpperCAmelCase = generator(
['This is great !', 'Something else'] , num_return_sequences=2 , batch_size=2 , do_sample=snake_case )
self.assertEqual(
snake_case , [
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
[{'generated_text': ANY(snake_case )}, {'generated_text': ANY(snake_case )}],
] , )
with self.assertRaises(snake_case ):
generator(4 )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='pt' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
_UpperCAmelCase = 3
_UpperCAmelCase = generator(
'Something there' , num_return_sequences=snake_case , num_beams=snake_case , )
_UpperCAmelCase = [
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
{'generated_text': ''},
]
self.assertEqual(snake_case , snake_case )
_UpperCAmelCase = generator('This is a test' , do_sample=snake_case , num_return_sequences=2 , return_tensors=snake_case )
self.assertEqual(
snake_case , [
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
] , )
_UpperCAmelCase = generator.model.config.eos_token_id
_UpperCAmelCase = '<pad>'
_UpperCAmelCase = generator(
['This is a test', 'This is a second test'] , do_sample=snake_case , num_return_sequences=2 , batch_size=2 , return_tensors=snake_case , )
self.assertEqual(
snake_case , [
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
[
{'generated_token_ids': ANY(torch.Tensor )},
{'generated_token_ids': ANY(torch.Tensor )},
],
] , )
@require_tf
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = pipeline('text2text-generation' , model='patrickvonplaten/t5-tiny-random' , framework='tf' )
# do_sample=False necessary for reproducibility
_UpperCAmelCase = generator('Something there' , do_sample=snake_case )
self.assertEqual(snake_case , [{'generated_text': ''}] )
| 24 | 1 |
"""simple docstring"""
def UpperCAmelCase ( A : Any , A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = [1]
for i in range(2 , A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
_UpperCAmelCase = []
_UpperCAmelCase = list(range(A ) )
# Find permutation
while factorials:
_UpperCAmelCase = factorials.pop()
_UpperCAmelCase , _UpperCAmelCase = divmod(A , A )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 24 |
"""simple docstring"""
def UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = [[0 for _ in range(A )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_UpperCAmelCase = 1
for n in range(m + 1 ):
for k in range(1 , A ):
memo[n][k] += memo[n][k - 1]
if n - k > 0:
memo[n][k] += memo[n - k - 1][k]
return memo[m][m - 1]
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
try:
lowercase = int(input('''Enter a number: ''').strip())
print(partition(n))
except ValueError:
print('''Please enter a number.''')
else:
try:
lowercase = int(sys.argv[1])
print(partition(n))
except ValueError:
print('''Please pass a number.''')
| 24 | 1 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCAmelCase ( A : Union[dict, list, tuple, torch.Tensor] ):
'''simple docstring'''
_UpperCAmelCase = []
if isinstance(A , A ):
for v in tree.values():
shapes.extend(_fetch_dims(A ) )
elif isinstance(A , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(A ) )
elif isinstance(A , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('Not supported' )
return shapes
@torch.jit.ignore
def UpperCAmelCase ( A : int , A : Tuple[int, ...] ):
'''simple docstring'''
_UpperCAmelCase = []
for d in reversed(A ):
idx.append(flat_idx % d )
_UpperCAmelCase = flat_idx // d
return tuple(reversed(A ) )
@torch.jit.ignore
def UpperCAmelCase ( A : Sequence[int] , A : Sequence[int] , A : Sequence[int] , A : Optional[Sequence[bool]] = None , A : Optional[Sequence[bool]] = None , ):
'''simple docstring'''
def reduce_edge_list(A : List[bool] ) -> None:
_UpperCAmelCase = True
for i in range(len(A ) ):
_UpperCAmelCase = -1 * (i + 1)
l[reversed_idx] &= tally
_UpperCAmelCase = l[reversed_idx]
if start_edges is None:
_UpperCAmelCase = [s == 0 for s in start]
reduce_edge_list(A )
if end_edges is None:
_UpperCAmelCase = [e == (d - 1) for e, d in zip(A , A )]
reduce_edge_list(A )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(A ) == 0:
return [()]
elif len(A ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
_UpperCAmelCase = []
_UpperCAmelCase = []
# Dimensions common to start and end can be selected directly
for s, e in zip(A , A ):
if s == e:
path_list.append(slice(A , s + 1 ) )
else:
break
_UpperCAmelCase = tuple(A )
_UpperCAmelCase = len(A )
# start == end, and we're done
if divergence_idx == len(A ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
_UpperCAmelCase = start[divergence_idx]
return tuple(
path + (slice(A , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
_UpperCAmelCase = end[divergence_idx]
return tuple(
path + (slice(A , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
_UpperCAmelCase = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def UpperCAmelCase ( A : torch.Tensor , A : int , A : int , A : int ):
'''simple docstring'''
_UpperCAmelCase = t.shape[:no_batch_dims]
_UpperCAmelCase = list(_flat_idx_to_idx(A , A ) )
# _get_minimal_slice_set is inclusive
_UpperCAmelCase = list(_flat_idx_to_idx(flat_end - 1 , A ) )
# Get an ordered list of slices to perform
_UpperCAmelCase = _get_minimal_slice_set(
A , A , A , )
_UpperCAmelCase = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def UpperCAmelCase ( A : Callable , A : Dict[str, Any] , A : int , A : int , A : bool = False , A : Any = None , A : bool = False , ):
'''simple docstring'''
if not (len(A ) > 0):
raise ValueError('Must provide at least one input' )
_UpperCAmelCase = [shape[:no_batch_dims] for shape in _fetch_dims(A )]
_UpperCAmelCase = tuple([max(A ) for s in zip(*A )] )
def _prep_inputs(A : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
_UpperCAmelCase = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
_UpperCAmelCase = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
_UpperCAmelCase = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
_UpperCAmelCase = tensor_tree_map(_prep_inputs , A )
_UpperCAmelCase = None
if _out is not None:
_UpperCAmelCase = tensor_tree_map(lambda A : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
_UpperCAmelCase = 1
for d in orig_batch_dims:
flat_batch_dim *= d
_UpperCAmelCase = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(A : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
_UpperCAmelCase = 0
_UpperCAmelCase = prepped_outputs
for _ in range(A ):
# Chunk the input
if not low_mem:
_UpperCAmelCase = _select_chunk
else:
_UpperCAmelCase = partial(
_chunk_slice , flat_start=A , flat_end=min(A , i + chunk_size ) , no_batch_dims=len(A ) , )
_UpperCAmelCase = tensor_tree_map(A , A )
# Run the layer on the chunk
_UpperCAmelCase = layer(**A )
# Allocate space for the output
if out is None:
_UpperCAmelCase = tensor_tree_map(lambda A : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , A )
# Put the chunk in its pre-allocated space
if isinstance(A , A ):
def assign(A : dict , A : dict ) -> None:
for k, v in da.items():
if isinstance(A , A ):
assign(A , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
_UpperCAmelCase = da[k]
assign(A , A )
elif isinstance(A , A ):
for xa, xa in zip(A , A ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
_UpperCAmelCase = xa
elif isinstance(A , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
_UpperCAmelCase = output_chunk
else:
raise ValueError('Not supported' )
i += chunk_size
_UpperCAmelCase = tensor_tree_map(lambda A : t.view(orig_batch_dims + t.shape[1:] ) , A )
return out
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case = 512 , ) -> str:
_UpperCAmelCase = max_chunk_size
_UpperCAmelCase = None
_UpperCAmelCase = None
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> int:
logging.info('Tuning chunk size...' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
_UpperCAmelCase = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
_UpperCAmelCase = [c for c in candidates if c > min_chunk_size]
_UpperCAmelCase = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(snake_case ) -> bool:
try:
with torch.no_grad():
fn(*snake_case , chunk_size=snake_case )
return True
except RuntimeError:
return False
_UpperCAmelCase = 0
_UpperCAmelCase = len(snake_case ) - 1
while i > min_viable_chunk_size_index:
_UpperCAmelCase = test_chunk_size(candidates[i] )
if not viable:
_UpperCAmelCase = (min_viable_chunk_size_index + i) // 2
else:
_UpperCAmelCase = i
_UpperCAmelCase = (i + len(snake_case ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def lowerCamelCase_ ( self , snake_case , snake_case ) -> bool:
_UpperCAmelCase = True
for aa, aa in zip(snake_case , snake_case ):
assert type(snake_case ) == type(snake_case )
if isinstance(snake_case , (list, tuple) ):
consistent &= self._compare_arg_caches(snake_case , snake_case )
elif isinstance(snake_case , snake_case ):
_UpperCAmelCase = [v for _, v in sorted(aa.items() , key=lambda snake_case : x[0] )]
_UpperCAmelCase = [v for _, v in sorted(aa.items() , key=lambda snake_case : x[0] )]
consistent &= self._compare_arg_caches(snake_case , snake_case )
else:
consistent &= aa == aa
return consistent
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , ) -> int:
_UpperCAmelCase = True
_UpperCAmelCase = tree_map(lambda snake_case : a.shape if isinstance(snake_case , torch.Tensor ) else a , snake_case , snake_case )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(snake_case )
_UpperCAmelCase = self._compare_arg_caches(self.cached_arg_data , snake_case )
else:
# Otherwise, we can reuse the precomputed value
_UpperCAmelCase = False
if not consistent:
_UpperCAmelCase = self._determine_favorable_chunk_size(
snake_case , snake_case , snake_case , )
_UpperCAmelCase = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 24 |
"""simple docstring"""
import os
lowercase = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def UpperCAmelCase ( A : str ):
'''simple docstring'''
_UpperCAmelCase = 0
_UpperCAmelCase = 0
while index < len(A ) - 1:
_UpperCAmelCase = SYMBOLS[numerals[index]]
_UpperCAmelCase = 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 UpperCAmelCase ( A : int ):
'''simple docstring'''
_UpperCAmelCase = ''
_UpperCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_UpperCAmelCase = 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
_UpperCAmelCase = 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 UpperCAmelCase ( A : str = "/p089_roman.txt" ):
'''simple docstring'''
_UpperCAmelCase = 0
with open(os.path.dirname(A ) + roman_numerals_filename ) as filea:
_UpperCAmelCase = filea.readlines()
for line in lines:
_UpperCAmelCase = line.strip()
_UpperCAmelCase = parse_roman_numerals(A )
_UpperCAmelCase = generate_roman_numerals(A )
savings += len(A ) - len(A )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
PIL_INTERPOLATION,
randn_tensor,
)
def UpperCAmelCase ( A : List[Any] , A : List[str] , A : Optional[int] ):
'''simple docstring'''
if isinstance(A , torch.Tensor ):
return image
elif isinstance(A , PIL.Image.Image ):
_UpperCAmelCase = [image]
if isinstance(image[0] , PIL.Image.Image ):
_UpperCAmelCase = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] ) )[None, :] for i in image]
_UpperCAmelCase = np.concatenate(A , axis=0 )
_UpperCAmelCase = np.array(A ).astype(np.floataa ) / 255.0
_UpperCAmelCase = image.transpose(0 , 3 , 1 , 2 )
_UpperCAmelCase = 2.0 * image - 1.0
_UpperCAmelCase = torch.from_numpy(A )
elif isinstance(image[0] , torch.Tensor ):
_UpperCAmelCase = torch.cat(A , dim=0 )
return image
def UpperCAmelCase ( A : Optional[Any] , A : Optional[int] , A : Any , A : Dict=0.9995 ):
'''simple docstring'''
if not isinstance(A , np.ndarray ):
_UpperCAmelCase = True
_UpperCAmelCase = va.device
_UpperCAmelCase = va.cpu().numpy()
_UpperCAmelCase = va.cpu().numpy()
_UpperCAmelCase = np.sum(va * va / (np.linalg.norm(A ) * np.linalg.norm(A )) )
if np.abs(A ) > DOT_THRESHOLD:
_UpperCAmelCase = (1 - t) * va + t * va
else:
_UpperCAmelCase = np.arccos(A )
_UpperCAmelCase = np.sin(A )
_UpperCAmelCase = theta_a * t
_UpperCAmelCase = np.sin(A )
_UpperCAmelCase = np.sin(theta_a - theta_t ) / sin_theta_a
_UpperCAmelCase = sin_theta_t / sin_theta_a
_UpperCAmelCase = sa * va + sa * va
if inputs_are_torch:
_UpperCAmelCase = torch.from_numpy(A ).to(A )
return va
def UpperCAmelCase ( A : Union[str, Any] , A : str ):
'''simple docstring'''
_UpperCAmelCase = F.normalize(A , dim=-1 )
_UpperCAmelCase = F.normalize(A , dim=-1 )
return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def UpperCAmelCase ( A : List[str] , A : str ):
'''simple docstring'''
for param in model.parameters():
_UpperCAmelCase = value
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , ) -> Union[str, Any]:
super().__init__()
self.register_modules(
vae=snake_case , text_encoder=snake_case , clip_model=snake_case , tokenizer=snake_case , unet=snake_case , scheduler=snake_case , feature_extractor=snake_case , coca_model=snake_case , coca_tokenizer=snake_case , coca_transform=snake_case , )
_UpperCAmelCase = (
feature_extractor.size
if isinstance(feature_extractor.size , snake_case )
else feature_extractor.size['shortest_edge']
)
_UpperCAmelCase = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std )
set_requires_grad(self.text_encoder , snake_case )
set_requires_grad(self.clip_model , snake_case )
def lowerCamelCase_ ( self , snake_case = "auto" ) -> Dict:
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_UpperCAmelCase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
self.enable_attention_slicing(snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
set_requires_grad(self.vae , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
set_requires_grad(self.vae , snake_case )
def lowerCamelCase_ ( self ) -> Dict:
set_requires_grad(self.unet , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
set_requires_grad(self.unet , snake_case )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Any:
# get the original timestep using init_timestep
_UpperCAmelCase = min(int(num_inference_steps * strength ) , snake_case )
_UpperCAmelCase = max(num_inference_steps - init_timestep , 0 )
_UpperCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=None ) -> int:
if not isinstance(snake_case , torch.Tensor ):
raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(snake_case )}' )
_UpperCAmelCase = image.to(device=snake_case , dtype=snake_case )
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = [
self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(snake_case )
]
_UpperCAmelCase = torch.cat(snake_case , dim=0 )
else:
_UpperCAmelCase = self.vae.encode(snake_case ).latent_dist.sample(snake_case )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCAmelCase = 0.18215 * init_latents
_UpperCAmelCase = init_latents.repeat_interleave(snake_case , dim=0 )
_UpperCAmelCase = randn_tensor(init_latents.shape , generator=snake_case , device=snake_case , dtype=snake_case )
# get latents
_UpperCAmelCase = self.scheduler.add_noise(snake_case , snake_case , snake_case )
_UpperCAmelCase = init_latents
return latents
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = self.coca_transform(snake_case ).unsqueeze(0 )
with torch.no_grad(), torch.cuda.amp.autocast():
_UpperCAmelCase = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) )
_UpperCAmelCase = self.coca_tokenizer.decode(generated[0].cpu().numpy() )
return generated.split('<end_of_text>' )[0].replace('<start_of_text>' , '' ).rstrip(' .,' )
def lowerCamelCase_ ( self , snake_case , snake_case ) -> Tuple:
_UpperCAmelCase = self.feature_extractor.preprocess(snake_case )
_UpperCAmelCase = torch.from_numpy(clip_image_input['pixel_values'][0] ).unsqueeze(0 ).to(self.device ).half()
_UpperCAmelCase = self.clip_model.get_image_features(snake_case )
_UpperCAmelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=snake_case )
_UpperCAmelCase = image_embeddings_clip.repeat_interleave(snake_case , dim=0 )
return image_embeddings_clip
@torch.enable_grad()
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , ) -> Dict:
_UpperCAmelCase = latents.detach().requires_grad_()
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case , snake_case )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case , snake_case , encoder_hidden_states=snake_case ).sample
if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
_UpperCAmelCase = self.scheduler.alphas_cumprod[timestep]
_UpperCAmelCase = 1 - alpha_prod_t
# compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_UpperCAmelCase = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5
_UpperCAmelCase = torch.sqrt(snake_case )
_UpperCAmelCase = pred_original_sample * (fac) + latents * (1 - fac)
elif isinstance(self.scheduler , snake_case ):
_UpperCAmelCase = self.scheduler.sigmas[index]
_UpperCAmelCase = latents - sigma * noise_pred
else:
raise ValueError(f'scheduler type {type(self.scheduler )} not supported' )
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCAmelCase = 1 / 0.18215 * sample
_UpperCAmelCase = self.vae.decode(snake_case ).sample
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = transforms.Resize(self.feature_extractor_size )(snake_case )
_UpperCAmelCase = self.normalize(snake_case ).to(latents.dtype )
_UpperCAmelCase = self.clip_model.get_image_features(snake_case )
_UpperCAmelCase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=snake_case )
_UpperCAmelCase = spherical_dist_loss(snake_case , snake_case ).mean() * clip_guidance_scale
_UpperCAmelCase = -torch.autograd.grad(snake_case , snake_case )[0]
if isinstance(self.scheduler , snake_case ):
_UpperCAmelCase = latents.detach() + grads * (sigma**2)
_UpperCAmelCase = noise_pred_original
else:
_UpperCAmelCase = noise_pred_original - torch.sqrt(snake_case ) * grads
return noise_pred, latents
@torch.no_grad()
def __call__( self , snake_case , snake_case , snake_case = None , snake_case = None , snake_case = 512 , snake_case = 512 , snake_case = 0.6 , snake_case = 50 , snake_case = 7.5 , snake_case = 1 , snake_case = 0.0 , snake_case = 100 , snake_case = None , snake_case = "pil" , snake_case = True , snake_case = 0.8 , snake_case = 0.1 , snake_case = 0.1 , ) -> Tuple:
if isinstance(snake_case , snake_case ) and len(snake_case ) != batch_size:
raise ValueError(f'You have passed {batch_size} batch_size, but only {len(snake_case )} generators.' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if isinstance(snake_case , torch.Generator ) and batch_size > 1:
_UpperCAmelCase = [generator] + [None] * (batch_size - 1)
_UpperCAmelCase = [
('model', self.coca_model is None),
('tokenizer', self.coca_tokenizer is None),
('transform', self.coca_transform is None),
]
_UpperCAmelCase = [x[0] for x in coca_is_none if x[1]]
_UpperCAmelCase = ', '.join(snake_case )
# generate prompts with coca model if prompt is None
if content_prompt is None:
if len(snake_case ):
raise ValueError(
f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
_UpperCAmelCase = self.get_image_description(snake_case )
if style_prompt is None:
if len(snake_case ):
raise ValueError(
f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.' )
_UpperCAmelCase = self.get_image_description(snake_case )
# get prompt text embeddings for content and style
_UpperCAmelCase = self.tokenizer(
snake_case , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=snake_case , return_tensors='pt' , )
_UpperCAmelCase = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]
_UpperCAmelCase = self.tokenizer(
snake_case , padding='max_length' , max_length=self.tokenizer.model_max_length , truncation=snake_case , return_tensors='pt' , )
_UpperCAmelCase = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]
_UpperCAmelCase = slerp(snake_case , snake_case , snake_case )
# duplicate text embeddings for each generation per prompt
_UpperCAmelCase = text_embeddings.repeat_interleave(snake_case , dim=0 )
# set timesteps
_UpperCAmelCase = 'offset' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_offset:
_UpperCAmelCase = 1
self.scheduler.set_timesteps(snake_case , **snake_case )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
self.scheduler.timesteps.to(self.device )
_UpperCAmelCase , _UpperCAmelCase = self.get_timesteps(snake_case , snake_case , self.device )
_UpperCAmelCase = timesteps[:1].repeat(snake_case )
# Preprocess image
_UpperCAmelCase = preprocess(snake_case , snake_case , snake_case )
_UpperCAmelCase = self.prepare_latents(
snake_case , snake_case , snake_case , text_embeddings.dtype , self.device , snake_case )
_UpperCAmelCase = preprocess(snake_case , snake_case , snake_case )
_UpperCAmelCase = self.prepare_latents(
snake_case , snake_case , snake_case , text_embeddings.dtype , self.device , snake_case )
_UpperCAmelCase = slerp(snake_case , snake_case , snake_case )
if clip_guidance_scale > 0:
_UpperCAmelCase = self.get_clip_image_embeddings(snake_case , snake_case )
_UpperCAmelCase = self.get_clip_image_embeddings(snake_case , snake_case )
_UpperCAmelCase = slerp(
snake_case , snake_case , snake_case )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
_UpperCAmelCase = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
_UpperCAmelCase = content_text_input.input_ids.shape[-1]
_UpperCAmelCase = self.tokenizer([''] , padding='max_length' , max_length=snake_case , return_tensors='pt' )
_UpperCAmelCase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt
_UpperCAmelCase = uncond_embeddings.repeat_interleave(snake_case , dim=0 )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_UpperCAmelCase = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
_UpperCAmelCase = (batch_size, self.unet.config.in_channels, height // 8, width // 8)
_UpperCAmelCase = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not work reproducibly on mps
_UpperCAmelCase = torch.randn(snake_case , generator=snake_case , device='cpu' , dtype=snake_case ).to(
self.device )
else:
_UpperCAmelCase = torch.randn(snake_case , generator=snake_case , device=self.device , dtype=snake_case )
else:
if latents.shape != latents_shape:
raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
_UpperCAmelCase = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
_UpperCAmelCase = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_UpperCAmelCase = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_UpperCAmelCase = {}
if accepts_eta:
_UpperCAmelCase = eta
# check if the scheduler accepts generator
_UpperCAmelCase = 'generator' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
if accepts_generator:
_UpperCAmelCase = generator
with self.progress_bar(total=snake_case ):
for i, t in enumerate(snake_case ):
# expand the latents if we are doing classifier free guidance
_UpperCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_UpperCAmelCase = self.scheduler.scale_model_input(snake_case , snake_case )
# predict the noise residual
_UpperCAmelCase = self.unet(snake_case , snake_case , encoder_hidden_states=snake_case ).sample
# perform classifier free guidance
if do_classifier_free_guidance:
_UpperCAmelCase , _UpperCAmelCase = noise_pred.chunk(2 )
_UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# perform clip guidance
if clip_guidance_scale > 0:
_UpperCAmelCase = (
text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
)
_UpperCAmelCase , _UpperCAmelCase = self.cond_fn(
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , )
# compute the previous noisy sample x_t -> x_t-1
_UpperCAmelCase = self.scheduler.step(snake_case , snake_case , snake_case , **snake_case ).prev_sample
# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
_UpperCAmelCase = 1 / 0.18215 * latents
_UpperCAmelCase = self.vae.decode(snake_case ).sample
_UpperCAmelCase = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase = self.numpy_to_pil(snake_case )
if not return_dict:
return (image, None)
return StableDiffusionPipelineOutput(images=snake_case , nsfw_content_detected=snake_case )
| 24 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = {
'task_specific_params': {
'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
}
}
_UpperCAmelCase = {
'task_specific_params.summarization.length_penalty': 1.0,
'task_specific_params.summarization.max_length': 128,
'task_specific_params.summarization.min_length': 12,
'task_specific_params.summarization.num_beams': 4,
'task_specific_params.summarization_cnn.length_penalty': 2.0,
'task_specific_params.summarization_cnn.max_length': 142,
'task_specific_params.summarization_cnn.min_length': 56,
'task_specific_params.summarization_cnn.num_beams': 4,
'task_specific_params.summarization_xsum.length_penalty': 1.0,
'task_specific_params.summarization_xsum.max_length': 62,
'task_specific_params.summarization_xsum.min_length': 11,
'task_specific_params.summarization_xsum.num_beams': 6,
}
self.assertEqual(flatten_dict(snake_case ) , snake_case )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(snake_case ) , x.transpose() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , transpose(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , transpose(snake_case , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case ) , np.asarray(transpose(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(transpose(snake_case , axes=(1, 2, 0) ) , np.asarray(transpose(snake_case , axes=(1, 2, 0) ) ) ) )
def lowerCamelCase_ ( self ) -> List[str]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.reshape(snake_case , (4, 3) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.reshape(snake_case , (12, 5) ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , reshape(snake_case , (4, 3) ).numpy() ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , reshape(snake_case , (12, 5) ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (4, 3) ) , np.asarray(reshape(snake_case , (4, 3) ) ) ) )
_UpperCAmelCase = np.random.randn(3 , 4 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(reshape(snake_case , (12, 5) ) , np.asarray(reshape(snake_case , (12, 5) ) ) ) )
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.squeeze(snake_case ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.squeeze(snake_case , axis=2 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , squeeze(snake_case ).numpy() ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , squeeze(snake_case , axis=2 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(1 , 3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case ) , np.asarray(squeeze(snake_case ) ) ) )
_UpperCAmelCase = np.random.randn(1 , 4 , 1 , 5 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(squeeze(snake_case , axis=2 ) , np.asarray(squeeze(snake_case , axis=2 ) ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.expand_dims(snake_case , axis=1 ) ) )
@require_torch
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = torch.tensor(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_tf
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = tf.constant(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , expand_dims(snake_case , axis=1 ).numpy() ) )
@require_flax
def lowerCamelCase_ ( self ) -> str:
_UpperCAmelCase = np.random.randn(3 , 4 )
_UpperCAmelCase = jnp.array(snake_case )
self.assertTrue(np.allclose(expand_dims(snake_case , axis=1 ) , np.asarray(expand_dims(snake_case , axis=1 ) ) ) )
| 24 | 1 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCAmelCase ( A : Tuple ):
'''simple docstring'''
for param in module.parameters():
_UpperCAmelCase = False
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = 'cuda' if torch.cuda.is_available() else 'cpu'
if torch.backends.mps.is_available() and torch.backends.mps.is_built():
_UpperCAmelCase = 'mps'
if device == "mps":
print(
'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
' with generations.' )
return device
def UpperCAmelCase ( A : Dict ):
'''simple docstring'''
_UpperCAmelCase = plt.imshow(A )
fig.axes.get_xaxis().set_visible(A )
fig.axes.get_yaxis().set_visible(A )
plt.show()
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = datetime.now()
_UpperCAmelCase = current_time.strftime('%H:%M:%S' )
return timestamp
| 24 |
"""simple docstring"""
import os
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = os.path.join(os.path.dirname(A ) , 'num.txt' )
with open(A ) as file_hand:
return str(sum(int(A ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 24 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowercase__ ( A ):
'''simple docstring'''
@staticmethod
@abstractmethod
def lowerCamelCase_ ( snake_case ) -> Any:
raise NotImplementedError()
@abstractmethod
def lowerCamelCase_ ( self ) -> Dict:
raise NotImplementedError()
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
'''tokenization_roberta''': ['''RobertaTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''RobertaTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RobertaForCausalLM''',
'''RobertaForMaskedLM''',
'''RobertaForMultipleChoice''',
'''RobertaForQuestionAnswering''',
'''RobertaForSequenceClassification''',
'''RobertaForTokenClassification''',
'''RobertaModel''',
'''RobertaPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFRobertaForCausalLM''',
'''TFRobertaForMaskedLM''',
'''TFRobertaForMultipleChoice''',
'''TFRobertaForQuestionAnswering''',
'''TFRobertaForSequenceClassification''',
'''TFRobertaForTokenClassification''',
'''TFRobertaMainLayer''',
'''TFRobertaModel''',
'''TFRobertaPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''FlaxRobertaForCausalLM''',
'''FlaxRobertaForMaskedLM''',
'''FlaxRobertaForMultipleChoice''',
'''FlaxRobertaForQuestionAnswering''',
'''FlaxRobertaForSequenceClassification''',
'''FlaxRobertaForTokenClassification''',
'''FlaxRobertaModel''',
'''FlaxRobertaPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 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 lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = 'ylacombe/bark-small'
_UpperCAmelCase = tempfile.mkdtemp()
_UpperCAmelCase = 'en_speaker_1'
_UpperCAmelCase = 'This is a test string'
_UpperCAmelCase = 'speaker_embeddings_path.json'
_UpperCAmelCase = 'speaker_embeddings'
def lowerCamelCase_ ( self , **snake_case ) -> Optional[int]:
return AutoTokenizer.from_pretrained(self.checkpoint , **snake_case )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
shutil.rmtree(self.tmpdirname )
def lowerCamelCase_ ( self ) -> Tuple:
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BarkProcessor(tokenizer=snake_case )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = 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 , )
_UpperCAmelCase = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
_UpperCAmelCase = 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 lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
_UpperCAmelCase = 35
_UpperCAmelCase = 2
_UpperCAmelCase = 8
_UpperCAmelCase = {
'semantic_prompt': np.ones(snake_case ),
'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len) ),
'fine_prompt': np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
_UpperCAmelCase = processor(text=self.input_string , voice_preset=snake_case )
_UpperCAmelCase = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(snake_case , np.array([] ) ).tolist() )
# test loading voice preset from npz file
_UpperCAmelCase = os.path.join(self.tmpdirname , 'file.npz' )
np.savez(snake_case , **snake_case )
_UpperCAmelCase = processor(text=self.input_string , voice_preset=snake_case )
_UpperCAmelCase = inputs['history_prompt']
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(snake_case , np.array([] ) ).tolist() )
# test loading voice preset from the hub
_UpperCAmelCase = processor(text=self.input_string , voice_preset=self.voice_preset )
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = self.get_tokenizer()
_UpperCAmelCase = BarkProcessor(tokenizer=snake_case )
_UpperCAmelCase = processor(text=self.input_string )
_UpperCAmelCase = tokenizer(
self.input_string , padding='max_length' , max_length=256 , add_special_tokens=snake_case , return_attention_mask=snake_case , return_token_type_ids=snake_case , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 24 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , *snake_case , **snake_case ) -> None:
warnings.warn(
'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use YolosImageProcessor instead.' , snake_case , )
super().__init__(*snake_case , **snake_case )
| 24 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
_UpperCAmelCase = get_activation('gelu' )
self.assertTrue(torch.allclose(gelu_python(snake_case ) , torch_builtin(snake_case ) ) )
self.assertFalse(torch.allclose(gelu_python(snake_case ) , gelu_new(snake_case ) ) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
_UpperCAmelCase = get_activation('gelu' )
_UpperCAmelCase = get_activation('gelu_10' )
_UpperCAmelCase = torch_builtin(snake_case )
_UpperCAmelCase = geluaa(snake_case )
_UpperCAmelCase = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(snake_case ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def lowerCamelCase_ ( self ) -> Tuple:
get_activation('gelu' )
get_activation('gelu_10' )
get_activation('gelu_fast' )
get_activation('gelu_new' )
get_activation('gelu_python' )
get_activation('gelu_pytorch_tanh' )
get_activation('linear' )
get_activation('mish' )
get_activation('quick_gelu' )
get_activation('relu' )
get_activation('sigmoid' )
get_activation('silu' )
get_activation('swish' )
get_activation('tanh' )
with self.assertRaises(snake_case ):
get_activation('bogus' )
with self.assertRaises(snake_case ):
get_activation(snake_case )
def lowerCamelCase_ ( self ) -> Optional[Any]:
_UpperCAmelCase = get_activation('gelu' )
_UpperCAmelCase = 1
_UpperCAmelCase = get_activation('gelu' )
self.assertEqual(acta.a , 1 )
with self.assertRaises(snake_case ):
_UpperCAmelCase = acta.a
| 24 |
"""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
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''beit'''
def __init__( self , snake_case=8192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1E-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ) -> str:
super().__init__(**snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = use_mask_token
_UpperCAmelCase = use_absolute_position_embeddings
_UpperCAmelCase = use_relative_position_bias
_UpperCAmelCase = use_shared_relative_position_bias
_UpperCAmelCase = layer_scale_init_value
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_UpperCAmelCase = out_indices
_UpperCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_UpperCAmelCase = use_auxiliary_head
_UpperCAmelCase = auxiliary_loss_weight
_UpperCAmelCase = auxiliary_channels
_UpperCAmelCase = auxiliary_num_convs
_UpperCAmelCase = auxiliary_concat_input
_UpperCAmelCase = semantic_loss_ignore_index
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
lowercase = [
'''openmmlab/upernet-convnext-tiny''',
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
lowercase = '''UperNetConfig'''
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case = 0 , snake_case = False , snake_case = 1 , ) -> None:
super().__init__()
_UpperCAmelCase = nn.Convad(
in_channels=snake_case , out_channels=snake_case , kernel_size=snake_case , padding=snake_case , bias=snake_case , dilation=snake_case , )
_UpperCAmelCase = nn.BatchNormad(snake_case )
_UpperCAmelCase = nn.ReLU()
def lowerCamelCase_ ( self , snake_case ) -> torch.Tensor:
_UpperCAmelCase = self.conv(snake_case )
_UpperCAmelCase = self.batch_norm(snake_case )
_UpperCAmelCase = self.activation(snake_case )
return output
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case ) -> None:
super().__init__()
_UpperCAmelCase = [
nn.AdaptiveAvgPoolad(snake_case ),
UperNetConvModule(snake_case , snake_case , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(snake_case ) , snake_case )
def lowerCamelCase_ ( self , snake_case ) -> torch.Tensor:
_UpperCAmelCase = input
for layer in self.layers:
_UpperCAmelCase = layer(snake_case )
return hidden_state
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case , snake_case , snake_case ) -> None:
super().__init__()
_UpperCAmelCase = pool_scales
_UpperCAmelCase = align_corners
_UpperCAmelCase = in_channels
_UpperCAmelCase = channels
_UpperCAmelCase = []
for i, pool_scale in enumerate(snake_case ):
_UpperCAmelCase = UperNetPyramidPoolingBlock(pool_scale=snake_case , in_channels=snake_case , channels=snake_case )
self.blocks.append(snake_case )
self.add_module(str(snake_case ) , snake_case )
def lowerCamelCase_ ( self , snake_case ) -> List[torch.Tensor]:
_UpperCAmelCase = []
for ppm in self.blocks:
_UpperCAmelCase = ppm(snake_case )
_UpperCAmelCase = nn.functional.interpolate(
snake_case , size=x.size()[2:] , mode='bilinear' , align_corners=self.align_corners )
ppm_outs.append(snake_case )
return ppm_outs
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case ) -> List[str]:
super().__init__()
_UpperCAmelCase = config
_UpperCAmelCase = config.pool_scales # e.g. (1, 2, 3, 6)
_UpperCAmelCase = in_channels
_UpperCAmelCase = config.hidden_size
_UpperCAmelCase = False
_UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
_UpperCAmelCase = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
_UpperCAmelCase = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
_UpperCAmelCase = nn.ModuleList()
_UpperCAmelCase = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
_UpperCAmelCase = UperNetConvModule(snake_case , self.channels , kernel_size=1 )
_UpperCAmelCase = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(snake_case )
self.fpn_convs.append(snake_case )
_UpperCAmelCase = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def lowerCamelCase_ ( self ) -> Optional[int]:
self.apply(self._init_weights )
def lowerCamelCase_ ( self , snake_case ) -> Tuple:
if isinstance(snake_case , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def lowerCamelCase_ ( self , snake_case ) -> Optional[int]:
_UpperCAmelCase = inputs[-1]
_UpperCAmelCase = [x]
psp_outs.extend(self.psp_modules(snake_case ) )
_UpperCAmelCase = torch.cat(snake_case , dim=1 )
_UpperCAmelCase = self.bottleneck(snake_case )
return output
def lowerCamelCase_ ( self , snake_case ) -> torch.Tensor:
# build laterals
_UpperCAmelCase = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(snake_case ) )
# build top-down path
_UpperCAmelCase = len(snake_case )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_UpperCAmelCase = laterals[i - 1].shape[2:]
_UpperCAmelCase = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=snake_case , mode='bilinear' , align_corners=self.align_corners )
# build outputs
_UpperCAmelCase = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_UpperCAmelCase = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='bilinear' , align_corners=self.align_corners )
_UpperCAmelCase = torch.cat(snake_case , dim=1 )
_UpperCAmelCase = self.fpn_bottleneck(snake_case )
_UpperCAmelCase = self.classifier(snake_case )
return output
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case , snake_case = 2 , snake_case = 3 , snake_case = 1 ) -> None:
super().__init__()
_UpperCAmelCase = config
_UpperCAmelCase = config.auxiliary_in_channels
_UpperCAmelCase = config.auxiliary_channels
_UpperCAmelCase = config.auxiliary_num_convs
_UpperCAmelCase = config.auxiliary_concat_input
_UpperCAmelCase = in_index
_UpperCAmelCase = (kernel_size // 2) * dilation
_UpperCAmelCase = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=snake_case , padding=snake_case , dilation=snake_case ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=snake_case , padding=snake_case , dilation=snake_case ) )
if self.num_convs == 0:
_UpperCAmelCase = nn.Identity()
else:
_UpperCAmelCase = nn.Sequential(*snake_case )
if self.concat_input:
_UpperCAmelCase = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=snake_case , padding=kernel_size // 2 )
_UpperCAmelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def lowerCamelCase_ ( self ) -> Dict:
self.apply(self._init_weights )
def lowerCamelCase_ ( self , snake_case ) -> Union[str, Any]:
if isinstance(snake_case , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def lowerCamelCase_ ( self , snake_case ) -> torch.Tensor:
# just take the relevant feature maps
_UpperCAmelCase = encoder_hidden_states[self.in_index]
_UpperCAmelCase = self.convs(snake_case )
if self.concat_input:
_UpperCAmelCase = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
_UpperCAmelCase = self.classifier(snake_case )
return output
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = UperNetConfig
_UpperCAmelCase = '''pixel_values'''
_UpperCAmelCase = True
def lowerCamelCase_ ( self , snake_case ) -> List[str]:
if isinstance(snake_case , snake_case ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def lowerCamelCase_ ( self ) -> Optional[int]:
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def lowerCamelCase_ ( self , snake_case , snake_case=False ) -> Any:
if isinstance(snake_case , snake_case ):
_UpperCAmelCase = value
lowercase = r'''
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
lowercase = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'''UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.''', A, )
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case ) -> Dict:
super().__init__(snake_case )
_UpperCAmelCase = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
_UpperCAmelCase = UperNetHead(snake_case , in_channels=self.backbone.channels )
_UpperCAmelCase = UperNetFCNHead(snake_case ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length' ) )
@replace_return_docstrings(output_type=snake_case , config_class=_CONFIG_FOR_DOC )
def lowerCamelCase_ ( self , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = None , ) -> Union[tuple, SemanticSegmenterOutput]:
_UpperCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_UpperCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_UpperCAmelCase = output_attentions if output_attentions is not None else self.config.output_attentions
_UpperCAmelCase = self.backbone.forward_with_filtered_kwargs(
snake_case , output_hidden_states=snake_case , output_attentions=snake_case )
_UpperCAmelCase = outputs.feature_maps
_UpperCAmelCase = self.decode_head(snake_case )
_UpperCAmelCase = nn.functional.interpolate(snake_case , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=snake_case )
_UpperCAmelCase = None
if self.auxiliary_head is not None:
_UpperCAmelCase = self.auxiliary_head(snake_case )
_UpperCAmelCase = nn.functional.interpolate(
snake_case , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=snake_case )
_UpperCAmelCase = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('The number of labels should be greater than one' )
else:
# compute weighted loss
_UpperCAmelCase = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
_UpperCAmelCase = loss_fct(snake_case , snake_case )
_UpperCAmelCase = loss_fct(snake_case , snake_case )
_UpperCAmelCase = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
_UpperCAmelCase = (logits,) + outputs[1:]
else:
_UpperCAmelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=snake_case , logits=snake_case , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 24 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase = logging.getLogger(__name__)
if __name__ == "__main__":
lowercase = argparse.ArgumentParser(
description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
)
parser.add_argument(
'''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
)
parser.add_argument(
'''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
)
parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
lowercase = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, '''rb''') as fp:
lowercase = pickle.load(fp)
logger.info('''Counting occurrences for MLM.''')
lowercase = Counter()
for tk_ids in data:
counter.update(tk_ids)
lowercase = [0] * args.vocab_size
for k, v in counter.items():
lowercase = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, '''wb''') as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 24 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
lowercase = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 24 |
"""simple docstring"""
from itertools import permutations
def UpperCAmelCase ( A : tuple ):
'''simple docstring'''
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
_UpperCAmelCase = [7, 11, 13, 17]
for i, test in enumerate(A ):
if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
return False
return True
def UpperCAmelCase ( A : int = 10 ):
'''simple docstring'''
return sum(
int(''.join(map(A , A ) ) )
for num in permutations(range(A ) )
if is_substring_divisible(A ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 24 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''gpt_neox'''
def __init__( self , snake_case=50432 , snake_case=6144 , snake_case=44 , snake_case=64 , snake_case=24576 , snake_case="gelu" , snake_case=0.25 , snake_case=10000 , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case=2048 , snake_case=0.02 , snake_case=1E-5 , snake_case=True , snake_case=0 , snake_case=2 , snake_case=False , snake_case=True , snake_case=None , **snake_case , ) -> Union[str, Any]:
super().__init__(bos_token_id=snake_case , eos_token_id=snake_case , **snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = rotary_pct
_UpperCAmelCase = rotary_emb_base
_UpperCAmelCase = attention_dropout
_UpperCAmelCase = hidden_dropout
_UpperCAmelCase = classifier_dropout
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = use_cache
_UpperCAmelCase = tie_word_embeddings
_UpperCAmelCase = use_parallel_residual
_UpperCAmelCase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
'The hidden size is not divisble by the number of attention heads! Make sure to update them!' )
def lowerCamelCase_ ( self ) -> Dict:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , snake_case ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
f'got {self.rope_scaling}' )
_UpperCAmelCase = self.rope_scaling.get('type' , snake_case )
_UpperCAmelCase = self.rope_scaling.get('factor' , snake_case )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(snake_case , snake_case ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
'''tokenization_mvp''': ['''MvpTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = ['''MvpTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MvpForCausalLM''',
'''MvpForConditionalGeneration''',
'''MvpForQuestionAnswering''',
'''MvpForSequenceClassification''',
'''MvpModel''',
'''MvpPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
from .tokenization_mvp import MvpTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mvp_fast import MvpTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mvp import (
MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
MvpForCausalLM,
MvpForConditionalGeneration,
MvpForQuestionAnswering,
MvpForSequenceClassification,
MvpModel,
MvpPreTrainedModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowercase = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
'''processing_trocr''': ['''TrOCRProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TrOCRForCausalLM''',
'''TrOCRPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionConfig''',
],
'''processing_clipseg''': ['''CLIPSegProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [
'''CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPSegModel''',
'''CLIPSegPreTrainedModel''',
'''CLIPSegTextModel''',
'''CLIPSegVisionModel''',
'''CLIPSegForImageSegmentation''',
]
if TYPE_CHECKING:
from .configuration_clipseg import (
CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPSegConfig,
CLIPSegTextConfig,
CLIPSegVisionConfig,
)
from .processing_clipseg import CLIPSegProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clipseg import (
CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPSegForImageSegmentation,
CLIPSegModel,
CLIPSegPreTrainedModel,
CLIPSegTextModel,
CLIPSegVisionModel,
)
else:
import sys
lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 24 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
def UpperCAmelCase ( A : int , A : str , A : Tuple ):
'''simple docstring'''
_UpperCAmelCase = WavaVecaForSequenceClassification.from_pretrained(A , config=A )
_UpperCAmelCase = downstream_dict['projector.weight']
_UpperCAmelCase = downstream_dict['projector.bias']
_UpperCAmelCase = downstream_dict['model.post_net.linear.weight']
_UpperCAmelCase = downstream_dict['model.post_net.linear.bias']
return model
def UpperCAmelCase ( A : str , A : int , A : Optional[Any] ):
'''simple docstring'''
_UpperCAmelCase = WavaVecaForAudioFrameClassification.from_pretrained(A , config=A )
_UpperCAmelCase = downstream_dict['model.linear.weight']
_UpperCAmelCase = downstream_dict['model.linear.bias']
return model
def UpperCAmelCase ( A : int , A : Optional[Any] , A : Dict ):
'''simple docstring'''
_UpperCAmelCase = WavaVecaForXVector.from_pretrained(A , config=A )
_UpperCAmelCase = downstream_dict['connector.weight']
_UpperCAmelCase = downstream_dict['connector.bias']
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_UpperCAmelCase = downstream_dict[
f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
]
_UpperCAmelCase = downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']
_UpperCAmelCase = downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
_UpperCAmelCase = downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
_UpperCAmelCase = downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
_UpperCAmelCase = downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
_UpperCAmelCase = downstream_dict['objective.W']
return model
@torch.no_grad()
def UpperCAmelCase ( A : Optional[int] , A : Optional[Any] , A : str , A : Dict ):
'''simple docstring'''
_UpperCAmelCase = torch.load(A , map_location='cpu' )
_UpperCAmelCase = checkpoint['Downstream']
_UpperCAmelCase = WavaVecaConfig.from_pretrained(A )
_UpperCAmelCase = WavaVecaFeatureExtractor.from_pretrained(
A , return_attention_mask=A , do_normalize=A )
_UpperCAmelCase = hf_config.architectures[0]
if arch.endswith('ForSequenceClassification' ):
_UpperCAmelCase = convert_classification(A , A , A )
elif arch.endswith('ForAudioFrameClassification' ):
_UpperCAmelCase = convert_diarization(A , A , A )
elif arch.endswith('ForXVector' ):
_UpperCAmelCase = convert_xvector(A , A , A )
else:
raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )
if hf_config.use_weighted_layer_sum:
_UpperCAmelCase = checkpoint['Featurizer']['weights']
hf_feature_extractor.save_pretrained(A )
hf_model.save_pretrained(A )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
lowercase = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 24 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase = logging.get_logger(__name__)
lowercase = {
'''microsoft/swin-tiny-patch4-window7-224''': (
'''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowercase__ ( A, A ):
'''simple docstring'''
_UpperCAmelCase = '''swin'''
_UpperCAmelCase = {
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self , snake_case=224 , snake_case=4 , snake_case=3 , snake_case=96 , snake_case=[2, 2, 6, 2] , snake_case=[3, 6, 12, 24] , snake_case=7 , snake_case=4.0 , snake_case=True , snake_case=0.0 , snake_case=0.0 , snake_case=0.1 , snake_case="gelu" , snake_case=False , snake_case=0.02 , snake_case=1E-5 , snake_case=32 , snake_case=None , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(**snake_case )
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_size
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = len(snake_case )
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
_UpperCAmelCase = mlp_ratio
_UpperCAmelCase = qkv_bias
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = drop_path_rate
_UpperCAmelCase = hidden_act
_UpperCAmelCase = use_absolute_embeddings
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = initializer_range
_UpperCAmelCase = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_UpperCAmelCase = int(embed_dim * 2 ** (len(snake_case ) - 1) )
_UpperCAmelCase = ['stem'] + [f'stage{idx}' for idx in range(1 , len(snake_case ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices(
out_features=snake_case , out_indices=snake_case , stage_names=self.stage_names )
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self ) -> float:
return 1E-4
| 24 | 1 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
lowercase = logging.get_logger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case=None , **snake_case ) -> List[Any]:
warnings.warn(
'`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
'instead.' , snake_case , )
super().__init__(args=snake_case , **snake_case )
| 24 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowercase__ ( nn.Module ):
'''simple docstring'''
def __init__( self , snake_case = 16 , snake_case = 88 , snake_case = None , snake_case = 1 , snake_case = 0.0 , snake_case = 32 , snake_case = None , snake_case = False , snake_case = None , snake_case = None , snake_case = "geglu" , snake_case = None , ) -> str:
super().__init__()
_UpperCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=snake_case , attention_head_dim=snake_case , in_channels=snake_case , num_layers=snake_case , dropout=snake_case , norm_num_groups=snake_case , cross_attention_dim=snake_case , attention_bias=snake_case , sample_size=snake_case , num_vector_embeds=snake_case , activation_fn=snake_case , num_embeds_ada_norm=snake_case , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_UpperCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_UpperCAmelCase = [77, 257]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_UpperCAmelCase = [1, 0]
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case=None , snake_case=None , snake_case=None , snake_case = True , ) -> Any:
_UpperCAmelCase = hidden_states
_UpperCAmelCase = []
_UpperCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_UpperCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_UpperCAmelCase = self.transformer_index_for_condition[i]
_UpperCAmelCase = self.transformers[transformer_index](
snake_case , encoder_hidden_states=snake_case , timestep=snake_case , cross_attention_kwargs=snake_case , return_dict=snake_case , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_UpperCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_UpperCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=snake_case )
| 24 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case = None , snake_case = None , snake_case = None , snake_case = None , snake_case = False , snake_case = False , snake_case = None , **snake_case , ) -> Tuple:
_UpperCAmelCase = path_or_paths
_UpperCAmelCase = split if split or isinstance(snake_case , snake_case ) else 'train'
_UpperCAmelCase = features
_UpperCAmelCase = cache_dir
_UpperCAmelCase = keep_in_memory
_UpperCAmelCase = streaming
_UpperCAmelCase = num_proc
_UpperCAmelCase = kwargs
@abstractmethod
def lowerCamelCase_ ( self ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
pass
class lowercase__ ( A ):
'''simple docstring'''
def __init__( self , snake_case = None , snake_case = None , snake_case = False , snake_case = False , snake_case = None , **snake_case , ) -> Tuple:
_UpperCAmelCase = features
_UpperCAmelCase = cache_dir
_UpperCAmelCase = keep_in_memory
_UpperCAmelCase = streaming
_UpperCAmelCase = num_proc
_UpperCAmelCase = kwargs
@abstractmethod
def lowerCamelCase_ ( self ) -> Union[Dataset, IterableDataset]:
pass
| 24 |
"""simple docstring"""
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import CvtForImageClassification, CvtModel
from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase__ ( A ):
'''simple docstring'''
def lowerCamelCase_ ( self ) -> int:
_UpperCAmelCase = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case , 'embed_dim' ) )
self.parent.assertTrue(hasattr(snake_case , 'num_heads' ) )
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case , snake_case=13 , snake_case=64 , snake_case=3 , snake_case=[16, 48, 96] , snake_case=[1, 3, 6] , snake_case=[1, 2, 10] , snake_case=[7, 3, 3] , snake_case=[4, 2, 2] , snake_case=[2, 1, 1] , snake_case=[2, 2, 2] , snake_case=[False, False, True] , snake_case=[0.0, 0.0, 0.0] , snake_case=0.02 , snake_case=1E-12 , snake_case=True , snake_case=True , snake_case=2 , ) -> Tuple:
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = image_size
_UpperCAmelCase = patch_sizes
_UpperCAmelCase = patch_stride
_UpperCAmelCase = patch_padding
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = num_labels
_UpperCAmelCase = num_channels
_UpperCAmelCase = embed_dim
_UpperCAmelCase = num_heads
_UpperCAmelCase = stride_kv
_UpperCAmelCase = depth
_UpperCAmelCase = cls_token
_UpperCAmelCase = attention_drop_rate
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase = None
if self.use_labels:
_UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self ) -> List[str]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[int]:
_UpperCAmelCase = CvtModel(config=snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case )
_UpperCAmelCase = (self.image_size, self.image_size)
_UpperCAmelCase , _UpperCAmelCase = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_UpperCAmelCase = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_UpperCAmelCase = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowerCamelCase_ ( self , snake_case , snake_case , snake_case ) -> Optional[Any]:
_UpperCAmelCase = self.num_labels
_UpperCAmelCase = CvtForImageClassification(snake_case )
model.to(snake_case )
model.eval()
_UpperCAmelCase = model(snake_case , labels=snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = config_and_inputs
_UpperCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( A, A, unittest.TestCase ):
'''simple docstring'''
_UpperCAmelCase = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
_UpperCAmelCase = (
{'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
if is_torch_available()
else {}
)
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
_UpperCAmelCase = False
def lowerCamelCase_ ( self ) -> Union[str, Any]:
_UpperCAmelCase = CvtModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=snake_case , has_text_modality=snake_case , hidden_size=37 )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCamelCase_ ( self ) -> Union[str, Any]:
return
@unittest.skip(reason='Cvt does not output attentions' )
def lowerCamelCase_ ( self ) -> str:
pass
@unittest.skip(reason='Cvt does not use inputs_embeds' )
def lowerCamelCase_ ( self ) -> int:
pass
@unittest.skip(reason='Cvt does not support input and output embeddings' )
def lowerCamelCase_ ( self ) -> Union[str, Any]:
pass
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(snake_case )
_UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase = [*signature.parameters.keys()]
_UpperCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case )
def lowerCamelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(snake_case , snake_case , snake_case ):
_UpperCAmelCase = model_class(snake_case )
model.to(snake_case )
model.eval()
with torch.no_grad():
_UpperCAmelCase = model(**self._prepare_for_class(snake_case , snake_case ) )
_UpperCAmelCase = outputs.hidden_states
_UpperCAmelCase = len(self.model_tester.depth )
self.assertEqual(len(snake_case ) , snake_case )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase = True
check_hidden_states_output(snake_case , snake_case , snake_case )
def lowerCamelCase_ ( self ) -> Any:
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case )
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowerCamelCase_ ( self ) -> Dict:
pass
@slow
def lowerCamelCase_ ( self ) -> Dict:
for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase = CvtModel.from_pretrained(snake_case )
self.assertIsNotNone(snake_case )
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCamelCase_ ( self ) -> List[Any]:
return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCamelCase_ ( self ) -> Dict:
_UpperCAmelCase = CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(snake_case )
_UpperCAmelCase = self.default_image_processor
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=snake_case , return_tensors='pt' ).to(snake_case )
# forward pass
with torch.no_grad():
_UpperCAmelCase = model(**snake_case )
# verify the logits
_UpperCAmelCase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , snake_case )
_UpperCAmelCase = torch.tensor([0.9285, 0.9015, -0.3150] ).to(snake_case )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case , atol=1E-4 ) )
| 24 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowercase = ['''small''', '''medium''', '''large''']
lowercase = '''lm_head.decoder.weight'''
lowercase = '''lm_head.weight'''
def UpperCAmelCase ( A : str , A : str ):
'''simple docstring'''
_UpperCAmelCase = torch.load(A )
_UpperCAmelCase = d.pop(A )
os.makedirs(A , exist_ok=A )
torch.save(A , os.path.join(A , A ) )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
lowercase = parser.parse_args()
for MODEL in DIALOGPT_MODELS:
lowercase = os.path.join(args.dialogpt_path, F'''{MODEL}_ft.pkl''')
lowercase = F'''./DialoGPT-{MODEL}'''
convert_dialogpt_checkpoint(
checkpoint_path,
pytorch_dump_folder_path,
)
| 24 |
"""simple docstring"""
from __future__ import annotations
from cmath import sqrt
def UpperCAmelCase ( A : int , A : int , A : int ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
_UpperCAmelCase = b * b - 4 * a * c
_UpperCAmelCase = (-b + sqrt(A )) / (2 * a)
_UpperCAmelCase = (-b - sqrt(A )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCAmelCase ( ):
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 24 | 1 |
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