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from __future__ import annotations from random import random from typing import Generic, TypeVar SCREAMING_SNAKE_CASE = TypeVar('KT') SCREAMING_SNAKE_CASE = TypeVar('VT') class __UpperCAmelCase ( Generic[KT, VT] ): """simple docstring""" def __init__( self , __A = "root" , __A = None ): __a = key __a = value __a = [] def __repr__( self ): return f'''Node({self.key}: {self.value})''' @property def snake_case_ ( self ): return len(self.forward ) class __UpperCAmelCase ( Generic[KT, VT] ): """simple docstring""" def __init__( self , __A = 0.5 , __A = 16 ): __a = Node[KT, VT]() __a = 0 __a = p __a = max_level def __str__( self ): __a = list(self ) if len(__A ) == 0: return f'''SkipList(level={self.level})''' __a = max((len(str(__A ) ) for item in items) , default=4 ) __a = max(__A , 4 ) + 4 __a = self.head __a = [] __a = node.forward.copy() lines.append(f'''[{node.key}]'''.ljust(__A , """-""" ) + """* """ * len(__A ) ) lines.append(""" """ * label_size + """| """ * len(__A ) ) while len(node.forward ) != 0: __a = node.forward[0] lines.append( f'''[{node.key}]'''.ljust(__A , """-""" ) + """ """.join(str(n.key ) if n.key == node.key else """|""" for n in forwards ) ) lines.append(""" """ * label_size + """| """ * len(__A ) ) __a = node.forward lines.append("""None""".ljust(__A ) + """* """ * len(__A ) ) return f'''SkipList(level={self.level})\n''' + "\n".join(__A ) def __iter__( self ): __a = self.head while len(node.forward ) != 0: yield node.forward[0].key __a = node.forward[0] def snake_case_ ( self ): __a = 1 while random() < self.p and level < self.max_level: level += 1 return level def snake_case_ ( self , __A ): __a = [] __a = self.head for i in reversed(range(self.level ) ): # i < node.level - When node level is lesser than `i` decrement `i`. # node.forward[i].key < key - Jumping to node with key value higher # or equal to searched key would result # in skipping searched key. while i < node.level and node.forward[i].key < key: __a = node.forward[i] # Each leftmost node (relative to searched node) will potentially have to # be updated. update_vector.append(__A ) update_vector.reverse() # Note that we were inserting values in reverse order. # len(node.forward) != 0 - If current node doesn't contain any further # references then searched key is not present. # node.forward[0].key == key - Next node key should be equal to search key # if key is present. if len(node.forward ) != 0 and node.forward[0].key == key: return node.forward[0], update_vector else: return None, update_vector def snake_case_ ( self , __A ): __a , __a = self._locate_node(__A ) if node is not None: for i, update_node in enumerate(__A ): # Remove or replace all references to removed node. if update_node.level > i and update_node.forward[i].key == key: if node.level > i: __a = node.forward[i] else: __a = update_node.forward[:i] def snake_case_ ( self , __A , __A ): __a , __a = self._locate_node(__A ) if node is not None: __a = value else: __a = self.random_level() if level > self.level: # After level increase we have to add additional nodes to head. for _ in range(self.level - 1 , __A ): update_vector.append(self.head ) __a = level __a = Node(__A , __A ) for i, update_node in enumerate(update_vector[:level] ): # Change references to pass through new node. if update_node.level > i: new_node.forward.append(update_node.forward[i] ) if update_node.level < i + 1: update_node.forward.append(__A ) else: __a = new_node def snake_case_ ( self , __A ): __a , __a = self._locate_node(__A ) if node is not None: return node.value return None def a (): __a = SkipList() skip_list.insert("""Key1""" , 3 ) skip_list.insert("""Key2""" , 12 ) skip_list.insert("""Key3""" , 41 ) skip_list.insert("""Key4""" , -19 ) __a = skip_list.head __a = {} while node.level != 0: __a = node.forward[0] __a = node.value assert len(lowerCAmelCase__ ) == 4 assert all_values["Key1"] == 3 assert all_values["Key2"] == 12 assert all_values["Key3"] == 41 assert all_values["Key4"] == -19 def a (): __a = SkipList() skip_list.insert("""Key1""" , 10 ) skip_list.insert("""Key1""" , 12 ) skip_list.insert("""Key5""" , 7 ) skip_list.insert("""Key7""" , 10 ) skip_list.insert("""Key10""" , 5 ) skip_list.insert("""Key7""" , 7 ) skip_list.insert("""Key5""" , 5 ) skip_list.insert("""Key10""" , 10 ) __a = skip_list.head __a = {} while node.level != 0: __a = node.forward[0] __a = node.value if len(lowerCAmelCase__ ) != 4: print() assert len(lowerCAmelCase__ ) == 4 assert all_values["Key1"] == 12 assert all_values["Key7"] == 7 assert all_values["Key5"] == 5 assert all_values["Key10"] == 10 def a (): __a = SkipList() assert skip_list.find("""Some key""" ) is None def a (): __a = SkipList() skip_list.insert("""Key2""" , 20 ) assert skip_list.find("""Key2""" ) == 20 skip_list.insert("""Some Key""" , 10 ) skip_list.insert("""Key2""" , 8 ) skip_list.insert("""V""" , 13 ) assert skip_list.find("""Y""" ) is None assert skip_list.find("""Key2""" ) == 8 assert skip_list.find("""Some Key""" ) == 10 assert skip_list.find("""V""" ) == 13 def a (): __a = SkipList() skip_list.delete("""Some key""" ) assert len(skip_list.head.forward ) == 0 def a (): __a = SkipList() skip_list.insert("""Key1""" , 12 ) skip_list.insert("""V""" , 13 ) skip_list.insert("""X""" , 14 ) skip_list.insert("""Key2""" , 15 ) skip_list.delete("""V""" ) skip_list.delete("""Key2""" ) assert skip_list.find("""V""" ) is None assert skip_list.find("""Key2""" ) is None def a (): __a = SkipList() skip_list.insert("""Key1""" , 12 ) skip_list.insert("""V""" , 13 ) skip_list.insert("""X""" , 14 ) skip_list.insert("""Key2""" , 15 ) skip_list.delete("""V""" ) assert skip_list.find("""V""" ) is None assert skip_list.find("""X""" ) == 14 assert skip_list.find("""Key1""" ) == 12 assert skip_list.find("""Key2""" ) == 15 skip_list.delete("""X""" ) assert skip_list.find("""V""" ) is None assert skip_list.find("""X""" ) is None assert skip_list.find("""Key1""" ) == 12 assert skip_list.find("""Key2""" ) == 15 skip_list.delete("""Key1""" ) assert skip_list.find("""V""" ) is None assert skip_list.find("""X""" ) is None assert skip_list.find("""Key1""" ) is None assert skip_list.find("""Key2""" ) == 15 skip_list.delete("""Key2""" ) assert skip_list.find("""V""" ) is None assert skip_list.find("""X""" ) is None assert skip_list.find("""Key1""" ) is None assert skip_list.find("""Key2""" ) is None def a (): __a = SkipList() skip_list.insert("""Key1""" , 12 ) skip_list.insert("""V""" , 13 ) skip_list.insert("""X""" , 142 ) skip_list.insert("""Key2""" , 15 ) skip_list.delete("""X""" ) def traverse_keys(lowerCAmelCase__ ): yield node.key for forward_node in node.forward: yield from traverse_keys(lowerCAmelCase__ ) assert len(set(traverse_keys(skip_list.head ) ) ) == 4 def a (): def is_sorted(lowerCAmelCase__ ): return all(next_item >= item for item, next_item in zip(lowerCAmelCase__ , lst[1:] ) ) __a = SkipList() for i in range(10 ): skip_list.insert(lowerCAmelCase__ , lowerCAmelCase__ ) assert is_sorted(list(lowerCAmelCase__ ) ) skip_list.delete(5 ) skip_list.delete(8 ) skip_list.delete(2 ) assert is_sorted(list(lowerCAmelCase__ ) ) skip_list.insert(-12 , -12 ) skip_list.insert(77 , 77 ) assert is_sorted(list(lowerCAmelCase__ ) ) def a (): for _ in range(100 ): # Repeat test 100 times due to the probabilistic nature of skip list # random values == random bugs test_insert() test_insert_overrides_existing_value() test_searching_empty_list_returns_none() test_search() test_deleting_item_from_empty_list_do_nothing() test_deleted_items_are_not_founded_by_find_method() test_delete_removes_only_given_key() test_delete_doesnt_leave_dead_nodes() test_iter_always_yields_sorted_values() def a (): __a = SkipList() skip_list.insert(2 , """2""" ) skip_list.insert(4 , """4""" ) skip_list.insert(6 , """4""" ) skip_list.insert(4 , """5""" ) skip_list.insert(8 , """4""" ) skip_list.insert(9 , """4""" ) skip_list.delete(4 ) print(lowerCAmelCase__ ) if __name__ == "__main__": import doctest doctest.testmod() main()
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'''simple docstring''' def A_ ( snake_case = 600851475143 ): try: SCREAMING_SNAKE_CASE:str = int(snake_case ) except (TypeError, ValueError): raise TypeError("Parameter n must be int or castable to int." ) if n <= 0: raise ValueError("Parameter n must be greater than or equal to one." ) SCREAMING_SNAKE_CASE:Optional[Any] = 1 SCREAMING_SNAKE_CASE:Optional[Any] = 2 while i * i <= n: while n % i == 0: SCREAMING_SNAKE_CASE:List[Any] = i n //= i i += 1 if n > 1: SCREAMING_SNAKE_CASE:List[str] = n return int(snake_case ) if __name__ == "__main__": print(f'''{solution() = }''')
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) UpperCAmelCase = { 'configuration_blenderbot': [ 'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BlenderbotConfig', 'BlenderbotOnnxConfig', ], 'tokenization_blenderbot': ['BlenderbotTokenizer'], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase = ['BlenderbotTokenizerFast'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase = [ 'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST', 'BlenderbotForCausalLM', 'BlenderbotForConditionalGeneration', 'BlenderbotModel', 'BlenderbotPreTrainedModel', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase = [ 'TFBlenderbotForConditionalGeneration', 'TFBlenderbotModel', 'TFBlenderbotPreTrainedModel', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase = [ 'FlaxBlenderbotForConditionalGeneration', 'FlaxBlenderbotModel', 'FlaxBlenderbotPreTrainedModel', ] if TYPE_CHECKING: from .configuration_blenderbot import ( BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig, BlenderbotOnnxConfig, ) from .tokenization_blenderbot import BlenderbotTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_blenderbot_fast import BlenderbotTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_blenderbot import ( BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST, BlenderbotForCausalLM, BlenderbotForConditionalGeneration, BlenderbotModel, BlenderbotPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_blenderbot import ( TFBlenderbotForConditionalGeneration, TFBlenderbotModel, TFBlenderbotPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_blenderbot import ( FlaxBlenderbotForConditionalGeneration, FlaxBlenderbotModel, FlaxBlenderbotPreTrainedModel, ) else: import sys UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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'''simple docstring''' def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] ) -> str: """simple docstring""" global f # a global dp table for knapsack if f[i][j] < 0: if j < wt[i - 1]: lowerCAmelCase = mf_knapsack(i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else: lowerCAmelCase = max( mf_knapsack(i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , mf_knapsack(i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , j - wt[i - 1] ) + val[i - 1] , ) lowerCAmelCase = val return f[i][j] def _snake_case ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : str ) -> Any: """simple docstring""" lowerCAmelCase = [[0] * (w + 1) for _ in range(n + 1 )] for i in range(1 , n + 1 ): for w_ in range(1 , w + 1 ): if wt[i - 1] <= w_: lowerCAmelCase = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] ) else: lowerCAmelCase = dp[i - 1][w_] return dp[n][w_], dp def _snake_case ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list ) -> List[str]: """simple docstring""" if not (isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ) and isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) )): raise ValueError( """Both the weights and values vectors must be either lists or tuples""" ) lowerCAmelCase = len(_SCREAMING_SNAKE_CASE ) if num_items != len(_SCREAMING_SNAKE_CASE ): lowerCAmelCase = ( """The number of weights must be the same as the number of values.\n""" f'But got {num_items} weights and {len(_SCREAMING_SNAKE_CASE )} values' ) raise ValueError(_SCREAMING_SNAKE_CASE ) for i in range(_SCREAMING_SNAKE_CASE ): if not isinstance(wt[i] , _SCREAMING_SNAKE_CASE ): lowerCAmelCase = ( """All weights must be integers but got weight of """ f'type {type(wt[i] )} at index {i}' ) raise TypeError(_SCREAMING_SNAKE_CASE ) lowerCAmelCase, lowerCAmelCase = knapsack(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) lowerCAmelCase = set() _construct_solution(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) return optimal_val, example_optional_set def _snake_case ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : set ) -> str: """simple docstring""" # for the current item i at a maximum weight j to be part of an optimal subset, # the optimal value at (i, j) must be greater than the optimal value at (i-1, j). # where i - 1 means considering only the previous items at the given maximum weight if i > 0 and j > 0: if dp[i - 1][j] == dp[i][j]: _construct_solution(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , i - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else: optimal_set.add(_SCREAMING_SNAKE_CASE ) _construct_solution(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , i - 1 , j - wt[i - 1] , _SCREAMING_SNAKE_CASE ) if __name__ == "__main__": UpperCAmelCase = [3, 2, 4, 4] UpperCAmelCase = [4, 3, 2, 3] UpperCAmelCase = 4 UpperCAmelCase = 6 UpperCAmelCase = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)] UpperCAmelCase , UpperCAmelCase = knapsack(w, wt, val, n) print(optimal_solution) print(mf_knapsack(n, wt, val, w)) # switched the n and w # testing the dynamic programming problem with example # the optimal subset for the above example are items 3 and 4 UpperCAmelCase , UpperCAmelCase = knapsack_with_example_solution(w, wt, val) assert optimal_solution == 8 assert optimal_subset == {3, 4} print('optimal_value = ', optimal_solution) print('An optimal subset corresponding to the optimal value', optimal_subset)
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from collections import namedtuple import requests from lxml import html # type: ignore lowercase_ = namedtuple('covid_data', 'cases deaths recovered') def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = "https://www.worldometers.info/coronavirus/" ) -> covid_data: _a = '''//div[@class = "maincounter-number"]/span/text()''' return covid_data(*html.fromstring(requests.get(UpperCAmelCase_ ).content ).xpath(UpperCAmelCase_ ) ) lowercase_ = 'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}' print(fmt.format(*covid_stats()))
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from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCamelCase__ = logging.get_logger(__name__) UpperCamelCase__ = { 'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json', } class UpperCAmelCase__ ( A_ ): '''simple docstring''' UpperCAmelCase_ = '''mra''' def __init__( self : Union[str, Any] , UpperCamelCase : Optional[Any]=5_02_65 , UpperCamelCase : Any=7_68 , UpperCamelCase : Tuple=12 , UpperCamelCase : Union[str, Any]=12 , UpperCamelCase : int=30_72 , UpperCamelCase : Any="gelu" , UpperCamelCase : Any=0.1 , UpperCamelCase : List[Any]=0.1 , UpperCamelCase : Optional[Any]=5_12 , UpperCamelCase : Union[str, Any]=1 , UpperCamelCase : Tuple=0.02 , UpperCamelCase : Union[str, Any]=1E-5 , UpperCamelCase : Tuple="absolute" , UpperCamelCase : Union[str, Any]=4 , UpperCamelCase : Optional[Any]="full" , UpperCamelCase : List[str]=0 , UpperCamelCase : Tuple=0 , UpperCamelCase : Any=1 , UpperCamelCase : Any=0 , UpperCamelCase : Optional[int]=2 , **UpperCamelCase : Union[str, Any] , ): """simple docstring""" super().__init__(pad_token_id=UpperCamelCase , bos_token_id=UpperCamelCase , eos_token_id=UpperCamelCase , **UpperCamelCase ) _lowercase : List[Any] = vocab_size _lowercase : Any = max_position_embeddings _lowercase : List[str] = hidden_size _lowercase : Union[str, Any] = num_hidden_layers _lowercase : Optional[Any] = num_attention_heads _lowercase : Any = intermediate_size _lowercase : Any = hidden_act _lowercase : str = hidden_dropout_prob _lowercase : str = attention_probs_dropout_prob _lowercase : int = initializer_range _lowercase : Dict = type_vocab_size _lowercase : Union[str, Any] = layer_norm_eps _lowercase : Tuple = position_embedding_type _lowercase : List[str] = block_per_row _lowercase : int = approx_mode _lowercase : Optional[Any] = initial_prior_first_n_blocks _lowercase : Dict = initial_prior_diagonal_n_blocks
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from .integrations import ( is_optuna_available, is_ray_available, is_sigopt_available, is_wandb_available, run_hp_search_optuna, run_hp_search_ray, run_hp_search_sigopt, run_hp_search_wandb, ) from .trainer_utils import ( HPSearchBackend, default_hp_space_optuna, default_hp_space_ray, default_hp_space_sigopt, default_hp_space_wandb, ) from .utils import logging a__ : List[Any] = logging.get_logger(__name__) class a_ : """simple docstring""" __SCREAMING_SNAKE_CASE : str __SCREAMING_SNAKE_CASE : str = None @staticmethod def __lowerCAmelCase ( ) ->str: raise NotImplementedError def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Optional[int]: raise NotImplementedError def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[Any]: raise NotImplementedError def __lowerCAmelCase ( self ) ->Tuple: if not self.is_available(): raise RuntimeError( F"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" ) @classmethod def __lowerCAmelCase ( cls ) ->Union[str, Any]: return F"""`pip install {cls.pip_package or cls.name}`""" class a_ ( a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE : str = 'optuna' @staticmethod def __lowerCAmelCase ( ) ->Union[str, Any]: return is_optuna_available() def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Union[str, Any]: return run_hp_search_optuna(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[Any]: return default_hp_space_optuna(_lowerCamelCase ) class a_ ( a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE : Optional[int] = 'ray' __SCREAMING_SNAKE_CASE : Union[str, Any] = '\'ray[tune]\'' @staticmethod def __lowerCAmelCase ( ) ->int: return is_ray_available() def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Dict: return run_hp_search_ray(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[int]: return default_hp_space_ray(_lowerCamelCase ) class a_ ( a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE : Union[str, Any] = 'sigopt' @staticmethod def __lowerCAmelCase ( ) ->Tuple: return is_sigopt_available() def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Optional[Any]: return run_hp_search_sigopt(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) def __lowerCAmelCase ( self , _lowerCamelCase ) ->Tuple: return default_hp_space_sigopt(_lowerCamelCase ) class a_ ( a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE : Any = 'wandb' @staticmethod def __lowerCAmelCase ( ) ->Optional[int]: return is_wandb_available() def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Optional[Any]: return run_hp_search_wandb(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) def __lowerCAmelCase ( self , _lowerCamelCase ) ->Tuple: return default_hp_space_wandb(_lowerCamelCase ) a__ : int = { HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend] } def UpperCAmelCase_( ): """simple docstring""" SCREAMING_SNAKE_CASE : Union[str, Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()] if len(a__ ) > 0: SCREAMING_SNAKE_CASE : Optional[int] = available_backends[0].name if len(a__ ) > 1: logger.info( F"""{len(a__ )} hyperparameter search backends available. Using {name} as the default.""" ) return name raise RuntimeError( '''No hyperparameter search backend available.\n''' + '''\n'''.join( F""" - To install {backend.name} run {backend.pip_install()}""" for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) a__ : Tuple = { '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''], '''tokenization_roberta''': ['''RobertaTokenizer'''], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a__ : str = ['''RobertaTokenizerFast'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a__ : Union[str, Any] = [ '''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: a__ : Dict = [ '''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: a__ : Dict = [ '''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 a__ : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import argparse import datetime import json import time import warnings from logging import getLogger from pathlib import Path from typing import Dict, List import torch from tqdm import tqdm from transformers import AutoModelForSeqaSeqLM, AutoTokenizer from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params __A : List[Any] = getLogger(__name__) __A : Dict = 'cuda' if torch.cuda.is_available() else 'cpu' def __a ( A__ : List[str] , A__ : str , A__ : str , A__ : int = 8 , A__ : str = DEFAULT_DEVICE , A__ : List[str]=False , A__ : Tuple="summarization" , A__ : int=None , **A__ : List[Any] , ): SCREAMING_SNAKE_CASE = Path(A__ ).open("w" , encoding="utf-8" ) SCREAMING_SNAKE_CASE = str(A__ ) SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(A__ ).to(A__ ) if fpaa: SCREAMING_SNAKE_CASE = model.half() SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(A__ ) logger.info(F"Inferred tokenizer type: {tokenizer.__class__}" ) # if this is wrong, check config.model_type. SCREAMING_SNAKE_CASE = time.time() # update config with task specific params use_task_specific_params(A__ , A__ ) if prefix is None: SCREAMING_SNAKE_CASE = prefix or getattr(model.config , "prefix" , "" ) or "" for examples_chunk in tqdm(list(chunks(A__ , A__ ) ) ): SCREAMING_SNAKE_CASE = [prefix + text for text in examples_chunk] SCREAMING_SNAKE_CASE = tokenizer(A__ , return_tensors="pt" , truncation=A__ , padding="longest" ).to(A__ ) SCREAMING_SNAKE_CASE = model.generate( input_ids=batch.input_ids , attention_mask=batch.attention_mask , **A__ , ) SCREAMING_SNAKE_CASE = tokenizer.batch_decode(A__ , skip_special_tokens=A__ , clean_up_tokenization_spaces=A__ ) for hypothesis in dec: fout.write(hypothesis + "\n" ) fout.flush() fout.close() SCREAMING_SNAKE_CASE = int(time.time() - start_time ) # seconds SCREAMING_SNAKE_CASE = len(A__ ) return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs , 4 )} def __a ( ): return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" ) def __a ( A__ : List[Any]=True ): SCREAMING_SNAKE_CASE = argparse.ArgumentParser() parser.add_argument("model_name" , type=A__ , help="like facebook/bart-large-cnn,t5-base, etc." ) parser.add_argument("input_path" , type=A__ , help="like cnn_dm/test.source" ) parser.add_argument("save_path" , type=A__ , help="where to save summaries" ) parser.add_argument("--reference_path" , type=A__ , required=A__ , help="like cnn_dm/test.target" ) parser.add_argument("--score_path" , type=A__ , required=A__ , default="metrics.json" , help="where to save metrics" ) parser.add_argument("--device" , type=A__ , required=A__ , default=A__ , help="cuda, cuda:1, cpu etc." ) parser.add_argument( "--prefix" , type=A__ , required=A__ , default=A__ , help="will be added to the begininng of src examples" ) parser.add_argument("--task" , type=A__ , default="summarization" , help="used for task_specific_params + metrics" ) parser.add_argument("--bs" , type=A__ , default=8 , required=A__ , help="batch size" ) parser.add_argument( "--n_obs" , type=A__ , default=-1 , required=A__ , help="How many observations. Defaults to all." ) parser.add_argument("--fp16" , action="store_true" ) parser.add_argument("--dump-args" , action="store_true" , help="print the custom hparams with the results" ) parser.add_argument( "--info" , nargs="?" , type=A__ , const=datetime_now() , help=( "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g." " lang=en-ru. If no value is passed, the current datetime string will be used." ) , ) # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = parser.parse_known_args() SCREAMING_SNAKE_CASE = parse_numeric_n_bool_cl_kwargs(A__ ) if parsed_args and verbose: print(F"parsed the following generate kwargs: {parsed_args}" ) SCREAMING_SNAKE_CASE = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path ).readlines()] if args.n_obs > 0: SCREAMING_SNAKE_CASE = examples[: args.n_obs] Path(args.save_path ).parent.mkdir(exist_ok=A__ ) if args.reference_path is None and Path(args.score_path ).exists(): warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c." ) if args.device == "cpu" and args.fpaa: # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half' raise ValueError("Can't mix --fp16 and --device cpu" ) SCREAMING_SNAKE_CASE = generate_summaries_or_translations( A__ , args.save_path , args.model_name , batch_size=args.bs , device=args.device , fpaa=args.fpaa , task=args.task , prefix=args.prefix , **A__ , ) if args.reference_path is None: return {} # Compute scores SCREAMING_SNAKE_CASE = calculate_bleu if "translation" in args.task else calculate_rouge SCREAMING_SNAKE_CASE = [x.rstrip() for x in open(args.save_path ).readlines()] SCREAMING_SNAKE_CASE = [x.rstrip() for x in open(args.reference_path ).readlines()][: len(A__ )] SCREAMING_SNAKE_CASE = score_fn(A__ , A__ ) scores.update(A__ ) if args.dump_args: scores.update(A__ ) if args.info: SCREAMING_SNAKE_CASE = args.info if verbose: print(A__ ) if args.score_path is not None: json.dump(A__ , open(args.score_path , "w" ) ) return scores if __name__ == "__main__": # Usage for MT: # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json --task translation $@ run_generate(verbose=True)
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import torch from diffusers import KDPMaDiscreteScheduler from diffusers.utils import torch_device from .test_schedulers import SchedulerCommonTest class a__ ( __SCREAMING_SNAKE_CASE ): _A = (KDPMaDiscreteScheduler,) _A = 10 def lowerCAmelCase ( self : List[str] , **A_ : List[str] ) -> Optional[Any]: """simple docstring""" lowerCamelCase_: str = { """num_train_timesteps""": 11_00, """beta_start""": 0.0001, """beta_end""": 0.02, """beta_schedule""": """linear""", } config.update(**A_ ) return config def lowerCAmelCase ( self : Optional[Any] ) -> Optional[Any]: """simple docstring""" for timesteps in [10, 50, 1_00, 10_00]: self.check_over_configs(num_train_timesteps=A_ ) def lowerCAmelCase ( self : Optional[int] ) -> Any: """simple docstring""" for beta_start, beta_end in zip([0.00001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ): self.check_over_configs(beta_start=A_ , beta_end=A_ ) def lowerCAmelCase ( self : List[Any] ) -> str: """simple docstring""" for schedule in ["linear", "scaled_linear"]: self.check_over_configs(beta_schedule=A_ ) def lowerCAmelCase ( self : List[Any] ) -> Optional[Any]: """simple docstring""" for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=A_ ) def lowerCAmelCase ( self : Tuple ) -> str: """simple docstring""" lowerCamelCase_: int = self.scheduler_classes[0] lowerCamelCase_: List[str] = self.get_scheduler_config(prediction_type="""v_prediction""" ) lowerCamelCase_: List[str] = scheduler_class(**A_ ) scheduler.set_timesteps(self.num_inference_steps ) lowerCamelCase_: Dict = self.dummy_model() lowerCamelCase_: Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma lowerCamelCase_: Optional[int] = sample.to(A_ ) for i, t in enumerate(scheduler.timesteps ): lowerCamelCase_: str = scheduler.scale_model_input(A_ , A_ ) lowerCamelCase_: str = model(A_ , A_ ) lowerCamelCase_: Union[str, Any] = scheduler.step(A_ , A_ , A_ ) lowerCamelCase_: List[str] = output.prev_sample lowerCamelCase_: List[Any] = torch.sum(torch.abs(A_ ) ) lowerCamelCase_: Dict = torch.mean(torch.abs(A_ ) ) if torch_device in ["cpu", "mps"]: assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2 assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3 else: # CUDA assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2 assert abs(result_mean.item() - 0.0002 ) < 1e-3 def lowerCAmelCase ( self : Optional[Any] ) -> List[Any]: """simple docstring""" if torch_device == "mps": return lowerCamelCase_: Optional[Any] = self.scheduler_classes[0] lowerCamelCase_: List[Any] = self.get_scheduler_config() lowerCamelCase_: Optional[Any] = scheduler_class(**A_ ) scheduler.set_timesteps(self.num_inference_steps ) lowerCamelCase_: str = self.dummy_model() lowerCamelCase_: Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma lowerCamelCase_: int = sample.to(A_ ) for i, t in enumerate(scheduler.timesteps ): lowerCamelCase_: Dict = scheduler.scale_model_input(A_ , A_ ) lowerCamelCase_: Union[str, Any] = model(A_ , A_ ) lowerCamelCase_: Tuple = scheduler.step(A_ , A_ , A_ ) lowerCamelCase_: List[str] = output.prev_sample lowerCamelCase_: Union[str, Any] = torch.sum(torch.abs(A_ ) ) lowerCamelCase_: Dict = torch.mean(torch.abs(A_ ) ) if torch_device in ["cpu", "mps"]: assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3 else: # CUDA assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3 def lowerCAmelCase ( self : List[str] ) -> Union[str, Any]: """simple docstring""" if torch_device == "mps": return lowerCamelCase_: List[Any] = self.scheduler_classes[0] lowerCamelCase_: str = self.get_scheduler_config() lowerCamelCase_: List[Any] = scheduler_class(**A_ ) scheduler.set_timesteps(self.num_inference_steps , device=A_ ) lowerCamelCase_: Union[str, Any] = self.dummy_model() lowerCamelCase_: Tuple = self.dummy_sample_deter.to(A_ ) * scheduler.init_noise_sigma for t in scheduler.timesteps: lowerCamelCase_: Optional[Any] = scheduler.scale_model_input(A_ , A_ ) lowerCamelCase_: Optional[Any] = model(A_ , A_ ) lowerCamelCase_: Optional[int] = scheduler.step(A_ , A_ , A_ ) lowerCamelCase_: Optional[int] = output.prev_sample lowerCamelCase_: List[str] = torch.sum(torch.abs(A_ ) ) lowerCamelCase_: Optional[int] = torch.mean(torch.abs(A_ ) ) if str(A_ ).startswith("""cpu""" ): # The following sum varies between 148 and 156 on mps. Why? assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3 else: # CUDA assert abs(result_sum.item() - 20.4125 ) < 1e-2 assert abs(result_mean.item() - 0.0266 ) < 1e-3
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def _lowercase ( lowercase__ = 1_0_0_0 ): __lowerCAmelCase : Dict = -1 __lowerCAmelCase : Optional[Any] = 0 for a in range(1 , n // 3 ): # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c __lowerCAmelCase : List[str] = (n * n - 2 * a * n) // (2 * n - 2 * a) __lowerCAmelCase : Tuple = n - a - b if c * c == (a * a + b * b): __lowerCAmelCase : str = a * b * c if candidate >= product: __lowerCAmelCase : Dict = candidate return product if __name__ == "__main__": print(F"{solution() = }")
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from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_torch_available from ...utils import OptionalDependencyNotAvailable _UpperCamelCase = { "configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"], "tokenization_gpt_neox_japanese": ["GPTNeoXJapaneseTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _UpperCamelCase = [ "GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXJapaneseForCausalLM", "GPTNeoXJapaneseLayer", "GPTNeoXJapaneseModel", "GPTNeoXJapanesePreTrainedModel", ] if TYPE_CHECKING: from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gpt_neox_japanese import ( GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseLayer, GPTNeoXJapaneseModel, GPTNeoXJapanesePreTrainedModel, ) else: import sys _UpperCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import os import socket from contextlib import contextmanager import torch from ..commands.config.default import write_basic_config # noqa: F401 from ..state import PartialState from .dataclasses import DistributedType from .imports import is_deepspeed_available, is_tpu_available from .transformer_engine import convert_model from .versions import is_torch_version if is_deepspeed_available(): from deepspeed import DeepSpeedEngine if is_tpu_available(check_device=False): import torch_xla.core.xla_model as xm def A(__a: List[Any] ): if is_torch_version("<" , "2.0.0" ) or not hasattr(A_ , "_dynamo" ): return False return isinstance(A_ , torch._dynamo.eval_frame.OptimizedModule ) def A(__a: List[str] , __a: bool = True ): lowerCAmelCase_ = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel) lowerCAmelCase_ = is_compiled_module(A_ ) if is_compiled: lowerCAmelCase_ = model lowerCAmelCase_ = model._orig_mod if is_deepspeed_available(): options += (DeepSpeedEngine,) while isinstance(A_ , A_ ): lowerCAmelCase_ = model.module if not keep_fpaa_wrapper: lowerCAmelCase_ = getattr(A_ , "forward" ) lowerCAmelCase_ = model.__dict__.pop("_original_forward" , A_ ) if original_forward is not None: while hasattr(A_ , "__wrapped__" ): lowerCAmelCase_ = forward.__wrapped__ if forward == original_forward: break lowerCAmelCase_ = forward if getattr(A_ , "_converted_to_transformer_engine" , A_ ): convert_model(A_ , to_transformer_engine=A_ ) if is_compiled: lowerCAmelCase_ = model lowerCAmelCase_ = compiled_model return model def A(): PartialState().wait_for_everyone() def A(__a: Optional[int] , __a: str ): if PartialState().distributed_type == DistributedType.TPU: xm.save(A_ , A_ ) elif PartialState().local_process_index == 0: torch.save(A_ , A_ ) @contextmanager def A(**__a: Any ): for key, value in kwargs.items(): lowerCAmelCase_ = str(A_ ) yield for key in kwargs: if key.upper() in os.environ: del os.environ[key.upper()] def A(__a: Any ): if not hasattr(A_ , "__qualname__" ) and not hasattr(A_ , "__name__" ): lowerCAmelCase_ = getattr(A_ , "__class__" , A_ ) if hasattr(A_ , "__qualname__" ): return obj.__qualname__ if hasattr(A_ , "__name__" ): return obj.__name__ return str(A_ ) def A(__a: Any , __a: Dict ): for key, value in source.items(): if isinstance(A_ , A_ ): lowerCAmelCase_ = destination.setdefault(A_ , {} ) merge_dicts(A_ , A_ ) else: lowerCAmelCase_ = value return destination def A(__a: int = None ): if port is None: lowerCAmelCase_ = 2_9500 with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s: return s.connect_ex(("localhost", port) ) == 0
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"""simple docstring""" import inspect import unittest from huggingface_hub import hf_hub_download from transformers import ASTConfig from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_torchaudio_available 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 torch import nn from transformers import ASTForAudioClassification, ASTModel from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import ( AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ) if is_torchaudio_available(): import torchaudio from transformers import ASTFeatureExtractor class __snake_case : def __init__( self : Dict , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple=1_3 , __lowerCAmelCase : Any=2 , __lowerCAmelCase : List[str]=2_4 , __lowerCAmelCase : str=1_6 , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : Optional[Any]=3_2 , __lowerCAmelCase : List[Any]=5 , __lowerCAmelCase : int=4 , __lowerCAmelCase : int=3_7 , __lowerCAmelCase : Union[str, Any]="gelu" , __lowerCAmelCase : Dict=0.1 , __lowerCAmelCase : str=0.1 , __lowerCAmelCase : int=1_0 , __lowerCAmelCase : List[Any]=0.02 , __lowerCAmelCase : str=None , __lowerCAmelCase : List[str]=2 , __lowerCAmelCase : Union[str, Any]=2 , ): """simple docstring""" _lowerCamelCase : List[str] = parent _lowerCamelCase : str = batch_size _lowerCamelCase : Tuple = patch_size _lowerCamelCase : Optional[int] = max_length _lowerCamelCase : List[Any] = num_mel_bins _lowerCamelCase : int = is_training _lowerCamelCase : Union[str, Any] = use_labels _lowerCamelCase : Dict = hidden_size _lowerCamelCase : Tuple = num_hidden_layers _lowerCamelCase : int = num_attention_heads _lowerCamelCase : Tuple = intermediate_size _lowerCamelCase : List[str] = hidden_act _lowerCamelCase : Dict = hidden_dropout_prob _lowerCamelCase : int = attention_probs_dropout_prob _lowerCamelCase : List[Any] = type_sequence_label_size _lowerCamelCase : Tuple = initializer_range _lowerCamelCase : List[str] = scope _lowerCamelCase : Optional[int] = frequency_stride _lowerCamelCase : List[Any] = time_stride # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens) _lowerCamelCase : Optional[int] = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1 _lowerCamelCase : Union[str, Any] = (self.max_length - self.patch_size) // self.time_stride + 1 _lowerCamelCase : Any = frequency_out_dimension * time_out_dimension _lowerCamelCase : List[Any] = num_patches + 2 def SCREAMING_SNAKE_CASE ( self : Dict ): """simple docstring""" _lowerCamelCase : int = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] ) _lowerCamelCase : str = None if self.use_labels: _lowerCamelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size ) _lowerCamelCase : Optional[int] = self.get_config() return config, input_values, labels def SCREAMING_SNAKE_CASE ( self : str ): """simple docstring""" return ASTConfig( patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__lowerCAmelCase , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , ) def SCREAMING_SNAKE_CASE ( self : Any , __lowerCAmelCase : Tuple , __lowerCAmelCase : Any , __lowerCAmelCase : Dict ): """simple docstring""" _lowerCamelCase : List[Any] = ASTModel(config=__lowerCAmelCase ) model.to(__lowerCAmelCase ) model.eval() _lowerCamelCase : List[Any] = model(__lowerCAmelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def SCREAMING_SNAKE_CASE ( self : Optional[int] ): """simple docstring""" _lowerCamelCase : int = self.prepare_config_and_inputs() ( ( _lowerCamelCase ) , ( _lowerCamelCase ) , ( _lowerCamelCase ) , ) : Optional[Any] = config_and_inputs _lowerCamelCase : int = {'''input_values''': input_values} return config, inputs_dict @require_torch class __snake_case ( _lowercase , _lowercase , unittest.TestCase): snake_case__ : List[Any] = ( ( ASTModel, ASTForAudioClassification, ) if is_torch_available() else () ) snake_case__ : Tuple = ( {"audio-classification": ASTForAudioClassification, "feature-extraction": ASTModel} if is_torch_available() else {} ) snake_case__ : Any = False snake_case__ : List[Any] = False snake_case__ : Optional[Any] = False snake_case__ : Optional[Any] = False def SCREAMING_SNAKE_CASE ( self : Dict , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Optional[Any] ): """simple docstring""" if pipeline_test_casse_name == "AudioClassificationPipelineTests": return True return False def SCREAMING_SNAKE_CASE ( self : Any ): """simple docstring""" _lowerCamelCase : Optional[int] = ASTModelTester(self ) _lowerCamelCase : Any = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=3_7 ) def SCREAMING_SNAKE_CASE ( self : str ): """simple docstring""" self.config_tester.run_common_tests() @unittest.skip(reason='''AST does not use inputs_embeds''' ) def SCREAMING_SNAKE_CASE ( self : Optional[int] ): """simple docstring""" pass def SCREAMING_SNAKE_CASE ( self : Any ): """simple docstring""" _lowerCamelCase , _lowerCamelCase : Any = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCamelCase : Dict = model_class(__lowerCAmelCase ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) _lowerCamelCase : List[str] = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__lowerCAmelCase , nn.Linear ) ) def SCREAMING_SNAKE_CASE ( self : Tuple ): """simple docstring""" _lowerCamelCase , _lowerCamelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _lowerCamelCase : str = model_class(__lowerCAmelCase ) _lowerCamelCase : Tuple = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic _lowerCamelCase : Any = [*signature.parameters.keys()] _lowerCamelCase : str = ['''input_values'''] self.assertListEqual(arg_names[:1] , __lowerCAmelCase ) def SCREAMING_SNAKE_CASE ( self : Optional[Any] ): """simple docstring""" _lowerCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__lowerCAmelCase ) @slow def SCREAMING_SNAKE_CASE ( self : Optional[Any] ): """simple docstring""" for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _lowerCamelCase : Union[str, Any] = ASTModel.from_pretrained(__lowerCAmelCase ) self.assertIsNotNone(__lowerCAmelCase ) def snake_case_ ( ): '''simple docstring''' _lowerCamelCase : List[str] = hf_hub_download( repo_id='''nielsr/audio-spectogram-transformer-checkpoint''', filename='''sample_audio.flac''', repo_type='''dataset''' ) _lowerCamelCase , _lowerCamelCase : str = torchaudio.load(A_ ) return audio, sampling_rate @require_torch @require_torchaudio class __snake_case ( unittest.TestCase): @cached_property def SCREAMING_SNAKE_CASE ( self : Tuple ): """simple docstring""" return ( ASTFeatureExtractor.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ) if is_torchaudio_available() else None ) @slow def SCREAMING_SNAKE_CASE ( self : Optional[Any] ): """simple docstring""" _lowerCamelCase : int = self.default_feature_extractor _lowerCamelCase : Union[str, Any] = ASTForAudioClassification.from_pretrained('''MIT/ast-finetuned-audioset-10-10-0.4593''' ).to(__lowerCAmelCase ) _lowerCamelCase : List[Any] = self.default_feature_extractor _lowerCamelCase , _lowerCamelCase : List[Any] = prepare_audio() _lowerCamelCase : Dict = audio.squeeze().numpy() _lowerCamelCase : Tuple = feature_extractor(__lowerCAmelCase , sampling_rate=__lowerCAmelCase , return_tensors='''pt''' ).to(__lowerCAmelCase ) # forward pass with torch.no_grad(): _lowerCamelCase : Tuple = model(**__lowerCAmelCase ) # verify the logits _lowerCamelCase : Tuple = torch.Size((1, 5_2_7) ) self.assertEqual(outputs.logits.shape , __lowerCAmelCase ) _lowerCamelCase : Optional[int] = torch.tensor([-0.87_60, -7.00_42, -8.66_02] ).to(__lowerCAmelCase ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
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"""simple docstring""" import logging import os from logging import ( CRITICAL, # NOQA DEBUG, # NOQA ERROR, # NOQA FATAL, # NOQA INFO, # NOQA NOTSET, # NOQA WARN, # NOQA WARNING, # NOQA ) from typing import Optional from tqdm import auto as tqdm_lib SCREAMING_SNAKE_CASE = { 'debug': logging.DEBUG, 'info': logging.INFO, 'warning': logging.WARNING, 'error': logging.ERROR, 'critical': logging.CRITICAL, } SCREAMING_SNAKE_CASE = logging.WARNING def __lowerCAmelCase( ): """simple docstring""" _lowercase : List[Any] = os.getenv('DATASETS_VERBOSITY' ,__UpperCAmelCase ) if env_level_str: if env_level_str in log_levels: return log_levels[env_level_str] else: logging.getLogger().warning( F'''Unknown option DATASETS_VERBOSITY={env_level_str}, ''' F'''has to be one of: { ', '.join(log_levels.keys() ) }''' ) return _default_log_level def __lowerCAmelCase( ): """simple docstring""" return __name__.split('.' )[0] def __lowerCAmelCase( ): """simple docstring""" return logging.getLogger(_get_library_name() ) def __lowerCAmelCase( ): """simple docstring""" _lowercase : Union[str, Any] = _get_library_root_logger() library_root_logger.setLevel(_get_default_logging_level() ) def __lowerCAmelCase( ): """simple docstring""" _lowercase : Any = _get_library_root_logger() library_root_logger.setLevel(logging.NOTSET ) def __lowerCAmelCase( __UpperCAmelCase = None ): """simple docstring""" if name is None: _lowercase : str = _get_library_name() return logging.getLogger(__UpperCAmelCase ) def __lowerCAmelCase( ): """simple docstring""" return _get_library_root_logger().getEffectiveLevel() def __lowerCAmelCase( __UpperCAmelCase ): """simple docstring""" _get_library_root_logger().setLevel(__UpperCAmelCase ) def __lowerCAmelCase( ): """simple docstring""" return set_verbosity(__UpperCAmelCase ) def __lowerCAmelCase( ): """simple docstring""" return set_verbosity(__UpperCAmelCase ) def __lowerCAmelCase( ): """simple docstring""" return set_verbosity(__UpperCAmelCase ) def __lowerCAmelCase( ): """simple docstring""" return set_verbosity(__UpperCAmelCase ) def __lowerCAmelCase( ): """simple docstring""" _lowercase : Union[str, Any] = False def __lowerCAmelCase( ): """simple docstring""" _lowercase : int = True # Configure the library root logger at the module level (singleton-like) _configure_library_root_logger() class _lowerCamelCase : def __init__( self : Optional[Any] , *lowerCamelCase_ : Union[str, Any] , **lowerCamelCase_ : List[Any] ): # pylint: disable=unused-argument """simple docstring""" _lowercase : List[str] = args[0] if args else None def __iter__( self : Any ): """simple docstring""" return iter(self._iterator ) def __getattr__( self : Optional[Any] , lowerCamelCase_ : str ): """simple docstring""" def empty_fn(*lowerCamelCase_ : int , **lowerCamelCase_ : Tuple ): # pylint: disable=unused-argument return return empty_fn def __enter__( self : str ): """simple docstring""" return self def __exit__( self : List[str] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : int ): """simple docstring""" return SCREAMING_SNAKE_CASE = True class _lowerCamelCase : def __call__( self : Optional[int] , *lowerCamelCase_ : List[str] , lowerCamelCase_ : str=False , **lowerCamelCase_ : Dict ): """simple docstring""" if _tqdm_active and not disable: return tqdm_lib.tqdm(*lowerCamelCase_ , **lowerCamelCase_ ) else: return EmptyTqdm(*lowerCamelCase_ , **lowerCamelCase_ ) def __UpperCAmelCase ( self : Dict , *lowerCamelCase_ : Union[str, Any] , **lowerCamelCase_ : Optional[Any] ): """simple docstring""" _lowercase : Dict = None if _tqdm_active: return tqdm_lib.tqdm.set_lock(*lowerCamelCase_ , **lowerCamelCase_ ) def __UpperCAmelCase ( self : List[Any] ): """simple docstring""" if _tqdm_active: return tqdm_lib.tqdm.get_lock() SCREAMING_SNAKE_CASE = _tqdm_cls() def __lowerCAmelCase( ): """simple docstring""" global _tqdm_active return bool(_tqdm_active ) def __lowerCAmelCase( ): """simple docstring""" global _tqdm_active _lowercase : List[str] = True def __lowerCAmelCase( ): """simple docstring""" global _tqdm_active _lowercase : List[Any] = False
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"""simple docstring""" import math def __lowerCAmelCase( __UpperCAmelCase ): """simple docstring""" if not isinstance(__UpperCAmelCase ,__UpperCAmelCase ): _lowercase : List[Any] = F'''Input value of [number={number}] must be an integer''' raise TypeError(__UpperCAmelCase ) if number < 1: _lowercase : List[Any] = F'''Input value of [number={number}] must be > 0''' raise ValueError(__UpperCAmelCase ) elif number == 1: return 3 elif number == 2: return 5 else: _lowercase : str = int(math.log(number // 3 ,2 ) ) + 2 _lowercase : Union[str, Any] = [3, 5] _lowercase : Optional[int] = 2 _lowercase : List[Any] = 3 for block in range(1 ,__UpperCAmelCase ): for _ in range(__UpperCAmelCase ): proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] ) proth_index += 1 increment *= 2 return proth_list[number - 1] if __name__ == "__main__": import doctest doctest.testmod() for number in range(11): SCREAMING_SNAKE_CASE = 0 try: SCREAMING_SNAKE_CASE = proth(number) except ValueError: print(f"""ValueError: there is no {number}th Proth number""") continue print(f"""The {number}th Proth number: {value}""")
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import argparse import pickle import numpy as np import torch from torch import nn from transformers import ReformerConfig, ReformerModelWithLMHead from transformers.utils import logging logging.set_verbosity_info() def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=None ) -> Union[str, Any]: # set parameter of one layer assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match''' SCREAMING_SNAKE_CASE__ = nn.Parameter(lowerCAmelCase_ ) if bias is not None: assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match''' SCREAMING_SNAKE_CASE__ = nn.Parameter(lowerCAmelCase_ ) def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Any: # set torch weights for 1-to-1 comparison SCREAMING_SNAKE_CASE__ = np.asarray(weights[0] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[1] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[2] ) set_param( torch_layer.self_attention.query_key , torch.tensor(lowerCAmelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCAmelCase_ ) , ) set_param( torch_layer.self_attention.value , torch.tensor(lowerCAmelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCAmelCase_ ) , ) set_param( torch_layer.output.dense , torch.tensor(lowerCAmelCase_ ).view(-1 , lowerCAmelCase_ ).contiguous().transpose(0 , 1 ) , ) def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Optional[Any]: # set torch weights for 1-to-1 comparison SCREAMING_SNAKE_CASE__ = np.asarray(weights[0] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[1] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[2] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[3] ) set_param( torch_layer.self_attention.query , torch.tensor(lowerCAmelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCAmelCase_ ) , ) set_param( torch_layer.self_attention.key , torch.tensor(lowerCAmelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCAmelCase_ ) , ) set_param( torch_layer.self_attention.value , torch.tensor(lowerCAmelCase_ ).transpose(1 , 2 ).contiguous().view(-1 , lowerCAmelCase_ ) , ) set_param( torch_layer.output.dense , torch.tensor(lowerCAmelCase_ ).view(-1 , lowerCAmelCase_ ).contiguous().transpose(0 , 1 ) , ) def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]: # layernorm 1 SCREAMING_SNAKE_CASE__ = weights[0][0][0] SCREAMING_SNAKE_CASE__ = np.asarray(layer_norm_a[0] ) SCREAMING_SNAKE_CASE__ = np.asarray(layer_norm_a[1] ) set_param( torch_block.attention.layer_norm , torch.tensor(lowerCAmelCase_ ) , torch.tensor(lowerCAmelCase_ ) , ) # lsh weights + output SCREAMING_SNAKE_CASE__ = weights[0][1] if len(lowerCAmelCase_ ) < 4: set_layer_weights_in_torch_lsh(lowerCAmelCase_ , torch_block.attention , lowerCAmelCase_ ) else: set_layer_weights_in_torch_local(lowerCAmelCase_ , torch_block.attention , lowerCAmelCase_ ) # intermediate weighs SCREAMING_SNAKE_CASE__ = weights[2][0][1][2] # Chunked Feed Forward if len(lowerCAmelCase_ ) == 4: SCREAMING_SNAKE_CASE__ = intermediate_weights[2] # layernorm 2 SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[0][0] ) SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[0][1] ) set_param( torch_block.feed_forward.layer_norm , torch.tensor(lowerCAmelCase_ ) , torch.tensor(lowerCAmelCase_ ) , ) # intermediate dense SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[1][0] ) SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[1][1] ) set_param( torch_block.feed_forward.dense.dense , torch.tensor(lowerCAmelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCAmelCase_ ) , ) # intermediate out SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[4][0] ) SCREAMING_SNAKE_CASE__ = np.asarray(intermediate_weights[4][1] ) set_param( torch_block.feed_forward.output.dense , torch.tensor(lowerCAmelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCAmelCase_ ) , ) def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Dict: # reformer model SCREAMING_SNAKE_CASE__ = torch_model.reformer # word embeds SCREAMING_SNAKE_CASE__ = np.asarray(weights[1] ) set_param( torch_model_reformer.embeddings.word_embeddings , torch.tensor(lowerCAmelCase_ ) , ) if isinstance(weights[3] , lowerCAmelCase_ ): SCREAMING_SNAKE_CASE__ = torch_model_reformer.embeddings.position_embeddings for emb_idx in range(len(position_embeddings.weights ) ): SCREAMING_SNAKE_CASE__ = np.asarray(weights[3][emb_idx][0] ) assert ( position_embeddings.weights[emb_idx].shape == emb_weights.shape ), f'''{position_embeddings[emb_idx]} emb does not match''' SCREAMING_SNAKE_CASE__ = nn.Parameter(torch.tensor(lowerCAmelCase_ ) ) SCREAMING_SNAKE_CASE__ = weights[5] assert len(torch_model_reformer.encoder.layers ) * 4 == len( lowerCAmelCase_ ), "HF and trax model do not have the same number of layers" for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ): SCREAMING_SNAKE_CASE__ = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)] set_block_weights_in_torch(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) # output layer norm SCREAMING_SNAKE_CASE__ = np.asarray(weights[7][0] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[7][1] ) set_param( torch_model_reformer.encoder.layer_norm , torch.tensor(lowerCAmelCase_ ) , torch.tensor(lowerCAmelCase_ ) , ) # output embeddings SCREAMING_SNAKE_CASE__ = np.asarray(weights[9][0] ) SCREAMING_SNAKE_CASE__ = np.asarray(weights[9][1] ) set_param( torch_model.lm_head.decoder , torch.tensor(lowerCAmelCase_ ).transpose(0 , 1 ).contiguous() , torch.tensor(lowerCAmelCase_ ) , ) def __snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> str: # Initialise PyTorch model SCREAMING_SNAKE_CASE__ = ReformerConfig.from_json_file(lowerCAmelCase_ ) print(f'''Building PyTorch model from configuration: {config}''' ) SCREAMING_SNAKE_CASE__ = ReformerModelWithLMHead(lowerCAmelCase_ ) with open(lowerCAmelCase_ , '''rb''' ) as f: SCREAMING_SNAKE_CASE__ = pickle.load(lowerCAmelCase_ )['''weights'''] set_model_weights_in_torch(lowerCAmelCase_ , lowerCAmelCase_ , config.hidden_size ) # Save pytorch-model print(f'''Save PyTorch model to {pytorch_dump_path}''' ) torch.save(model.state_dict() , lowerCAmelCase_ ) if __name__ == "__main__": _A : Union[str, Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( """--trax_model_pkl_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 Reformer 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.""" ) _A : List[Any] = parser.parse_args() convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
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'''simple docstring''' # Author: OMKAR PATHAK, Nwachukwu Chidiebere # Use a Python dictionary to construct the graph. from __future__ import annotations from pprint import pformat from typing import Generic, TypeVar A : int = TypeVar("""T""") class lowerCAmelCase_ ( Generic[T] ): def __init__( self : int, _snake_case : bool = True ): '''simple docstring''' snake_case : dict[T, list[T]] ={} # dictionary of lists snake_case : Optional[int] =directed def __snake_case ( self : Any, _snake_case : T, _snake_case : T ): '''simple docstring''' if not self.directed: # For undirected graphs # if both source vertex and destination vertex are both present in the # adjacency list, add destination vertex to source vertex list of adjacent # vertices and add source vertex to destination vertex list of adjacent # vertices. if source_vertex in self.adj_list and destination_vertex in self.adj_list: self.adj_list[source_vertex].append(_snake_case ) self.adj_list[destination_vertex].append(_snake_case ) # if only source vertex is present in adjacency list, add destination vertex # to source vertex list of adjacent vertices, then create a new vertex with # destination vertex as key and assign a list containing the source vertex # as it's first adjacent vertex. elif source_vertex in self.adj_list: self.adj_list[source_vertex].append(_snake_case ) snake_case : Any =[source_vertex] # if only destination vertex is present in adjacency list, add source vertex # to destination vertex list of adjacent vertices, then create a new vertex # with source vertex as key and assign a list containing the source vertex # as it's first adjacent vertex. elif destination_vertex in self.adj_list: self.adj_list[destination_vertex].append(_snake_case ) snake_case : int =[destination_vertex] # if both source vertex and destination vertex are not present in adjacency # list, create a new vertex with source vertex as key and assign a list # containing the destination vertex as it's first adjacent vertex also # create a new vertex with destination vertex as key and assign a list # containing the source vertex as it's first adjacent vertex. else: snake_case : Union[str, Any] =[destination_vertex] snake_case : str =[source_vertex] else: # For directed graphs # if both source vertex and destination vertex are present in adjacency # list, add destination vertex to source vertex list of adjacent vertices. if source_vertex in self.adj_list and destination_vertex in self.adj_list: self.adj_list[source_vertex].append(_snake_case ) # if only source vertex is present in adjacency list, add destination # vertex to source vertex list of adjacent vertices and create a new vertex # with destination vertex as key, which has no adjacent vertex elif source_vertex in self.adj_list: self.adj_list[source_vertex].append(_snake_case ) snake_case : Optional[Any] =[] # if only destination vertex is present in adjacency list, create a new # vertex with source vertex as key and assign a list containing destination # vertex as first adjacent vertex elif destination_vertex in self.adj_list: snake_case : Any =[destination_vertex] # if both source vertex and destination vertex are not present in adjacency # list, create a new vertex with source vertex as key and a list containing # destination vertex as it's first adjacent vertex. Then create a new vertex # with destination vertex as key, which has no adjacent vertex else: snake_case : int =[destination_vertex] snake_case : Optional[Any] =[] return self def __repr__( self : int ): '''simple docstring''' return pformat(self.adj_list )
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import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import SchedulerMixin, SchedulerOutput class snake_case_ ( __A , __A ): '''simple docstring''' lowerCamelCase = 1 @register_to_config def __init__( self : str , __magic_name__ : int = 1000 , __magic_name__ : Optional[Union[np.ndarray, List[float]]] = None ) -> List[str]: # set `betas`, `alphas`, `timesteps` self.set_timesteps(__magic_name__ ) # standard deviation of the initial noise distribution lowerCamelCase_ : List[Any] = 1.0 # For now we only support F-PNDM, i.e. the runge-kutta method # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf # mainly at formula (9), (12), (13) and the Algorithm 2. lowerCamelCase_ : Optional[Any] = 4 # running values lowerCamelCase_ : Union[str, Any] = [] def __SCREAMING_SNAKE_CASE ( self : int , __magic_name__ : int , __magic_name__ : Union[str, torch.device] = None ) -> List[Any]: lowerCamelCase_ : Tuple = num_inference_steps lowerCamelCase_ : int = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1] lowerCamelCase_ : Union[str, Any] = torch.cat([steps, torch.tensor([0.0] )] ) if self.config.trained_betas is not None: lowerCamelCase_ : int = torch.tensor(self.config.trained_betas , dtype=torch.floataa ) else: lowerCamelCase_ : Tuple = torch.sin(steps * math.pi / 2 ) ** 2 lowerCamelCase_ : Tuple = (1.0 - self.betas**2) ** 0.5 lowerCamelCase_ : Union[str, Any] = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1] lowerCamelCase_ : List[str] = timesteps.to(__magic_name__ ) lowerCamelCase_ : Any = [] def __SCREAMING_SNAKE_CASE ( self : List[str] , __magic_name__ : torch.FloatTensor , __magic_name__ : int , __magic_name__ : torch.FloatTensor , __magic_name__ : bool = True , ) -> Union[SchedulerOutput, Tuple]: if self.num_inference_steps is None: raise ValueError( "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" ) lowerCamelCase_ : str = (self.timesteps == timestep).nonzero().item() lowerCamelCase_ : Union[str, Any] = timestep_index + 1 lowerCamelCase_ : List[str] = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index] self.ets.append(__magic_name__ ) if len(self.ets ) == 1: lowerCamelCase_ : str = self.ets[-1] elif len(self.ets ) == 2: lowerCamelCase_ : Optional[Any] = (3 * self.ets[-1] - self.ets[-2]) / 2 elif len(self.ets ) == 3: lowerCamelCase_ : Any = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12 else: lowerCamelCase_ : Tuple = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4]) lowerCamelCase_ : Union[str, Any] = self._get_prev_sample(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=__magic_name__ ) def __SCREAMING_SNAKE_CASE ( self : Any , __magic_name__ : torch.FloatTensor , *__magic_name__ : Optional[Any] , **__magic_name__ : Tuple ) -> torch.FloatTensor: return sample def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __magic_name__ : str , __magic_name__ : Any , __magic_name__ : int , __magic_name__ : List[str] ) -> Optional[int]: lowerCamelCase_ : List[str] = self.alphas[timestep_index] lowerCamelCase_ : int = self.betas[timestep_index] lowerCamelCase_ : Union[str, Any] = self.alphas[prev_timestep_index] lowerCamelCase_ : Tuple = self.betas[prev_timestep_index] lowerCamelCase_ : Tuple = (sample - sigma * ets) / max(__magic_name__ , 1e-8 ) lowerCamelCase_ : int = next_alpha * pred + ets * next_sigma return prev_sample def __len__( self : int ) -> Union[str, Any]: return self.config.num_train_timesteps
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from __future__ import annotations def __a ( __UpperCAmelCase : list[int | str] ) -> None: """simple docstring""" create_state_space_tree(__UpperCAmelCase , [] , 0 , [0 for i in range(len(__UpperCAmelCase ) )] ) def __a ( __UpperCAmelCase : list[int | str] , __UpperCAmelCase : list[int | str] , __UpperCAmelCase : int , __UpperCAmelCase : list[int] , ) -> None: """simple docstring""" if index == len(__UpperCAmelCase ): print(__UpperCAmelCase ) return for i in range(len(__UpperCAmelCase ) ): if not index_used[i]: current_sequence.append(sequence[i] ) lowerCamelCase_ : str = True create_state_space_tree(__UpperCAmelCase , __UpperCAmelCase , index + 1 , __UpperCAmelCase ) current_sequence.pop() lowerCamelCase_ : Dict = False snake_case_ : list[int | str] = [3, 1, 2, 4] generate_all_permutations(sequence) snake_case_ : list[int | str] = ["A", "B", "C"] generate_all_permutations(sequence_a)
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"""simple docstring""" import warnings from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging A__ : int = logging.get_logger(__name__) A__ : List[Any] = { 'nvidia/segformer-b0-finetuned-ade-512-512': ( 'https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json' ), # See all SegFormer models at https://huggingface.co/models?filter=segformer } class lowercase__ ( snake_case__ ): _UpperCAmelCase :Any = """segformer""" def __init__( self : Optional[Any] , snake_case__ : Dict=3 , snake_case__ : int=4 , snake_case__ : Tuple=[2, 2, 2, 2] , snake_case__ : int=[8, 4, 2, 1] , snake_case__ : Optional[int]=[32, 64, 160, 256] , snake_case__ : List[str]=[7, 3, 3, 3] , snake_case__ : Optional[Any]=[4, 2, 2, 2] , snake_case__ : List[str]=[1, 2, 5, 8] , snake_case__ : Optional[int]=[4, 4, 4, 4] , snake_case__ : Optional[int]="gelu" , snake_case__ : Optional[Any]=0.0 , snake_case__ : int=0.0 , snake_case__ : List[Any]=0.1 , snake_case__ : Dict=0.02 , snake_case__ : List[str]=0.1 , snake_case__ : Optional[Any]=1E-6 , snake_case__ : str=256 , snake_case__ : int=255 , **snake_case__ : List[str] , ): super().__init__(**snake_case__ ) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be" " removed, as the behaviour will default to that of reshape_last_stage = True." , snake_case__ , ) lowerCamelCase_ : Optional[int] =num_channels lowerCamelCase_ : Tuple =num_encoder_blocks lowerCamelCase_ : Optional[int] =depths lowerCamelCase_ : Tuple =sr_ratios lowerCamelCase_ : List[Any] =hidden_sizes lowerCamelCase_ : int =patch_sizes lowerCamelCase_ : List[str] =strides lowerCamelCase_ : List[Any] =mlp_ratios lowerCamelCase_ : List[Any] =num_attention_heads lowerCamelCase_ : Any =hidden_act lowerCamelCase_ : Optional[Any] =hidden_dropout_prob lowerCamelCase_ : int =attention_probs_dropout_prob lowerCamelCase_ : Any =classifier_dropout_prob lowerCamelCase_ : str =initializer_range lowerCamelCase_ : Optional[int] =drop_path_rate lowerCamelCase_ : Optional[Any] =layer_norm_eps lowerCamelCase_ : int =decoder_hidden_size lowerCamelCase_ : Optional[int] =kwargs.get("reshape_last_stage" , snake_case__ ) lowerCamelCase_ : int =semantic_loss_ignore_index class lowercase__ ( snake_case__ ): _UpperCAmelCase :Union[str, Any] = version.parse("1.11" ) @property def UpperCAmelCase__ ( self : str ): return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def UpperCAmelCase__ ( self : List[Any] ): return 1E-4 @property def UpperCAmelCase__ ( self : List[str] ): return 12
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def lowercase ( a , a , a , a ): '''simple docstring''' SCREAMING_SNAKE_CASE_ :int = [False] * len(a ) SCREAMING_SNAKE_CASE_ :List[Any] = [] queue.append(a ) SCREAMING_SNAKE_CASE_ :int = True while queue: SCREAMING_SNAKE_CASE_ :int = queue.pop(0 ) for ind in range(len(graph[u] ) ): if visited[ind] is False and graph[u][ind] > 0: queue.append(a ) SCREAMING_SNAKE_CASE_ :Tuple = True SCREAMING_SNAKE_CASE_ :Optional[int] = u return visited[t] def lowercase ( a , a , a ): '''simple docstring''' SCREAMING_SNAKE_CASE_ :Any = [-1] * (len(a )) SCREAMING_SNAKE_CASE_ :Tuple = 0 while bfs(a , a , a , a ): SCREAMING_SNAKE_CASE_ :List[Any] = float("Inf" ) SCREAMING_SNAKE_CASE_ :str = sink while s != source: # Find the minimum value in select path SCREAMING_SNAKE_CASE_ :str = min(a , graph[parent[s]][s] ) SCREAMING_SNAKE_CASE_ :Optional[Any] = parent[s] max_flow += path_flow SCREAMING_SNAKE_CASE_ :Dict = sink while v != source: SCREAMING_SNAKE_CASE_ :int = parent[v] graph[u][v] -= path_flow graph[v][u] += path_flow SCREAMING_SNAKE_CASE_ :Any = parent[v] return max_flow SCREAMING_SNAKE_CASE__ = [ [0, 16, 13, 0, 0, 0], [0, 0, 10, 12, 0, 0], [0, 4, 0, 0, 14, 0], [0, 0, 9, 0, 0, 20], [0, 0, 0, 7, 0, 4], [0, 0, 0, 0, 0, 0], ] SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0, 5 print(ford_fulkerson(graph, source, sink))
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'''simple docstring''' import collections import gzip import os import urllib import numpy from tensorflow.python.framework import dtypes, random_seed from tensorflow.python.platform import gfile from tensorflow.python.util.deprecation import deprecated A_ : List[str] = collections.namedtuple("_Datasets", ["train", "validation", "test"]) # CVDF mirror of http://yann.lecun.com/exdb/mnist/ A_ : List[Any] = 'https://storage.googleapis.com/cvdf-datasets/mnist/' def UpperCamelCase__ ( __magic_name__ : List[Any] ) -> Optional[int]: '''simple docstring''' snake_case__ : str = numpy.dtype(numpy.uintaa ).newbyteorder(""">""" ) return numpy.frombuffer(bytestream.read(4 ) , dtype=_lowerCamelCase )[0] @deprecated(_lowerCamelCase , """Please use tf.data to implement this functionality.""" ) def UpperCamelCase__ ( __magic_name__ : int ) -> Any: '''simple docstring''' print("""Extracting""" , f.name ) with gzip.GzipFile(fileobj=_lowerCamelCase ) as bytestream: snake_case__ : str = _readaa(_lowerCamelCase ) if magic != 20_51: raise ValueError( """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name) ) snake_case__ : List[str] = _readaa(_lowerCamelCase ) snake_case__ : Dict = _readaa(_lowerCamelCase ) snake_case__ : Optional[int] = _readaa(_lowerCamelCase ) snake_case__ : Dict = bytestream.read(rows * cols * num_images ) snake_case__ : Optional[int] = numpy.frombuffer(_lowerCamelCase , dtype=numpy.uinta ) snake_case__ : Dict = data.reshape(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , 1 ) return data @deprecated(_lowerCamelCase , """Please use tf.one_hot on tensors.""" ) def UpperCamelCase__ ( __magic_name__ : List[str] , __magic_name__ : List[str] ) -> str: '''simple docstring''' snake_case__ : str = labels_dense.shape[0] snake_case__ : str = numpy.arange(_lowerCamelCase ) * num_classes snake_case__ : str = numpy.zeros((num_labels, num_classes) ) snake_case__ : Tuple = 1 return labels_one_hot @deprecated(_lowerCamelCase , """Please use tf.data to implement this functionality.""" ) def UpperCamelCase__ ( __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int]=False , __magic_name__ : Optional[int]=10 ) -> Dict: '''simple docstring''' print("""Extracting""" , f.name ) with gzip.GzipFile(fileobj=_lowerCamelCase ) as bytestream: snake_case__ : int = _readaa(_lowerCamelCase ) if magic != 20_49: raise ValueError( """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name) ) snake_case__ : Any = _readaa(_lowerCamelCase ) snake_case__ : List[Any] = bytestream.read(_lowerCamelCase ) snake_case__ : Dict = numpy.frombuffer(_lowerCamelCase , dtype=numpy.uinta ) if one_hot: return _dense_to_one_hot(_lowerCamelCase , _lowerCamelCase ) return labels class __snake_case : '''simple docstring''' @deprecated( __SCREAMING_SNAKE_CASE , """Please use alternatives such as official/mnist/_DataSet.py""" """ from tensorflow/models.""" , ) def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=dtypes.floataa , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=None , ): snake_case__ : Optional[int] = random_seed.get_seed(__SCREAMING_SNAKE_CASE ) # If op level seed is not set, use whatever graph level seed is returned numpy.random.seed(seeda if seed is None else seeda ) snake_case__ : Optional[Any] = dtypes.as_dtype(__SCREAMING_SNAKE_CASE ).base_dtype if dtype not in (dtypes.uinta, dtypes.floataa): raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype ) if fake_data: snake_case__ : str = 1_0_0_0_0 snake_case__ : Optional[Any] = one_hot else: assert ( images.shape[0] == labels.shape[0] ), f"images.shape: {images.shape} labels.shape: {labels.shape}" snake_case__ : Any = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) if reshape: assert images.shape[3] == 1 snake_case__ : Union[str, Any] = images.reshape( images.shape[0] , images.shape[1] * images.shape[2] ) if dtype == dtypes.floataa: # Convert from [0, 255] -> [0.0, 1.0]. snake_case__ : List[Any] = images.astype(numpy.floataa ) snake_case__ : Optional[Any] = numpy.multiply(__SCREAMING_SNAKE_CASE , 1.0 / 255.0 ) snake_case__ : Optional[Any] = images snake_case__ : List[Any] = labels snake_case__ : str = 0 snake_case__ : Union[str, Any] = 0 @property def __UpperCamelCase ( self ): return self._images @property def __UpperCamelCase ( self ): return self._labels @property def __UpperCamelCase ( self ): return self._num_examples @property def __UpperCamelCase ( self ): return self._epochs_completed def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False , __SCREAMING_SNAKE_CASE=True ): if fake_data: snake_case__ : Any = [1] * 7_8_4 snake_case__ : Union[str, Any] = [1] + [0] * 9 if self.one_hot else 0 return ( [fake_image for _ in range(__SCREAMING_SNAKE_CASE )], [fake_label for _ in range(__SCREAMING_SNAKE_CASE )], ) snake_case__ : str = self._index_in_epoch # Shuffle for the first epoch if self._epochs_completed == 0 and start == 0 and shuffle: snake_case__ : Any = numpy.arange(self._num_examples ) numpy.random.shuffle(__SCREAMING_SNAKE_CASE ) snake_case__ : int = self.images[perma] snake_case__ : str = self.labels[perma] # Go to the next epoch if start + batch_size > self._num_examples: # Finished epoch self._epochs_completed += 1 # Get the rest examples in this epoch snake_case__ : Optional[int] = self._num_examples - start snake_case__ : Optional[int] = self._images[start : self._num_examples] snake_case__ : int = self._labels[start : self._num_examples] # Shuffle the data if shuffle: snake_case__ : Optional[int] = numpy.arange(self._num_examples ) numpy.random.shuffle(__SCREAMING_SNAKE_CASE ) snake_case__ : Optional[Any] = self.images[perm] snake_case__ : Tuple = self.labels[perm] # Start next epoch snake_case__ : Tuple = 0 snake_case__ : Union[str, Any] = batch_size - rest_num_examples snake_case__ : List[str] = self._index_in_epoch snake_case__ : Dict = self._images[start:end] snake_case__ : str = self._labels[start:end] return ( numpy.concatenate((images_rest_part, images_new_part) , axis=0 ), numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ), ) else: self._index_in_epoch += batch_size snake_case__ : Union[str, Any] = self._index_in_epoch return self._images[start:end], self._labels[start:end] @deprecated(_lowerCamelCase , """Please write your own downloading logic.""" ) def UpperCamelCase__ ( __magic_name__ : Tuple , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] ) -> Tuple: '''simple docstring''' if not gfile.Exists(_lowerCamelCase ): gfile.MakeDirs(_lowerCamelCase ) snake_case__ : Optional[Any] = os.path.join(_lowerCamelCase , _lowerCamelCase ) if not gfile.Exists(_lowerCamelCase ): urllib.request.urlretrieve(_lowerCamelCase , _lowerCamelCase ) # noqa: S310 with gfile.GFile(_lowerCamelCase ) as f: snake_case__ : Any = f.size() print("""Successfully downloaded""" , _lowerCamelCase , _lowerCamelCase , """bytes.""" ) return filepath @deprecated( _lowerCamelCase , """Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')""" ) def UpperCamelCase__ ( __magic_name__ : str , __magic_name__ : List[Any]=False , __magic_name__ : str=False , __magic_name__ : List[str]=dtypes.floataa , __magic_name__ : Any=True , __magic_name__ : Union[str, Any]=50_00 , __magic_name__ : str=None , __magic_name__ : Optional[int]=DEFAULT_SOURCE_URL , ) -> List[Any]: '''simple docstring''' if fake_data: def fake(): return _DataSet( [] , [] , fake_data=_lowerCamelCase , one_hot=_lowerCamelCase , dtype=_lowerCamelCase , seed=_lowerCamelCase ) snake_case__ : Optional[int] = fake() snake_case__ : Tuple = fake() snake_case__ : List[str] = fake() return _Datasets(train=_lowerCamelCase , validation=_lowerCamelCase , test=_lowerCamelCase ) if not source_url: # empty string check snake_case__ : str = DEFAULT_SOURCE_URL snake_case__ : Optional[int] = "train-images-idx3-ubyte.gz" snake_case__ : Dict = "train-labels-idx1-ubyte.gz" snake_case__ : List[str] = "t10k-images-idx3-ubyte.gz" snake_case__ : List[str] = "t10k-labels-idx1-ubyte.gz" snake_case__ : Optional[int] = _maybe_download( _lowerCamelCase , _lowerCamelCase , source_url + train_images_file ) with gfile.Open(_lowerCamelCase , """rb""" ) as f: snake_case__ : int = _extract_images(_lowerCamelCase ) snake_case__ : Optional[Any] = _maybe_download( _lowerCamelCase , _lowerCamelCase , source_url + train_labels_file ) with gfile.Open(_lowerCamelCase , """rb""" ) as f: snake_case__ : int = _extract_labels(_lowerCamelCase , one_hot=_lowerCamelCase ) snake_case__ : int = _maybe_download( _lowerCamelCase , _lowerCamelCase , source_url + test_images_file ) with gfile.Open(_lowerCamelCase , """rb""" ) as f: snake_case__ : Optional[int] = _extract_images(_lowerCamelCase ) snake_case__ : str = _maybe_download( _lowerCamelCase , _lowerCamelCase , source_url + test_labels_file ) with gfile.Open(_lowerCamelCase , """rb""" ) as f: snake_case__ : List[str] = _extract_labels(_lowerCamelCase , one_hot=_lowerCamelCase ) if not 0 <= validation_size <= len(_lowerCamelCase ): snake_case__ : str = ( "Validation size should be between 0 and " f"{len(_lowerCamelCase )}. Received: {validation_size}." ) raise ValueError(_lowerCamelCase ) snake_case__ : Any = train_images[:validation_size] snake_case__ : Optional[Any] = train_labels[:validation_size] snake_case__ : Optional[int] = train_images[validation_size:] snake_case__ : Tuple = train_labels[validation_size:] snake_case__ : List[str] = {"dtype": dtype, "reshape": reshape, "seed": seed} snake_case__ : Union[str, Any] = _DataSet(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) snake_case__ : str = _DataSet(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) snake_case__ : Optional[Any] = _DataSet(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) return _Datasets(train=_lowerCamelCase , validation=_lowerCamelCase , test=_lowerCamelCase )
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'''simple docstring''' import argparse import json import os from collections import OrderedDict import torch from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer from transformers.tokenization_utils_base import AddedToken @torch.no_grad() def UpperCamelCase__ ( __magic_name__ : Tuple , __magic_name__ : int , __magic_name__ : str , __magic_name__ : Optional[Any] , __magic_name__ : List[Any] ) -> int: '''simple docstring''' with open(__magic_name__ ) as metadata_file: snake_case__ : Optional[Any] = json.load(__magic_name__ ) snake_case__ : Tuple = LukeConfig(use_entity_aware_attention=__magic_name__ , **metadata["""model_config"""] ) # Load in the weights from the checkpoint_path snake_case__ : Tuple = torch.load(__magic_name__ , map_location="""cpu""" )["""module"""] # Load the entity vocab file snake_case__ : Any = load_original_entity_vocab(__magic_name__ ) # add an entry for [MASK2] snake_case__ : List[Any] = max(entity_vocab.values() ) + 1 config.entity_vocab_size += 1 snake_case__ : List[str] = XLMRobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""] ) # Add special tokens to the token vocabulary for downstream tasks snake_case__ : Optional[Any] = AddedToken("""<ent>""" , lstrip=__magic_name__ , rstrip=__magic_name__ ) snake_case__ : Any = AddedToken("""<ent2>""" , lstrip=__magic_name__ , rstrip=__magic_name__ ) tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]} ) config.vocab_size += 2 print(f"Saving tokenizer to {pytorch_dump_folder_path}" ) tokenizer.save_pretrained(__magic_name__ ) with open(os.path.join(__magic_name__ , """tokenizer_config.json""" ) , """r""" ) as f: snake_case__ : Union[str, Any] = json.load(__magic_name__ ) snake_case__ : Optional[Any] = """MLukeTokenizer""" with open(os.path.join(__magic_name__ , """tokenizer_config.json""" ) , """w""" ) as f: json.dump(__magic_name__ , __magic_name__ ) with open(os.path.join(__magic_name__ , MLukeTokenizer.vocab_files_names["""entity_vocab_file"""] ) , """w""" ) as f: json.dump(__magic_name__ , __magic_name__ ) snake_case__ : List[Any] = MLukeTokenizer.from_pretrained(__magic_name__ ) # Initialize the embeddings of the special tokens snake_case__ : List[str] = tokenizer.convert_tokens_to_ids(["""@"""] )[0] snake_case__ : List[Any] = tokenizer.convert_tokens_to_ids(["""#"""] )[0] snake_case__ : Optional[Any] = state_dict["""embeddings.word_embeddings.weight"""] snake_case__ : List[str] = word_emb[ent_init_index].unsqueeze(0 ) snake_case__ : List[str] = word_emb[enta_init_index].unsqueeze(0 ) snake_case__ : Union[str, Any] = torch.cat([word_emb, ent_emb, enta_emb] ) # add special tokens for 'entity_predictions.bias' for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]: snake_case__ : List[str] = state_dict[bias_name] snake_case__ : List[str] = decoder_bias[ent_init_index].unsqueeze(0 ) snake_case__ : Dict = decoder_bias[enta_init_index].unsqueeze(0 ) snake_case__ : str = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] ) # Initialize the query layers of the entity-aware self-attention mechanism for layer_index in range(config.num_hidden_layers ): for matrix_name in ["query.weight", "query.bias"]: snake_case__ : Union[str, Any] = f"encoder.layer.{layer_index}.attention.self." snake_case__ : Tuple = state_dict[prefix + matrix_name] snake_case__ : str = state_dict[prefix + matrix_name] snake_case__ : Dict = state_dict[prefix + matrix_name] # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks snake_case__ : Union[str, Any] = state_dict["""entity_embeddings.entity_embeddings.weight"""] snake_case__ : Union[str, Any] = entity_emb[entity_vocab["""[MASK]"""]].unsqueeze(0 ) snake_case__ : List[Any] = torch.cat([entity_emb, entity_mask_emb] ) # add [MASK2] for 'entity_predictions.bias' snake_case__ : Optional[Any] = state_dict["""entity_predictions.bias"""] snake_case__ : Optional[Any] = entity_prediction_bias[entity_vocab["""[MASK]"""]].unsqueeze(0 ) snake_case__ : Any = torch.cat([entity_prediction_bias, entity_mask_bias] ) snake_case__ : int = LukeForMaskedLM(config=__magic_name__ ).eval() state_dict.pop("""entity_predictions.decoder.weight""" ) state_dict.pop("""lm_head.decoder.weight""" ) state_dict.pop("""lm_head.decoder.bias""" ) snake_case__ : Tuple = OrderedDict() for key, value in state_dict.items(): if not (key.startswith("""lm_head""" ) or key.startswith("""entity_predictions""" )): snake_case__ : Optional[Any] = state_dict[key] else: snake_case__ : Optional[int] = state_dict[key] snake_case__ , snake_case__ : Any = model.load_state_dict(__magic_name__ , strict=__magic_name__ ) if set(__magic_name__ ) != {"luke.embeddings.position_ids"}: raise ValueError(f"Unexpected unexpected_keys: {unexpected_keys}" ) if set(__magic_name__ ) != { "lm_head.decoder.weight", "lm_head.decoder.bias", "entity_predictions.decoder.weight", }: raise ValueError(f"Unexpected missing_keys: {missing_keys}" ) model.tie_weights() assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all() assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all() # Check outputs snake_case__ : List[Any] = MLukeTokenizer.from_pretrained(__magic_name__ , task="""entity_classification""" ) snake_case__ : int = """ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan).""" snake_case__ : Union[str, Any] = (0, 9) snake_case__ : str = tokenizer(__magic_name__ , entity_spans=[span] , return_tensors="""pt""" ) snake_case__ : List[Any] = model(**__magic_name__ ) # Verify word hidden states if model_size == "large": raise NotImplementedError else: # base snake_case__ : List[Any] = torch.Size((1, 33, 7_68) ) snake_case__ : Optional[int] = torch.tensor([[0.0892, 0.0596, -0.2819], [0.0134, 0.1199, 0.0573], [-0.0169, 0.0927, 0.0644]] ) if not (outputs.last_hidden_state.shape == expected_shape): raise ValueError( f"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" ) if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , __magic_name__ , atol=1E-4 ): raise ValueError # Verify entity hidden states if model_size == "large": raise NotImplementedError else: # base snake_case__ : Tuple = torch.Size((1, 1, 7_68) ) snake_case__ : int = torch.tensor([[-0.1482, 0.0609, 0.0322]] ) if not (outputs.entity_last_hidden_state.shape == expected_shape): raise ValueError( f"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is" f" {expected_shape}" ) if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , __magic_name__ , atol=1E-4 ): raise ValueError # Verify masked word/entity prediction snake_case__ : Optional[Any] = MLukeTokenizer.from_pretrained(__magic_name__ ) snake_case__ : Any = """Tokyo is the capital of <mask>.""" snake_case__ : str = (24, 30) snake_case__ : List[str] = tokenizer(__magic_name__ , entity_spans=[span] , return_tensors="""pt""" ) snake_case__ : Optional[int] = model(**__magic_name__ ) snake_case__ : List[Any] = encoding["""input_ids"""][0].tolist() snake_case__ : Tuple = input_ids.index(tokenizer.convert_tokens_to_ids("""<mask>""" ) ) snake_case__ : int = outputs.logits[0][mask_position_id].argmax(dim=-1 ) assert "Japan" == tokenizer.decode(__magic_name__ ) snake_case__ : str = outputs.entity_logits[0][0].argmax().item() snake_case__ : Tuple = [ entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id ] assert [e for e in multilingual_predicted_entities if e.startswith("""en:""" )][0] == "en:Japan" # Finally, save our PyTorch model and tokenizer print("""Saving PyTorch model to {}""".format(__magic_name__ ) ) model.save_pretrained(__magic_name__ ) def UpperCamelCase__ ( __magic_name__ : Any ) -> Optional[int]: '''simple docstring''' snake_case__ : Any = ["""[MASK]""", """[PAD]""", """[UNK]"""] snake_case__ : str = [json.loads(__magic_name__ ) for line in open(__magic_name__ )] snake_case__ : List[str] = {} for entry in data: snake_case__ : Dict = entry["""id"""] for entity_name, language in entry["entities"]: if entity_name in SPECIAL_TOKENS: snake_case__ : List[Any] = entity_id break snake_case__ : Optional[Any] = f"{language}:{entity_name}" snake_case__ : Any = entity_id return new_mapping if __name__ == "__main__": A_ : str = argparse.ArgumentParser() # Required parameters parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.") parser.add_argument( "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration." ) parser.add_argument( "--entity_vocab_path", default=None, type=str, help="Path to an entity_vocab.tsv file, containing the entity vocabulary.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model." ) parser.add_argument( "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted." ) A_ : Dict = parser.parse_args() convert_luke_checkpoint( args.checkpoint_path, args.metadata_path, args.entity_vocab_path, args.pytorch_dump_folder_path, args.model_size, )
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { 'caidas/swin2sr-classicalsr-x2-64': ( 'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json' ), } class lowerCamelCase__ ( A__ ): __lowerCamelCase = """swin2sr""" __lowerCamelCase = { """hidden_size""": """embed_dim""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers""", } def __init__( self : str , __a : Optional[Any]=64 , __a : List[str]=1 , __a : Dict=3 , __a : Dict=180 , __a : Union[str, Any]=[6, 6, 6, 6, 6, 6] , __a : Tuple=[6, 6, 6, 6, 6, 6] , __a : Optional[Any]=8 , __a : Dict=2.0 , __a : List[str]=True , __a : Dict=0.0 , __a : int=0.0 , __a : str=0.1 , __a : str="gelu" , __a : List[str]=False , __a : Optional[int]=0.02 , __a : List[Any]=1e-5 , __a : Dict=2 , __a : str=1.0 , __a : str="1conv" , __a : Tuple="pixelshuffle" , **__a : List[str] , ): '''simple docstring''' super().__init__(**__a ) lowerCamelCase__: Dict = image_size lowerCamelCase__: Optional[Any] = patch_size lowerCamelCase__: List[str] = num_channels lowerCamelCase__: str = embed_dim lowerCamelCase__: List[Any] = depths lowerCamelCase__: Union[str, Any] = len(__a ) lowerCamelCase__: Tuple = num_heads lowerCamelCase__: Tuple = window_size lowerCamelCase__: List[Any] = mlp_ratio lowerCamelCase__: List[Any] = qkv_bias lowerCamelCase__: Optional[Any] = hidden_dropout_prob lowerCamelCase__: List[Any] = attention_probs_dropout_prob lowerCamelCase__: Optional[int] = drop_path_rate lowerCamelCase__: Tuple = hidden_act lowerCamelCase__: Optional[int] = use_absolute_embeddings lowerCamelCase__: Optional[Any] = layer_norm_eps lowerCamelCase__: Tuple = initializer_range lowerCamelCase__: List[Any] = upscale lowerCamelCase__: Any = img_range lowerCamelCase__: Union[str, Any] = resi_connection lowerCamelCase__: Tuple = upsampler
306
from __future__ import annotations _lowercase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0] _lowercase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1] def __lowerCAmelCase ( _UpperCamelCase ) -> list[float]: '''simple docstring''' lowerCamelCase__: str = [] lowerCamelCase__: List[str] = len(_UpperCamelCase ) for i in range(_UpperCamelCase ): lowerCamelCase__: float = -1 for j in range(i + 1 , _UpperCamelCase ): if arr[i] < arr[j]: lowerCamelCase__: Dict = arr[j] break result.append(_UpperCamelCase ) return result def __lowerCAmelCase ( _UpperCamelCase ) -> list[float]: '''simple docstring''' lowerCamelCase__: Tuple = [] for i, outer in enumerate(_UpperCamelCase ): lowerCamelCase__: float = -1 for inner in arr[i + 1 :]: if outer < inner: lowerCamelCase__: Dict = inner break result.append(_UpperCamelCase ) return result def __lowerCAmelCase ( _UpperCamelCase ) -> list[float]: '''simple docstring''' lowerCamelCase__: Optional[Any] = len(_UpperCamelCase ) lowerCamelCase__: list[float] = [] lowerCamelCase__: list[float] = [-1] * arr_size for index in reversed(range(_UpperCamelCase ) ): if stack: while stack[-1] <= arr[index]: stack.pop() if not stack: break if stack: lowerCamelCase__: Dict = stack[-1] stack.append(arr[index] ) return result if __name__ == "__main__": from doctest import testmod from timeit import timeit testmod() print(next_greatest_element_slow(arr)) print(next_greatest_element_fast(arr)) print(next_greatest_element(arr)) _lowercase = ( 'from __main__ import arr, next_greatest_element_slow, ' 'next_greatest_element_fast, next_greatest_element' ) print( 'next_greatest_element_slow():', timeit('next_greatest_element_slow(arr)', setup=setup), ) print( 'next_greatest_element_fast():', timeit('next_greatest_element_fast(arr)', setup=setup), ) print( ' next_greatest_element():', timeit('next_greatest_element(arr)', setup=setup), )
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1
from __future__ import annotations import unittest from transformers import XGLMConfig, XGLMTokenizer, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers.models.xglm.modeling_tf_xglm import ( TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, TFXGLMForCausalLM, TFXGLMModel, ) @require_tf class _a : """simple docstring""" snake_case_ = XGLMConfig snake_case_ = {} snake_case_ = "gelu" def __init__( self : int , a : Tuple , a : Optional[Any]=14 , a : Dict=7 , a : Union[str, Any]=True , a : List[Any]=True , a : Optional[int]=True , a : Dict=99 , a : Optional[int]=32 , a : Any=2 , a : Optional[int]=4 , a : List[str]=37 , a : Union[str, Any]="gelu" , a : List[str]=0.1 , a : Optional[Any]=0.1 , a : Tuple=5_12 , a : Any=0.02 , ) ->Optional[int]: SCREAMING_SNAKE_CASE__ : List[Any] = parent SCREAMING_SNAKE_CASE__ : Optional[int] = batch_size SCREAMING_SNAKE_CASE__ : Tuple = seq_length SCREAMING_SNAKE_CASE__ : Dict = is_training SCREAMING_SNAKE_CASE__ : Optional[Any] = use_input_mask SCREAMING_SNAKE_CASE__ : List[Any] = use_labels SCREAMING_SNAKE_CASE__ : Optional[int] = vocab_size SCREAMING_SNAKE_CASE__ : List[str] = d_model SCREAMING_SNAKE_CASE__ : int = num_hidden_layers SCREAMING_SNAKE_CASE__ : Tuple = num_attention_heads SCREAMING_SNAKE_CASE__ : Optional[int] = ffn_dim SCREAMING_SNAKE_CASE__ : List[str] = activation_function SCREAMING_SNAKE_CASE__ : Union[str, Any] = activation_dropout SCREAMING_SNAKE_CASE__ : List[str] = attention_dropout SCREAMING_SNAKE_CASE__ : List[str] = max_position_embeddings SCREAMING_SNAKE_CASE__ : Union[str, Any] = initializer_range SCREAMING_SNAKE_CASE__ : Tuple = None SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0 SCREAMING_SNAKE_CASE__ : int = 2 SCREAMING_SNAKE_CASE__ : Tuple = 1 def A_ ( self : Optional[Any] ) ->Union[str, Any]: return XGLMConfig.from_pretrained("facebook/xglm-564M" ) def A_ ( self : Tuple ) ->Union[str, Any]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.clip_by_value( ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 ) SCREAMING_SNAKE_CASE__ : Any = None if self.use_input_mask: SCREAMING_SNAKE_CASE__ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE__ : List[Any] = self.get_config() SCREAMING_SNAKE_CASE__ : Dict = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 ) return ( config, input_ids, input_mask, head_mask, ) def A_ ( self : Optional[int] ) ->int: return XGLMConfig( vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=A_ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=A_ , ) def A_ ( self : Optional[int] ) ->Tuple: SCREAMING_SNAKE_CASE__ : List[Any] = self.prepare_config_and_inputs() ( ( SCREAMING_SNAKE_CASE__ ), ( SCREAMING_SNAKE_CASE__ ), ( SCREAMING_SNAKE_CASE__ ), ( SCREAMING_SNAKE_CASE__ ), ) : Optional[Any] = config_and_inputs SCREAMING_SNAKE_CASE__ : Tuple = { "input_ids": input_ids, "head_mask": head_mask, } return config, inputs_dict @require_tf class _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ): """simple docstring""" snake_case_ = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else () snake_case_ = (TFXGLMForCausalLM,) if is_tf_available() else () snake_case_ = ( {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {} ) snake_case_ = False snake_case_ = False snake_case_ = False def A_ ( self : Optional[Any] ) ->Tuple: SCREAMING_SNAKE_CASE__ : List[str] = TFXGLMModelTester(self ) SCREAMING_SNAKE_CASE__ : Any = ConfigTester(self , config_class=A_ , n_embd=37 ) def A_ ( self : List[str] ) ->Optional[int]: self.config_tester.run_common_tests() @slow def A_ ( self : Optional[int] ) ->Dict: for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE__ : List[str] = TFXGLMModel.from_pretrained(A_ ) self.assertIsNotNone(A_ ) @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor." ) def A_ ( self : List[Any] ) ->Any: super().test_resize_token_embeddings() @require_tf class _a ( unittest.TestCase ): """simple docstring""" @slow def A_ ( self : str , a : Any=True ) ->List[str]: SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = tf.convert_to_tensor([[2, 2_68, 98_65]] , dtype=tf.intaa ) # The dog # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other # fmt: off SCREAMING_SNAKE_CASE__ : Any = [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81] # fmt: on SCREAMING_SNAKE_CASE__ : Dict = model.generate(A_ , do_sample=A_ , num_beams=1 ) if verify_outputs: self.assertListEqual(output_ids[0].numpy().tolist() , A_ ) @slow def A_ ( self : Optional[int] ) ->Optional[Any]: SCREAMING_SNAKE_CASE__ : Tuple = XGLMTokenizer.from_pretrained("facebook/xglm-564M" ) SCREAMING_SNAKE_CASE__ : List[str] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" ) tf.random.set_seed(0 ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = tokenizer("Today is a nice day and" , return_tensors="tf" ) SCREAMING_SNAKE_CASE__ : Dict = tokenized.input_ids # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices) with tf.device(":/CPU:0" ): SCREAMING_SNAKE_CASE__ : List[str] = model.generate(A_ , do_sample=A_ , seed=[7, 0] ) SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.decode(output_ids[0] , skip_special_tokens=A_ ) SCREAMING_SNAKE_CASE__ : Optional[Any] = ( "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due" ) self.assertEqual(A_ , A_ ) @slow def A_ ( self : Tuple ) ->Tuple: SCREAMING_SNAKE_CASE__ : Optional[int] = TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M" ) SCREAMING_SNAKE_CASE__ : List[str] = XGLMTokenizer.from_pretrained("facebook/xglm-564M" ) SCREAMING_SNAKE_CASE__ : List[str] = "left" # use different length sentences to test batching SCREAMING_SNAKE_CASE__ : Dict = [ "This is an extremelly long sentence that only exists to test the ability of the model to cope with " "left-padding, such as in batched generation. The output for the sequence below should be the same " "regardless of whether left padding is applied or not. When", "Hello, my dog is a little", ] SCREAMING_SNAKE_CASE__ : List[str] = tokenizer(A_ , return_tensors="tf" , padding=A_ ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = inputs["input_ids"] SCREAMING_SNAKE_CASE__ : str = model.generate(input_ids=A_ , attention_mask=inputs["attention_mask"] , max_new_tokens=12 ) SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer(sentences[0] , return_tensors="tf" ).input_ids SCREAMING_SNAKE_CASE__ : Any = model.generate(input_ids=A_ , max_new_tokens=12 ) SCREAMING_SNAKE_CASE__ : str = tokenizer(sentences[1] , return_tensors="tf" ).input_ids SCREAMING_SNAKE_CASE__ : int = model.generate(input_ids=A_ , max_new_tokens=12 ) SCREAMING_SNAKE_CASE__ : Dict = tokenizer.batch_decode(A_ , skip_special_tokens=A_ ) SCREAMING_SNAKE_CASE__ : Optional[int] = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A_ ) SCREAMING_SNAKE_CASE__ : int = tokenizer.decode(output_padded[0] , skip_special_tokens=A_ ) SCREAMING_SNAKE_CASE__ : Optional[int] = [ "This is an extremelly long sentence that only exists to test the ability of the model to cope with " "left-padding, such as in batched generation. The output for the sequence below should be the same " "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be " "a single", "Hello, my dog is a little bit of a shy one, but he is very friendly", ] self.assertListEqual(A_ , A_ ) self.assertListEqual(A_ , [non_padded_sentence, padded_sentence] )
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from collections import OrderedDict from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import TensorType, logging if TYPE_CHECKING: from ...onnx.config import PatchingSpec from ...tokenization_utils_base import PreTrainedTokenizerBase __lowercase :List[Any] = logging.get_logger(__name__) __lowercase :Optional[int] = { "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json", "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json", "allenai/longformer-large-4096-finetuned-triviaqa": ( "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json" ), "allenai/longformer-base-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json" ), "allenai/longformer-large-4096-extra.pos.embd.only": ( "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json" ), } class _a ( lowercase__ ): """simple docstring""" snake_case_ = "longformer" def __init__( self : List[str] , a : Union[List[int], int] = 5_12 , a : int = 2 , a : int = 1 , a : int = 0 , a : int = 2 , a : int = 3_05_22 , a : int = 7_68 , a : int = 12 , a : int = 12 , a : int = 30_72 , a : str = "gelu" , a : float = 0.1 , a : float = 0.1 , a : int = 5_12 , a : int = 2 , a : float = 0.02 , a : float = 1E-12 , a : bool = False , **a : Dict , ) ->Tuple: super().__init__(pad_token_id=a , **a ) SCREAMING_SNAKE_CASE__ : int = attention_window SCREAMING_SNAKE_CASE__ : Any = sep_token_id SCREAMING_SNAKE_CASE__ : str = bos_token_id SCREAMING_SNAKE_CASE__ : List[str] = eos_token_id SCREAMING_SNAKE_CASE__ : List[str] = vocab_size SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_size SCREAMING_SNAKE_CASE__ : List[str] = num_hidden_layers SCREAMING_SNAKE_CASE__ : Optional[int] = num_attention_heads SCREAMING_SNAKE_CASE__ : List[Any] = hidden_act SCREAMING_SNAKE_CASE__ : Optional[int] = intermediate_size SCREAMING_SNAKE_CASE__ : List[str] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : Dict = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : Optional[Any] = max_position_embeddings SCREAMING_SNAKE_CASE__ : str = type_vocab_size SCREAMING_SNAKE_CASE__ : Any = initializer_range SCREAMING_SNAKE_CASE__ : List[Any] = layer_norm_eps SCREAMING_SNAKE_CASE__ : Any = onnx_export class _a ( lowercase__ ): """simple docstring""" def __init__( self : int , a : "PretrainedConfig" , a : str = "default" , a : "List[PatchingSpec]" = None ) ->str: super().__init__(a , a , a ) SCREAMING_SNAKE_CASE__ : Any = True @property def A_ ( self : int ) ->Mapping[str, Mapping[int, str]]: if self.task == "multiple-choice": SCREAMING_SNAKE_CASE__ : int = {0: "batch", 1: "choice", 2: "sequence"} else: SCREAMING_SNAKE_CASE__ : str = {0: "batch", 1: "sequence"} return OrderedDict( [ ("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("global_attention_mask", dynamic_axis), ] ) @property def A_ ( self : Optional[Any] ) ->Mapping[str, Mapping[int, str]]: SCREAMING_SNAKE_CASE__ : Optional[Any] = super().outputs if self.task == "default": SCREAMING_SNAKE_CASE__ : List[str] = {0: "batch"} return outputs @property def A_ ( self : str ) ->float: return 1E-4 @property def A_ ( self : Any ) ->int: # needs to be >= 14 to support tril operator return max(super().default_onnx_opset , 14 ) def A_ ( self : str , a : "PreTrainedTokenizerBase" , a : int = -1 , a : int = -1 , a : bool = False , a : Optional[TensorType] = None , ) ->Mapping[str, Any]: SCREAMING_SNAKE_CASE__ : Tuple = super().generate_dummy_inputs( preprocessor=a , batch_size=a , seq_length=a , is_pair=a , framework=a ) import torch # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64) # makes the export fail randomly SCREAMING_SNAKE_CASE__ : Any = torch.zeros_like(inputs["input_ids"] ) # make every second token global SCREAMING_SNAKE_CASE__ : str = 1 return inputs
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import warnings from ...utils import is_sklearn_available, requires_backends if is_sklearn_available(): from scipy.stats import pearsonr, spearmanr from sklearn.metrics import fa_score, matthews_corrcoef _snake_case : Union[str, Any] = ( 'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate ' 'library. You can have a look at this example script for pointers: ' 'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' ) def a_ ( lowerCAmelCase_ : Optional[int], lowerCAmelCase_ : Union[str, Any] ): warnings.warn(lowerCAmelCase_, lowerCAmelCase_ ) requires_backends(lowerCAmelCase_, 'sklearn' ) return (preds == labels).mean() def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : List[str] ): warnings.warn(lowerCAmelCase_, lowerCAmelCase_ ) requires_backends(lowerCAmelCase_, 'sklearn' ) __lowerCAmelCase = simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ ) __lowerCAmelCase = fa_score(y_true=lowerCAmelCase_, y_pred=lowerCAmelCase_ ) return { "acc": acc, "f1": fa, "acc_and_f1": (acc + fa) / 2, } def a_ ( lowerCAmelCase_ : List[str], lowerCAmelCase_ : Optional[int] ): warnings.warn(lowerCAmelCase_, lowerCAmelCase_ ) requires_backends(lowerCAmelCase_, 'sklearn' ) __lowerCAmelCase = pearsonr(lowerCAmelCase_, lowerCAmelCase_ )[0] __lowerCAmelCase = spearmanr(lowerCAmelCase_, lowerCAmelCase_ )[0] return { "pearson": pearson_corr, "spearmanr": spearman_corr, "corr": (pearson_corr + spearman_corr) / 2, } def a_ ( lowerCAmelCase_ : List[Any], lowerCAmelCase_ : List[str], lowerCAmelCase_ : Union[str, Any] ): warnings.warn(lowerCAmelCase_, lowerCAmelCase_ ) requires_backends(lowerCAmelCase_, 'sklearn' ) assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ), F"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_ )} and {len(lowerCAmelCase_ )}""" if task_name == "cola": return {"mcc": matthews_corrcoef(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "sst-2": return {"acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "mrpc": return acc_and_fa(lowerCAmelCase_, lowerCAmelCase_ ) elif task_name == "sts-b": return pearson_and_spearman(lowerCAmelCase_, lowerCAmelCase_ ) elif task_name == "qqp": return acc_and_fa(lowerCAmelCase_, lowerCAmelCase_ ) elif task_name == "mnli": return {"mnli/acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "mnli-mm": return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "qnli": return {"acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "rte": return {"acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "wnli": return {"acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} elif task_name == "hans": return {"acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} else: raise KeyError(lowerCAmelCase_ ) def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : int, lowerCAmelCase_ : Union[str, Any] ): warnings.warn(lowerCAmelCase_, lowerCAmelCase_ ) requires_backends(lowerCAmelCase_, 'sklearn' ) if len(lowerCAmelCase_ ) != len(lowerCAmelCase_ ): raise ValueError(F"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_ )} and {len(lowerCAmelCase_ )}""" ) if task_name == "xnli": return {"acc": simple_accuracy(lowerCAmelCase_, lowerCAmelCase_ )} else: raise KeyError(lowerCAmelCase_ )
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def _UpperCamelCase ( lowercase__ = 10**9 ): __SCREAMING_SNAKE_CASE : List[str] = 1 __SCREAMING_SNAKE_CASE : int = 2 __SCREAMING_SNAKE_CASE : Union[str, Any] = 0 __SCREAMING_SNAKE_CASE : Dict = 0 __SCREAMING_SNAKE_CASE : Any = 0 while perimeter <= max_perimeter: perimeters_sum += perimeter prev_value += 2 * value value += prev_value __SCREAMING_SNAKE_CASE : Dict = 2 * value + 2 if i % 2 == 0 else 2 * value - 2 i += 1 return perimeters_sum if __name__ == "__main__": print(f"""{solution() = }""")
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import argparse import copy def __magic_name__( SCREAMING_SNAKE_CASE__ : List[str] ) -> int: '''simple docstring''' A__ = {} with open(SCREAMING_SNAKE_CASE__ ) as f: for line in f: if line.split()[0] not in dict_of_neighbours: A__ = [] _list.append([line.split()[1], line.split()[2]] ) A__ = _list else: dict_of_neighbours[line.split()[0]].append( [line.split()[1], line.split()[2]] ) if line.split()[1] not in dict_of_neighbours: A__ = [] _list.append([line.split()[0], line.split()[2]] ) A__ = _list else: dict_of_neighbours[line.split()[1]].append( [line.split()[0], line.split()[2]] ) return dict_of_neighbours def __magic_name__( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Any ) -> str: '''simple docstring''' with open(SCREAMING_SNAKE_CASE__ ) as f: A__ = f.read(1 ) A__ = start_node A__ = [] A__ = start_node A__ = 0 while visiting not in first_solution: A__ = 10000 for k in dict_of_neighbours[visiting]: if int(k[1] ) < int(SCREAMING_SNAKE_CASE__ ) and k[0] not in first_solution: A__ = k[1] A__ = k[0] first_solution.append(SCREAMING_SNAKE_CASE__ ) A__ = distance_of_first_solution + int(SCREAMING_SNAKE_CASE__ ) A__ = best_node first_solution.append(SCREAMING_SNAKE_CASE__ ) A__ = 0 for k in dict_of_neighbours[first_solution[-2]]: if k[0] == start_node: break position += 1 A__ = ( distance_of_first_solution + int(dict_of_neighbours[first_solution[-2]][position][1] ) - 10000 ) return first_solution, distance_of_first_solution def __magic_name__( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> List[Any]: '''simple docstring''' A__ = [] for n in solution[1:-1]: A__ = solution.index(SCREAMING_SNAKE_CASE__ ) for kn in solution[1:-1]: A__ = solution.index(SCREAMING_SNAKE_CASE__ ) if n == kn: continue A__ = copy.deepcopy(SCREAMING_SNAKE_CASE__ ) A__ = kn A__ = n A__ = 0 for k in _tmp[:-1]: A__ = _tmp[_tmp.index(SCREAMING_SNAKE_CASE__ ) + 1] for i in dict_of_neighbours[k]: if i[0] == next_node: A__ = distance + int(i[1] ) _tmp.append(SCREAMING_SNAKE_CASE__ ) if _tmp not in neighborhood_of_solution: neighborhood_of_solution.append(_tmp ) A__ = len(neighborhood_of_solution[0] ) - 1 neighborhood_of_solution.sort(key=lambda SCREAMING_SNAKE_CASE__ : x[index_of_last_item_in_the_list] ) return neighborhood_of_solution def __magic_name__( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> Optional[int]: '''simple docstring''' A__ = 1 A__ = first_solution A__ = [] A__ = distance_of_first_solution A__ = solution while count <= iters: A__ = find_neighborhood(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) A__ = 0 A__ = neighborhood[index_of_best_solution] A__ = len(SCREAMING_SNAKE_CASE__ ) - 1 A__ = False while not found: A__ = 0 while i < len(SCREAMING_SNAKE_CASE__ ): if best_solution[i] != solution[i]: A__ = best_solution[i] A__ = solution[i] break A__ = i + 1 if [first_exchange_node, second_exchange_node] not in tabu_list and [ second_exchange_node, first_exchange_node, ] not in tabu_list: tabu_list.append([first_exchange_node, second_exchange_node] ) A__ = True A__ = best_solution[:-1] A__ = neighborhood[index_of_best_solution][best_cost_index] if cost < best_cost: A__ = cost A__ = solution else: A__ = index_of_best_solution + 1 A__ = neighborhood[index_of_best_solution] if len(SCREAMING_SNAKE_CASE__ ) >= size: tabu_list.pop(0 ) A__ = count + 1 return best_solution_ever, best_cost def __magic_name__( SCREAMING_SNAKE_CASE__ : Dict=None ) -> int: '''simple docstring''' A__ = generate_neighbours(args.File ) A__ , A__ = generate_first_solution( args.File , SCREAMING_SNAKE_CASE__ ) A__ , A__ = tabu_search( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , args.Iterations , args.Size , ) print(f'Best solution: {best_sol}, with total distance: {best_cost}.' ) if __name__ == "__main__": lowercase_ = argparse.ArgumentParser(description="Tabu Search") parser.add_argument( "-f", "--File", type=str, help="Path to the file containing the data", required=True, ) parser.add_argument( "-i", "--Iterations", type=int, help="How many iterations the algorithm should perform", required=True, ) parser.add_argument( "-s", "--Size", type=int, help="Size of the tabu list", required=True ) # Pass the arguments to main method main(parser.parse_args())
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import unittest from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin lowercase_ = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece @require_tokenizers class A ( _UpperCAmelCase , unittest.TestCase ): """simple docstring""" lowerCamelCase = ReformerTokenizer lowerCamelCase = ReformerTokenizerFast lowerCamelCase = True lowerCamelCase = False lowerCamelCase = True def snake_case__ ( self : Any )-> Union[str, Any]: '''simple docstring''' super().setUp() A__ = ReformerTokenizer(lowercase_,keep_accents=lowercase_ ) tokenizer.save_pretrained(self.tmpdirname ) def snake_case__ ( self : int )-> int: '''simple docstring''' A__ = '<s>' A__ = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ),lowercase_ ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ),lowercase_ ) def snake_case__ ( self : List[str] )-> Dict: '''simple docstring''' A__ = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0],'<unk>' ) self.assertEqual(vocab_keys[1],'<s>' ) self.assertEqual(vocab_keys[-1],'j' ) self.assertEqual(len(lowercase_ ),1_0_0_0 ) def snake_case__ ( self : Any )-> int: '''simple docstring''' self.assertEqual(self.get_tokenizer().vocab_size,1_0_0_0 ) def snake_case__ ( self : Any )-> Tuple: '''simple docstring''' if not self.test_rust_tokenizer: return A__ = self.get_tokenizer() A__ = self.get_rust_tokenizer() A__ = 'I was born in 92000, and this is falsé.' A__ = tokenizer.tokenize(lowercase_ ) A__ = rust_tokenizer.tokenize(lowercase_ ) self.assertListEqual(lowercase_,lowercase_ ) A__ = tokenizer.encode(lowercase_,add_special_tokens=lowercase_ ) A__ = rust_tokenizer.encode(lowercase_,add_special_tokens=lowercase_ ) self.assertListEqual(lowercase_,lowercase_ ) A__ = self.get_rust_tokenizer() A__ = tokenizer.encode(lowercase_ ) A__ = rust_tokenizer.encode(lowercase_ ) self.assertListEqual(lowercase_,lowercase_ ) def snake_case__ ( self : str,lowercase_ : int=1_5 )-> Union[str, Any]: '''simple docstring''' for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})' ): A__ = self.rust_tokenizer_class.from_pretrained(lowercase_,**lowercase_ ) # Simple input A__ = 'This is a simple input' A__ = ['This is a simple input 1', 'This is a simple input 2'] A__ = ('This is a simple input', 'This is a pair') A__ = [ ('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(lowercase_,tokenizer_r.encode,lowercase_,max_length=lowercase_,padding='max_length' ) # Simple input self.assertRaises(lowercase_,tokenizer_r.encode_plus,lowercase_,max_length=lowercase_,padding='max_length' ) # Simple input self.assertRaises( lowercase_,tokenizer_r.batch_encode_plus,lowercase_,max_length=lowercase_,padding='max_length',) # Pair input self.assertRaises(lowercase_,tokenizer_r.encode,lowercase_,max_length=lowercase_,padding='max_length' ) # Pair input self.assertRaises(lowercase_,tokenizer_r.encode_plus,lowercase_,max_length=lowercase_,padding='max_length' ) # Pair input self.assertRaises( lowercase_,tokenizer_r.batch_encode_plus,lowercase_,max_length=lowercase_,padding='max_length',) def snake_case__ ( self : Any )-> Optional[Any]: '''simple docstring''' pass def snake_case__ ( self : Dict )-> Dict: '''simple docstring''' A__ = ReformerTokenizer(lowercase_,keep_accents=lowercase_ ) A__ = tokenizer.tokenize('This is a test' ) self.assertListEqual(lowercase_,['▁This', '▁is', '▁a', '▁t', 'est'] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(lowercase_ ),[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2],) A__ = tokenizer.tokenize('I was born in 92000, and this is falsé.' ) self.assertListEqual( lowercase_,[ SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.', ],) A__ = tokenizer.convert_tokens_to_ids(lowercase_ ) self.assertListEqual( lowercase_,[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4],) A__ = tokenizer.convert_ids_to_tokens(lowercase_ ) self.assertListEqual( lowercase_,[ SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.', ],) @cached_property def snake_case__ ( self : str )-> Optional[int]: '''simple docstring''' return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment' ) @slow def snake_case__ ( self : Optional[Any] )-> int: '''simple docstring''' A__ = 'Hello World!' A__ = [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7] self.assertListEqual(lowercase_,self.big_tokenizer.encode(lowercase_ ) ) @slow def snake_case__ ( self : Dict )-> Dict: '''simple docstring''' A__ = ( 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will' ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth' ) A__ = [ 1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 3_5, 2_8, 2_7_5, 3, 2_5_9, 2_9_7, 2_6_0, 8_4, 4, 3_5, 1_1_0, 4_4, 8, 2_5_9, 9_1, 2_6_8, 2_1, 1_1, 2_0_9, 2_7_4, 1_0_9, 2_6_6, 2_7_7, 1_1_7, 8_6, 9_3, 3_1_5, 2_5_8, 2_7_8, 2_5_8, 2_7_7, 2_5_8, 0, 2_5_8, 2_8_8, 2_5_8, 3_1_9, 2_5_8, 0, 2_5_8, 0, 2_5_8, 0, 2_5_8, 0, 2_5_8, 2_8_7, 2_5_8, 3_1_5, 2_5_8, 2_8_9, 2_5_8, 2_7_8, 9_9, 2_6_9, 2_6_6, 2_6_2, 8, 2_5_9, 2_4_1, 4, 2_1_7, 2_3_0, 2_6_8, 2_6_6, 5_5, 1_6_8, 1_0_6, 7_5, 1_9_3, 2_6_6, 2_2_3, 2_7, 4_9, 2_6, 2_8_2, 2_5, 2_6_4, 2_9_9, 1_9, 2_6, 0, 2_5_8, 2_7_7, 1_1_7, 8_6, 9_3, 1_7_6, 1_8_3, 2_7_0, 1_1, 2_6_2, 4_2, 6_1, 2_6_5, ] self.assertListEqual(lowercase_,self.big_tokenizer.encode(lowercase_ ) ) @require_torch @slow def snake_case__ ( self : Union[str, Any] )-> int: '''simple docstring''' import torch from transformers import ReformerConfig, ReformerModel # Build sequence A__ = list(self.big_tokenizer.get_vocab().keys() )[:1_0] A__ = ' '.join(lowercase_ ) A__ = self.big_tokenizer.encode_plus(lowercase_,return_tensors='pt' ) A__ = self.big_tokenizer.batch_encode_plus([sequence, sequence],return_tensors='pt' ) A__ = ReformerConfig() # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024) A__ = encoded_sequence['input_ids'].shape A__ = ReformerModel(lowercase_ ) # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320) assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size with torch.no_grad(): model(**lowercase_ ) model(**lowercase_ ) @slow def snake_case__ ( self : Tuple )-> Dict: '''simple docstring''' A__ = {'input_ids': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 # fmt: on # This tokenizer does not know some characters like ")". # That is the reason why we use very simple texts here. # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064 A__ = [ 'This is a very simple sentence.', 'The quick brown fox jumps over the lazy dog.', ] self.tokenizer_integration_test_util( expected_encoding=lowercase_,model_name='google/reformer-crime-and-punishment',revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a',padding=lowercase_,sequences=lowercase_,)
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import importlib import sys from argparse import REMAINDER, ArgumentParser from pathlib import Path import torch_xla.distributed.xla_multiprocessing as xmp def __magic_name__ ( ): '''simple docstring''' UpperCamelCase__ = ArgumentParser( description=( """PyTorch TPU distributed training launch """ """helper utility that will spawn up """ """multiple distributed processes""" ) ) # Optional arguments for the launch helper parser.add_argument("""--num_cores""" , type=__a , default=1 , help="""Number of TPU cores to use (1 or 8).""" ) # positional parser.add_argument( """training_script""" , type=__a , help=( """The full path to the single TPU training """ """program/script to be launched in parallel, """ """followed by all the arguments for the """ """training script""" ) , ) # rest from the training program parser.add_argument("""training_script_args""" , nargs=__a ) return parser.parse_args() def __magic_name__ ( ): '''simple docstring''' UpperCamelCase__ = parse_args() # Import training_script as a module. UpperCamelCase__ = Path(args.training_script ) sys.path.append(str(script_fpath.parent.resolve() ) ) UpperCamelCase__ = script_fpath.stem UpperCamelCase__ = importlib.import_module(__a ) # Patch sys.argv UpperCamelCase__ = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )] xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores ) if __name__ == "__main__": main()
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import gc import unittest import numpy as np import torch import torch.nn.functional as F from transformers import ( ClapTextConfig, ClapTextModelWithProjection, RobertaTokenizer, SpeechTaHifiGan, SpeechTaHifiGanConfig, ) from diffusers import ( AudioLDMPipeline, AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel, ) from diffusers.utils import is_xformers_available, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() class __A( __lowerCamelCase , unittest.TestCase ): """simple docstring""" SCREAMING_SNAKE_CASE__ = AudioLDMPipeline SCREAMING_SNAKE_CASE__ = TEXT_TO_AUDIO_PARAMS SCREAMING_SNAKE_CASE__ = TEXT_TO_AUDIO_BATCH_PARAMS SCREAMING_SNAKE_CASE__ = frozenset( [ """num_inference_steps""", """num_waveforms_per_prompt""", """generator""", """latents""", """output_type""", """return_dict""", """callback""", """callback_steps""", ] ) def UpperCAmelCase_ (self ): 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, 64) , class_embed_type="""simple_projection""" , projection_class_embeddings_input_dim=32 , class_embeddings_concat=SCREAMING_SNAKE_CASE_ , ) UpperCamelCase__ = DDIMScheduler( beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=SCREAMING_SNAKE_CASE_ , set_alpha_to_one=SCREAMING_SNAKE_CASE_ , ) torch.manual_seed(0 ) UpperCamelCase__ = AutoencoderKL( block_out_channels=[32, 64] , in_channels=1 , out_channels=1 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , ) torch.manual_seed(0 ) UpperCamelCase__ = ClapTextConfig( 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=10_00 , projection_dim=32 , ) UpperCamelCase__ = ClapTextModelWithProjection(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" , model_max_length=77 ) UpperCamelCase__ = SpeechTaHifiGanConfig( model_in_dim=8 , sampling_rate=1_60_00 , upsample_initial_channel=16 , upsample_rates=[2, 2] , upsample_kernel_sizes=[4, 4] , resblock_kernel_sizes=[3, 7] , resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] , normalize_before=SCREAMING_SNAKE_CASE_ , ) UpperCamelCase__ = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = { """unet""": unet, """scheduler""": scheduler, """vae""": vae, """text_encoder""": text_encoder, """tokenizer""": tokenizer, """vocoder""": vocoder, } return components def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0 ): if str(SCREAMING_SNAKE_CASE_ ).startswith("""mps""" ): UpperCamelCase__ = torch.manual_seed(SCREAMING_SNAKE_CASE_ ) else: UpperCamelCase__ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = { """prompt""": """A hammer hitting a wooden surface""", """generator""": generator, """num_inference_steps""": 2, """guidance_scale""": 6.0, } return inputs def UpperCAmelCase_ (self ): UpperCamelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] assert audio.ndim == 1 assert len(SCREAMING_SNAKE_CASE_ ) == 2_56 UpperCamelCase__ = audio[:10] UpperCamelCase__ = np.array( [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] ) assert np.abs(audio_slice - expected_slice ).max() < 1E-2 def UpperCAmelCase_ (self ): UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = 3 * [inputs["""prompt"""]] # forward UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = 3 * [inputs.pop("""prompt""" )] UpperCamelCase__ = audioldm_pipe.tokenizer( SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , ) UpperCamelCase__ = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.text_encoder( SCREAMING_SNAKE_CASE_ , ) UpperCamelCase__ = prompt_embeds.text_embeds # additional L_2 normalization over each hidden-state UpperCamelCase__ = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 ) UpperCamelCase__ = prompt_embeds # forward UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] assert np.abs(audio_a - audio_a ).max() < 1E-2 def UpperCAmelCase_ (self ): UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = 3 * ["""this is a negative prompt"""] UpperCamelCase__ = negative_prompt UpperCamelCase__ = 3 * [inputs["""prompt"""]] # forward UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = 3 * [inputs.pop("""prompt""" )] UpperCamelCase__ = [] for p in [prompt, negative_prompt]: UpperCamelCase__ = audioldm_pipe.tokenizer( SCREAMING_SNAKE_CASE_ , padding="""max_length""" , max_length=audioldm_pipe.tokenizer.model_max_length , truncation=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , ) UpperCamelCase__ = text_inputs["""input_ids"""].to(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.text_encoder( SCREAMING_SNAKE_CASE_ , ) UpperCamelCase__ = text_embeds.text_embeds # additional L_2 normalization over each hidden-state UpperCamelCase__ = F.normalize(SCREAMING_SNAKE_CASE_ , dim=-1 ) embeds.append(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ , UpperCamelCase__ = embeds # forward UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] assert np.abs(audio_a - audio_a ).max() < 1E-2 def UpperCAmelCase_ (self ): UpperCamelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = """egg cracking""" UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ , negative_prompt=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] assert audio.ndim == 1 assert len(SCREAMING_SNAKE_CASE_ ) == 2_56 UpperCamelCase__ = audio[:10] UpperCamelCase__ = np.array( [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] ) assert np.abs(audio_slice - expected_slice ).max() < 1E-2 def UpperCAmelCase_ (self ): UpperCamelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = """A hammer hitting a wooden surface""" # test num_waveforms_per_prompt=1 (default) UpperCamelCase__ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 ).audios assert audios.shape == (1, 2_56) # test num_waveforms_per_prompt=1 (default) for batch of prompts UpperCamelCase__ = 2 UpperCamelCase__ = audioldm_pipe([prompt] * batch_size , num_inference_steps=2 ).audios assert audios.shape == (batch_size, 2_56) # test num_waveforms_per_prompt for single prompt UpperCamelCase__ = 2 UpperCamelCase__ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios assert audios.shape == (num_waveforms_per_prompt, 2_56) # test num_waveforms_per_prompt for batch of prompts UpperCamelCase__ = 2 UpperCamelCase__ = audioldm_pipe( [prompt] * batch_size , num_inference_steps=2 , num_waveforms_per_prompt=SCREAMING_SNAKE_CASE_ ).audios assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56) def UpperCAmelCase_ (self ): UpperCamelCase__ = """cpu""" # ensure determinism for the device-dependent torch.Generator UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.vocoder.config.sampling_rate UpperCamelCase__ = self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe(audio_length_in_s=0.016 , **SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] assert audio.ndim == 1 assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.016 UpperCamelCase__ = audioldm_pipe(audio_length_in_s=0.032 , **SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = output.audios[0] assert audio.ndim == 1 assert len(SCREAMING_SNAKE_CASE_ ) / vocoder_sampling_rate == 0.032 def UpperCAmelCase_ (self ): UpperCamelCase__ = self.get_dummy_components() UpperCamelCase__ = AudioLDMPipeline(**SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = ["""hey"""] UpperCamelCase__ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 ) UpperCamelCase__ = output.audios.shape assert audio_shape == (1, 2_56) UpperCamelCase__ = audioldm_pipe.vocoder.config config.model_in_dim *= 2 UpperCamelCase__ = SpeechTaHifiGan(SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe(SCREAMING_SNAKE_CASE_ , num_inference_steps=1 ) UpperCamelCase__ = output.audios.shape # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram assert audio_shape == (1, 2_56) def UpperCAmelCase_ (self ): self._test_attention_slicing_forward_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ ) def UpperCAmelCase_ (self ): self._test_inference_batch_single_identical(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ ) @unittest.skipIf( torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , ) def UpperCAmelCase_ (self ): self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=SCREAMING_SNAKE_CASE_ ) @slow class __A( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ (self ): super().tearDown() gc.collect() torch.cuda.empty_cache() def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="cpu" , SCREAMING_SNAKE_CASE_=torch.floataa , SCREAMING_SNAKE_CASE_=0 ): UpperCamelCase__ = torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = np.random.RandomState(SCREAMING_SNAKE_CASE_ ).standard_normal((1, 8, 1_28, 16) ) UpperCamelCase__ = torch.from_numpy(SCREAMING_SNAKE_CASE_ ).to(device=SCREAMING_SNAKE_CASE_ , dtype=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = { """prompt""": """A hammer hitting a wooden surface""", """latents""": latents, """generator""": generator, """num_inference_steps""": 3, """guidance_scale""": 2.5, } return inputs def UpperCAmelCase_ (self ): UpperCamelCase__ = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = self.get_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = 25 UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0] assert audio.ndim == 1 assert len(SCREAMING_SNAKE_CASE_ ) == 8_19_20 UpperCamelCase__ = audio[7_72_30:7_72_40] UpperCamelCase__ = np.array( [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] ) UpperCamelCase__ = np.abs(expected_slice - audio_slice ).max() assert max_diff < 1E-2 def UpperCAmelCase_ (self ): UpperCamelCase__ = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" ) UpperCamelCase__ = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config ) UpperCamelCase__ = audioldm_pipe.to(SCREAMING_SNAKE_CASE_ ) audioldm_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = self.get_inputs(SCREAMING_SNAKE_CASE_ ) UpperCamelCase__ = audioldm_pipe(**SCREAMING_SNAKE_CASE_ ).audios[0] assert audio.ndim == 1 assert len(SCREAMING_SNAKE_CASE_ ) == 8_19_20 UpperCamelCase__ = audio[2_77_80:2_77_90] UpperCamelCase__ = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] ) UpperCamelCase__ = np.abs(expected_slice - audio_slice ).max() assert max_diff < 3E-2
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1
'''simple docstring''' import inspect import unittest from transformers import ViTMSNConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available 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 torch import nn from transformers import ViTMSNForImageClassification, ViTMSNModel from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class __lowercase : def __init__( self , UpperCamelCase , UpperCamelCase=13 , UpperCamelCase=30 , UpperCamelCase=2 , UpperCamelCase=3 , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=32 , UpperCamelCase=5 , UpperCamelCase=4 , UpperCamelCase=37 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=10 , UpperCamelCase=0.02 , UpperCamelCase=None , ) -> int: __a = parent __a = batch_size __a = image_size __a = patch_size __a = num_channels __a = is_training __a = use_labels __a = hidden_size __a = num_hidden_layers __a = num_attention_heads __a = intermediate_size __a = hidden_act __a = hidden_dropout_prob __a = attention_probs_dropout_prob __a = type_sequence_label_size __a = initializer_range __a = scope # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) __a = (image_size // patch_size) ** 2 __a = num_patches + 1 def UpperCamelCase__ ( self ) -> Dict: __a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) __a = None if self.use_labels: __a = ids_tensor([self.batch_size] , self.type_sequence_label_size ) __a = self.get_config() return config, pixel_values, labels def UpperCamelCase__ ( self ) -> str: return ViTMSNConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , ) def UpperCamelCase__ ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Optional[Any]: __a = ViTMSNModel(config=UpperCamelCase ) model.to(UpperCamelCase ) model.eval() __a = model(UpperCamelCase ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def UpperCamelCase__ ( self , UpperCamelCase , UpperCamelCase , UpperCamelCase ) -> Tuple: __a = self.type_sequence_label_size __a = ViTMSNForImageClassification(UpperCamelCase ) model.to(UpperCamelCase ) model.eval() __a = model(UpperCamelCase , labels=UpperCamelCase ) print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}' ) print('Labels: {labels}' ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) # test greyscale images __a = 1 __a = ViTMSNForImageClassification(UpperCamelCase ) model.to(UpperCamelCase ) model.eval() __a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) __a = model(UpperCamelCase ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def UpperCamelCase__ ( self ) -> Optional[int]: __a = self.prepare_config_and_inputs() __a , __a , __a = config_and_inputs __a = {'pixel_values': pixel_values} return config, inputs_dict @require_torch class __lowercase ( __magic_name__ , __magic_name__ , unittest.TestCase ): _a = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else () _a = ( {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification} if is_torch_available() else {} ) _a = False _a = False _a = False _a = False def UpperCamelCase__ ( self ) -> List[Any]: __a = ViTMSNModelTester(self ) __a = ConfigTester(self , config_class=UpperCamelCase , has_text_modality=UpperCamelCase , hidden_size=37 ) def UpperCamelCase__ ( self ) -> Any: self.config_tester.run_common_tests() @unittest.skip(reason='ViTMSN does not use inputs_embeds' ) def UpperCamelCase__ ( self ) -> str: pass def UpperCamelCase__ ( self ) -> Tuple: __a , __a = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __a = model_class(UpperCamelCase ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) __a = model.get_output_embeddings() self.assertTrue(x is None or isinstance(UpperCamelCase , nn.Linear ) ) def UpperCamelCase__ ( self ) -> Any: __a , __a = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __a = model_class(UpperCamelCase ) __a = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic __a = [*signature.parameters.keys()] __a = ['pixel_values'] self.assertListEqual(arg_names[:1] , UpperCamelCase ) def UpperCamelCase__ ( self ) -> Dict: __a = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*UpperCamelCase ) def UpperCamelCase__ ( self ) -> Any: __a = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*UpperCamelCase ) @slow def UpperCamelCase__ ( self ) -> Optional[Any]: for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: __a = ViTMSNModel.from_pretrained(UpperCamelCase ) self.assertIsNotNone(UpperCamelCase ) def SCREAMING_SNAKE_CASE ( ): __a = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ) return image @require_torch @require_vision class __lowercase ( unittest.TestCase ): @cached_property def UpperCamelCase__ ( self ) -> Dict: return ViTImageProcessor.from_pretrained('facebook/vit-msn-small' ) if is_vision_available() else None @slow def UpperCamelCase__ ( self ) -> Optional[int]: torch.manual_seed(2 ) __a = ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small' ).to(UpperCamelCase ) __a = self.default_image_processor __a = prepare_img() __a = image_processor(images=UpperCamelCase , return_tensors='pt' ).to(UpperCamelCase ) # forward pass with torch.no_grad(): __a = model(**UpperCamelCase ) # verify the logits __a = torch.Size((1, 1000) ) self.assertEqual(outputs.logits.shape , UpperCamelCase ) __a = torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(UpperCamelCase ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase , atol=1e-4 ) )
490
'''simple docstring''' import argparse import json from typing import List from ltp import LTP from transformers import BertTokenizer def SCREAMING_SNAKE_CASE ( a_ : Tuple ): # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ( (cp >= 0X4e00 and cp <= 0X9fff) or (cp >= 0X3400 and cp <= 0X4dbf) # or (cp >= 0X20000 and cp <= 0X2a6df) # or (cp >= 0X2a700 and cp <= 0X2b73f) # or (cp >= 0X2b740 and cp <= 0X2b81f) # or (cp >= 0X2b820 and cp <= 0X2ceaf) # or (cp >= 0Xf900 and cp <= 0Xfaff) or (cp >= 0X2f800 and cp <= 0X2fa1f) # ): # return True return False def SCREAMING_SNAKE_CASE ( a_ : str ): # word like '180' or '身高' or '神' for char in word: __a = ord(a_ ) if not _is_chinese_char(a_ ): return 0 return 1 def SCREAMING_SNAKE_CASE ( a_ : List[str] ): __a = set() for token in tokens: __a = len(a_ ) > 1 and is_chinese(a_ ) if chinese_word: word_set.add(a_ ) __a = list(a_ ) return word_list def SCREAMING_SNAKE_CASE ( a_ : List[str] , a_ : set() ): if not chinese_word_set: return bert_tokens __a = max([len(a_ ) for w in chinese_word_set] ) __a = bert_tokens __a , __a = 0, len(a_ ) while start < end: __a = True if is_chinese(bert_word[start] ): __a = min(end - start , a_ ) for i in range(a_ , 1 , -1 ): __a = ''.join(bert_word[start : start + i] ) if whole_word in chinese_word_set: for j in range(start + 1 , start + i ): __a = '##' + bert_word[j] __a = start + i __a = False break if single_word: start += 1 return bert_word def SCREAMING_SNAKE_CASE ( a_ : List[str] , a_ : LTP , a_ : BertTokenizer ): __a = [] for i in range(0 , len(a_ ) , 100 ): __a = ltp_tokenizer.seg(lines[i : i + 100] )[0] __a = [get_chinese_word(a_ ) for r in res] ltp_res.extend(a_ ) assert len(a_ ) == len(a_ ) __a = [] for i in range(0 , len(a_ ) , 100 ): __a = bert_tokenizer(lines[i : i + 100] , add_special_tokens=a_ , truncation=a_ , max_length=512 ) bert_res.extend(res['input_ids'] ) assert len(a_ ) == len(a_ ) __a = [] for input_ids, chinese_word in zip(a_ , a_ ): __a = [] for id in input_ids: __a = bert_tokenizer._convert_id_to_token(a_ ) input_tokens.append(a_ ) __a = add_sub_symbol(a_ , a_ ) __a = [] # We only save pos of chinese subwords start with ##, which mean is part of a whole word. for i, token in enumerate(a_ ): if token[:2] == "##": __a = token[2:] # save chinese tokens' pos if len(a_ ) == 1 and _is_chinese_char(ord(a_ ) ): ref_id.append(a_ ) ref_ids.append(a_ ) assert len(a_ ) == len(a_ ) return ref_ids def SCREAMING_SNAKE_CASE ( a_ : str ): # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm) # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp) with open(args.file_name , 'r' , encoding='utf-8' ) as f: __a = f.readlines() __a = [line.strip() for line in data if len(a_ ) > 0 and not line.isspace()] # avoid delimiter like '\u2029' __a = LTP(args.ltp ) # faster in GPU device __a = BertTokenizer.from_pretrained(args.bert ) __a = prepare_ref(a_ , a_ , a_ ) with open(args.save_path , 'w' , encoding='utf-8' ) as f: __a = [json.dumps(a_ ) + '\n' for ref in ref_ids] f.writelines(a_ ) if __name__ == "__main__": UpperCAmelCase_ = argparse.ArgumentParser(description="prepare_chinese_ref") parser.add_argument( "--file_name", type=str, default="./resources/chinese-demo.txt", help="file need process, same as training data in lm", ) parser.add_argument( "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path" ) parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer") parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res") UpperCAmelCase_ = parser.parse_args() main(args)
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1
"""simple docstring""" import os import tempfile import unittest from transformers import FlaubertConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_gpu, 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 import ( FlaubertForMultipleChoice, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertModel, FlaubertWithLMHeadModel, ) from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST class a ( UpperCamelCase_ ): def __init__( self : str , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=13 , __SCREAMING_SNAKE_CASE : List[Any]=7 , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Tuple=False , __SCREAMING_SNAKE_CASE : Optional[int]=False , __SCREAMING_SNAKE_CASE : Union[str, Any]=False , __SCREAMING_SNAKE_CASE : int=2 , __SCREAMING_SNAKE_CASE : Dict=99 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0 , __SCREAMING_SNAKE_CASE : str=32 , __SCREAMING_SNAKE_CASE : Union[str, Any]=5 , __SCREAMING_SNAKE_CASE : Any=4 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Any=0.1 , __SCREAMING_SNAKE_CASE : List[str]=512 , __SCREAMING_SNAKE_CASE : Optional[int]=12 , __SCREAMING_SNAKE_CASE : Union[str, Any]=2 , __SCREAMING_SNAKE_CASE : List[Any]=0.02 , __SCREAMING_SNAKE_CASE : Tuple=3 , __SCREAMING_SNAKE_CASE : int=4 , __SCREAMING_SNAKE_CASE : Optional[int]="last" , __SCREAMING_SNAKE_CASE : List[str]=None , __SCREAMING_SNAKE_CASE : List[Any]=None , ) -> Optional[int]: lowerCamelCase_ = parent lowerCamelCase_ = batch_size lowerCamelCase_ = seq_length lowerCamelCase_ = is_training lowerCamelCase_ = use_input_lengths lowerCamelCase_ = use_token_type_ids lowerCamelCase_ = use_labels lowerCamelCase_ = gelu_activation lowerCamelCase_ = sinusoidal_embeddings lowerCamelCase_ = causal lowerCamelCase_ = asm lowerCamelCase_ = n_langs lowerCamelCase_ = vocab_size lowerCamelCase_ = n_special lowerCamelCase_ = hidden_size lowerCamelCase_ = num_hidden_layers lowerCamelCase_ = num_attention_heads lowerCamelCase_ = hidden_dropout_prob lowerCamelCase_ = attention_probs_dropout_prob lowerCamelCase_ = max_position_embeddings lowerCamelCase_ = type_vocab_size lowerCamelCase_ = type_sequence_label_size lowerCamelCase_ = initializer_range lowerCamelCase_ = num_labels lowerCamelCase_ = num_choices lowerCamelCase_ = summary_type lowerCamelCase_ = use_proj lowerCamelCase_ = scope def UpperCamelCase ( self : Optional[int] ) -> Optional[int]: lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] ) lowerCamelCase_ = None if self.use_input_lengths: lowerCamelCase_ = ( ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2 ) # small variation of seq_length lowerCamelCase_ = None if self.use_token_type_ids: lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs ) lowerCamelCase_ = None lowerCamelCase_ = None lowerCamelCase_ = None if self.use_labels: lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) lowerCamelCase_ = ids_tensor([self.batch_size] , 2 ).float() lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices ) lowerCamelCase_ = self.get_config() return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def UpperCamelCase ( self : int ) -> Any: return FlaubertConfig( vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , ) def UpperCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Any , ) -> Union[str, Any]: lowerCamelCase_ = FlaubertModel(config=a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = model(a_ , lengths=a_ , langs=a_ ) lowerCamelCase_ = model(a_ , langs=a_ ) lowerCamelCase_ = model(a_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : str , ) -> Optional[Any]: lowerCamelCase_ = FlaubertWithLMHeadModel(a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = model(a_ , token_type_ids=a_ , labels=a_ ) self.parent.assertEqual(result.loss.shape , () ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , ) -> Optional[int]: lowerCamelCase_ = FlaubertForQuestionAnsweringSimple(a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = model(a_ ) lowerCamelCase_ = model(a_ , start_positions=a_ , end_positions=a_ ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def UpperCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : int , ) -> Tuple: lowerCamelCase_ = FlaubertForQuestionAnswering(a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = model(a_ ) lowerCamelCase_ = model( a_ , start_positions=a_ , end_positions=a_ , cls_index=a_ , is_impossible=a_ , p_mask=a_ , ) lowerCamelCase_ = model( a_ , start_positions=a_ , end_positions=a_ , cls_index=a_ , is_impossible=a_ , ) (lowerCamelCase_ ) = result_with_labels.to_tuple() lowerCamelCase_ = model(a_ , start_positions=a_ , end_positions=a_ ) (lowerCamelCase_ ) = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape , () ) self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) ) self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) ) self.parent.assertEqual( result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) ) def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , ) -> Union[str, Any]: lowerCamelCase_ = FlaubertForSequenceClassification(a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = model(a_ ) lowerCamelCase_ = model(a_ , labels=a_ ) self.parent.assertEqual(result.loss.shape , () ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def UpperCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , ) -> Tuple: lowerCamelCase_ = self.num_labels lowerCamelCase_ = FlaubertForTokenClassification(a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = model(a_ , attention_mask=a_ , labels=a_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , ) -> Dict: lowerCamelCase_ = self.num_choices lowerCamelCase_ = FlaubertForMultipleChoice(config=a_ ) model.to(a_ ) model.eval() lowerCamelCase_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() lowerCamelCase_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() lowerCamelCase_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() lowerCamelCase_ = model( a_ , attention_mask=a_ , token_type_ids=a_ , labels=a_ , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]: lowerCamelCase_ = self.prepare_config_and_inputs() ( lowerCamelCase_ ) = config_and_inputs lowerCamelCase_ = { 'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths, 'attention_mask': input_mask, } return config, inputs_dict @require_torch class a ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ): SCREAMING_SNAKE_CASE : Optional[int] = ( ( FlaubertModel, FlaubertWithLMHeadModel, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertForMultipleChoice, ) if is_torch_available() else () ) SCREAMING_SNAKE_CASE : List[Any] = ( { """feature-extraction""": FlaubertModel, """fill-mask""": FlaubertWithLMHeadModel, """question-answering""": FlaubertForQuestionAnsweringSimple, """text-classification""": FlaubertForSequenceClassification, """token-classification""": FlaubertForTokenClassification, """zero-shot""": FlaubertForSequenceClassification, } if is_torch_available() else {} ) def UpperCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Union[str, Any] ) -> int: if ( pipeline_test_casse_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith('Fast' ) ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False def UpperCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[str]=False ) -> Dict: lowerCamelCase_ = super()._prepare_for_class(a_ , a_ , return_labels=a_ ) if return_labels: if model_class.__name__ == "FlaubertForQuestionAnswering": lowerCamelCase_ = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=a_ ) lowerCamelCase_ = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=a_ ) return inputs_dict def UpperCamelCase ( self : List[str] ) -> List[Any]: lowerCamelCase_ = FlaubertModelTester(self ) lowerCamelCase_ = ConfigTester(self , config_class=a_ , emb_dim=37 ) def UpperCamelCase ( self : Dict ) -> Any: self.config_tester.run_common_tests() def UpperCamelCase ( self : Tuple ) -> Optional[Any]: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_model(*a_ ) def UpperCamelCase ( self : Optional[Any] ) -> Dict: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_lm_head(*a_ ) def UpperCamelCase ( self : Union[str, Any] ) -> int: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_simple_qa(*a_ ) def UpperCamelCase ( self : Union[str, Any] ) -> List[Any]: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_qa(*a_ ) def UpperCamelCase ( self : List[Any] ) -> int: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_sequence_classif(*a_ ) def UpperCamelCase ( self : Dict ) -> Tuple: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_token_classif(*a_ ) def UpperCamelCase ( self : Tuple ) -> str: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_multiple_choice(*a_ ) @slow def UpperCamelCase ( self : int ) -> List[Any]: for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: lowerCamelCase_ = FlaubertModel.from_pretrained(a_ ) self.assertIsNotNone(a_ ) @slow @require_torch_gpu def UpperCamelCase ( self : str ) -> Optional[int]: lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # FlauBertForMultipleChoice behaves incorrectly in JIT environments. if model_class == FlaubertForMultipleChoice: return lowerCamelCase_ = True lowerCamelCase_ = model_class(config=a_ ) lowerCamelCase_ = self._prepare_for_class(a_ , a_ ) lowerCamelCase_ = torch.jit.trace( a_ , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(a_ , os.path.join(a_ , 'traced_model.pt' ) ) lowerCamelCase_ = torch.jit.load(os.path.join(a_ , 'traced_model.pt' ) , map_location=a_ ) loaded(inputs_dict['input_ids'].to(a_ ) , inputs_dict['attention_mask'].to(a_ ) ) @require_torch class a ( unittest.TestCase ): @slow def UpperCamelCase ( self : Union[str, Any] ) -> str: lowerCamelCase_ = FlaubertModel.from_pretrained('flaubert/flaubert_base_cased' ) lowerCamelCase_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] ) with torch.no_grad(): lowerCamelCase_ = model(a_ )[0] lowerCamelCase_ = torch.Size((1, 11, 768) ) self.assertEqual(output.shape , a_ ) lowerCamelCase_ = torch.tensor( [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]] ) self.assertTrue(torch.allclose(output[:, :3, :3] , a_ , atol=1e-4 ) )
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import os import pytest from attr import dataclass SCREAMING_SNAKE_CASE__ : int = "us-east-1" # defaults region @dataclass class snake_case : lowercase_ = 42 lowercase_ = 'arn:aws:iam::558105141721:role/sagemaker_execution_role' lowercase_ = { 'task_name': 'mnli', 'per_device_train_batch_size': 16, 'per_device_eval_batch_size': 16, 'do_train': True, 'do_eval': True, 'do_predict': True, 'output_dir': '/opt/ml/model', 'overwrite_output_dir': True, 'max_steps': 500, 'save_steps': 5_500, } lowercase_ = {**hyperparameters, 'max_steps': 1_000} @property def __lowercase( self : List[str] )-> str: """simple docstring""" if self.framework == "pytorch": return [ {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"}, {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"}, ] else: return [ {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"}, {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"}, {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"}, ] @property def __lowercase( self : Union[str, Any] )-> str: """simple docstring""" return F'''{self.framework}-transfromers-test''' @property def __lowercase( self : int )-> str: """simple docstring""" return F'''./tests/sagemaker/scripts/{self.framework}''' @property def __lowercase( self : Tuple )-> str: """simple docstring""" if self.framework == "pytorch": return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04" else: return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04" @pytest.fixture(scope='class' ) def _a ( lowercase__ : Dict ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : List[Any] = SageMakerTestEnvironment(framework=request.cls.framework )
85
0
import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging lowercase : List[str] = logging.get_logger(__name__) lowercase : Dict = { 'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json', # See all SEW-D models at https://huggingface.co/models?filter=sew-d } class __A( a__ ): __A = """sew-d""" def __init__( self, A=32, A=768, A=12, A=12, A=3072, A=2, A=512, A=256, A=True, A=True, A=("p2c", "c2p"), A="layer_norm", A="gelu_python", A=0.1, A=0.1, A=0.1, A=0.0, A=0.1, A=0.02, A=1E-7, A=1E-5, A="group", A="gelu", A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512), A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1), A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1), A=False, A=128, A=16, A=True, A=0.05, A=10, A=2, A=0.0, A=10, A=0, A="mean", A=False, A=False, A=256, A=0, A=1, A=2, **A, ): """simple docstring""" super().__init__(**lowercase__, pad_token_id=lowercase__, bos_token_id=lowercase__, eos_token_id=lowercase__ ) _UpperCamelCase = hidden_size _UpperCamelCase = feat_extract_norm _UpperCamelCase = feat_extract_activation _UpperCamelCase = list(lowercase__ ) _UpperCamelCase = list(lowercase__ ) _UpperCamelCase = list(lowercase__ ) _UpperCamelCase = conv_bias _UpperCamelCase = num_conv_pos_embeddings _UpperCamelCase = num_conv_pos_embedding_groups _UpperCamelCase = len(self.conv_dim ) _UpperCamelCase = num_hidden_layers _UpperCamelCase = intermediate_size _UpperCamelCase = squeeze_factor _UpperCamelCase = max_position_embeddings _UpperCamelCase = position_buckets _UpperCamelCase = share_att_key _UpperCamelCase = relative_attention _UpperCamelCase = norm_rel_ebd _UpperCamelCase = list(lowercase__ ) _UpperCamelCase = hidden_act _UpperCamelCase = num_attention_heads _UpperCamelCase = hidden_dropout _UpperCamelCase = attention_dropout _UpperCamelCase = activation_dropout _UpperCamelCase = feat_proj_dropout _UpperCamelCase = final_dropout _UpperCamelCase = layer_norm_eps _UpperCamelCase = feature_layer_norm_eps _UpperCamelCase = initializer_range _UpperCamelCase = vocab_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( '''Configuration for convolutional layers is incorrect.''' '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,''' F'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)''' F'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 _UpperCamelCase = apply_spec_augment _UpperCamelCase = mask_time_prob _UpperCamelCase = mask_time_length _UpperCamelCase = mask_time_min_masks _UpperCamelCase = mask_feature_prob _UpperCamelCase = mask_feature_length _UpperCamelCase = mask_feature_min_masks # ctc loss _UpperCamelCase = ctc_loss_reduction _UpperCamelCase = ctc_zero_infinity # sequence classification _UpperCamelCase = use_weighted_layer_sum _UpperCamelCase = classifier_proj_size @property def _UpperCamelCase ( self ): """simple docstring""" return functools.reduce(operator.mul, self.conv_stride, 1 )
703
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 __A( unittest.TestCase ): def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split() _UpperCamelCase = dict(zip(A, range(len(A ) ) ) ) _UpperCamelCase = { '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>''', } _UpperCamelCase = { '''feature_size''': 1, '''padding_value''': 0.0, '''sampling_rate''': 1_6000, '''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, A ) with open(self.vocab_file, '''w''', encoding='''utf-8''' ) as fp: fp.write(json.dumps(A ) + '''\n''' ) with open(self.feature_extraction_file, '''w''', encoding='''utf-8''' ) as fp: fp.write(json.dumps(A ) + '''\n''' ) # load decoder from hub _UpperCamelCase = '''hf-internal-testing/ngram-beam-search-decoder''' def _UpperCamelCase ( self, **A ): """simple docstring""" _UpperCamelCase = self.add_kwargs_tokens_map.copy() kwargs.update(A ) return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname, **A ) def _UpperCamelCase ( self, **A ): """simple docstring""" return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname, **A ) def _UpperCamelCase ( self, **A ): """simple docstring""" return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name, **A ) def _UpperCamelCase ( self ): """simple docstring""" shutil.rmtree(self.tmpdirname ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) 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, A ) # feature extractor self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string() ) self.assertIsInstance(processor.feature_extractor, A ) # 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, A ) def _UpperCamelCase ( self ): """simple docstring""" _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 _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_tokenizer() # add token to trigger raise tokenizer.add_tokens(['''xx'''] ) with self.assertRaisesRegex(A, '''include''' ): WavaVecaProcessorWithLM( tokenizer=A, feature_extractor=self.get_feature_extractor(), decoder=self.get_decoder() ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) _UpperCamelCase = floats_list((3, 1000) ) _UpperCamelCase = feature_extractor(A, return_tensors='''np''' ) _UpperCamelCase = processor(A, 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 _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) _UpperCamelCase = '''This is a test string''' _UpperCamelCase = processor(text=A ) _UpperCamelCase = tokenizer(A ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key], encoded_processor[key] ) def _UpperCamelCase ( self, A=(2, 10, 16), A=77 ): """simple docstring""" np.random.seed(A ) return np.random.rand(*A ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) _UpperCamelCase = self._get_dummy_logits(shape=(10, 16), seed=13 ) _UpperCamelCase = processor.decode(A ) _UpperCamelCase = decoder.decode_beams(A )[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 _UpperCamelCase ( self, A ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) _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(A ) else: with get_context(A ).Pool() as pool: _UpperCamelCase = processor.batch_decode(A, A ) _UpperCamelCase = list(A ) with get_context('''fork''' ).Pool() as p: _UpperCamelCase = decoder.decode_beams_batch(A, A ) _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(A, decoded_processor.text ) self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''], decoded_processor.text ) self.assertListEqual(A, decoded_processor.logit_score ) self.assertListEqual(A, decoded_processor.lm_score ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) _UpperCamelCase = self._get_dummy_logits() _UpperCamelCase = 15 _UpperCamelCase = -20.0 _UpperCamelCase = -4.0 _UpperCamelCase = processor.batch_decode( A, beam_width=A, beam_prune_logp=A, token_min_logp=A, ) _UpperCamelCase = decoded_processor_out.text _UpperCamelCase = list(A ) with get_context('''fork''' ).Pool() as pool: _UpperCamelCase = decoder.decode_beams_batch( A, A, beam_width=A, beam_prune_logp=A, token_min_logp=A, ) _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(A, A ) self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''], A ) self.assertTrue(np.array_equal(A, decoded_processor_out.logit_score ) ) self.assertTrue(np.allclose([-20.054, -18.447], A, atol=1E-3 ) ) self.assertTrue(np.array_equal(A, decoded_processor_out.lm_score ) ) self.assertTrue(np.allclose([-15.554, -13.9_474], A, atol=1E-3 ) ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) _UpperCamelCase = self._get_dummy_logits() _UpperCamelCase = 2.0 _UpperCamelCase = 5.0 _UpperCamelCase = -20.0 _UpperCamelCase = True _UpperCamelCase = processor.batch_decode( A, alpha=A, beta=A, unk_score_offset=A, lm_score_boundary=A, ) _UpperCamelCase = decoded_processor_out.text _UpperCamelCase = list(A ) decoder.reset_params( alpha=A, beta=A, unk_score_offset=A, lm_score_boundary=A, ) with get_context('''fork''' ).Pool() as pool: _UpperCamelCase = decoder.decode_beams_batch( A, A, ) _UpperCamelCase = [d[0][0] for d in decoded_decoder_out] self.assertListEqual(A, A ) self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''], A ) _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, A ) def _UpperCamelCase ( self ): """simple docstring""" _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(A ) _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(A, A ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = snapshot_download('''hf-internal-testing/processor_with_lm''' ) _UpperCamelCase = WavaVecaProcessorWithLM.from_pretrained(A ) _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(A ) _UpperCamelCase = os.listdir(A ) local_decoder_files.sort() expected_decoder_files.sort() # test that both decoder form hub and local files in cache are the same self.assertListEqual(A, A ) def _UpperCamelCase ( self ): """simple docstring""" _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(A, return_tensors='''np''' ) _UpperCamelCase = processor_auto(A, 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(A ) _UpperCamelCase = processor_auto.batch_decode(A ) self.assertListEqual(decoded_wavaveca.text, decoded_auto.text ) def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = self.get_feature_extractor() _UpperCamelCase = self.get_tokenizer() _UpperCamelCase = self.get_decoder() _UpperCamelCase = WavaVecaProcessorWithLM(tokenizer=A, feature_extractor=A, decoder=A ) self.assertListEqual( processor.model_input_names, feature_extractor.model_input_names, msg='''`processor` and `feature_extractor` model input names do not match''', ) @staticmethod def _UpperCamelCase ( A, A ): """simple docstring""" _UpperCamelCase = [d[key] for d in offsets] return retrieved_list def _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' ) _UpperCamelCase = self._get_dummy_logits()[0] _UpperCamelCase = processor.decode(A, output_word_offsets=A ) # 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(A, A ) ) 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 _UpperCamelCase ( self ): """simple docstring""" _UpperCamelCase = WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' ) _UpperCamelCase = self._get_dummy_logits() _UpperCamelCase = processor.batch_decode(A, output_word_offsets=A ) # 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(A, A ) ) self.assertListEqual( [''' '''.join(self.get_from_offsets(A, '''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 _UpperCamelCase ( self ): """simple docstring""" import torch _UpperCamelCase = load_dataset('''common_voice''', '''en''', split='''train''', streaming=A ) _UpperCamelCase = ds.cast_column('''audio''', datasets.Audio(sampling_rate=1_6000 ) ) _UpperCamelCase = iter(A ) _UpperCamelCase = next(A ) _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(A ).logits.cpu().numpy() _UpperCamelCase = processor.decode(logits[0], output_word_offsets=A ) _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(A, '''word''' ) ), A ) self.assertEqual(''' '''.join(self.get_from_offsets(A, '''word''' ) ), output.text ) # output times _UpperCamelCase = torch.tensor(self.get_from_offsets(A, '''start_time''' ) ) _UpperCamelCase = torch.tensor(self.get_from_offsets(A, '''end_time''' ) ) # fmt: off _UpperCamelCase = torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599] ) _UpperCamelCase = torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94] ) # fmt: on self.assertTrue(torch.allclose(A, A, atol=0.01 ) ) self.assertTrue(torch.allclose(A, A, atol=0.01 ) )
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0
"""simple docstring""" from __future__ import annotations from collections.abc import Callable from typing import Any, Generic, TypeVar _A = TypeVar('T') class lowerCamelCase (Generic[T] ): '''simple docstring''' def __init__( self : Tuple , _snake_case : list[T] , _snake_case : Callable[[T, T], T] ) -> None: SCREAMING_SNAKE_CASE__ = None SCREAMING_SNAKE_CASE__ = len(_snake_case ) SCREAMING_SNAKE_CASE__ = [any_type for _ in range(self.N )] + arr SCREAMING_SNAKE_CASE__ = fnc self.build() def lowerCAmelCase_ ( self : int ) -> None: for p in range(self.N - 1 , 0 , -1 ): SCREAMING_SNAKE_CASE__ = self.fn(self.st[p * 2] , self.st[p * 2 + 1] ) def lowerCAmelCase_ ( self : Tuple , _snake_case : int , _snake_case : T ) -> None: p += self.N SCREAMING_SNAKE_CASE__ = v while p > 1: SCREAMING_SNAKE_CASE__ = p // 2 SCREAMING_SNAKE_CASE__ = self.fn(self.st[p * 2] , self.st[p * 2 + 1] ) def lowerCAmelCase_ ( self : List[Any] , _snake_case : int , _snake_case : int ) -> T | None: # noqa: E741 SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = l + self.N, r + self.N SCREAMING_SNAKE_CASE__ = None while l <= r: if l % 2 == 1: SCREAMING_SNAKE_CASE__ = self.st[l] if res is None else self.fn(_snake_case , self.st[l] ) if r % 2 == 0: SCREAMING_SNAKE_CASE__ = self.st[r] if res is None else self.fn(_snake_case , self.st[r] ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = (l + 1) // 2, (r - 1) // 2 return res if __name__ == "__main__": from functools import reduce _A = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2] _A = { 0: 7, 1: 2, 2: 6, 3: -1_4, 4: 5, 5: 4, 6: 7, 7: -1_0, 8: 9, 9: 1_0, 1_0: 1_2, 1_1: 1, } _A = SegmentTree(test_array, min) _A = SegmentTree(test_array, max) _A = SegmentTree(test_array, lambda a, b: a + b) def SCREAMING_SNAKE_CASE ( ) -> None: for i in range(len(__UpperCAmelCase ) ): for j in range(__UpperCAmelCase , len(__UpperCAmelCase ) ): SCREAMING_SNAKE_CASE__ = reduce(__UpperCAmelCase , test_array[i : j + 1] ) SCREAMING_SNAKE_CASE__ = reduce(__UpperCAmelCase , test_array[i : j + 1] ) SCREAMING_SNAKE_CASE__ = reduce(lambda __UpperCAmelCase , __UpperCAmelCase : a + b , test_array[i : j + 1] ) assert min_range == min_segment_tree.query(__UpperCAmelCase , __UpperCAmelCase ) assert max_range == max_segment_tree.query(__UpperCAmelCase , __UpperCAmelCase ) assert sum_range == sum_segment_tree.query(__UpperCAmelCase , __UpperCAmelCase ) test_all_segments() for index, value in test_updates.items(): _A = value min_segment_tree.update(index, value) max_segment_tree.update(index, value) sum_segment_tree.update(index, value) test_all_segments()
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"""simple docstring""" import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename _A = 'http://www.mocksite.com/file1.txt' _A = '"text": ["foo", "foo"]' _A = '6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8' class lowerCamelCase : '''simple docstring''' a = 2_0_0 a = {"Content-Length": "100"} a = {} def lowerCAmelCase_ ( self : Union[str, Any] , **_snake_case : str ) -> Any: return [bytes(_snake_case , "utf-8" )] def SCREAMING_SNAKE_CASE ( *__UpperCAmelCase , **__UpperCAmelCase ) -> Any: return MockResponse() @pytest.mark.parametrize("urls_type" , [str, list, dict] ) def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple: import requests monkeypatch.setattr(__UpperCAmelCase , "request" , __UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = URL if issubclass(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = url elif issubclass(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = [url] elif issubclass(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = {"train": url} SCREAMING_SNAKE_CASE__ = "dummy" SCREAMING_SNAKE_CASE__ = "downloads" SCREAMING_SNAKE_CASE__ = tmp_path SCREAMING_SNAKE_CASE__ = DownloadConfig( cache_dir=os.path.join(__UpperCAmelCase , __UpperCAmelCase ) , use_etag=__UpperCAmelCase , ) SCREAMING_SNAKE_CASE__ = DownloadManager(dataset_name=__UpperCAmelCase , download_config=__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = dl_manager.download(__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = urls for downloaded_paths in [downloaded_paths]: if isinstance(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = [downloaded_paths] SCREAMING_SNAKE_CASE__ = [urls] elif isinstance(__UpperCAmelCase , __UpperCAmelCase ): assert "train" in downloaded_paths.keys() SCREAMING_SNAKE_CASE__ = downloaded_paths.values() SCREAMING_SNAKE_CASE__ = urls.values() assert downloaded_paths for downloaded_path, input_url in zip(__UpperCAmelCase , __UpperCAmelCase ): assert downloaded_path == dl_manager.downloaded_paths[input_url] SCREAMING_SNAKE_CASE__ = Path(__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = downloaded_path.parts assert parts[-1] == HASH assert parts[-2] == cache_subdir assert downloaded_path.exists() SCREAMING_SNAKE_CASE__ = downloaded_path.read_text() assert content == CONTENT SCREAMING_SNAKE_CASE__ = downloaded_path.with_suffix(".json" ) assert metadata_downloaded_path.exists() SCREAMING_SNAKE_CASE__ = json.loads(metadata_downloaded_path.read_text() ) assert metadata_content == {"url": URL, "etag": None} @pytest.mark.parametrize("paths_type" , [str, list, dict] ) def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple: SCREAMING_SNAKE_CASE__ = str(__UpperCAmelCase ) if issubclass(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = filename elif issubclass(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = [filename] elif issubclass(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = {"train": filename} SCREAMING_SNAKE_CASE__ = "dummy" SCREAMING_SNAKE_CASE__ = xz_file.parent SCREAMING_SNAKE_CASE__ = "extracted" SCREAMING_SNAKE_CASE__ = DownloadConfig( cache_dir=__UpperCAmelCase , use_etag=__UpperCAmelCase , ) SCREAMING_SNAKE_CASE__ = DownloadManager(dataset_name=__UpperCAmelCase , download_config=__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = dl_manager.extract(__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = paths for extracted_paths in [extracted_paths]: if isinstance(__UpperCAmelCase , __UpperCAmelCase ): SCREAMING_SNAKE_CASE__ = [extracted_paths] SCREAMING_SNAKE_CASE__ = [paths] elif isinstance(__UpperCAmelCase , __UpperCAmelCase ): assert "train" in extracted_paths.keys() SCREAMING_SNAKE_CASE__ = extracted_paths.values() SCREAMING_SNAKE_CASE__ = paths.values() assert extracted_paths for extracted_path, input_path in zip(__UpperCAmelCase , __UpperCAmelCase ): assert extracted_path == dl_manager.extracted_paths[input_path] SCREAMING_SNAKE_CASE__ = Path(__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = extracted_path.parts assert parts[-1] == hash_url_to_filename(__UpperCAmelCase , etag=__UpperCAmelCase ) assert parts[-2] == extracted_subdir assert extracted_path.exists() SCREAMING_SNAKE_CASE__ = extracted_path.read_text() SCREAMING_SNAKE_CASE__ = text_file.read_text() assert extracted_file_content == expected_file_content def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> int: assert path.endswith(".jsonl" ) for num_items, line in enumerate(__UpperCAmelCase , start=1 ): SCREAMING_SNAKE_CASE__ = json.loads(line.decode("utf-8" ) ) assert item.keys() == {"col_1", "col_2", "col_3"} assert num_items == 4 @pytest.mark.parametrize("archive_jsonl" , ["tar_jsonl_path", "zip_jsonl_path"] ) def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ = request.getfixturevalue(__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = DownloadManager() for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(__UpperCAmelCase ) , start=1 ): _test_jsonl(__UpperCAmelCase , __UpperCAmelCase ) assert num_jsonl == 2 @pytest.mark.parametrize("archive_nested_jsonl" , ["tar_nested_jsonl_path", "zip_nested_jsonl_path"] ) def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> Any: SCREAMING_SNAKE_CASE__ = request.getfixturevalue(__UpperCAmelCase ) SCREAMING_SNAKE_CASE__ = DownloadManager() for num_tar, (path, file) in enumerate(dl_manager.iter_archive(__UpperCAmelCase ) , start=1 ): for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(__UpperCAmelCase ) , start=1 ): _test_jsonl(__UpperCAmelCase , __UpperCAmelCase ) assert num_tar == 1 assert num_jsonl == 2 def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> Tuple: SCREAMING_SNAKE_CASE__ = DownloadManager() for num_file, file in enumerate(dl_manager.iter_files(__UpperCAmelCase ) , start=1 ): assert os.path.basename(__UpperCAmelCase ) == ("test.txt" if num_file == 1 else "train.txt") assert num_file == 2
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1
import unittest from transformers import CamembertTokenizer, CamembertTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import is_torch_available from ...test_tokenization_common import TokenizerTesterMixin _lowerCamelCase = get_tests_dir('fixtures/test_sentencepiece.model') _lowerCamelCase = get_tests_dir('fixtures/test_sentencepiece_bpe.model') _lowerCamelCase = 'pt' if is_torch_available() else 'tf' @require_sentencepiece @require_tokenizers class __A ( lowerCamelCase__ ,unittest.TestCase ): """simple docstring""" UpperCAmelCase__ = CamembertTokenizer UpperCAmelCase__ = CamembertTokenizerFast UpperCAmelCase__ = True UpperCAmelCase__ = True def __snake_case ( self): """simple docstring""" super().setUp() # We have a SentencePiece fixture for testing _lowerCamelCase : Optional[Any] = CamembertTokenizer(a__) tokenizer.save_pretrained(self.tmpdirname) def __snake_case ( self): """simple docstring""" _lowerCamelCase : Tuple = '''<pad>''' _lowerCamelCase : Tuple = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__) , a__) self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__) , a__) def __snake_case ( self): """simple docstring""" _lowerCamelCase : List[Any] = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0] , '''<s>NOTUSED''') self.assertEqual(vocab_keys[1] , '''<pad>''') self.assertEqual(vocab_keys[-1] , '''<mask>''') self.assertEqual(len(a__) , 1004) def __snake_case ( self): """simple docstring""" self.assertEqual(self.get_tokenizer().vocab_size , 1005) def __snake_case ( self): """simple docstring""" _lowerCamelCase : Optional[Any] = CamembertTokenizer(a__) tokenizer.save_pretrained(self.tmpdirname) _lowerCamelCase : Optional[Any] = CamembertTokenizerFast.from_pretrained(self.tmpdirname) _lowerCamelCase : Optional[int] = '''I was born in 92000, and this is falsé.''' _lowerCamelCase : Optional[int] = tokenizer.encode(a__) _lowerCamelCase : List[Any] = rust_tokenizer.encode(a__) self.assertListEqual(a__ , a__) _lowerCamelCase : Optional[Any] = tokenizer.encode(a__ , add_special_tokens=a__) _lowerCamelCase : Optional[Any] = rust_tokenizer.encode(a__ , add_special_tokens=a__) self.assertListEqual(a__ , a__) # <unk> tokens are not the same for `rust` than for `slow`. # Because spm gives back raw token instead of `unk` in EncodeAsPieces # tokens = tokenizer.tokenize(sequence) _lowerCamelCase : Tuple = tokenizer.convert_ids_to_tokens(a__) _lowerCamelCase : List[str] = rust_tokenizer.tokenize(a__) self.assertListEqual(a__ , a__) def __snake_case ( self): """simple docstring""" if not self.test_rust_tokenizer: return _lowerCamelCase : List[str] = self.get_tokenizer() _lowerCamelCase : int = self.get_rust_tokenizer() _lowerCamelCase : Tuple = '''I was born in 92000, and this is falsé.''' _lowerCamelCase : List[Any] = tokenizer.tokenize(a__) _lowerCamelCase : Optional[Any] = rust_tokenizer.tokenize(a__) self.assertListEqual(a__ , a__) _lowerCamelCase : Dict = tokenizer.encode(a__ , add_special_tokens=a__) _lowerCamelCase : List[str] = rust_tokenizer.encode(a__ , add_special_tokens=a__) self.assertListEqual(a__ , a__) _lowerCamelCase : Tuple = self.get_rust_tokenizer() _lowerCamelCase : Union[str, Any] = tokenizer.encode(a__) _lowerCamelCase : Optional[Any] = rust_tokenizer.encode(a__) self.assertListEqual(a__ , a__) @slow def __snake_case ( self): """simple docstring""" _lowerCamelCase : Any = {'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''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, 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]]} # noqa: E501 # fmt: on # camembert is a french model. So we also use french texts. _lowerCamelCase : str = [ '''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=a__ , model_name='''camembert-base''' , revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf''' , sequences=a__ , )
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def __UpperCAmelCase( lowercase_ ): # if the collection is empty, returns empty if collection == []: return [] # get some information about the collection _lowerCamelCase : Tuple = len(lowercase_ ) _lowerCamelCase : List[str] = max(lowercase_ ) _lowerCamelCase : Any = min(lowercase_ ) # create the counting array _lowerCamelCase : str = coll_max + 1 - coll_min _lowerCamelCase : Union[str, Any] = [0] * counting_arr_length # count how much a number appears in the collection for number in collection: counting_arr[number - coll_min] += 1 # sum each position with it's predecessors. now, counting_arr[i] tells # us how many elements <= i has in the collection for i in range(1 , lowercase_ ): _lowerCamelCase : Union[str, Any] = counting_arr[i] + counting_arr[i - 1] # create the output collection _lowerCamelCase : Dict = [0] * coll_len # place the elements in the output, respecting the original order (stable # sort) from end to begin, updating counting_arr for i in reversed(range(0 , lowercase_ ) ): _lowerCamelCase : Union[str, Any] = collection[i] counting_arr[collection[i] - coll_min] -= 1 return ordered def __UpperCAmelCase( lowercase_ ): return "".join([chr(lowercase_ ) for i in counting_sort([ord(lowercase_ ) for c in string] )] ) if __name__ == "__main__": # Test string sort assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt" _lowerCamelCase = input('Enter numbers separated by a comma:\n').strip() _lowerCamelCase = [int(item) for item in user_input.split(',')] print(counting_sort(unsorted))
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1
from __future__ import annotations from collections.abc import Generator def snake_case ( ) -> Generator[int, None, None]: _A = {} _A = 2 while True: _A = factor_map.pop(snake_case__ , snake_case__) if factor: _A = factor + prime while x in factor_map: x += factor _A = factor else: _A = prime yield prime prime += 1 def snake_case ( snake_case__ :float = 1E10) -> int: _A = sieve() _A = 1 while True: _A = next(snake_case__) if (2 * prime * n) > limit: return n # Ignore the next prime as the reminder will be 2. next(snake_case__) n += 2 if __name__ == "__main__": print(solution())
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from transformers import DistilBertTokenizer, DistilBertTokenizerFast from transformers.testing_utils import require_tokenizers, slow from ..bert.test_tokenization_bert import BertTokenizationTest @require_tokenizers class a ( __lowerCAmelCase ): """simple docstring""" lowerCamelCase :Any = DistilBertTokenizer lowerCamelCase :Optional[Any] = DistilBertTokenizerFast lowerCamelCase :Union[str, Any] = True @slow def UpperCAmelCase ( self ) -> Optional[int]: _A = DistilBertTokenizer.from_pretrained("""distilbert-base-uncased""" ) _A = tokenizer.encode("""sequence builders""" , add_special_tokens=lowerCAmelCase_ ) _A = tokenizer.encode("""multi-sequence build""" , add_special_tokens=lowerCAmelCase_ ) _A = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ ) _A = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ , lowerCAmelCase_ ) assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [ tokenizer.sep_token_id ]
401
1
'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging UpperCAmelCase__ :Optional[Any] = logging.get_logger(__name__) UpperCAmelCase__ :List[str] = { """SCUT-DLVCLab/lilt-roberta-en-base""": ( """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json""" ), } class SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ): snake_case__ : Optional[int] = 'lilt' def __init__( self : List[Any] , A__ : List[Any]=30522 , A__ : Tuple=768 , A__ : List[Any]=12 , A__ : Optional[int]=12 , A__ : Any=3072 , A__ : Dict="gelu" , A__ : Any=0.1 , A__ : Dict=0.1 , A__ : Tuple=512 , A__ : int=2 , A__ : Dict=0.02 , A__ : str=1e-1_2 , A__ : Tuple=0 , A__ : int="absolute" , A__ : int=None , A__ : Optional[Any]=4 , A__ : Optional[Any]=1024 , **A__ : List[Any] , ): """simple docstring""" super().__init__(pad_token_id=_a , **_a ) __lowerCamelCase : Optional[int] = vocab_size __lowerCamelCase : str = hidden_size __lowerCamelCase : Any = num_hidden_layers __lowerCamelCase : Optional[int] = num_attention_heads __lowerCamelCase : Tuple = hidden_act __lowerCamelCase : List[str] = intermediate_size __lowerCamelCase : Any = hidden_dropout_prob __lowerCamelCase : Union[str, Any] = attention_probs_dropout_prob __lowerCamelCase : Dict = max_position_embeddings __lowerCamelCase : Any = type_vocab_size __lowerCamelCase : Any = initializer_range __lowerCamelCase : int = layer_norm_eps __lowerCamelCase : Dict = position_embedding_type __lowerCamelCase : int = classifier_dropout __lowerCamelCase : List[Any] = channel_shrink_ratio __lowerCamelCase : Tuple = max_ad_position_embeddings
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'''simple docstring''' from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments def __lowercase () -> str: """simple docstring""" __lowerCamelCase : Any = HfArgumentParser(_lowercase ) __lowerCamelCase : List[str] = parser.parse_args_into_dataclasses()[0] __lowerCamelCase : Dict = TensorFlowBenchmark(args=_lowercase ) try: __lowerCamelCase : List[str] = parser.parse_args_into_dataclasses()[0] except ValueError as e: __lowerCamelCase : Tuple = """Arg --no_{0} is no longer used, please use --no-{0} instead.""" __lowerCamelCase : List[Any] = """ """.join(str(_lowercase ).split(""" """ )[:-1] ) __lowerCamelCase : Tuple = """""" __lowerCamelCase : List[str] = eval(str(_lowercase ).split(""" """ )[-1] ) __lowerCamelCase : Tuple = [] for arg in depreciated_args: # arg[2:] removes '--' if arg[2:] in TensorFlowBenchmark.deprecated_args: # arg[5:] removes '--no_' full_error_msg += arg_error_msg.format(arg[5:] ) else: wrong_args.append(_lowercase ) if len(_lowercase ) > 0: __lowerCamelCase : Tuple = full_error_msg + begin_error_msg + str(_lowercase ) raise ValueError(_lowercase ) benchmark.run() if __name__ == "__main__": main()
483
0
"""simple docstring""" from ....configuration_utils import PretrainedConfig from ....utils import logging lowerCamelCase__ = logging.get_logger(__name__) lowerCamelCase__ = { "Visual-Attention-Network/van-base": ( "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json" ), } class _UpperCamelCase ( _lowerCAmelCase): __lowerCamelCase = "van" def __init__(self , lowerCamelCase__=2_2_4 , lowerCamelCase__=3 , lowerCamelCase__=[7, 3, 3, 3] , lowerCamelCase__=[4, 2, 2, 2] , lowerCamelCase__=[6_4, 1_2_8, 3_2_0, 5_1_2] , lowerCamelCase__=[3, 3, 1_2, 3] , lowerCamelCase__=[8, 8, 4, 4] , lowerCamelCase__="gelu" , lowerCamelCase__=0.0_2 , lowerCamelCase__=1E-6 , lowerCamelCase__=1E-2 , lowerCamelCase__=0.0 , lowerCamelCase__=0.0 , **lowerCamelCase__ , ): """simple docstring""" super().__init__(**lowerCamelCase__ ) A__ = image_size A__ = num_channels A__ = patch_sizes A__ = strides A__ = hidden_sizes A__ = depths A__ = mlp_ratios A__ = hidden_act A__ = initializer_range A__ = layer_norm_eps A__ = layer_scale_init_value A__ = drop_path_rate A__ = dropout_rate
574
import gc import unittest import torch from parameterized import parameterized from diffusers import AutoencoderKL from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import enable_full_determinism from .test_modeling_common import ModelTesterMixin, UNetTesterMixin enable_full_determinism() class a (_lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ): """simple docstring""" __UpperCAmelCase : str = AutoencoderKL __UpperCAmelCase : Optional[Any] = "sample" __UpperCAmelCase : Optional[int] = 1e-2 @property def __snake_case ( self : Dict ) -> Optional[Any]: __snake_case : Optional[Any] = 4 __snake_case : Tuple = 3 __snake_case : List[str] = (32, 32) __snake_case : str = floats_tensor((batch_size, num_channels) + sizes ).to(lowerCamelCase ) return {"sample": image} @property def __snake_case ( self : Union[str, Any] ) -> Tuple: return (3, 32, 32) @property def __snake_case ( self : int ) -> int: return (3, 32, 32) def __snake_case ( self : Optional[Any] ) -> Dict: __snake_case : Optional[Any] = { "block_out_channels": [32, 64], "in_channels": 3, "out_channels": 3, "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"], "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"], "latent_channels": 4, } __snake_case : Any = self.dummy_input return init_dict, inputs_dict def __snake_case ( self : str ) -> Dict: pass def __snake_case ( self : Tuple ) -> List[str]: pass @unittest.skipIf(torch_device == "mps" , "Gradient checkpointing skipped on MPS" ) def __snake_case ( self : Any ) -> Optional[Any]: # enable deterministic behavior for gradient checkpointing __snake_case , __snake_case : int = self.prepare_init_args_and_inputs_for_common() __snake_case : str = self.model_class(**lowerCamelCase ) model.to(lowerCamelCase ) assert not model.is_gradient_checkpointing and model.training __snake_case : str = model(**lowerCamelCase ).sample # run the backwards pass on the model. For backwards pass, for simplicity purpose, # we won't calculate the loss and rather backprop on out.sum() model.zero_grad() __snake_case : Any = torch.randn_like(lowerCamelCase ) __snake_case : str = (out - labels).mean() loss.backward() # re-instantiate the model now enabling gradient checkpointing __snake_case : Optional[int] = self.model_class(**lowerCamelCase ) # clone model model_a.load_state_dict(model.state_dict() ) model_a.to(lowerCamelCase ) model_a.enable_gradient_checkpointing() assert model_a.is_gradient_checkpointing and model_a.training __snake_case : int = model_a(**lowerCamelCase ).sample # run the backwards pass on the model. For backwards pass, for simplicity purpose, # we won't calculate the loss and rather backprop on out.sum() model_a.zero_grad() __snake_case : Union[str, Any] = (out_a - labels).mean() loss_a.backward() # compare the output and parameters gradients self.assertTrue((loss - loss_a).abs() < 1E-5 ) __snake_case : Optional[int] = dict(model.named_parameters() ) __snake_case : List[Any] = dict(model_a.named_parameters() ) for name, param in named_params.items(): self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5E-5 ) ) def __snake_case ( self : List[Any] ) -> Optional[int]: __snake_case , __snake_case : Optional[Any] = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" , output_loading_info=lowerCamelCase ) self.assertIsNotNone(lowerCamelCase ) self.assertEqual(len(loading_info["missing_keys"] ) , 0 ) model.to(lowerCamelCase ) __snake_case : Optional[Any] = model(**self.dummy_input ) assert image is not None, "Make sure output is not None" def __snake_case ( self : Optional[Any] ) -> Union[str, Any]: __snake_case : Tuple = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" ) __snake_case : Dict = model.to(lowerCamelCase ) model.eval() if torch_device == "mps": __snake_case : int = torch.manual_seed(0 ) else: __snake_case : str = torch.Generator(device=lowerCamelCase ).manual_seed(0 ) __snake_case : List[str] = torch.randn( 1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , ) __snake_case : Union[str, Any] = image.to(lowerCamelCase ) with torch.no_grad(): __snake_case : str = model(lowerCamelCase , sample_posterior=lowerCamelCase , generator=lowerCamelCase ).sample __snake_case : List[Any] = output[0, -1, -3:, -3:].flatten().cpu() # Since the VAE Gaussian prior's generator is seeded on the appropriate device, # the expected output slices are not the same for CPU and GPU. if torch_device == "mps": __snake_case : Union[str, Any] = torch.tensor( [ -4.0078E-01, -3.8323E-04, -1.2681E-01, -1.1462E-01, 2.0095E-01, 1.0893E-01, -8.8247E-02, -3.0361E-01, -9.8644E-03, ] ) elif torch_device == "cpu": __snake_case : Tuple = torch.tensor( [-0.13_52, 0.08_78, 0.04_19, -0.08_18, -0.10_69, 0.06_88, -0.14_58, -0.44_46, -0.00_26] ) else: __snake_case : List[str] = torch.tensor( [-0.24_21, 0.46_42, 0.25_07, -0.04_38, 0.06_82, 0.31_60, -0.20_18, -0.07_27, 0.24_85] ) self.assertTrue(torch_all_close(lowerCamelCase , lowerCamelCase , rtol=1E-2 ) ) @slow class a (unittest.TestCase ): """simple docstring""" def __snake_case ( self : int , lowerCamelCase : Dict , lowerCamelCase : Optional[Any] ) -> List[str]: return F'gaussian_noise_s={seed}_shape={"_".join([str(lowerCamelCase ) for s in shape] )}.npy' def __snake_case ( self : List[Any] ) -> int: # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def __snake_case ( self : Tuple , lowerCamelCase : List[Any]=0 , lowerCamelCase : Tuple=(4, 3, 512, 512) , lowerCamelCase : Optional[int]=False ) -> str: __snake_case : List[Any] = torch.floataa if fpaa else torch.floataa __snake_case : Tuple = torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCamelCase , lowerCamelCase ) ) ).to(lowerCamelCase ).to(lowerCamelCase ) return image def __snake_case ( self : Optional[Any] , lowerCamelCase : int="CompVis/stable-diffusion-v1-4" , lowerCamelCase : int=False ) -> int: __snake_case : str = "fp16" if fpaa else None __snake_case : int = torch.floataa if fpaa else torch.floataa __snake_case : int = AutoencoderKL.from_pretrained( lowerCamelCase , subfolder="vae" , torch_dtype=lowerCamelCase , revision=lowerCamelCase , ) model.to(lowerCamelCase ).eval() return model def __snake_case ( self : str , lowerCamelCase : int=0 ) -> Optional[Any]: if torch_device == "mps": return torch.manual_seed(lowerCamelCase ) return torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase ) @parameterized.expand( [ # fmt: off [33, [-0.16_03, 0.98_78, -0.04_95, -0.07_90, -0.27_09, 0.83_75, -0.20_60, -0.08_24], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]], [47, [-0.23_76, 0.11_68, 0.13_32, -0.48_40, -0.25_08, -0.07_91, -0.04_93, -0.40_89], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]], # fmt: on ] ) def __snake_case ( self : List[str] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[Any] ) -> List[Any]: __snake_case : Optional[Any] = self.get_sd_vae_model() __snake_case : List[Any] = self.get_sd_image(lowerCamelCase ) __snake_case : Tuple = self.get_generator(lowerCamelCase ) with torch.no_grad(): __snake_case : Optional[Any] = model(lowerCamelCase , generator=lowerCamelCase , sample_posterior=lowerCamelCase ).sample assert sample.shape == image.shape __snake_case : List[Any] = sample[-1, -2:, -2:, :2].flatten().float().cpu() __snake_case : int = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice ) assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=3E-3 ) @parameterized.expand( [ # fmt: off [33, [-0.05_13, 0.02_89, 1.37_99, 0.21_66, -0.25_73, -0.08_71, 0.51_03, -0.09_99]], [47, [-0.41_28, -0.13_20, -0.37_04, 0.19_65, -0.41_16, -0.23_32, -0.33_40, 0.22_47]], # fmt: on ] ) @require_torch_gpu def __snake_case ( self : Any , lowerCamelCase : List[str] , lowerCamelCase : List[str] ) -> Tuple: __snake_case : Any = self.get_sd_vae_model(fpaa=lowerCamelCase ) __snake_case : List[Any] = self.get_sd_image(lowerCamelCase , fpaa=lowerCamelCase ) __snake_case : List[Any] = self.get_generator(lowerCamelCase ) with torch.no_grad(): __snake_case : str = model(lowerCamelCase , generator=lowerCamelCase , sample_posterior=lowerCamelCase ).sample assert sample.shape == image.shape __snake_case : Optional[Any] = sample[-1, -2:, :2, -2:].flatten().float().cpu() __snake_case : Any = torch.tensor(lowerCamelCase ) assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=1E-2 ) @parameterized.expand( [ # fmt: off [33, [-0.16_09, 0.98_66, -0.04_87, -0.07_77, -0.27_16, 0.83_68, -0.20_55, -0.08_14], [-0.23_95, 0.00_98, 0.01_02, -0.07_09, -0.28_40, -0.02_74, -0.07_18, -0.18_24]], [47, [-0.23_77, 0.11_47, 0.13_33, -0.48_41, -0.25_06, -0.08_05, -0.04_91, -0.40_85], [0.03_50, 0.08_47, 0.04_67, 0.03_44, -0.08_42, -0.05_47, -0.06_33, -0.11_31]], # fmt: on ] ) def __snake_case ( self : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Any , lowerCamelCase : Dict ) -> int: __snake_case : int = self.get_sd_vae_model() __snake_case : List[Any] = self.get_sd_image(lowerCamelCase ) with torch.no_grad(): __snake_case : int = model(lowerCamelCase ).sample assert sample.shape == image.shape __snake_case : Union[str, Any] = sample[-1, -2:, -2:, :2].flatten().float().cpu() __snake_case : List[str] = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice ) assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=3E-3 ) @parameterized.expand( [ # fmt: off [13, [-0.20_51, -0.18_03, -0.23_11, -0.21_14, -0.32_92, -0.35_74, -0.29_53, -0.33_23]], [37, [-0.26_32, -0.26_25, -0.21_99, -0.27_41, -0.45_39, -0.49_90, -0.37_20, -0.49_25]], # fmt: on ] ) @require_torch_gpu def __snake_case ( self : List[str] , lowerCamelCase : Tuple , lowerCamelCase : Any ) -> Optional[Any]: __snake_case : List[str] = self.get_sd_vae_model() __snake_case : List[Any] = self.get_sd_image(lowerCamelCase , shape=(3, 4, 64, 64) ) with torch.no_grad(): __snake_case : str = model.decode(lowerCamelCase ).sample assert list(sample.shape ) == [3, 3, 512, 512] __snake_case : str = sample[-1, -2:, :2, -2:].flatten().cpu() __snake_case : Optional[int] = torch.tensor(lowerCamelCase ) assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=1E-3 ) @parameterized.expand( [ # fmt: off [27, [-0.03_69, 0.02_07, -0.07_76, -0.06_82, -0.17_47, -0.19_30, -0.14_65, -0.20_39]], [16, [-0.16_28, -0.21_34, -0.27_47, -0.26_42, -0.37_74, -0.44_04, -0.36_87, -0.42_77]], # fmt: on ] ) @require_torch_gpu def __snake_case ( self : str , lowerCamelCase : Optional[int] , lowerCamelCase : Dict ) -> int: __snake_case : int = self.get_sd_vae_model(fpaa=lowerCamelCase ) __snake_case : List[str] = self.get_sd_image(lowerCamelCase , shape=(3, 4, 64, 64) , fpaa=lowerCamelCase ) with torch.no_grad(): __snake_case : Union[str, Any] = model.decode(lowerCamelCase ).sample assert list(sample.shape ) == [3, 3, 512, 512] __snake_case : Optional[Any] = sample[-1, -2:, :2, -2:].flatten().float().cpu() __snake_case : Optional[Any] = torch.tensor(lowerCamelCase ) assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=5E-3 ) @parameterized.expand([(13,), (16,), (27,)] ) @require_torch_gpu @unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." ) def __snake_case ( self : Tuple , lowerCamelCase : List[Any] ) -> Tuple: __snake_case : Dict = self.get_sd_vae_model(fpaa=lowerCamelCase ) __snake_case : Any = self.get_sd_image(lowerCamelCase , shape=(3, 4, 64, 64) , fpaa=lowerCamelCase ) with torch.no_grad(): __snake_case : str = model.decode(lowerCamelCase ).sample model.enable_xformers_memory_efficient_attention() with torch.no_grad(): __snake_case : Any = model.decode(lowerCamelCase ).sample assert list(sample.shape ) == [3, 3, 512, 512] assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=1E-1 ) @parameterized.expand([(13,), (16,), (37,)] ) @require_torch_gpu @unittest.skipIf(not is_xformers_available() , reason="xformers is not required when using PyTorch 2.0." ) def __snake_case ( self : List[Any] , lowerCamelCase : Any ) -> Optional[int]: __snake_case : str = self.get_sd_vae_model() __snake_case : Union[str, Any] = self.get_sd_image(lowerCamelCase , shape=(3, 4, 64, 64) ) with torch.no_grad(): __snake_case : List[Any] = model.decode(lowerCamelCase ).sample model.enable_xformers_memory_efficient_attention() with torch.no_grad(): __snake_case : Dict = model.decode(lowerCamelCase ).sample assert list(sample.shape ) == [3, 3, 512, 512] assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=1E-2 ) @parameterized.expand( [ # fmt: off [33, [-0.30_01, 0.09_18, -2.69_84, -3.97_20, -3.20_99, -5.03_53, 1.73_38, -0.20_65, 3.42_67]], [47, [-1.50_30, -4.38_71, -6.03_55, -9.11_57, -1.66_61, -2.78_53, 2.16_07, -5.08_23, 2.56_33]], # fmt: on ] ) def __snake_case ( self : List[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Dict ) -> Optional[int]: __snake_case : str = self.get_sd_vae_model() __snake_case : int = self.get_sd_image(lowerCamelCase ) __snake_case : int = self.get_generator(lowerCamelCase ) with torch.no_grad(): __snake_case : Optional[Any] = model.encode(lowerCamelCase ).latent_dist __snake_case : Dict = dist.sample(generator=lowerCamelCase ) assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]] __snake_case : List[str] = sample[0, -1, -3:, -3:].flatten().cpu() __snake_case : Dict = torch.tensor(lowerCamelCase ) __snake_case : Dict = 3E-3 if torch_device != "mps" else 1E-2 assert torch_all_close(lowerCamelCase , lowerCamelCase , atol=lowerCamelCase )
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"""simple docstring""" import collections from typing import List, Optional, Union from ...tokenization_utils_base import BatchEncoding from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging from ..bert.tokenization_bert import BertTokenizer __UpperCAmelCase =logging.get_logger(__name__) __UpperCAmelCase ={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""} __UpperCAmelCase ={ """vocab_file""": { """facebook/dpr-ctx_encoder-single-nq-base""": ( """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt""" ), """facebook/dpr-ctx_encoder-multiset-base""": ( """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt""" ), }, """tokenizer_file""": { """facebook/dpr-ctx_encoder-single-nq-base""": ( """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json""" ), """facebook/dpr-ctx_encoder-multiset-base""": ( """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json""" ), }, } __UpperCAmelCase ={ """vocab_file""": { """facebook/dpr-question_encoder-single-nq-base""": ( """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt""" ), """facebook/dpr-question_encoder-multiset-base""": ( """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt""" ), }, """tokenizer_file""": { """facebook/dpr-question_encoder-single-nq-base""": ( """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json""" ), """facebook/dpr-question_encoder-multiset-base""": ( """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json""" ), }, } __UpperCAmelCase ={ """vocab_file""": { """facebook/dpr-reader-single-nq-base""": ( """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt""" ), """facebook/dpr-reader-multiset-base""": ( """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt""" ), }, """tokenizer_file""": { """facebook/dpr-reader-single-nq-base""": ( """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json""" ), """facebook/dpr-reader-multiset-base""": ( """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json""" ), }, } __UpperCAmelCase ={ """facebook/dpr-ctx_encoder-single-nq-base""": 512, """facebook/dpr-ctx_encoder-multiset-base""": 512, } __UpperCAmelCase ={ """facebook/dpr-question_encoder-single-nq-base""": 512, """facebook/dpr-question_encoder-multiset-base""": 512, } __UpperCAmelCase ={ """facebook/dpr-reader-single-nq-base""": 512, """facebook/dpr-reader-multiset-base""": 512, } __UpperCAmelCase ={ """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True}, """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True}, } __UpperCAmelCase ={ """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True}, """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True}, } __UpperCAmelCase ={ """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True}, """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True}, } class lowerCAmelCase__ ( UpperCAmelCase_ ): lowercase__ : str = VOCAB_FILES_NAMES lowercase__ : Any = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP lowercase__ : int = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase__ : Dict = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION class lowerCAmelCase__ ( UpperCAmelCase_ ): lowercase__ : List[Any] = VOCAB_FILES_NAMES lowercase__ : Any = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP lowercase__ : Union[str, Any] = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase__ : Optional[Any] = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION __UpperCAmelCase =collections.namedtuple( """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""] ) __UpperCAmelCase =collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""]) __UpperCAmelCase =r""" Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`. It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers), using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)` with the format: ``` [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids> ``` Args: questions (`str` or `List[str]`): The questions to be encoded. You can specify one question for many passages. In this case, the question will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in `titles` or `texts`. titles (`str` or `List[str]`): The passages titles to be encoded. This can be a string or a list of strings if there are several passages. texts (`str` or `List[str]`): The passages texts to be encoded. This can be a string or a list of strings if there are several passages. padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`): Activates and controls padding. Accepts the following values: - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`): Activates and controls truncation. Accepts the following values: - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will truncate token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch of pairs) is provided. - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. This will only truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided. - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater than the model maximum admissible input size). max_length (`int`, *optional*): Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to `None`, this will use the predefined model maximum length if a maximum length is required by one of the truncation/padding parameters. If the model has no specific maximum input length (like XLNet) truncation/padding to a maximum length will be deactivated. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors instead of list of python integers. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return Numpy `np.ndarray` objects. return_attention_mask (`bool`, *optional*): Whether or not to return the attention mask. If not set, will return the attention mask according to the specific tokenizer's default, defined by the `return_outputs` attribute. [What are attention masks?](../glossary#attention-mask) Returns: `Dict[str, List[List[int]]]`: A dictionary with the following keys: - `input_ids`: List of token ids to be fed to a model. - `attention_mask`: List of indices specifying which tokens should be attended to by the model. """ @add_start_docstrings(UpperCAmelCase_ ) class lowerCAmelCase__ : def __call__( self , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = False , UpperCamelCase__ = False , UpperCamelCase__ = None , UpperCamelCase__ = None , UpperCamelCase__ = None , **UpperCamelCase__ , ): '''simple docstring''' if titles is None and texts is None: return super().__call__( UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , return_tensors=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , **UpperCamelCase__ , ) elif titles is None or texts is None: A__ = titles if texts is None else texts return super().__call__( UpperCamelCase__ , UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , return_tensors=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , **UpperCamelCase__ , ) A__ = titles if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) else [titles] A__ = texts if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) else [texts] A__ = len(UpperCamelCase__ ) A__ = questions if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) else [questions] * n_passages if len(UpperCamelCase__ ) != len(UpperCamelCase__ ): raise ValueError( f"""There should be as many titles than texts but got {len(UpperCamelCase__ )} titles and {len(UpperCamelCase__ )} texts.""" ) A__ = super().__call__(UpperCamelCase__ , UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ )["input_ids"] A__ = super().__call__(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , padding=UpperCamelCase__ , truncation=UpperCamelCase__ )["input_ids"] A__ = { "input_ids": [ (encoded_question_and_title + encoded_text)[:max_length] if max_length is not None and truncation else encoded_question_and_title + encoded_text for encoded_question_and_title, encoded_text in zip(UpperCamelCase__ , UpperCamelCase__ ) ] } if return_attention_mask is not False: A__ = [] for input_ids in encoded_inputs["input_ids"]: attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] ) A__ = attention_mask return self.pad(UpperCamelCase__ , padding=UpperCamelCase__ , max_length=UpperCamelCase__ , return_tensors=UpperCamelCase__ ) def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 16 , UpperCamelCase__ = 64 , UpperCamelCase__ = 4 , ): '''simple docstring''' A__ = reader_input["input_ids"] A__ , A__ , A__ = reader_output[:3] A__ = len(UpperCamelCase__ ) A__ = sorted(range(UpperCamelCase__ ) , reverse=UpperCamelCase__ , key=relevance_logits.__getitem__ ) A__ = [] for doc_id in sorted_docs: A__ = list(input_ids[doc_id] ) # assuming question & title information is at the beginning of the sequence A__ = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id if sequence_ids[-1] == self.pad_token_id: A__ = sequence_ids.index(self.pad_token_id ) else: A__ = len(UpperCamelCase__ ) A__ = self._get_best_spans( start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=UpperCamelCase__ , top_spans=UpperCamelCase__ , ) for start_index, end_index in best_spans: start_index += passage_offset end_index += passage_offset nbest_spans_predictions.append( DPRSpanPrediction( span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=UpperCamelCase__ , start_index=UpperCamelCase__ , end_index=UpperCamelCase__ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) ) if len(UpperCamelCase__ ) >= num_spans: break return nbest_spans_predictions[:num_spans] def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , ): '''simple docstring''' A__ = [] for start_index, start_score in enumerate(UpperCamelCase__ ): for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ): scores.append(((start_index, start_index + answer_length), start_score + end_score) ) A__ = sorted(UpperCamelCase__ , key=lambda UpperCamelCase__ : x[1] , reverse=UpperCamelCase__ ) A__ = [] for (start_index, end_index), score in scores: if start_index > end_index: raise ValueError(f"""Wrong span indices: [{start_index}:{end_index}]""" ) A__ = end_index - start_index + 1 if length > max_answer_length: raise ValueError(f"""Span is too long: {length} > {max_answer_length}""" ) if any( start_index <= prev_start_index <= prev_end_index <= end_index or prev_start_index <= start_index <= end_index <= prev_end_index for (prev_start_index, prev_end_index) in chosen_span_intervals ): continue chosen_span_intervals.append((start_index, end_index) ) if len(UpperCamelCase__ ) == top_spans: break return chosen_span_intervals @add_end_docstrings(UpperCAmelCase_ ) class lowerCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ ): lowercase__ : Optional[int] = VOCAB_FILES_NAMES lowercase__ : str = READER_PRETRAINED_VOCAB_FILES_MAP lowercase__ : Dict = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase__ : Optional[int] = READER_PRETRAINED_INIT_CONFIGURATION lowercase__ : Optional[int] = ["""input_ids""", """attention_mask"""]
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"""simple docstring""" from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFXLMRobertaModel @require_tf @require_sentencepiece @require_tokenizers class lowerCAmelCase__ ( unittest.TestCase ): @slow def lowercase_ ( self ): '''simple docstring''' A__ = TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base" ) A__ = { "input_ids": tf.convert_to_tensor([[0, 26_46, 1_02_69, 83, 9_99_42, 2]] , dtype=tf.intaa ), # "My dog is cute" "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]] , dtype=tf.intaa ), } A__ = model(UpperCamelCase__ )["last_hidden_state"] A__ = tf.TensorShape((1, 6, 7_68) ) self.assertEqual(output.shape , UpperCamelCase__ ) # compare the actual values for a slice. A__ = tf.convert_to_tensor( [ [ [0.068_1762, 0.1089_4451, 0.0677_2504], [-0.0642_3668, 0.0236_6615, 0.0432_9344], [-0.0605_7295, 0.0997_4135, -0.0007_0584], ] ] , dtype=tf.floataa , ) self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
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import math from dataclasses import dataclass from typing import Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, randn_tensor from .scheduling_utils import SchedulerMixin @dataclass # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP class a_ ( __SCREAMING_SNAKE_CASE ): a : torch.FloatTensor a : Optional[torch.FloatTensor] = None def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Optional[int]=0.9_99 , SCREAMING_SNAKE_CASE_ : List[Any]="cosine" , ) -> Union[str, Any]: if alpha_transform_type == "cosine": def alpha_bar_fn(SCREAMING_SNAKE_CASE_ : Union[str, Any] ): return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2 elif alpha_transform_type == "exp": def alpha_bar_fn(SCREAMING_SNAKE_CASE_ : Optional[Any] ): return math.exp(t * -12.0 ) else: raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}""" ) _lowercase = [] for i in range(_SCREAMING_SNAKE_CASE ): _lowercase = i / num_diffusion_timesteps _lowercase = (i + 1) / num_diffusion_timesteps betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE ) ) return torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.floataa ) class a_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ): @register_to_config def __init__( self , __UpperCamelCase = 1_000 , __UpperCamelCase = "fixed_small_log" , __UpperCamelCase = True , __UpperCamelCase = 1.0 , __UpperCamelCase = "epsilon" , __UpperCamelCase = "squaredcos_cap_v2" , ): if beta_schedule != "squaredcos_cap_v2": raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" ) _lowercase = betas_for_alpha_bar(_snake_case ) _lowercase = 1.0 - self.betas _lowercase = torch.cumprod(self.alphas , dim=0 ) _lowercase = torch.tensor(1.0 ) # standard deviation of the initial noise distribution _lowercase = 1.0 # setable values _lowercase = None _lowercase = torch.from_numpy(np.arange(0 , _snake_case )[::-1].copy() ) _lowercase = variance_type def UpperCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ): return sample def UpperCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase = None ): _lowercase = num_inference_steps _lowercase = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1) _lowercase = (np.arange(0 , _snake_case ) * step_ratio).round()[::-1].copy().astype(np.intaa ) _lowercase = torch.from_numpy(_snake_case ).to(_snake_case ) def UpperCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None ): if prev_timestep is None: _lowercase = t - 1 _lowercase = self.alphas_cumprod[t] _lowercase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one _lowercase = 1 - alpha_prod_t _lowercase = 1 - alpha_prod_t_prev if prev_timestep == t - 1: _lowercase = self.betas[t] else: _lowercase = 1 - alpha_prod_t / alpha_prod_t_prev # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf) # and sample from it to get previous sample # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample _lowercase = beta_prod_t_prev / beta_prod_t * beta if variance_type is None: _lowercase = self.config.variance_type # hacks - were probably added for training stability if variance_type == "fixed_small_log": _lowercase = torch.log(torch.clamp(_snake_case , min=1E-20 ) ) _lowercase = torch.exp(0.5 * variance ) elif variance_type == "learned_range": # NOTE difference with DDPM scheduler _lowercase = variance.log() _lowercase = beta.log() _lowercase = (predicted_variance + 1) / 2 _lowercase = frac * max_log + (1 - frac) * min_log return variance def UpperCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase=None , __UpperCamelCase = True , ): _lowercase = timestep if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range": _lowercase = torch.split(_snake_case , sample.shape[1] , dim=1 ) else: _lowercase = None # 1. compute alphas, betas if prev_timestep is None: _lowercase = t - 1 _lowercase = self.alphas_cumprod[t] _lowercase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one _lowercase = 1 - alpha_prod_t _lowercase = 1 - alpha_prod_t_prev if prev_timestep == t - 1: _lowercase = self.betas[t] _lowercase = self.alphas[t] else: _lowercase = 1 - alpha_prod_t / alpha_prod_t_prev _lowercase = 1 - beta # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if self.config.prediction_type == "epsilon": _lowercase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 elif self.config.prediction_type == "sample": _lowercase = model_output else: raise ValueError( f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`""" """ for the UnCLIPScheduler.""" ) # 3. Clip "predicted x_0" if self.config.clip_sample: _lowercase = torch.clamp( _snake_case , -self.config.clip_sample_range , self.config.clip_sample_range ) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf _lowercase = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t _lowercase = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf _lowercase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise _lowercase = 0 if t > 0: _lowercase = randn_tensor( model_output.shape , dtype=model_output.dtype , generator=_snake_case , device=model_output.device ) _lowercase = self._get_variance( _snake_case , predicted_variance=_snake_case , prev_timestep=_snake_case , ) if self.variance_type == "fixed_small_log": _lowercase = variance elif self.variance_type == "learned_range": _lowercase = (0.5 * variance).exp() else: raise ValueError( f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`""" """ for the UnCLIPScheduler.""" ) _lowercase = variance * variance_noise _lowercase = pred_prev_sample + variance if not return_dict: return (pred_prev_sample,) return UnCLIPSchedulerOutput(prev_sample=_snake_case , pred_original_sample=_snake_case ) def UpperCamelCase_ ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ): # Make sure alphas_cumprod and timestep have same device and dtype as original_samples _lowercase = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype ) _lowercase = timesteps.to(original_samples.device ) _lowercase = alphas_cumprod[timesteps] ** 0.5 _lowercase = sqrt_alpha_prod.flatten() while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ): _lowercase = sqrt_alpha_prod.unsqueeze(-1 ) _lowercase = (1 - alphas_cumprod[timesteps]) ** 0.5 _lowercase = sqrt_one_minus_alpha_prod.flatten() while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ): _lowercase = sqrt_one_minus_alpha_prod.unsqueeze(-1 ) _lowercase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise return noisy_samples
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'''simple docstring''' import heapq def a__ ( _SCREAMING_SNAKE_CASE : dict ) -> set[int]: """simple docstring""" UpperCAmelCase_ : list[list] = [] # for each node and his adjacency list add them and the rank of the node to queue # using heapq module the queue will be filled like a Priority Queue # heapq works with a min priority queue, so I used -1*len(v) to build it for key, value in graph.items(): # O(log(n)) heapq.heappush(_SCREAMING_SNAKE_CASE , [-1 * len(_SCREAMING_SNAKE_CASE ), (key, value)] ) # chosen_vertices = set of chosen vertices UpperCAmelCase_ : Optional[int] = set() # while queue isn't empty and there are still edges # (queue[0][0] is the rank of the node with max rank) while queue and queue[0][0] != 0: # extract vertex with max rank from queue and add it to chosen_vertices UpperCAmelCase_ : Tuple = heapq.heappop(_SCREAMING_SNAKE_CASE )[1][0] chosen_vertices.add(_SCREAMING_SNAKE_CASE ) # Remove all arcs adjacent to argmax for elem in queue: # if v haven't adjacent node, skip if elem[0] == 0: continue # if argmax is reachable from elem # remove argmax from elem's adjacent list and update his rank if argmax in elem[1][1]: UpperCAmelCase_ : Any = elem[1][1].index(_SCREAMING_SNAKE_CASE ) del elem[1][1][index] elem[0] += 1 # re-order the queue heapq.heapify(_SCREAMING_SNAKE_CASE ) return chosen_vertices if __name__ == "__main__": import doctest doctest.testmod() _lowerCamelCase = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]} print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging __SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__) __SCREAMING_SNAKE_CASE :int = {} class A_ ( _UpperCAmelCase ): _lowerCamelCase : Tuple = """llama""" _lowerCamelCase : List[str] = ["""past_key_values"""] def __init__( self : List[str] , snake_case_ : Union[str, Any]=3_2_0_0_0 , snake_case_ : List[str]=4_0_9_6 , snake_case_ : List[Any]=1_1_0_0_8 , snake_case_ : List[Any]=3_2 , snake_case_ : Dict=3_2 , snake_case_ : Tuple=None , snake_case_ : int="silu" , snake_case_ : Optional[Any]=2_0_4_8 , snake_case_ : List[str]=0.0_2 , snake_case_ : Union[str, Any]=1e-6 , snake_case_ : Optional[int]=True , snake_case_ : Optional[int]=0 , snake_case_ : Union[str, Any]=1 , snake_case_ : Dict=2 , snake_case_ : Optional[Any]=1 , snake_case_ : str=False , snake_case_ : str=None , **snake_case_ : List[Any] , ): _UpperCAmelCase = vocab_size _UpperCAmelCase = max_position_embeddings _UpperCAmelCase = hidden_size _UpperCAmelCase = intermediate_size _UpperCAmelCase = num_hidden_layers _UpperCAmelCase = num_attention_heads # for backward compatibility if num_key_value_heads is None: _UpperCAmelCase = num_attention_heads _UpperCAmelCase = num_key_value_heads _UpperCAmelCase = hidden_act _UpperCAmelCase = initializer_range _UpperCAmelCase = rms_norm_eps _UpperCAmelCase = pretraining_tp _UpperCAmelCase = use_cache _UpperCAmelCase = rope_scaling self._rope_scaling_validation() super().__init__( pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , tie_word_embeddings=lowercase__ , **lowercase__ , ) def lowercase ( self : Union[str, Any] ): if self.rope_scaling is None: return if not isinstance(self.rope_scaling , lowercase__ ) 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" , lowercase__ ) _UpperCAmelCase = self.rope_scaling.get("factor" , lowercase__ ) 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(lowercase__ , lowercase__ ) or rope_scaling_factor <= 1.0: raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
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'''simple docstring''' import sys from collections import defaultdict class A_ : def __init__( self : Dict ): _UpperCAmelCase = [] def lowercase ( self : Union[str, Any] , snake_case_ : List[str] ): return self.node_position[vertex] def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : Union[str, Any] ): _UpperCAmelCase = pos def lowercase ( self : Optional[Any] , snake_case_ : Dict , snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Optional[Any] ): if start > size // 2 - 1: return else: if 2 * start + 2 >= size: _UpperCAmelCase = 2 * start + 1 else: if heap[2 * start + 1] < heap[2 * start + 2]: _UpperCAmelCase = 2 * start + 1 else: _UpperCAmelCase = 2 * start + 2 if heap[smallest_child] < heap[start]: _UpperCAmelCase , _UpperCAmelCase = heap[smallest_child], positions[smallest_child] _UpperCAmelCase , _UpperCAmelCase = ( heap[start], positions[start], ) _UpperCAmelCase , _UpperCAmelCase = temp, tempa _UpperCAmelCase = self.get_position(positions[smallest_child] ) self.set_position( positions[smallest_child] , self.get_position(positions[start] ) ) self.set_position(positions[start] , snake_case_ ) self.top_to_bottom(snake_case_ , snake_case_ , snake_case_ , snake_case_ ) def lowercase ( self : List[Any] , snake_case_ : Optional[Any] , snake_case_ : Optional[int] , snake_case_ : Optional[int] , snake_case_ : Any ): _UpperCAmelCase = position[index] while index != 0: _UpperCAmelCase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 ) if val < heap[parent]: _UpperCAmelCase = heap[parent] _UpperCAmelCase = position[parent] self.set_position(position[parent] , snake_case_ ) else: _UpperCAmelCase = val _UpperCAmelCase = temp self.set_position(snake_case_ , snake_case_ ) break _UpperCAmelCase = parent else: _UpperCAmelCase = val _UpperCAmelCase = temp self.set_position(snake_case_ , 0 ) def lowercase ( self : Optional[int] , snake_case_ : List[str] , snake_case_ : Any ): _UpperCAmelCase = len(snake_case_ ) // 2 - 1 for i in range(snake_case_ , -1 , -1 ): self.top_to_bottom(snake_case_ , snake_case_ , len(snake_case_ ) , snake_case_ ) def lowercase ( self : Any , snake_case_ : str , snake_case_ : str ): _UpperCAmelCase = positions[0] _UpperCAmelCase = sys.maxsize self.top_to_bottom(snake_case_ , 0 , len(snake_case_ ) , snake_case_ ) return temp def UpperCAmelCase_ ( __lowercase : Any ) -> Optional[Any]: '''simple docstring''' _UpperCAmelCase = Heap() _UpperCAmelCase = [0] * len(__lowercase ) _UpperCAmelCase = [-1] * len(__lowercase ) # Neighboring Tree Vertex of selected vertex # Minimum Distance of explored vertex with neighboring vertex of partial tree # formed in graph _UpperCAmelCase = [] # Heap of Distance of vertices from their neighboring vertex _UpperCAmelCase = [] for vertex in range(len(__lowercase ) ): distance_tv.append(sys.maxsize ) positions.append(__lowercase ) heap.node_position.append(__lowercase ) _UpperCAmelCase = [] _UpperCAmelCase = 1 _UpperCAmelCase = sys.maxsize for neighbor, distance in adjacency_list[0]: _UpperCAmelCase = 0 _UpperCAmelCase = distance heap.heapify(__lowercase , __lowercase ) for _ in range(1 , len(__lowercase ) ): _UpperCAmelCase = heap.delete_minimum(__lowercase , __lowercase ) if visited[vertex] == 0: tree_edges.append((nbr_tv[vertex], vertex) ) _UpperCAmelCase = 1 for neighbor, distance in adjacency_list[vertex]: if ( visited[neighbor] == 0 and distance < distance_tv[heap.get_position(__lowercase )] ): _UpperCAmelCase = distance heap.bottom_to_top( __lowercase , heap.get_position(__lowercase ) , __lowercase , __lowercase ) _UpperCAmelCase = vertex return tree_edges if __name__ == "__main__": # pragma: no cover # < --------- Prims Algorithm --------- > __SCREAMING_SNAKE_CASE :Optional[int] = int(input('''Enter number of edges: ''').strip()) __SCREAMING_SNAKE_CASE :Optional[int] = defaultdict(list) for _ in range(edges_number): __SCREAMING_SNAKE_CASE :Dict = [int(x) for x in input().strip().split()] adjacency_list[edge[0]].append([edge[1], edge[2]]) adjacency_list[edge[1]].append([edge[0], edge[2]]) print(prisms_algorithm(adjacency_list))
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"""simple docstring""" import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __SCREAMING_SNAKE_CASE = logging.get_logger(__name__) __SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'} __SCREAMING_SNAKE_CASE = { 'vocab_file': { 'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json', 'allenai/longformer-large-4096': ( 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json' ), 'allenai/longformer-large-4096-finetuned-triviaqa': ( 'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json' ), 'allenai/longformer-base-4096-extra.pos.embd.only': ( 'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json' ), 'allenai/longformer-large-4096-extra.pos.embd.only': ( 'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json' ), }, 'merges_file': { 'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt', 'allenai/longformer-large-4096': ( 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt' ), 'allenai/longformer-large-4096-finetuned-triviaqa': ( 'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt' ), 'allenai/longformer-base-4096-extra.pos.embd.only': ( 'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt' ), 'allenai/longformer-large-4096-extra.pos.embd.only': ( 'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt' ), }, } __SCREAMING_SNAKE_CASE = { 'allenai/longformer-base-4096': 4_096, 'allenai/longformer-large-4096': 4_096, 'allenai/longformer-large-4096-finetuned-triviaqa': 4_096, 'allenai/longformer-base-4096-extra.pos.embd.only': 4_096, 'allenai/longformer-large-4096-extra.pos.embd.only': 4_096, } @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def A_ ( ): UpperCamelCase_ : Tuple =( list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) ) ) UpperCamelCase_ : Optional[int] =bs[:] UpperCamelCase_ : List[str] =0 for b in range(2**8 ): if b not in bs: bs.append(__lowercase ) cs.append(2**8 + n ) n += 1 UpperCamelCase_ : Dict =[chr(__lowercase ) for n in cs] return dict(zip(__lowercase , __lowercase ) ) def A_ ( __lowercase ): UpperCamelCase_ : Optional[Any] =set() UpperCamelCase_ : Dict =word[0] for char in word[1:]: pairs.add((prev_char, char) ) UpperCamelCase_ : Tuple =char return pairs class a__ ( A__ ): UpperCAmelCase__ = VOCAB_FILES_NAMES UpperCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP UpperCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES UpperCAmelCase__ = ['''input_ids''', '''attention_mask'''] def __init__( self :Dict , _lowerCamelCase :List[Any] , _lowerCamelCase :Any , _lowerCamelCase :Dict="replace" , _lowerCamelCase :List[str]="<s>" , _lowerCamelCase :int="</s>" , _lowerCamelCase :Dict="</s>" , _lowerCamelCase :Optional[int]="<s>" , _lowerCamelCase :Union[str, Any]="<unk>" , _lowerCamelCase :Optional[Any]="<pad>" , _lowerCamelCase :Union[str, Any]="<mask>" , _lowerCamelCase :Tuple=False , **_lowerCamelCase :Tuple , ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else bos_token UpperCamelCase_ : int =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else eos_token UpperCamelCase_ : str =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else sep_token UpperCamelCase_ : Optional[int] =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else cls_token UpperCamelCase_ : Dict =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else unk_token UpperCamelCase_ : str =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else pad_token # Mask token behave like a normal word, i.e. include the space before it UpperCamelCase_ : int =AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase ) if isinstance(_lowerCamelCase , _lowerCamelCase ) else mask_token super().__init__( errors=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , add_prefix_space=_lowerCamelCase , **_lowerCamelCase , ) with open(_lowerCamelCase , encoding='utf-8' ) as vocab_handle: UpperCamelCase_ : Union[str, Any] =json.load(_lowerCamelCase ) UpperCamelCase_ : Tuple ={v: k for k, v in self.encoder.items()} UpperCamelCase_ : Optional[Any] =errors # how to handle errors in decoding UpperCamelCase_ : int =bytes_to_unicode() UpperCamelCase_ : Optional[int] ={v: k for k, v in self.byte_encoder.items()} with open(_lowerCamelCase , encoding='utf-8' ) as merges_handle: UpperCamelCase_ : int =merges_handle.read().split('\n' )[1:-1] UpperCamelCase_ : Optional[Any] =[tuple(merge.split() ) for merge in bpe_merges] UpperCamelCase_ : Any =dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) ) UpperCamelCase_ : Tuple ={} UpperCamelCase_ : List[str] =add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions UpperCamelCase_ : List[Any] =re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' ) @property def lowerCamelCase_ ( self :Optional[Any] ): '''simple docstring''' return len(self.encoder ) def lowerCamelCase_ ( self :Optional[int] ): '''simple docstring''' return dict(self.encoder , **self.added_tokens_encoder ) def lowerCamelCase_ ( self :Tuple , _lowerCamelCase :int ): '''simple docstring''' if token in self.cache: return self.cache[token] UpperCamelCase_ : Dict =tuple(_lowerCamelCase ) UpperCamelCase_ : Union[str, Any] =get_pairs(_lowerCamelCase ) if not pairs: return token while True: UpperCamelCase_ : int =min(_lowerCamelCase , key=lambda _lowerCamelCase : self.bpe_ranks.get(_lowerCamelCase , float('inf' ) ) ) if bigram not in self.bpe_ranks: break UpperCamelCase_ , UpperCamelCase_ : Dict =bigram UpperCamelCase_ : str =[] UpperCamelCase_ : List[str] =0 while i < len(_lowerCamelCase ): try: UpperCamelCase_ : Union[str, Any] =word.index(_lowerCamelCase , _lowerCamelCase ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) UpperCamelCase_ : Any =j if word[i] == first and i < len(_lowerCamelCase ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 UpperCamelCase_ : Any =tuple(_lowerCamelCase ) UpperCamelCase_ : Optional[int] =new_word if len(_lowerCamelCase ) == 1: break else: UpperCamelCase_ : str =get_pairs(_lowerCamelCase ) UpperCamelCase_ : Dict =' '.join(_lowerCamelCase ) UpperCamelCase_ : Union[str, Any] =word return word def lowerCamelCase_ ( self :int , _lowerCamelCase :int ): '''simple docstring''' UpperCamelCase_ : Dict =[] for token in re.findall(self.pat , _lowerCamelCase ): UpperCamelCase_ : Tuple =''.join( self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase ).split(' ' ) ) return bpe_tokens def lowerCamelCase_ ( self :Any , _lowerCamelCase :str ): '''simple docstring''' return self.encoder.get(_lowerCamelCase , self.encoder.get(self.unk_token ) ) def lowerCamelCase_ ( self :Union[str, Any] , _lowerCamelCase :List[Any] ): '''simple docstring''' return self.decoder.get(_lowerCamelCase ) def lowerCamelCase_ ( self :str , _lowerCamelCase :int ): '''simple docstring''' UpperCamelCase_ : Optional[int] =''.join(_lowerCamelCase ) UpperCamelCase_ : List[Any] =bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors ) return text def lowerCamelCase_ ( self :List[str] , _lowerCamelCase :str , _lowerCamelCase :Optional[str] = None ): '''simple docstring''' if not os.path.isdir(_lowerCamelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return UpperCamelCase_ : Dict =os.path.join( _lowerCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) UpperCamelCase_ : Optional[Any] =os.path.join( _lowerCamelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] ) with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as f: f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCamelCase , ensure_ascii=_lowerCamelCase ) + '\n' ) UpperCamelCase_ : List[str] =0 with open(_lowerCamelCase , 'w' , encoding='utf-8' ) as writer: writer.write('#version: 0.2\n' ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCamelCase : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' ' Please check that the tokenizer is not corrupted!' ) UpperCamelCase_ : List[Any] =token_index writer.write(' '.join(_lowerCamelCase ) + '\n' ) index += 1 return vocab_file, merge_file def lowerCamelCase_ ( self :str , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ): '''simple docstring''' if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] UpperCamelCase_ : Dict =[self.cls_token_id] UpperCamelCase_ : Optional[int] =[self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def lowerCamelCase_ ( self :str , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None , _lowerCamelCase :bool = False ): '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase ) if token_ids_a is None: return [1] + ([0] * len(_lowerCamelCase )) + [1] return [1] + ([0] * len(_lowerCamelCase )) + [1, 1] + ([0] * len(_lowerCamelCase )) + [1] def lowerCamelCase_ ( self :Any , _lowerCamelCase :List[int] , _lowerCamelCase :Optional[List[int]] = None ): '''simple docstring''' UpperCamelCase_ : Union[str, Any] =[self.sep_token_id] UpperCamelCase_ : List[Any] =[self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def lowerCamelCase_ ( self :Tuple , _lowerCamelCase :Any , _lowerCamelCase :Optional[int]=False , **_lowerCamelCase :Optional[int] ): '''simple docstring''' UpperCamelCase_ : Dict =kwargs.pop('add_prefix_space' , self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase ) > 0 and not text[0].isspace()): UpperCamelCase_ : List[Any] =' ' + text return (text, kwargs)
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"""simple docstring""" def A_ ( __lowercase , __lowercase , __lowercase ): if len(__lowercase ) != len(__lowercase ): raise ValueError('The length of profit and weight must be same.' ) if max_weight <= 0: raise ValueError('max_weight must greater than zero.' ) if any(p < 0 for p in profit ): raise ValueError('Profit can not be negative.' ) if any(w < 0 for w in weight ): raise ValueError('Weight can not be negative.' ) # List created to store profit gained for the 1kg in case of each weight # respectively. Calculate and append profit/weight for each element. UpperCamelCase_ : int =[p / w for p, w in zip(__lowercase , __lowercase )] # Creating a copy of the list and sorting profit/weight in ascending order UpperCamelCase_ : Optional[int] =sorted(__lowercase ) # declaring useful variables UpperCamelCase_ : Optional[int] =len(__lowercase ) UpperCamelCase_ : Optional[int] =0 UpperCamelCase_ : List[Any] =0 UpperCamelCase_ : Optional[int] =0 # loop till the total weight do not reach max limit e.g. 15 kg and till i<length while limit <= max_weight and i < length: # flag value for encountered greatest element in sorted_profit_by_weight UpperCamelCase_ : List[str] =sorted_profit_by_weight[length - i - 1] UpperCamelCase_ : Optional[Any] =profit_by_weight.index(__lowercase ) UpperCamelCase_ : str =-1 # check if the weight encountered is less than the total weight # encountered before. if max_weight - limit >= weight[index]: limit += weight[index] # Adding profit gained for the given weight 1 === # weight[index]/weight[index] gain += 1 * profit[index] else: # Since the weight encountered is greater than limit, therefore take the # required number of remaining kgs and calculate profit for it. # weight remaining / weight[index] gain += (max_weight - limit) / weight[index] * profit[index] break i += 1 return gain if __name__ == "__main__": print( 'Input profits, weights, and then max_weight (all positive ints) separated by ' 'spaces.' ) __SCREAMING_SNAKE_CASE = [int(x) for x in input('Input profits separated by spaces: ').split()] __SCREAMING_SNAKE_CASE = [int(x) for x in input('Input weights separated by spaces: ').split()] __SCREAMING_SNAKE_CASE = int(input('Max weight allowed: ')) # Function Call calc_profit(profit, weight, max_weight)
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"""simple docstring""" from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class __UpperCAmelCase ( lowerCAmelCase__ ): A__ : Dict = ["image_processor", "tokenizer"] A__ : List[str] = "AutoImageProcessor" A__ : int = "AutoTokenizer" def __init__( self , _lowerCamelCase , _lowerCamelCase ): super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) lowerCamelCase__ =self.image_processor def __call__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase ): if text is None and images is None: raise ValueError("You have to specify either text or images. Both cannot be none." ) if text is not None: lowerCamelCase__ =self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) if images is not None: lowerCamelCase__ =self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) if text is not None and images is not None: lowerCamelCase__ =image_features.pixel_values return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE ) , tensor_type=_SCREAMING_SNAKE_CASE ) def _a ( self , *_lowerCamelCase , **_lowerCamelCase ): return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) def _a ( self , *_lowerCamelCase , **_lowerCamelCase ): return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) @property def _a ( self ): return ["input_ids", "attention_mask", "pixel_values"]
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) a ={ 'configuration_albert': ['ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'AlbertConfig', 'AlbertOnnxConfig'], } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a =['AlbertTokenizer'] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a =['AlbertTokenizerFast'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a =[ 'ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST', 'AlbertForMaskedLM', 'AlbertForMultipleChoice', 'AlbertForPreTraining', 'AlbertForQuestionAnswering', 'AlbertForSequenceClassification', 'AlbertForTokenClassification', 'AlbertModel', 'AlbertPreTrainedModel', 'load_tf_weights_in_albert', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a =[ 'TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFAlbertForMaskedLM', 'TFAlbertForMultipleChoice', 'TFAlbertForPreTraining', 'TFAlbertForQuestionAnswering', 'TFAlbertForSequenceClassification', 'TFAlbertForTokenClassification', 'TFAlbertMainLayer', 'TFAlbertModel', 'TFAlbertPreTrainedModel', ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a =[ 'FlaxAlbertForMaskedLM', 'FlaxAlbertForMultipleChoice', 'FlaxAlbertForPreTraining', 'FlaxAlbertForQuestionAnswering', 'FlaxAlbertForSequenceClassification', 'FlaxAlbertForTokenClassification', 'FlaxAlbertModel', 'FlaxAlbertPreTrainedModel', ] if TYPE_CHECKING: from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_albert import AlbertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_albert_fast import AlbertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_albert import ( ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, AlbertForMaskedLM, AlbertForMultipleChoice, AlbertForPreTraining, AlbertForQuestionAnswering, AlbertForSequenceClassification, AlbertForTokenClassification, AlbertModel, AlbertPreTrainedModel, load_tf_weights_in_albert, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_albert import ( TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFAlbertForMaskedLM, TFAlbertForMultipleChoice, TFAlbertForPreTraining, TFAlbertForQuestionAnswering, TFAlbertForSequenceClassification, TFAlbertForTokenClassification, TFAlbertMainLayer, TFAlbertModel, TFAlbertPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_albert import ( FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForPreTraining, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertModel, FlaxAlbertPreTrainedModel, ) else: import sys a =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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'''simple docstring''' from __future__ import annotations from collections import namedtuple from dataclasses import dataclass @dataclass class UpperCamelCase_ : """simple docstring""" UpperCAmelCase__ : int UpperCAmelCase__ : TreeNode | None = None UpperCAmelCase__ : TreeNode | None = None __magic_name__ : Optional[int] =namedtuple('CoinsDistribResult', 'moves excess') def __snake_case ( lowerCamelCase_ : TreeNode | None ): '''simple docstring''' if root is None: return 0 # Validation def count_nodes(lowerCamelCase_ : TreeNode | None ) -> int: if node is None: return 0 return count_nodes(node.left ) + count_nodes(node.right ) + 1 def count_coins(lowerCamelCase_ : TreeNode | None ) -> int: if node is None: return 0 return count_coins(node.left ) + count_coins(node.right ) + node.data if count_nodes(lowerCamelCase_ ) != count_coins(lowerCamelCase_ ): raise ValueError("The nodes number should be same as the number of coins" ) # Main calculation def get_distrib(lowerCamelCase_ : TreeNode | None ) -> CoinsDistribResult: if node is None: return CoinsDistribResult(0 , 1 ) __magic_name__ , __magic_name__ = get_distrib(node.left ) __magic_name__ , __magic_name__ = get_distrib(node.right ) __magic_name__ = 1 - left_distrib_excess __magic_name__ = 1 - right_distrib_excess __magic_name__ = ( left_distrib_moves + right_distrib_moves + abs(lowerCamelCase_ ) + abs(lowerCamelCase_ ) ) __magic_name__ = node.data - coins_to_left - coins_to_right return CoinsDistribResult(lowerCamelCase_ , lowerCamelCase_ ) return get_distrib(lowerCamelCase_ )[0] if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import functools import logging import os import sys import threading from logging import ( CRITICAL, # NOQA DEBUG, # NOQA ERROR, # NOQA FATAL, # NOQA INFO, # NOQA NOTSET, # NOQA WARN, # NOQA WARNING, # NOQA ) from typing import Optional import huggingface_hub.utils as hf_hub_utils from tqdm import auto as tqdm_lib __magic_name__ : Tuple =threading.Lock() __magic_name__ : Optional[logging.Handler] =None __magic_name__ : List[str] ={ 'debug': logging.DEBUG, 'info': logging.INFO, 'warning': logging.WARNING, 'error': logging.ERROR, 'critical': logging.CRITICAL, } __magic_name__ : str =logging.WARNING __magic_name__ : Any =True def __snake_case ( ): '''simple docstring''' __magic_name__ = os.getenv("TRANSFORMERS_VERBOSITY" , lowerCamelCase_ ) if env_level_str: if env_level_str in log_levels: return log_levels[env_level_str] else: logging.getLogger().warning( F'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, ' F'has to be one of: { ", ".join(log_levels.keys() ) }' ) return _default_log_level def __snake_case ( ): '''simple docstring''' return __name__.split("." )[0] def __snake_case ( ): '''simple docstring''' return logging.getLogger(_get_library_name() ) def __snake_case ( ): '''simple docstring''' global _default_handler with _lock: if _default_handler: # This library has already configured the library root logger. return __magic_name__ = logging.StreamHandler() # Set sys.stderr as stream. __magic_name__ = sys.stderr.flush # Apply our default configuration to the library root logger. __magic_name__ = _get_library_root_logger() library_root_logger.addHandler(_default_handler ) library_root_logger.setLevel(_get_default_logging_level() ) __magic_name__ = False def __snake_case ( ): '''simple docstring''' global _default_handler with _lock: if not _default_handler: return __magic_name__ = _get_library_root_logger() library_root_logger.removeHandler(_default_handler ) library_root_logger.setLevel(logging.NOTSET ) __magic_name__ = None def __snake_case ( ): '''simple docstring''' return log_levels def __snake_case ( lowerCamelCase_ : Optional[str] = None ): '''simple docstring''' if name is None: __magic_name__ = _get_library_name() _configure_library_root_logger() return logging.getLogger(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' _configure_library_root_logger() return _get_library_root_logger().getEffectiveLevel() def __snake_case ( lowerCamelCase_ : int ): '''simple docstring''' _configure_library_root_logger() _get_library_root_logger().setLevel(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' return set_verbosity(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' return set_verbosity(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' return set_verbosity(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' return set_verbosity(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' _configure_library_root_logger() assert _default_handler is not None _get_library_root_logger().removeHandler(_default_handler ) def __snake_case ( ): '''simple docstring''' _configure_library_root_logger() assert _default_handler is not None _get_library_root_logger().addHandler(_default_handler ) def __snake_case ( lowerCamelCase_ : logging.Handler ): '''simple docstring''' _configure_library_root_logger() assert handler is not None _get_library_root_logger().addHandler(lowerCamelCase_ ) def __snake_case ( lowerCamelCase_ : logging.Handler ): '''simple docstring''' _configure_library_root_logger() assert handler is not None and handler not in _get_library_root_logger().handlers _get_library_root_logger().removeHandler(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' _configure_library_root_logger() __magic_name__ = False def __snake_case ( ): '''simple docstring''' _configure_library_root_logger() __magic_name__ = True def __snake_case ( ): '''simple docstring''' __magic_name__ = _get_library_root_logger().handlers for handler in handlers: __magic_name__ = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" ) handler.setFormatter(lowerCamelCase_ ) def __snake_case ( ): '''simple docstring''' __magic_name__ = _get_library_root_logger().handlers for handler in handlers: handler.setFormatter(lowerCamelCase_ ) def __snake_case ( self : Union[str, Any] , *lowerCamelCase_ : str , **lowerCamelCase_ : Any ): '''simple docstring''' __magic_name__ = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , lowerCamelCase_ ) if no_advisory_warnings: return self.warning(*lowerCamelCase_ , **lowerCamelCase_ ) __magic_name__ : int =warning_advice @functools.lru_cache(lowerCamelCase_ ) def __snake_case ( self : Dict , *lowerCamelCase_ : int , **lowerCamelCase_ : int ): '''simple docstring''' self.warning(*lowerCamelCase_ , **lowerCamelCase_ ) __magic_name__ : Optional[int] =warning_once class UpperCamelCase_ : """simple docstring""" def __init__( self : int , *_lowerCamelCase : Tuple , **_lowerCamelCase : Optional[Any] ) -> Any: # pylint: disable=unused-argument __magic_name__ = args[0] if args else None def __iter__( self : int ) -> Tuple: return iter(self._iterator ) def __getattr__( self : List[Any] , _lowerCamelCase : int ) -> List[Any]: def empty_fn(*_lowerCamelCase : List[str] , **_lowerCamelCase : List[str] ): # pylint: disable=unused-argument return return empty_fn def __enter__( self : Optional[Any] ) -> Any: return self def __exit__( self : int , _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[str] ) -> Dict: return class UpperCamelCase_ : """simple docstring""" def __call__( self : Any , *_lowerCamelCase : Optional[Any] , **_lowerCamelCase : Any ) -> List[Any]: if _tqdm_active: return tqdm_lib.tqdm(*_lowerCamelCase , **_lowerCamelCase ) else: return EmptyTqdm(*_lowerCamelCase , **_lowerCamelCase ) def __A ( self : Optional[Any] , *_lowerCamelCase : Optional[Any] , **_lowerCamelCase : Dict ) -> Union[str, Any]: __magic_name__ = None if _tqdm_active: return tqdm_lib.tqdm.set_lock(*_lowerCamelCase , **_lowerCamelCase ) def __A ( self : str ) -> Any: if _tqdm_active: return tqdm_lib.tqdm.get_lock() __magic_name__ : List[Any] =_tqdm_cls() def __snake_case ( ): '''simple docstring''' global _tqdm_active return bool(_tqdm_active ) def __snake_case ( ): '''simple docstring''' global _tqdm_active __magic_name__ = True hf_hub_utils.enable_progress_bars() def __snake_case ( ): '''simple docstring''' global _tqdm_active __magic_name__ = False hf_hub_utils.disable_progress_bars()
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'''simple docstring''' import faiss # noqa: F401 # Here to have a nice missing dependency error message early on import numpy # noqa: F401 # Here to have a nice missing dependency error message early on import requests # noqa: F401 # Here to have a nice missing dependency error message early on import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on from mauve import compute_mauve # From: mauve-text import datasets __snake_case = """\ @inproceedings{pillutla-etal:mauve:neurips2021, title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers}, author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid}, booktitle = {NeurIPS}, year = {2021} } """ __snake_case = """\ MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure. MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences. For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021). This metrics is a wrapper around the official implementation of MAUVE: https://github.com/krishnap25/mauve """ __snake_case = """ Calculates MAUVE scores between two lists of generated text and reference text. Args: predictions: list of generated text to score. Each predictions should be a string with tokens separated by spaces. references: list of reference for each prediction. Each reference should be a string with tokens separated by spaces. Optional Args: num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1 kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9 kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5 kmeans_max_iter: maximum number of k-means iterations. Default 500 featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl']. device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU max_text_length: maximum number of tokens to consider. Default 1024 divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25 mauve_scaling_factor: \"c\" from the paper. Default 5. verbose: If True (default), print running time updates seed: random seed to initialize k-means cluster assignments. Returns: mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer, frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer, divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve, p_hist: a discrete distribution, which is a quantized version of the text distribution p_text, q_hist: same as above, but with q_text. Examples: >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest >>> import datasets >>> mauve = datasets.load_metric('mauve') >>> predictions = [\"hello there\", \"general kenobi\"] >>> references = [\"hello there\", \"general kenobi\"] >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP >>> print(out.mauve) # doctest: +SKIP 1.0 """ @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class _a ( datasets.Metric ): """simple docstring""" def lowerCamelCase__ ( self : Optional[Any] ): '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/krishnap25/mauve""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""string""" , id="""sequence""" ), """references""": datasets.Value("""string""" , id="""sequence""" ), } ) , codebase_urls=["""https://github.com/krishnap25/mauve"""] , reference_urls=[ """https://arxiv.org/abs/2102.01454""", """https://github.com/krishnap25/mauve""", ] , ) def lowerCamelCase__ ( self : Union[str, Any] , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : List[Any]=None , lowercase_ : Any=None , lowercase_ : Optional[Any]=None , lowercase_ : int=None , lowercase_ : Optional[Any]="auto" , lowercase_ : Optional[Any]=-1 , lowercase_ : str=0.9 , lowercase_ : Optional[Any]=5 , lowercase_ : str=500 , lowercase_ : str="gpt2-large" , lowercase_ : int=-1 , lowercase_ : int=1_024 , lowercase_ : Dict=25 , lowercase_ : Union[str, Any]=5 , lowercase_ : Tuple=True , lowercase_ : Optional[Any]=25 , ): '''simple docstring''' lowercase_ = compute_mauve( p_text=lowercase_ , q_text=lowercase_ , p_features=lowercase_ , q_features=lowercase_ , p_tokens=lowercase_ , q_tokens=lowercase_ , num_buckets=lowercase_ , pca_max_data=lowercase_ , kmeans_explained_var=lowercase_ , kmeans_num_redo=lowercase_ , kmeans_max_iter=lowercase_ , featurize_model_name=lowercase_ , device_id=lowercase_ , max_text_length=lowercase_ , divergence_curve_discretization_size=lowercase_ , mauve_scaling_factor=lowercase_ , verbose=lowercase_ , seed=lowercase_ , ) return out
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'''simple docstring''' import argparse __snake_case = """docs/source/_static/js/custom.js""" def A_ ( SCREAMING_SNAKE_CASE_ ) ->Any: with open(SCREAMING_SNAKE_CASE_ , encoding="""utf-8""" , newline="""\n""" ) as f: lowercase_ = f.readlines() lowercase_ = 0 # First let's put the right version while not lines[index].startswith("""const stableVersion =""" ): index += 1 lowercase_ = f"""const stableVersion = \"v{version}\"\n""" # Then update the dictionary while not lines[index].startswith("""const versionMapping = {""" ): index += 1 # We go until the end while not lines[index].startswith("""}""" ): index += 1 # We add the new version at the end lines[index - 1] += f""" \"v{version}\": \"v{version}\",\n""" with open(SCREAMING_SNAKE_CASE_ , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f: f.writelines(SCREAMING_SNAKE_CASE_ ) if __name__ == "__main__": __snake_case = argparse.ArgumentParser() parser.add_argument("""--version""", help="""Release version.""") __snake_case = parser.parse_args() update_custom_js(args.version)
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available A_ : Tuple = { 'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'], 'configuration_maskformer_swin': ['MaskFormerSwinConfig'], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: A_ : Any = ['MaskFormerFeatureExtractor'] A_ : Optional[int] = ['MaskFormerImageProcessor'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: A_ : List[str] = [ 'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST', 'MaskFormerForInstanceSegmentation', 'MaskFormerModel', 'MaskFormerPreTrainedModel', ] A_ : Optional[int] = [ 'MaskFormerSwinBackbone', 'MaskFormerSwinModel', 'MaskFormerSwinPreTrainedModel', ] if TYPE_CHECKING: from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig from .configuration_maskformer_swin import MaskFormerSwinConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_maskformer import MaskFormerFeatureExtractor from .image_processing_maskformer import MaskFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_maskformer import ( MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, MaskFormerForInstanceSegmentation, MaskFormerModel, MaskFormerPreTrainedModel, ) from .modeling_maskformer_swin import ( MaskFormerSwinBackbone, MaskFormerSwinModel, MaskFormerSwinPreTrainedModel, ) else: import sys A_ : int = _LazyModule(__name__, globals()['__file__'], _import_structure)
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import json import os import unittest from transformers.models.roc_bert.tokenization_roc_bert import ( VOCAB_FILES_NAMES, RoCBertBasicTokenizer, RoCBertTokenizer, RoCBertWordpieceTokenizer, _is_control, _is_punctuation, _is_whitespace, ) from transformers.testing_utils import require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english @require_tokenizers class _a (__magic_name__ , unittest.TestCase ): '''simple docstring''' UpperCAmelCase__: List[str] = RoCBertTokenizer UpperCAmelCase__: Dict = None UpperCAmelCase__: Optional[Any] = False UpperCAmelCase__: Union[str, Any] = True UpperCAmelCase__: Union[str, Any] = filter_non_english def __A ( self ): super().setUp() A__ : Tuple = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""] A__ : Union[str, Any] = {} A__ : Dict = {} for i, value in enumerate(A__ ): A__ : Optional[int] = i A__ : Optional[int] = i A__ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) A__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_shape_file"""] ) A__ : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""word_pronunciation_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer: vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) ) with open(self.word_shape_file , """w""" , encoding="""utf-8""" ) as word_shape_writer: json.dump(A__ , A__ , ensure_ascii=A__ ) with open(self.word_pronunciation_file , """w""" , encoding="""utf-8""" ) as word_pronunciation_writer: json.dump(A__ , A__ , ensure_ascii=A__ ) def __A ( self ): A__ : Dict = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file ) A__ : Optional[Any] = tokenizer.tokenize("""你好[SEP]你是谁""" ) self.assertListEqual(A__ , ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [5, 6, 2, 5, 7, 8] ) self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(A__ ) , [5, 6, 2, 5, 7, 8] ) self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(A__ ) , [5, 6, 2, 5, 7, 8] ) def __A ( self ): A__ : Dict = RoCBertBasicTokenizer() self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] ) def __A ( self ): A__ : List[str] = RoCBertBasicTokenizer(do_lower_case=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def __A ( self ): A__ : int = RoCBertBasicTokenizer(do_lower_case=A__ , strip_accents=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] ) def __A ( self ): A__ : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=A__ , strip_accents=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def __A ( self ): A__ : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] ) self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] ) def __A ( self ): A__ : Union[str, Any] = RoCBertBasicTokenizer(do_lower_case=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def __A ( self ): A__ : Dict = RoCBertBasicTokenizer(do_lower_case=A__ , strip_accents=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def __A ( self ): A__ : Optional[Any] = RoCBertBasicTokenizer(do_lower_case=A__ , strip_accents=A__ ) self.assertListEqual( tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] ) def __A ( self ): A__ : Dict = RoCBertBasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] ) self.assertListEqual( tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] ) def __A ( self ): A__ : List[Any] = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""] A__ : Any = {} for i, token in enumerate(A__ ): A__ : Optional[int] = i A__ : Optional[Any] = RoCBertWordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" ) self.assertListEqual(tokenizer.tokenize("""""" ) , [] ) self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] ) self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] ) def __A ( self ): self.assertTrue(_is_whitespace(""" """ ) ) self.assertTrue(_is_whitespace("""\t""" ) ) self.assertTrue(_is_whitespace("""\r""" ) ) self.assertTrue(_is_whitespace("""\n""" ) ) self.assertTrue(_is_whitespace("""\u00A0""" ) ) self.assertFalse(_is_whitespace("""A""" ) ) self.assertFalse(_is_whitespace("""-""" ) ) def __A ( self ): self.assertTrue(_is_control("""\u0005""" ) ) self.assertFalse(_is_control("""A""" ) ) self.assertFalse(_is_control(""" """ ) ) self.assertFalse(_is_control("""\t""" ) ) self.assertFalse(_is_control("""\r""" ) ) def __A ( self ): self.assertTrue(_is_punctuation("""-""" ) ) self.assertTrue(_is_punctuation("""$""" ) ) self.assertTrue(_is_punctuation("""`""" ) ) self.assertTrue(_is_punctuation(""".""" ) ) self.assertFalse(_is_punctuation("""A""" ) ) self.assertFalse(_is_punctuation(""" """ ) ) def __A ( self ): A__ : Optional[int] = self.get_tokenizer() # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340 self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) if self.test_rust_tokenizer: A__ : Tuple = self.get_rust_tokenizer() self.assertListEqual( [rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] ) def __A ( self ): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): A__ : Dict = self.rust_tokenizer_class.from_pretrained(A__ , **A__ ) A__ : str = F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence.""" A__ : Union[str, Any] = tokenizer_r.encode_plus( A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , ) A__ : Any = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False A__ : List[str] = ( [ ((0, 0), tokenizer_r.cls_token), ((0, 1), """A"""), ((1, 2), ""","""), ((3, 5), """na"""), ((5, 6), """##ï"""), ((6, 8), """##ve"""), ((9, 15), tokenizer_r.mask_token), ((16, 21), """Allen"""), ((21, 23), """##NL"""), ((23, 24), """##P"""), ((25, 33), """sentence"""), ((33, 34), """."""), ((0, 0), tokenizer_r.sep_token), ] if not do_lower_case else [ ((0, 0), tokenizer_r.cls_token), ((0, 1), """a"""), ((1, 2), ""","""), ((3, 8), """naive"""), ((9, 15), tokenizer_r.mask_token), ((16, 21), """allen"""), ((21, 23), """##nl"""), ((23, 24), """##p"""), ((25, 33), """sentence"""), ((33, 34), """."""), ((0, 0), tokenizer_r.sep_token), ] ) self.assertEqual( [e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) ) self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] ) def __A ( self ): A__ : Union[str, Any] = ["""的""", """人""", """有"""] A__ : List[str] = """""".join(A__ ) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): A__ : Any = True A__ : int = self.tokenizer_class.from_pretrained(A__ , **A__ ) A__ : Dict = self.rust_tokenizer_class.from_pretrained(A__ , **A__ ) A__ : List[str] = tokenizer_p.encode(A__ , add_special_tokens=A__ ) A__ : List[Any] = tokenizer_r.encode(A__ , add_special_tokens=A__ ) A__ : Tuple = tokenizer_r.convert_ids_to_tokens(A__ ) A__ : int = tokenizer_p.convert_ids_to_tokens(A__ ) # it is expected that each Chinese character is not preceded by "##" self.assertListEqual(A__ , A__ ) self.assertListEqual(A__ , A__ ) A__ : Optional[int] = False A__ : Union[str, Any] = self.rust_tokenizer_class.from_pretrained(A__ , **A__ ) A__ : str = self.tokenizer_class.from_pretrained(A__ , **A__ ) A__ : Tuple = tokenizer_r.encode(A__ , add_special_tokens=A__ ) A__ : List[str] = tokenizer_p.encode(A__ , add_special_tokens=A__ ) A__ : Union[str, Any] = tokenizer_r.convert_ids_to_tokens(A__ ) A__ : Tuple = tokenizer_p.convert_ids_to_tokens(A__ ) # it is expected that only the first Chinese character is not preceded by "##". A__ : str = [ F"""##{token}""" if idx != 0 else token for idx, token in enumerate(A__ ) ] self.assertListEqual(A__ , A__ ) self.assertListEqual(A__ , A__ ) @slow def __A ( self ): A__ : str = self.tokenizer_class(self.vocab_file , self.word_shape_file , self.word_pronunciation_file ) A__ : Optional[Any] = tokenizer.encode("""你好""" , add_special_tokens=A__ ) A__ : List[Any] = tokenizer.encode("""你是谁""" , add_special_tokens=A__ ) A__ : Any = tokenizer.build_inputs_with_special_tokens(A__ ) A__ : Any = tokenizer.build_inputs_with_special_tokens(A__ , A__ ) assert encoded_sentence == [1] + text + [2] assert encoded_pair == [1] + text + [2] + text_a + [2] def __A ( self ): A__ : List[str] = self.get_tokenizers(do_lower_case=A__ ) for tokenizer in tokenizers: with self.subTest(F"""{tokenizer.__class__.__name__}""" ): A__ : Optional[int] = """你好,你是谁""" A__ : List[str] = tokenizer.tokenize(A__ ) A__ : Union[str, Any] = tokenizer.convert_tokens_to_ids(A__ ) A__ : str = tokenizer.convert_tokens_to_shape_ids(A__ ) A__ : Optional[int] = tokenizer.convert_tokens_to_pronunciation_ids(A__ ) A__ : Union[str, Any] = tokenizer.prepare_for_model( A__ , A__ , A__ , add_special_tokens=A__ ) A__ : int = tokenizer.encode_plus(A__ , add_special_tokens=A__ ) self.assertEqual(A__ , A__ )
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1
from ...configuration_utils import PretrainedConfig from ...utils import logging __a : List[Any] = logging.get_logger(__name__) __a : Any = { """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""", # See all XGLM models at https://huggingface.co/models?filter=xglm } class _UpperCamelCase ( _UpperCAmelCase ): """simple docstring""" __a : List[Any] = '''xglm''' __a : str = ['''past_key_values'''] __a : Tuple = { '''num_attention_heads''': '''attention_heads''', '''hidden_size''': '''d_model''', '''num_hidden_layers''': '''num_layers''', } def __init__( self , lowerCAmelCase__=25_60_08 , lowerCAmelCase__=20_48 , lowerCAmelCase__=10_24 , lowerCAmelCase__=40_96 , lowerCAmelCase__=24 , lowerCAmelCase__=16 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.02 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=2 , lowerCAmelCase__=1 , lowerCAmelCase__=0 , lowerCAmelCase__=2 , **lowerCAmelCase__ , ) -> str: '''simple docstring''' __lowercase = vocab_size __lowercase = max_position_embeddings __lowercase = d_model __lowercase = ffn_dim __lowercase = num_layers __lowercase = attention_heads __lowercase = activation_function __lowercase = dropout __lowercase = attention_dropout __lowercase = activation_dropout __lowercase = layerdrop __lowercase = init_std __lowercase = scale_embedding # scale factor will be sqrt(d_model) if True __lowercase = use_cache super().__init__( pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , decoder_start_token_id=lowerCAmelCase__ , **lowerCAmelCase__ , )
522
import argparse import logging import pickle import random import time import numpy as np from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO ) __a : Optional[Any] = logging.getLogger(__name__) def UpperCAmelCase ( ): """simple docstring""" __lowercase = argparse.ArgumentParser( description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).''' ) parser.add_argument('''--file_path''' , type=lowercase , default='''data/dump.txt''' , help='''The path to the data.''' ) parser.add_argument('''--tokenizer_type''' , type=lowercase , default='''bert''' , choices=['''bert''', '''roberta''', '''gpt2'''] ) parser.add_argument('''--tokenizer_name''' , type=lowercase , default='''bert-base-uncased''' , help='''The tokenizer to use.''' ) parser.add_argument('''--dump_file''' , type=lowercase , default='''data/dump''' , help='''The dump file prefix.''' ) __lowercase = parser.parse_args() logger.info(F"Loading Tokenizer ({args.tokenizer_name})" ) if args.tokenizer_type == "bert": __lowercase = BertTokenizer.from_pretrained(args.tokenizer_name ) __lowercase = tokenizer.special_tokens_map['''cls_token'''] # `[CLS]` __lowercase = tokenizer.special_tokens_map['''sep_token'''] # `[SEP]` elif args.tokenizer_type == "roberta": __lowercase = RobertaTokenizer.from_pretrained(args.tokenizer_name ) __lowercase = tokenizer.special_tokens_map['''cls_token'''] # `<s>` __lowercase = tokenizer.special_tokens_map['''sep_token'''] # `</s>` elif args.tokenizer_type == "gpt2": __lowercase = GPTaTokenizer.from_pretrained(args.tokenizer_name ) __lowercase = tokenizer.special_tokens_map['''bos_token'''] # `<|endoftext|>` __lowercase = tokenizer.special_tokens_map['''eos_token'''] # `<|endoftext|>` logger.info(F"Loading text from {args.file_path}" ) with open(args.file_path , '''r''' , encoding='''utf8''' ) as fp: __lowercase = fp.readlines() logger.info('''Start encoding''' ) logger.info(F"{len(lowercase )} examples to process." ) __lowercase = [] __lowercase = 0 __lowercase = 10000 __lowercase = time.time() for text in data: __lowercase = F"{bos} {text.strip()} {sep}" __lowercase = tokenizer.encode(lowercase , add_special_tokens=lowercase ) rslt.append(lowercase ) iter += 1 if iter % interval == 0: __lowercase = time.time() logger.info(F"{iter} examples processed. - {(end-start):.2f}s/{interval}expl" ) __lowercase = time.time() logger.info('''Finished binarization''' ) logger.info(F"{len(lowercase )} examples processed." ) __lowercase = F"{args.dump_file}.{args.tokenizer_name}.pickle" __lowercase = tokenizer.vocab_size if vocab_size < (1 << 16): __lowercase = [np.uintaa(lowercase ) for d in rslt] else: __lowercase = [np.intaa(lowercase ) for d in rslt] random.shuffle(rslt_ ) logger.info(F"Dump to {dp_file}" ) with open(lowercase , '''wb''' ) as handle: pickle.dump(rslt_ , lowercase , protocol=pickle.HIGHEST_PROTOCOL ) if __name__ == "__main__": main()
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1
from collections.abc import Sequence def lowerCAmelCase_ ( lowerCamelCase = None ): if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __magic_name__ : str =nums[0] for i in range(1 , len(lowerCamelCase ) ): __magic_name__ : Any =nums[i] __magic_name__ : Dict =max(lowerCamelCase , ans + num , lowerCamelCase ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user UpperCAmelCase_ : List[str] = int(input("Enter number of elements : ").strip()) UpperCAmelCase_ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n] print(max_subsequence_sum(array))
21
from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available, ) _lowercase = { "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"], "processing_layoutlmv2": ["LayoutLMv2Processor"], "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ["LayoutLMv2TokenizerFast"] try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ["LayoutLMv2FeatureExtractor"] _lowercase = ["LayoutLMv2ImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST", "LayoutLMv2ForQuestionAnswering", "LayoutLMv2ForSequenceClassification", "LayoutLMv2ForTokenClassification", "LayoutLMv2Layer", "LayoutLMv2Model", "LayoutLMv2PreTrainedModel", ] if TYPE_CHECKING: from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig from .processing_layoutlmva import LayoutLMvaProcessor from .tokenization_layoutlmva import LayoutLMvaTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_layoutlmva import ( LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST, LayoutLMvaForQuestionAnswering, LayoutLMvaForSequenceClassification, LayoutLMvaForTokenClassification, LayoutLMvaLayer, LayoutLMvaModel, LayoutLMvaPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
632
0
import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditionModel, ) from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() def A (__A : Dict ) -> List[Any]: """simple docstring""" UpperCAmelCase_ = [tensor.shape for tensor in tensor_list] return all(shape == shapes[0] for shape in shapes[1:] ) class __snake_case ( a , a , a , unittest.TestCase ): UpperCAmelCase__ : Dict = StableDiffusionLatentUpscalePipeline UpperCAmelCase__ : Any = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - { '''height''', '''width''', '''cross_attention_kwargs''', '''negative_prompt_embeds''', '''prompt_embeds''', } UpperCAmelCase__ : List[str] = PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''} UpperCAmelCase__ : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS UpperCAmelCase__ : Optional[Any] = frozenset( [] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess UpperCAmelCase__ : Tuple = frozenset([] ) UpperCAmelCase__ : Optional[Any] = True @property def lowerCamelCase ( self : Union[str, Any]): """simple docstring""" UpperCAmelCase_ = 1 UpperCAmelCase_ = 4 UpperCAmelCase_ = (16, 16) UpperCAmelCase_ = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0)).to(_snake_case) return image def lowerCamelCase ( self : Optional[int]): """simple docstring""" torch.manual_seed(0) UpperCAmelCase_ = UNetaDConditionModel( act_fn='''gelu''' , attention_head_dim=8 , norm_num_groups=_snake_case , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=( '''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', ) , in_channels=8 , mid_block_type=_snake_case , only_cross_attention=_snake_case , out_channels=5 , resnet_time_scale_shift='''scale_shift''' , time_embedding_type='''fourier''' , timestep_post_act='''gelu''' , up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') , ) UpperCAmelCase_ = AutoencoderKL( block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[ '''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''', ] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) UpperCAmelCase_ = EulerDiscreteScheduler(prediction_type='''sample''') 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 , hidden_act='''quick_gelu''' , projection_dim=512 , ) UpperCAmelCase_ = CLIPTextModel(_snake_case) UpperCAmelCase_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''') UpperCAmelCase_ = { '''unet''': model.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, } return components def lowerCamelCase ( self : Union[str, Any] , _snake_case : Union[str, Any] , _snake_case : int=0): """simple docstring""" 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''', '''image''': self.dummy_image.cpu(), '''generator''': generator, '''num_inference_steps''': 2, '''output_type''': '''numpy''', } return inputs def lowerCamelCase ( self : Tuple): """simple docstring""" 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, 256, 256, 3)) UpperCAmelCase_ = np.array( [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]) UpperCAmelCase_ = np.abs(image_slice.flatten() - expected_slice).max() self.assertLessEqual(_snake_case , 1e-3) def lowerCamelCase ( self : str): """simple docstring""" super().test_attention_slicing_forward_pass(expected_max_diff=7e-3) def lowerCamelCase ( self : List[Any]): """simple docstring""" super().test_cpu_offload_forward_pass(expected_max_diff=3e-3) def lowerCamelCase ( self : List[str]): """simple docstring""" super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3) def lowerCamelCase ( self : int): """simple docstring""" super().test_inference_batch_single_identical(expected_max_diff=7e-3) def lowerCamelCase ( self : str): """simple docstring""" super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3) def lowerCamelCase ( self : Union[str, Any]): """simple docstring""" super().test_save_load_local(expected_max_difference=3e-3) def lowerCamelCase ( self : Union[str, Any]): """simple docstring""" super().test_save_load_optional_components(expected_max_difference=3e-3) def lowerCamelCase ( self : str): """simple docstring""" UpperCAmelCase_ = [ '''DDIMScheduler''', '''DDPMScheduler''', '''PNDMScheduler''', '''HeunDiscreteScheduler''', '''EulerAncestralDiscreteScheduler''', '''KDPM2DiscreteScheduler''', '''KDPM2AncestralDiscreteScheduler''', '''DPMSolverSDEScheduler''', ] UpperCAmelCase_ = self.get_dummy_components() UpperCAmelCase_ = self.pipeline_class(**_snake_case) # make sure that PNDM does not need warm-up pipe.scheduler.register_to_config(skip_prk_steps=_snake_case) pipe.to(_snake_case) pipe.set_progress_bar_config(disable=_snake_case) UpperCAmelCase_ = self.get_dummy_inputs(_snake_case) UpperCAmelCase_ = 2 UpperCAmelCase_ = [] for scheduler_enum in KarrasDiffusionSchedulers: if scheduler_enum.name in skip_schedulers: # no sigma schedulers are not supported # no schedulers continue UpperCAmelCase_ = getattr(_snake_case , scheduler_enum.name) UpperCAmelCase_ = scheduler_cls.from_config(pipe.scheduler.config) UpperCAmelCase_ = pipe(**_snake_case)[0] outputs.append(_snake_case) assert check_same_shape(_snake_case) @require_torch_gpu @slow class __snake_case ( unittest.TestCase ): def lowerCamelCase ( self : Optional[int]): """simple docstring""" super().tearDown() gc.collect() torch.cuda.empty_cache() def lowerCamelCase ( self : Tuple): """simple docstring""" UpperCAmelCase_ = torch.manual_seed(33) UpperCAmelCase_ = StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' , torch_dtype=torch.floataa) pipe.to('''cuda''') UpperCAmelCase_ = StableDiffusionLatentUpscalePipeline.from_pretrained( '''stabilityai/sd-x2-latent-upscaler''' , torch_dtype=torch.floataa) upscaler.to('''cuda''') UpperCAmelCase_ = '''a photo of an astronaut high resolution, unreal engine, ultra realistic''' UpperCAmelCase_ = pipe(_snake_case , generator=_snake_case , output_type='''latent''').images UpperCAmelCase_ = upscaler( prompt=_snake_case , image=_snake_case , num_inference_steps=20 , guidance_scale=0 , generator=_snake_case , output_type='''np''' , ).images[0] UpperCAmelCase_ = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''') assert np.abs((expected_image - image).mean()) < 5e-2 def lowerCamelCase ( self : str): """simple docstring""" UpperCAmelCase_ = torch.manual_seed(33) UpperCAmelCase_ = StableDiffusionLatentUpscalePipeline.from_pretrained( '''stabilityai/sd-x2-latent-upscaler''' , torch_dtype=torch.floataa) upscaler.to('''cuda''') UpperCAmelCase_ = '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas''' UpperCAmelCase_ = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''') UpperCAmelCase_ = upscaler( prompt=_snake_case , image=_snake_case , num_inference_steps=20 , guidance_scale=0 , generator=_snake_case , output_type='''np''' , ).images[0] UpperCAmelCase_ = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''') assert np.abs((expected_image - image).max()) < 5e-2
169
def A (__A : str ) -> list: """simple docstring""" return [ txt[:a] + txt[a].upper() + txt[a + 1 :] for a in range(len(__A ) ) if txt[a].isalpha() ] if __name__ == "__main__": __import__("doctest").testmod()
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1
import numpy class _snake_case : def __init__( self : Dict, __lowercase : numpy.ndarray, __lowercase : numpy.ndarray ): lowercase__ = input_array # Random initial weights are assigned where first argument is the # number of nodes in previous layer and second argument is the # number of nodes in the next layer. # Random initial weights are assigned. # self.input_array.shape[1] is used to represent number of nodes in input layer. # First hidden layer consists of 4 nodes. lowercase__ = numpy.random.rand( self.input_array.shape[1], 4 ) # Random initial values for the first hidden layer. # First hidden layer has 4 nodes. # Second hidden layer has 3 nodes. lowercase__ = numpy.random.rand( 4, 3 ) # Random initial values for the second hidden layer. # Second hidden layer has 3 nodes. # Output layer has 1 node. lowercase__ = numpy.random.rand(3, 1 ) # Real output values provided. lowercase__ = output_array # Predicted output values by the neural network. # Predicted_output array initially consists of zeroes. lowercase__ = numpy.zeros(output_array.shape ) def A__ ( self : List[Any] ): lowercase__ = sigmoid( numpy.dot(self.input_array, self.input_layer_and_first_hidden_layer_weights ) ) # layer_between_first_hidden_layer_and_second_hidden_layer is the layer # connecting the first hidden set of nodes with the second hidden set of nodes. lowercase__ = sigmoid( numpy.dot( self.layer_between_input_and_first_hidden_layer, self.first_hidden_layer_and_second_hidden_layer_weights, ) ) # layer_between_second_hidden_layer_and_output is the layer connecting # second hidden layer with the output node. lowercase__ = sigmoid( numpy.dot( self.layer_between_first_hidden_layer_and_second_hidden_layer, self.second_hidden_layer_and_output_layer_weights, ) ) return self.layer_between_second_hidden_layer_and_output def A__ ( self : Optional[Any] ): lowercase__ = numpy.dot( self.layer_between_first_hidden_layer_and_second_hidden_layer.T, 2 * (self.output_array - self.predicted_output) * sigmoid_derivative(self.predicted_output ), ) lowercase__ = numpy.dot( self.layer_between_input_and_first_hidden_layer.T, numpy.dot( 2 * (self.output_array - self.predicted_output) * sigmoid_derivative(self.predicted_output ), self.second_hidden_layer_and_output_layer_weights.T, ) * sigmoid_derivative( self.layer_between_first_hidden_layer_and_second_hidden_layer ), ) lowercase__ = numpy.dot( self.input_array.T, numpy.dot( numpy.dot( 2 * (self.output_array - self.predicted_output) * sigmoid_derivative(self.predicted_output ), self.second_hidden_layer_and_output_layer_weights.T, ) * sigmoid_derivative( self.layer_between_first_hidden_layer_and_second_hidden_layer ), self.first_hidden_layer_and_second_hidden_layer_weights.T, ) * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ), ) self.input_layer_and_first_hidden_layer_weights += ( updated_input_layer_and_first_hidden_layer_weights ) self.first_hidden_layer_and_second_hidden_layer_weights += ( updated_first_hidden_layer_and_second_hidden_layer_weights ) self.second_hidden_layer_and_output_layer_weights += ( updated_second_hidden_layer_and_output_layer_weights ) def A__ ( self : Any, __lowercase : numpy.ndarray, __lowercase : int, __lowercase : bool ): for iteration in range(1, iterations + 1 ): lowercase__ = self.feedforward() self.back_propagation() if give_loss: lowercase__ = numpy.mean(numpy.square(output - self.feedforward() ) ) print(F'''Iteration {iteration} Loss: {loss}''' ) def A__ ( self : Union[str, Any], __lowercase : numpy.ndarray ): lowercase__ = input_arr lowercase__ = sigmoid( numpy.dot(self.array, self.input_layer_and_first_hidden_layer_weights ) ) lowercase__ = sigmoid( numpy.dot( self.layer_between_input_and_first_hidden_layer, self.first_hidden_layer_and_second_hidden_layer_weights, ) ) lowercase__ = sigmoid( numpy.dot( self.layer_between_first_hidden_layer_and_second_hidden_layer, self.second_hidden_layer_and_output_layer_weights, ) ) return int(self.layer_between_second_hidden_layer_and_output > 0.6 ) def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): return 1 / (1 + numpy.exp(-value )) def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): return (value) * (1 - (value)) def __lowerCAmelCase ( ): lowercase__ = numpy.array( ( [0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1], [1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1], ) , dtype=numpy.floataa , ) # True output values for the given input values. lowercase__ = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa ) # Calling neural network class. lowercase__ = TwoHiddenLayerNeuralNetwork( input_array=SCREAMING_SNAKE_CASE_ , output_array=SCREAMING_SNAKE_CASE_ ) # Calling training function. # Set give_loss to True if you want to see loss in every iteration. neural_network.train(output=SCREAMING_SNAKE_CASE_ , iterations=10 , give_loss=SCREAMING_SNAKE_CASE_ ) return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) ) if __name__ == "__main__": example()
413
import json import os import subprocess import unittest from ast import literal_eval import pytest from parameterized import parameterized, parameterized_class from . import is_sagemaker_available if is_sagemaker_available(): from sagemaker import Session, TrainingJobAnalytics from sagemaker.huggingface import HuggingFace @pytest.mark.skipif( literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""")) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , ) @pytest.mark.usefixtures("""sm_env""") @parameterized_class( [ { """framework""": """pytorch""", """script""": """run_glue_model_parallelism.py""", """model_name_or_path""": """roberta-large""", """instance_type""": """ml.p3dn.24xlarge""", """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2}, }, { """framework""": """pytorch""", """script""": """run_glue.py""", """model_name_or_path""": """roberta-large""", """instance_type""": """ml.p3dn.24xlarge""", """results""": {"""train_runtime""": 1_6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 1.2}, }, ]) class _snake_case ( unittest.TestCase): def A__ ( self : Dict ): if self.framework == "pytorch": subprocess.run( F'''cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'''.split(), encoding="utf-8", check=__lowercase, ) assert hasattr(self, "env" ) def A__ ( self : Tuple, __lowercase : Tuple ): # configuration for running training on smdistributed Model Parallel lowercase__ = { "enabled": True, "processes_per_host": 8, } lowercase__ = { "enabled": True, "parameters": { "microbatches": 4, "placement_strategy": "spread", "pipeline": "interleaved", "optimize": "speed", "partitions": 4, "ddp": True, }, } lowercase__ = {"smdistributed": {"modelparallel": smp_options}, "mpi": mpi_options} lowercase__ = "trainer" if self.script == "run_glue.py" else "smtrainer" # creates estimator return HuggingFace( entry_point=self.script, source_dir=self.env.test_path, role=self.env.role, image_uri=self.env.image_uri, base_job_name=F'''{self.env.base_job_name}-{instance_count}-smp-{name_extension}''', instance_count=__lowercase, instance_type=self.instance_type, debugger_hook_config=__lowercase, hyperparameters={ **self.env.hyperparameters, "model_name_or_path": self.model_name_or_path, "max_steps": 500, }, metric_definitions=self.env.metric_definitions, distribution=__lowercase, py_version="py36", ) def A__ ( self : Union[str, Any], __lowercase : List[Any] ): TrainingJobAnalytics(__lowercase ).export_csv(F'''{self.env.test_path}/{job_name}_metrics.csv''' ) @parameterized.expand([(1,)] ) def A__ ( self : Dict, __lowercase : Optional[int] ): # create estimator lowercase__ = self.create_estimator(__lowercase ) # run training estimator.fit() # result dataframe lowercase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe() # extract kpis lowercase__ = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] ) lowercase__ = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] ) # get train time from SageMaker job, this includes starting, preprocessing, stopping lowercase__ = ( Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds", 99_9999 ) ) # assert kpis assert train_runtime <= self.results["train_runtime"] assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy ) assert all(t <= self.results["eval_loss"] for t in eval_loss ) # dump tests result into json file to share in PR with open(F'''{estimator.latest_training_job.name}.json''', "w" ) as outfile: json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss}, __lowercase )
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1
import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditionModel, ) from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() def a ( A__ : int ) -> Dict: """simple docstring""" _lowercase =[tensor.shape for tensor in tensor_list] return all(shape == shapes[0] for shape in shapes[1:] ) class __lowerCAmelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ): _a = StableDiffusionLatentUpscalePipeline _a = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - { """height""", """width""", """cross_attention_kwargs""", """negative_prompt_embeds""", """prompt_embeds""", } _a = PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""} _a = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS _a = frozenset( [] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess _a = frozenset([] ) _a = True @property def A__ ( self ) -> str: '''simple docstring''' _lowercase =1 _lowercase =4 _lowercase =(16, 16) _lowercase =floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(lowerCAmelCase ) return image def A__ ( self ) -> Optional[Any]: '''simple docstring''' torch.manual_seed(0 ) _lowercase =UNetaDConditionModel( act_fn='gelu' , attention_head_dim=8 , norm_num_groups=lowerCAmelCase , block_out_channels=[32, 32, 64, 64] , time_cond_proj_dim=160 , conv_in_kernel=1 , conv_out_kernel=1 , cross_attention_dim=32 , down_block_types=( 'KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', ) , in_channels=8 , mid_block_type=lowerCAmelCase , only_cross_attention=lowerCAmelCase , out_channels=5 , resnet_time_scale_shift='scale_shift' , time_embedding_type='fourier' , timestep_post_act='gelu' , up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D') , ) _lowercase =AutoencoderKL( block_out_channels=[32, 32, 64, 64] , in_channels=3 , out_channels=3 , down_block_types=[ 'DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D', ] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , ) _lowercase =EulerDiscreteScheduler(prediction_type='sample' ) _lowercase =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=1_000 , hidden_act='quick_gelu' , projection_dim=512 , ) _lowercase =CLIPTextModel(lowerCAmelCase ) _lowercase =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' ) _lowercase ={ 'unet': model.eval(), 'vae': vae.eval(), 'scheduler': scheduler, 'text_encoder': text_encoder, 'tokenizer': tokenizer, } return components def A__ ( self , lowerCAmelCase , lowerCAmelCase=0 ) -> str: '''simple docstring''' if str(lowerCAmelCase ).startswith('mps' ): _lowercase =torch.manual_seed(lowerCAmelCase ) else: _lowercase =torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase ) _lowercase ={ 'prompt': 'A painting of a squirrel eating a burger', 'image': self.dummy_image.cpu(), 'generator': generator, 'num_inference_steps': 2, 'output_type': 'numpy', } return inputs def A__ ( self ) -> Tuple: '''simple docstring''' _lowercase ='cpu' _lowercase =self.get_dummy_components() _lowercase =self.pipeline_class(**lowerCAmelCase ) pipe.to(lowerCAmelCase ) pipe.set_progress_bar_config(disable=lowerCAmelCase ) _lowercase =self.get_dummy_inputs(lowerCAmelCase ) _lowercase =pipe(**lowerCAmelCase ).images _lowercase =image[0, -3:, -3:, -1] self.assertEqual(image.shape , (1, 256, 256, 3) ) _lowercase =np.array( [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055] ) _lowercase =np.abs(image_slice.flatten() - expected_slice ).max() self.assertLessEqual(lowerCAmelCase , 1e-3 ) def A__ ( self ) -> int: '''simple docstring''' super().test_attention_slicing_forward_pass(expected_max_diff=7e-3 ) def A__ ( self ) -> int: '''simple docstring''' super().test_cpu_offload_forward_pass(expected_max_diff=3e-3 ) def A__ ( self ) -> List[Any]: '''simple docstring''' super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 ) def A__ ( self ) -> Union[str, Any]: '''simple docstring''' super().test_inference_batch_single_identical(expected_max_diff=7e-3 ) def A__ ( self ) -> Any: '''simple docstring''' super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3 ) def A__ ( self ) -> Optional[int]: '''simple docstring''' super().test_save_load_local(expected_max_difference=3e-3 ) def A__ ( self ) -> int: '''simple docstring''' super().test_save_load_optional_components(expected_max_difference=3e-3 ) def A__ ( self ) -> Any: '''simple docstring''' _lowercase =[ 'DDIMScheduler', 'DDPMScheduler', 'PNDMScheduler', 'HeunDiscreteScheduler', 'EulerAncestralDiscreteScheduler', 'KDPM2DiscreteScheduler', 'KDPM2AncestralDiscreteScheduler', 'DPMSolverSDEScheduler', ] _lowercase =self.get_dummy_components() _lowercase =self.pipeline_class(**lowerCAmelCase ) # make sure that PNDM does not need warm-up pipe.scheduler.register_to_config(skip_prk_steps=lowerCAmelCase ) pipe.to(lowerCAmelCase ) pipe.set_progress_bar_config(disable=lowerCAmelCase ) _lowercase =self.get_dummy_inputs(lowerCAmelCase ) _lowercase =2 _lowercase =[] for scheduler_enum in KarrasDiffusionSchedulers: if scheduler_enum.name in skip_schedulers: # no sigma schedulers are not supported # no schedulers continue _lowercase =getattr(lowerCAmelCase , scheduler_enum.name ) _lowercase =scheduler_cls.from_config(pipe.scheduler.config ) _lowercase =pipe(**lowerCAmelCase )[0] outputs.append(lowerCAmelCase ) assert check_same_shape(lowerCAmelCase ) @require_torch_gpu @slow class __lowerCAmelCase ( unittest.TestCase ): def A__ ( self ) -> Dict: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def A__ ( self ) -> Any: '''simple docstring''' _lowercase =torch.manual_seed(33 ) _lowercase =StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4' , torch_dtype=torch.floataa ) pipe.to('cuda' ) _lowercase =StableDiffusionLatentUpscalePipeline.from_pretrained( 'stabilityai/sd-x2-latent-upscaler' , torch_dtype=torch.floataa ) upscaler.to('cuda' ) _lowercase ='a photo of an astronaut high resolution, unreal engine, ultra realistic' _lowercase =pipe(lowerCAmelCase , generator=lowerCAmelCase , output_type='latent' ).images _lowercase =upscaler( prompt=lowerCAmelCase , image=lowerCAmelCase , num_inference_steps=20 , guidance_scale=0 , generator=lowerCAmelCase , output_type='np' , ).images[0] _lowercase =load_numpy( 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy' ) assert np.abs((expected_image - image).mean() ) < 5e-2 def A__ ( self ) -> Optional[Any]: '''simple docstring''' _lowercase =torch.manual_seed(33 ) _lowercase =StableDiffusionLatentUpscalePipeline.from_pretrained( 'stabilityai/sd-x2-latent-upscaler' , torch_dtype=torch.floataa ) upscaler.to('cuda' ) _lowercase ='the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas' _lowercase =load_image( 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png' ) _lowercase =upscaler( prompt=lowerCAmelCase , image=lowerCAmelCase , num_inference_steps=20 , guidance_scale=0 , generator=lowerCAmelCase , output_type='np' , ).images[0] _lowercase =load_numpy( 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy' ) assert np.abs((expected_image - image).max() ) < 5e-2
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import argparse import os from . import ( ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BART_PRETRAINED_MODEL_ARCHIVE_LIST, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, BartConfig, BertConfig, CamembertConfig, CTRLConfig, DistilBertConfig, DPRConfig, ElectraConfig, FlaubertConfig, GPTaConfig, LayoutLMConfig, LxmertConfig, OpenAIGPTConfig, RobertaConfig, TaConfig, TFAlbertForPreTraining, TFBartForConditionalGeneration, TFBartForSequenceClassification, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFCamembertForMaskedLM, TFCTRLLMHeadModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, TFElectraForPreTraining, TFFlaubertWithLMHeadModel, TFGPTaLMHeadModel, TFLayoutLMForMaskedLM, TFLxmertForPreTraining, TFLxmertVisualFeatureEncoder, TFOpenAIGPTLMHeadModel, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TFTaForConditionalGeneration, TFTransfoXLLMHeadModel, TFWavaVecaModel, TFXLMRobertaForMaskedLM, TFXLMWithLMHeadModel, TFXLNetLMHeadModel, TransfoXLConfig, WavaVecaConfig, WavaVecaModel, XLMConfig, XLMRobertaConfig, XLNetConfig, is_torch_available, load_pytorch_checkpoint_in_tfa_model, ) from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging if is_torch_available(): import numpy as np import torch from . import ( AlbertForPreTraining, BartForConditionalGeneration, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, CamembertForMaskedLM, CTRLLMHeadModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DPRContextEncoder, DPRQuestionEncoder, DPRReader, ElectraForPreTraining, FlaubertWithLMHeadModel, GPTaLMHeadModel, LayoutLMForMaskedLM, LxmertForPreTraining, LxmertVisualFeatureEncoder, OpenAIGPTLMHeadModel, RobertaForMaskedLM, RobertaForSequenceClassification, TaForConditionalGeneration, TransfoXLLMHeadModel, XLMRobertaForMaskedLM, XLMWithLMHeadModel, XLNetLMHeadModel, ) logging.set_verbosity_info() lowercase_ = { 'bart': ( BartConfig, TFBartForConditionalGeneration, TFBartForSequenceClassification, BartForConditionalGeneration, BART_PRETRAINED_MODEL_ARCHIVE_LIST, ), 'bert': ( BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'bert-large-uncased-whole-word-masking-finetuned-squad': ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'bert-large-cased-whole-word-masking-finetuned-squad': ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'bert-base-cased-finetuned-mrpc': ( BertConfig, TFBertForSequenceClassification, BertForSequenceClassification, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'dpr': ( DPRConfig, TFDPRQuestionEncoder, TFDPRContextEncoder, TFDPRReader, DPRQuestionEncoder, DPRContextEncoder, DPRReader, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ), 'gpt2': ( GPTaConfig, TFGPTaLMHeadModel, GPTaLMHeadModel, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'xlnet': ( XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'xlm': ( XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'xlm-roberta': ( XLMRobertaConfig, TFXLMRobertaForMaskedLM, XLMRobertaForMaskedLM, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'transfo-xl': ( TransfoXLConfig, TFTransfoXLLMHeadModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'openai-gpt': ( OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'roberta': ( RobertaConfig, TFRobertaForCausalLM, TFRobertaForMaskedLM, RobertaForMaskedLM, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'layoutlm': ( LayoutLMConfig, TFLayoutLMForMaskedLM, LayoutLMForMaskedLM, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, ), 'roberta-large-mnli': ( RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'camembert': ( CamembertConfig, TFCamembertForMaskedLM, CamembertForMaskedLM, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'flaubert': ( FlaubertConfig, TFFlaubertWithLMHeadModel, FlaubertWithLMHeadModel, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'distilbert': ( DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'distilbert-base-distilled-squad': ( DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'lxmert': ( LxmertConfig, TFLxmertForPreTraining, LxmertForPreTraining, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'lxmert-visual-feature-encoder': ( LxmertConfig, TFLxmertVisualFeatureEncoder, LxmertVisualFeatureEncoder, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'ctrl': ( CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'albert': ( AlbertConfig, TFAlbertForPreTraining, AlbertForPreTraining, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 't5': ( TaConfig, TFTaForConditionalGeneration, TaForConditionalGeneration, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'electra': ( ElectraConfig, TFElectraForPreTraining, ElectraForPreTraining, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), 'wav2vec2': ( WavaVecaConfig, TFWavaVecaModel, WavaVecaModel, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), } def a ( A__ : Tuple , A__ : List[Any] , A__ : Optional[int] , A__ : Dict , A__ : Any=False , A__ : str=True ) -> str: """simple docstring""" if model_type not in MODEL_CLASSES: raise ValueError(F'''Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.''' ) _lowercase , _lowercase , _lowercase , _lowercase =MODEL_CLASSES[model_type] # Initialise TF model if config_file in aws_config_map: _lowercase =cached_file(A__ , A__ , force_download=not use_cached_models ) _lowercase =config_class.from_json_file(A__ ) _lowercase =True _lowercase =True print(F'''Building TensorFlow model from configuration: {config}''' ) _lowercase =model_class(A__ ) # Load weights from tf checkpoint if pytorch_checkpoint_path in aws_config_map.keys(): _lowercase =cached_file( A__ , A__ , force_download=not use_cached_models ) # Load PyTorch checkpoint in tf2 model: _lowercase =load_pytorch_checkpoint_in_tfa_model(A__ , A__ ) if compare_with_pt_model: _lowercase =tf_model(tf_model.dummy_inputs , training=A__ ) # build the network _lowercase =torch.load(A__ , map_location='cpu' ) _lowercase =pt_model_class.from_pretrained( pretrained_model_name_or_path=A__ , config=A__ , state_dict=A__ ) with torch.no_grad(): _lowercase =pt_model(**pt_model.dummy_inputs ) _lowercase =pto[0].numpy() _lowercase =tfo[0].numpy() _lowercase =np.amax(np.abs(np_pt - np_tf ) ) print(F'''Max absolute difference between models outputs {diff}''' ) assert diff <= 2e-2, F'''Error, model absolute difference is >2e-2: {diff}''' # Save pytorch-model print(F'''Save TensorFlow model to {tf_dump_path}''' ) tf_model.save_weights(A__ , save_format='h5' ) def a ( A__ : str , A__ : str , A__ : Optional[Any]=None , A__ : Any=None , A__ : Optional[int]=False , A__ : Optional[int]=False , A__ : int=False , A__ : str=False , ) -> List[Any]: """simple docstring""" if args_model_type is None: _lowercase =list(MODEL_CLASSES.keys() ) else: _lowercase =[args_model_type] for j, model_type in enumerate(A__ , start=1 ): print('=' * 100 ) print(F''' Converting model type {j}/{len(A__ )}: {model_type}''' ) print('=' * 100 ) if model_type not in MODEL_CLASSES: raise ValueError(F'''Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.''' ) _lowercase , _lowercase , _lowercase , _lowercase , _lowercase =MODEL_CLASSES[model_type] if model_shortcut_names_or_path is None: _lowercase =list(aws_model_maps.keys() ) if config_shortcut_names_or_path is None: _lowercase =model_shortcut_names_or_path for i, (model_shortcut_name, config_shortcut_name) in enumerate( zip(A__ , A__ ) , start=1 ): print('-' * 100 ) if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name: if not only_convert_finetuned_models: print(F''' Skipping finetuned checkpoint {model_shortcut_name}''' ) continue _lowercase =model_shortcut_name elif only_convert_finetuned_models: print(F''' Skipping not finetuned checkpoint {model_shortcut_name}''' ) continue print( F''' Converting checkpoint {i}/{len(A__ )}: {model_shortcut_name} - model_type {model_type}''' ) print('-' * 100 ) if config_shortcut_name in aws_config_map: _lowercase =cached_file(A__ , A__ , force_download=not use_cached_models ) else: _lowercase =config_shortcut_name if model_shortcut_name in aws_model_maps: _lowercase =cached_file(A__ , A__ , force_download=not use_cached_models ) else: _lowercase =model_shortcut_name if os.path.isfile(A__ ): _lowercase ='converted_model' convert_pt_checkpoint_to_tf( model_type=A__ , pytorch_checkpoint_path=A__ , config_file=A__ , tf_dump_path=os.path.join(A__ , model_shortcut_name + '-tf_model.h5' ) , compare_with_pt_model=A__ , ) if remove_cached_files: os.remove(A__ ) os.remove(A__ ) if __name__ == "__main__": lowercase_ = argparse.ArgumentParser() # Required parameters parser.add_argument( '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.' ) parser.add_argument( '--model_type', default=None, type=str, help=( f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and " 'convert all the models from AWS.' ), ) parser.add_argument( '--pytorch_checkpoint_path', default=None, type=str, help=( 'Path to the PyTorch checkpoint path or shortcut name to download from AWS. ' 'If not given, will download and convert all the checkpoints from AWS.' ), ) parser.add_argument( '--config_file', default=None, type=str, help=( 'The config json file corresponding to the pre-trained model. \n' 'This specifies the model architecture. If not given and ' '--pytorch_checkpoint_path is not given or is a shortcut name ' 'use the configuration associated to the shortcut name on the AWS' ), ) parser.add_argument( '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.' ) parser.add_argument( '--use_cached_models', action='store_true', help='Use cached models if possible instead of updating to latest checkpoint versions.', ) parser.add_argument( '--remove_cached_files', action='store_true', help='Remove pytorch models after conversion (save memory when converting in batches).', ) parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.') lowercase_ = parser.parse_args() # if args.pytorch_checkpoint_path is not None: # convert_pt_checkpoint_to_tf(args.model_type.lower(), # args.pytorch_checkpoint_path, # args.config_file if args.config_file is not None else args.pytorch_checkpoint_path, # args.tf_dump_path, # compare_with_pt_model=args.compare_with_pt_model, # use_cached_models=args.use_cached_models) # else: convert_all_pt_checkpoints_to_tf( args.model_type.lower() if args.model_type is not None else None, args.tf_dump_path, model_shortcut_names_or_path=[args.pytorch_checkpoint_path] if args.pytorch_checkpoint_path is not None else None, config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None, compare_with_pt_model=args.compare_with_pt_model, use_cached_models=args.use_cached_models, remove_cached_files=args.remove_cached_files, only_convert_finetuned_models=args.only_convert_finetuned_models, )
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0
"""simple docstring""" from typing import List, Optional from tokenizers import ByteLevelBPETokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_blenderbot_small import BlenderbotSmallTokenizer lowerCAmelCase__ = logging.get_logger(__name__) lowerCAmelCase__ = { '''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_config_file''': '''tokenizer_config.json''', } lowerCAmelCase__ = { '''vocab_file''': { '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json''' }, '''merges_file''': { '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt''' }, '''tokenizer_config_file''': { '''facebook/blenderbot_small-90M''': ( '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json''' ) }, } lowerCAmelCase__ = { '''facebook/blenderbot_small-90M''': 512, } class SCREAMING_SNAKE_CASE__ ( lowercase ): """simple docstring""" a : List[Any] =VOCAB_FILES_NAMES a : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP a : Optional[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a : Optional[Any] =BlenderbotSmallTokenizer def __init__( self , snake_case__=None , snake_case__=None , snake_case__="<|endoftext|>" , snake_case__="<|endoftext|>" , snake_case__="<|endoftext|>" , snake_case__=False , snake_case__=True , **snake_case__ , ): """simple docstring""" super().__init__( ByteLevelBPETokenizer( vocab=snake_case__ , merges=snake_case__ , add_prefix_space=snake_case__ , trim_offsets=snake_case__ , ) , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , **snake_case__ , ) lowerCAmelCase : Optional[Any] = add_prefix_space def lowercase__ ( self , snake_case__ , snake_case__=None ): """simple docstring""" lowerCAmelCase : int = [self.bos_token_id] + token_ids_a + [self.eos_token_id] if token_ids_a is None: return output return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id] def lowercase__ ( self , snake_case__ , snake_case__ = None ): """simple docstring""" lowerCAmelCase : Union[str, Any] = [self.sep_token_id] lowerCAmelCase : int = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
645
"""simple docstring""" from ...processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE__ ( lowercase ): """simple docstring""" a : int ="SpeechT5FeatureExtractor" a : Any ="SpeechT5Tokenizer" def __init__( self , snake_case__ , snake_case__ ): """simple docstring""" super().__init__(snake_case__ , snake_case__ ) def __call__( self , *snake_case__ , **snake_case__ ): """simple docstring""" lowerCAmelCase : str = kwargs.pop("audio" , snake_case__ ) lowerCAmelCase : Tuple = kwargs.pop("text" , snake_case__ ) lowerCAmelCase : str = kwargs.pop("text_target" , snake_case__ ) lowerCAmelCase : List[str] = kwargs.pop("audio_target" , snake_case__ ) lowerCAmelCase : Union[str, Any] = kwargs.pop("sampling_rate" , snake_case__ ) if audio is not None and text is not None: raise ValueError( "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?" ) if audio_target is not None and text_target is not None: raise ValueError( "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?" ) if audio is None and audio_target is None and text is None and text_target is None: raise ValueError( "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process." ) if audio is not None: lowerCAmelCase : int = self.feature_extractor(snake_case__ , *snake_case__ , sampling_rate=snake_case__ , **snake_case__ ) elif text is not None: lowerCAmelCase : Optional[int] = self.tokenizer(snake_case__ , **snake_case__ ) else: lowerCAmelCase : Union[str, Any] = None if audio_target is not None: lowerCAmelCase : Optional[Any] = self.feature_extractor(audio_target=snake_case__ , *snake_case__ , sampling_rate=snake_case__ , **snake_case__ ) lowerCAmelCase : Any = targets["input_values"] elif text_target is not None: lowerCAmelCase : Tuple = self.tokenizer(snake_case__ , **snake_case__ ) lowerCAmelCase : str = targets["input_ids"] else: lowerCAmelCase : str = None if inputs is None: return targets if targets is not None: lowerCAmelCase : List[str] = labels lowerCAmelCase : List[Any] = targets.get("attention_mask" ) if decoder_attention_mask is not None: lowerCAmelCase : Union[str, Any] = decoder_attention_mask return inputs def lowercase__ ( self , *snake_case__ , **snake_case__ ): """simple docstring""" lowerCAmelCase : int = kwargs.pop("input_values" , snake_case__ ) lowerCAmelCase : List[Any] = kwargs.pop("input_ids" , snake_case__ ) lowerCAmelCase : Dict = kwargs.pop("labels" , snake_case__ ) if input_values is not None and input_ids is not None: raise ValueError("Cannot process both `input_values` and `input_ids` inputs." ) if input_values is None and input_ids is None and labels is None: raise ValueError( "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded." ) if input_values is not None: lowerCAmelCase : int = self.feature_extractor.pad(snake_case__ , *snake_case__ , **snake_case__ ) elif input_ids is not None: lowerCAmelCase : Optional[Any] = self.tokenizer.pad(snake_case__ , **snake_case__ ) else: lowerCAmelCase : Optional[Any] = None if labels is not None: if "input_ids" in labels or (isinstance(snake_case__ , snake_case__ ) and "input_ids" in labels[0]): lowerCAmelCase : Tuple = self.tokenizer.pad(snake_case__ , **snake_case__ ) lowerCAmelCase : Any = targets["input_ids"] else: lowerCAmelCase : List[Any] = self.feature_extractor.feature_size lowerCAmelCase : Optional[int] = self.feature_extractor.num_mel_bins lowerCAmelCase : str = self.feature_extractor.pad(snake_case__ , *snake_case__ , **snake_case__ ) lowerCAmelCase : Optional[Any] = feature_size_hack lowerCAmelCase : Optional[Any] = targets["input_values"] else: lowerCAmelCase : List[Any] = None if inputs is None: return targets if targets is not None: lowerCAmelCase : int = labels lowerCAmelCase : Optional[int] = targets.get("attention_mask" ) if decoder_attention_mask is not None: lowerCAmelCase : List[Any] = decoder_attention_mask return inputs def lowercase__ ( self , *snake_case__ , **snake_case__ ): """simple docstring""" return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ ) def lowercase__ ( self , *snake_case__ , **snake_case__ ): """simple docstring""" return self.tokenizer.decode(*snake_case__ , **snake_case__ )
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1
'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging snake_case_ = logging.get_logger(__name__) snake_case_ = { """tanreinama/GPTSAN-2.8B-spout_is_uniform""": ( """https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json""" ), } class a__ ( _lowercase ): __magic_name__ : Union[str, Any] = "gptsan-japanese" __magic_name__ : int = [ "past_key_values", ] __magic_name__ : str = { "hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers", } def __init__(self : List[str], __UpperCAmelCase : str=36000, __UpperCAmelCase : Union[str, Any]=1280, __UpperCAmelCase : List[str]=1024, __UpperCAmelCase : Optional[int]=8192, __UpperCAmelCase : Dict=4096, __UpperCAmelCase : Optional[Any]=128, __UpperCAmelCase : Dict=10, __UpperCAmelCase : Any=0, __UpperCAmelCase : List[Any]=16, __UpperCAmelCase : Optional[int]=16, __UpperCAmelCase : Dict=128, __UpperCAmelCase : int=0.0, __UpperCAmelCase : List[Any]=1e-5, __UpperCAmelCase : List[str]=False, __UpperCAmelCase : Optional[int]=0.0, __UpperCAmelCase : List[str]="float32", __UpperCAmelCase : Dict=False, __UpperCAmelCase : List[Any]=False, __UpperCAmelCase : Union[str, Any]=False, __UpperCAmelCase : Optional[int]=0.002, __UpperCAmelCase : int=False, __UpperCAmelCase : Dict=True, __UpperCAmelCase : str=35998, __UpperCAmelCase : Any=35995, __UpperCAmelCase : int=35999, **__UpperCAmelCase : str, ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE : Tuple = vocab_size SCREAMING_SNAKE_CASE : List[str] = max_position_embeddings SCREAMING_SNAKE_CASE : Optional[int] = d_model SCREAMING_SNAKE_CASE : int = d_ff SCREAMING_SNAKE_CASE : int = d_ext SCREAMING_SNAKE_CASE : Optional[int] = d_spout SCREAMING_SNAKE_CASE : Dict = num_switch_layers SCREAMING_SNAKE_CASE : str = num_ext_layers SCREAMING_SNAKE_CASE : int = num_switch_layers + num_ext_layers SCREAMING_SNAKE_CASE : Union[str, Any] = num_heads SCREAMING_SNAKE_CASE : List[str] = num_experts SCREAMING_SNAKE_CASE : Tuple = expert_capacity SCREAMING_SNAKE_CASE : List[Any] = dropout_rate SCREAMING_SNAKE_CASE : List[Any] = layer_norm_epsilon SCREAMING_SNAKE_CASE : Optional[Any] = router_bias SCREAMING_SNAKE_CASE : Dict = router_jitter_noise SCREAMING_SNAKE_CASE : str = router_dtype SCREAMING_SNAKE_CASE : str = router_ignore_padding_tokens SCREAMING_SNAKE_CASE : List[Any] = output_hidden_states SCREAMING_SNAKE_CASE : Tuple = output_attentions SCREAMING_SNAKE_CASE : Optional[Any] = initializer_factor SCREAMING_SNAKE_CASE : str = output_router_logits SCREAMING_SNAKE_CASE : Optional[int] = use_cache super().__init__( separator_token_id=__UpperCAmelCase, pad_token_id=__UpperCAmelCase, eos_token_id=__UpperCAmelCase, **__UpperCAmelCase, )
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'''simple docstring''' import unittest from transformers.testing_utils import CaptureStdout from transformers.tools.python_interpreter import evaluate def __lowercase (_SCREAMING_SNAKE_CASE :List[Any] ): return x + 2 class a__ ( unittest.TestCase ): def lowercase__ (self : List[str] ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE : Union[str, Any] = '''x = 3''' SCREAMING_SNAKE_CASE : Dict = {} SCREAMING_SNAKE_CASE : Any = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) assert result == 3 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3} ) SCREAMING_SNAKE_CASE : str = '''x = y''' SCREAMING_SNAKE_CASE : int = {'''y''': 5} SCREAMING_SNAKE_CASE : Optional[int] = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(__UpperCAmelCase, {'''x''': 5, '''y''': 5} ) def lowercase__ (self : Optional[int] ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE : int = '''y = add_two(x)''' SCREAMING_SNAKE_CASE : Optional[Any] = {'''x''': 3} SCREAMING_SNAKE_CASE : str = evaluate(__UpperCAmelCase, {'''add_two''': add_two}, state=__UpperCAmelCase ) assert result == 5 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''y''': 5} ) # Won't work without the tool with CaptureStdout() as out: SCREAMING_SNAKE_CASE : Tuple = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) assert result is None assert "tried to execute add_two" in out.out def lowercase__ (self : Any ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Any = '''x = 3''' SCREAMING_SNAKE_CASE : Any = {} SCREAMING_SNAKE_CASE : str = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) assert result == 3 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3} ) def lowercase__ (self : Optional[int] ) -> Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Optional[int] = '''test_dict = {\'x\': x, \'y\': add_two(x)}''' SCREAMING_SNAKE_CASE : Tuple = {'''x''': 3} SCREAMING_SNAKE_CASE : List[Any] = evaluate(__UpperCAmelCase, {'''add_two''': add_two}, state=__UpperCAmelCase ) self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''y''': 5} ) self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} ) def lowercase__ (self : Optional[Any] ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE : Union[str, Any] = '''x = 3\ny = 5''' SCREAMING_SNAKE_CASE : Tuple = {} SCREAMING_SNAKE_CASE : Optional[Any] = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''y''': 5} ) def lowercase__ (self : Tuple ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Any = '''text = f\'This is x: {x}.\'''' SCREAMING_SNAKE_CASE : List[str] = {'''x''': 3} SCREAMING_SNAKE_CASE : Dict = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == "This is x: 3." self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''text''': '''This is x: 3.'''} ) def lowercase__ (self : int ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE : Optional[int] = '''if x <= 3:\n y = 2\nelse:\n y = 5''' SCREAMING_SNAKE_CASE : int = {'''x''': 3} SCREAMING_SNAKE_CASE : List[str] = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 2 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''y''': 2} ) SCREAMING_SNAKE_CASE : Any = {'''x''': 8} SCREAMING_SNAKE_CASE : int = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) # evaluate returns the value of the last assignment. assert result == 5 self.assertDictEqual(__UpperCAmelCase, {'''x''': 8, '''y''': 5} ) def lowercase__ (self : Any ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE : Any = '''test_list = [x, add_two(x)]''' SCREAMING_SNAKE_CASE : List[str] = {'''x''': 3} SCREAMING_SNAKE_CASE : Tuple = evaluate(__UpperCAmelCase, {'''add_two''': add_two}, state=__UpperCAmelCase ) self.assertListEqual(__UpperCAmelCase, [3, 5] ) self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''test_list''': [3, 5]} ) def lowercase__ (self : int ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE : Tuple = '''y = x''' SCREAMING_SNAKE_CASE : Tuple = {'''x''': 3} SCREAMING_SNAKE_CASE : str = evaluate(__UpperCAmelCase, {}, state=__UpperCAmelCase ) assert result == 3 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''y''': 3} ) def lowercase__ (self : Optional[Any] ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Dict = '''test_list = [x, add_two(x)]\ntest_list[1]''' SCREAMING_SNAKE_CASE : int = {'''x''': 3} SCREAMING_SNAKE_CASE : Optional[int] = evaluate(__UpperCAmelCase, {'''add_two''': add_two}, state=__UpperCAmelCase ) assert result == 5 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''test_list''': [3, 5]} ) SCREAMING_SNAKE_CASE : Dict = '''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']''' SCREAMING_SNAKE_CASE : Tuple = {'''x''': 3} SCREAMING_SNAKE_CASE : Optional[Any] = evaluate(__UpperCAmelCase, {'''add_two''': add_two}, state=__UpperCAmelCase ) assert result == 5 self.assertDictEqual(__UpperCAmelCase, {'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}} ) def lowercase__ (self : Optional[Any] ) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE : Any = '''x = 0\nfor i in range(3):\n x = i''' SCREAMING_SNAKE_CASE : List[Any] = {} SCREAMING_SNAKE_CASE : List[str] = evaluate(__UpperCAmelCase, {'''range''': range}, state=__UpperCAmelCase ) assert result == 2 self.assertDictEqual(__UpperCAmelCase, {'''x''': 2, '''i''': 2} )
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'''simple docstring''' from typing import Dict, List, Optional, Tuple, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_torch_available, is_torch_tensor, logging if is_torch_available(): import torch UpperCAmelCase = logging.get_logger(__name__) class __snake_case( _lowerCAmelCase ): '''simple docstring''' UpperCAmelCase : Optional[int] = ["pixel_values"] def __init__( self , A_ = True , A_ = None , A_ = PILImageResampling.BILINEAR , A_ = True , A_ = None , A_ = True , A_ = 1 / 255 , A_ = True , A_ = None , A_ = None , **A_ , ) -> None: super().__init__(**A_ ) lowerCAmelCase = size if size is not None else {"""shortest_edge""": 256} lowerCAmelCase = get_size_dict(A_ , default_to_square=A_ ) lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 224, """width""": 224} lowerCAmelCase = get_size_dict(A_ , param_name="""crop_size""" ) lowerCAmelCase = do_resize lowerCAmelCase = size lowerCAmelCase = resample lowerCAmelCase = do_center_crop lowerCAmelCase = crop_size lowerCAmelCase = do_rescale lowerCAmelCase = rescale_factor lowerCAmelCase = do_normalize lowerCAmelCase = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN lowerCAmelCase = image_std if image_std is not None else IMAGENET_STANDARD_STD def __snake_case ( self , A_ , A_ , A_ = PILImageResampling.BICUBIC , A_ = None , **A_ , ) -> np.ndarray: lowerCAmelCase = get_size_dict(A_ , default_to_square=A_ ) if "shortest_edge" not in size: raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' ) lowerCAmelCase = get_resize_output_image_size(A_ , size=size["""shortest_edge"""] , default_to_square=A_ ) return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ ) def __snake_case ( self , A_ , A_ , A_ = None , **A_ , ) -> np.ndarray: lowerCAmelCase = get_size_dict(A_ ) if "height" not in size or "width" not in size: raise ValueError(f'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}' ) return center_crop(A_ , size=(size["""height"""], size["""width"""]) , data_format=A_ , **A_ ) def __snake_case ( self , A_ , A_ , A_ = None , **A_ ) -> np.ndarray: return rescale(A_ , scale=A_ , data_format=A_ , **A_ ) def __snake_case ( self , A_ , A_ , A_ , A_ = None , **A_ , ) -> np.ndarray: return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ ) def __snake_case ( self , A_ , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = None , A_ = ChannelDimension.FIRST , **A_ , ) -> List[str]: lowerCAmelCase = do_resize if do_resize is not None else self.do_resize lowerCAmelCase = size if size is not None else self.size lowerCAmelCase = get_size_dict(A_ , default_to_square=A_ ) lowerCAmelCase = resample if resample is not None else self.resample lowerCAmelCase = do_center_crop if do_center_crop is not None else self.do_center_crop lowerCAmelCase = crop_size if crop_size is not None else self.crop_size lowerCAmelCase = get_size_dict(A_ , param_name="""crop_size""" ) lowerCAmelCase = do_rescale if do_rescale is not None else self.do_rescale lowerCAmelCase = rescale_factor if rescale_factor is not None else self.rescale_factor lowerCAmelCase = do_normalize if do_normalize is not None else self.do_normalize lowerCAmelCase = image_mean if image_mean is not None else self.image_mean lowerCAmelCase = image_std if image_std is not None else self.image_std lowerCAmelCase = make_list_of_images(A_ ) if not valid_images(A_ ): raise ValueError( """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """ """torch.Tensor, tf.Tensor or jax.ndarray.""" ) if do_resize and size is None: raise ValueError("""Size must be specified if do_resize is True.""" ) if do_center_crop and crop_size is None: raise ValueError("""Crop size must be specified if do_center_crop is True.""" ) if do_rescale and rescale_factor is None: raise ValueError("""Rescale factor must be specified if do_rescale is True.""" ) if do_normalize and (image_mean is None or image_std is None): raise ValueError("""Image mean and std must be specified if do_normalize is True.""" ) # All transformations expect numpy arrays. lowerCAmelCase = [to_numpy_array(A_ ) for image in images] if do_resize: lowerCAmelCase = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images] if do_center_crop: lowerCAmelCase = [self.center_crop(image=A_ , size=A_ ) for image in images] if do_rescale: lowerCAmelCase = [self.rescale(image=A_ , scale=A_ ) for image in images] if do_normalize: lowerCAmelCase = [self.normalize(image=A_ , mean=A_ , std=A_ ) for image in images] lowerCAmelCase = [to_channel_dimension_format(A_ , A_ ) for image in images] lowerCAmelCase = {"""pixel_values""": images} return BatchFeature(data=A_ , tensor_type=A_ ) def __snake_case ( self , A_ , A_ = None ) -> Union[str, Any]: lowerCAmelCase = outputs.logits # Resize logits and compute semantic segmentation maps if target_sizes is not None: if len(A_ ) != len(A_ ): raise ValueError( """Make sure that you pass in as many target sizes as the batch dimension of the logits""" ) if is_torch_tensor(A_ ): lowerCAmelCase = target_sizes.numpy() lowerCAmelCase = [] for idx in range(len(A_ ) ): lowerCAmelCase = torch.nn.functional.interpolate( logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=A_ ) lowerCAmelCase = resized_logits[0].argmax(dim=0 ) semantic_segmentation.append(A_ ) else: lowerCAmelCase = logits.argmax(dim=1 ) lowerCAmelCase = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )] return semantic_segmentation
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'''simple docstring''' import argparse import torch from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt if __name__ == "__main__": UpperCAmelCase = argparse.ArgumentParser() parser.add_argument( '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.' ) # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml parser.add_argument( '--original_config_file', default=None, type=str, help='The YAML config file corresponding to the original architecture.', ) parser.add_argument( '--num_in_channels', default=None, type=int, help='The number of input channels. If `None` number of input channels will be automatically inferred.', ) parser.add_argument( '--scheduler_type', default='pndm', type=str, help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']', ) parser.add_argument( '--pipeline_type', default=None, type=str, help=( 'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\'' '. If `None` pipeline will be automatically inferred.' ), ) parser.add_argument( '--image_size', default=None, type=int, help=( 'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2' ' Base. Use 768 for Stable Diffusion v2.' ), ) parser.add_argument( '--prediction_type', default=None, type=str, help=( 'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable' ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.' ), ) parser.add_argument( '--extract_ema', action='store_true', help=( 'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights' ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield' ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.' ), ) parser.add_argument( '--upcast_attention', action='store_true', help=( 'Whether the attention computation should always be upcasted. This is necessary when running stable' ' diffusion 2.1.' ), ) parser.add_argument( '--from_safetensors', action='store_true', help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.', ) parser.add_argument( '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.', ) parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.') parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)') parser.add_argument( '--stable_unclip', type=str, default=None, required=False, help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.', ) parser.add_argument( '--stable_unclip_prior', type=str, default=None, required=False, help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.', ) parser.add_argument( '--clip_stats_path', type=str, help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.', required=False, ) parser.add_argument( '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.' ) parser.add_argument('--half', action='store_true', help='Save weights in half precision.') parser.add_argument( '--vae_path', type=str, default=None, required=False, help='Set to a path, hub id to an already converted vae to not convert it again.', ) UpperCAmelCase = parser.parse_args() UpperCAmelCase = download_from_original_stable_diffusion_ckpt( checkpoint_path=args.checkpoint_path, original_config_file=args.original_config_file, image_size=args.image_size, prediction_type=args.prediction_type, model_type=args.pipeline_type, extract_ema=args.extract_ema, scheduler_type=args.scheduler_type, num_in_channels=args.num_in_channels, upcast_attention=args.upcast_attention, from_safetensors=args.from_safetensors, device=args.device, stable_unclip=args.stable_unclip, stable_unclip_prior=args.stable_unclip_prior, clip_stats_path=args.clip_stats_path, controlnet=args.controlnet, vae_path=args.vae_path, ) if args.half: pipe.to(torch_dtype=torch.floataa) if args.controlnet: # only save the controlnet model pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) else: pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
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"""simple docstring""" def UpperCamelCase_ ( lowerCamelCase : int , lowerCamelCase : Tuple ) -> Tuple: """simple docstring""" __magic_name__ : Dict = 1 # To kept the Calculated Value # Since C(n, k) = C(n, n-k) if k > (n - k): __magic_name__ : Dict = n - k # Calculate C(n,k) for i in range(lowerCamelCase_ ): result *= n - i result //= i + 1 return result def UpperCamelCase_ ( lowerCamelCase : Tuple ) -> Dict: """simple docstring""" return binomial_coefficient(2 * node_count , lowerCamelCase_ ) // (node_count + 1) def UpperCamelCase_ ( lowerCamelCase : Dict ) -> str: """simple docstring""" if n < 0: raise ValueError('''factorial() not defined for negative values''' ) __magic_name__ : List[str] = 1 for i in range(1 , n + 1 ): result *= i return result def UpperCamelCase_ ( lowerCamelCase : int ) -> Tuple: """simple docstring""" return catalan_number(lowerCamelCase_ ) * factorial(lowerCamelCase_ ) if __name__ == "__main__": A = int(input("""Enter the number of nodes: """).strip() or 0) if node_count <= 0: raise ValueError("""We need some nodes to work with.""") print( F"""Given {node_count} nodes, there are {binary_tree_count(node_count)} """ F"""binary trees and {catalan_number(node_count)} binary search trees.""" )
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"""simple docstring""" from __future__ import annotations from collections.abc import Callable from typing import Generic, TypeVar A = TypeVar("""T""") A = TypeVar("""U""") class _UpperCamelCase ( Generic[T, U] ): """simple docstring""" def __init__( self : Any , snake_case : T | None , snake_case : U | None ) -> Tuple: '''simple docstring''' __magic_name__ : Optional[Any] = key __magic_name__ : str = val __magic_name__ : DoubleLinkedListNode[T, U] | None = None __magic_name__ : DoubleLinkedListNode[T, U] | None = None def __repr__( self : str ) -> str: '''simple docstring''' return ( f"""Node: key: {self.key}, val: {self.val}, """ f"""has next: {bool(self.next )}, has prev: {bool(self.prev )}""" ) class _UpperCamelCase ( Generic[T, U] ): """simple docstring""" def __init__( self : str ) -> None: '''simple docstring''' __magic_name__ : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(snake_case , snake_case ) __magic_name__ : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(snake_case , snake_case ) __magic_name__ , __magic_name__ : Tuple = self.rear, self.head def __repr__( self : str ) -> str: '''simple docstring''' __magic_name__ : List[str] = ['''DoubleLinkedList'''] __magic_name__ : Optional[Any] = self.head while node.next is not None: rep.append(str(snake_case ) ) __magic_name__ : Any = node.next rep.append(str(self.rear ) ) return ",\n ".join(snake_case ) def _UpperCAmelCase ( self : List[str] , snake_case : DoubleLinkedListNode[T, U] ) -> None: '''simple docstring''' __magic_name__ : Tuple = self.rear.prev # All nodes other than self.head are guaranteed to have non-None previous assert previous is not None __magic_name__ : Dict = node __magic_name__ : Optional[int] = previous __magic_name__ : Tuple = node __magic_name__ : Optional[int] = self.rear def _UpperCAmelCase ( self : str , snake_case : DoubleLinkedListNode[T, U] ) -> DoubleLinkedListNode[T, U] | None: '''simple docstring''' if node.prev is None or node.next is None: return None __magic_name__ : str = node.next __magic_name__ : Dict = node.prev __magic_name__ : Any = None __magic_name__ : Dict = None return node class _UpperCamelCase ( Generic[T, U] ): """simple docstring""" snake_case_ = {} def __init__( self : Dict , snake_case : int ) -> Optional[Any]: '''simple docstring''' __magic_name__ : DoubleLinkedList[T, U] = DoubleLinkedList() __magic_name__ : str = capacity __magic_name__ : Tuple = 0 __magic_name__ : Optional[Any] = 0 __magic_name__ : List[str] = 0 __magic_name__ : dict[T, DoubleLinkedListNode[T, U]] = {} def __repr__( self : Tuple ) -> str: '''simple docstring''' return ( f"""CacheInfo(hits={self.hits}, misses={self.miss}, """ f"""capacity={self.capacity}, current size={self.num_keys})""" ) def __contains__( self : List[str] , snake_case : T ) -> bool: '''simple docstring''' return key in self.cache def _UpperCAmelCase ( self : Optional[int] , snake_case : T ) -> U | None: '''simple docstring''' if key in self.cache: self.hits += 1 __magic_name__ : DoubleLinkedListNode[T, U] = self.cache[key] __magic_name__ : Optional[Any] = self.list.remove(self.cache[key] ) assert node == value_node # node is guaranteed not None because it is in self.cache assert node is not None self.list.add(snake_case ) return node.val self.miss += 1 return None def _UpperCAmelCase ( self : str , snake_case : T , snake_case : U ) -> None: '''simple docstring''' if key not in self.cache: if self.num_keys >= self.capacity: # delete first node (oldest) when over capacity __magic_name__ : Optional[Any] = self.list.head.next # guaranteed to have a non-None first node when num_keys > 0 # explain to type checker via assertions assert first_node is not None assert first_node.key is not None assert ( self.list.remove(snake_case ) is not None ) # node guaranteed to be in list assert node.key is not None del self.cache[first_node.key] self.num_keys -= 1 __magic_name__ : List[str] = DoubleLinkedListNode(snake_case , snake_case ) self.list.add(self.cache[key] ) self.num_keys += 1 else: # bump node to the end of the list, update value __magic_name__ : Dict = self.list.remove(self.cache[key] ) assert node is not None # node guaranteed to be in list __magic_name__ : Any = value self.list.add(snake_case ) @classmethod def _UpperCAmelCase ( cls : Tuple , snake_case : int = 128 ) -> Callable[[Callable[[T], U]], Callable[..., U]]: '''simple docstring''' def cache_decorator_inner(snake_case : Callable[[T], U] ) -> Callable[..., U]: def cache_decorator_wrapper(*snake_case : T ) -> U: if func not in cls.decorator_function_to_instance_map: __magic_name__ : Any = LRUCache(snake_case ) __magic_name__ : Optional[Any] = cls.decorator_function_to_instance_map[func].get(args[0] ) if result is None: __magic_name__ : Any = func(*snake_case ) cls.decorator_function_to_instance_map[func].put(args[0] , snake_case ) return result def cache_info() -> LRUCache[T, U]: return cls.decorator_function_to_instance_map[func] setattr(snake_case , '''cache_info''' , snake_case ) # noqa: B010 return cache_decorator_wrapper return cache_decorator_inner if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from __future__ import annotations from collections import deque class _a : '''simple docstring''' def __init__( self, A ): '''simple docstring''' SCREAMING_SNAKE_CASE : list[dict] = [] self.adlist.append( {'value': '', 'next_states': [], 'fail_state': 0, 'output': []} ) for keyword in keywords: self.add_keyword(A ) self.set_fail_transitions() def UpperCamelCase_ ( self, A, A ): '''simple docstring''' for state in self.adlist[current_state]["next_states"]: if char == self.adlist[state]["value"]: return state return None def UpperCamelCase_ ( self, A ): '''simple docstring''' SCREAMING_SNAKE_CASE : List[str] = 0 for character in keyword: SCREAMING_SNAKE_CASE : Any = self.find_next_state(A, A ) if next_state is None: self.adlist.append( { 'value': character, 'next_states': [], 'fail_state': 0, 'output': [], } ) self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 ) SCREAMING_SNAKE_CASE : List[str] = len(self.adlist ) - 1 else: SCREAMING_SNAKE_CASE : int = next_state self.adlist[current_state]["output"].append(A ) def UpperCamelCase_ ( self ): '''simple docstring''' SCREAMING_SNAKE_CASE : deque = deque() for node in self.adlist[0]["next_states"]: q.append(A ) SCREAMING_SNAKE_CASE : Optional[Any] = 0 while q: SCREAMING_SNAKE_CASE : List[Any] = q.popleft() for child in self.adlist[r]["next_states"]: q.append(A ) SCREAMING_SNAKE_CASE : Optional[Any] = self.adlist[r]['fail_state'] while ( self.find_next_state(A, self.adlist[child]['value'] ) is None and state != 0 ): SCREAMING_SNAKE_CASE : str = self.adlist[state]['fail_state'] SCREAMING_SNAKE_CASE : int = self.find_next_state( A, self.adlist[child]['value'] ) if self.adlist[child]["fail_state"] is None: SCREAMING_SNAKE_CASE : Optional[int] = 0 SCREAMING_SNAKE_CASE : int = ( self.adlist[child]['output'] + self.adlist[self.adlist[child]['fail_state']]['output'] ) def UpperCamelCase_ ( self, A ): '''simple docstring''' SCREAMING_SNAKE_CASE : dict = {} # returns a dict with keywords and list of its occurrences SCREAMING_SNAKE_CASE : Optional[Any] = 0 for i in range(len(A ) ): while ( self.find_next_state(A, string[i] ) is None and current_state != 0 ): SCREAMING_SNAKE_CASE : Union[str, Any] = self.adlist[current_state]['fail_state'] SCREAMING_SNAKE_CASE : Tuple = self.find_next_state(A, string[i] ) if next_state is None: SCREAMING_SNAKE_CASE : Any = 0 else: SCREAMING_SNAKE_CASE : Dict = next_state for key in self.adlist[current_state]["output"]: if key not in result: SCREAMING_SNAKE_CASE : Dict = [] result[key].append(i - len(A ) + 1 ) return result if __name__ == "__main__": import doctest doctest.testmod()
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import unittest import numpy as np 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 if is_vision_available(): from PIL import Image from transformers import LevitImageProcessor class SCREAMING_SNAKE_CASE ( unittest.TestCase ): def __init__( self : Optional[Any] , __lowercase : Any , __lowercase : Union[str, Any]=7 , __lowercase : List[str]=3 , __lowercase : List[Any]=18 , __lowercase : str=30 , __lowercase : Optional[Any]=400 , __lowercase : Dict=True , __lowercase : int=None , __lowercase : Tuple=True , __lowercase : Optional[Any]=None , __lowercase : List[str]=True , __lowercase : List[Any]=[0.5, 0.5, 0.5] , __lowercase : Any=[0.5, 0.5, 0.5] , ): '''simple docstring''' __a = size if size is not None else {"""shortest_edge""": 18} __a = crop_size if crop_size is not None else {"""height""": 18, """width""": 18} __a = parent __a = batch_size __a = num_channels __a = image_size __a = min_resolution __a = max_resolution __a = do_resize __a = size __a = do_center_crop __a = crop_size __a = do_normalize __a = image_mean __a = image_std def UpperCamelCase_ ( self : Optional[int] ): '''simple docstring''' return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "do_center_crop": self.do_center_crop, "size": self.size, "crop_size": self.crop_size, } @require_torch @require_vision class SCREAMING_SNAKE_CASE ( lowerCamelCase__ , unittest.TestCase ): __lowerCamelCase : Dict =LevitImageProcessor if is_vision_available() else None def UpperCamelCase_ ( self : Optional[int] ): '''simple docstring''' __a = LevitImageProcessingTester(self ) @property def UpperCamelCase_ ( self : Optional[Any] ): '''simple docstring''' return self.image_processor_tester.prepare_image_processor_dict() def UpperCamelCase_ ( self : int ): '''simple docstring''' __a = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__lowercase , """image_mean""" ) ) self.assertTrue(hasattr(__lowercase , """image_std""" ) ) self.assertTrue(hasattr(__lowercase , """do_normalize""" ) ) self.assertTrue(hasattr(__lowercase , """do_resize""" ) ) self.assertTrue(hasattr(__lowercase , """do_center_crop""" ) ) self.assertTrue(hasattr(__lowercase , """size""" ) ) def UpperCamelCase_ ( self : int ): '''simple docstring''' __a = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {"""shortest_edge""": 18} ) self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} ) __a = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 ) self.assertEqual(image_processor.size , {"""shortest_edge""": 42} ) self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} ) def UpperCamelCase_ ( self : Tuple ): '''simple docstring''' pass def UpperCamelCase_ ( self : Optional[Any] ): '''simple docstring''' # Initialize image_processing __a = self.image_processing_class(**self.image_processor_dict ) # create random PIL images __a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase ) for image in image_inputs: self.assertIsInstance(__lowercase , Image.Image ) # Test not batched input __a = 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.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) # Test batched __a = image_processing(__lowercase , 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.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) def UpperCamelCase_ ( self : Optional[Any] ): '''simple docstring''' # Initialize image_processing __a = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors __a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , numpify=__lowercase ) for image in image_inputs: self.assertIsInstance(__lowercase , np.ndarray ) # Test not batched input __a = 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.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) # Test batched __a = image_processing(__lowercase , 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.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) def UpperCamelCase_ ( self : str ): '''simple docstring''' # Initialize image_processing __a = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors __a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__lowercase , torchify=__lowercase ) for image in image_inputs: self.assertIsInstance(__lowercase , torch.Tensor ) # Test not batched input __a = 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.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , ) # Test batched __a = image_processing(__lowercase , 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.crop_size["""height"""], self.image_processor_tester.crop_size["""width"""], ) , )
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"""simple docstring""" import argparse import json import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD torch.set_grad_enabled(False) def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__=False ) -> Optional[Any]: A = [] for i in range(config.num_hidden_layers ): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""") ) rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""") ) rename_keys.append( (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""") ) rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""") ) rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""") ) rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""") ) rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""") ) rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""") ) rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""") ) rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""") ) # projection layer + position embeddings rename_keys.extend( [ ('module.cls_token', 'vit.embeddings.cls_token'), ('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'), ('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'), ('module.pos_embed', 'vit.embeddings.position_embeddings'), ] ) if base_model: # layernorm + pooler rename_keys.extend( [ ('module.norm.weight', 'layernorm.weight'), ('module.norm.bias', 'layernorm.bias'), ] ) # if just the base model, we should remove "vit" from all keys that start with "vit" A = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys] else: # layernorm + classification head rename_keys.extend( [ ('norm.weight', 'vit.layernorm.weight'), ('norm.bias', 'vit.layernorm.bias'), ('head.weight', 'classifier.weight'), ('head.bias', 'classifier.bias'), ] ) return rename_keys def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Dict: for i in range(config.num_hidden_layers ): if base_model: A = '' else: A = 'vit.' # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) A = state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight""" ) A = state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias""" ) # next, add query, keys and values (in that order) to the state dict A = in_proj_weight[ : config.hidden_size, : ] A = in_proj_bias[: config.hidden_size] A = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] A = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] A = in_proj_weight[ -config.hidden_size :, : ] A = in_proj_bias[-config.hidden_size :] def lowerCAmelCase__ ( lowerCamelCase__ ) -> int: A = ['head.weight', 'head.bias'] for k in ignore_keys: state_dict.pop(lowerCamelCase__ , lowerCamelCase__ ) def lowerCAmelCase__ ( lowerCamelCase__ ) -> Optional[int]: # projection head is used in the self-supervised pre-training in MSN, # for downstream task it's not needed. A = [ 'module.fc.fc1.weight', 'module.fc.fc1.bias', 'module.fc.bn1.weight', 'module.fc.bn1.bias', 'module.fc.bn1.running_mean', 'module.fc.bn1.running_var', 'module.fc.bn1.num_batches_tracked', 'module.fc.fc2.weight', 'module.fc.fc2.bias', 'module.fc.bn2.weight', 'module.fc.bn2.bias', 'module.fc.bn2.running_mean', 'module.fc.bn2.running_var', 'module.fc.bn2.num_batches_tracked', 'module.fc.fc3.weight', 'module.fc.fc3.bias', ] for k in ignore_keys: state_dict.pop(lowerCamelCase__ , lowerCamelCase__ ) def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]: A = dct.pop(lowerCamelCase__ ) A = val def lowerCAmelCase__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]: A = ViTMSNConfig() A = 1000 A = 'datasets/huggingface/label-files' A = 'imagenet-1k-id2label.json' A = json.load(open(hf_hub_download(lowerCamelCase__ , lowerCamelCase__ ) , 'r' ) ) A = {int(lowerCamelCase__ ): v for k, v in idalabel.items()} A = idalabel A = {v: k for k, v in idalabel.items()} if "s16" in checkpoint_url: A = 384 A = 1536 A = 6 elif "l16" in checkpoint_url: A = 1024 A = 4096 A = 24 A = 16 A = 0.1 elif "b4" in checkpoint_url: A = 4 elif "l7" in checkpoint_url: A = 7 A = 1024 A = 4096 A = 24 A = 16 A = 0.1 A = ViTMSNModel(lowerCamelCase__ ) A = torch.hub.load_state_dict_from_url(lowerCamelCase__ , map_location='cpu' )['target_encoder'] A = ViTImageProcessor(size=config.image_size ) remove_projection_head(lowerCamelCase__ ) A = create_rename_keys(lowerCamelCase__ , base_model=lowerCamelCase__ ) for src, dest in rename_keys: rename_key(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) read_in_q_k_v(lowerCamelCase__ , lowerCamelCase__ , base_model=lowerCamelCase__ ) model.load_state_dict(lowerCamelCase__ ) model.eval() A = 'http://images.cocodataset.org/val2017/000000039769.jpg' A = Image.open(requests.get(lowerCamelCase__ , stream=lowerCamelCase__ ).raw ) A = ViTImageProcessor( size=config.image_size , image_mean=lowerCamelCase__ , image_std=lowerCamelCase__ ) A = image_processor(images=lowerCamelCase__ , return_tensors='pt' ) # forward pass torch.manual_seed(2 ) A = model(**lowerCamelCase__ ) A = outputs.last_hidden_state # The following Colab Notebook was used to generate these outputs: # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb if "s16" in checkpoint_url: A = torch.tensor([[-1.09_15, -1.48_76, -1.18_09]] ) elif "b16" in checkpoint_url: A = torch.tensor([[14.28_89, -18.90_45, 11.72_81]] ) elif "l16" in checkpoint_url: A = torch.tensor([[41.50_28, -22.86_81, 45.64_75]] ) elif "b4" in checkpoint_url: A = torch.tensor([[-4.38_68, 5.29_32, -0.41_37]] ) else: A = torch.tensor([[-0.17_92, -0.64_65, 2.42_63]] ) # verify logits assert torch.allclose(last_hidden_state[:, 0, :3] , lowerCamelCase__ , atol=1E-4 ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(lowerCamelCase__ ) print(f"""Saving image processor to {pytorch_dump_folder_path}""" ) image_processor.save_pretrained(lowerCamelCase__ ) if __name__ == "__main__": A = argparse.ArgumentParser() # Required parameters parser.add_argument( '--checkpoint_url', default='https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar', type=str, help='URL of the checkpoint you\'d like to convert.', ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.' ) A = parser.parse_args() convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
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"""simple docstring""" import os import unittest from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class UpperCAmelCase__ ( UpperCamelCase ,unittest.TestCase ): lowerCAmelCase_ : Tuple = LayoutLMTokenizer lowerCAmelCase_ : Any = LayoutLMTokenizerFast lowerCAmelCase_ : Optional[int] = True lowerCAmelCase_ : List[Any] = True def A_ ( self : Optional[Any] ) -> Optional[Any]: '''simple docstring''' super().setUp() A = [ '[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest', ] A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] ) with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer: vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) ) def A_ ( self : str , **snake_case : Tuple ) -> Union[str, Any]: '''simple docstring''' return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **snake_case ) def A_ ( self : List[str] , snake_case : int ) -> List[Any]: '''simple docstring''' A = 'UNwant\u00E9d,running' A = 'unwanted, running' return input_text, output_text def A_ ( self : Tuple ) -> Optional[int]: '''simple docstring''' A = self.tokenizer_class(self.vocab_file ) A = tokenizer.tokenize('UNwant\u00E9d,running' ) self.assertListEqual(snake_case , ['un', '##want', '##ed', ',', 'runn', '##ing'] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case ) , [7, 4, 5, 10, 8, 9] ) def A_ ( self : Any ) -> List[Any]: '''simple docstring''' pass
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# Lint as: python3 # pylint: enable=line-too-long # pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position lowerCamelCase__ : int = '2.13.1' import platform import pyarrow from packaging import version if version.parse(platform.python_version()) < version.parse('3.7'): raise ImportWarning( 'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.' ) if version.parse(pyarrow.__version__).major < 8: raise ImportWarning( 'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n' 'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.' ) del platform del pyarrow del version from .arrow_dataset import Dataset from .arrow_reader import ReadInstruction from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder from .combine import concatenate_datasets, interleave_datasets from .dataset_dict import DatasetDict, IterableDatasetDict from .download import * from .features import * from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled from .info import DatasetInfo, MetricInfo from .inspect import ( get_dataset_config_info, get_dataset_config_names, get_dataset_infos, get_dataset_split_names, inspect_dataset, inspect_metric, list_datasets, list_metrics, ) from .iterable_dataset import IterableDataset from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric from .metric import Metric from .splits import ( NamedSplit, NamedSplitAll, Split, SplitBase, SplitDict, SplitGenerator, SplitInfo, SubSplitInfo, percent, ) from .tasks import * from .utils import * from .utils import logging # deprecated modules from datasets import arrow_dataset as _arrow_dataset # isort:skip from datasets import utils as _utils # isort:skip from datasets.utils import download_manager as _deprecated_download_manager # isort:skip lowerCamelCase__ : int = concatenate_datasets lowerCamelCase__ : List[Any] = DownloadConfig lowerCamelCase__ : int = DownloadManager lowerCamelCase__ : List[str] = DownloadMode lowerCamelCase__ : List[str] = DownloadConfig lowerCamelCase__ : int = DownloadMode lowerCamelCase__ : List[Any] = DownloadManager del _arrow_dataset, _utils, _deprecated_download_manager
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import operator as op def UpperCAmelCase_ ( __UpperCAmelCase : str ) -> Any: SCREAMING_SNAKE_CASE_ = [] SCREAMING_SNAKE_CASE_ = lambda __UpperCAmelCase , __UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation SCREAMING_SNAKE_CASE_ = { '^': op.pow, '*': op.mul, '/': div, '+': op.add, '-': op.sub, } # operators & their respective operation # print table header print('Symbol'.center(8 ) , 'Action'.center(12 ) , 'Stack' , sep=' | ' ) print('-' * (30 + len(__UpperCAmelCase )) ) for x in post_fix: if x.isdigit(): # if x in digit stack.append(__UpperCAmelCase ) # append x to stack # output in tabular format print(x.rjust(8 ) , ('push(' + x + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' ) else: SCREAMING_SNAKE_CASE_ = stack.pop() # pop stack # output in tabular format print(''.rjust(8 ) , ('pop(' + b + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' ) SCREAMING_SNAKE_CASE_ = stack.pop() # pop stack # output in tabular format print(''.rjust(8 ) , ('pop(' + a + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' ) stack.append( str(opr[x](int(__UpperCAmelCase ) , int(__UpperCAmelCase ) ) ) ) # evaluate the 2 values popped from stack & push result to stack # output in tabular format print( x.rjust(8 ) , ('push(' + a + x + b + ')').ljust(12 ) , ','.join(__UpperCAmelCase ) , sep=' | ' , ) return int(stack[0] ) if __name__ == "__main__": lowerCamelCase__ : Tuple = input('\n\nEnter a Postfix Equation (space separated) = ').split(' ') print('\n\tResult = ', solve(Postfix))
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import os def __SCREAMING_SNAKE_CASE ( ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ = os.path.dirname(os.path.realpath(__UpperCamelCase ) ) SCREAMING_SNAKE_CASE__ = os.path.join(__UpperCamelCase , """triangle.txt""" ) with open(__UpperCamelCase ) as f: SCREAMING_SNAKE_CASE__ = f.readlines() SCREAMING_SNAKE_CASE__ = [] for line in triangle: SCREAMING_SNAKE_CASE__ = [] for number in line.strip().split(""" """ ): numbers_from_line.append(int(__UpperCamelCase ) ) a.append(__UpperCamelCase ) for i in range(1 , len(__UpperCamelCase ) ): for j in range(len(a[i] ) ): SCREAMING_SNAKE_CASE__ = a[i - 1][j] if j != len(a[i - 1] ) else 0 SCREAMING_SNAKE_CASE__ = a[i - 1][j - 1] if j > 0 else 0 a[i][j] += max(__UpperCamelCase , __UpperCamelCase ) return max(a[-1] ) if __name__ == "__main__": print(solution())
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import itertools from dataclasses import dataclass from typing import List, Optional import pyarrow as pa import pyarrow.parquet as pq import datasets from datasets.table import table_cast __lowerCamelCase : Union[str, Any] = datasets.utils.logging.get_logger(__name__) @dataclass class __snake_case ( datasets.BuilderConfig ): lowerCAmelCase_ = 1_00_00 lowerCAmelCase_ = None lowerCAmelCase_ = None class __snake_case ( datasets.ArrowBasedBuilder ): lowerCAmelCase_ = ParquetConfig def __a ( self : List[str] ): """simple docstring""" return datasets.DatasetInfo(features=self.config.features ) def __a ( self : Dict , _lowercase : List[str] ): """simple docstring""" if not self.config.data_files: raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}""" ) SCREAMING_SNAKE_CASE__ = dl_manager.download_and_extract(self.config.data_files ) if isinstance(_lowercase , (str, list, tuple) ): SCREAMING_SNAKE_CASE__ = data_files if isinstance(_lowercase , _lowercase ): SCREAMING_SNAKE_CASE__ = [files] # Use `dl_manager.iter_files` to skip hidden files in an extracted archive SCREAMING_SNAKE_CASE__ = [dl_manager.iter_files(_lowercase ) for file in files] return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )] SCREAMING_SNAKE_CASE__ = [] for split_name, files in data_files.items(): if isinstance(_lowercase , _lowercase ): SCREAMING_SNAKE_CASE__ = [files] # Use `dl_manager.iter_files` to skip hidden files in an extracted archive SCREAMING_SNAKE_CASE__ = [dl_manager.iter_files(_lowercase ) for file in files] # Infer features is they are stoed in the arrow schema if self.info.features is None: for file in itertools.chain.from_iterable(_lowercase ): with open(_lowercase , """rb""" ) as f: SCREAMING_SNAKE_CASE__ = datasets.Features.from_arrow_schema(pq.read_schema(_lowercase ) ) break splits.append(datasets.SplitGenerator(name=_lowercase , gen_kwargs={"""files""": files} ) ) return splits def __a ( self : Optional[Any] , _lowercase : pa.Table ): """simple docstring""" if self.info.features is not None: # more expensive cast to support nested features with keys in a different order # allows str <-> int/float or str to Audio for example SCREAMING_SNAKE_CASE__ = table_cast(_lowercase , self.info.features.arrow_schema ) return pa_table def __a ( self : Dict , _lowercase : List[Any] ): """simple docstring""" SCREAMING_SNAKE_CASE__ = self.info.features.arrow_schema if self.info.features is not None else None if self.info.features is not None and self.config.columns is not None: if sorted(field.name for field in schema ) != sorted(self.config.columns ): raise ValueError( f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" ) for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase ) ): with open(_lowercase , """rb""" ) as f: SCREAMING_SNAKE_CASE__ = pq.ParquetFile(_lowercase ) try: for batch_idx, record_batch in enumerate( parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ): SCREAMING_SNAKE_CASE__ = pa.Table.from_batches([record_batch] ) # Uncomment for debugging (will print the Arrow table size and elements) # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}") # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows))) yield f"""{file_idx}_{batch_idx}""", self._cast_table(_lowercase ) except ValueError as e: logger.error(f"""Failed to read file '{file}' with error {type(_lowercase )}: {e}""" ) raise
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"""simple docstring""" from collections import OrderedDict from typing import Any, List, Mapping, Optional from ... import PreTrainedTokenizer, TensorType, is_torch_available from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfigWithPast, PatchingSpec from ...utils import logging lowerCAmelCase__ =logging.get_logger(__name__) lowerCAmelCase__ ={ "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json", "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json", "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json", "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json", "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json", "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json", "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json", "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json", "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json", "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json", "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json", "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json", } class A__( __magic_name__ ): lowerCAmelCase = '''codegen''' lowerCAmelCase = { '''max_position_embeddings''': '''n_positions''', '''hidden_size''': '''n_embd''', '''num_attention_heads''': '''n_head''', '''num_hidden_layers''': '''n_layer''', } def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Tuple=5_04_00 , __SCREAMING_SNAKE_CASE : Optional[Any]=20_48 , __SCREAMING_SNAKE_CASE : str=20_48 , __SCREAMING_SNAKE_CASE : Optional[int]=40_96 , __SCREAMING_SNAKE_CASE : Dict=28 , __SCREAMING_SNAKE_CASE : List[str]=16 , __SCREAMING_SNAKE_CASE : List[Any]=64 , __SCREAMING_SNAKE_CASE : int=None , __SCREAMING_SNAKE_CASE : Dict="gelu_new" , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Union[str, Any]=1E-5 , __SCREAMING_SNAKE_CASE : Optional[int]=0.02 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=5_02_56 , __SCREAMING_SNAKE_CASE : List[Any]=5_02_56 , __SCREAMING_SNAKE_CASE : List[Any]=False , **__SCREAMING_SNAKE_CASE : Optional[int] , ) -> Optional[int]: """simple docstring""" __SCREAMING_SNAKE_CASE = vocab_size __SCREAMING_SNAKE_CASE = n_ctx __SCREAMING_SNAKE_CASE = n_positions __SCREAMING_SNAKE_CASE = n_embd __SCREAMING_SNAKE_CASE = n_layer __SCREAMING_SNAKE_CASE = n_head __SCREAMING_SNAKE_CASE = n_inner __SCREAMING_SNAKE_CASE = rotary_dim __SCREAMING_SNAKE_CASE = activation_function __SCREAMING_SNAKE_CASE = resid_pdrop __SCREAMING_SNAKE_CASE = embd_pdrop __SCREAMING_SNAKE_CASE = attn_pdrop __SCREAMING_SNAKE_CASE = layer_norm_epsilon __SCREAMING_SNAKE_CASE = initializer_range __SCREAMING_SNAKE_CASE = use_cache __SCREAMING_SNAKE_CASE = bos_token_id __SCREAMING_SNAKE_CASE = eos_token_id super().__init__( bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , tie_word_embeddings=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE ) class A__( __magic_name__ ): def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : PretrainedConfig , __SCREAMING_SNAKE_CASE : str = "default" , __SCREAMING_SNAKE_CASE : List[PatchingSpec] = None , __SCREAMING_SNAKE_CASE : bool = False , ) -> List[Any]: """simple docstring""" super().__init__(__SCREAMING_SNAKE_CASE , task=__SCREAMING_SNAKE_CASE , patching_specs=__SCREAMING_SNAKE_CASE , use_past=__SCREAMING_SNAKE_CASE ) if not getattr(self._config , '''pad_token_id''' , __SCREAMING_SNAKE_CASE ): # TODO: how to do that better? __SCREAMING_SNAKE_CASE = 0 @property def _a ( self : List[Any] ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" __SCREAMING_SNAKE_CASE = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} ) if self.use_past: self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE , direction='''inputs''' ) __SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''past_sequence + sequence'''} else: __SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''} return common_inputs @property def _a ( self : Any ) -> int: """simple docstring""" return self._config.n_layer @property def _a ( self : List[Any] ) -> int: """simple docstring""" return self._config.n_head def _a ( self : Dict , __SCREAMING_SNAKE_CASE : PreTrainedTokenizer , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : int = -1 , __SCREAMING_SNAKE_CASE : bool = False , __SCREAMING_SNAKE_CASE : Optional[TensorType] = None , ) -> Mapping[str, Any]: """simple docstring""" __SCREAMING_SNAKE_CASE = super(__SCREAMING_SNAKE_CASE , self ).generate_dummy_inputs( __SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , seq_length=__SCREAMING_SNAKE_CASE , is_pair=__SCREAMING_SNAKE_CASE , framework=__SCREAMING_SNAKE_CASE ) # We need to order the input in the way they appears in the forward() __SCREAMING_SNAKE_CASE = 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 __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = common_inputs['''input_ids'''].shape # Not using the same length for past_key_values __SCREAMING_SNAKE_CASE = seqlen + 2 __SCREAMING_SNAKE_CASE = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) __SCREAMING_SNAKE_CASE = [ (torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers ) ] __SCREAMING_SNAKE_CASE = common_inputs['''attention_mask'''] if self.use_past: __SCREAMING_SNAKE_CASE = ordered_inputs['''attention_mask'''].dtype __SCREAMING_SNAKE_CASE = torch.cat( [ordered_inputs['''attention_mask'''], torch.ones(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , dtype=__SCREAMING_SNAKE_CASE )] , dim=1 ) return ordered_inputs @property def _a ( self : List[Any] ) -> int: """simple docstring""" return 13
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"""simple docstring""" import unittest import torch from torch import nn from diffusers.models.activations import get_activation class A__( unittest.TestCase ): def _a ( self : List[str] ) -> str: """simple docstring""" __SCREAMING_SNAKE_CASE = get_activation('''swish''' ) self.assertIsInstance(__SCREAMING_SNAKE_CASE , nn.SiLU ) self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 ) self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 ) def _a ( self : List[Any] ) -> Tuple: """simple docstring""" __SCREAMING_SNAKE_CASE = get_activation('''silu''' ) self.assertIsInstance(__SCREAMING_SNAKE_CASE , nn.SiLU ) self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 ) self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 ) def _a ( self : List[str] ) -> Tuple: """simple docstring""" __SCREAMING_SNAKE_CASE = get_activation('''mish''' ) self.assertIsInstance(__SCREAMING_SNAKE_CASE , nn.Mish ) self.assertEqual(act(torch.tensor(-2_00 , dtype=torch.floataa ) ).item() , 0 ) self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 ) def _a ( self : int ) -> Any: """simple docstring""" __SCREAMING_SNAKE_CASE = get_activation('''gelu''' ) self.assertIsInstance(__SCREAMING_SNAKE_CASE , nn.GELU ) self.assertEqual(act(torch.tensor(-1_00 , dtype=torch.floataa ) ).item() , 0 ) self.assertNotEqual(act(torch.tensor(-1 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(0 , dtype=torch.floataa ) ).item() , 0 ) self.assertEqual(act(torch.tensor(20 , dtype=torch.floataa ) ).item() , 20 )
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"""simple docstring""" from __future__ import annotations import time __snake_case = list[tuple[int, int]] __snake_case = [ [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], ] __snake_case = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right class __lowerCamelCase : '''simple docstring''' def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]: _a = pos_x _a = pos_y _a = (pos_y, pos_x) _a = goal_x _a = goal_y _a = parent class __lowerCamelCase : '''simple docstring''' def __init__( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]: _a = Node(start[1] , start[0] , goal[1] , goal[0] , __UpperCAmelCase ) _a = Node(goal[1] , goal[0] , goal[1] , goal[0] , __UpperCAmelCase ) _a = [self.start] _a = False def _UpperCAmelCase ( self ) -> Path | None: while self.node_queue: _a = self.node_queue.pop(0 ) if current_node.pos == self.target.pos: _a = True return self.retrace_path(__UpperCAmelCase ) _a = self.get_successors(__UpperCAmelCase ) for node in successors: self.node_queue.append(__UpperCAmelCase ) if not self.reached: return [self.start.pos] return None def _UpperCAmelCase ( self , __UpperCAmelCase ) -> list[Node]: _a = [] for action in delta: _a = parent.pos_x + action[1] _a = parent.pos_y + action[0] if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(__UpperCAmelCase ) - 1): continue if grid[pos_y][pos_x] != 0: continue successors.append( Node(__UpperCAmelCase , __UpperCAmelCase , self.target.pos_y , self.target.pos_x , __UpperCAmelCase ) ) return successors def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Path: _a = node _a = [] while current_node is not None: path.append((current_node.pos_y, current_node.pos_x) ) _a = current_node.parent path.reverse() return path class __lowerCamelCase : '''simple docstring''' def __init__( self , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple: _a = BreadthFirstSearch(__UpperCAmelCase , __UpperCAmelCase ) _a = BreadthFirstSearch(__UpperCAmelCase , __UpperCAmelCase ) _a = False def _UpperCAmelCase ( self ) -> Path | None: while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue: _a = self.fwd_bfs.node_queue.pop(0 ) _a = self.bwd_bfs.node_queue.pop(0 ) if current_bwd_node.pos == current_fwd_node.pos: _a = True return self.retrace_bidirectional_path( __UpperCAmelCase , __UpperCAmelCase ) _a = current_bwd_node _a = current_fwd_node _a = { self.fwd_bfs: self.fwd_bfs.get_successors(__UpperCAmelCase ), self.bwd_bfs: self.bwd_bfs.get_successors(__UpperCAmelCase ), } for bfs in [self.fwd_bfs, self.bwd_bfs]: for node in successors[bfs]: bfs.node_queue.append(__UpperCAmelCase ) if not self.reached: return [self.fwd_bfs.start.pos] return None def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> Path: _a = self.fwd_bfs.retrace_path(__UpperCAmelCase ) _a = self.bwd_bfs.retrace_path(__UpperCAmelCase ) bwd_path.pop() bwd_path.reverse() _a = fwd_path + bwd_path return path if __name__ == "__main__": # all coordinates are given in format [y,x] import doctest doctest.testmod() __snake_case = (0, 0) __snake_case = (len(grid) - 1, len(grid[0]) - 1) for elem in grid: print(elem) __snake_case = time.time() __snake_case = BreadthFirstSearch(init, goal) __snake_case = bfs.search() __snake_case = time.time() - start_bfs_time print('''Unidirectional BFS computation time : ''', bfs_time) __snake_case = time.time() __snake_case = BidirectionalBreadthFirstSearch(init, goal) __snake_case = bd_bfs.search() __snake_case = time.time() - start_bd_bfs_time print('''Bidirectional BFS computation time : ''', bd_bfs_time)
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"""simple docstring""" def A_ ( _lowerCAmelCase : int = 10_00 ): """simple docstring""" _a , _a = 1, 1 _a = 2 while True: _a = 0 _a = fa + fa _a , _a = fa, f index += 1 for _ in str(_lowerCAmelCase ): i += 1 if i == n: break return index if __name__ == "__main__": print(solution(int(str(input()).strip())))
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# Copyright 2023 The HuggingFace Inc. 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 numpy as np import torch from ..models.clipseg import CLIPSegForImageSegmentation from ..utils import is_vision_available, requires_backends from .base import PipelineTool if is_vision_available(): from PIL import Image class __UpperCAmelCase ( __A ): """simple docstring""" _lowerCamelCase = ( """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.""" """It takes two arguments named `image` which should be the original image, and `label` which should be a text """ """describing the elements what should be identified in the segmentation mask. The tool returns the mask.""" ) _lowerCamelCase = """CIDAS/clipseg-rd64-refined""" _lowerCamelCase = """image_segmenter""" _lowerCamelCase = CLIPSegForImageSegmentation _lowerCamelCase = ["""image""", """text"""] _lowerCamelCase = ["""image"""] def __init__( self , *__A , **__A ): requires_backends(self , ["""vision"""] ) super().__init__(*__A , **__A ) def snake_case_ ( self , __A , __A ): return self.pre_processor(text=[label] , images=[image] , padding=__A , return_tensors="""pt""" ) def snake_case_ ( self , __A ): with torch.no_grad(): __a = self.model(**__A ).logits return logits def snake_case_ ( self , __A ): __a = outputs.cpu().detach().numpy() __a = 0 __a = 1 return Image.fromarray((array * 255).astype(np.uinta ) )
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from __future__ import annotations def lowerCAmelCase__(__snake_case ,__snake_case ) -> list[int]: '''simple docstring''' lowerCamelCase__ = 0 lowerCamelCase__ = len(__snake_case ) - 1 while i < j: if nums[i] + nums[j] == target: return [i, j] elif nums[i] + nums[j] < target: lowerCamelCase__ = i + 1 else: lowerCamelCase__ = j - 1 return [] if __name__ == "__main__": import doctest doctest.testmod() print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")
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'''simple docstring''' from collections import OrderedDict from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import TensorType, logging if TYPE_CHECKING: from ...onnx.config import PatchingSpec from ...tokenization_utils_base import PreTrainedTokenizerBase _lowerCamelCase : List[Any] = logging.get_logger(__name__) _lowerCamelCase : Tuple = { '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''', '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''', '''allenai/longformer-large-4096-finetuned-triviaqa''': ( '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json''' ), '''allenai/longformer-base-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json''' ), '''allenai/longformer-large-4096-extra.pos.embd.only''': ( '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json''' ), } class SCREAMING_SNAKE_CASE ( a__ ): """simple docstring""" _SCREAMING_SNAKE_CASE = """longformer""" def __init__( self : Union[str, Any] , UpperCamelCase__ : List[str] = 5_1_2 , UpperCamelCase__ : List[str] = 2 , UpperCamelCase__ : str = 1 , UpperCamelCase__ : List[str] = 0 , UpperCamelCase__ : List[str] = 2 , UpperCamelCase__ : Dict = 3_0_5_2_2 , UpperCamelCase__ : Tuple = 7_6_8 , UpperCamelCase__ : Optional[Any] = 1_2 , UpperCamelCase__ : int = 1_2 , UpperCamelCase__ : int = 3_0_7_2 , UpperCamelCase__ : str = "gelu" , UpperCamelCase__ : List[str] = 0.1 , UpperCamelCase__ : str = 0.1 , UpperCamelCase__ : Dict = 5_1_2 , UpperCamelCase__ : Optional[int] = 2 , UpperCamelCase__ : List[Any] = 0.0_2 , UpperCamelCase__ : Dict = 1E-1_2 , UpperCamelCase__ : Any = False , **UpperCamelCase__ : Any , ): """simple docstring""" super().__init__(pad_token_id=lowerCAmelCase__ , **lowerCAmelCase__ ) UpperCamelCase = attention_window UpperCamelCase = sep_token_id UpperCamelCase = bos_token_id UpperCamelCase = eos_token_id UpperCamelCase = vocab_size UpperCamelCase = hidden_size UpperCamelCase = num_hidden_layers UpperCamelCase = num_attention_heads UpperCamelCase = hidden_act UpperCamelCase = intermediate_size UpperCamelCase = hidden_dropout_prob UpperCamelCase = attention_probs_dropout_prob UpperCamelCase = max_position_embeddings UpperCamelCase = type_vocab_size UpperCamelCase = initializer_range UpperCamelCase = layer_norm_eps UpperCamelCase = onnx_export class SCREAMING_SNAKE_CASE ( a__ ): """simple docstring""" def __init__( self : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Optional[Any] = "default" , UpperCamelCase__ : List[str] = None ): """simple docstring""" super().__init__(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) UpperCamelCase = True @property def A ( self : List[str] ): """simple docstring""" if self.task == "multiple-choice": UpperCamelCase = {0: "batch", 1: "choice", 2: "sequence"} else: UpperCamelCase = {0: "batch", 1: "sequence"} return OrderedDict( [ ('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('global_attention_mask', dynamic_axis), ] ) @property def A ( self : Optional[int] ): """simple docstring""" UpperCamelCase = super().outputs if self.task == "default": UpperCamelCase = {0: "batch"} return outputs @property def A ( self : Any ): """simple docstring""" return 1E-4 @property def A ( self : Tuple ): """simple docstring""" return max(super().default_onnx_opset , 1_4 ) def A ( self : Union[str, Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] = -1 , UpperCamelCase__ : Any = -1 , UpperCamelCase__ : Dict = False , UpperCamelCase__ : str = None , ): """simple docstring""" UpperCamelCase = super().generate_dummy_inputs( preprocessor=lowerCAmelCase__ , batch_size=lowerCAmelCase__ , seq_length=lowerCAmelCase__ , is_pair=lowerCAmelCase__ , framework=lowerCAmelCase__ ) import torch # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64) # makes the export fail randomly UpperCamelCase = torch.zeros_like(inputs['input_ids'] ) # make every second token global UpperCamelCase = 1 return inputs
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'''simple docstring''' import inspect from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel, VQModel from ...schedulers import DDIMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class SCREAMING_SNAKE_CASE ( _a ): """simple docstring""" def __init__( self : Tuple , UpperCamelCase__ : VQModel , UpperCamelCase__ : UNetaDModel , UpperCamelCase__ : DDIMScheduler ): """simple docstring""" super().__init__() self.register_modules(vqvae=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ ) @torch.no_grad() def __call__( self : Any , UpperCamelCase__ : int = 1 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : int = 5_0 , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , **UpperCamelCase__ : Any , ): """simple docstring""" UpperCamelCase = randn_tensor( (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=UpperCamelCase__ , ) UpperCamelCase = latents.to(self.device ) # scale the initial noise by the standard deviation required by the scheduler UpperCamelCase = latents * self.scheduler.init_noise_sigma self.scheduler.set_timesteps(UpperCamelCase__ ) # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature UpperCamelCase = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() ) UpperCamelCase = {} if accepts_eta: UpperCamelCase = eta for t in self.progress_bar(self.scheduler.timesteps ): UpperCamelCase = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ ) # predict the noise residual UpperCamelCase = self.unet(UpperCamelCase__ , UpperCamelCase__ ).sample # compute the previous noisy sample x_t -> x_t-1 UpperCamelCase = self.scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample # decode the image latents with the VAE UpperCamelCase = self.vqvae.decode(UpperCamelCase__ ).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(UpperCamelCase__ ) if not return_dict: return (image,) return ImagePipelineOutput(images=UpperCamelCase__ )
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'''simple docstring''' import argparse import re import requests import torch # git clone https://github.com/salesforce/BLIP.git from models.blip import blip_decoder from models.blip_itm import blip_itm from models.blip_vqa import blip_vqa from PIL import Image from torchvision import transforms from torchvision.transforms.functional import InterpolationMode from transformers import ( BertTokenizer, BlipConfig, BlipForConditionalGeneration, BlipForImageTextRetrieval, BlipForQuestionAnswering, ) def _lowerCAmelCase ( lowercase : str , lowercase : Dict ) ->int: """simple docstring""" lowercase__ = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' lowercase__ = Image.open(requests.get(lowercase , stream=lowercase ).raw ).convert('''RGB''' ) lowercase__ = transforms.Compose( [ transforms.Resize((image_size, image_size) , interpolation=InterpolationMode.BICUBIC ), transforms.ToTensor(), transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73) , (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11) ), ] ) lowercase__ = transform(lowercase ).unsqueeze(0 ).to(lowercase ) return image def _lowerCAmelCase ( lowercase : str ) ->List[str]: """simple docstring""" if "visual_encoder" in key: lowercase__ = re.sub('''visual_encoder*''' , '''vision_model.encoder''' , lowercase ) if "blocks" in key: lowercase__ = re.sub(R'''blocks''' , '''layers''' , lowercase ) if "attn" in key: lowercase__ = re.sub(R'''attn''' , '''self_attn''' , lowercase ) if "norm1" in key: lowercase__ = re.sub(R'''norm1''' , '''layer_norm1''' , lowercase ) if "norm2" in key: lowercase__ = re.sub(R'''norm2''' , '''layer_norm2''' , lowercase ) if "encoder.norm" in key: lowercase__ = re.sub(R'''encoder.norm''' , '''post_layernorm''' , lowercase ) if "encoder.patch_embed.proj" in key: lowercase__ = re.sub(R'''encoder.patch_embed.proj''' , '''embeddings.patch_embedding''' , lowercase ) if "encoder.pos_embed" in key: lowercase__ = re.sub(R'''encoder.pos_embed''' , '''embeddings.position_embedding''' , lowercase ) if "encoder.cls_token" in key: lowercase__ = re.sub(R'''encoder.cls_token''' , '''embeddings.class_embedding''' , lowercase ) if "self_attn" in key: lowercase__ = re.sub(R'''self_attn.proj''' , '''self_attn.projection''' , lowercase ) return key @torch.no_grad() def _lowerCAmelCase ( lowercase : List[str] , lowercase : Any=None ) ->Dict: """simple docstring""" if config_path is not None: lowercase__ = BlipConfig.from_pretrained(lowercase ) else: lowercase__ = BlipConfig(projection_dim=5_1_2 , text_config={} , vision_config={} ) lowercase__ = BlipForConditionalGeneration(lowercase ).eval() lowercase__ = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth' lowercase__ = blip_decoder(pretrained=lowercase , image_size=3_8_4 , vit='''base''' ) lowercase__ = pt_model.eval() lowercase__ = pt_model.state_dict() for key in modified_state_dict.copy(): lowercase__ = modified_state_dict.pop(lowercase ) lowercase__ = rename_key(lowercase ) lowercase__ = value hf_model.load_state_dict(lowercase ) lowercase__ = 3_8_4 lowercase__ = load_demo_image(image_size=lowercase , device='''cpu''' ) lowercase__ = BertTokenizer.from_pretrained('''bert-base-uncased''' ) lowercase__ = tokenizer(['''a picture of'''] ).input_ids lowercase__ = hf_model.generate(lowercase , lowercase ) assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2] lowercase__ = hf_model.generate(lowercase ) assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2] if pytorch_dump_folder_path is not None: hf_model.save_pretrained(lowercase ) # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth' lowercase__ = ( 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth' ) lowercase__ = blip_vqa(pretrained=lowercase , image_size=lowercase , vit='''base''' ) vqa_model.eval() lowercase__ = vqa_model.state_dict() for key in modified_state_dict.copy(): lowercase__ = modified_state_dict.pop(lowercase ) lowercase__ = rename_key(lowercase ) lowercase__ = value lowercase__ = BlipForQuestionAnswering(lowercase ) hf_vqa_model.load_state_dict(lowercase ) lowercase__ = ['How many dogs are in this image?'] lowercase__ = tokenizer(lowercase , return_tensors='''pt''' ).input_ids lowercase__ = hf_vqa_model.generate(lowercase , lowercase ) print(tokenizer.decode(answer[0] ) ) assert tokenizer.decode(answer[0] ) == "[UNK] 1 [SEP]" if pytorch_dump_folder_path is not None: hf_vqa_model.save_pretrained(pytorch_dump_folder_path + '''_vqa''' ) lowercase__ = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' lowercase__ = blip_itm(pretrained=lowercase , image_size=lowercase , vit='''base''' ) itm_model.eval() lowercase__ = itm_model.state_dict() for key in modified_state_dict.copy(): lowercase__ = modified_state_dict.pop(lowercase ) lowercase__ = rename_key(lowercase ) lowercase__ = value lowercase__ = BlipForImageTextRetrieval(lowercase ) lowercase__ = ['A picture of a woman with a dog sitting in a beach'] lowercase__ = tokenizer( lowercase , return_tensors='''pt''' , padding='''max_length''' , truncation=lowercase , max_length=3_5 , ).input_ids hf_itm_model.load_state_dict(lowercase ) hf_itm_model.eval() lowercase__ = hf_itm_model(lowercase , lowercase , use_itm_head=lowercase ) lowercase__ = hf_itm_model(lowercase , lowercase , use_itm_head=lowercase ) assert out[0].item() == 0.21_10_68_74_94_27_79_54 assert torch.nn.functional.softmax(out_itm[0] , dim=1 )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27 if pytorch_dump_folder_path is not None: hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm''' ) if __name__ == "__main__": _lowerCAmelCase = argparse.ArgumentParser() parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert") _lowerCAmelCase = parser.parse_args() convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
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def lowerCamelCase_ ( lowerCAmelCase: str )-> str: _snake_case : str = 0 # if input_string is "aba" than new_input_string become "a|b|a" _snake_case : List[Any] = '' _snake_case : Dict = '' # append each character + "|" in new_string for range(0, length-1) for i in input_string[: len(lowerCAmelCase ) - 1]: new_input_string += i + "|" # append last character new_input_string += input_string[-1] # we will store the starting and ending of previous furthest ending palindromic # substring _snake_case , _snake_case : Union[str, Any] = 0, 0 # length[i] shows the length of palindromic substring with center i _snake_case : Optional[Any] = [1 for i in range(len(lowerCAmelCase ) )] # for each character in new_string find corresponding palindromic string _snake_case : Any = 0 for j in range(len(lowerCAmelCase ) ): _snake_case : Tuple = 1 if j > r else min(length[l + r - j] // 2 , r - j + 1 ) while ( j - k >= 0 and j + k < len(lowerCAmelCase ) and new_input_string[k + j] == new_input_string[j - k] ): k += 1 _snake_case : str = 2 * k - 1 # does this string is ending after the previously explored end (that is r) ? # if yes the update the new r to the last index of this if j + k - 1 > r: _snake_case : List[str] = j - k + 1 # noqa: E741 _snake_case : List[Any] = j + k - 1 # update max_length and start position if max_length < length[j]: _snake_case : List[Any] = length[j] _snake_case : Optional[Any] = j # create that string _snake_case : Any = new_input_string[start - max_length // 2 : start + max_length // 2 + 1] for i in s: if i != "|": output_string += i return output_string if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations import unittest from transformers import LEDConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFLEDForConditionalGeneration, TFLEDModel @require_tf class _UpperCamelCase : '''simple docstring''' _A : Any = LEDConfig _A : str = {} _A : List[str] = "gelu" def __init__( self : List[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[Any]=1_3 , lowerCAmelCase__ : int=7 , lowerCAmelCase__ : str=True , lowerCAmelCase__ : Any=False , lowerCAmelCase__ : str=9_9 , lowerCAmelCase__ : str=3_2 , lowerCAmelCase__ : Union[str, Any]=2 , lowerCAmelCase__ : Optional[Any]=4 , lowerCAmelCase__ : List[Any]=3_7 , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Tuple=0.1 , lowerCAmelCase__ : Dict=2_0 , lowerCAmelCase__ : str=2 , lowerCAmelCase__ : Dict=1 , lowerCAmelCase__ : Any=0 , lowerCAmelCase__ : List[Any]=4 , ): """simple docstring""" __SCREAMING_SNAKE_CASE : Optional[Any] = parent __SCREAMING_SNAKE_CASE : List[str] = batch_size __SCREAMING_SNAKE_CASE : str = seq_length __SCREAMING_SNAKE_CASE : str = is_training __SCREAMING_SNAKE_CASE : Any = use_labels __SCREAMING_SNAKE_CASE : Any = vocab_size __SCREAMING_SNAKE_CASE : List[str] = hidden_size __SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers __SCREAMING_SNAKE_CASE : Dict = num_attention_heads __SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size __SCREAMING_SNAKE_CASE : int = hidden_dropout_prob __SCREAMING_SNAKE_CASE : Dict = attention_probs_dropout_prob __SCREAMING_SNAKE_CASE : str = max_position_embeddings __SCREAMING_SNAKE_CASE : int = eos_token_id __SCREAMING_SNAKE_CASE : Dict = pad_token_id __SCREAMING_SNAKE_CASE : Optional[Any] = bos_token_id __SCREAMING_SNAKE_CASE : str = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window` and one before and one after __SCREAMING_SNAKE_CASE : List[str] = self.attention_window + 2 # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for # the `test_attention_outputs` and `test_hidden_states_output` tests __SCREAMING_SNAKE_CASE : int = ( self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window ) def UpperCamelCase__ ( self : int ): """simple docstring""" __SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) __SCREAMING_SNAKE_CASE : Optional[Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 ) __SCREAMING_SNAKE_CASE : Tuple = tf.concat([input_ids, eos_tensor] , axis=1 ) __SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __SCREAMING_SNAKE_CASE : List[Any] = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , ) __SCREAMING_SNAKE_CASE : Dict = prepare_led_inputs_dict(__a , __a , __a ) __SCREAMING_SNAKE_CASE : Union[str, Any] = tf.concat( [tf.zeros_like(__a )[:, :-1], tf.ones_like(__a )[:, -1:]] , axis=-1 , ) __SCREAMING_SNAKE_CASE : Union[str, Any] = global_attention_mask return config, inputs_dict def UpperCamelCase__ ( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int ): """simple docstring""" __SCREAMING_SNAKE_CASE : Tuple = TFLEDModel(config=__a ).get_decoder() __SCREAMING_SNAKE_CASE : Tuple = inputs_dict["input_ids"] __SCREAMING_SNAKE_CASE : int = input_ids[:1, :] __SCREAMING_SNAKE_CASE : List[str] = inputs_dict["attention_mask"][:1, :] __SCREAMING_SNAKE_CASE : List[Any] = 1 # first forward pass __SCREAMING_SNAKE_CASE : Any = model(__a , attention_mask=__a , use_cache=__a ) __SCREAMING_SNAKE_CASE : Union[str, Any] = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids __SCREAMING_SNAKE_CASE : Any = ids_tensor((self.batch_size, 3) , config.vocab_size ) __SCREAMING_SNAKE_CASE : Any = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta ) # append to next input_ids and __SCREAMING_SNAKE_CASE : List[str] = tf.concat([input_ids, next_tokens] , axis=-1 ) __SCREAMING_SNAKE_CASE : Union[str, Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 ) __SCREAMING_SNAKE_CASE : Tuple = model(__a , attention_mask=__a )[0] __SCREAMING_SNAKE_CASE : int = model(__a , attention_mask=__a , past_key_values=__a )[0] self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] ) # select random slice __SCREAMING_SNAKE_CASE : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) ) __SCREAMING_SNAKE_CASE : List[str] = output_from_no_past[:, -3:, random_slice_idx] __SCREAMING_SNAKE_CASE : Optional[int] = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(__a , __a , rtol=1E-3 ) def lowerCAmelCase_ ( _lowerCamelCase: Any , _lowerCamelCase: str , _lowerCamelCase: Dict , _lowerCamelCase: Union[str, Any]=None , _lowerCamelCase: Optional[int]=None , _lowerCamelCase: Optional[Any]=None , _lowerCamelCase: str=None , ): if attention_mask is None: __SCREAMING_SNAKE_CASE : str = tf.cast(tf.math.not_equal(lowercase_ , config.pad_token_id ) , tf.inta ) if decoder_attention_mask is None: __SCREAMING_SNAKE_CASE : str = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ), ] , axis=-1 , ) if head_mask is None: __SCREAMING_SNAKE_CASE : List[str] = tf.ones((config.encoder_layers, config.encoder_attention_heads) ) if decoder_head_mask is None: __SCREAMING_SNAKE_CASE : List[str] = tf.ones((config.decoder_layers, config.decoder_attention_heads) ) return { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, } @require_tf class _UpperCamelCase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ): '''simple docstring''' _A : Any = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else () _A : List[str] = (TFLEDForConditionalGeneration,) if is_tf_available() else () _A : List[str] = ( { "conversational": TFLEDForConditionalGeneration, "feature-extraction": TFLEDModel, "summarization": TFLEDForConditionalGeneration, "text2text-generation": TFLEDForConditionalGeneration, "translation": TFLEDForConditionalGeneration, } if is_tf_available() else {} ) _A : Tuple = True _A : str = False _A : Optional[Any] = False _A : int = False def UpperCamelCase__ ( self : str ): """simple docstring""" __SCREAMING_SNAKE_CASE : int = TFLEDModelTester(self ) __SCREAMING_SNAKE_CASE : Any = ConfigTester(self , config_class=__a ) def UpperCamelCase__ ( self : Optional[Any] ): """simple docstring""" self.config_tester.run_common_tests() def UpperCamelCase__ ( self : List[str] ): """simple docstring""" __SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*__a ) def UpperCamelCase__ ( self : List[str] ): """simple docstring""" __SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() __SCREAMING_SNAKE_CASE : Optional[int] = tf.zeros_like(inputs_dict["""attention_mask"""] ) __SCREAMING_SNAKE_CASE : Union[str, Any] = 2 __SCREAMING_SNAKE_CASE : str = tf.where( tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict["""global_attention_mask"""] , ) __SCREAMING_SNAKE_CASE : Dict = True __SCREAMING_SNAKE_CASE : str = self.model_tester.seq_length __SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.encoder_seq_length def check_decoder_attentions_output(lowerCAmelCase__ : Optional[int] ): __SCREAMING_SNAKE_CASE : Optional[int] = outputs.decoder_attentions self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , ) def check_encoder_attentions_output(lowerCAmelCase__ : Optional[Any] ): __SCREAMING_SNAKE_CASE : Union[str, Any] = [t.numpy() for t in outputs.encoder_attentions] __SCREAMING_SNAKE_CASE : List[Any] = [t.numpy() for t in outputs.encoder_global_attentions] self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , ) self.assertListEqual( list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , ) for model_class in self.all_model_classes: __SCREAMING_SNAKE_CASE : Dict = True __SCREAMING_SNAKE_CASE : Optional[Any] = False __SCREAMING_SNAKE_CASE : int = False __SCREAMING_SNAKE_CASE : Optional[int] = model_class(__a ) __SCREAMING_SNAKE_CASE : int = model(self._prepare_for_class(__a , __a ) ) __SCREAMING_SNAKE_CASE : Any = len(__a ) self.assertEqual(config.output_hidden_states , __a ) check_encoder_attentions_output(__a ) if self.is_encoder_decoder: __SCREAMING_SNAKE_CASE : Optional[Any] = model_class(__a ) __SCREAMING_SNAKE_CASE : List[Any] = model(self._prepare_for_class(__a , __a ) ) self.assertEqual(config.output_hidden_states , __a ) check_decoder_attentions_output(__a ) # Check that output attentions can also be changed via the config del inputs_dict["output_attentions"] __SCREAMING_SNAKE_CASE : int = True __SCREAMING_SNAKE_CASE : Tuple = model_class(__a ) __SCREAMING_SNAKE_CASE : str = model(self._prepare_for_class(__a , __a ) ) self.assertEqual(config.output_hidden_states , __a ) check_encoder_attentions_output(__a ) # Check attention is always last and order is fine __SCREAMING_SNAKE_CASE : Any = True __SCREAMING_SNAKE_CASE : List[str] = True __SCREAMING_SNAKE_CASE : Tuple = model_class(__a ) __SCREAMING_SNAKE_CASE : int = model(self._prepare_for_class(__a , __a ) ) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__a ) ) self.assertEqual(model.config.output_hidden_states , __a ) check_encoder_attentions_output(__a ) @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing.""" ) def UpperCamelCase__ ( self : str ): """simple docstring""" pass def UpperCamelCase__ ( self : Optional[int] ): """simple docstring""" pass def lowerCAmelCase_ ( _lowerCamelCase: List[Any] ): return tf.constant(lowercase_ , dtype=tf.intaa ) UpperCamelCase__ : Any = 1E-4 @slow @require_tf class _UpperCamelCase ( unittest.TestCase ): '''simple docstring''' def UpperCamelCase__ ( self : Tuple ): """simple docstring""" __SCREAMING_SNAKE_CASE : Any = TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" ).led # change to intended input here __SCREAMING_SNAKE_CASE : int = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] ) __SCREAMING_SNAKE_CASE : Tuple = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] ) __SCREAMING_SNAKE_CASE : Optional[Any] = prepare_led_inputs_dict(model.config , __a , __a ) __SCREAMING_SNAKE_CASE : Optional[int] = model(**__a )[0] __SCREAMING_SNAKE_CASE : Optional[int] = (1, 1_0_2_4, 7_6_8) self.assertEqual(output.shape , __a ) # change to expected output here __SCREAMING_SNAKE_CASE : Tuple = tf.convert_to_tensor( [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]] , ) tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1E-3 ) def UpperCamelCase__ ( self : Dict ): """simple docstring""" __SCREAMING_SNAKE_CASE : Optional[int] = TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384""" ) # change to intended input here __SCREAMING_SNAKE_CASE : Optional[int] = _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] ) __SCREAMING_SNAKE_CASE : List[str] = _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]] ) __SCREAMING_SNAKE_CASE : Optional[Any] = prepare_led_inputs_dict(model.config , __a , __a ) __SCREAMING_SNAKE_CASE : Union[str, Any] = model(**__a )[0] __SCREAMING_SNAKE_CASE : int = (1, 1_0_2_4, model.config.vocab_size) self.assertEqual(output.shape , __a ) # change to expected output here __SCREAMING_SNAKE_CASE : Optional[int] = tf.convert_to_tensor( [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]] , ) tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1E-3 , rtol=1E-3 )
707
'''simple docstring''' import inspect import tempfile from collections import OrderedDict, UserDict from collections.abc import MutableMapping from contextlib import ExitStack, contextmanager from dataclasses import fields from enum import Enum from typing import Any, ContextManager, List, Tuple import numpy as np from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy if is_flax_available(): import jax.numpy as jnp class _UpperCamelCase ( lowerCamelCase__ ): '''simple docstring''' def __get__( self : str , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[Any]=None ): """simple docstring""" if obj is None: return self if self.fget is None: raise AttributeError("""unreadable attribute""" ) __SCREAMING_SNAKE_CASE : Any = """__cached_""" + self.fget.__name__ __SCREAMING_SNAKE_CASE : str = getattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) if cached is None: __SCREAMING_SNAKE_CASE : int = self.fget(lowerCAmelCase__ ) setattr(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) return cached def lowerCAmelCase_ ( _lowerCamelCase: Tuple ): __SCREAMING_SNAKE_CASE : List[str] = val.lower() if val in {"y", "yes", "t", "true", "on", "1"}: return 1 if val in {"n", "no", "f", "false", "off", "0"}: return 0 raise ValueError(F"invalid truth value {val!r}" ) def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] ): if is_torch_fx_proxy(_lowerCamelCase ): return True if is_torch_available(): import torch if isinstance(_lowerCamelCase , torch.Tensor ): return True if is_tf_available(): import tensorflow as tf if isinstance(_lowerCamelCase , tf.Tensor ): return True if is_flax_available(): import jax.numpy as jnp from jax.core import Tracer if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ): return True return isinstance(_lowerCamelCase , np.ndarray ) def lowerCAmelCase_ ( _lowerCamelCase: Optional[Any] ): return isinstance(_lowerCamelCase , np.ndarray ) def lowerCAmelCase_ ( _lowerCamelCase: Dict ): return _is_numpy(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: Any ): import torch return isinstance(_lowerCamelCase , torch.Tensor ) def lowerCAmelCase_ ( _lowerCamelCase: str ): return False if not is_torch_available() else _is_torch(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: Dict ): import torch return isinstance(_lowerCamelCase , torch.device ) def lowerCAmelCase_ ( _lowerCamelCase: Any ): return False if not is_torch_available() else _is_torch_device(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: Any ): import torch if isinstance(_lowerCamelCase , _lowerCamelCase ): if hasattr(_lowerCamelCase , _lowerCamelCase ): __SCREAMING_SNAKE_CASE : Tuple = getattr(_lowerCamelCase , _lowerCamelCase ) else: return False return isinstance(_lowerCamelCase , torch.dtype ) def lowerCAmelCase_ ( _lowerCamelCase: Optional[Any] ): return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: str ): import tensorflow as tf return isinstance(_lowerCamelCase , tf.Tensor ) def lowerCAmelCase_ ( _lowerCamelCase: int ): return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: Any ): import tensorflow as tf # the `is_symbolic_tensor` predicate is only available starting with TF 2.14 if hasattr(_lowerCamelCase , """is_symbolic_tensor""" ): return tf.is_symbolic_tensor(_lowerCamelCase ) return type(_lowerCamelCase ) == tf.Tensor def lowerCAmelCase_ ( _lowerCamelCase: Any ): return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: Any ): import jax.numpy as jnp # noqa: F811 return isinstance(_lowerCamelCase , jnp.ndarray ) def lowerCAmelCase_ ( _lowerCamelCase: List[str] ): return False if not is_flax_available() else _is_jax(_lowerCamelCase ) def lowerCAmelCase_ ( _lowerCamelCase: int ): if isinstance(_lowerCamelCase , (dict, UserDict) ): return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()} elif isinstance(_lowerCamelCase , (list, tuple) ): return [to_py_obj(_lowerCamelCase ) for o in obj] elif is_tf_tensor(_lowerCamelCase ): return obj.numpy().tolist() elif is_torch_tensor(_lowerCamelCase ): return obj.detach().cpu().tolist() elif is_jax_tensor(_lowerCamelCase ): return np.asarray(_lowerCamelCase ).tolist() elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays return obj.tolist() else: return obj def lowerCAmelCase_ ( _lowerCamelCase: Any ): if isinstance(_lowerCamelCase , (dict, UserDict) ): return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()} elif isinstance(_lowerCamelCase , (list, tuple) ): return np.array(_lowerCamelCase ) elif is_tf_tensor(_lowerCamelCase ): return obj.numpy() elif is_torch_tensor(_lowerCamelCase ): return obj.detach().cpu().numpy() elif is_jax_tensor(_lowerCamelCase ): return np.asarray(_lowerCamelCase ) else: return obj class _UpperCamelCase ( lowerCamelCase__ ): '''simple docstring''' def UpperCamelCase__ ( self : Dict ): """simple docstring""" __SCREAMING_SNAKE_CASE : int = fields(self ) # Safety and consistency checks if not len(lowerCAmelCase__ ): raise ValueError(F"{self.__class__.__name__} has no fields." ) if not all(field.default is None for field in class_fields[1:] ): raise ValueError(F"{self.__class__.__name__} should not have more than one required field." ) __SCREAMING_SNAKE_CASE : Dict = getattr(self , class_fields[0].name ) __SCREAMING_SNAKE_CASE : Dict = all(getattr(self , field.name ) is None for field in class_fields[1:] ) if other_fields_are_none and not is_tensor(lowerCAmelCase__ ): if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): __SCREAMING_SNAKE_CASE : Any = first_field.items() __SCREAMING_SNAKE_CASE : Dict = True else: try: __SCREAMING_SNAKE_CASE : List[Any] = iter(lowerCAmelCase__ ) __SCREAMING_SNAKE_CASE : Optional[int] = True except TypeError: __SCREAMING_SNAKE_CASE : int = False # if we provided an iterator as first field and the iterator is a (key, value) iterator # set the associated fields if first_field_iterator: for idx, element in enumerate(lowerCAmelCase__ ): if ( not isinstance(lowerCAmelCase__ , (list, tuple) ) or not len(lowerCAmelCase__ ) == 2 or not isinstance(element[0] , lowerCAmelCase__ ) ): if idx == 0: # If we do not have an iterator of key/values, set it as attribute __SCREAMING_SNAKE_CASE : str = first_field else: # If we have a mixed iterator, raise an error raise ValueError( F"Cannot set key/value for {element}. It needs to be a tuple (key, value)." ) break setattr(self , element[0] , element[1] ) if element[1] is not None: __SCREAMING_SNAKE_CASE : Optional[int] = element[1] elif first_field is not None: __SCREAMING_SNAKE_CASE : Optional[int] = first_field else: for field in class_fields: __SCREAMING_SNAKE_CASE : List[Any] = getattr(self , field.name ) if v is not None: __SCREAMING_SNAKE_CASE : Optional[int] = v def __delitem__( self : Optional[Any] , *lowerCAmelCase__ : int , **lowerCAmelCase__ : Tuple ): """simple docstring""" raise Exception(F"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance." ) def UpperCamelCase__ ( self : str , *lowerCAmelCase__ : Optional[Any] , **lowerCAmelCase__ : Dict ): """simple docstring""" raise Exception(F"You cannot use ``setdefault`` on a {self.__class__.__name__} instance." ) def UpperCamelCase__ ( self : str , *lowerCAmelCase__ : Any , **lowerCAmelCase__ : List[Any] ): """simple docstring""" raise Exception(F"You cannot use ``pop`` on a {self.__class__.__name__} instance." ) def UpperCamelCase__ ( self : int , *lowerCAmelCase__ : List[str] , **lowerCAmelCase__ : List[Any] ): """simple docstring""" raise Exception(F"You cannot use ``update`` on a {self.__class__.__name__} instance." ) def __getitem__( self : Union[str, Any] , lowerCAmelCase__ : Optional[Any] ): """simple docstring""" if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): __SCREAMING_SNAKE_CASE : Tuple = dict(self.items() ) return inner_dict[k] else: return self.to_tuple()[k] def __setattr__( self : str , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Any ): """simple docstring""" if name in self.keys() and value is not None: # Don't call self.__setitem__ to avoid recursion errors super().__setitem__(lowerCAmelCase__ , lowerCAmelCase__ ) super().__setattr__(lowerCAmelCase__ , lowerCAmelCase__ ) def __setitem__( self : Tuple , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str ): """simple docstring""" super().__setitem__(lowerCAmelCase__ , lowerCAmelCase__ ) # Don't call self.__setattr__ to avoid recursion errors super().__setattr__(lowerCAmelCase__ , lowerCAmelCase__ ) def UpperCamelCase__ ( self : Optional[Any] ): """simple docstring""" return tuple(self[k] for k in self.keys() ) class _UpperCamelCase ( lowerCamelCase__ , lowerCamelCase__ ): '''simple docstring''' @classmethod def UpperCamelCase__ ( cls : List[Any] , lowerCAmelCase__ : Tuple ): """simple docstring""" raise ValueError( F"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}" ) class _UpperCamelCase ( lowerCamelCase__ ): '''simple docstring''' _A : Dict = '''longest''' _A : Optional[Any] = '''max_length''' _A : Tuple = '''do_not_pad''' class _UpperCamelCase ( lowerCamelCase__ ): '''simple docstring''' _A : Optional[int] = '''pt''' _A : Union[str, Any] = '''tf''' _A : Union[str, Any] = '''np''' _A : Dict = '''jax''' class _UpperCamelCase : '''simple docstring''' def __init__( self : str , lowerCAmelCase__ : List[ContextManager] ): """simple docstring""" __SCREAMING_SNAKE_CASE : int = context_managers __SCREAMING_SNAKE_CASE : Dict = ExitStack() def __enter__( self : Union[str, Any] ): """simple docstring""" for context_manager in self.context_managers: self.stack.enter_context(lowerCAmelCase__ ) def __exit__( self : Any , *lowerCAmelCase__ : Any , **lowerCAmelCase__ : List[str] ): """simple docstring""" self.stack.__exit__(*lowerCAmelCase__ , **lowerCAmelCase__ ) def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] ): __SCREAMING_SNAKE_CASE : Optional[Any] = infer_framework(_lowerCamelCase ) if framework == "tf": __SCREAMING_SNAKE_CASE : Optional[Any] = inspect.signature(model_class.call ) # TensorFlow models elif framework == "pt": __SCREAMING_SNAKE_CASE : int = inspect.signature(model_class.forward ) # PyTorch models else: __SCREAMING_SNAKE_CASE : List[Any] = inspect.signature(model_class.__call__ ) # Flax models for p in signature.parameters: if p == "return_loss" and signature.parameters[p].default is True: return True return False def lowerCAmelCase_ ( _lowerCamelCase: Tuple ): __SCREAMING_SNAKE_CASE : Optional[Any] = model_class.__name__ __SCREAMING_SNAKE_CASE : List[Any] = infer_framework(_lowerCamelCase ) if framework == "tf": __SCREAMING_SNAKE_CASE : Union[str, Any] = inspect.signature(model_class.call ) # TensorFlow models elif framework == "pt": __SCREAMING_SNAKE_CASE : Dict = inspect.signature(model_class.forward ) # PyTorch models else: __SCREAMING_SNAKE_CASE : List[Any] = inspect.signature(model_class.__call__ ) # Flax models if "QuestionAnswering" in model_name: return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")] else: return [p for p in signature.parameters if "label" in p] def lowerCAmelCase_ ( _lowerCamelCase: MutableMapping , _lowerCamelCase: str = "" , _lowerCamelCase: str = "." ): def _flatten_dict(_lowerCamelCase: str , _lowerCamelCase: Any="" , _lowerCamelCase: List[Any]="." ): for k, v in d.items(): __SCREAMING_SNAKE_CASE : Tuple = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k if v and isinstance(_lowerCamelCase , _lowerCamelCase ): yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items() else: yield key, v return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ) @contextmanager def lowerCAmelCase_ ( _lowerCamelCase: Dict , _lowerCamelCase: bool = False ): if use_temp_dir: with tempfile.TemporaryDirectory() as tmp_dir: yield tmp_dir else: yield working_dir def lowerCAmelCase_ ( _lowerCamelCase: Union[str, Any] , _lowerCamelCase: Optional[Any]=None ): if is_numpy_array(_lowerCamelCase ): return np.transpose(_lowerCamelCase , axes=_lowerCamelCase ) elif is_torch_tensor(_lowerCamelCase ): return array.T if axes is None else array.permute(*_lowerCamelCase ) elif is_tf_tensor(_lowerCamelCase ): import tensorflow as tf return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase ) elif is_jax_tensor(_lowerCamelCase ): return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase ) else: raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." ) def lowerCAmelCase_ ( _lowerCamelCase: Union[str, Any] , _lowerCamelCase: Dict ): if is_numpy_array(_lowerCamelCase ): return np.reshape(_lowerCamelCase , _lowerCamelCase ) elif is_torch_tensor(_lowerCamelCase ): return array.reshape(*_lowerCamelCase ) elif is_tf_tensor(_lowerCamelCase ): import tensorflow as tf return tf.reshape(_lowerCamelCase , _lowerCamelCase ) elif is_jax_tensor(_lowerCamelCase ): return jnp.reshape(_lowerCamelCase , _lowerCamelCase ) else: raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." ) def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] , _lowerCamelCase: Dict=None ): if is_numpy_array(_lowerCamelCase ): return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase ) elif is_torch_tensor(_lowerCamelCase ): return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase ) elif is_tf_tensor(_lowerCamelCase ): import tensorflow as tf return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase ) elif is_jax_tensor(_lowerCamelCase ): return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase ) else: raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." ) def lowerCAmelCase_ ( _lowerCamelCase: List[str] , _lowerCamelCase: List[str] ): if is_numpy_array(_lowerCamelCase ): return np.expand_dims(_lowerCamelCase , _lowerCamelCase ) elif is_torch_tensor(_lowerCamelCase ): return array.unsqueeze(dim=_lowerCamelCase ) elif is_tf_tensor(_lowerCamelCase ): import tensorflow as tf return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase ) elif is_jax_tensor(_lowerCamelCase ): return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase ) else: raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." ) def lowerCAmelCase_ ( _lowerCamelCase: int ): if is_numpy_array(_lowerCamelCase ): return np.size(_lowerCamelCase ) elif is_torch_tensor(_lowerCamelCase ): return array.numel() elif is_tf_tensor(_lowerCamelCase ): import tensorflow as tf return tf.size(_lowerCamelCase ) elif is_jax_tensor(_lowerCamelCase ): return array.size else: raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." ) def lowerCAmelCase_ ( _lowerCamelCase: str , _lowerCamelCase: Any ): for key, value in auto_map.items(): if isinstance(_lowerCamelCase , (tuple, list) ): __SCREAMING_SNAKE_CASE : Dict = [F"{repo_id}--{v}" if (v is not None and """--""" not in v) else v for v in value] elif value is not None and "--" not in value: __SCREAMING_SNAKE_CASE : Any = F"{repo_id}--{value}" return auto_map def lowerCAmelCase_ ( _lowerCamelCase: Optional[int] ): for base_class in inspect.getmro(_lowerCamelCase ): __SCREAMING_SNAKE_CASE : Optional[Any] = base_class.__module__ __SCREAMING_SNAKE_CASE : Any = base_class.__name__ if module.startswith("""tensorflow""" ) or module.startswith("""keras""" ) or name == "TFPreTrainedModel": return "tf" elif module.startswith("""torch""" ) or name == "PreTrainedModel": return "pt" elif module.startswith("""flax""" ) or module.startswith("""jax""" ) or name == "FlaxPreTrainedModel": return "flax" else: raise TypeError(F"Could not infer framework from class {model_class}." )
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0
'''simple docstring''' # Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForVisionaSeq from ..utils import requires_backends from .base import PipelineTool if TYPE_CHECKING: from PIL import Image class _A ( __lowercase ): lowercase__: Optional[int] = '''Salesforce/blip-image-captioning-base''' lowercase__: Optional[Any] = ( '''This is a tool that generates a description of an image. It takes an input named `image` which should be the ''' '''image to caption, and returns a text that contains the description in English.''' ) lowercase__: int = '''image_captioner''' lowercase__: Optional[Any] = AutoModelForVisionaSeq lowercase__: int = ['''image'''] lowercase__: List[str] = ['''text'''] def __init__( self : Tuple , *__magic_name__ : List[str] , **__magic_name__ : List[Any] ) -> List[str]: """simple docstring""" requires_backends(self , ["""vision"""] ) super().__init__(*__magic_name__ , **__magic_name__ ) def lowercase__ ( self : Optional[int] , __magic_name__ : "Image" ) -> Optional[Any]: """simple docstring""" return self.pre_processor(images=__magic_name__ , return_tensors="""pt""" ) def lowercase__ ( self : Optional[Any] , __magic_name__ : int ) -> Optional[Any]: """simple docstring""" return self.model.generate(**__magic_name__ ) def lowercase__ ( self : Optional[int] , __magic_name__ : str ) -> str: """simple docstring""" return self.pre_processor.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ )[0].strip()
26
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 SCREAMING_SNAKE_CASE ( lowerCamelCase__ , unittest.TestCase ): __lowerCamelCase : Dict =GPTSanJapaneseTokenizer __lowerCamelCase : List[Any] =False __lowerCamelCase : List[str] ={'do_clean_text': False, 'add_prefix_space': False} def UpperCamelCase_ ( self : str ): '''simple docstring''' super().setUp() # fmt: off __a = ["""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 __a = {"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}} # 😀 __a = {"""unk_token""": """<unk>"""} __a = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) __a = 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(__lowercase ) ) def UpperCamelCase_ ( self : Dict , **__lowercase : Union[str, Any] ): '''simple docstring''' kwargs.update(self.special_tokens_map ) return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **__lowercase ) def UpperCamelCase_ ( self : Any , __lowercase : str ): '''simple docstring''' __a = """こんにちは、世界。 \nこんばんは、㔺界。😀""" __a = """こんにちは、世界。 \nこんばんは、世界。😀""" return input_text, output_text def UpperCamelCase_ ( self : Optional[Any] , __lowercase : Union[str, Any] ): '''simple docstring''' __a , __a = self.get_input_output_texts(__lowercase ) __a = tokenizer.encode(__lowercase , add_special_tokens=__lowercase ) __a = tokenizer.decode(__lowercase , clean_up_tokenization_spaces=__lowercase ) return text, ids def UpperCamelCase_ ( self : Optional[Any] ): '''simple docstring''' pass # TODO add if relevant def UpperCamelCase_ ( self : Optional[int] ): '''simple docstring''' pass # TODO add if relevant def UpperCamelCase_ ( self : Optional[int] ): '''simple docstring''' pass # TODO add if relevant def UpperCamelCase_ ( self : List[Any] ): '''simple docstring''' __a = self.get_tokenizer() # Testing tokenization __a = """こんにちは、世界。 こんばんは、㔺界。""" __a = ["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""] __a = tokenizer.tokenize(__lowercase ) self.assertListEqual(__lowercase , __lowercase ) # Testing conversion to ids without special tokens __a = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6] __a = tokenizer.convert_tokens_to_ids(__lowercase ) self.assertListEqual(__lowercase , __lowercase ) # Testing conversion to ids with special tokens __a = tokens + [tokenizer.unk_token] __a = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19] __a = tokenizer.convert_tokens_to_ids(__lowercase ) self.assertListEqual(__lowercase , __lowercase ) def UpperCamelCase_ ( self : Optional[Any] ): '''simple docstring''' __a = self.get_tokenizer() # Testing tokenization __a = """こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。""" __a = """こんにちは、、、、世界。こんばんは、、、、世界。""" __a = tokenizer.encode(__lowercase ) __a = tokenizer.decode(__lowercase ) self.assertEqual(__lowercase , __lowercase ) @slow def UpperCamelCase_ ( self : int ): '''simple docstring''' __a = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" ) # Testing tokenization __a = """こんにちは、世界。""" __a = """こんばんは、㔺界。😀""" __a = """こんにちは、世界。こんばんは、世界。😀""" __a = tokenizer.encode(prefix_text + input_text ) __a = tokenizer.encode("""""" , prefix_text=prefix_text + input_text ) __a = tokenizer.encode(__lowercase , prefix_text=__lowercase ) __a = tokenizer.decode(__lowercase ) __a = tokenizer.decode(__lowercase ) __a = tokenizer.decode(__lowercase ) self.assertEqual(__lowercase , __lowercase ) self.assertEqual(__lowercase , __lowercase ) self.assertEqual(__lowercase , __lowercase ) @slow def UpperCamelCase_ ( self : Tuple ): '''simple docstring''' __a = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" ) # Testing tokenization __a = """こんにちは、世界。""" __a = """こんばんは、㔺界。😀""" __a = len(tokenizer.encode(__lowercase ) ) - 2 __a = len(tokenizer.encode(__lowercase ) ) - 2 __a = [1] + [0] * (len_prefix + len_text + 1) __a = [1] * (len_prefix + len_text + 1) + [0] __a = [1] + [1] * (len_prefix) + [0] * (len_text + 1) __a = tokenizer(prefix_text + input_text ).token_type_ids __a = tokenizer("""""" , prefix_text=prefix_text + input_text ).token_type_ids __a = tokenizer(__lowercase , prefix_text=__lowercase ).token_type_ids self.assertListEqual(__lowercase , __lowercase ) self.assertListEqual(__lowercase , __lowercase ) self.assertListEqual(__lowercase , __lowercase ) @slow def UpperCamelCase_ ( self : int ): '''simple docstring''' __a = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" ) __a = tokenizer.encode("""あンいワ""" ) __a = tokenizer.encode("""""" , prefix_text="""あンいワ""" ) __a = tokenizer.encode("""いワ""" , prefix_text="""あン""" ) self.assertEqual(tokenizer.decode(__lowercase ) , tokenizer.decode(__lowercase ) ) self.assertEqual(tokenizer.decode(__lowercase ) , tokenizer.decode(__lowercase ) ) self.assertNotEqual(__lowercase , __lowercase ) self.assertNotEqual(__lowercase , __lowercase ) 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 UpperCamelCase_ ( self : List[str] ): '''simple docstring''' __a = self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese""" ) __a = [["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]] __a = tokenizer(__lowercase , padding=__lowercase ) __a = tokenizer.batch_encode_plus(__lowercase , padding=__lowercase ) # fmt: off __a = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]] __a = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]] __a = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]] # fmt: on self.assertListEqual(x_token.input_ids , __lowercase ) self.assertListEqual(x_token.token_type_ids , __lowercase ) self.assertListEqual(x_token.attention_mask , __lowercase ) self.assertListEqual(x_token_a.input_ids , __lowercase ) self.assertListEqual(x_token_a.token_type_ids , __lowercase ) self.assertListEqual(x_token_a.attention_mask , __lowercase ) def UpperCamelCase_ ( self : Union[str, Any] ): '''simple docstring''' # Intentionally convert some words to accommodate character fluctuations unique to Japanese pass def UpperCamelCase_ ( self : Union[str, Any] ): '''simple docstring''' # tokenizer has no padding token pass
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0
'''simple docstring''' import warnings from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding class UpperCAmelCase ( __a): '''simple docstring''' __magic_name__ : int = ["image_processor", "tokenizer"] __magic_name__ : Optional[int] = "CLIPImageProcessor" __magic_name__ : Dict = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast") def __init__( self , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_) -> List[str]: """simple docstring""" a_ =None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." , lowerCAmelCase_ , ) a_ =kwargs.pop("feature_extractor") a_ =image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`.") if tokenizer is None: raise ValueError("You need to specify a `tokenizer`.") super().__init__(lowerCAmelCase_ , lowerCAmelCase_) def __call__( self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_=None , **lowerCAmelCase_) -> Optional[int]: """simple docstring""" if text is None and images is None: raise ValueError("You have to specify either text or images. Both cannot be none.") if text is not None: a_ =self.tokenizer(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_) if images is not None: a_ =self.image_processor(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_) if text is not None and images is not None: a_ =image_features.pixel_values return encoding elif text is not None: return encoding else: return BatchEncoding(data=dict(**lowerCAmelCase_) , tensor_type=lowerCAmelCase_) def lowercase_ ( self , *lowerCAmelCase_ , **lowerCAmelCase_) -> Tuple: """simple docstring""" return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_) def lowercase_ ( self , *lowerCAmelCase_ , **lowerCAmelCase_) -> Tuple: """simple docstring""" return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_) @property def lowercase_ ( self) -> Optional[Any]: """simple docstring""" a_ =self.tokenizer.model_input_names a_ =self.image_processor.model_input_names return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
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'''simple docstring''' import argparse import json import os import numpy as np import PIL import requests import tensorflow.keras.applications.efficientnet as efficientnet import torch from huggingface_hub import hf_hub_download from PIL import Image from tensorflow.keras.preprocessing import image from transformers import ( EfficientNetConfig, EfficientNetForImageClassification, EfficientNetImageProcessor, ) from transformers.utils import logging logging.set_verbosity_info() lowercase = logging.get_logger(__name__) lowercase = { '''b0''': efficientnet.EfficientNetBa, '''b1''': efficientnet.EfficientNetBa, '''b2''': efficientnet.EfficientNetBa, '''b3''': efficientnet.EfficientNetBa, '''b4''': efficientnet.EfficientNetBa, '''b5''': efficientnet.EfficientNetBa, '''b6''': efficientnet.EfficientNetBa, '''b7''': efficientnet.EfficientNetBa, } lowercase = { '''b0''': { '''hidden_dim''': 1_280, '''width_coef''': 1.0, '''depth_coef''': 1.0, '''image_size''': 224, '''dropout_rate''': 0.2, '''dw_padding''': [], }, '''b1''': { '''hidden_dim''': 1_280, '''width_coef''': 1.0, '''depth_coef''': 1.1, '''image_size''': 240, '''dropout_rate''': 0.2, '''dw_padding''': [16], }, '''b2''': { '''hidden_dim''': 1_408, '''width_coef''': 1.1, '''depth_coef''': 1.2, '''image_size''': 260, '''dropout_rate''': 0.3, '''dw_padding''': [5, 8, 16], }, '''b3''': { '''hidden_dim''': 1_536, '''width_coef''': 1.2, '''depth_coef''': 1.4, '''image_size''': 300, '''dropout_rate''': 0.3, '''dw_padding''': [5, 18], }, '''b4''': { '''hidden_dim''': 1_792, '''width_coef''': 1.4, '''depth_coef''': 1.8, '''image_size''': 380, '''dropout_rate''': 0.4, '''dw_padding''': [6], }, '''b5''': { '''hidden_dim''': 2_048, '''width_coef''': 1.6, '''depth_coef''': 2.2, '''image_size''': 456, '''dropout_rate''': 0.4, '''dw_padding''': [13, 27], }, '''b6''': { '''hidden_dim''': 2_304, '''width_coef''': 1.8, '''depth_coef''': 2.6, '''image_size''': 528, '''dropout_rate''': 0.5, '''dw_padding''': [31], }, '''b7''': { '''hidden_dim''': 2_560, '''width_coef''': 2.0, '''depth_coef''': 3.1, '''image_size''': 600, '''dropout_rate''': 0.5, '''dw_padding''': [18], }, } def UpperCAmelCase_ ( lowercase__ ): '''simple docstring''' a_ =EfficientNetConfig() a_ =CONFIG_MAP[model_name]["hidden_dim"] a_ =CONFIG_MAP[model_name]["width_coef"] a_ =CONFIG_MAP[model_name]["depth_coef"] a_ =CONFIG_MAP[model_name]["image_size"] a_ =CONFIG_MAP[model_name]["dropout_rate"] a_ =CONFIG_MAP[model_name]["dw_padding"] a_ ="huggingface/label-files" a_ ="imagenet-1k-id2label.json" a_ =1_0_0_0 a_ =json.load(open(hf_hub_download(lowercase__ , lowercase__ , repo_type="dataset" ) , "r" ) ) a_ ={int(lowercase__ ): v for k, v in idalabel.items()} a_ =idalabel a_ ={v: k for k, v in idalabel.items()} return config def UpperCAmelCase_ ( ): '''simple docstring''' a_ ="http://images.cocodataset.org/val2017/000000039769.jpg" a_ =Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw ) return im def UpperCAmelCase_ ( lowercase__ ): '''simple docstring''' a_ =CONFIG_MAP[model_name]["image_size"] a_ =EfficientNetImageProcessor( size={"height": size, "width": size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase__ , ) return preprocessor def UpperCAmelCase_ ( lowercase__ ): '''simple docstring''' a_ =[v.split("_" )[0].split("block" )[1] for v in original_param_names if v.startswith("block" )] a_ =sorted(set(lowercase__ ) ) a_ =len(lowercase__ ) a_ ={b: str(lowercase__ ) for b, i in zip(lowercase__ , range(lowercase__ ) )} a_ =[] rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight") ) rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight") ) rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias") ) rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean") ) rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var") ) for b in block_names: a_ =block_name_mapping[b] rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") ) rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") ) rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") ) rename_keys.append( (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") ) rename_keys.append( (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") ) rename_keys.append( (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") ) rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") ) rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") ) rename_keys.append( (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") ) rename_keys.append( (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") ) rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") ) rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") ) rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") ) rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") ) rename_keys.append( (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") ) rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") ) rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") ) rename_keys.append( (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") ) rename_keys.append( (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") ) rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight") ) rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight") ) rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias") ) rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean") ) rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var") ) a_ ={} for item in rename_keys: if item[0] in original_param_names: a_ ="efficientnet." + item[1] a_ ="classifier.weight" a_ ="classifier.bias" return key_mapping def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ ): '''simple docstring''' for key, value in tf_params.items(): if "normalization" in key: continue a_ =key_mapping[key] if "_conv" in key and "kernel" in key: a_ =torch.from_numpy(lowercase__ ).permute(3 , 2 , 0 , 1 ) elif "depthwise_kernel" in key: a_ =torch.from_numpy(lowercase__ ).permute(2 , 3 , 0 , 1 ) elif "kernel" in key: a_ =torch.from_numpy(np.transpose(lowercase__ ) ) else: a_ =torch.from_numpy(lowercase__ ) # Replace HF parameters with original TF model parameters assert hf_params[hf_key].shape == new_hf_value.shape hf_params[hf_key].copy_(lowercase__ ) @torch.no_grad() def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ): '''simple docstring''' a_ =model_classes[model_name]( include_top=lowercase__ , weights="imagenet" , input_tensor=lowercase__ , input_shape=lowercase__ , pooling=lowercase__ , classes=1_0_0_0 , classifier_activation="softmax" , ) a_ =original_model.trainable_variables a_ =original_model.non_trainable_variables a_ ={param.name: param.numpy() for param in tf_params} for param in tf_non_train_params: a_ =param.numpy() a_ =list(tf_params.keys() ) # Load HuggingFace model a_ =get_efficientnet_config(lowercase__ ) a_ =EfficientNetForImageClassification(lowercase__ ).eval() a_ =hf_model.state_dict() # Create src-to-dst parameter name mapping dictionary print("Converting parameters..." ) a_ =rename_keys(lowercase__ ) replace_params(lowercase__ , lowercase__ , lowercase__ ) # Initialize preprocessor and preprocess input image a_ =convert_image_processor(lowercase__ ) a_ =preprocessor(images=prepare_img() , return_tensors="pt" ) # HF model inference hf_model.eval() with torch.no_grad(): a_ =hf_model(**lowercase__ ) a_ =outputs.logits.detach().numpy() # Original model inference a_ =False a_ =CONFIG_MAP[model_name]["image_size"] a_ =prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST ) a_ =image.img_to_array(lowercase__ ) a_ =np.expand_dims(lowercase__ , axis=0 ) a_ =original_model.predict(lowercase__ ) # Check whether original and HF model outputs match -> np.allclose assert np.allclose(lowercase__ , lowercase__ , atol=1E-3 ), "The predicted logits are not the same." print("Model outputs match!" ) if save_model: # Create folder to save model if not os.path.isdir(lowercase__ ): os.mkdir(lowercase__ ) # Save converted model and image processor hf_model.save_pretrained(lowercase__ ) preprocessor.save_pretrained(lowercase__ ) if push_to_hub: # Push model and image processor to hub print(F"""Pushing converted {model_name} to the hub...""" ) a_ =F"""efficientnet-{model_name}""" preprocessor.push_to_hub(lowercase__ ) hf_model.push_to_hub(lowercase__ ) if __name__ == "__main__": lowercase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default='''b0''', type=str, help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''', ) parser.add_argument( '''--pytorch_dump_folder_path''', default='''hf_model''', type=str, help='''Path to the output PyTorch model directory.''', ) parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''') parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''') lowercase = parser.parse_args() convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
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1
import argparse import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType ######################################################################## # This is a fully working simple example to use Accelerate # # This example trains a Bert base model on GLUE MRPC # in any of the following settings (with the same script): # - single CPU or single GPU # - multi GPUS (using PyTorch distributed mode) # - (multi) TPUs # - fp16 (mixed-precision) or fp32 (normal precision) # # To run it in each of these various modes, follow the instructions # in the readme for examples: # https://github.com/huggingface/accelerate/tree/main/examples # ######################################################################## _lowerCamelCase : Optional[Any] = 16 _lowerCamelCase : List[Any] = 32 def _lowerCAmelCase ( __magic_name__ :Accelerator , __magic_name__ :int = 1_6 ): UpperCAmelCase_ = AutoTokenizer.from_pretrained('''bert-base-cased''' ) UpperCAmelCase_ = load_dataset('''glue''' , '''mrpc''' ) def tokenize_function(__magic_name__ :int ): # max_length=None => use the model max length (it's actually the default) UpperCAmelCase_ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=__magic_name__ , max_length=__magic_name__ ) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset # starting with the main process first: with accelerator.main_process_first(): UpperCAmelCase_ = datasets.map( __magic_name__ , batched=__magic_name__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library UpperCAmelCase_ = tokenized_datasets.rename_column('''label''' , '''labels''' ) def collate_fn(__magic_name__ :List[Any] ): # On TPU it's best to pad everything to the same length or training will be very slow. UpperCAmelCase_ = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None # When using mixed precision we want round multiples of 8/16 if accelerator.mixed_precision == "fp8": UpperCAmelCase_ = 1_6 elif accelerator.mixed_precision != "no": UpperCAmelCase_ = 8 else: UpperCAmelCase_ = None return tokenizer.pad( __magic_name__ , padding='''longest''' , max_length=__magic_name__ , pad_to_multiple_of=__magic_name__ , return_tensors='''pt''' , ) # Instantiate dataloaders. UpperCAmelCase_ = DataLoader( tokenized_datasets['''train'''] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=__magic_name__ , drop_last=__magic_name__ ) UpperCAmelCase_ = DataLoader( tokenized_datasets['''validation'''] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=__magic_name__ , drop_last=(accelerator.mixed_precision == '''fp8''') , ) return train_dataloader, eval_dataloader def _lowerCAmelCase ( __magic_name__ :Tuple , __magic_name__ :List[Any] ): # Initialize accelerator UpperCAmelCase_ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision ) # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs UpperCAmelCase_ = config['''lr'''] UpperCAmelCase_ = int(config['''num_epochs'''] ) UpperCAmelCase_ = int(config['''seed'''] ) UpperCAmelCase_ = int(config['''batch_size'''] ) UpperCAmelCase_ = evaluate.load('''glue''' , '''mrpc''' ) # If the batch size is too big we use gradient accumulation UpperCAmelCase_ = 1 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU: UpperCAmelCase_ = batch_size // MAX_GPU_BATCH_SIZE UpperCAmelCase_ = MAX_GPU_BATCH_SIZE set_seed(__magic_name__ ) UpperCAmelCase_, UpperCAmelCase_ = get_dataloaders(__magic_name__ , __magic_name__ ) # Instantiate the model (we build the model here so that the seed also control new weights initialization) UpperCAmelCase_ = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=__magic_name__ ) # We could avoid this line since the accelerator is set with `device_placement=True` (default value). # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that). UpperCAmelCase_ = model.to(accelerator.device ) # Instantiate optimizer UpperCAmelCase_ = AdamW(params=model.parameters() , lr=__magic_name__ ) # Instantiate scheduler UpperCAmelCase_ = get_linear_schedule_with_warmup( optimizer=__magic_name__ , num_warmup_steps=1_0_0 , num_training_steps=(len(__magic_name__ ) * num_epochs) // gradient_accumulation_steps , ) # Prepare everything # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the # prepare method. UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ = accelerator.prepare( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) # Now we train the model for epoch in range(__magic_name__ ): model.train() for step, batch in enumerate(__magic_name__ ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) UpperCAmelCase_ = model(**__magic_name__ ) UpperCAmelCase_ = outputs.loss UpperCAmelCase_ = loss / gradient_accumulation_steps accelerator.backward(__magic_name__ ) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() model.eval() for step, batch in enumerate(__magic_name__ ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) with torch.no_grad(): UpperCAmelCase_ = model(**__magic_name__ ) UpperCAmelCase_ = outputs.logits.argmax(dim=-1 ) UpperCAmelCase_, UpperCAmelCase_ = accelerator.gather_for_metrics((predictions, batch['''labels''']) ) metric.add_batch( predictions=__magic_name__ , references=__magic_name__ , ) UpperCAmelCase_ = metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(F'''epoch {epoch}:''' , __magic_name__ ) def _lowerCAmelCase ( ): UpperCAmelCase_ = argparse.ArgumentParser(description='''Simple example of training script.''' ) parser.add_argument( '''--mixed_precision''' , type=__magic_name__ , default=__magic_name__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose''' '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.''' '''and an Nvidia Ampere GPU.''' , ) parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' ) UpperCAmelCase_ = parser.parse_args() UpperCAmelCase_ = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6} training_function(__magic_name__ , __magic_name__ ) if __name__ == "__main__": main()
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import random import sys import numpy as np from matplotlib import pyplot as plt from matplotlib.colors import ListedColormap _lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>' _lowerCamelCase : Dict = [0] * 100 + [1] * 10 random.shuffle(choice) def _lowerCAmelCase ( __magic_name__ :int ): UpperCAmelCase_ = [[False for i in range(__magic_name__ )] for j in range(__magic_name__ )] return canvas def _lowerCAmelCase ( __magic_name__ :list[list[bool]] ): for i, row in enumerate(__magic_name__ ): for j, _ in enumerate(__magic_name__ ): UpperCAmelCase_ = bool(random.getrandbits(1 ) ) def _lowerCAmelCase ( __magic_name__ :list[list[bool]] ): UpperCAmelCase_ = np.array(__magic_name__ ) UpperCAmelCase_ = np.array(create_canvas(current_canvas.shape[0] ) ) for r, row in enumerate(__magic_name__ ): for c, pt in enumerate(__magic_name__ ): UpperCAmelCase_ = __judge_point( __magic_name__ , current_canvas[r - 1 : r + 2, c - 1 : c + 2] ) UpperCAmelCase_ = next_gen_canvas del next_gen_canvas # cleaning memory as we move on. UpperCAmelCase_ = current_canvas.tolist() return return_canvas def _lowerCAmelCase ( __magic_name__ :bool , __magic_name__ :list[list[bool]] ): UpperCAmelCase_ = 0 UpperCAmelCase_ = 0 # finding dead or alive neighbours count. for i in neighbours: for status in i: if status: alive += 1 else: dead += 1 # handling duplicate entry for focus pt. if pt: alive -= 1 else: dead -= 1 # running the rules of game here. UpperCAmelCase_ = pt if pt: if alive < 2: UpperCAmelCase_ = False elif alive == 2 or alive == 3: UpperCAmelCase_ = True elif alive > 3: UpperCAmelCase_ = False else: if alive == 3: UpperCAmelCase_ = True return state if __name__ == "__main__": if len(sys.argv) != 2: raise Exception(usage_doc) _lowerCamelCase : Union[str, Any] = int(sys.argv[1]) # main working structure of this module. _lowerCamelCase : Any = create_canvas(canvas_size) seed(c) _lowerCamelCase , _lowerCamelCase : Any = plt.subplots() fig.show() _lowerCamelCase : Optional[int] = ListedColormap(['w', 'k']) try: while True: _lowerCamelCase : Union[str, Any] = run(c) ax.matshow(c, cmap=cmap) fig.canvas.draw() ax.cla() except KeyboardInterrupt: # do nothing. pass
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from dataclasses import dataclass from enum import Enum from typing import List, Optional, Union import numpy as np import PIL from PIL import Image from ...utils import BaseOutput, is_torch_available, is_transformers_available @dataclass class a__ ( _snake_case ): """simple docstring""" A__ : Union[List[PIL.Image.Image], np.ndarray] A__ : Optional[List[bool]] if is_transformers_available() and is_torch_available(): from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
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import json import sys import tempfile import unittest from pathlib import Path import transformers from transformers import ( CONFIG_MAPPING, FEATURE_EXTRACTOR_MAPPING, AutoConfig, AutoFeatureExtractor, WavaVecaConfig, WavaVecaFeatureExtractor, ) from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils''')) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402 __magic_name__ = get_tests_dir('''fixtures''') __magic_name__ = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''') __magic_name__ = get_tests_dir('''fixtures/dummy-config.json''') class a__ ( unittest.TestCase ): """simple docstring""" def __UpperCAmelCase ( self :str ): lowercase = 0 def __UpperCAmelCase ( self :Tuple ): lowercase = AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h' ) self.assertIsInstance(lowercase__ , lowercase__ ) def __UpperCAmelCase ( self :Any ): lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ ) self.assertIsInstance(lowercase__ , lowercase__ ) def __UpperCAmelCase ( self :int ): with tempfile.TemporaryDirectory() as tmpdirname: lowercase = WavaVecaConfig() # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ ).to_dict() config_dict.pop('feature_extractor_type' ) lowercase = WavaVecaFeatureExtractor(**lowercase__ ) # save in new folder model_config.save_pretrained(lowercase__ ) config.save_pretrained(lowercase__ ) lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ ) # make sure private variable is not incorrectly saved lowercase = json.loads(config.to_json_string() ) self.assertTrue('_processor_class' not in dict_as_saved ) self.assertIsInstance(lowercase__ , lowercase__ ) def __UpperCAmelCase ( self :List[Any] ): lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ ) self.assertIsInstance(lowercase__ , lowercase__ ) def __UpperCAmelCase ( self :List[Any] ): with self.assertRaisesRegex( lowercase__ , 'bert-base is not a local folder and is not a valid model identifier' ): lowercase = AutoFeatureExtractor.from_pretrained('bert-base' ) def __UpperCAmelCase ( self :List[str] ): with self.assertRaisesRegex( lowercase__ , R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)' ): lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ , revision='aaaaaa' ) def __UpperCAmelCase ( self :Any ): with self.assertRaisesRegex( lowercase__ , 'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.' , ): lowercase = AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model' ) def __UpperCAmelCase ( self :Tuple ): # If remote code is not set, we will time out when asking whether to load the model. with self.assertRaises(lowercase__ ): lowercase = AutoFeatureExtractor.from_pretrained( 'hf-internal-testing/test_dynamic_feature_extractor' ) # If remote code is disabled, we can't load this config. with self.assertRaises(lowercase__ ): lowercase = AutoFeatureExtractor.from_pretrained( 'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ ) lowercase = AutoFeatureExtractor.from_pretrained( 'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ ) self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' ) # Test feature extractor can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained(lowercase__ ) lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ , trust_remote_code=lowercase__ ) self.assertEqual(reloaded_feature_extractor.__class__.__name__ , 'NewFeatureExtractor' ) def __UpperCAmelCase ( self :Optional[int] ): try: AutoConfig.register('custom' , lowercase__ ) AutoFeatureExtractor.register(lowercase__ , lowercase__ ) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(lowercase__ ): AutoFeatureExtractor.register(lowercase__ , lowercase__ ) # Now that the config is registered, it can be used as any other config with the auto-API lowercase = CustomFeatureExtractor.from_pretrained(lowercase__ ) with tempfile.TemporaryDirectory() as tmp_dir: feature_extractor.save_pretrained(lowercase__ ) lowercase = AutoFeatureExtractor.from_pretrained(lowercase__ ) self.assertIsInstance(lowercase__ , lowercase__ ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig] def __UpperCAmelCase ( self :Any ): class a__ ( _snake_case ): """simple docstring""" A__ : Union[str, Any] = True try: AutoConfig.register('custom' , lowercase__ ) AutoFeatureExtractor.register(lowercase__ , lowercase__ ) # If remote code is not set, the default is to use local lowercase = AutoFeatureExtractor.from_pretrained( 'hf-internal-testing/test_dynamic_feature_extractor' ) self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' ) self.assertTrue(feature_extractor.is_local ) # If remote code is disabled, we load the local one. lowercase = AutoFeatureExtractor.from_pretrained( 'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ ) self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' ) self.assertTrue(feature_extractor.is_local ) # If remote is enabled, we load from the Hub lowercase = AutoFeatureExtractor.from_pretrained( 'hf-internal-testing/test_dynamic_feature_extractor' , trust_remote_code=lowercase__ ) self.assertEqual(feature_extractor.__class__.__name__ , 'NewFeatureExtractor' ) self.assertTrue(not hasattr(lowercase__ , 'is_local' ) ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content: del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
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'''simple docstring''' from collections import deque from .hash_table import HashTable class lowerCamelCase ( lowerCamelCase ): '''simple docstring''' def __init__( self : Union[str, Any] , *UpperCAmelCase__ : Optional[Any] , **UpperCAmelCase__ : Dict ) ->List[Any]: super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ ) def lowerCAmelCase__ ( self : str , UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] ) ->List[Any]: UpperCAmelCase_ = deque([] ) if self.values[key] is None else self.values[key] self.values[key].appendleft(UpperCAmelCase__ ) UpperCAmelCase_ = self.values[key] def lowerCAmelCase__ ( self : List[Any] ) ->str: return ( sum(self.charge_factor - len(UpperCAmelCase__ ) for slot in self.values ) / self.size_table * self.charge_factor ) def lowerCAmelCase__ ( self : Tuple , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Dict=None ) ->str: if not ( len(self.values[key] ) == self.charge_factor and self.values.count(UpperCAmelCase__ ) == 0 ): return key return super()._collision_resolution(UpperCAmelCase__ , UpperCAmelCase__ )
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'''simple docstring''' import argparse import json import os import re import torch from transformers import BloomConfig, BloomModel from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME from transformers.utils import logging logging.set_verbosity_info() lowercase__ : Union[str, Any] = [ "word_embeddings_layernorm.weight", "word_embeddings_layernorm.bias", "input_layernorm.weight", "input_layernorm.bias", "post_attention_layernorm.weight", "post_attention_layernorm.bias", "self_attention.dense.bias", "mlp.dense_4h_to_h.bias", "ln_f.weight", "ln_f.bias", ] lowercase__ : Dict = [ "mlp.dense_4h_to_h.weight", "self_attention.dense.weight", ] def __lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : Tuple ): '''simple docstring''' UpperCAmelCase_ = { '''word_embeddings.weight''': '''word_embeddings.weight''', '''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''', '''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''', '''weight''': '''ln_f.weight''', '''bias''': '''ln_f.bias''', } if key in layer_rename_map: return layer_rename_map[key] # Handle transformer blocks UpperCAmelCase_ = int(re.match(R'''.*layer_(\d*).*''' , _UpperCamelCase )[1] ) layer_number -= 3 return F"""h.{layer_number}.""" + key def __lowerCamelCase ( _UpperCamelCase : Optional[Any] ): '''simple docstring''' if dtype == torch.bool: return 1 / 8 UpperCAmelCase_ = re.search(R'''[^\d](\d+)$''' , str(_UpperCamelCase ) ) if bit_search is None: raise ValueError(F"""`dtype` is not a valid dtype: {dtype}.""" ) UpperCAmelCase_ = int(bit_search.groups()[0] ) return bit_size // 8 def __lowerCamelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : str , _UpperCamelCase : Tuple , _UpperCamelCase : Dict , _UpperCamelCase : Dict ): '''simple docstring''' if bloom_config_file == "": UpperCAmelCase_ = BloomConfig() else: UpperCAmelCase_ = BloomConfig.from_json_file(_UpperCamelCase ) if shard_model: UpperCAmelCase_ = os.listdir(_UpperCamelCase ) UpperCAmelCase_ = sorted(filter(lambda _UpperCamelCase : s.startswith('''layer''' ) and "model_00" in s , _UpperCamelCase ) ) UpperCAmelCase_ = {'''weight_map''': {}, '''metadata''': {}} UpperCAmelCase_ = 0 UpperCAmelCase_ = None UpperCAmelCase_ = BloomConfig() for j, file in enumerate(_UpperCamelCase ): print('''Processing file: {}'''.format(_UpperCamelCase ) ) UpperCAmelCase_ = None for i in range(_UpperCamelCase ): # load all TP files UpperCAmelCase_ = file.replace('''model_00''' , F"""model_0{i}""" ) UpperCAmelCase_ = torch.load(os.path.join(_UpperCamelCase , _UpperCamelCase ) , map_location='''cpu''' ) # Rename keys in the transformers names UpperCAmelCase_ = list(temp.keys() ) for key in keys: UpperCAmelCase_ = temp.pop(_UpperCamelCase ) if tensors is None: UpperCAmelCase_ = temp else: for key in tensors.keys(): if any(key.endswith(_UpperCamelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ): # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425) tensors[key] += temp[key] else: # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel UpperCAmelCase_ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0 # We concatenate these weights accross TP ranks UpperCAmelCase_ = torch.cat([tensors[key], temp[key]] , dim=_UpperCamelCase ) # Divide by the number of TP the weights we want to average for key in tensors.keys(): if any(key.endswith(_UpperCamelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ): UpperCAmelCase_ = tensors[key] / pretraining_tp torch.save( _UpperCamelCase , os.path.join( _UpperCamelCase , '''pytorch_model_{}-of-{}.bin'''.format(str(j + 1 ).zfill(5 ) , str(len(_UpperCamelCase ) ).zfill(5 ) ) , ) , ) for key in tensors.keys(): UpperCAmelCase_ = tensors[key] total_size += value.numel() * get_dtype_size(value.dtype ) if key not in index_dict["weight_map"]: UpperCAmelCase_ = '''pytorch_model_{}-of-{}.bin'''.format( str(j + 1 ).zfill(5 ) , str(len(_UpperCamelCase ) ).zfill(5 ) ) UpperCAmelCase_ = BloomConfig() UpperCAmelCase_ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME UpperCAmelCase_ = total_size with open(_UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f: f.write(config.to_json_string() ) with open(os.path.join(_UpperCamelCase , WEIGHTS_NAME + '''.index.json''' ) , '''w''' , encoding='''utf-8''' ) as f: UpperCAmelCase_ = json.dumps(_UpperCamelCase , indent=2 , sort_keys=_UpperCamelCase ) + '''\n''' f.write(_UpperCamelCase ) else: UpperCAmelCase_ = BloomModel(_UpperCamelCase ) UpperCAmelCase_ = os.listdir(_UpperCamelCase ) UpperCAmelCase_ = sorted(filter(lambda _UpperCamelCase : s.startswith('''layer''' ) and "model_00" in s , _UpperCamelCase ) ) UpperCAmelCase_ = None for i, file in enumerate(_UpperCamelCase ): UpperCAmelCase_ = None for i in range(_UpperCamelCase ): # load all TP files UpperCAmelCase_ = file.replace('''model_00''' , F"""model_0{i}""" ) UpperCAmelCase_ = torch.load(os.path.join(_UpperCamelCase , _UpperCamelCase ) , map_location='''cpu''' ) # Rename keys in the transformers names UpperCAmelCase_ = list(temp.keys() ) for key in keys: UpperCAmelCase_ = temp.pop(_UpperCamelCase ) if tensors is None: UpperCAmelCase_ = temp else: for key in tensors.keys(): # We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425) if any(key.endswith(_UpperCamelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ): tensors[key] += temp[key] else: # Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel UpperCAmelCase_ = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0 # We concatenate these weights accross TP ranks UpperCAmelCase_ = torch.cat([tensors[key], temp[key]] , dim=_UpperCamelCase ) # Divide by the number of TP the weights we want to average for key in tensors.keys(): if any(key.endswith(_UpperCamelCase ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ): UpperCAmelCase_ = tensors[key] / pretraining_tp UpperCAmelCase_ = model.load_state_dict(_UpperCamelCase , strict=_UpperCamelCase ) assert not other_keys.unexpected_keys, F"""The keys {other_keys.unexpected_keys} are unexpected""" if missing_keys is None: UpperCAmelCase_ = set(other_keys.missing_keys ) else: UpperCAmelCase_ = missing_keys.intersection(set(other_keys.missing_keys ) ) assert not missing_keys, F"""The keys {missing_keys} are missing""" # Save pytorch-model os.makedirs(_UpperCamelCase , exist_ok=_UpperCamelCase ) UpperCAmelCase_ = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME UpperCAmelCase_ = pytorch_dump_folder_path + '''/''' + CONFIG_NAME print(F"""Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}""" ) if config.torch_dtype is not None: UpperCAmelCase_ = model.to(config.torch_dtype ) torch.save(model.state_dict() , _UpperCamelCase ) print(F"""Save configuration file to {pytorch_config_dump_path}""" ) with open(_UpperCamelCase , '''w''' , encoding='''utf-8''' ) as f: f.write(config.to_json_string() ) if __name__ == "__main__": lowercase__ : str = argparse.ArgumentParser() # Required parameters parser.add_argument( "--bloom_checkpoint_path", default=None, type=str, required=True, help="Path to the Megatron-LM 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( "--bloom_config_file", default="", type=str, help=( "An optional config json file corresponding to the pre-trained model. \n" "This specifies the model architecture." ), ) parser.add_argument( "--shard_model", action="store_true", help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint", ) parser.add_argument( "--pretraining_tp", default=4, type=int, help="Pretraining TP rank that has been used when training the model in Megatron-LM \n", ) lowercase__ : Any = parser.parse_args() convert_bloom_checkpoint_to_pytorch( args.bloom_checkpoint_path, args.bloom_config_file, args.pytorch_dump_folder_path, args.shard_model, args.pretraining_tp, )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available lowercase : Union[str, Any] = { "configuration_rag": ["RagConfig"], "retrieval_rag": ["RagRetriever"], "tokenization_rag": ["RagTokenizer"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase : Dict = [ "RagModel", "RagPreTrainedModel", "RagSequenceForGeneration", "RagTokenForGeneration", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase : Optional[Any] = [ "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 : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import warnings from pathlib import Path from typing import List, Tuple, Union import fire from torch import nn from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel from transformers.utils import logging lowercase : int = logging.get_logger(__name__) def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ): A : str = nn.ModuleList([src_layers[i] for i in layers_to_copy] ) assert len(lowerCamelCase_ ) == len(lowerCamelCase_ ), F'{len(lowerCamelCase_ )} != {len(lowerCamelCase_ )}' dest_layers.load_state_dict(layers_to_copy.state_dict() ) lowercase : Optional[int] = { # maps num layers in teacher -> num_layers in student -> which teacher layers to copy. # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP 12: { 1: [0], # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher 2: [0, 6], 3: [0, 6, 11], 4: [0, 4, 8, 11], 6: [0, 2, 4, 7, 9, 11], 9: [0, 1, 2, 4, 5, 7, 9, 10, 11], 12: list(range(12)), }, 16: { # maps num layers in student -> which teacher layers to copy 1: [0], 2: [0, 15], 3: [0, 8, 15], 4: [0, 5, 10, 15], 6: [0, 3, 6, 9, 12, 15], 8: [0, 2, 4, 6, 8, 10, 12, 15], 9: [0, 1, 3, 5, 7, 9, 11, 13, 15], 12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15], 16: list(range(16)), }, 6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))}, } lowercase : Union[str, Any] = { # maps num layers in student -> which teacher layers to copy. 6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]}, 12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]}, 16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]}, } def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ): try: A : Dict = LAYERS_TO_COPY[n_teacher][n_student] return val except KeyError: if n_student != n_teacher: warnings.warn( F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first' F' {n_student}' ) return list(range(lowerCamelCase_ ) ) def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ): if n_student > n_teacher: raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' ) elif n_teacher == n_student: return list(range(lowerCamelCase_ ) ) elif n_student == 1: return [n_teacher - 1] else: return LAYERS_TO_SUPERVISE[n_teacher][n_student] def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ = "student" , lowerCamelCase_ = None , lowerCamelCase_ = None , lowerCamelCase_=False , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ , ): A : List[str] = '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.''' assert (e is not None) or (d is not None), _msg if isinstance(lowerCamelCase_ , lowerCamelCase_ ): AutoTokenizer.from_pretrained(lowerCamelCase_ ).save_pretrained(lowerCamelCase_ ) # purely for convenience A : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_ ).eval() else: assert isinstance(lowerCamelCase_ , lowerCamelCase_ ), F'teacher must be a model or string got type {type(lowerCamelCase_ )}' A : Tuple = teacher.config.to_diff_dict() try: A , A : str = teacher.config.encoder_layers, teacher.config.decoder_layers if e is None: A : str = teacher_e if d is None: A : List[str] = teacher_d init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d} ) except AttributeError: # T5 if hasattr(teacher.config , '''num_encoder_layers''' ): A , A : str = teacher.config.num_encoder_layers, teacher.config.num_decoder_layers else: A , A : List[str] = teacher.config.num_layers, teacher.config.num_decoder_layers if e is None: A : Union[str, Any] = teacher_e if d is None: A : Any = teacher_d if hasattr(teacher.config , '''num_encoder_layers''' ): init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d} ) else: init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d} ) # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs init_kwargs.update(lowerCamelCase_ ) # Copy weights A : Dict = teacher.config_class(**lowerCamelCase_ ) A : List[str] = AutoModelForSeqaSeqLM.from_config(lowerCamelCase_ ) # Start by copying the full teacher state dict this will copy the first N teacher layers to the student. A : int = student.load_state_dict(teacher.state_dict() , strict=lowerCamelCase_ ) assert info.missing_keys == [], info.missing_keys # every student key should have a teacher keys. if copy_first_teacher_layers: # Our copying is done. We just log and save A , A : Tuple = list(range(lowerCamelCase_ ) ), list(range(lowerCamelCase_ ) ) logger.info( F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to' F' {save_path}' ) student.save_pretrained(lowerCamelCase_ ) return student, e_layers_to_copy, d_layers_to_copy # Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer. if e_layers_to_copy is None: A : List[int] = pick_layers_to_copy(lowerCamelCase_ , lowerCamelCase_ ) if d_layers_to_copy is None: A : List[int] = pick_layers_to_copy(lowerCamelCase_ , lowerCamelCase_ ) try: if hasattr( lowerCamelCase_ , '''prophetnet''' ): # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers copy_layers(teacher.prophetnet.encoder.layers , student.prophetnet.encoder.layers , lowerCamelCase_ ) copy_layers(teacher.prophetnet.decoder.layers , student.prophetnet.decoder.layers , lowerCamelCase_ ) else: copy_layers(teacher.model.encoder.layers , student.model.encoder.layers , lowerCamelCase_ ) copy_layers(teacher.model.decoder.layers , student.model.decoder.layers , lowerCamelCase_ ) except AttributeError: # For t5, student.model.encoder.layers is called student.encoder.block copy_layers(teacher.encoder.block , student.encoder.block , lowerCamelCase_ ) copy_layers(teacher.decoder.block , student.decoder.block , lowerCamelCase_ ) logger.info( F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' ) A : Tuple = { '''teacher_type''': teacher.config.model_type, '''copied_encoder_layers''': e_layers_to_copy, '''copied_decoder_layers''': d_layers_to_copy, } student.save_pretrained(lowerCamelCase_ ) # Save information about copying for easier reproducibility return student, e_layers_to_copy, d_layers_to_copy if __name__ == "__main__": fire.Fire(create_student_by_copying_alternating_layers)
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'''simple docstring''' import builtins import sys from ...utils.imports import _is_package_available from . import cursor, input from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor from .keymap import KEYMAP _UpperCamelCase : Any = False try: _UpperCamelCase : Any = _is_package_available("google.colab") except ModuleNotFoundError: pass @input.register class _snake_case : def __init__( self , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = [] ): '''simple docstring''' lowerCAmelCase = 0 lowerCAmelCase = choices lowerCAmelCase = prompt if sys.platform == "win32": lowerCAmelCase = '*' else: lowerCAmelCase = '➔ ' def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "" ): '''simple docstring''' if sys.platform != "win32": writeColor(self.choices[index] , 32 , _SCREAMING_SNAKE_CASE ) else: forceWrite(self.choices[index] , _SCREAMING_SNAKE_CASE ) def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ): '''simple docstring''' if index == self.position: forceWrite(F' {self.arrow_char} ' ) self.write_choice(_SCREAMING_SNAKE_CASE ) else: forceWrite(F' {self.choices[index]}' ) reset_cursor() def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 1 ): '''simple docstring''' lowerCAmelCase = self.position if direction == Direction.DOWN: if self.position + 1 >= len(self.choices ): return self.position += num_spaces else: if self.position - 1 < 0: return self.position -= num_spaces clear_line() self.print_choice(_SCREAMING_SNAKE_CASE ) move_cursor(_SCREAMING_SNAKE_CASE , direction.name ) self.print_choice(self.position ) @input.mark(KEYMAP['up'] ) def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' self.move_direction(Direction.UP ) @input.mark(KEYMAP['down'] ) def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' self.move_direction(Direction.DOWN ) @input.mark(KEYMAP['newline'] ) def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' move_cursor(len(self.choices ) - self.position , 'DOWN' ) return self.position @input.mark(KEYMAP['interrupt'] ) def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' move_cursor(len(self.choices ) - self.position , 'DOWN' ) raise KeyboardInterrupt @input.mark_multiple(*[KEYMAP[str(_SCREAMING_SNAKE_CASE )] for number in range(10 )] ) def _SCREAMING_SNAKE_CASE ( self ): '''simple docstring''' lowerCAmelCase = int(chr(self.current_selection ) ) lowerCAmelCase = index - self.position if index == self.position: return if index < len(self.choices ): if self.position > index: self.move_direction(Direction.UP , -movement ) elif self.position < index: self.move_direction(Direction.DOWN , _SCREAMING_SNAKE_CASE ) else: return else: return def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE = 0 ): '''simple docstring''' if self.prompt: linebreak() forceWrite(self.prompt , '\n' ) if in_colab: forceWrite('Please input a choice index (starting from 0), and press enter' , '\n' ) else: forceWrite('Please select a choice using the arrow or number keys, and selecting with enter' , '\n' ) lowerCAmelCase = default_choice for i in range(len(self.choices ) ): self.print_choice(_SCREAMING_SNAKE_CASE ) forceWrite('\n' ) move_cursor(len(self.choices ) - self.position , 'UP' ) with cursor.hide(): while True: if in_colab: try: lowerCAmelCase = int(builtins.input() ) except ValueError: lowerCAmelCase = default_choice else: lowerCAmelCase = self.handle_input() if choice is not None: reset_cursor() for _ in range(len(self.choices ) + 1 ): move_cursor(1 , 'UP' ) clear_line() self.write_choice(_SCREAMING_SNAKE_CASE , '\n' ) return choice
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'''simple docstring''' import warnings from typing import Dict import numpy as np from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline if is_tf_available(): from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING def snake_case ( snake_case : Any ) -> Any: """simple docstring""" return 1.0 / (1.0 + np.exp(-_outputs )) def snake_case ( snake_case : Dict ) -> str: """simple docstring""" lowerCAmelCase = np.max(_outputs , axis=-1 , keepdims=snake_case ) lowerCAmelCase = np.exp(_outputs - maxes ) return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=snake_case ) class _snake_case ( a_ ): SCREAMING_SNAKE_CASE : List[str] = '''sigmoid''' SCREAMING_SNAKE_CASE : List[Any] = '''softmax''' SCREAMING_SNAKE_CASE : Optional[Any] = '''none''' @add_end_docstrings( a_ , R''' return_all_scores (`bool`, *optional*, defaults to `False`): Whether to return all prediction scores or just the one of the predicted class. function_to_apply (`str`, *optional*, defaults to `"default"`): The function to apply to the model outputs in order to retrieve the scores. Accepts four different values: - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model has several labels, will apply the softmax function on the output. - `"sigmoid"`: Applies the sigmoid function on the output. - `"softmax"`: Applies the softmax function on the output. - `"none"`: Does not apply any function on the output. ''' , ) class _snake_case ( a_ ): SCREAMING_SNAKE_CASE : Union[str, Any] = False SCREAMING_SNAKE_CASE : Dict = ClassificationFunction.NONE def __init__( self , **_SCREAMING_SNAKE_CASE ): '''simple docstring''' super().__init__(**_SCREAMING_SNAKE_CASE ) self.check_model_type( TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING if self.framework == 'tf' else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING ) def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="" , **_SCREAMING_SNAKE_CASE ): '''simple docstring''' lowerCAmelCase = tokenizer_kwargs lowerCAmelCase = {} if hasattr(self.model.config , 'return_all_scores' ) and return_all_scores is None: lowerCAmelCase = self.model.config.return_all_scores if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or top_k is None: lowerCAmelCase = top_k lowerCAmelCase = False elif return_all_scores is not None: warnings.warn( '`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of' ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.' , _SCREAMING_SNAKE_CASE , ) if return_all_scores: lowerCAmelCase = None else: lowerCAmelCase = 1 if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): lowerCAmelCase = ClassificationFunction[function_to_apply.upper()] if function_to_apply is not None: lowerCAmelCase = function_to_apply return preprocess_params, {}, postprocess_params def __call__( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ): '''simple docstring''' lowerCAmelCase = super().__call__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) # TODO try and retrieve it in a nicer way from _sanitize_parameters. lowerCAmelCase = 'top_k' not in kwargs if isinstance(args[0] , _SCREAMING_SNAKE_CASE ) and _legacy: # This pipeline is odd, and return a list when single item is run return [result] else: return result def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ): '''simple docstring''' lowerCAmelCase = self.framework if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): return self.tokenizer(**_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) == 1 and isinstance(inputs[0] , _SCREAMING_SNAKE_CASE ) and len(inputs[0] ) == 2: # It used to be valid to use a list of list of list for text pairs, keeping this path for BC return self.tokenizer( text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): # This is likely an invalid usage of the pipeline attempting to pass text pairs. raise ValueError( 'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a' ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.' ) return self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ): '''simple docstring''' return self.model(**_SCREAMING_SNAKE_CASE ) def _SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=True ): '''simple docstring''' if function_to_apply is None: if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1: lowerCAmelCase = ClassificationFunction.SIGMOID elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1: lowerCAmelCase = ClassificationFunction.SOFTMAX elif hasattr(self.model.config , 'function_to_apply' ) and function_to_apply is None: lowerCAmelCase = self.model.config.function_to_apply else: lowerCAmelCase = ClassificationFunction.NONE lowerCAmelCase = model_outputs['logits'][0] lowerCAmelCase = outputs.numpy() if function_to_apply == ClassificationFunction.SIGMOID: lowerCAmelCase = sigmoid(_SCREAMING_SNAKE_CASE ) elif function_to_apply == ClassificationFunction.SOFTMAX: lowerCAmelCase = softmax(_SCREAMING_SNAKE_CASE ) elif function_to_apply == ClassificationFunction.NONE: lowerCAmelCase = outputs else: raise ValueError(F'Unrecognized `function_to_apply` argument: {function_to_apply}' ) if top_k == 1 and _legacy: return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()} lowerCAmelCase = [ {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(_SCREAMING_SNAKE_CASE ) ] if not _legacy: dict_scores.sort(key=lambda _SCREAMING_SNAKE_CASE : x["score"] , reverse=_SCREAMING_SNAKE_CASE ) if top_k is not None: lowerCAmelCase = dict_scores[:top_k] return dict_scores
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def __magic_name__ ( __a : List[Any] , __a : Optional[Any] ): '''simple docstring''' UpperCamelCase__ = len(__a ) UpperCamelCase__ = [] for i in range(len(__a ) - pat_len + 1 ): UpperCamelCase__ = True for j in range(__a ): if s[i + j] != pattern[j]: UpperCamelCase__ = False break if match_found: position.append(__a ) return position if __name__ == "__main__": assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3] print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
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from __future__ import annotations lowerCamelCase_ = '''#''' class __A: """simple docstring""" def __init__(self ): UpperCamelCase__ = {} def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ): UpperCamelCase__ = self._trie for char in text: if char not in trie: UpperCamelCase__ = {} UpperCamelCase__ = trie[char] UpperCamelCase__ = True def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ): UpperCamelCase__ = self._trie for char in prefix: if char in trie: UpperCamelCase__ = trie[char] else: return [] return self._elements(SCREAMING_SNAKE_CASE_ ) def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ): UpperCamelCase__ = [] for c, v in d.items(): UpperCamelCase__ = [""" """] if c == END else [(c + s) for s in self._elements(SCREAMING_SNAKE_CASE_ )] result.extend(SCREAMING_SNAKE_CASE_ ) return tuple(SCREAMING_SNAKE_CASE_ ) lowerCamelCase_ = Trie() lowerCamelCase_ = ('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''') for word in words: trie.insert_word(word) def __magic_name__ ( __a : str ): '''simple docstring''' UpperCamelCase__ = trie.find_word(__a ) return tuple(string + word for word in suffixes ) def __magic_name__ ( ): '''simple docstring''' print(autocomplete_using_trie("""de""" ) ) if __name__ == "__main__": import doctest doctest.testmod() main()
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import inspect import os import unittest import torch import accelerate from accelerate import debug_launcher from accelerate.test_utils import ( execute_subprocess_async, require_cpu, require_huggingface_suite, require_multi_gpu, require_single_gpu, ) from accelerate.utils import patch_environment @require_huggingface_suite class _A ( unittest.TestCase): def UpperCAmelCase ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = inspect.getfile(accelerate.test_utils ) SCREAMING_SNAKE_CASE_ : str = os.path.sep.join( mod_file.split(os.path.sep )[:-1] + ['scripts', 'external_deps', 'test_metrics.py'] ) from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401 SCREAMING_SNAKE_CASE_ : str = test_metrics @require_cpu def UpperCAmelCase ( self ): """simple docstring""" debug_launcher(self.test_metrics.main , num_processes=1 ) @require_cpu def UpperCAmelCase ( self ): """simple docstring""" debug_launcher(self.test_metrics.main ) @require_single_gpu def UpperCAmelCase ( self ): """simple docstring""" self.test_metrics.main() @require_multi_gpu def UpperCAmelCase ( self ): """simple docstring""" print(f"Found {torch.cuda.device_count()} devices." ) SCREAMING_SNAKE_CASE_ : int = ['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path] with patch_environment(omp_num_threads=1 ): execute_subprocess_async(_SCREAMING_SNAKE_CASE , env=os.environ.copy() )
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import os import re import warnings from shutil import copyfile from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer if TYPE_CHECKING: from ...tokenization_utils_base import TextInput from ...utils import logging lowerCAmelCase : Optional[Any] = logging.get_logger(__name__) lowerCAmelCase : List[str] = {'vocab_file': 'spiece.model'} lowerCAmelCase : Optional[int] = { 'vocab_file': { 't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model', 't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model', 't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model', 't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model', 't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model', } } # TODO(PVP) - this should be removed in Transformers v5 lowerCAmelCase : Optional[int] = { 't5-small': 5_12, 't5-base': 5_12, 't5-large': 5_12, 't5-3b': 5_12, 't5-11b': 5_12, } lowerCAmelCase : Optional[int] = '▁' class _A ( __magic_name__): SCREAMING_SNAKE_CASE : Tuple = VOCAB_FILES_NAMES SCREAMING_SNAKE_CASE : Optional[int] = PRETRAINED_VOCAB_FILES_MAP SCREAMING_SNAKE_CASE : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES SCREAMING_SNAKE_CASE : Tuple = ['''input_ids''', '''attention_mask'''] def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE=100 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE=True , **_SCREAMING_SNAKE_CASE , ): """simple docstring""" if extra_ids > 0 and additional_special_tokens is None: SCREAMING_SNAKE_CASE_ : List[str] = [f"<extra_id_{i}>" for i in range(_SCREAMING_SNAKE_CASE )] elif extra_ids > 0 and additional_special_tokens is not None: # Check that we have the right number of extra_id special tokens SCREAMING_SNAKE_CASE_ : Dict = len(set(filter(lambda _SCREAMING_SNAKE_CASE : bool('extra_id' in str(_SCREAMING_SNAKE_CASE ) ) , _SCREAMING_SNAKE_CASE ) ) ) if extra_tokens != extra_ids: raise ValueError( f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are" ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids' ' tokens' ) if legacy: logger.warning_once( f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to" ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565' ) SCREAMING_SNAKE_CASE_ : int = legacy SCREAMING_SNAKE_CASE_ : int = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , extra_ids=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , legacy=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , ) SCREAMING_SNAKE_CASE_ : Any = vocab_file SCREAMING_SNAKE_CASE_ : Optional[Any] = extra_ids SCREAMING_SNAKE_CASE_ : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_SCREAMING_SNAKE_CASE ) @staticmethod def UpperCAmelCase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): """simple docstring""" if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes: SCREAMING_SNAKE_CASE_ : int = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path] if init_max_model_length is not None and init_max_model_length != max_model_length: return init_max_model_length elif init_max_model_length is None: warnings.warn( 'This tokenizer was incorrectly instantiated with a model max length of' f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this" ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with' ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on' f" {pretrained_model_name_or_path} automatically truncating your input to" f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences" f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with" ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please' ' instantiate this tokenizer with `model_max_length` set to your preferred value.' , _SCREAMING_SNAKE_CASE , ) return max_model_length @property def UpperCAmelCase ( self ): """simple docstring""" return self.sp_model.get_piece_size() + self._extra_ids def UpperCAmelCase ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_SCREAMING_SNAKE_CASE , token_ids_a=_SCREAMING_SNAKE_CASE , already_has_special_tokens=_SCREAMING_SNAKE_CASE ) # normal case: some special tokens if token_ids_a is None: return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] return ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] def UpperCAmelCase ( self ): """simple docstring""" return list( set(filter(lambda _SCREAMING_SNAKE_CASE : bool(re.search(r'<extra_id_\d+>' , _SCREAMING_SNAKE_CASE ) ) is not None , self.additional_special_tokens ) ) ) def UpperCAmelCase ( self ): """simple docstring""" return [self._convert_token_to_id(_SCREAMING_SNAKE_CASE ) for token in self.get_sentinel_tokens()] def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ): """simple docstring""" if len(_SCREAMING_SNAKE_CASE ) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated" ' eos tokens being added.' ) return token_ids else: return token_ids + [self.eos_token_id] def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ): """simple docstring""" SCREAMING_SNAKE_CASE_ : str = [self.eos_token_id] if token_ids_a is None: return len(token_ids_a + eos ) * [0] return len(token_ids_a + eos + token_ids_a + eos ) * [0] def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE ) if token_ids_a is None: return token_ids_a else: SCREAMING_SNAKE_CASE_ : int = self._add_eos_if_not_present(_SCREAMING_SNAKE_CASE ) return token_ids_a + token_ids_a def __getstate__( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = self.__dict__.copy() SCREAMING_SNAKE_CASE_ : Optional[Any] = None return state def __setstate__( self , _SCREAMING_SNAKE_CASE ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] = d # for backward compatibility if not hasattr(self , 'sp_model_kwargs' ): SCREAMING_SNAKE_CASE_ : str = {} SCREAMING_SNAKE_CASE_ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ): """simple docstring""" if not self.legacy: SCREAMING_SNAKE_CASE_ : Dict = SPIECE_UNDERLINE + text.replace(_SCREAMING_SNAKE_CASE , ' ' ) return super().tokenize(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ): """simple docstring""" if not self.legacy: SCREAMING_SNAKE_CASE_ : List[str] = text.startswith(_SCREAMING_SNAKE_CASE ) if is_first: SCREAMING_SNAKE_CASE_ : Optional[int] = text[1:] SCREAMING_SNAKE_CASE_ : Tuple = self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE ) if not self.legacy and not is_first and not text.startswith(' ' ) and tokens[0].startswith(_SCREAMING_SNAKE_CASE ): SCREAMING_SNAKE_CASE_ : Any = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:] return tokens def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ): """simple docstring""" if token.startswith('<extra_id_' ): SCREAMING_SNAKE_CASE_ : Union[str, Any] = re.match(r'<extra_id_(\d+)>' , _SCREAMING_SNAKE_CASE ) SCREAMING_SNAKE_CASE_ : Dict = int(match.group(1 ) ) return self.vocab_size - num - 1 return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE ) def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ): """simple docstring""" if index < self.sp_model.get_piece_size(): SCREAMING_SNAKE_CASE_ : str = self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE ) else: SCREAMING_SNAKE_CASE_ : List[Any] = f"<extra_id_{self.vocab_size - 1 - index}>" return token def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] = [] SCREAMING_SNAKE_CASE_ : str = '' SCREAMING_SNAKE_CASE_ : Dict = False for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special: out_string += " " out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token SCREAMING_SNAKE_CASE_ : int = True SCREAMING_SNAKE_CASE_ : Dict = [] else: current_sub_tokens.append(_SCREAMING_SNAKE_CASE ) SCREAMING_SNAKE_CASE_ : Dict = False out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) return out_string.strip() def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ): """simple docstring""" if not os.path.isdir(_SCREAMING_SNAKE_CASE ): logger.error(f"Vocabulary path ({save_directory}) should be a directory" ) return SCREAMING_SNAKE_CASE_ : Dict = os.path.join( _SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE ) elif not os.path.isfile(self.vocab_file ): with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fi: SCREAMING_SNAKE_CASE_ : Optional[Any] = self.sp_model.serialized_model_proto() fi.write(_SCREAMING_SNAKE_CASE ) return (out_vocab_file,)
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import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_mbart import MBartTokenizer else: __A : Tuple = None __A : Any = logging.get_logger(__name__) __A : Union[str, Any] = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""} __A : List[Any] = { """vocab_file""": { """facebook/mbart-large-en-ro""": ( """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model""" ), """facebook/mbart-large-cc25""": ( """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model""" ), }, """tokenizer_file""": { """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""", """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""", }, } __A : int = { """facebook/mbart-large-en-ro""": 1_0_2_4, """facebook/mbart-large-cc25""": 1_0_2_4, } # fmt: off __A : List[Any] = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""] class UpperCAmelCase_ ( A ): '''simple docstring''' a__ = VOCAB_FILES_NAMES a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a__ = PRETRAINED_VOCAB_FILES_MAP a__ = ['''input_ids''', '''attention_mask'''] a__ = MBartTokenizer a__ = [] a__ = [] def __init__( self : str , a : List[Any]=None , a : Optional[int]=None , a : str="<s>" , a : Tuple="</s>" , a : Optional[int]="</s>" , a : int="<s>" , a : List[str]="<unk>" , a : Tuple="<pad>" , a : Dict="<mask>" , a : Optional[Any]=None , a : List[Any]=None , a : Any=None , **a : Optional[Any] , ) -> Any: # Mask token behave like a normal word, i.e. include the space before it SCREAMING_SNAKE_CASE = AddedToken(a , lstrip=a , rstrip=a ) if isinstance(a , a ) else mask_token super().__init__( vocab_file=a , tokenizer_file=a , bos_token=a , eos_token=a , sep_token=a , cls_token=a , unk_token=a , pad_token=a , mask_token=a , src_lang=a , tgt_lang=a , additional_special_tokens=a , **a , ) SCREAMING_SNAKE_CASE = vocab_file SCREAMING_SNAKE_CASE = False if not self.vocab_file else True SCREAMING_SNAKE_CASE = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) SCREAMING_SNAKE_CASE = { lang_code: self.convert_tokens_to_ids(a ) for lang_code in FAIRSEQ_LANGUAGE_CODES } SCREAMING_SNAKE_CASE = src_lang if src_lang is not None else """en_XX""" SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(self._src_lang ) SCREAMING_SNAKE_CASE = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _UpperCAmelCase ( self : int ) -> str: return self._src_lang @src_lang.setter def _UpperCAmelCase ( self : List[Any] , a : str ) -> None: SCREAMING_SNAKE_CASE = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _UpperCAmelCase ( self : Dict , a : List[int] , a : Optional[List[int]] = None ) -> List[int]: if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _UpperCAmelCase ( self : Tuple , a : List[int] , a : Optional[List[int]] = None ) -> List[int]: SCREAMING_SNAKE_CASE = [self.sep_token_id] SCREAMING_SNAKE_CASE = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _UpperCAmelCase ( self : Optional[Any] , a : Optional[int] , a : str , a : Optional[str] , a : Optional[str] , **a : Optional[Any] ) -> Optional[Any]: if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) SCREAMING_SNAKE_CASE = src_lang SCREAMING_SNAKE_CASE = self(a , add_special_tokens=a , return_tensors=a , **a ) SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(a ) SCREAMING_SNAKE_CASE = tgt_lang_id return inputs def _UpperCAmelCase ( self : str , a : List[str] , a : str = "en_XX" , a : Optional[List[str]] = None , a : str = "ro_RO" , **a : List[str] , ) -> BatchEncoding: SCREAMING_SNAKE_CASE = src_lang SCREAMING_SNAKE_CASE = tgt_lang return super().prepare_seqaseq_batch(a , a , **a ) def _UpperCAmelCase ( self : Optional[Any] ) -> str: return self.set_src_lang_special_tokens(self.src_lang ) def _UpperCAmelCase ( self : str ) -> List[str]: return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _UpperCAmelCase ( self : Dict , a : List[str] ) -> None: SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(a ) SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code] SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _UpperCAmelCase ( self : Dict , a : str ) -> None: SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(a ) SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code] SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.prefix_tokens ) SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.suffix_tokens ) SCREAMING_SNAKE_CASE = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _UpperCAmelCase ( self : Dict , a : str , a : Optional[str] = None ) -> Tuple[str]: if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(a ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" ) return SCREAMING_SNAKE_CASE = os.path.join( a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(a ): copyfile(self.vocab_file , a ) return (out_vocab_file,)
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import os import shutil from pathlib import Path from typing import Optional, Union import numpy as np from huggingface_hub import hf_hub_download from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging if is_onnx_available(): import onnxruntime as ort __A : str = logging.get_logger(__name__) __A : str = { """tensor(bool)""": np.bool_, """tensor(int8)""": np.inta, """tensor(uint8)""": np.uinta, """tensor(int16)""": np.intaa, """tensor(uint16)""": np.uintaa, """tensor(int32)""": np.intaa, """tensor(uint32)""": np.uintaa, """tensor(int64)""": np.intaa, """tensor(uint64)""": np.uintaa, """tensor(float16)""": np.floataa, """tensor(float)""": np.floataa, """tensor(double)""": np.floataa, } class UpperCAmelCase_ : '''simple docstring''' def __init__( self : List[str] , a : Tuple=None , **a : Any ) -> List[str]: logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future.""" ) SCREAMING_SNAKE_CASE = model SCREAMING_SNAKE_CASE = kwargs.get("""model_save_dir""" , a ) SCREAMING_SNAKE_CASE = kwargs.get("""latest_model_name""" , a ) def __call__( self : List[str] , **a : Any ) -> List[Any]: SCREAMING_SNAKE_CASE = {k: np.array(a ) for k, v in kwargs.items()} return self.model.run(a , a ) @staticmethod def _UpperCAmelCase ( a : Union[str, Path] , a : Any=None , a : Optional[int]=None ) -> Optional[Any]: if provider is None: logger.info("""No onnxruntime provider specified, using CPUExecutionProvider""" ) SCREAMING_SNAKE_CASE = """CPUExecutionProvider""" return ort.InferenceSession(a , providers=[provider] , sess_options=a ) def _UpperCAmelCase ( self : str , a : Union[str, Path] , a : Optional[str] = None , **a : str ) -> Tuple: SCREAMING_SNAKE_CASE = file_name if file_name is not None else ONNX_WEIGHTS_NAME SCREAMING_SNAKE_CASE = self.model_save_dir.joinpath(self.latest_model_name ) SCREAMING_SNAKE_CASE = Path(a ).joinpath(a ) try: shutil.copyfile(a , a ) except shutil.SameFileError: pass # copy external weights (for models >2GB) SCREAMING_SNAKE_CASE = self.model_save_dir.joinpath(a ) if src_path.exists(): SCREAMING_SNAKE_CASE = Path(a ).joinpath(a ) try: shutil.copyfile(a , a ) except shutil.SameFileError: pass def _UpperCAmelCase ( self : Dict , a : Union[str, os.PathLike] , **a : Tuple , ) -> str: if os.path.isfile(a ): logger.error(f"""Provided path ({save_directory}) should be a directory, not a file""" ) return os.makedirs(a , exist_ok=a ) # saving model weights/files self._save_pretrained(a , **a ) @classmethod def _UpperCAmelCase ( cls : List[Any] , a : Union[str, Path] , a : Optional[Union[bool, str, None]] = None , a : Optional[Union[str, None]] = None , a : bool = False , a : Optional[str] = None , a : Optional[str] = None , a : Optional[str] = None , a : Optional["ort.SessionOptions"] = None , **a : Union[str, Any] , ) -> List[str]: SCREAMING_SNAKE_CASE = file_name if file_name is not None else ONNX_WEIGHTS_NAME # load model from local directory if os.path.isdir(a ): SCREAMING_SNAKE_CASE = OnnxRuntimeModel.load_model( os.path.join(a , a ) , provider=a , sess_options=a ) SCREAMING_SNAKE_CASE = Path(a ) # load model from hub else: # download model SCREAMING_SNAKE_CASE = hf_hub_download( repo_id=a , filename=a , use_auth_token=a , revision=a , cache_dir=a , force_download=a , ) SCREAMING_SNAKE_CASE = Path(a ).parent SCREAMING_SNAKE_CASE = Path(a ).name SCREAMING_SNAKE_CASE = OnnxRuntimeModel.load_model(a , provider=a , sess_options=a ) return cls(model=a , **a ) @classmethod def _UpperCAmelCase ( cls : List[Any] , a : Union[str, Path] , a : bool = True , a : Optional[str] = None , a : Optional[str] = None , **a : Union[str, Any] , ) -> Any: SCREAMING_SNAKE_CASE = None if len(str(a ).split("""@""" ) ) == 2: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = model_id.split("""@""" ) return cls._from_pretrained( model_id=a , revision=a , cache_dir=a , force_download=a , use_auth_token=a , **a , )
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"""simple docstring""" from __future__ import annotations __A = list[tuple[int, int]] __A = [ [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], ] __A = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right class snake_case : def __init__( self : Union[str, Any] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : Node | None , )-> Union[str, Any]: '''simple docstring''' __lowerCAmelCase: List[str] = pos_x __lowerCAmelCase: str = pos_y __lowerCAmelCase: int = (pos_y, pos_x) __lowerCAmelCase: Union[str, Any] = goal_x __lowerCAmelCase: Any = goal_y __lowerCAmelCase: Dict = g_cost __lowerCAmelCase: List[str] = parent __lowerCAmelCase: List[str] = self.calculate_heuristic() def lowercase_ ( self : Optional[Any])-> Dict: '''simple docstring''' __lowerCAmelCase: Union[str, Any] = abs(self.pos_x - self.goal_x) __lowerCAmelCase: int = abs(self.pos_y - self.goal_y) return dx + dy def __lt__( self : Optional[int] , UpperCamelCase__ : List[str])-> int: '''simple docstring''' return self.f_cost < other.f_cost class snake_case : def __init__( self : Any , UpperCamelCase__ : tuple[int, int] , UpperCamelCase__ : tuple[int, int])-> List[Any]: '''simple docstring''' __lowerCAmelCase: Optional[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , __lowercase) __lowerCAmelCase: Union[str, Any] = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , __lowercase) __lowerCAmelCase: Union[str, Any] = [self.start] __lowerCAmelCase: str = [] __lowerCAmelCase: Any = False def lowercase_ ( self : str)-> Union[str, Any]: '''simple docstring''' while self.open_nodes: # Open Nodes are sorted using __lt__ self.open_nodes.sort() __lowerCAmelCase: List[Any] = self.open_nodes.pop(0) if current_node.pos == self.target.pos: __lowerCAmelCase: str = True return self.retrace_path(__lowercase) self.closed_nodes.append(__lowercase) __lowerCAmelCase: Dict = self.get_successors(__lowercase) for child_node in successors: if child_node in self.closed_nodes: continue if child_node not in self.open_nodes: self.open_nodes.append(__lowercase) else: # retrieve the best current path __lowerCAmelCase: str = self.open_nodes.pop(self.open_nodes.index(__lowercase)) if child_node.g_cost < better_node.g_cost: self.open_nodes.append(__lowercase) else: self.open_nodes.append(__lowercase) if not self.reached: return [self.start.pos] return None def lowercase_ ( self : Optional[Any] , UpperCamelCase__ : Node)-> Dict: '''simple docstring''' __lowerCAmelCase: Optional[int] = [] for action in delta: __lowerCAmelCase: List[Any] = parent.pos_x + action[1] __lowerCAmelCase: Optional[int] = parent.pos_y + action[0] if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(__lowercase) - 1): continue if grid[pos_y][pos_x] != 0: continue successors.append( Node( __lowercase , __lowercase , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , __lowercase , )) return successors def lowercase_ ( self : Any , UpperCamelCase__ : Node | None)-> List[str]: '''simple docstring''' __lowerCAmelCase: Dict = node __lowerCAmelCase: Dict = [] while current_node is not None: path.append((current_node.pos_y, current_node.pos_x)) __lowerCAmelCase: str = current_node.parent path.reverse() return path if __name__ == "__main__": __A = (0, 0) __A = (len(grid) - 1, len(grid[0]) - 1) for elem in grid: print(elem) print("------") __A = GreedyBestFirst(init, goal) __A = greedy_bf.search() if path: for pos_x, pos_y in path: __A = 2 for elem in grid: print(elem)
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'''simple docstring''' import enum import warnings from ..tokenization_utils import TruncationStrategy from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_tf_available(): import tensorflow as tf from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING UpperCAmelCase = logging.get_logger(__name__) class lowerCAmelCase ( enum.Enum ): lowerCAmelCase_ = 0 lowerCAmelCase_ = 1 @add_end_docstrings(A ) class lowerCAmelCase ( A ): lowerCAmelCase_ = "generated" def __init__( self : Tuple , *__lowercase : Tuple , **__lowercase : Optional[int] ): """simple docstring""" super().__init__(*__lowercase , **__lowercase ) self.check_model_type( TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING ) def snake_case ( self : Dict , __lowercase : Any=None , __lowercase : int=None , __lowercase : List[str]=None , __lowercase : int=None , __lowercase : str=None , __lowercase : List[Any]=None , **__lowercase : Union[str, Any] , ): """simple docstring""" __lowercase ={} if truncation is not None: __lowercase =truncation __lowercase =generate_kwargs __lowercase ={} if return_tensors is not None and return_type is None: __lowercase =ReturnType.TENSORS if return_tensors else ReturnType.TEXT if return_type is not None: __lowercase =return_type if clean_up_tokenization_spaces is not None: __lowercase =clean_up_tokenization_spaces if stop_sequence is not None: __lowercase =self.tokenizer.encode(__lowercase , add_special_tokens=__lowercase ) if len(__lowercase ) > 1: warnings.warn( 'Stopping on a multiple token sequence is not yet supported on transformers. The first token of' ' the stop sequence will be used as the stop sequence string in the interim.' ) __lowercase =stop_sequence_ids[0] return preprocess_params, forward_params, postprocess_params def snake_case ( self : List[Any] , __lowercase : int , __lowercase : int , __lowercase : int ): """simple docstring""" return True def snake_case ( self : Optional[Any] , *__lowercase : Optional[int] , __lowercase : Union[str, Any] ): """simple docstring""" __lowercase =self.model.config.prefix if self.model.config.prefix is not None else '' if isinstance(args[0] , __lowercase ): if self.tokenizer.pad_token_id is None: raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input' ) __lowercase =([prefix + arg for arg in args[0]],) __lowercase =True elif isinstance(args[0] , __lowercase ): __lowercase =(prefix + args[0],) __lowercase =False else: raise ValueError( f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`''' ) __lowercase =self.tokenizer(*__lowercase , padding=__lowercase , truncation=__lowercase , return_tensors=self.framework ) # This is produced by tokenizers but is an invalid generate kwargs if "token_type_ids" in inputs: del inputs["token_type_ids"] return inputs def __call__( self : Any , *__lowercase : int , **__lowercase : Optional[Any] ): """simple docstring""" __lowercase =super().__call__(*__lowercase , **__lowercase ) if ( isinstance(args[0] , __lowercase ) and all(isinstance(__lowercase , __lowercase ) for el in args[0] ) and all(len(__lowercase ) == 1 for res in result ) ): return [res[0] for res in result] return result def snake_case ( self : List[str] , __lowercase : Tuple , __lowercase : List[Any]=TruncationStrategy.DO_NOT_TRUNCATE , **__lowercase : Dict ): """simple docstring""" __lowercase =self._parse_and_tokenize(__lowercase , truncation=__lowercase , **__lowercase ) return inputs def snake_case ( self : Optional[int] , __lowercase : Tuple , **__lowercase : Tuple ): """simple docstring""" if self.framework == "pt": __lowercase , __lowercase =model_inputs['input_ids'].shape elif self.framework == "tf": __lowercase , __lowercase =tf.shape(model_inputs['input_ids'] ).numpy() __lowercase =generate_kwargs.get('min_length' , self.model.config.min_length ) __lowercase =generate_kwargs.get('max_length' , self.model.config.max_length ) self.check_inputs(__lowercase , generate_kwargs['min_length'] , generate_kwargs['max_length'] ) __lowercase =self.model.generate(**__lowercase , **__lowercase ) __lowercase =output_ids.shape[0] if self.framework == "pt": __lowercase =output_ids.reshape(__lowercase , out_b // in_b , *output_ids.shape[1:] ) elif self.framework == "tf": __lowercase =tf.reshape(__lowercase , (in_b, out_b // in_b, *output_ids.shape[1:]) ) return {"output_ids": output_ids} def snake_case ( self : str , __lowercase : Optional[Any] , __lowercase : Optional[int]=ReturnType.TEXT , __lowercase : Union[str, Any]=False ): """simple docstring""" __lowercase =[] for output_ids in model_outputs["output_ids"][0]: if return_type == ReturnType.TENSORS: __lowercase ={f'''{self.return_name}_token_ids''': output_ids} elif return_type == ReturnType.TEXT: __lowercase ={ f'''{self.return_name}_text''': self.tokenizer.decode( __lowercase , skip_special_tokens=__lowercase , clean_up_tokenization_spaces=__lowercase , ) } records.append(__lowercase ) return records @add_end_docstrings(A ) class lowerCAmelCase ( A ): lowerCAmelCase_ = "summary" def __call__( self : Dict , *__lowercase : Dict , **__lowercase : Dict ): """simple docstring""" return super().__call__(*__lowercase , **__lowercase ) def snake_case ( self : str , __lowercase : int , __lowercase : int , __lowercase : int ): """simple docstring""" if max_length < min_length: logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.''' ) if input_length < max_length: logger.warning( f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is ''' 'a summarization task, where outputs shorter than the input are typically wanted, you might ' f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})''' ) @add_end_docstrings(A ) class lowerCAmelCase ( A ): lowerCAmelCase_ = "translation" def snake_case ( self : int , __lowercase : int , __lowercase : int , __lowercase : int ): """simple docstring""" if input_length > 0.9 * max_length: logger.warning( f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider ''' 'increasing your max_length manually, e.g. translator(\'...\', max_length=400)' ) return True def snake_case ( self : int , *__lowercase : str , __lowercase : List[str]=TruncationStrategy.DO_NOT_TRUNCATE , __lowercase : List[Any]=None , __lowercase : Optional[int]=None ): """simple docstring""" if getattr(self.tokenizer , '_build_translation_inputs' , __lowercase ): return self.tokenizer._build_translation_inputs( *__lowercase , return_tensors=self.framework , truncation=__lowercase , src_lang=__lowercase , tgt_lang=__lowercase ) else: return super()._parse_and_tokenize(*__lowercase , truncation=__lowercase ) def snake_case ( self : Optional[int] , __lowercase : List[str]=None , __lowercase : Optional[Any]=None , **__lowercase : int ): """simple docstring""" __lowercase , __lowercase , __lowercase =super()._sanitize_parameters(**__lowercase ) if src_lang is not None: __lowercase =src_lang if tgt_lang is not None: __lowercase =tgt_lang if src_lang is None and tgt_lang is None: # Backward compatibility, direct arguments use is preferred. __lowercase =kwargs.get('task' , self.task ) __lowercase =task.split('_' ) if task and len(__lowercase ) == 4: # translation, XX, to YY __lowercase =items[1] __lowercase =items[3] return preprocess_params, forward_params, postprocess_params def __call__( self : Optional[Any] , *__lowercase : Any , **__lowercase : List[Any] ): """simple docstring""" return super().__call__(*__lowercase , **__lowercase )
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'''simple docstring''' from sklearn.metrics import mean_squared_error import datasets a : Union[str, Any] = """\ @article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} } """ a : int = """\ Mean Squared Error(MSE) is the average of the square of difference between the predicted and actual values. """ a : int = """ Args: predictions: array-like of shape (n_samples,) or (n_samples, n_outputs) Estimated target values. references: array-like of shape (n_samples,) or (n_samples, n_outputs) Ground truth (correct) target values. sample_weight: array-like of shape (n_samples,), default=None Sample weights. multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\" Defines aggregating of multiple output values. Array-like value defines weights used to average errors. \"raw_values\" : Returns a full set of errors in case of multioutput input. \"uniform_average\" : Errors of all outputs are averaged with uniform weight. squared : bool, default=True If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value. Returns: mse : mean squared error. Examples: >>> mse_metric = datasets.load_metric(\"mse\") >>> predictions = [2.5, 0.0, 2, 8] >>> references = [3, -0.5, 2, 7] >>> results = mse_metric.compute(predictions=predictions, references=references) >>> print(results) {'mse': 0.375} >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False) >>> print(rmse_result) {'mse': 0.6123724356957945} If you're using multi-dimensional lists, then set the config as follows : >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\") >>> predictions = [[0.5, 1], [-1, 1], [7, -6]] >>> references = [[0, 2], [-1, 2], [8, -5]] >>> results = mse_metric.compute(predictions=predictions, references=references) >>> print(results) {'mse': 0.7083333333333334} >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values') >>> print(results) # doctest: +NORMALIZE_WHITESPACE {'mse': array([0.41666667, 1. ])} """ @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class UpperCamelCase_ ( datasets.Metric ): def _lowercase( self ) -> List[str]: return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , reference_urls=[ """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html""" ] , ) def _lowercase( self ) -> List[Any]: if self.config_name == "multilist": return { "predictions": datasets.Sequence(datasets.Value("""float""" ) ), "references": datasets.Sequence(datasets.Value("""float""" ) ), } else: return { "predictions": datasets.Value("""float""" ), "references": datasets.Value("""float""" ), } def _lowercase( self , A , A , A=None , A="uniform_average" , A=True ) -> List[Any]: UpperCAmelCase : List[Any] = mean_squared_error( A , A , sample_weight=A , multioutput=A , squared=A ) return {"mse": mse}
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'''simple docstring''' from collections.abc import Callable import numpy as np def __lowerCamelCase ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> np.array: UpperCAmelCase : Optional[Any] = int(np.ceil((x_end - xa) / step_size ) ) UpperCAmelCase : str = np.zeros((n + 1,) ) UpperCAmelCase : Optional[Any] = ya UpperCAmelCase : Union[str, Any] = xa for k in range(_lowercase ): UpperCAmelCase : Dict = y[k] + step_size * ode_func(_lowercase , y[k] ) UpperCAmelCase : Optional[int] = y[k] + ( (step_size / 2) * (ode_func(_lowercase , y[k] ) + ode_func(x + step_size , _lowercase )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_tokenizers_available, is_torch_available, ) a = { "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"], "tokenization_deberta": ["DebertaTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a = ["DebertaTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a = [ "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "DebertaForMaskedLM", "DebertaForQuestionAnswering", "DebertaForSequenceClassification", "DebertaForTokenClassification", "DebertaModel", "DebertaPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: a = [ "TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST", "TFDebertaForMaskedLM", "TFDebertaForQuestionAnswering", "TFDebertaForSequenceClassification", "TFDebertaForTokenClassification", "TFDebertaModel", "TFDebertaPreTrainedModel", ] if TYPE_CHECKING: from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig from .tokenization_deberta import DebertaTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_deberta_fast import DebertaTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deberta import ( DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, DebertaForMaskedLM, DebertaForQuestionAnswering, DebertaForSequenceClassification, DebertaForTokenClassification, DebertaModel, DebertaPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_deberta import ( TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST, TFDebertaForMaskedLM, TFDebertaForQuestionAnswering, TFDebertaForSequenceClassification, TFDebertaForTokenClassification, TFDebertaModel, TFDebertaPreTrainedModel, ) else: import sys a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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'''simple docstring''' import collections import json import os import re from typing import TYPE_CHECKING, List, Optional, Tuple import numpy as np from ...tokenization_utils_fast import PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation a = logging.get_logger(__name__) a = {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"} a = { "vocab_file": { "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt", }, "emoji_file": { "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json", }, } a = { "abeja/gpt-neox-japanese-2.7b": 2048, } def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Any: '''simple docstring''' with open(__UpperCAmelCase , """r""" , encoding="""utf-8""" ) as f: __SCREAMING_SNAKE_CASE = json.loads(f.read() ) __SCREAMING_SNAKE_CASE = collections.OrderedDict() __SCREAMING_SNAKE_CASE = collections.OrderedDict() __SCREAMING_SNAKE_CASE = collections.OrderedDict() with open(__UpperCAmelCase , """r""" , encoding="""utf-8""" ) as f: __SCREAMING_SNAKE_CASE = f.readlines() __SCREAMING_SNAKE_CASE = [[t.rstrip("""\n""" )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n""" ).split(""",""" ) for t in token] for idx, b in enumerate(__UpperCAmelCase ): __SCREAMING_SNAKE_CASE = b __SCREAMING_SNAKE_CASE = idx for wd in b: __SCREAMING_SNAKE_CASE = idx return vocab, raw_vocab, ids_to_tokens, emoji class __a ( _snake_case ): __UpperCamelCase : Tuple = VOCAB_FILES_NAMES __UpperCamelCase : Tuple = PRETRAINED_VOCAB_FILES_MAP __UpperCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES __UpperCamelCase : Union[str, Any] = ['input_ids', 'attention_mask'] def __init__( self : Dict ,lowerCamelCase : Union[str, Any] ,lowerCamelCase : int ,lowerCamelCase : str="<|endoftext|>" ,lowerCamelCase : List[Any]="<|endoftext|>" ,lowerCamelCase : str="<|startoftext|>" ,lowerCamelCase : Dict="<|endoftext|>" ,lowerCamelCase : int=False ,**lowerCamelCase : str ,): '''simple docstring''' super().__init__( unk_token=lowerCamelCase ,pad_token=lowerCamelCase ,bos_token=lowerCamelCase ,eos_token=lowerCamelCase ,do_clean_text=lowerCamelCase ,**lowerCamelCase ,) if not os.path.isfile(lowerCamelCase ): raise ValueError( f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained""" """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" ) if not os.path.isfile(lowerCamelCase ): raise ValueError( f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google""" """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`""" ) __SCREAMING_SNAKE_CASE = do_clean_text __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = load_vocab_and_emoji(lowerCamelCase ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = SubWordJapaneseTokenizer( vocab=self.vocab ,ids_to_tokens=self.ids_to_tokens ,emoji=self.emoji ) @property def UpperCAmelCase__ ( self : Optional[Any] ): '''simple docstring''' return len(self.raw_vocab ) def UpperCAmelCase__ ( self : List[str] ): '''simple docstring''' return dict(self.raw_vocab ,**self.added_tokens_encoder ) def UpperCAmelCase__ ( self : Dict ,lowerCamelCase : str ): '''simple docstring''' return self.subword_tokenizer.tokenize(lowerCamelCase ,clean=self.do_clean_text ) def UpperCAmelCase__ ( self : Union[str, Any] ,lowerCamelCase : Union[str, Any] ): '''simple docstring''' return self.vocab.get(lowerCamelCase ,self.vocab.get(self.unk_token ) ) def UpperCAmelCase__ ( self : List[Any] ,lowerCamelCase : Optional[Any] ): '''simple docstring''' return self.subword_tokenizer.convert_id_to_token(lowerCamelCase ) def UpperCAmelCase__ ( self : List[str] ,lowerCamelCase : str ): '''simple docstring''' __SCREAMING_SNAKE_CASE = """""".join(lowerCamelCase ).strip() return out_string def UpperCAmelCase__ ( self : List[str] ,lowerCamelCase : "Conversation" ): '''simple docstring''' __SCREAMING_SNAKE_CASE = [] for is_user, text in conversation.iter_texts(): input_ids.extend(self.encode(lowerCamelCase ,add_special_tokens=lowerCamelCase ) + [self.eos_token_id] ) if len(lowerCamelCase ) > self.model_max_length: __SCREAMING_SNAKE_CASE = input_ids[-self.model_max_length :] return input_ids def UpperCAmelCase__ ( self : Dict ,lowerCamelCase : str ,lowerCamelCase : Optional[str] = None ): '''simple docstring''' __SCREAMING_SNAKE_CASE = 0 if os.path.isdir(lowerCamelCase ): __SCREAMING_SNAKE_CASE = os.path.join( lowerCamelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) __SCREAMING_SNAKE_CASE = os.path.join( lowerCamelCase ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""] ) else: __SCREAMING_SNAKE_CASE = ( (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""] ) __SCREAMING_SNAKE_CASE = ( (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""] ) with open(lowerCamelCase ,"""w""" ,encoding="""utf-8""" ) as writer: for token_index, token in self.ids_to_tokens.items(): if index != token_index: logger.warning( f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.""" """ Please check that the vocabulary is not corrupted!""" ) __SCREAMING_SNAKE_CASE = token_index writer.write(""",""".join(lowerCamelCase ) + """\n""" ) index += 1 with open(lowerCamelCase ,"""w""" ,encoding="""utf-8""" ) as writer: json.dump(self.emoji ,lowerCamelCase ) return vocab_file, emoji_file class __a ( _snake_case ): def __init__( self : Tuple ,lowerCamelCase : List[str] ,lowerCamelCase : Union[str, Any] ,lowerCamelCase : Optional[int] ): '''simple docstring''' __SCREAMING_SNAKE_CASE = vocab # same as swe __SCREAMING_SNAKE_CASE = ids_to_tokens # same as bpe __SCREAMING_SNAKE_CASE = emoji __SCREAMING_SNAKE_CASE = np.max([len(lowerCamelCase ) for w in self.vocab.keys()] ) __SCREAMING_SNAKE_CASE = re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)""" ) __SCREAMING_SNAKE_CASE = re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*""" ) __SCREAMING_SNAKE_CASE = re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}""" ) __SCREAMING_SNAKE_CASE = re.compile( r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" ) __SCREAMING_SNAKE_CASE = re.compile( r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*""" ) __SCREAMING_SNAKE_CASE = re.compile( r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*""" ) __SCREAMING_SNAKE_CASE = """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿""" __SCREAMING_SNAKE_CASE = """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟""" __SCREAMING_SNAKE_CASE = str.maketrans({k: """<BLOCK>""" for k in keisen + blocks} ) def __len__( self : List[str] ): '''simple docstring''' return len(self.ids_to_tokens ) def UpperCAmelCase__ ( self : Optional[int] ,lowerCamelCase : Dict ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.content_repattera.sub("""<URL>""" ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = self.content_repattera.sub("""<EMAIL>""" ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = self.content_repattera.sub("""<TEL>""" ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = self.content_repattera.sub("""<DATE>""" ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = self.content_repattera.sub("""<DATE>""" ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = self.content_repattera.sub("""<PRICE>""" ,lowerCamelCase ) __SCREAMING_SNAKE_CASE = content.translate(self.content_transa ) while "<BLOCK><BLOCK>" in content: __SCREAMING_SNAKE_CASE = content.replace("""<BLOCK><BLOCK>""" ,"""<BLOCK>""" ) return content def UpperCAmelCase__ ( self : Dict ,lowerCamelCase : Optional[Any] ,lowerCamelCase : List[str]=False ): '''simple docstring''' __SCREAMING_SNAKE_CASE = text.replace(""" """ ,"""<SP>""" ) __SCREAMING_SNAKE_CASE = text.replace(""" """ ,"""<SP>""" ) __SCREAMING_SNAKE_CASE = text.replace("""\r\n""" ,"""<BR>""" ) __SCREAMING_SNAKE_CASE = text.replace("""\n""" ,"""<BR>""" ) __SCREAMING_SNAKE_CASE = text.replace("""\r""" ,"""<BR>""" ) __SCREAMING_SNAKE_CASE = text.replace("""\t""" ,"""<TAB>""" ) __SCREAMING_SNAKE_CASE = text.replace("""—""" ,"""ー""" ) __SCREAMING_SNAKE_CASE = text.replace("""−""" ,"""ー""" ) for k, v in self.emoji["emoji"].items(): if k in text: __SCREAMING_SNAKE_CASE = text.replace(lowerCamelCase ,lowerCamelCase ) if clean: __SCREAMING_SNAKE_CASE = self.clean_text(lowerCamelCase ) def check_simbol(lowerCamelCase : List[str] ): __SCREAMING_SNAKE_CASE = x.encode() if len(lowerCamelCase ) == 1 and len(lowerCamelCase ) == 2: __SCREAMING_SNAKE_CASE = (int(e[0] ) << 8) + int(e[1] ) if ( (c >= 0xC_2A1 and c <= 0xC_2BF) or (c >= 0xC_780 and c <= 0xC_783) or (c >= 0xC_AB9 and c <= 0xC_BBF) or (c >= 0xC_C80 and c <= 0xC_DA2) ): return True return False def checkuae(lowerCamelCase : Union[str, Any] ): __SCREAMING_SNAKE_CASE = x.encode() if len(lowerCamelCase ) == 1 and len(lowerCamelCase ) == 3: __SCREAMING_SNAKE_CASE = (int(e[0] ) << 16) + (int(e[1] ) << 8) + int(e[2] ) if c >= 0xE28_080 and c <= 0xE2B_07F: return True return False __SCREAMING_SNAKE_CASE = 0 __SCREAMING_SNAKE_CASE = [] while pos < len(lowerCamelCase ): __SCREAMING_SNAKE_CASE = min(len(lowerCamelCase ) ,pos + self.maxlen + 1 ) if text[pos] == """<""" else pos + 3 __SCREAMING_SNAKE_CASE = [] # (token_id, token, pos) for e in range(lowerCamelCase ,lowerCamelCase ,-1 ): __SCREAMING_SNAKE_CASE = text[pos:e] if wd in self.vocab: if wd[0] == "<" and len(lowerCamelCase ) > 2: __SCREAMING_SNAKE_CASE = [(self.vocab[wd], wd, e)] break else: candidates.append((self.vocab[wd], wd, e) ) if len(lowerCamelCase ) > 0: # the smallest token_id is adopted __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = sorted(lowerCamelCase ,key=lambda lowerCamelCase : x[0] )[0] result.append(lowerCamelCase ) __SCREAMING_SNAKE_CASE = e else: __SCREAMING_SNAKE_CASE = pos + 1 __SCREAMING_SNAKE_CASE = text[pos:end] if check_simbol(lowerCamelCase ): result.append("""<KIGOU>""" ) elif checkuae(lowerCamelCase ): result.append("""<U2000U2BFF>""" ) else: for i in wd.encode("""utf-8""" ): result.append("""<|byte%d|>""" % i ) __SCREAMING_SNAKE_CASE = end return result def UpperCAmelCase__ ( self : Optional[Any] ,lowerCamelCase : Dict ,lowerCamelCase : Optional[Any]="\n" ): '''simple docstring''' __SCREAMING_SNAKE_CASE = [] __SCREAMING_SNAKE_CASE = [] __SCREAMING_SNAKE_CASE = self.ids_to_tokens[index][0] if word[:6] == "<|byte" and word[-2:] == "|>": byte_tokens.append(int(word[6:-2] ) ) else: if len(lowerCamelCase ) > 0: words.append(bytearray(lowerCamelCase ).decode("""utf-8""" ,errors="""replace""" ) ) __SCREAMING_SNAKE_CASE = [] if word[:7] == "<|emoji" and word[-2:] == "|>": words.append(self.emoji["""emoji_inv"""][word] ) elif word == "<SP>": words.append(""" """ ) elif word == "<BR>": words.append(lowerCamelCase ) elif word == "<TAB>": words.append("""\t""" ) elif word == "<BLOCK>": words.append("""▀""" ) elif word == "<KIGOU>": words.append("""ǀ""" ) elif word == "<U2000U2BFF>": words.append("""‖""" ) else: words.append(lowerCamelCase ) if len(lowerCamelCase ) > 0: words.append(bytearray(lowerCamelCase ).decode("""utf-8""" ,errors="""replace""" ) ) __SCREAMING_SNAKE_CASE = """""".join(lowerCamelCase ) return text
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def _A ( _UpperCamelCase , _UpperCamelCase ): _UpperCAmelCase : Tuple = len(_UpperCamelCase ) _UpperCAmelCase : Tuple = len(_UpperCamelCase ) _UpperCAmelCase : Dict = [[False for _ in range(m + 1 )] for _ in range(n + 1 )] _UpperCAmelCase : List[Any] = True for i in range(_UpperCamelCase ): for j in range(m + 1 ): if dp[i][j]: if j < m and a[i].upper() == b[j]: _UpperCAmelCase : List[Any] = True if a[i].islower(): _UpperCAmelCase : str = True return dp[n][m] if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations import math import random from collections.abc import Collection from typing import overload class lowerCAmelCase_ : def __init__( self : Any , UpperCAmelCase_ : Collection[float] | None = None ) -> None: '''simple docstring''' if components is None: _UpperCAmelCase : Optional[Any] = [] _UpperCAmelCase : Any = list(UpperCAmelCase_ ) def __len__( self : Tuple ) -> int: '''simple docstring''' return len(self.__components ) def __str__( self : List[Any] ) -> str: '''simple docstring''' return "(" + ",".join(map(UpperCAmelCase_ , self.__components ) ) + ")" def __add__( self : List[str] , UpperCAmelCase_ : Vector ) -> Vector: '''simple docstring''' _UpperCAmelCase : Optional[int] = len(self ) if size == len(UpperCAmelCase_ ): _UpperCAmelCase : List[str] = [self.__components[i] + other.component(UpperCAmelCase_ ) for i in range(UpperCAmelCase_ )] return Vector(UpperCAmelCase_ ) else: raise Exception('''must have the same size''' ) def __sub__( self : Optional[int] , UpperCAmelCase_ : Vector ) -> Vector: '''simple docstring''' _UpperCAmelCase : str = len(self ) if size == len(UpperCAmelCase_ ): _UpperCAmelCase : Any = [self.__components[i] - other.component(UpperCAmelCase_ ) for i in range(UpperCAmelCase_ )] return Vector(UpperCAmelCase_ ) else: # error case raise Exception('''must have the same size''' ) @overload def __mul__( self : List[Any] , UpperCAmelCase_ : float ) -> Vector: '''simple docstring''' ... @overload def __mul__( self : Dict , UpperCAmelCase_ : Vector ) -> float: '''simple docstring''' ... def __mul__( self : Dict , UpperCAmelCase_ : float | Vector ) -> float | Vector: '''simple docstring''' if isinstance(UpperCAmelCase_ , (float, int) ): _UpperCAmelCase : Dict = [c * other for c in self.__components] return Vector(UpperCAmelCase_ ) elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and len(self ) == len(UpperCAmelCase_ ): _UpperCAmelCase : Optional[int] = len(self ) _UpperCAmelCase : Optional[Any] = [self.__components[i] * other.component(UpperCAmelCase_ ) for i in range(UpperCAmelCase_ )] return sum(UpperCAmelCase_ ) else: # error case raise Exception('''invalid operand!''' ) def a_ ( self : Optional[int] ) -> Vector: '''simple docstring''' return Vector(self.__components ) def a_ ( self : Optional[int] , UpperCAmelCase_ : int ) -> float: '''simple docstring''' if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and -len(self.__components ) <= i < len(self.__components ): return self.__components[i] else: raise Exception('''index out of range''' ) def a_ ( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : float ) -> None: '''simple docstring''' assert -len(self.__components ) <= pos < len(self.__components ) _UpperCAmelCase : Optional[Any] = value def a_ ( self : List[str] ) -> float: '''simple docstring''' if len(self.__components ) == 0: raise Exception('''Vector is empty''' ) _UpperCAmelCase : str = [c**2 for c in self.__components] return math.sqrt(sum(UpperCAmelCase_ ) ) def a_ ( self : Optional[int] , UpperCAmelCase_ : Vector , UpperCAmelCase_ : bool = False ) -> float: '''simple docstring''' _UpperCAmelCase : Optional[Any] = self * other _UpperCAmelCase : Union[str, Any] = self.euclidean_length() * other.euclidean_length() if deg: return math.degrees(math.acos(num / den ) ) else: return math.acos(num / den ) def _A ( _UpperCamelCase ): assert isinstance(_UpperCamelCase , _UpperCamelCase ) return Vector([0] * dimension ) def _A ( _UpperCamelCase , _UpperCamelCase ): assert isinstance(_UpperCamelCase , _UpperCamelCase ) and (isinstance(_UpperCamelCase , _UpperCamelCase )) _UpperCAmelCase : Optional[Any] = [0] * dimension _UpperCAmelCase : Optional[Any] = 1 return Vector(_UpperCamelCase ) def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ): assert ( isinstance(_UpperCamelCase , _UpperCamelCase ) and isinstance(_UpperCamelCase , _UpperCamelCase ) and (isinstance(_UpperCamelCase , (int, float) )) ) return x * scalar + y def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ): random.seed(_UpperCamelCase ) _UpperCAmelCase : Tuple = [random.randint(_UpperCamelCase , _UpperCamelCase ) for _ in range(_UpperCamelCase )] return Vector(_UpperCamelCase ) class lowerCAmelCase_ : def __init__( self : int , UpperCAmelCase_ : list[list[float]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> None: '''simple docstring''' _UpperCAmelCase : Tuple = matrix _UpperCAmelCase : Tuple = w _UpperCAmelCase : Tuple = h def __str__( self : str ) -> str: '''simple docstring''' _UpperCAmelCase : Optional[Any] = '''''' for i in range(self.__height ): ans += "|" for j in range(self.__width ): if j < self.__width - 1: ans += str(self.__matrix[i][j] ) + "," else: ans += str(self.__matrix[i][j] ) + "|\n" return ans def __add__( self : str , UpperCAmelCase_ : Matrix ) -> Matrix: '''simple docstring''' if self.__width == other.width() and self.__height == other.height(): _UpperCAmelCase : Optional[int] = [] for i in range(self.__height ): _UpperCAmelCase : Union[str, Any] = [ self.__matrix[i][j] + other.component(UpperCAmelCase_ , UpperCAmelCase_ ) for j in range(self.__width ) ] matrix.append(UpperCAmelCase_ ) return Matrix(UpperCAmelCase_ , self.__width , self.__height ) else: raise Exception('''matrix must have the same dimension!''' ) def __sub__( self : Dict , UpperCAmelCase_ : Matrix ) -> Matrix: '''simple docstring''' if self.__width == other.width() and self.__height == other.height(): _UpperCAmelCase : Optional[Any] = [] for i in range(self.__height ): _UpperCAmelCase : Union[str, Any] = [ self.__matrix[i][j] - other.component(UpperCAmelCase_ , UpperCAmelCase_ ) for j in range(self.__width ) ] matrix.append(UpperCAmelCase_ ) return Matrix(UpperCAmelCase_ , self.__width , self.__height ) else: raise Exception('''matrices must have the same dimension!''' ) @overload def __mul__( self : int , UpperCAmelCase_ : float ) -> Matrix: '''simple docstring''' ... @overload def __mul__( self : Optional[Any] , UpperCAmelCase_ : Vector ) -> Vector: '''simple docstring''' ... def __mul__( self : List[str] , UpperCAmelCase_ : float | Vector ) -> Vector | Matrix: '''simple docstring''' if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ): # matrix-vector if len(UpperCAmelCase_ ) == self.__width: _UpperCAmelCase : int = zero_vector(self.__height ) for i in range(self.__height ): _UpperCAmelCase : Optional[int] = [ self.__matrix[i][j] * other.component(UpperCAmelCase_ ) for j in range(self.__width ) ] ans.change_component(UpperCAmelCase_ , sum(UpperCAmelCase_ ) ) return ans else: raise Exception( '''vector must have the same size as the ''' '''number of columns of the matrix!''' ) elif isinstance(UpperCAmelCase_ , (int, float) ): # matrix-scalar _UpperCAmelCase : str = [ [self.__matrix[i][j] * other for j in range(self.__width )] for i in range(self.__height ) ] return Matrix(UpperCAmelCase_ , self.__width , self.__height ) return None def a_ ( self : Optional[int] ) -> int: '''simple docstring''' return self.__height def a_ ( self : Tuple ) -> int: '''simple docstring''' return self.__width def a_ ( self : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> float: '''simple docstring''' if 0 <= x < self.__height and 0 <= y < self.__width: return self.__matrix[x][y] else: raise Exception('''change_component: indices out of bounds''' ) def a_ ( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float ) -> None: '''simple docstring''' if 0 <= x < self.__height and 0 <= y < self.__width: _UpperCAmelCase : Dict = value else: raise Exception('''change_component: indices out of bounds''' ) def a_ ( self : str , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> float: '''simple docstring''' if self.__height != self.__width: raise Exception('''Matrix is not square''' ) _UpperCAmelCase : Any = self.__matrix[:x] + self.__matrix[x + 1 :] for i in range(len(UpperCAmelCase_ ) ): _UpperCAmelCase : Dict = minor[i][:y] + minor[i][y + 1 :] return Matrix(UpperCAmelCase_ , self.__width - 1 , self.__height - 1 ).determinant() def a_ ( self : Any , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> float: '''simple docstring''' if self.__height != self.__width: raise Exception('''Matrix is not square''' ) if 0 <= x < self.__height and 0 <= y < self.__width: return (-1) ** (x + y) * self.minor(UpperCAmelCase_ , UpperCAmelCase_ ) else: raise Exception('''Indices out of bounds''' ) def a_ ( self : Optional[int] ) -> float: '''simple docstring''' if self.__height != self.__width: raise Exception('''Matrix is not square''' ) if self.__height < 1: raise Exception('''Matrix has no element''' ) elif self.__height == 1: return self.__matrix[0][0] elif self.__height == 2: return ( self.__matrix[0][0] * self.__matrix[1][1] - self.__matrix[0][1] * self.__matrix[1][0] ) else: _UpperCAmelCase : Optional[int] = [ self.__matrix[0][y] * self.cofactor(0 , UpperCAmelCase_ ) for y in range(self.__width ) ] return sum(UpperCAmelCase_ ) def _A ( _UpperCamelCase ): _UpperCAmelCase : list[list[float]] = [[0] * n for _ in range(_UpperCamelCase )] return Matrix(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) def _A ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ): random.seed(_UpperCamelCase ) _UpperCAmelCase : list[list[float]] = [ [random.randint(_UpperCamelCase , _UpperCamelCase ) for _ in range(_UpperCamelCase )] for _ in range(_UpperCamelCase ) ] return Matrix(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
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from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin @dataclass class lowerCAmelCase_ ( __lowercase ): UpperCAmelCase = 42 class lowerCAmelCase_ ( __lowercase, __lowercase ): @register_to_config def __init__( self : int , _A : int = 32 , _A : int = 64 , _A : int = 20 , _A : int = 768 , _A : Union[str, Any]=77 , _A : Optional[Any]=4 , _A : float = 0.0 , _A : str = "silu" , _A : Optional[str] = None , _A : Optional[str] = None , _A : Optional[str] = "linear" , _A : Optional[str] = "prd" , _A : Optional[int] = None , _A : Optional[int] = None , _A : Optional[int] = None , ): super().__init__() _UpperCamelCase = num_attention_heads _UpperCamelCase = attention_head_dim _UpperCamelCase = num_attention_heads * attention_head_dim _UpperCamelCase = additional_embeddings _UpperCamelCase = time_embed_dim or inner_dim _UpperCamelCase = embedding_proj_dim or embedding_dim _UpperCamelCase = clip_embed_dim or embedding_dim _UpperCamelCase = Timesteps(_A , _A , 0 ) _UpperCamelCase = TimestepEmbedding(_A , _A , out_dim=_A , act_fn=_A ) _UpperCamelCase = nn.Linear(_A , _A ) if embedding_proj_norm_type is None: _UpperCamelCase = None elif embedding_proj_norm_type == "layer": _UpperCamelCase = nn.LayerNorm(_A ) else: raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}""" ) _UpperCamelCase = nn.Linear(_A , _A ) if encoder_hid_proj_type is None: _UpperCamelCase = None elif encoder_hid_proj_type == "linear": _UpperCamelCase = nn.Linear(_A , _A ) else: raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}""" ) _UpperCamelCase = nn.Parameter(torch.zeros(1 , num_embeddings + additional_embeddings , _A ) ) if added_emb_type == "prd": _UpperCamelCase = nn.Parameter(torch.zeros(1 , 1 , _A ) ) elif added_emb_type is None: _UpperCamelCase = None else: raise ValueError( F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`.""" ) _UpperCamelCase = nn.ModuleList( [ BasicTransformerBlock( _A , _A , _A , dropout=_A , activation_fn='''gelu''' , attention_bias=_A , ) for d in range(_A ) ] ) if norm_in_type == "layer": _UpperCamelCase = nn.LayerNorm(_A ) elif norm_in_type is None: _UpperCamelCase = None else: raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}.""" ) _UpperCamelCase = nn.LayerNorm(_A ) _UpperCamelCase = nn.Linear(_A , _A ) _UpperCamelCase = torch.full( [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings] , -1_0000.0 ) causal_attention_mask.triu_(1 ) _UpperCamelCase = causal_attention_mask[None, ...] self.register_buffer('''causal_attention_mask''' , _A , persistent=_A ) _UpperCamelCase = nn.Parameter(torch.zeros(1 , _A ) ) _UpperCamelCase = nn.Parameter(torch.zeros(1 , _A ) ) @property # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors def UpperCamelCase_ ( self : Optional[int] ): _UpperCamelCase = {} def fn_recursive_add_processors(_A : str , _A : torch.nn.Module , _A : Dict[str, AttentionProcessor] ): if hasattr(_A , '''set_processor''' ): _UpperCamelCase = module.processor for sub_name, child in module.named_children(): fn_recursive_add_processors(F"""{name}.{sub_name}""" , _A , _A ) return processors for name, module in self.named_children(): fn_recursive_add_processors(_A , _A , _A ) return processors def UpperCamelCase_ ( self : Optional[Any] , _A : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ): _UpperCamelCase = len(self.attn_processors.keys() ) if isinstance(_A , _A ) and len(_A ) != count: raise ValueError( F"""A dict of processors was passed, but the number of processors {len(_A )} does not match the""" F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" ) def fn_recursive_attn_processor(_A : str , _A : torch.nn.Module , _A : List[str] ): if hasattr(_A , '''set_processor''' ): if not isinstance(_A , _A ): module.set_processor(_A ) else: module.set_processor(processor.pop(F"""{name}.processor""" ) ) for sub_name, child in module.named_children(): fn_recursive_attn_processor(F"""{name}.{sub_name}""" , _A , _A ) for name, module in self.named_children(): fn_recursive_attn_processor(_A , _A , _A ) def UpperCamelCase_ ( self : Union[str, Any] ): self.set_attn_processor(AttnProcessor() ) def UpperCamelCase_ ( self : Union[str, Any] , _A : Optional[Any] , _A : Union[torch.Tensor, float, int] , _A : torch.FloatTensor , _A : Optional[torch.FloatTensor] = None , _A : Optional[torch.BoolTensor] = None , _A : bool = True , ): _UpperCamelCase = hidden_states.shape[0] _UpperCamelCase = timestep if not torch.is_tensor(_A ): _UpperCamelCase = torch.tensor([timesteps] , dtype=torch.long , device=hidden_states.device ) elif torch.is_tensor(_A ) and len(timesteps.shape ) == 0: _UpperCamelCase = timesteps[None].to(hidden_states.device ) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML _UpperCamelCase = timesteps * torch.ones(_A , dtype=timesteps.dtype , device=timesteps.device ) _UpperCamelCase = self.time_proj(_A ) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might be fp16, so we need to cast here. _UpperCamelCase = timesteps_projected.to(dtype=self.dtype ) _UpperCamelCase = self.time_embedding(_A ) if self.embedding_proj_norm is not None: _UpperCamelCase = self.embedding_proj_norm(_A ) _UpperCamelCase = self.embedding_proj(_A ) if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None: _UpperCamelCase = self.encoder_hidden_states_proj(_A ) elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None: raise ValueError('''`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set''' ) _UpperCamelCase = self.proj_in(_A ) _UpperCamelCase = self.positional_embedding.to(hidden_states.dtype ) _UpperCamelCase = [] _UpperCamelCase = 0 if encoder_hidden_states is not None: additional_embeds.append(_A ) additional_embeddings_len += encoder_hidden_states.shape[1] if len(proj_embeddings.shape ) == 2: _UpperCamelCase = proj_embeddings[:, None, :] if len(hidden_states.shape ) == 2: _UpperCamelCase = hidden_states[:, None, :] _UpperCamelCase = additional_embeds + [ proj_embeddings, time_embeddings[:, None, :], hidden_states, ] if self.prd_embedding is not None: _UpperCamelCase = self.prd_embedding.to(hidden_states.dtype ).expand(_A , -1 , -1 ) additional_embeds.append(_A ) _UpperCamelCase = torch.cat( _A , dim=1 , ) # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens _UpperCamelCase = additional_embeddings_len + proj_embeddings.shape[1] + 1 if positional_embeddings.shape[1] < hidden_states.shape[1]: _UpperCamelCase = F.pad( _A , ( 0, 0, additional_embeddings_len, self.prd_embedding.shape[1] if self.prd_embedding is not None else 0, ) , value=0.0 , ) _UpperCamelCase = hidden_states + positional_embeddings if attention_mask is not None: _UpperCamelCase = (1 - attention_mask.to(hidden_states.dtype )) * -1_0000.0 _UpperCamelCase = F.pad(_A , (0, self.additional_embeddings) , value=0.0 ) _UpperCamelCase = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype ) _UpperCamelCase = attention_mask.repeat_interleave(self.config.num_attention_heads , dim=0 ) if self.norm_in is not None: _UpperCamelCase = self.norm_in(_A ) for block in self.transformer_blocks: _UpperCamelCase = block(_A , attention_mask=_A ) _UpperCamelCase = self.norm_out(_A ) if self.prd_embedding is not None: _UpperCamelCase = hidden_states[:, -1] else: _UpperCamelCase = hidden_states[:, additional_embeddings_len:] _UpperCamelCase = self.proj_to_clip_embeddings(_A ) if not return_dict: return (predicted_image_embedding,) return PriorTransformerOutput(predicted_image_embedding=_A ) def UpperCamelCase_ ( self : List[Any] , _A : Dict ): _UpperCamelCase = (prior_latents * self.clip_std) + self.clip_mean return prior_latents
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def lowerCamelCase__ ( snake_case_ : int = 1000 ) -> int: __snake_case = 2**power __snake_case = str(snake_case_ ) __snake_case = list(snake_case_ ) __snake_case = 0 for i in list_num: sum_of_num += int(snake_case_ ) return sum_of_num if __name__ == "__main__": snake_case_ = int(input('Enter the power of 2: ').strip()) print('2 ^ ', power, ' = ', 2**power) snake_case_ = solution(power) print('Sum of the digits is: ', result)
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0
import builtins import sys from ...utils.imports import _is_package_available from . import cursor, input from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor from .keymap import KEYMAP A__ = False try: A__ = _is_package_available("google.colab") except ModuleNotFoundError: pass @input.register class __UpperCamelCase : def __init__( self: List[Any] , __UpperCamelCase: str = None , __UpperCamelCase: list = [] ): '''simple docstring''' __magic_name__ = 0 __magic_name__ = choices __magic_name__ = prompt if sys.platform == "win32": __magic_name__ = '*' else: __magic_name__ = '➔ ' def _SCREAMING_SNAKE_CASE ( self: Tuple , __UpperCamelCase: List[str] , __UpperCamelCase: str = "" ): '''simple docstring''' if sys.platform != "win32": writeColor(self.choices[index] , 32 , __UpperCamelCase ) else: forceWrite(self.choices[index] , __UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: List[str] , __UpperCamelCase: int ): '''simple docstring''' if index == self.position: forceWrite(F' {self.arrow_char} ' ) self.write_choice(__UpperCamelCase ) else: forceWrite(F' {self.choices[index]}' ) reset_cursor() def _SCREAMING_SNAKE_CASE ( self: List[Any] , __UpperCamelCase: Direction , __UpperCamelCase: int = 1 ): '''simple docstring''' __magic_name__ = self.position if direction == Direction.DOWN: if self.position + 1 >= len(self.choices ): return self.position += num_spaces else: if self.position - 1 < 0: return self.position -= num_spaces clear_line() self.print_choice(__UpperCamelCase ) move_cursor(__UpperCamelCase , direction.name ) self.print_choice(self.position ) @input.mark(KEYMAP['up'] ) def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' self.move_direction(Direction.UP ) @input.mark(KEYMAP['down'] ) def _SCREAMING_SNAKE_CASE ( self: int ): '''simple docstring''' self.move_direction(Direction.DOWN ) @input.mark(KEYMAP['newline'] ) def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' move_cursor(len(self.choices ) - self.position , 'DOWN' ) return self.position @input.mark(KEYMAP['interrupt'] ) def _SCREAMING_SNAKE_CASE ( self: Dict ): '''simple docstring''' move_cursor(len(self.choices ) - self.position , 'DOWN' ) raise KeyboardInterrupt @input.mark_multiple(*[KEYMAP[str(__UpperCamelCase )] for number in range(10 )] ) def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' __magic_name__ = int(chr(self.current_selection ) ) __magic_name__ = index - self.position if index == self.position: return if index < len(self.choices ): if self.position > index: self.move_direction(Direction.UP , -movement ) elif self.position < index: self.move_direction(Direction.DOWN , __UpperCamelCase ) else: return else: return def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] , __UpperCamelCase: int = 0 ): '''simple docstring''' if self.prompt: linebreak() forceWrite(self.prompt , '\n' ) if in_colab: forceWrite('Please input a choice index (starting from 0), and press enter' , '\n' ) else: forceWrite('Please select a choice using the arrow or number keys, and selecting with enter' , '\n' ) __magic_name__ = default_choice for i in range(len(self.choices ) ): self.print_choice(__UpperCamelCase ) forceWrite('\n' ) move_cursor(len(self.choices ) - self.position , 'UP' ) with cursor.hide(): while True: if in_colab: try: __magic_name__ = int(builtins.input() ) except ValueError: __magic_name__ = default_choice else: __magic_name__ = self.handle_input() if choice is not None: reset_cursor() for _ in range(len(self.choices ) + 1 ): move_cursor(1 , 'UP' ) clear_line() self.write_choice(__UpperCamelCase , '\n' ) return choice
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import unittest from diffusers.models.unet_ad_blocks import * # noqa F403 from diffusers.utils import torch_device from .test_unet_blocks_common import UNetBlockTesterMixin class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : str = DownBlockaD # noqa F405 _lowercase : Union[str, Any] = "down" def _SCREAMING_SNAKE_CASE ( self: List[str] ): '''simple docstring''' __magic_name__ = [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : List[str] = ResnetDownsampleBlockaD # noqa F405 _lowercase : Union[str, Any] = "down" def _SCREAMING_SNAKE_CASE ( self: int ): '''simple docstring''' __magic_name__ = [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Dict = AttnDownBlockaD # noqa F405 _lowercase : List[Any] = "down" def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' __magic_name__ = [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : int = CrossAttnDownBlockaD # noqa F405 _lowercase : Any = "down" def _SCREAMING_SNAKE_CASE ( self: int ): '''simple docstring''' __magic_name__, __magic_name__ = super().prepare_init_args_and_inputs_for_common() __magic_name__ = 32 return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: str ): '''simple docstring''' __magic_name__ = [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Union[str, Any] = SimpleCrossAttnDownBlockaD # noqa F405 _lowercase : List[str] = "down" @property def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' return super().get_dummy_input(include_encoder_hidden_states=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' __magic_name__, __magic_name__ = super().prepare_init_args_and_inputs_for_common() __magic_name__ = 32 return init_dict, inputs_dict @unittest.skipIf(torch_device == 'mps' , 'MPS result is not consistent' ) def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' __magic_name__ = [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : str = SkipDownBlockaD # noqa F405 _lowercase : Union[str, Any] = "down" @property def _SCREAMING_SNAKE_CASE ( self: Optional[int] ): '''simple docstring''' return super().get_dummy_input(include_skip_sample=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: List[str] ): '''simple docstring''' __magic_name__ = [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Tuple = AttnSkipDownBlockaD # noqa F405 _lowercase : str = "down" @property def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' return super().get_dummy_input(include_skip_sample=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Dict ): '''simple docstring''' __magic_name__ = [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Optional[int] = DownEncoderBlockaD # noqa F405 _lowercase : List[str] = "down" @property def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] ): '''simple docstring''' return super().get_dummy_input(include_temb=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Dict ): '''simple docstring''' __magic_name__ = { 'in_channels': 32, 'out_channels': 32, } __magic_name__ = self.dummy_input return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: List[str] ): '''simple docstring''' __magic_name__ = [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : List[Any] = AttnDownEncoderBlockaD # noqa F405 _lowercase : Optional[Any] = "down" @property def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' return super().get_dummy_input(include_temb=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Optional[Any] ): '''simple docstring''' __magic_name__ = { 'in_channels': 32, 'out_channels': 32, } __magic_name__ = self.dummy_input return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: Dict ): '''simple docstring''' __magic_name__ = [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : List[Any] = UNetMidBlockaD # noqa F405 _lowercase : Any = "mid" def _SCREAMING_SNAKE_CASE ( self: List[str] ): '''simple docstring''' __magic_name__ = { 'in_channels': 32, 'temb_channels': 1_28, } __magic_name__ = self.dummy_input return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: str ): '''simple docstring''' __magic_name__ = [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : str = UNetMidBlockaDCrossAttn # noqa F405 _lowercase : int = "mid" def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' __magic_name__, __magic_name__ = super().prepare_init_args_and_inputs_for_common() __magic_name__ = 32 return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: Tuple ): '''simple docstring''' __magic_name__ = [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Tuple = UNetMidBlockaDSimpleCrossAttn # noqa F405 _lowercase : str = "mid" @property def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' return super().get_dummy_input(include_encoder_hidden_states=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Tuple ): '''simple docstring''' __magic_name__, __magic_name__ = super().prepare_init_args_and_inputs_for_common() __magic_name__ = 32 return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' __magic_name__ = [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : List[Any] = UpBlockaD # noqa F405 _lowercase : List[Any] = "up" @property def _SCREAMING_SNAKE_CASE ( self: Tuple ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' __magic_name__ = [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : List[Any] = ResnetUpsampleBlockaD # noqa F405 _lowercase : Dict = "up" @property def _SCREAMING_SNAKE_CASE ( self: Optional[int] ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Dict ): '''simple docstring''' __magic_name__ = [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Any = CrossAttnUpBlockaD # noqa F405 _lowercase : Union[str, Any] = "up" @property def _SCREAMING_SNAKE_CASE ( self: Optional[int] ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: str ): '''simple docstring''' __magic_name__, __magic_name__ = super().prepare_init_args_and_inputs_for_common() __magic_name__ = 32 return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: Any ): '''simple docstring''' __magic_name__ = [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : str = SimpleCrossAttnUpBlockaD # noqa F405 _lowercase : Tuple = "up" @property def _SCREAMING_SNAKE_CASE ( self: List[str] ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase , include_encoder_hidden_states=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] ): '''simple docstring''' __magic_name__, __magic_name__ = super().prepare_init_args_and_inputs_for_common() __magic_name__ = 32 return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' __magic_name__ = [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Optional[Any] = AttnUpBlockaD # noqa F405 _lowercase : Optional[int] = "up" @property def _SCREAMING_SNAKE_CASE ( self: str ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase ) @unittest.skipIf(torch_device == 'mps' , 'MPS result is not consistent' ) def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] ): '''simple docstring''' __magic_name__ = [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Union[str, Any] = SkipUpBlockaD # noqa F405 _lowercase : int = "up" @property def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Union[str, Any] ): '''simple docstring''' __magic_name__ = [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Union[str, Any] = AttnSkipUpBlockaD # noqa F405 _lowercase : Optional[Any] = "up" @property def _SCREAMING_SNAKE_CASE ( self: Optional[Any] ): '''simple docstring''' return super().get_dummy_input(include_res_hidden_states_tuple=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: Tuple ): '''simple docstring''' __magic_name__ = [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : List[str] = UpDecoderBlockaD # noqa F405 _lowercase : List[str] = "up" @property def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' return super().get_dummy_input(include_temb=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' __magic_name__ = {'in_channels': 32, 'out_channels': 32} __magic_name__ = self.dummy_input return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: Optional[Any] ): '''simple docstring''' __magic_name__ = [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137] super().test_output(__UpperCamelCase ) class __UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ): _lowercase : Optional[Any] = AttnUpDecoderBlockaD # noqa F405 _lowercase : Any = "up" @property def _SCREAMING_SNAKE_CASE ( self: Dict ): '''simple docstring''' return super().get_dummy_input(include_temb=__UpperCamelCase ) def _SCREAMING_SNAKE_CASE ( self: str ): '''simple docstring''' __magic_name__ = {'in_channels': 32, 'out_channels': 32} __magic_name__ = self.dummy_input return init_dict, inputs_dict def _SCREAMING_SNAKE_CASE ( self: List[Any] ): '''simple docstring''' __magic_name__ = [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568] super().test_output(__UpperCamelCase )
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import re def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ): UpperCamelCase__ : Optional[int] = re.compile( R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' ) return bool(re.search(UpperCamelCase__ , UpperCamelCase__ ) ) if __name__ == "__main__": lowerCamelCase ="0094702343221" print(is_sri_lankan_phone_number(phone))
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import inspect import unittest from transformers import YolosConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import YolosForObjectDetection, YolosModel from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class _lowerCamelCase : """simple docstring""" def __init__( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=1_3 , __SCREAMING_SNAKE_CASE=[3_0, 3_0] , __SCREAMING_SNAKE_CASE=2 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=3_2 , __SCREAMING_SNAKE_CASE=5 , __SCREAMING_SNAKE_CASE=4 , __SCREAMING_SNAKE_CASE=3_7 , __SCREAMING_SNAKE_CASE="gelu" , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=0.1 , __SCREAMING_SNAKE_CASE=1_0 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=8 , __SCREAMING_SNAKE_CASE=1_0 , ) -> List[str]: """simple docstring""" UpperCamelCase__ : Any = parent UpperCamelCase__ : Optional[Any] = batch_size UpperCamelCase__ : str = image_size UpperCamelCase__ : Union[str, Any] = patch_size UpperCamelCase__ : Union[str, Any] = num_channels UpperCamelCase__ : Tuple = is_training UpperCamelCase__ : Optional[int] = use_labels UpperCamelCase__ : Optional[int] = hidden_size UpperCamelCase__ : Any = num_hidden_layers UpperCamelCase__ : Optional[Any] = num_attention_heads UpperCamelCase__ : int = intermediate_size UpperCamelCase__ : Tuple = hidden_act UpperCamelCase__ : int = hidden_dropout_prob UpperCamelCase__ : str = attention_probs_dropout_prob UpperCamelCase__ : str = type_sequence_label_size UpperCamelCase__ : str = initializer_range UpperCamelCase__ : Dict = num_labels UpperCamelCase__ : List[Any] = scope UpperCamelCase__ : str = n_targets UpperCamelCase__ : int = num_detection_tokens # we set the expected sequence length (which is used in several tests) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens UpperCamelCase__ : int = (image_size[1] // patch_size) * (image_size[0] // patch_size) UpperCamelCase__ : Any = num_patches + 1 + self.num_detection_tokens def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]: """simple docstring""" UpperCamelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] ) UpperCamelCase__ : int = None if self.use_labels: # labels is a list of Dict (each Dict being the labels for a given example in the batch) UpperCamelCase__ : Union[str, Any] = [] for i in range(self.batch_size ): UpperCamelCase__ : Optional[Any] = {} UpperCamelCase__ : str = torch.randint( high=self.num_labels , size=(self.n_targets,) , device=__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : List[Any] = torch.rand(self.n_targets , 4 , device=__SCREAMING_SNAKE_CASE ) labels.append(__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : Any = self.get_config() return config, pixel_values, labels def __SCREAMING_SNAKE_CASE ( self ) -> List[str]: """simple docstring""" return YolosConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , ) def __SCREAMING_SNAKE_CASE ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> List[Any]: """simple docstring""" UpperCamelCase__ : Dict = YolosModel(config=__SCREAMING_SNAKE_CASE ) model.to(__SCREAMING_SNAKE_CASE ) model.eval() UpperCamelCase__ : Optional[Any] = model(__SCREAMING_SNAKE_CASE ) self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) ) def __SCREAMING_SNAKE_CASE ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) -> str: """simple docstring""" UpperCamelCase__ : Optional[int] = YolosForObjectDetection(__SCREAMING_SNAKE_CASE ) model.to(__SCREAMING_SNAKE_CASE ) model.eval() UpperCamelCase__ : str = model(pixel_values=__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : Union[str, Any] = model(__SCREAMING_SNAKE_CASE ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) ) self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) ) UpperCamelCase__ : Optional[Any] = model(pixel_values=__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE ) self.parent.assertEqual(result.loss.shape , () ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) ) self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) ) def __SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]: """simple docstring""" UpperCamelCase__ : str = self.prepare_config_and_inputs() UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ : Union[str, Any] = config_and_inputs UpperCamelCase__ : Union[str, Any] = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class _lowerCamelCase ( UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ): """simple docstring""" SCREAMING_SNAKE_CASE_ = (YolosModel, YolosForObjectDetection) if is_torch_available() else () SCREAMING_SNAKE_CASE_ = ( {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {} ) SCREAMING_SNAKE_CASE_ = False SCREAMING_SNAKE_CASE_ = False SCREAMING_SNAKE_CASE_ = False SCREAMING_SNAKE_CASE_ = False def __SCREAMING_SNAKE_CASE ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=False ) -> List[str]: """simple docstring""" UpperCamelCase__ : int = super()._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE ) if return_labels: if model_class.__name__ == "YolosForObjectDetection": UpperCamelCase__ : List[Any] = [] for i in range(self.model_tester.batch_size ): UpperCamelCase__ : Optional[int] = {} UpperCamelCase__ : Union[str, Any] = torch.ones( size=(self.model_tester.n_targets,) , device=__SCREAMING_SNAKE_CASE , dtype=torch.long ) UpperCamelCase__ : Tuple = torch.ones( self.model_tester.n_targets , 4 , device=__SCREAMING_SNAKE_CASE , dtype=torch.float ) labels.append(__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : Optional[int] = labels return inputs_dict def __SCREAMING_SNAKE_CASE ( self ) -> int: """simple docstring""" UpperCamelCase__ : Any = YolosModelTester(self ) UpperCamelCase__ : Union[str, Any] = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE , hidden_size=3_7 ) def __SCREAMING_SNAKE_CASE ( self ) -> Tuple: """simple docstring""" self.config_tester.run_common_tests() def __SCREAMING_SNAKE_CASE ( self ) -> str: """simple docstring""" pass def __SCREAMING_SNAKE_CASE ( self ) -> str: """simple docstring""" UpperCamelCase__ ,UpperCamelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase__ : Dict = model_class(__SCREAMING_SNAKE_CASE ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) UpperCamelCase__ : Optional[int] = model.get_output_embeddings() self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , nn.Linear ) ) def __SCREAMING_SNAKE_CASE ( self ) -> Tuple: """simple docstring""" UpperCamelCase__ ,UpperCamelCase__ : Any = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase__ : Any = model_class(__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : Optional[Any] = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic UpperCamelCase__ : List[Any] = [*signature.parameters.keys()] UpperCamelCase__ : List[Any] = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE ) def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]: """simple docstring""" UpperCamelCase__ : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE ) def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]: """simple docstring""" UpperCamelCase__ ,UpperCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() UpperCamelCase__ : List[str] = True # in YOLOS, the seq_len is different UpperCamelCase__ : List[str] = self.model_tester.expected_seq_len for model_class in self.all_model_classes: UpperCamelCase__ : Tuple = True UpperCamelCase__ : int = False UpperCamelCase__ : Union[str, Any] = True UpperCamelCase__ : str = model_class(__SCREAMING_SNAKE_CASE ) model.to(__SCREAMING_SNAKE_CASE ) model.eval() with torch.no_grad(): UpperCamelCase__ : Tuple = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) UpperCamelCase__ : Tuple = outputs.attentions self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers ) # check that output_attentions also work using config del inputs_dict["output_attentions"] UpperCamelCase__ : str = True UpperCamelCase__ : Union[str, Any] = model_class(__SCREAMING_SNAKE_CASE ) model.to(__SCREAMING_SNAKE_CASE ) model.eval() with torch.no_grad(): UpperCamelCase__ : Optional[Any] = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) UpperCamelCase__ : List[Any] = outputs.attentions self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , ) UpperCamelCase__ : Optional[Any] = len(__SCREAMING_SNAKE_CASE ) # Check attention is always last and order is fine UpperCamelCase__ : Optional[Any] = True UpperCamelCase__ : List[Any] = True UpperCamelCase__ : List[Any] = model_class(__SCREAMING_SNAKE_CASE ) model.to(__SCREAMING_SNAKE_CASE ) model.eval() with torch.no_grad(): UpperCamelCase__ : Optional[Any] = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) UpperCamelCase__ : Optional[int] = 1 self.assertEqual(out_len + added_hidden_states , len(__SCREAMING_SNAKE_CASE ) ) UpperCamelCase__ : int = outputs.attentions self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , ) def __SCREAMING_SNAKE_CASE ( self ) -> Any: """simple docstring""" def check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ): UpperCamelCase__ : str = model_class(__SCREAMING_SNAKE_CASE ) model.to(__SCREAMING_SNAKE_CASE ) model.eval() with torch.no_grad(): UpperCamelCase__ : Optional[Any] = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) UpperCamelCase__ : Tuple = outputs.hidden_states UpperCamelCase__ : Tuple = getattr( self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE ) # YOLOS has a different seq_length UpperCamelCase__ : Dict = self.model_tester.expected_seq_len self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , ) UpperCamelCase__ ,UpperCamelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase__ : Optional[Any] = True check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] UpperCamelCase__ : Any = True check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) def __SCREAMING_SNAKE_CASE ( self ) -> Tuple: """simple docstring""" UpperCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_object_detection(*__SCREAMING_SNAKE_CASE ) @slow def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]: """simple docstring""" for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCamelCase__ : Dict = YolosModel.from_pretrained(__SCREAMING_SNAKE_CASE ) self.assertIsNotNone(__SCREAMING_SNAKE_CASE ) def SCREAMING_SNAKE_CASE_ ( ): UpperCamelCase__ : Dict = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class _lowerCamelCase ( unittest.TestCase ): """simple docstring""" @cached_property def __SCREAMING_SNAKE_CASE ( self ) -> str: """simple docstring""" return AutoImageProcessor.from_pretrained('''hustvl/yolos-small''' ) if is_vision_available() else None @slow def __SCREAMING_SNAKE_CASE ( self ) -> Dict: """simple docstring""" UpperCamelCase__ : List[Any] = YolosForObjectDetection.from_pretrained('''hustvl/yolos-small''' ).to(__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : List[Any] = self.default_image_processor UpperCamelCase__ : int = prepare_img() UpperCamelCase__ : Optional[Any] = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).to(__SCREAMING_SNAKE_CASE ) # forward pass with torch.no_grad(): UpperCamelCase__ : int = model(inputs.pixel_values ) # verify outputs UpperCamelCase__ : Dict = torch.Size((1, 1_0_0, 9_2) ) self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE ) UpperCamelCase__ : Optional[int] = torch.tensor( [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]] , device=__SCREAMING_SNAKE_CASE , ) UpperCamelCase__ : Dict = torch.tensor( [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]] , device=__SCREAMING_SNAKE_CASE ) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) ) self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) ) # verify postprocessing UpperCamelCase__ : Any = image_processor.post_process_object_detection( __SCREAMING_SNAKE_CASE , threshold=0.3 , target_sizes=[image.size[::-1]] )[0] UpperCamelCase__ : List[Any] = torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861] ).to(__SCREAMING_SNAKE_CASE ) UpperCamelCase__ : List[str] = [7_5, 7_5, 1_7, 6_3, 1_7] UpperCamelCase__ : List[str] = torch.tensor([335.0609, 79.3848, 375.4216, 187.2495] ).to(__SCREAMING_SNAKE_CASE ) self.assertEqual(len(results['''scores'''] ) , 5 ) self.assertTrue(torch.allclose(results['''scores'''] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) ) self.assertSequenceEqual(results['''labels'''].tolist() , __SCREAMING_SNAKE_CASE ) self.assertTrue(torch.allclose(results['''boxes'''][0, :] , __SCREAMING_SNAKE_CASE ) )
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import sys import webbrowser import requests from bsa import BeautifulSoup from fake_useragent import UserAgent if __name__ == "__main__": print('Googling.....') _SCREAMING_SNAKE_CASE = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:]) _SCREAMING_SNAKE_CASE = requests.get(url, headers={'UserAgent': UserAgent().random}) # res.raise_for_status() with open('project1a.html', 'wb') as out_file: # only for knowing the class for data in res.iter_content(10_000): out_file.write(data) _SCREAMING_SNAKE_CASE = BeautifulSoup(res.text, 'html.parser') _SCREAMING_SNAKE_CASE = list(soup.select('.eZt8xd'))[:5] print(len(links)) for link in links: if link.text == "Maps": webbrowser.open(link.get('href')) else: webbrowser.open(F'''https://google.com{link.get("href")}''')
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import colorsys from PIL import Image # type: ignore def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float: _A = x _A = y for step in range(snake_case__): # noqa: B007 _A = a * a - b * b + x _A = 2 * a * b + y _A = a_new # divergence happens for all complex number with an absolute value # greater than 4 if a * a + b * b > 4: break return step / (max_step - 1) def snake_case ( snake_case__ :float) -> tuple: if distance == 1: return (0, 0, 0) else: return (255, 255, 255) def snake_case ( snake_case__ :float) -> tuple: if distance == 1: return (0, 0, 0) else: return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(snake_case__ , 1 , 1)) def snake_case ( snake_case__ :int = 800 , snake_case__ :int = 600 , snake_case__ :float = -0.6 , snake_case__ :float = 0 , snake_case__ :float = 3.2 , snake_case__ :int = 50 , snake_case__ :bool = True , ) -> Image.Image: _A = Image.new("""RGB""" , (image_width, image_height)) _A = img.load() # loop through the image-coordinates for image_x in range(snake_case__): for image_y in range(snake_case__): # determine the figure-coordinates based on the image-coordinates _A = figure_width / image_width * image_height _A = figure_center_x + (image_x / image_width - 0.5) * figure_width _A = figure_center_y + (image_y / image_height - 0.5) * figure_height _A = get_distance(snake_case__ , snake_case__ , snake_case__) # color the corresponding pixel based on the selected coloring-function if use_distance_color_coding: _A = get_color_coded_rgb(snake_case__) else: _A = get_black_and_white_rgb(snake_case__) return img if __name__ == "__main__": import doctest doctest.testmod() # colored version, full figure _SCREAMING_SNAKE_CASE = get_image() # uncomment for colored version, different section, zoomed in # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4, # figure_width = 0.8) # uncomment for black and white version, full figure # img = get_image(use_distance_color_coding = False) # uncomment to save the image # img.save("mandelbrot.png") img.show()
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'''simple docstring''' from scipy.stats import spearmanr import datasets SCREAMING_SNAKE_CASE = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n' SCREAMING_SNAKE_CASE = '\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {\'spearmanr\': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results[\'spearmanr\'])\n -0.7\n >>> print(round(results[\'spearmanr_pvalue\'], 2))\n 0.19\n' SCREAMING_SNAKE_CASE = r'\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class UpperCAmelCase_ ( datasets.Metric ): """simple docstring""" def A__ ( self : Union[str, Any] ) -> List[Any]: '''simple docstring''' return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { '''predictions''': datasets.Value('''float''' ), '''references''': datasets.Value('''float''' ), } ) , reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html'''] , ) def A__ ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Any=False ) -> Dict: '''simple docstring''' lowercase : List[Any] =spearmanr(UpperCAmelCase , UpperCAmelCase ) if return_pvalue: return {"spearmanr": results[0], "spearmanr_pvalue": results[1]} else: return {"spearmanr": results[0]}
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'''simple docstring''' def UpperCamelCase_ ( _UpperCAmelCase : int ) -> list: """simple docstring""" _UpperCAmelCase : Tuple = int(_UpperCAmelCase ) if n_element < 1: _UpperCAmelCase : Tuple = ValueError("a should be a positive number" ) raise my_error _UpperCAmelCase : Optional[Any] = [1] _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = (0, 0, 0) _UpperCAmelCase : str = 1 while index < n_element: while hamming_list[i] * 2 <= hamming_list[-1]: i += 1 while hamming_list[j] * 3 <= hamming_list[-1]: j += 1 while hamming_list[k] * 5 <= hamming_list[-1]: k += 1 hamming_list.append( min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) ) index += 1 return hamming_list if __name__ == "__main__": __SCREAMING_SNAKE_CASE : Dict = input("""Enter the last number (nth term) of the Hamming Number Series: """) print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""") __SCREAMING_SNAKE_CASE : Union[str, Any] = hamming(int(n)) print("""-----------------------------------------------------""") print(F'The list with nth numbers is: {hamming_numbers}') print("""-----------------------------------------------------""")
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available SCREAMING_SNAKE_CASE = { 'configuration_ctrl': ['CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CTRLConfig'], 'tokenization_ctrl': ['CTRLTokenizer'], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE = [ 'CTRL_PRETRAINED_MODEL_ARCHIVE_LIST', 'CTRLForSequenceClassification', 'CTRLLMHeadModel', 'CTRLModel', 'CTRLPreTrainedModel', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE = [ 'TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFCTRLForSequenceClassification', 'TFCTRLLMHeadModel', 'TFCTRLModel', 'TFCTRLPreTrainedModel', ] if TYPE_CHECKING: from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig from .tokenization_ctrl import CTRLTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_ctrl import ( CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLForSequenceClassification, CTRLLMHeadModel, CTRLModel, CTRLPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_ctrl import ( TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, TFCTRLForSequenceClassification, TFCTRLLMHeadModel, TFCTRLModel, TFCTRLPreTrainedModel, ) else: import sys SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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"""simple docstring""" import importlib import json import os from collections import OrderedDict from typing import Dict, Optional, Union # Build the list of all feature extractors from ...configuration_utils import PretrainedConfig from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code from ...feature_extraction_utils import FeatureExtractionMixin from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging from .auto_factory import _LazyAutoMapping from .configuration_auto import ( CONFIG_MAPPING_NAMES, AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings, ) SCREAMING_SNAKE_CASE = logging.get_logger(__name__) SCREAMING_SNAKE_CASE = OrderedDict( [ ('audio-spectrogram-transformer', 'ASTFeatureExtractor'), ('beit', 'BeitFeatureExtractor'), ('chinese_clip', 'ChineseCLIPFeatureExtractor'), ('clap', 'ClapFeatureExtractor'), ('clip', 'CLIPFeatureExtractor'), ('clipseg', 'ViTFeatureExtractor'), ('conditional_detr', 'ConditionalDetrFeatureExtractor'), ('convnext', 'ConvNextFeatureExtractor'), ('cvt', 'ConvNextFeatureExtractor'), ('data2vec-audio', 'Wav2Vec2FeatureExtractor'), ('data2vec-vision', 'BeitFeatureExtractor'), ('deformable_detr', 'DeformableDetrFeatureExtractor'), ('deit', 'DeiTFeatureExtractor'), ('detr', 'DetrFeatureExtractor'), ('dinat', 'ViTFeatureExtractor'), ('donut-swin', 'DonutFeatureExtractor'), ('dpt', 'DPTFeatureExtractor'), ('encodec', 'EncodecFeatureExtractor'), ('flava', 'FlavaFeatureExtractor'), ('glpn', 'GLPNFeatureExtractor'), ('groupvit', 'CLIPFeatureExtractor'), ('hubert', 'Wav2Vec2FeatureExtractor'), ('imagegpt', 'ImageGPTFeatureExtractor'), ('layoutlmv2', 'LayoutLMv2FeatureExtractor'), ('layoutlmv3', 'LayoutLMv3FeatureExtractor'), ('levit', 'LevitFeatureExtractor'), ('maskformer', 'MaskFormerFeatureExtractor'), ('mctct', 'MCTCTFeatureExtractor'), ('mobilenet_v1', 'MobileNetV1FeatureExtractor'), ('mobilenet_v2', 'MobileNetV2FeatureExtractor'), ('mobilevit', 'MobileViTFeatureExtractor'), ('nat', 'ViTFeatureExtractor'), ('owlvit', 'OwlViTFeatureExtractor'), ('perceiver', 'PerceiverFeatureExtractor'), ('poolformer', 'PoolFormerFeatureExtractor'), ('regnet', 'ConvNextFeatureExtractor'), ('resnet', 'ConvNextFeatureExtractor'), ('segformer', 'SegformerFeatureExtractor'), ('sew', 'Wav2Vec2FeatureExtractor'), ('sew-d', 'Wav2Vec2FeatureExtractor'), ('speech_to_text', 'Speech2TextFeatureExtractor'), ('speecht5', 'SpeechT5FeatureExtractor'), ('swiftformer', 'ViTFeatureExtractor'), ('swin', 'ViTFeatureExtractor'), ('swinv2', 'ViTFeatureExtractor'), ('table-transformer', 'DetrFeatureExtractor'), ('timesformer', 'VideoMAEFeatureExtractor'), ('tvlt', 'TvltFeatureExtractor'), ('unispeech', 'Wav2Vec2FeatureExtractor'), ('unispeech-sat', 'Wav2Vec2FeatureExtractor'), ('van', 'ConvNextFeatureExtractor'), ('videomae', 'VideoMAEFeatureExtractor'), ('vilt', 'ViltFeatureExtractor'), ('vit', 'ViTFeatureExtractor'), ('vit_mae', 'ViTFeatureExtractor'), ('vit_msn', 'ViTFeatureExtractor'), ('wav2vec2', 'Wav2Vec2FeatureExtractor'), ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'), ('wavlm', 'Wav2Vec2FeatureExtractor'), ('whisper', 'WhisperFeatureExtractor'), ('xclip', 'CLIPFeatureExtractor'), ('yolos', 'YolosFeatureExtractor'), ] ) SCREAMING_SNAKE_CASE = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES) def __lowerCAmelCase( __UpperCAmelCase ): """simple docstring""" for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items(): if class_name in extractors: _lowercase : int = model_type_to_module_name(__UpperCAmelCase ) _lowercase : int = importlib.import_module(F'''.{module_name}''' ,'transformers.models' ) try: return getattr(__UpperCAmelCase ,__UpperCAmelCase ) except AttributeError: continue for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items(): if getattr(__UpperCAmelCase ,'__name__' ,__UpperCAmelCase ) == class_name: return extractor # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main # init and we return the proper dummy to get an appropriate error message. _lowercase : List[str] = importlib.import_module('transformers' ) if hasattr(__UpperCAmelCase ,__UpperCAmelCase ): return getattr(__UpperCAmelCase ,__UpperCAmelCase ) return None def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase = None ,__UpperCAmelCase = False ,__UpperCAmelCase = False ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,__UpperCAmelCase = None ,__UpperCAmelCase = False ,**__UpperCAmelCase ,): """simple docstring""" _lowercase : Any = get_file_from_repo( __UpperCAmelCase ,__UpperCAmelCase ,cache_dir=__UpperCAmelCase ,force_download=__UpperCAmelCase ,resume_download=__UpperCAmelCase ,proxies=__UpperCAmelCase ,use_auth_token=__UpperCAmelCase ,revision=__UpperCAmelCase ,local_files_only=__UpperCAmelCase ,) if resolved_config_file is None: logger.info( 'Could not locate the feature extractor configuration file, will try to use the model config instead.' ) return {} with open(__UpperCAmelCase ,encoding='utf-8' ) as reader: return json.load(__UpperCAmelCase ) class _lowerCamelCase : def __init__( self : List[Any] ): """simple docstring""" raise EnvironmentError( 'AutoFeatureExtractor is designed to be instantiated ' 'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.' ) @classmethod @replace_list_option_in_docstrings(lowerCamelCase_ ) def __UpperCAmelCase ( cls : Union[str, Any] , lowerCamelCase_ : str , **lowerCamelCase_ : int ): """simple docstring""" _lowercase : Optional[Any] = kwargs.pop('config' , lowerCamelCase_ ) _lowercase : Optional[Any] = kwargs.pop('trust_remote_code' , lowerCamelCase_ ) _lowercase : Optional[int] = True _lowercase , _lowercase : Dict = FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase_ , **lowerCamelCase_ ) _lowercase : Dict = config_dict.get('feature_extractor_type' , lowerCamelCase_ ) _lowercase : str = None if "AutoFeatureExtractor" in config_dict.get('auto_map' , {} ): _lowercase : int = config_dict['auto_map']['AutoFeatureExtractor'] # If we don't find the feature extractor class in the feature extractor config, let's try the model config. if feature_extractor_class is None and feature_extractor_auto_map is None: if not isinstance(lowerCamelCase_ , lowerCamelCase_ ): _lowercase : Optional[int] = AutoConfig.from_pretrained(lowerCamelCase_ , **lowerCamelCase_ ) # It could be in `config.feature_extractor_type`` _lowercase : Tuple = getattr(lowerCamelCase_ , 'feature_extractor_type' , lowerCamelCase_ ) if hasattr(lowerCamelCase_ , 'auto_map' ) and "AutoFeatureExtractor" in config.auto_map: _lowercase : Optional[int] = config.auto_map['AutoFeatureExtractor'] if feature_extractor_class is not None: _lowercase : Optional[Any] = feature_extractor_class_from_name(lowerCamelCase_ ) _lowercase : int = feature_extractor_auto_map is not None _lowercase : Dict = feature_extractor_class is not None or type(lowerCamelCase_ ) in FEATURE_EXTRACTOR_MAPPING _lowercase : List[str] = resolve_trust_remote_code( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) if has_remote_code and trust_remote_code: _lowercase : Any = get_class_from_dynamic_module( lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ) _lowercase : str = kwargs.pop('code_revision' , lowerCamelCase_ ) if os.path.isdir(lowerCamelCase_ ): feature_extractor_class.register_for_auto_class() return feature_extractor_class.from_dict(lowerCamelCase_ , **lowerCamelCase_ ) elif feature_extractor_class is not None: return feature_extractor_class.from_dict(lowerCamelCase_ , **lowerCamelCase_ ) # Last try: we use the FEATURE_EXTRACTOR_MAPPING. elif type(lowerCamelCase_ ) in FEATURE_EXTRACTOR_MAPPING: _lowercase : List[str] = FEATURE_EXTRACTOR_MAPPING[type(lowerCamelCase_ )] return feature_extractor_class.from_dict(lowerCamelCase_ , **lowerCamelCase_ ) raise ValueError( F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a ''' F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following ''' F'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys() )}''' ) @staticmethod def __UpperCAmelCase ( lowerCamelCase_ : Tuple , lowerCamelCase_ : Dict ): """simple docstring""" FEATURE_EXTRACTOR_MAPPING.register(lowerCamelCase_ , lowerCamelCase_ )
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import numpy as np def UpperCAmelCase__( __UpperCAmelCase : List[str] ): return 1 / (1 + np.exp(-vector )) def UpperCAmelCase__( __UpperCAmelCase : int ): return vector * sigmoid(SCREAMING_SNAKE_CASE__ ) if __name__ == "__main__": import doctest doctest.testmod()
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from typing import TYPE_CHECKING from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available lowercase : Union[str, Any] = { """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""], """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""], """processing_mctct""": ["""MCTCTProcessor"""], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase : Tuple = [ """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""", """MCTCTForCTC""", """MCTCTModel""", """MCTCTPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig from .feature_extraction_mctct import MCTCTFeatureExtractor from .processing_mctct import MCTCTProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel else: import sys lowercase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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import os def A_ ( ) ->Tuple: """simple docstring""" SCREAMING_SNAKE_CASE = os.path.join(os.path.dirname(lowercase_ ) , 'num.txt' ) with open(lowercase_ ) as file_hand: return str(sum(int(lowercase_ ) for line in file_hand ) )[:1_0] if __name__ == "__main__": print(solution())
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import numpy as np class a_: """simple docstring""" def __init__( self : Any) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE = (0, 0) SCREAMING_SNAKE_CASE = None SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = 0 SCREAMING_SNAKE_CASE = 0 def __eq__( self : int , lowerCAmelCase__ : List[Any]) -> Optional[int]: """simple docstring""" return self.position == cell.position def __UpperCamelCase ( self : int) -> int: """simple docstring""" print(self.position) class a_: """simple docstring""" def __init__( self : Optional[int] , lowerCAmelCase__ : List[str]=(5, 5)) -> List[str]: """simple docstring""" SCREAMING_SNAKE_CASE = np.zeros(lowerCAmelCase__) SCREAMING_SNAKE_CASE = world_size[0] SCREAMING_SNAKE_CASE = world_size[1] def __UpperCamelCase ( self : Optional[Any]) -> Tuple: """simple docstring""" print(self.w) def __UpperCamelCase ( self : Union[str, Any] , lowerCAmelCase__ : Tuple) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE = [ (-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1), ] SCREAMING_SNAKE_CASE = cell.position[0] SCREAMING_SNAKE_CASE = cell.position[1] SCREAMING_SNAKE_CASE = [] for n in neughbour_cord: SCREAMING_SNAKE_CASE = current_x + n[0] SCREAMING_SNAKE_CASE = current_y + n[1] if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit: SCREAMING_SNAKE_CASE = Cell() SCREAMING_SNAKE_CASE = (x, y) SCREAMING_SNAKE_CASE = cell neighbours.append(lowerCAmelCase__) return neighbours def A_ ( lowercase_ , lowercase_ , lowercase_ ) ->Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE = [] SCREAMING_SNAKE_CASE = [] _open.append(lowercase_ ) while _open: SCREAMING_SNAKE_CASE = np.argmin([n.f for n in _open] ) SCREAMING_SNAKE_CASE = _open[min_f] _closed.append(_open.pop(lowercase_ ) ) if current == goal: break for n in world.get_neigbours(lowercase_ ): for c in _closed: if c == n: continue SCREAMING_SNAKE_CASE = current.g + 1 SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = n.position SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = goal.position SCREAMING_SNAKE_CASE = (ya - ya) ** 2 + (xa - xa) ** 2 SCREAMING_SNAKE_CASE = n.h + n.g for c in _open: if c == n and c.f < n.f: continue _open.append(lowercase_ ) SCREAMING_SNAKE_CASE = [] while current.parent is not None: path.append(current.position ) SCREAMING_SNAKE_CASE = current.parent path.append(current.position ) return path[::-1] if __name__ == "__main__": __UpperCAmelCase = Gridworld() # Start position and goal __UpperCAmelCase = Cell() __UpperCAmelCase = (0, 0) __UpperCAmelCase = Cell() __UpperCAmelCase = (4, 4) print(f'path from {start.position} to {goal.position}') __UpperCAmelCase = astar(world, start, goal) # Just for visual reasons. for i in s: __UpperCAmelCase = 1 print(world.w)
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'''simple docstring''' from typing import List, Optional, Union import torch from ...models import UNetaDConditionModel, VQModel from ...pipelines import DiffusionPipeline from ...pipelines.pipeline_utils import ImagePipelineOutput from ...schedulers import DDPMScheduler from ...utils import ( is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring, ) SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__) # pylint: disable=invalid-name SCREAMING_SNAKE_CASE_ = "\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)[\"depth\"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline(\"depth-estimation\")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to(\"cuda\")\n\n\n >>> img = load_image(\n ... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n ... \"/kandinsky/cat.png\"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n >>> prompt = \"A robot, 4k photo\"\n >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save(\"robot_cat.png\")\n ```\n" def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=8 ): __a : List[Any] = height // scale_factor**2 if height % scale_factor**2 != 0: new_height += 1 __a : List[str] = width // scale_factor**2 if width % scale_factor**2 != 0: new_width += 1 return new_height * scale_factor, new_width * scale_factor class lowerCAmelCase ( lowerCamelCase__ ): """simple docstring""" def __init__( self , _A , _A , _A , ) -> Optional[Any]: super().__init__() self.register_modules( unet=_A , scheduler=_A , movq=_A , ) __a : List[str] = 2 ** (len(self.movq.config.block_out_channels ) - 1) def __magic_name__ ( self , _A , _A , _A , _A , _A , _A ) -> Union[str, Any]: if latents is None: __a : int = randn_tensor(_A , generator=_A , device=_A , dtype=_A ) else: if latents.shape != shape: raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' ) __a : Any = latents.to(_A ) __a : Optional[int] = latents * scheduler.init_noise_sigma return latents def __magic_name__ ( self , _A=0 ) -> Union[str, Any]: if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError('Please install accelerate via `pip install accelerate`' ) __a : List[str] = torch.device(f'''cuda:{gpu_id}''' ) __a : List[str] = [ self.unet, self.movq, ] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(_A , _A ) def __magic_name__ ( self , _A=0 ) -> List[str]: if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ): from accelerate import cpu_offload_with_hook else: raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' ) __a : Any = torch.device(f'''cuda:{gpu_id}''' ) if self.device.type != "cpu": self.to('cpu' , silence_dtype_warnings=_A ) torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist) __a : str = None for cpu_offloaded_model in [self.unet, self.movq]: __a , __a : str = cpu_offload_with_hook(_A , _A , prev_module_hook=_A ) # We'll offload the last model manually. __a : List[str] = hook @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def __magic_name__ ( self ) -> int: if not hasattr(self.unet , '_hf_hook' ): return self.device for module in self.unet.modules(): if ( hasattr(_A , '_hf_hook' ) and hasattr(module._hf_hook , 'execution_device' ) and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device ) return self.device @torch.no_grad() @replace_example_docstring(_A ) def __call__( self , _A , _A , _A , _A = 512 , _A = 512 , _A = 100 , _A = 4.0 , _A = 1 , _A = None , _A = None , _A = "pil" , _A = True , ) -> int: __a : List[str] = self._execution_device __a : str = guidance_scale > 1.0 if isinstance(_A , _A ): __a : List[Any] = torch.cat(_A , dim=0 ) if isinstance(_A , _A ): __a : Optional[Any] = torch.cat(_A , dim=0 ) if isinstance(_A , _A ): __a : int = torch.cat(_A , dim=0 ) __a : List[str] = image_embeds.shape[0] * num_images_per_prompt if do_classifier_free_guidance: __a : str = image_embeds.repeat_interleave(_A , dim=0 ) __a : Dict = negative_image_embeds.repeat_interleave(_A , dim=0 ) __a : str = hint.repeat_interleave(_A , dim=0 ) __a : Optional[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_A ) __a : List[str] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=_A ) self.scheduler.set_timesteps(_A , device=_A ) __a : int = self.scheduler.timesteps __a : Tuple = self.movq.config.latent_channels __a , __a : int = downscale_height_and_width(_A , _A , self.movq_scale_factor ) # create initial latent __a : Tuple = self.prepare_latents( (batch_size, num_channels_latents, height, width) , image_embeds.dtype , _A , _A , _A , self.scheduler , ) for i, t in enumerate(self.progress_bar(_A ) ): # expand the latents if we are doing classifier free guidance __a : Optional[int] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents __a : Optional[int] = {'image_embeds': image_embeds, 'hint': hint} __a : Optional[Any] = self.unet( sample=_A , timestep=_A , encoder_hidden_states=_A , added_cond_kwargs=_A , return_dict=_A , )[0] if do_classifier_free_guidance: __a , __a : Tuple = noise_pred.split(latents.shape[1] , dim=1 ) __a , __a : Optional[int] = noise_pred.chunk(2 ) __a , __a : Dict = variance_pred.chunk(2 ) __a : Tuple = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) __a : List[Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 ) if not ( hasattr(self.scheduler.config , 'variance_type' ) and self.scheduler.config.variance_type in ["learned", "learned_range"] ): __a , __a : List[str] = noise_pred.split(latents.shape[1] , dim=1 ) # compute the previous noisy sample x_t -> x_t-1 __a : str = self.scheduler.step( _A , _A , _A , generator=_A , )[0] # post-processing __a : int = self.movq.decode(_A , force_not_quantize=_A )['sample'] if output_type not in ["pt", "np", "pil"]: raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' ) if output_type in ["np", "pil"]: __a : List[Any] = image * 0.5 + 0.5 __a : Dict = image.clamp(0 , 1 ) __a : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy() if output_type == "pil": __a : int = self.numpy_to_pil(_A ) if not return_dict: return (image,) return ImagePipelineOutput(images=_A )
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'''simple docstring''' from statistics import mean, stdev def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 3 ): __a : List[str] = min(SCREAMING_SNAKE_CASE__ ) __a : Tuple = max(SCREAMING_SNAKE_CASE__ ) # normalize data return [round((x - x_min) / (x_max - x_min) , SCREAMING_SNAKE_CASE__ ) for x in data] def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 3 ): __a : Dict = mean(SCREAMING_SNAKE_CASE__ ) __a : str = stdev(SCREAMING_SNAKE_CASE__ ) # standardize data return [round((x - mu) / (sigma) , SCREAMING_SNAKE_CASE__ ) for x in data]
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from . import __version__ # Backward compatibility imports, to make sure all those objects can be found in file_utils from .utils import ( CLOUDFRONT_DISTRIB_PREFIX, CONFIG_NAME, DISABLE_TELEMETRY, DUMMY_INPUTS, DUMMY_MASK, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, FEATURE_EXTRACTOR_NAME, FLAX_WEIGHTS_NAME, HF_MODULES_CACHE, HUGGINGFACE_CO_PREFIX, HUGGINGFACE_CO_RESOLVE_ENDPOINT, MODEL_CARD_NAME, MULTIPLE_CHOICE_DUMMY_INPUTS, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, S3_BUCKET_PREFIX, SENTENCEPIECE_UNDERLINE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TORCH_FX_REQUIRED_VERSION, TRANSFORMERS_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, USE_JAX, USE_TF, USE_TORCH, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ContextManagers, DummyObject, EntryNotFoundError, ExplicitEnum, ModelOutput, PaddingStrategy, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, TensorType, _LazyModule, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, cached_property, copy_func, default_cache_path, define_sagemaker_information, get_cached_models, get_file_from_repo, get_full_repo_name, get_torch_version, has_file, http_user_agent, is_apex_available, is_bsa_available, is_coloredlogs_available, is_datasets_available, is_detectrona_available, is_faiss_available, is_flax_available, is_ftfy_available, is_in_notebook, is_ipex_available, is_librosa_available, is_offline_mode, is_onnx_available, is_pandas_available, is_phonemizer_available, is_protobuf_available, is_psutil_available, is_pyanvml_available, is_pyctcdecode_available, is_pytesseract_available, is_pytorch_quantization_available, is_rjieba_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_scipy_available, is_sentencepiece_available, is_seqio_available, is_sklearn_available, is_soundfile_availble, is_spacy_available, is_speech_available, is_tensor, is_tensorflow_probability_available, is_tfaonnx_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_bfaa_available, is_torch_cuda_available, is_torch_fx_available, is_torch_fx_proxy, is_torch_mps_available, is_torch_tfaa_available, is_torch_tpu_available, is_torchaudio_available, is_training_run_on_sagemaker, is_vision_available, replace_return_docstrings, requires_backends, to_numpy, to_py_obj, torch_only_method, )
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import argparse from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt if __name__ == "__main__": _lowercase = argparse.ArgumentParser() parser.add_argument( "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." ) parser.add_argument( "--original_config_file", type=str, required=True, help="The YAML config file corresponding to the original architecture.", ) parser.add_argument( "--num_in_channels", default=None, type=int, help="The number of input channels. If `None` number of input channels will be automatically inferred.", ) parser.add_argument( "--image_size", default=512, type=int, help=( "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2" " Base. Use 768 for Stable Diffusion v2." ), ) parser.add_argument( "--extract_ema", action="store_true", help=( "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." ), ) parser.add_argument( "--upcast_attention", action="store_true", help=( "Whether the attention computation should always be upcasted. This is necessary when running stable" " diffusion 2.1." ), ) parser.add_argument( "--from_safetensors", action="store_true", help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", ) parser.add_argument( "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not.", ) parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") def lowerCAmelCase__ ( UpperCamelCase_ : Any )-> Union[str, Any]: if string == "True": return True elif string == "False": return False else: raise ValueError(f"could not parse string as bool {string}" ) parser.add_argument( "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool ) parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int) _lowercase = parser.parse_args() _lowercase = download_controlnet_from_original_ckpt( checkpoint_path=args.checkpoint_path, original_config_file=args.original_config_file, image_size=args.image_size, extract_ema=args.extract_ema, num_in_channels=args.num_in_channels, upcast_attention=args.upcast_attention, from_safetensors=args.from_safetensors, device=args.device, use_linear_projection=args.use_linear_projection, cross_attention_dim=args.cross_attention_dim, ) controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
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0
"""simple docstring""" import inspect import re from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_config_docstrings.py a : Tuple = '''src/transformers''' # This is to make sure the transformers module imported is the one in the repo. a : Dict = direct_transformers_import(PATH_TO_TRANSFORMERS) a : Tuple = transformers.models.auto.configuration_auto.CONFIG_MAPPING # Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`. # For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)` a : Optional[Any] = re.compile(r'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''') a : Tuple = { '''DecisionTransformerConfig''', '''EncoderDecoderConfig''', '''MusicgenConfig''', '''RagConfig''', '''SpeechEncoderDecoderConfig''', '''TimmBackboneConfig''', '''VisionEncoderDecoderConfig''', '''VisionTextDualEncoderConfig''', '''LlamaConfig''', } def _UpperCamelCase ( _A ) -> Optional[Any]: """simple docstring""" _UpperCAmelCase = None # source code of `config_class` _UpperCAmelCase = inspect.getsource(_A ) _UpperCAmelCase = _re_checkpoint.findall(_A ) # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link. # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')` for ckpt_name, ckpt_link in checkpoints: # allow the link to end with `/` if ckpt_link.endswith("""/""" ): _UpperCAmelCase = ckpt_link[:-1] # verify the checkpoint name corresponds to the checkpoint link _UpperCAmelCase = F"""https://huggingface.co/{ckpt_name}""" if ckpt_link == ckpt_link_from_name: _UpperCAmelCase = ckpt_name break return checkpoint def _UpperCamelCase ( ) -> int: """simple docstring""" _UpperCAmelCase = [] for config_class in list(CONFIG_MAPPING.values() ): # Skip deprecated models if "models.deprecated" in config_class.__module__: continue _UpperCAmelCase = get_checkpoint_from_config_class(_A ) _UpperCAmelCase = config_class.__name__ if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK: configs_without_checkpoint.append(_A ) if len(_A ) > 0: _UpperCAmelCase = """\n""".join(sorted(_A ) ) raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}""" ) if __name__ == "__main__": check_config_docstrings_have_checkpoints()
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from tokenizers import processors from ...tokenization_utils import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_mbart import MBartTokenizer else: a : Optional[Any] = None a : str = logging.get_logger(__name__) a : str = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''} a : str = { '''vocab_file''': { '''facebook/mbart-large-en-ro''': ( '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model''' ), '''facebook/mbart-large-cc25''': ( '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model''' ), }, '''tokenizer_file''': { '''facebook/mbart-large-en-ro''': '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json''', '''facebook/mbart-large-cc25''': '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json''', }, } a : List[str] = { '''facebook/mbart-large-en-ro''': 1_0_2_4, '''facebook/mbart-large-cc25''': 1_0_2_4, } # fmt: off a : Optional[int] = ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN'''] class a_ ( _UpperCAmelCase ): a : Optional[int] = VOCAB_FILES_NAMES a : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a : Optional[Any] = PRETRAINED_VOCAB_FILES_MAP a : Optional[int] = ['input_ids', 'attention_mask'] a : str = MBartTokenizer a : List[int] = [] a : List[int] = [] def __init__( self : str , __UpperCamelCase : List[str]=None , __UpperCamelCase : List[Any]=None , __UpperCamelCase : Dict="<s>" , __UpperCamelCase : int="</s>" , __UpperCamelCase : str="</s>" , __UpperCamelCase : Union[str, Any]="<s>" , __UpperCamelCase : Any="<unk>" , __UpperCamelCase : Any="<pad>" , __UpperCamelCase : int="<mask>" , __UpperCamelCase : Any=None , __UpperCamelCase : Optional[Any]=None , __UpperCamelCase : Optional[Any]=None , **__UpperCamelCase : Optional[int] , ) ->Union[str, Any]: '''simple docstring''' _UpperCAmelCase = AddedToken(__UpperCamelCase , lstrip=__UpperCamelCase , rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase , __UpperCamelCase ) else mask_token super().__init__( vocab_file=__UpperCamelCase , tokenizer_file=__UpperCamelCase , bos_token=__UpperCamelCase , eos_token=__UpperCamelCase , sep_token=__UpperCamelCase , cls_token=__UpperCamelCase , unk_token=__UpperCamelCase , pad_token=__UpperCamelCase , mask_token=__UpperCamelCase , src_lang=__UpperCamelCase , tgt_lang=__UpperCamelCase , additional_special_tokens=__UpperCamelCase , **__UpperCamelCase , ) _UpperCAmelCase = vocab_file _UpperCAmelCase = False if not self.vocab_file else True _UpperCAmelCase = FAIRSEQ_LANGUAGE_CODES.copy() if additional_special_tokens is not None: # Only add those special tokens if they are not already there. _additional_special_tokens.extend( [t for t in additional_special_tokens if t not in _additional_special_tokens] ) self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} ) _UpperCAmelCase = { lang_code: self.convert_tokens_to_ids(__UpperCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES } _UpperCAmelCase = src_lang if src_lang is not None else """en_XX""" _UpperCAmelCase = self.convert_tokens_to_ids(self._src_lang ) _UpperCAmelCase = tgt_lang self.set_src_lang_special_tokens(self._src_lang ) @property def _snake_case ( self : Optional[Any] ) ->str: '''simple docstring''' return self._src_lang @src_lang.setter def _snake_case ( self : int , __UpperCamelCase : str ) ->None: '''simple docstring''' _UpperCAmelCase = new_src_lang self.set_src_lang_special_tokens(self._src_lang ) def _snake_case ( self : str , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ) ->List[int]: '''simple docstring''' if token_ids_a is None: return self.prefix_tokens + token_ids_a + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens def _snake_case ( self : Union[str, Any] , __UpperCamelCase : List[int] , __UpperCamelCase : Optional[List[int]] = None ) ->List[int]: '''simple docstring''' _UpperCAmelCase = [self.sep_token_id] _UpperCAmelCase = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _snake_case ( self : Dict , __UpperCamelCase : int , __UpperCamelCase : str , __UpperCamelCase : Optional[str] , __UpperCamelCase : Optional[str] , **__UpperCamelCase : Any ) ->Dict: '''simple docstring''' if src_lang is None or tgt_lang is None: raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" ) _UpperCAmelCase = src_lang _UpperCAmelCase = self(__UpperCamelCase , add_special_tokens=__UpperCamelCase , return_tensors=__UpperCamelCase , **__UpperCamelCase ) _UpperCAmelCase = self.convert_tokens_to_ids(__UpperCamelCase ) _UpperCAmelCase = tgt_lang_id return inputs def _snake_case ( self : Tuple , __UpperCamelCase : List[str] , __UpperCamelCase : str = "en_XX" , __UpperCamelCase : Optional[List[str]] = None , __UpperCamelCase : str = "ro_RO" , **__UpperCamelCase : Union[str, Any] , ) ->BatchEncoding: '''simple docstring''' _UpperCAmelCase = src_lang _UpperCAmelCase = tgt_lang return super().prepare_seqaseq_batch(__UpperCamelCase , __UpperCamelCase , **__UpperCamelCase ) def _snake_case ( self : Union[str, Any] ) ->int: '''simple docstring''' return self.set_src_lang_special_tokens(self.src_lang ) def _snake_case ( self : Union[str, Any] ) ->Tuple: '''simple docstring''' return self.set_tgt_lang_special_tokens(self.tgt_lang ) def _snake_case ( self : Union[str, Any] , __UpperCamelCase : Dict ) ->None: '''simple docstring''' _UpperCAmelCase = self.convert_tokens_to_ids(__UpperCamelCase ) _UpperCAmelCase = [] _UpperCAmelCase = [self.eos_token_id, self.cur_lang_code] _UpperCAmelCase = self.convert_ids_to_tokens(self.prefix_tokens ) _UpperCAmelCase = self.convert_ids_to_tokens(self.suffix_tokens ) _UpperCAmelCase = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _snake_case ( self : List[str] , __UpperCamelCase : str ) ->None: '''simple docstring''' _UpperCAmelCase = self.convert_tokens_to_ids(__UpperCamelCase ) _UpperCAmelCase = [] _UpperCAmelCase = [self.eos_token_id, self.cur_lang_code] _UpperCAmelCase = self.convert_ids_to_tokens(self.prefix_tokens ) _UpperCAmelCase = self.convert_ids_to_tokens(self.suffix_tokens ) _UpperCAmelCase = processors.TemplateProcessing( single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , ) def _snake_case ( self : List[Any] , __UpperCamelCase : str , __UpperCamelCase : Optional[str] = None ) ->Tuple[str]: '''simple docstring''' if not self.can_save_slow_tokenizer: raise ValueError( """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """ """tokenizer.""" ) if not os.path.isdir(__UpperCamelCase ): logger.error(f"""Vocabulary path ({save_directory}) should be a directory.""" ) return _UpperCAmelCase = os.path.join( __UpperCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ): copyfile(self.vocab_file , __UpperCamelCase ) return (out_vocab_file,)
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import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch if is_torch_available(): import torch from transformers.generation import DisjunctiveConstraint @require_torch class snake_case_ ( unittest.TestCase ): """simple docstring""" def A__ ( self ): # For consistency across different places the DisjunctiveConstraint is called, # dc.token_ids is a list of integers. It is also initialized only by integers. __lowerCAmelCase = [[1, 2, 4], [1, 2, 3, 4]] __lowerCAmelCase = DisjunctiveConstraint(_A ) self.assertTrue(isinstance(dc.token_ids , _A ) ) with self.assertRaises(_A ): DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) ) with self.assertRaises(_A ): DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] ) def A__ ( self ): # We can't have constraints that are complete subsets of another. This leads to a preverse # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint? # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it). __lowerCAmelCase = [[1, 2], [1, 2, 3, 4]] with self.assertRaises(_A ): DisjunctiveConstraint(_A ) # fails here def A__ ( self ): __lowerCAmelCase = [[1, 2, 3], [1, 2, 4]] __lowerCAmelCase = DisjunctiveConstraint(_A ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(1 ) __lowerCAmelCase = stepped is True and completed is False and reset is False self.assertTrue(_A ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(2 ) __lowerCAmelCase = stepped is True and completed is False and reset is False self.assertTrue(_A ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1, 2] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(3 ) __lowerCAmelCase = stepped is True and completed is True and reset is False self.assertTrue(_A ) self.assertTrue(dc.completed ) # Completed! self.assertTrue(dc.current_seq == [1, 2, 3] ) def A__ ( self ): __lowerCAmelCase = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]] __lowerCAmelCase = DisjunctiveConstraint(_A ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(1 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(2 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1, 2] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(4 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.current_seq == [1, 2, 4] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(5 ) self.assertTrue(dc.completed ) # Completed! self.assertTrue(dc.current_seq == [1, 2, 4, 5] ) dc.reset() __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(1 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.remaining() == 3 ) self.assertTrue(dc.current_seq == [1] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(2 ) self.assertTrue(not dc.completed ) self.assertTrue(dc.remaining() == 2 ) self.assertTrue(dc.current_seq == [1, 2] ) __lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase = dc.update(5 ) self.assertTrue(dc.completed ) # Completed! self.assertTrue(dc.remaining() == 0 ) self.assertTrue(dc.current_seq == [1, 2, 5] )
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import os import posixpath import uuid from dataclasses import dataclass from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union import numpy as np import pyarrow as pa import datasets from datasets.arrow_writer import ArrowWriter, ParquetWriter from datasets.config import MAX_SHARD_SIZE from datasets.filesystems import ( is_remote_filesystem, rename, ) from datasets.iterable_dataset import _BaseExamplesIterable from datasets.utils.py_utils import convert_file_size_to_int lowerCamelCase = datasets.utils.logging.get_logger(__name__) if TYPE_CHECKING: import pyspark @dataclass class snake_case_ ( datasets.BuilderConfig ): """simple docstring""" __UpperCAmelCase =None def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , ): """simple docstring""" import pyspark def generate_fn(): __lowerCAmelCase = df.select('*' , pyspark.sql.functions.spark_partition_id().alias('part_id' ) ) for partition_id in partition_order: __lowerCAmelCase = df_with_partition_id.select('*' ).where(F"""part_id = {partition_id}""" ).drop('part_id' ) __lowerCAmelCase = partition_df.collect() __lowerCAmelCase = 0 for row in rows: yield F"""{partition_id}_{row_id}""", row.asDict() row_id += 1 return generate_fn class snake_case_ ( _BaseExamplesIterable ): """simple docstring""" def __init__( self , _A , _A=None , ): __lowerCAmelCase = df __lowerCAmelCase = partition_order or range(self.df.rdd.getNumPartitions() ) __lowerCAmelCase = _generate_iterable_examples(self.df , self.partition_order ) def __iter__( self ): yield from self.generate_examples_fn() def A__ ( self , _A ): __lowerCAmelCase = list(range(self.df.rdd.getNumPartitions() ) ) generator.shuffle(_A ) return SparkExamplesIterable(self.df , partition_order=_A ) def A__ ( self , _A , _A ): __lowerCAmelCase = self.split_shard_indices_by_worker(_A , _A ) return SparkExamplesIterable(self.df , partition_order=_A ) @property def A__ ( self ): return len(self.partition_order ) class snake_case_ ( datasets.DatasetBuilder ): """simple docstring""" __UpperCAmelCase =SparkConfig def __init__( self , _A , _A = None , _A = None , **_A , ): import pyspark __lowerCAmelCase = pyspark.sql.SparkSession.builder.getOrCreate() __lowerCAmelCase = df __lowerCAmelCase = working_dir super().__init__( cache_dir=_A , config_name=str(self.df.semanticHash() ) , **_A , ) def A__ ( self ): # Returns the path of the created file. def create_cache_and_write_probe(_A ): # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories # already exist. os.makedirs(self._cache_dir , exist_ok=_A ) __lowerCAmelCase = os.path.join(self._cache_dir , 'fs_test' + uuid.uuida().hex ) # Opening the file in append mode will create a new file unless it already exists, in which case it will not # change the file contents. open(_A , 'a' ) return [probe_file] if self._spark.conf.get('spark.master' , '' ).startswith('local' ): return # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS # accessible to the driver. # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error. if self._cache_dir: __lowerCAmelCase = ( self._spark.sparkContext.parallelize(range(1 ) , 1 ).mapPartitions(_A ).collect() ) if os.path.isfile(probe[0] ): return raise ValueError( 'When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir' ) def A__ ( self ): return datasets.DatasetInfo(features=self.config.features ) def A__ ( self , _A ): return [datasets.SplitGenerator(name=datasets.Split.TRAIN )] def A__ ( self , _A ): import pyspark def get_arrow_batch_size(_A ): for batch in it: yield pa.RecordBatch.from_pydict({'batch_bytes': [batch.nbytes]} ) __lowerCAmelCase = self.df.count() __lowerCAmelCase = df_num_rows if df_num_rows <= 1_0_0 else 1_0_0 # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample. __lowerCAmelCase = ( self.df.limit(_A ) .repartition(1 ) .mapInArrow(_A , 'batch_bytes: long' ) .agg(pyspark.sql.functions.sum('batch_bytes' ).alias('sample_bytes' ) ) .collect()[0] .sample_bytes / sample_num_rows ) __lowerCAmelCase = approx_bytes_per_row * df_num_rows if approx_total_size > max_shard_size: # Make sure there is at least one row per partition. __lowerCAmelCase = min(_A , int(approx_total_size / max_shard_size ) ) __lowerCAmelCase = self.df.repartition(_A ) def A__ ( self , _A , _A , _A , ): import pyspark __lowerCAmelCase = ParquetWriter if file_format == 'parquet' else ArrowWriter __lowerCAmelCase = os.path.join(self._working_dir , os.path.basename(_A ) ) if self._working_dir else fpath __lowerCAmelCase = file_format == 'parquet' # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to # pickling the SparkContext. __lowerCAmelCase = self.config.features __lowerCAmelCase = self._writer_batch_size __lowerCAmelCase = self._fs.storage_options def write_arrow(_A ): # Within the same SparkContext, no two task attempts will share the same attempt ID. __lowerCAmelCase = pyspark.TaskContext().taskAttemptId() __lowerCAmelCase = next(_A , _A ) if first_batch is None: # Some partitions might not receive any data. return pa.RecordBatch.from_arrays( [[task_id], [0], [0]] , names=['task_id', 'num_examples', 'num_bytes'] , ) __lowerCAmelCase = 0 __lowerCAmelCase = writer_class( features=_A , path=working_fpath.replace('SSSSS' , F"""{shard_id:05d}""" ).replace('TTTTT' , F"""{task_id:05d}""" ) , writer_batch_size=_A , storage_options=_A , embed_local_files=_A , ) __lowerCAmelCase = pa.Table.from_batches([first_batch] ) writer.write_table(_A ) for batch in it: if max_shard_size is not None and writer._num_bytes >= max_shard_size: __lowerCAmelCase, __lowerCAmelCase = writer.finalize() writer.close() yield pa.RecordBatch.from_arrays( [[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , ) shard_id += 1 __lowerCAmelCase = writer_class( features=writer._features , path=working_fpath.replace('SSSSS' , F"""{shard_id:05d}""" ).replace('TTTTT' , F"""{task_id:05d}""" ) , writer_batch_size=_A , storage_options=_A , embed_local_files=_A , ) __lowerCAmelCase = pa.Table.from_batches([batch] ) writer.write_table(_A ) if writer._num_bytes > 0: __lowerCAmelCase, __lowerCAmelCase = writer.finalize() writer.close() yield pa.RecordBatch.from_arrays( [[task_id], [num_examples], [num_bytes]] , names=['task_id', 'num_examples', 'num_bytes'] , ) if working_fpath != fpath: for file in os.listdir(os.path.dirname(_A ) ): __lowerCAmelCase = os.path.join(os.path.dirname(_A ) , os.path.basename(_A ) ) shutil.move(_A , _A ) __lowerCAmelCase = ( self.df.mapInArrow(_A , 'task_id: long, num_examples: long, num_bytes: long' ) .groupBy('task_id' ) .agg( pyspark.sql.functions.sum('num_examples' ).alias('total_num_examples' ) , pyspark.sql.functions.sum('num_bytes' ).alias('total_num_bytes' ) , pyspark.sql.functions.count('num_bytes' ).alias('num_shards' ) , pyspark.sql.functions.collect_list('num_examples' ).alias('shard_lengths' ) , ) .collect() ) for row in stats: yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths) def A__ ( self , _A , _A = "arrow" , _A = None , _A = None , **_A , ): self._validate_cache_dir() __lowerCAmelCase = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE ) self._repartition_df_if_needed(_A ) __lowerCAmelCase = not is_remote_filesystem(self._fs ) __lowerCAmelCase = os.path.join if is_local else posixpath.join __lowerCAmelCase = '-TTTTT-SSSSS-of-NNNNN' __lowerCAmelCase = F"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}""" __lowerCAmelCase = path_join(self._output_dir , _A ) __lowerCAmelCase = 0 __lowerCAmelCase = 0 __lowerCAmelCase = 0 __lowerCAmelCase = [] __lowerCAmelCase = [] for task_id, content in self._prepare_split_single(_A , _A , _A ): ( ( __lowerCAmelCase ), ( __lowerCAmelCase ), ( __lowerCAmelCase ), ( __lowerCAmelCase ), ) = content if num_bytes > 0: total_num_examples += num_examples total_num_bytes += num_bytes total_shards += num_shards task_id_and_num_shards.append((task_id, num_shards) ) all_shard_lengths.extend(_A ) __lowerCAmelCase = total_num_examples __lowerCAmelCase = total_num_bytes # should rename everything at the end logger.debug(F"""Renaming {total_shards} shards.""" ) if total_shards > 1: __lowerCAmelCase = all_shard_lengths # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a # pickling error due to pickling the SparkContext. __lowerCAmelCase = self._fs # use the -SSSSS-of-NNNNN pattern def _rename_shard( _A , _A , _A , ): rename( _A , fpath.replace('SSSSS' , F"""{shard_id:05d}""" ).replace('TTTTT' , F"""{task_id:05d}""" ) , fpath.replace('TTTTT-SSSSS' , F"""{global_shard_id:05d}""" ).replace('NNNNN' , F"""{total_shards:05d}""" ) , ) __lowerCAmelCase = [] __lowerCAmelCase = 0 for i in range(len(_A ) ): __lowerCAmelCase, __lowerCAmelCase = task_id_and_num_shards[i] for shard_id in range(_A ): args.append([task_id, shard_id, global_shard_id] ) global_shard_id += 1 self._spark.sparkContext.parallelize(_A , len(_A ) ).map(lambda _A : _rename_shard(*_A ) ).collect() else: # don't use any pattern __lowerCAmelCase = 0 __lowerCAmelCase = task_id_and_num_shards[0][0] self._rename( fpath.replace('SSSSS' , F"""{shard_id:05d}""" ).replace('TTTTT' , F"""{task_id:05d}""" ) , fpath.replace(_A , '' ) , ) def A__ ( self , _A , ): return SparkExamplesIterable(self.df )
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from __future__ import annotations from collections.abc import Generator import requests from bsa import BeautifulSoup __snake_case :Tuple ='https://www.indeed.co.in/jobs?q=mobile+app+development&l=' def lowerCamelCase_ ( lowerCAmelCase__ : str = "mumbai" ) -> Generator[tuple[str, str], None, None]: '''simple docstring''' A = BeautifulSoup(requests.get(url + location ).content , 'html.parser' ) # This attribute finds out all the specifics listed in a job for job in soup.find_all('div' , attrs={'data-tn-component': 'organicJob'} ): A = job.find('a' , attrs={'data-tn-element': 'jobTitle'} ).text.strip() A = job.find('span' , {'class': 'company'} ).text.strip() yield job_title, company_name if __name__ == "__main__": for i, job in enumerate(fetch_jobs('Bangalore'), 1): print(F'''Job {i:>2} is {job[0]} at {job[1]}''')
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from pathlib import PurePosixPath from typing import Optional import fsspec from fsspec import AbstractFileSystem from huggingface_hub.hf_api import DatasetInfo from ..utils.file_utils import get_authentication_headers_for_url from ..utils.hub import hf_hub_url class lowercase__ ( __SCREAMING_SNAKE_CASE ): A__= '' A__= 'hf-legacy' # "hf://"" is reserved for hffs def __init__( self : List[str] , _lowercase : Optional[DatasetInfo] = None , _lowercase : Optional[str] = None , **_lowercase : Optional[Any] , ): """simple docstring""" super().__init__(self , **_lowercase ) UpperCAmelCase__ = repo_info UpperCAmelCase__ = token UpperCAmelCase__ = None def _UpperCAmelCase ( self : Optional[int] ): """simple docstring""" if self.dir_cache is None: UpperCAmelCase__ = {} for hf_file in self.repo_info.siblings: # TODO(QL): add sizes UpperCAmelCase__ = { "name": hf_file.rfilename, "size": None, "type": "file", } self.dir_cache.update( { str(_lowercase ): {"name": str(_lowercase ), "size": None, "type": "directory"} for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1] } ) def _UpperCAmelCase ( self : str , _lowercase : str , _lowercase : str = "rb" , **_lowercase : int , ): """simple docstring""" if not isinstance(self.repo_info , _lowercase ): raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}""" ) UpperCAmelCase__ = hf_hub_url(self.repo_info.id , _lowercase , revision=self.repo_info.sha ) return fsspec.open( _lowercase , mode=_lowercase , headers=get_authentication_headers_for_url(_lowercase , use_auth_token=self.token ) , client_kwargs={"trust_env": True} , ).open() def _UpperCAmelCase ( self : Union[str, Any] , _lowercase : Dict , **_lowercase : Optional[int] ): """simple docstring""" self._get_dirs() UpperCAmelCase__ = self._strip_protocol(_lowercase ) if path in self.dir_cache: return self.dir_cache[path] else: raise FileNotFoundError(_lowercase ) def _UpperCAmelCase ( self : int , _lowercase : Optional[Any] , _lowercase : Any=False , **_lowercase : int ): """simple docstring""" self._get_dirs() UpperCAmelCase__ = PurePosixPath(path.strip("/" ) ) UpperCAmelCase__ = {} for p, f in self.dir_cache.items(): UpperCAmelCase__ = PurePosixPath(p.strip("/" ) ) UpperCAmelCase__ = p.parent if root == path: UpperCAmelCase__ = f UpperCAmelCase__ = list(paths.values() ) if detail: return out else: return sorted(f["name"] for f in out )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available UpperCAmelCase : str = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase : Tuple = ['''BartphoTokenizer'''] if TYPE_CHECKING: try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_bartpho import BartphoTokenizer else: import sys UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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import unittest from accelerate import debug_launcher from accelerate.test_utils import require_cpu, test_ops, test_script @require_cpu class _A( unittest.TestCase ): """simple docstring""" def UpperCAmelCase_ ( self ): debug_launcher(test_script.main ) def UpperCAmelCase_ ( self ): debug_launcher(test_ops.main )
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0
'''simple docstring''' import math from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import SchedulerMixin, SchedulerOutput class lowerCAmelCase__ ( a , a ): """simple docstring""" lowerCAmelCase__ = 1 @register_to_config def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int = 1_000 , __SCREAMING_SNAKE_CASE : Optional[Union[np.ndarray, List[float]]] = None ) -> str: """simple docstring""" self.set_timesteps(__SCREAMING_SNAKE_CASE ) # standard deviation of the initial noise distribution __SCREAMING_SNAKE_CASE = 1.0 # For now we only support F-PNDM, i.e. the runge-kutta method # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf # mainly at formula (9), (12), (13) and the Algorithm 2. __SCREAMING_SNAKE_CASE = 4 # running values __SCREAMING_SNAKE_CASE = [] def UpperCAmelCase__ ( self : Tuple , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Union[str, torch.device] = None ) -> str: """simple docstring""" __SCREAMING_SNAKE_CASE = num_inference_steps __SCREAMING_SNAKE_CASE = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1] __SCREAMING_SNAKE_CASE = torch.cat([steps, torch.tensor([0.0] )] ) if self.config.trained_betas is not None: __SCREAMING_SNAKE_CASE = torch.tensor(self.config.trained_betas , dtype=torch.floataa ) else: __SCREAMING_SNAKE_CASE = torch.sin(steps * math.pi / 2 ) ** 2 __SCREAMING_SNAKE_CASE = (1.0 - self.betas**2) ** 0.5 __SCREAMING_SNAKE_CASE = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1] __SCREAMING_SNAKE_CASE = timesteps.to(__SCREAMING_SNAKE_CASE ) __SCREAMING_SNAKE_CASE = [] def UpperCAmelCase__ ( self : List[Any] , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : torch.FloatTensor , __SCREAMING_SNAKE_CASE : bool = True , ) -> Union[SchedulerOutput, Tuple]: """simple docstring""" if self.num_inference_steps is None: raise ValueError( """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" ) __SCREAMING_SNAKE_CASE = (self.timesteps == timestep).nonzero().item() __SCREAMING_SNAKE_CASE = timestep_index + 1 __SCREAMING_SNAKE_CASE = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index] self.ets.append(__SCREAMING_SNAKE_CASE ) if len(self.ets ) == 1: __SCREAMING_SNAKE_CASE = self.ets[-1] elif len(self.ets ) == 2: __SCREAMING_SNAKE_CASE = (3 * self.ets[-1] - self.ets[-2]) / 2 elif len(self.ets ) == 3: __SCREAMING_SNAKE_CASE = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12 else: __SCREAMING_SNAKE_CASE = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4]) __SCREAMING_SNAKE_CASE = self._get_prev_sample(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) if not return_dict: return (prev_sample,) return SchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE ) def UpperCAmelCase__ ( self : str , __SCREAMING_SNAKE_CASE : torch.FloatTensor , *__SCREAMING_SNAKE_CASE : Union[str, Any] , **__SCREAMING_SNAKE_CASE : Optional[int] ) -> torch.FloatTensor: """simple docstring""" return sample def UpperCAmelCase__ ( self : List[str] , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str ) -> Optional[int]: """simple docstring""" __SCREAMING_SNAKE_CASE = self.alphas[timestep_index] __SCREAMING_SNAKE_CASE = self.betas[timestep_index] __SCREAMING_SNAKE_CASE = self.alphas[prev_timestep_index] __SCREAMING_SNAKE_CASE = self.betas[prev_timestep_index] __SCREAMING_SNAKE_CASE = (sample - sigma * ets) / max(__SCREAMING_SNAKE_CASE , 1E-8 ) __SCREAMING_SNAKE_CASE = next_alpha * pred + ets * next_sigma return prev_sample def __len__( self : List[str] ) -> Optional[int]: """simple docstring""" return self.config.num_train_timesteps
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'''simple docstring''' import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConfig, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaForCTC, WavaVecaForPreTraining, WavaVecaProcessor, logging, ) from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification logging.set_verbosity_info() UpperCAmelCase : List[Any] = logging.get_logger(__name__) UpperCAmelCase : Optional[Any] = { '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', 'adapter_layer': 'encoder.layers.*.adapter_layer', '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': 'lm_head', 'mask_emb': 'masked_spec_embed', 'pooling_layer.linear': 'projector', 'pooling_layer.projection': 'classifier', } UpperCAmelCase : Any = [ 'lm_head', 'quantizer.weight_proj', 'quantizer.codevectors', 'project_q', 'project_hid', 'projector', 'classifier', ] def a__ ( a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE = {} with open(a__ , """r""" ) as file: for line_number, line in enumerate(a__ ): __SCREAMING_SNAKE_CASE = line.strip() if line: __SCREAMING_SNAKE_CASE = line.split() __SCREAMING_SNAKE_CASE = line_number __SCREAMING_SNAKE_CASE = words[0] __SCREAMING_SNAKE_CASE = value return result def a__ ( a__ , a__ , a__ , a__ , a__ ): """simple docstring""" for attribute in key.split(""".""" ): __SCREAMING_SNAKE_CASE = getattr(a__ , a__ ) __SCREAMING_SNAKE_CASE = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(a__ ): __SCREAMING_SNAKE_CASE = PARAM_MAPPING[full_name.split(""".""" )[-1]] __SCREAMING_SNAKE_CASE = """param""" if weight_type is not None and weight_type != "param": __SCREAMING_SNAKE_CASE = getattr(a__ , a__ ).shape elif weight_type is not None and weight_type == "param": __SCREAMING_SNAKE_CASE = hf_pointer for attribute in hf_param_name.split(""".""" ): __SCREAMING_SNAKE_CASE = getattr(a__ , a__ ) __SCREAMING_SNAKE_CASE = shape_pointer.shape # let's reduce dimension __SCREAMING_SNAKE_CASE = value[0] else: __SCREAMING_SNAKE_CASE = hf_pointer.shape if hf_shape != value.shape: raise ValueError( 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": __SCREAMING_SNAKE_CASE = value elif weight_type == "weight_g": __SCREAMING_SNAKE_CASE = value elif weight_type == "weight_v": __SCREAMING_SNAKE_CASE = value elif weight_type == "bias": __SCREAMING_SNAKE_CASE = value elif weight_type == "param": for attribute in hf_param_name.split(""".""" ): __SCREAMING_SNAKE_CASE = getattr(a__ , a__ ) __SCREAMING_SNAKE_CASE = value else: __SCREAMING_SNAKE_CASE = value logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' ) def a__ ( a__ , a__ , a__ , a__ , a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE = None for param_key in PARAM_MAPPING.keys(): if full_name.endswith(a__ ): __SCREAMING_SNAKE_CASE = PARAM_MAPPING[full_name.split(""".""" )[-1]] __SCREAMING_SNAKE_CASE = """param""" if weight_type is not None and weight_type != "param": __SCREAMING_SNAKE_CASE = """.""".join([key, weight_type] ) elif weight_type is not None and weight_type == "param": __SCREAMING_SNAKE_CASE = """.""".join([key, hf_param_name] ) else: __SCREAMING_SNAKE_CASE = key __SCREAMING_SNAKE_CASE = value if """lm_head""" in full_key else value[0] UpperCAmelCase : Any = { 'W_a': 'linear_1.weight', 'W_b': 'linear_2.weight', 'b_a': 'linear_1.bias', 'b_b': 'linear_2.bias', 'ln_W': 'norm.weight', 'ln_b': 'norm.bias', } def a__ ( a__ , a__ , a__=None , a__=None ): """simple docstring""" __SCREAMING_SNAKE_CASE = False for key, mapped_key in MAPPING.items(): __SCREAMING_SNAKE_CASE = """wav2vec2.""" + 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]: __SCREAMING_SNAKE_CASE = True if "*" in mapped_key: __SCREAMING_SNAKE_CASE = name.split(a__ )[0].split(""".""" )[-2] __SCREAMING_SNAKE_CASE = mapped_key.replace("""*""" , a__ ) if "weight_g" in name: __SCREAMING_SNAKE_CASE = """weight_g""" elif "weight_v" in name: __SCREAMING_SNAKE_CASE = """weight_v""" elif "bias" in name: __SCREAMING_SNAKE_CASE = """bias""" elif "weight" in name: # TODO: don't match quantizer.weight_proj __SCREAMING_SNAKE_CASE = """weight""" else: __SCREAMING_SNAKE_CASE = None if hf_dict is not None: rename_dict(a__ , a__ , a__ , a__ , a__ ) else: set_recursively(a__ , a__ , a__ , a__ , a__ ) return is_used return is_used def a__ ( a__ , a__ , a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE = [] __SCREAMING_SNAKE_CASE = fairseq_model.state_dict() __SCREAMING_SNAKE_CASE = hf_model.wavaveca.feature_extractor for name, value in fairseq_dict.items(): __SCREAMING_SNAKE_CASE = False if "conv_layers" in name: load_conv_layer( a__ , a__ , a__ , a__ , hf_model.config.feat_extract_norm == """group""" , ) __SCREAMING_SNAKE_CASE = True else: __SCREAMING_SNAKE_CASE = load_wavaveca_layer(a__ , a__ , a__ ) if not is_used: unused_weights.append(a__ ) logger.warning(F'Unused weights: {unused_weights}' ) def a__ ( a__ , a__ , a__ , a__ , a__ ): """simple docstring""" __SCREAMING_SNAKE_CASE = full_name.split("""conv_layers.""" )[-1] __SCREAMING_SNAKE_CASE = name.split(""".""" ) __SCREAMING_SNAKE_CASE = int(items[0] ) __SCREAMING_SNAKE_CASE = int(items[1] ) if type_id == 0: if "bias" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape: raise ValueError( F'{full_name} has size {value.shape}, but' F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' ) __SCREAMING_SNAKE_CASE = value logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape: raise ValueError( F'{full_name} has size {value.shape}, but' F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' ) __SCREAMING_SNAKE_CASE = 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: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape: raise ValueError( F'{full_name} has size {value.shape}, but' F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.' ) __SCREAMING_SNAKE_CASE = value logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' ) elif "weight" in name: if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape: raise ValueError( F'{full_name} has size {value.shape}, but' F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.' ) __SCREAMING_SNAKE_CASE = 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 a__ ( a__ , a__ , a__=None , a__=None , a__=True , a__=False ): """simple docstring""" if config_path is not None: __SCREAMING_SNAKE_CASE = WavaVecaConfig.from_pretrained(a__ ) else: __SCREAMING_SNAKE_CASE = WavaVecaConfig() if is_seq_class: __SCREAMING_SNAKE_CASE = read_txt_into_dict(a__ ) __SCREAMING_SNAKE_CASE = idalabel __SCREAMING_SNAKE_CASE = WavaVecaForSequenceClassification(a__ ) __SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=a__ , return_attention_mask=a__ , ) feature_extractor.save_pretrained(a__ ) elif is_finetuned: if dict_path: __SCREAMING_SNAKE_CASE = Dictionary.load(a__ ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq __SCREAMING_SNAKE_CASE = target_dict.pad_index __SCREAMING_SNAKE_CASE = target_dict.bos_index __SCREAMING_SNAKE_CASE = target_dict.eos_index __SCREAMING_SNAKE_CASE = len(target_dict.symbols ) __SCREAMING_SNAKE_CASE = 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__ ) __SCREAMING_SNAKE_CASE = target_dict.indices # fairseq has the <pad> and <s> switched __SCREAMING_SNAKE_CASE = 0 __SCREAMING_SNAKE_CASE = 1 with open(a__ , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(a__ , a__ ) __SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer( 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__ , ) __SCREAMING_SNAKE_CASE = True if config.feat_extract_norm == """layer""" else False __SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=a__ , return_attention_mask=a__ , ) __SCREAMING_SNAKE_CASE = WavaVecaProcessor(feature_extractor=a__ , tokenizer=a__ ) processor.save_pretrained(a__ ) __SCREAMING_SNAKE_CASE = WavaVecaForCTC(a__ ) else: __SCREAMING_SNAKE_CASE = WavaVecaForPreTraining(a__ ) if is_finetuned or is_seq_class: __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: __SCREAMING_SNAKE_CASE = argparse.Namespace(task="""audio_pretraining""" ) __SCREAMING_SNAKE_CASE = fairseq.tasks.setup_task(a__ ) __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=a__ ) __SCREAMING_SNAKE_CASE = model[0].eval() recursively_load_weights(a__ , a__ , not is_finetuned ) hf_wavavec.save_pretrained(a__ ) if __name__ == "__main__": UpperCAmelCase : int = 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' ) parser.add_argument( '--is_seq_class', action='store_true', help='Whether the model to convert is a fine-tuned sequence classification model or not', ) UpperCAmelCase : Optional[Any] = parser.parse_args() UpperCAmelCase : int = not args.not_finetuned and not args.is_seq_class convert_wavaveca_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, is_finetuned, args.is_seq_class, )
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1
'''simple docstring''' import pickle import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin _UpperCAmelCase : Optional[int] = get_tests_dir('''fixtures/test_sentencepiece.model''') @require_sentencepiece @require_tokenizers class __magic_name__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase ): UpperCamelCase__ = XLMRobertaTokenizer UpperCamelCase__ = XLMRobertaTokenizerFast UpperCamelCase__ = True UpperCamelCase__ = True def _A( self ): super().setUp() # We have a SentencePiece fixture for testing lowercase =XLMRobertaTokenizer(snake_case_ , keep_accents=snake_case_ ) tokenizer.save_pretrained(self.tmpdirname ) def _A( self ): lowercase ='''<pad>''' lowercase =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 _A( self ): lowercase =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_ ) , 10_02 ) def _A( self ): self.assertEqual(self.get_tokenizer().vocab_size , 10_02 ) def _A( self ): lowercase =XLMRobertaTokenizer(snake_case_ , keep_accents=snake_case_ ) lowercase =tokenizer.tokenize('''This is a test''' ) self.assertListEqual(snake_case_ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(snake_case_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , ) lowercase =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' ) self.assertListEqual( snake_case_ , [ SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''', ] , ) lowercase =tokenizer.convert_tokens_to_ids(snake_case_ ) self.assertListEqual( snake_case_ , [ value + tokenizer.fairseq_offset for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4] # ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^ ] , ) lowercase =tokenizer.convert_ids_to_tokens(snake_case_ ) self.assertListEqual( snake_case_ , [ SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''', ] , ) def _A( self ): if not self.test_slow_tokenizer: # as we don't have a slow version, we can't compare the outputs between slow and fast versions return lowercase =(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {}) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ): lowercase =self.rust_tokenizer_class.from_pretrained(snake_case_ , **snake_case_ ) lowercase =self.tokenizer_class.from_pretrained(snake_case_ , **snake_case_ ) lowercase =tempfile.mkdtemp() lowercase =tokenizer_r.save_pretrained(snake_case_ ) lowercase =tokenizer_p.save_pretrained(snake_case_ ) # Checks it save with the same files + the tokenizer.json file for the fast one self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) ) lowercase =tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f ) self.assertSequenceEqual(snake_case_ , snake_case_ ) # Checks everything loads correctly in the same way lowercase =tokenizer_r.from_pretrained(snake_case_ ) lowercase =tokenizer_p.from_pretrained(snake_case_ ) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(snake_case_ , snake_case_ ) ) # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key)) # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id")) shutil.rmtree(snake_case_ ) # Save tokenizer rust, legacy_format=True lowercase =tempfile.mkdtemp() lowercase =tokenizer_r.save_pretrained(snake_case_ , legacy_format=snake_case_ ) lowercase =tokenizer_p.save_pretrained(snake_case_ ) # Checks it save with the same files self.assertSequenceEqual(snake_case_ , snake_case_ ) # Checks everything loads correctly in the same way lowercase =tokenizer_r.from_pretrained(snake_case_ ) lowercase =tokenizer_p.from_pretrained(snake_case_ ) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(snake_case_ , snake_case_ ) ) shutil.rmtree(snake_case_ ) # Save tokenizer rust, legacy_format=False lowercase =tempfile.mkdtemp() lowercase =tokenizer_r.save_pretrained(snake_case_ , legacy_format=snake_case_ ) lowercase =tokenizer_p.save_pretrained(snake_case_ ) # Checks it saved the tokenizer.json file self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) ) # Checks everything loads correctly in the same way lowercase =tokenizer_r.from_pretrained(snake_case_ ) lowercase =tokenizer_p.from_pretrained(snake_case_ ) # Check special tokens are set accordingly on Rust and Python for key in tokenizer_pp.special_tokens_map: self.assertTrue(hasattr(snake_case_ , snake_case_ ) ) shutil.rmtree(snake_case_ ) @cached_property def _A( self ): return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' ) def _A( self ): with tempfile.NamedTemporaryFile() as f: shutil.copyfile(snake_case_ , f.name ) lowercase =XLMRobertaTokenizer(f.name , keep_accents=snake_case_ ) lowercase =pickle.dumps(snake_case_ ) pickle.loads(snake_case_ ) def _A( self ): if not self.test_rust_tokenizer: return lowercase =self.get_tokenizer() lowercase =self.get_rust_tokenizer() lowercase ='''I was born in 92000, and this is falsé.''' lowercase =tokenizer.tokenize(snake_case_ ) lowercase =rust_tokenizer.tokenize(snake_case_ ) self.assertListEqual(snake_case_ , snake_case_ ) lowercase =tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ ) lowercase =rust_tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ ) self.assertListEqual(snake_case_ , snake_case_ ) lowercase =self.get_rust_tokenizer() lowercase =tokenizer.encode(snake_case_ ) lowercase =rust_tokenizer.encode(snake_case_ ) self.assertListEqual(snake_case_ , snake_case_ ) @slow def _A( self ): lowercase ='''Hello World!''' lowercase =[0, 3_53_78, 66_61, 38, 2] # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer # xlmr.eval() # xlmr.encode(symbols) self.assertListEqual(snake_case_ , self.big_tokenizer.encode(snake_case_ ) ) @slow def _A( self ): lowercase =( '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will''' ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth''' ) lowercase =[ 0, 32_93, 83, 10, 45_52, 49_89, 79_86, 6_78, 10, 59_15, 1_11, 17_94_59, 12_48_50, 4, 60_44, 2_37, 12, 6, 5, 6, 4, 67_80, 7_05, 15, 13_88, 44, 3_78, 1_01_14, 7_11, 1_52, 20, 6, 5, 2_23_76, 6_42, 12_21, 1_51_90, 3_41_53, 4_50, 56_08, 9_59, 11_19, 5_77_02, 1_36, 1_86, 47, 10_98, 2_93_67, 47, # 4426, # What fairseq tokenizes from "<unk>": "_<" # 3678, # What fairseq tokenizes from "<unk>": "unk" # 2740, # What fairseq tokenizes from "<unk>": ">" 3, # What we tokenize from "<unk>": "<unk>" 6, # Residue from the tokenization: an extra sentencepiece underline 4, 60_44, 2_37, 62_84, 5_09_01, 5_28, 31, 90, 34, 9_27, 2, ] # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer # xlmr.eval() # xlmr.encode(symbols) self.assertListEqual(snake_case_ , self.big_tokenizer.encode(snake_case_ ) ) @slow def _A( self ): # fmt: off lowercase ={'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=snake_case_ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
145
'''simple docstring''' _UpperCAmelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ''' def UpperCamelCase ( ) -> None: '''simple docstring''' lowercase =input('''Enter message: ''' ) lowercase =input('''Enter key [alphanumeric]: ''' ) lowercase =input('''Encrypt/Decrypt [e/d]: ''' ) if mode.lower().startswith('''e''' ): lowercase ='''encrypt''' lowercase =encrypt_message(lowercase_ , lowercase_ ) elif mode.lower().startswith('''d''' ): lowercase ='''decrypt''' lowercase =decrypt_message(lowercase_ , lowercase_ ) print(f'\n{mode.title()}ed message:' ) print(lowercase_ ) def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> str: '''simple docstring''' return translate_message(lowercase_ , lowercase_ , '''encrypt''' ) def UpperCamelCase ( lowercase_ : str , lowercase_ : str ) -> str: '''simple docstring''' return translate_message(lowercase_ , lowercase_ , '''decrypt''' ) def UpperCamelCase ( lowercase_ : str , lowercase_ : str , lowercase_ : str ) -> str: '''simple docstring''' lowercase =[] lowercase =0 lowercase =key.upper() for symbol in message: lowercase =LETTERS.find(symbol.upper() ) if num != -1: if mode == "encrypt": num += LETTERS.find(key[key_index] ) elif mode == "decrypt": num -= LETTERS.find(key[key_index] ) num %= len(lowercase_ ) if symbol.isupper(): translated.append(LETTERS[num] ) elif symbol.islower(): translated.append(LETTERS[num].lower() ) key_index += 1 if key_index == len(lowercase_ ): lowercase =0 else: translated.append(lowercase_ ) return "".join(lowercase_ ) if __name__ == "__main__": main()
145
1
"""simple docstring""" import inspect import unittest from datasets import load_dataset from packaging import version from transformers import BeitConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( MODEL_MAPPING, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, ) from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): import PIL from PIL import Image from transformers import BeitImageProcessor class __lowerCAmelCase : '''simple docstring''' def __init__( self: Union[str, Any] , UpperCamelCase_: List[str] , UpperCamelCase_: Dict=100 , UpperCamelCase_: str=13 , UpperCamelCase_: List[str]=30 , UpperCamelCase_: List[Any]=2 , UpperCamelCase_: str=3 , UpperCamelCase_: List[Any]=True , UpperCamelCase_: int=True , UpperCamelCase_: Dict=32 , UpperCamelCase_: List[str]=4 , UpperCamelCase_: Union[str, Any]=4 , UpperCamelCase_: Dict=37 , UpperCamelCase_: str="gelu" , UpperCamelCase_: str=0.1 , UpperCamelCase_: Tuple=0.1 , UpperCamelCase_: Dict=10 , UpperCamelCase_: Optional[Any]=0.02 , UpperCamelCase_: Union[str, Any]=3 , UpperCamelCase_: List[Any]=None , UpperCamelCase_: Any=[0, 1, 2, 3] , ): UpperCamelCase_ =parent UpperCamelCase_ =100 UpperCamelCase_ =batch_size UpperCamelCase_ =image_size UpperCamelCase_ =patch_size UpperCamelCase_ =num_channels UpperCamelCase_ =is_training UpperCamelCase_ =use_labels 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_ =type_sequence_label_size UpperCamelCase_ =initializer_range UpperCamelCase_ =scope UpperCamelCase_ =out_indices UpperCamelCase_ =num_labels # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) UpperCamelCase_ =(image_size // patch_size) ** 2 UpperCamelCase_ =num_patches + 1 def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) 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.image_size, self.image_size] , self.num_labels ) UpperCamelCase_ =self.get_config() return config, pixel_values, labels, pixel_labels def UpperCamelCase__ ( self: Any ): return BeitConfig( vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCamelCase_ , initializer_range=self.initializer_range , out_indices=self.out_indices , ) def UpperCamelCase__ ( self: Any , UpperCamelCase_: Any , UpperCamelCase_: Optional[int] , UpperCamelCase_: Tuple , UpperCamelCase_: Union[str, Any] ): UpperCamelCase_ =BeitModel(config=UpperCamelCase_ ) model.to(UpperCamelCase_ ) model.eval() UpperCamelCase_ =model(UpperCamelCase_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def UpperCamelCase__ ( self: Union[str, Any] , UpperCamelCase_: Optional[int] , UpperCamelCase_: Any , UpperCamelCase_: Union[str, Any] , UpperCamelCase_: Union[str, Any] ): UpperCamelCase_ =BeitForMaskedImageModeling(config=UpperCamelCase_ ) model.to(UpperCamelCase_ ) model.eval() UpperCamelCase_ =model(UpperCamelCase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) ) def UpperCamelCase__ ( self: Optional[Any] , UpperCamelCase_: int , UpperCamelCase_: Optional[int] , UpperCamelCase_: List[Any] , UpperCamelCase_: Union[str, Any] ): UpperCamelCase_ =self.type_sequence_label_size UpperCamelCase_ =BeitForImageClassification(UpperCamelCase_ ) model.to(UpperCamelCase_ ) model.eval() UpperCamelCase_ =model(UpperCamelCase_ , labels=UpperCamelCase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) # test greyscale images UpperCamelCase_ =1 UpperCamelCase_ =BeitForImageClassification(UpperCamelCase_ ) model.to(UpperCamelCase_ ) model.eval() UpperCamelCase_ =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) UpperCamelCase_ =model(UpperCamelCase_ , labels=UpperCamelCase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def UpperCamelCase__ ( self: List[Any] , UpperCamelCase_: str , UpperCamelCase_: Dict , UpperCamelCase_: str , UpperCamelCase_: Any ): UpperCamelCase_ =self.num_labels UpperCamelCase_ =BeitForSemanticSegmentation(UpperCamelCase_ ) model.to(UpperCamelCase_ ) model.eval() UpperCamelCase_ =model(UpperCamelCase_ ) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) UpperCamelCase_ =model(UpperCamelCase_ , labels=UpperCamelCase_ ) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) def UpperCamelCase__ ( self: str ): UpperCamelCase_ =self.prepare_config_and_inputs() UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ =config_and_inputs UpperCamelCase_ ={"pixel_values": pixel_values} return config, inputs_dict @require_torch class __lowerCAmelCase ( UpperCAmelCase , UpperCAmelCase , unittest.TestCase ): '''simple docstring''' __lowerCamelCase : Union[str, Any] = ( (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation) if is_torch_available() else () ) __lowerCamelCase : Optional[Any] = ( { "feature-extraction": BeitModel, "image-classification": BeitForImageClassification, "image-segmentation": BeitForSemanticSegmentation, } if is_torch_available() else {} ) __lowerCamelCase : Tuple = False __lowerCamelCase : Optional[Any] = False __lowerCamelCase : Optional[Any] = False def UpperCamelCase__ ( self: Any ): UpperCamelCase_ =BeitModelTester(self ) UpperCamelCase_ =ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ , hidden_size=37 ) def UpperCamelCase__ ( self: List[str] ): self.config_tester.run_common_tests() @unittest.skip(reason="BEiT does not use inputs_embeds" ) def UpperCamelCase__ ( self: Tuple ): pass @require_torch_multi_gpu @unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" ) def UpperCamelCase__ ( self: int ): pass def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ , UpperCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase_ =model_class(UpperCamelCase_ ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) UpperCamelCase_ =model.get_output_embeddings() self.assertTrue(x is None or isinstance(UpperCamelCase_ , nn.Linear ) ) def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ , UpperCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: UpperCamelCase_ =model_class(UpperCamelCase_ ) 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] , UpperCamelCase_ ) def UpperCamelCase__ ( self: int ): UpperCamelCase_ =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*UpperCamelCase_ ) def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_ ) def UpperCamelCase__ ( self: Any ): UpperCamelCase_ =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ ) def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_ ) def UpperCamelCase__ ( self: Optional[int] ): 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: # we don't test BeitForMaskedImageModeling if model_class in [*get_values(UpperCamelCase_ ), BeitForMaskedImageModeling]: continue UpperCamelCase_ =model_class(UpperCamelCase_ ) model.to(UpperCamelCase_ ) model.train() UpperCamelCase_ =self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ ) UpperCamelCase_ =model(**UpperCamelCase_ ).loss loss.backward() def UpperCamelCase__ ( self: Union[str, Any] ): UpperCamelCase_ , UpperCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common() if not self.model_tester.is_training: return UpperCamelCase_ =False UpperCamelCase_ =True for model_class in self.all_model_classes: # we don't test BeitForMaskedImageModeling if ( model_class in [*get_values(UpperCamelCase_ ), BeitForMaskedImageModeling] or not model_class.supports_gradient_checkpointing ): continue UpperCamelCase_ =model_class(UpperCamelCase_ ) model.gradient_checkpointing_enable() model.to(UpperCamelCase_ ) model.train() UpperCamelCase_ =self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ ) UpperCamelCase_ =model(**UpperCamelCase_ ).loss loss.backward() def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ , UpperCamelCase_ =self.model_tester.prepare_config_and_inputs_for_common() UpperCamelCase_ =_config_zero_init(UpperCamelCase_ ) for model_class in self.all_model_classes: UpperCamelCase_ =model_class(config=UpperCamelCase_ ) for name, param in model.named_parameters(): # we skip lambda parameters as these require special initial values # determined by config.layer_scale_init_value if "lambda" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , ) @slow def UpperCamelCase__ ( self: int ): for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: UpperCamelCase_ =BeitModel.from_pretrained(UpperCamelCase_ ) self.assertIsNotNone(UpperCamelCase_ ) def _UpperCamelCase ( ): UpperCamelCase_ =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ) return image @require_torch @require_vision class __lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' @cached_property def UpperCamelCase__ ( self: str ): return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None @slow def UpperCamelCase__ ( self: Any ): UpperCamelCase_ =BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(UpperCamelCase_ ) UpperCamelCase_ =self.default_image_processor UpperCamelCase_ =prepare_img() UpperCamelCase_ =image_processor(images=UpperCamelCase_ , return_tensors="pt" ).pixel_values.to(UpperCamelCase_ ) # prepare bool_masked_pos UpperCamelCase_ =torch.ones((1, 196) , dtype=torch.bool ).to(UpperCamelCase_ ) # forward pass with torch.no_grad(): UpperCamelCase_ =model(pixel_values=UpperCamelCase_ , bool_masked_pos=UpperCamelCase_ ) UpperCamelCase_ =outputs.logits # verify the logits UpperCamelCase_ =torch.Size((1, 196, 8192) ) self.assertEqual(logits.shape , UpperCamelCase_ ) UpperCamelCase_ =torch.tensor( [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(UpperCamelCase_ ) self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , UpperCamelCase_ , atol=1e-2 ) ) @slow def UpperCamelCase__ ( self: Optional[Any] ): UpperCamelCase_ =BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(UpperCamelCase_ ) UpperCamelCase_ =self.default_image_processor UpperCamelCase_ =prepare_img() UpperCamelCase_ =image_processor(images=UpperCamelCase_ , return_tensors="pt" ).to(UpperCamelCase_ ) # forward pass with torch.no_grad(): UpperCamelCase_ =model(**UpperCamelCase_ ) UpperCamelCase_ =outputs.logits # verify the logits UpperCamelCase_ =torch.Size((1, 1000) ) self.assertEqual(logits.shape , UpperCamelCase_ ) UpperCamelCase_ =torch.tensor([-1.2385, -1.0987, -1.0108] ).to(UpperCamelCase_ ) self.assertTrue(torch.allclose(logits[0, :3] , UpperCamelCase_ , atol=1e-4 ) ) UpperCamelCase_ =281 self.assertEqual(logits.argmax(-1 ).item() , UpperCamelCase_ ) @slow def UpperCamelCase__ ( self: Optional[int] ): UpperCamelCase_ =BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to( UpperCamelCase_ ) UpperCamelCase_ =self.default_image_processor UpperCamelCase_ =prepare_img() UpperCamelCase_ =image_processor(images=UpperCamelCase_ , return_tensors="pt" ).to(UpperCamelCase_ ) # forward pass with torch.no_grad(): UpperCamelCase_ =model(**UpperCamelCase_ ) UpperCamelCase_ =outputs.logits # verify the logits UpperCamelCase_ =torch.Size((1, 2_1841) ) self.assertEqual(logits.shape , UpperCamelCase_ ) UpperCamelCase_ =torch.tensor([1.6881, -0.2787, 0.5901] ).to(UpperCamelCase_ ) self.assertTrue(torch.allclose(logits[0, :3] , UpperCamelCase_ , atol=1e-4 ) ) UpperCamelCase_ =2396 self.assertEqual(logits.argmax(-1 ).item() , UpperCamelCase_ ) @slow def UpperCamelCase__ ( self: int ): UpperCamelCase_ =BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" ) UpperCamelCase_ =model.to(UpperCamelCase_ ) UpperCamelCase_ =BeitImageProcessor(do_resize=UpperCamelCase_ , size=640 , do_center_crop=UpperCamelCase_ ) UpperCamelCase_ =load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" ) UpperCamelCase_ =Image.open(ds[0]["file"] ) UpperCamelCase_ =image_processor(images=UpperCamelCase_ , return_tensors="pt" ).to(UpperCamelCase_ ) # forward pass with torch.no_grad(): UpperCamelCase_ =model(**UpperCamelCase_ ) UpperCamelCase_ =outputs.logits # verify the logits UpperCamelCase_ =torch.Size((1, 150, 160, 160) ) self.assertEqual(logits.shape , UpperCamelCase_ ) UpperCamelCase_ =version.parse(PIL.__version__ ) < version.parse("9.0.0" ) if is_pillow_less_than_a: UpperCamelCase_ =torch.tensor( [ [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]], [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]], [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]], ] , device=UpperCamelCase_ , ) else: UpperCamelCase_ =torch.tensor( [ [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]], [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]], [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]], ] , device=UpperCamelCase_ , ) self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , UpperCamelCase_ , atol=1e-4 ) ) @slow def UpperCamelCase__ ( self: Optional[int] ): UpperCamelCase_ =BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" ) UpperCamelCase_ =model.to(UpperCamelCase_ ) UpperCamelCase_ =BeitImageProcessor(do_resize=UpperCamelCase_ , size=640 , do_center_crop=UpperCamelCase_ ) UpperCamelCase_ =load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" ) UpperCamelCase_ =Image.open(ds[0]["file"] ) UpperCamelCase_ =image_processor(images=UpperCamelCase_ , return_tensors="pt" ).to(UpperCamelCase_ ) # forward pass with torch.no_grad(): UpperCamelCase_ =model(**UpperCamelCase_ ) UpperCamelCase_ =outputs.logits.detach().cpu() UpperCamelCase_ =image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_ , target_sizes=[(500, 300)] ) UpperCamelCase_ =torch.Size((500, 300) ) self.assertEqual(segmentation[0].shape , UpperCamelCase_ ) UpperCamelCase_ =image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase_ ) UpperCamelCase_ =torch.Size((160, 160) ) self.assertEqual(segmentation[0].shape , UpperCamelCase_ )
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"""simple docstring""" import os import pickle import unittest from transformers import AutoTokenizer from transformers.models.bert.tokenization_bert import BertTokenizer from transformers.models.bert_japanese.tokenization_bert_japanese import ( VOCAB_FILES_NAMES, BertJapaneseTokenizer, CharacterTokenizer, JumanppTokenizer, MecabTokenizer, SudachiTokenizer, WordpieceTokenizer, ) from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi from ...test_tokenization_common import TokenizerTesterMixin @custom_tokenizers class __lowerCAmelCase ( UpperCAmelCase , unittest.TestCase ): '''simple docstring''' __lowerCamelCase : Tuple = BertJapaneseTokenizer __lowerCamelCase : Optional[Any] = False __lowerCamelCase : Optional[int] = True def UpperCamelCase__ ( self: int ): super().setUp() UpperCamelCase_ =[ "[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは", "世界", "##世界", "、", "##、", "。", "##。", ] UpperCamelCase_ =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] ) with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) ) def UpperCamelCase__ ( self: Optional[int] , UpperCamelCase_: Tuple ): UpperCamelCase_ ="こんにちは、世界。 \nこんばんは、世界。" UpperCamelCase_ ="こんにちは 、 世界 。 こんばんは 、 世界 。" return input_text, output_text def UpperCamelCase__ ( self: Tuple , UpperCamelCase_: Any ): UpperCamelCase_ , UpperCamelCase_ =self.get_input_output_texts(UpperCamelCase_ ) UpperCamelCase_ =tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ ) UpperCamelCase_ =tokenizer.decode(UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_ ) return text, ids def UpperCamelCase__ ( self: Optional[int] ): pass # TODO add if relevant def UpperCamelCase__ ( self: int ): pass # TODO add if relevant def UpperCamelCase__ ( self: Union[str, Any] ): pass # TODO add if relevant def UpperCamelCase__ ( self: Union[str, Any] ): UpperCamelCase_ =self.tokenizer_class(self.vocab_file ) UpperCamelCase_ =tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。" ) self.assertListEqual(UpperCamelCase_ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] ) def UpperCamelCase__ ( self: Dict ): UpperCamelCase_ =self.tokenizer_class(self.vocab_file , word_tokenizer_type="mecab" ) self.assertIsNotNone(UpperCamelCase_ ) UpperCamelCase_ ="こんにちは、世界。\nこんばんは、世界。" UpperCamelCase_ =tokenizer.tokenize(UpperCamelCase_ ) self.assertListEqual(UpperCamelCase_ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] ) UpperCamelCase_ =os.path.join(self.tmpdirname , "tokenizer.bin" ) with open(UpperCamelCase_ , "wb" ) as handle: pickle.dump(UpperCamelCase_ , UpperCamelCase_ ) with open(UpperCamelCase_ , "rb" ) as handle: UpperCamelCase_ =pickle.load(UpperCamelCase_ ) UpperCamelCase_ =tokenizer_new.tokenize(UpperCamelCase_ ) self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ ) def UpperCamelCase__ ( self: int ): UpperCamelCase_ =MecabTokenizer(mecab_dic="ipadic" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , ) def UpperCamelCase__ ( self: Optional[Any] ): try: UpperCamelCase_ =MecabTokenizer(mecab_dic="unidic_lite" ) except ModuleNotFoundError: return self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , ) def UpperCamelCase__ ( self: List[str] ): try: UpperCamelCase_ =MecabTokenizer(mecab_dic="unidic" ) except ModuleNotFoundError: return self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , ) def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ =MecabTokenizer(do_lower_case=UpperCamelCase_ , mecab_dic="ipadic" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"] , ) def UpperCamelCase__ ( self: Dict ): try: UpperCamelCase_ =MecabTokenizer( do_lower_case=UpperCamelCase_ , normalize_text=UpperCamelCase_ , mecab_option="-d /usr/local/lib/mecab/dic/jumandic" ) except RuntimeError: # if dict doesn't exist in the system, previous code raises this error. return self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "\u3000", "。"] , ) def UpperCamelCase__ ( self: Optional[int] ): UpperCamelCase_ =MecabTokenizer(normalize_text=UpperCamelCase_ , mecab_dic="ipadic" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", " ", "。"] , ) @require_sudachi def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =self.tokenizer_class(self.vocab_file , word_tokenizer_type="sudachi" ) self.assertIsNotNone(UpperCamelCase_ ) UpperCamelCase_ ="こんにちは、世界。\nこんばんは、世界。" UpperCamelCase_ =tokenizer.tokenize(UpperCamelCase_ ) self.assertListEqual(UpperCamelCase_ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] ) UpperCamelCase_ =os.path.join(self.tmpdirname , "tokenizer.bin" ) with open(UpperCamelCase_ , "wb" ) as handle: pickle.dump(UpperCamelCase_ , UpperCamelCase_ ) with open(UpperCamelCase_ , "rb" ) as handle: UpperCamelCase_ =pickle.load(UpperCamelCase_ ) UpperCamelCase_ =tokenizer_new.tokenize(UpperCamelCase_ ) self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ ) @require_sudachi def UpperCamelCase__ ( self: Union[str, Any] ): UpperCamelCase_ =SudachiTokenizer(sudachi_dict_type="core" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] , ) @require_sudachi def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ =SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="A" ) self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国", "人", "参政", "権"] ) @require_sudachi def UpperCamelCase__ ( self: List[str] ): UpperCamelCase_ =SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="B" ) self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国人", "参政権"] ) @require_sudachi def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ =SudachiTokenizer(sudachi_dict_type="core" , sudachi_split_mode="C" ) self.assertListEqual(tokenizer.tokenize("外国人参政権" ) , ["外国人参政権"] ) @require_sudachi def UpperCamelCase__ ( self: int ): UpperCamelCase_ =SudachiTokenizer(do_lower_case=UpperCamelCase_ , sudachi_dict_type="core" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "] , ) @require_sudachi def UpperCamelCase__ ( self: Optional[int] ): UpperCamelCase_ =SudachiTokenizer(normalize_text=UpperCamelCase_ , sudachi_dict_type="core" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "] , ) @require_sudachi def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =SudachiTokenizer(trim_whitespace=UpperCamelCase_ , sudachi_dict_type="core" ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"] , ) @require_jumanpp def UpperCamelCase__ ( self: Union[str, Any] ): UpperCamelCase_ =self.tokenizer_class(self.vocab_file , word_tokenizer_type="jumanpp" ) self.assertIsNotNone(UpperCamelCase_ ) UpperCamelCase_ ="こんにちは、世界。\nこんばんは、世界。" UpperCamelCase_ =tokenizer.tokenize(UpperCamelCase_ ) self.assertListEqual(UpperCamelCase_ , ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] ) UpperCamelCase_ =os.path.join(self.tmpdirname , "tokenizer.bin" ) with open(UpperCamelCase_ , "wb" ) as handle: pickle.dump(UpperCamelCase_ , UpperCamelCase_ ) with open(UpperCamelCase_ , "rb" ) as handle: UpperCamelCase_ =pickle.load(UpperCamelCase_ ) UpperCamelCase_ =tokenizer_new.tokenize(UpperCamelCase_ ) self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ ) @require_jumanpp def UpperCamelCase__ ( self: Any ): UpperCamelCase_ =JumanppTokenizer() self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , ) @require_jumanpp def UpperCamelCase__ ( self: Any ): UpperCamelCase_ =JumanppTokenizer(do_lower_case=UpperCamelCase_ ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , ) @require_jumanpp def UpperCamelCase__ ( self: int ): UpperCamelCase_ =JumanppTokenizer(normalize_text=UpperCamelCase_ ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["ア", "ッ", "フ", "゚", "ル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"] , ) @require_jumanpp def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =JumanppTokenizer(trim_whitespace=UpperCamelCase_ ) self.assertListEqual( tokenizer.tokenize(" \tアップルストアでiPhone8 が \n 発売された 。 " ) , ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "。"] , ) @require_jumanpp def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =JumanppTokenizer() self.assertListEqual( tokenizer.tokenize("ありがとうございますm(_ _)m見つけるのが大変です。" ) , ["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"] , ) def UpperCamelCase__ ( self: Optional[Any] ): UpperCamelCase_ =["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"] UpperCamelCase_ ={} for i, token in enumerate(UpperCamelCase_ ): UpperCamelCase_ =i UpperCamelCase_ =WordpieceTokenizer(vocab=UpperCamelCase_ , unk_token="[UNK]" ) self.assertListEqual(tokenizer.tokenize("" ) , [] ) self.assertListEqual(tokenizer.tokenize("こんにちは" ) , ["こんにちは"] ) self.assertListEqual(tokenizer.tokenize("こんばんは" ) , ["こん", "##ばんは"] ) self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは" ) , ["こん", "##ばんは", "[UNK]", "こんにちは"] ) def UpperCamelCase__ ( self: List[Any] ): UpperCamelCase_ =BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp" ) UpperCamelCase_ =tokenizer.subword_tokenizer UpperCamelCase_ =subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。" ) self.assertListEqual(UpperCamelCase_ , ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"] ) UpperCamelCase_ =subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは" ) self.assertListEqual(UpperCamelCase_ , ["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"] ) def UpperCamelCase__ ( self: str ): UpperCamelCase_ =self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese" ) UpperCamelCase_ =tokenizer.encode("ありがとう。" , add_special_tokens=UpperCamelCase_ ) UpperCamelCase_ =tokenizer.encode("どういたしまして。" , add_special_tokens=UpperCamelCase_ ) UpperCamelCase_ =tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ ) UpperCamelCase_ =tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ , UpperCamelCase_ ) # 2 is for "[CLS]", 3 is for "[SEP]" assert encoded_sentence == [2] + text + [3] assert encoded_pair == [2] + text + [3] + text_a + [3] @custom_tokenizers class __lowerCAmelCase ( UpperCAmelCase , unittest.TestCase ): '''simple docstring''' __lowerCamelCase : Tuple = BertJapaneseTokenizer __lowerCamelCase : Optional[int] = False def UpperCamelCase__ ( self: Any ): super().setUp() UpperCamelCase_ =["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"] UpperCamelCase_ =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] ) with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer: vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) ) def UpperCamelCase__ ( self: Dict , **UpperCamelCase_: List[Any] ): return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="character" , **UpperCamelCase_ ) def UpperCamelCase__ ( self: Optional[int] , UpperCamelCase_: Dict ): UpperCamelCase_ ="こんにちは、世界。 \nこんばんは、世界。" UpperCamelCase_ ="こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。" return input_text, output_text def UpperCamelCase__ ( self: int ): pass # TODO add if relevant def UpperCamelCase__ ( self: Tuple ): pass # TODO add if relevant def UpperCamelCase__ ( self: Dict ): pass # TODO add if relevant def UpperCamelCase__ ( self: Optional[int] ): UpperCamelCase_ =self.tokenizer_class(self.vocab_file , subword_tokenizer_type="character" ) UpperCamelCase_ =tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。" ) self.assertListEqual( UpperCamelCase_ , ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] ) def UpperCamelCase__ ( self: Tuple ): UpperCamelCase_ =["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"] UpperCamelCase_ ={} for i, token in enumerate(UpperCamelCase_ ): UpperCamelCase_ =i UpperCamelCase_ =CharacterTokenizer(vocab=UpperCamelCase_ , unk_token="[UNK]" ) self.assertListEqual(tokenizer.tokenize("" ) , [] ) self.assertListEqual(tokenizer.tokenize("こんにちは" ) , ["こ", "ん", "に", "ち", "は"] ) self.assertListEqual(tokenizer.tokenize("こんにちほ" ) , ["こ", "ん", "に", "ち", "[UNK]"] ) def UpperCamelCase__ ( self: Optional[Any] ): UpperCamelCase_ =self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char" ) UpperCamelCase_ =tokenizer.encode("ありがとう。" , add_special_tokens=UpperCamelCase_ ) UpperCamelCase_ =tokenizer.encode("どういたしまして。" , add_special_tokens=UpperCamelCase_ ) UpperCamelCase_ =tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ ) UpperCamelCase_ =tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ , UpperCamelCase_ ) # 2 is for "[CLS]", 3 is for "[SEP]" assert encoded_sentence == [2] + text + [3] assert encoded_pair == [2] + text + [3] + text_a + [3] @custom_tokenizers class __lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' def UpperCamelCase__ ( self: Dict ): UpperCamelCase_ ="cl-tohoku/bert-base-japanese" UpperCamelCase_ =AutoTokenizer.from_pretrained(UpperCamelCase_ ) self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_ ) class __lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' def UpperCamelCase__ ( self: List[str] ): UpperCamelCase_ ="cl-tohoku/bert-base-japanese" with self.assertLogs("transformers" , level="WARNING" ) as cm: BertTokenizer.from_pretrained(UpperCamelCase_ ) self.assertTrue( cm.records[0].message.startswith( "The tokenizer class you load from this checkpoint is not the same type as the class this function" " is called from." ) ) UpperCamelCase_ ="bert-base-cased" with self.assertLogs("transformers" , level="WARNING" ) as cm: BertJapaneseTokenizer.from_pretrained(UpperCamelCase_ ) self.assertTrue( cm.records[0].message.startswith( "The tokenizer class you load from this checkpoint is not the same type as the class this function" " is called from." ) )
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1
import argparse import json import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from torchvision import transforms from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' lowercase = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2] lowercase = True if '''large''' in model_name or '''huge''' in model_name else False lowercase = True if '''large''' in model_name or '''huge''' in model_name else False lowercase = True if '''large''' in model_name or '''huge''' in model_name else False if "large" in model_name or "xlarge" in model_name or "huge" in model_name: if "fl3" in model_name: lowercase = [3, 3, 3, 3] lowercase = [5, 5, 5, 5] elif "fl4" in model_name: lowercase = [4, 4, 4, 4] lowercase = [3, 3, 3, 3] if "tiny" in model_name or "small" in model_name or "base" in model_name: lowercase = [3, 3, 3, 3] if "lrf" in model_name: lowercase = [3, 3, 3, 3] else: lowercase = [2, 2, 2, 2] if "tiny" in model_name: lowercase = 96 elif "small" in model_name: lowercase = 96 elif "base" in model_name: lowercase = 128 elif "large" in model_name: lowercase = 192 elif "xlarge" in model_name: lowercase = 256 elif "huge" in model_name: lowercase = 352 # set label information lowercase = '''huggingface/label-files''' if "large" in model_name or "huge" in model_name: lowercase = '''imagenet-22k-id2label.json''' else: lowercase = '''imagenet-1k-id2label.json''' lowercase = json.load(open(hf_hub_download(lowerCAmelCase__ , lowerCAmelCase__ , repo_type='''dataset''' ) , '''r''' ) ) lowercase = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()} lowercase = {v: k for k, v in idalabel.items()} lowercase = FocalNetConfig( embed_dim=lowerCAmelCase__ , depths=lowerCAmelCase__ , focal_levels=lowerCAmelCase__ , focal_windows=lowerCAmelCase__ , use_conv_embed=lowerCAmelCase__ , idalabel=lowerCAmelCase__ , labelaid=lowerCAmelCase__ , use_post_layernorm=lowerCAmelCase__ , use_layerscale=lowerCAmelCase__ , ) return config def UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' if "patch_embed.proj" in name: lowercase = name.replace('''patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' ) if "patch_embed.norm" in name: lowercase = name.replace('''patch_embed.norm''' , '''embeddings.norm''' ) if "layers" in name: lowercase = '''encoder.''' + name if "encoder.layers" in name: lowercase = name.replace('''encoder.layers''' , '''encoder.stages''' ) if "downsample.proj" in name: lowercase = name.replace('''downsample.proj''' , '''downsample.projection''' ) if "blocks" in name: lowercase = name.replace('''blocks''' , '''layers''' ) if "modulation.f.weight" in name or "modulation.f.bias" in name: lowercase = name.replace('''modulation.f''' , '''modulation.projection_in''' ) if "modulation.h.weight" in name or "modulation.h.bias" in name: lowercase = name.replace('''modulation.h''' , '''modulation.projection_context''' ) if "modulation.proj.weight" in name or "modulation.proj.bias" in name: lowercase = name.replace('''modulation.proj''' , '''modulation.projection_out''' ) if name == "norm.weight": lowercase = '''layernorm.weight''' if name == "norm.bias": lowercase = '''layernorm.bias''' if "head" in name: lowercase = name.replace('''head''' , '''classifier''' ) else: lowercase = '''focalnet.''' + name return name def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ): '''simple docstring''' # fmt: off lowercase = { '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''', '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''', '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''', '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''', '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''', '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''', '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''', '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''', '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''', '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''', } # fmt: on lowercase = model_name_to_url[model_name] print('''Checkpoint URL: ''' , lowerCAmelCase__ ) lowercase = torch.hub.load_state_dict_from_url(lowerCAmelCase__ , map_location='''cpu''' )['''model'''] # rename keys for key in state_dict.copy().keys(): lowercase = state_dict.pop(lowerCAmelCase__ ) lowercase = val lowercase = get_focalnet_config(lowerCAmelCase__ ) lowercase = FocalNetForImageClassification(lowerCAmelCase__ ) model.eval() # load state dict model.load_state_dict(lowerCAmelCase__ ) # verify conversion lowercase = '''http://images.cocodataset.org/val2017/000000039769.jpg''' lowercase = BitImageProcessor( do_resize=lowerCAmelCase__ , size={'''shortest_edge''': 256} , resample=PILImageResampling.BILINEAR , do_center_crop=lowerCAmelCase__ , crop_size=224 , do_normalize=lowerCAmelCase__ , image_mean=lowerCAmelCase__ , image_std=lowerCAmelCase__ , ) lowercase = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ) lowercase = processor(images=lowerCAmelCase__ , return_tensors='''pt''' ) lowercase = transforms.Compose( [ transforms.Resize(256 ), transforms.CenterCrop(224 ), transforms.ToTensor(), transforms.Normalize(mean=[0.4_85, 0.4_56, 0.4_06] , std=[0.2_29, 0.2_24, 0.2_25] ), ] ) lowercase = image_transforms(lowerCAmelCase__ ).unsqueeze(0 ) # verify pixel_values assert torch.allclose(inputs.pixel_values , lowerCAmelCase__ , atol=1E-4 ) lowercase = model(**lowerCAmelCase__ ) lowercase = outputs.logits.argmax(-1 ).item() print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] ) print('''First values of logits:''' , outputs.logits[0, :3] ) if model_name == "focalnet-tiny": lowercase = torch.tensor([0.21_66, -0.43_68, 0.21_91] ) elif model_name == "focalnet-tiny-lrf": lowercase = torch.tensor([1.16_69, 0.01_25, -0.16_95] ) elif model_name == "focalnet-small": lowercase = torch.tensor([0.49_17, -0.04_30, 0.13_41] ) elif model_name == "focalnet-small-lrf": lowercase = torch.tensor([-0.25_88, -0.53_42, -0.23_31] ) elif model_name == "focalnet-base": lowercase = torch.tensor([-0.16_55, -0.40_90, -0.17_30] ) elif model_name == "focalnet-base-lrf": lowercase = torch.tensor([0.53_06, -0.04_83, -0.39_28] ) assert torch.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1E-4 ) print('''Looks ok!''' ) if pytorch_dump_folder_path is not None: print(f'Saving model and processor of {model_name} to {pytorch_dump_folder_path}' ) model.save_pretrained(lowerCAmelCase__ ) processor.save_pretrained(lowerCAmelCase__ ) if push_to_hub: print(f'Pushing model and processor of {model_name} to the hub...' ) model.push_to_hub(f'{model_name}' ) processor.push_to_hub(f'{model_name}' ) if __name__ == "__main__": lowercase__ :Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default="focalnet-tiny", type=str, help="Name of the FocalNet 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." ) parser.add_argument( "--push_to_hub", action="store_true", help="Whether to push the model and processor to the hub.", ) lowercase__ :Any = parser.parse_args() convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
633
from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available lowercase__ :Tuple = { "configuration_instructblip": [ "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP", "InstructBlipConfig", "InstructBlipQFormerConfig", "InstructBlipVisionConfig", ], "processing_instructblip": ["InstructBlipProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ :List[str] = [ "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST", "InstructBlipQFormerModel", "InstructBlipPreTrainedModel", "InstructBlipForConditionalGeneration", "InstructBlipVisionModel", ] if TYPE_CHECKING: from .configuration_instructblip import ( INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, InstructBlipConfig, InstructBlipQFormerConfig, InstructBlipVisionConfig, ) from .processing_instructblip import InstructBlipProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_instructblip import ( INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST, InstructBlipForConditionalGeneration, InstructBlipPreTrainedModel, InstructBlipQFormerModel, InstructBlipVisionModel, ) else: import sys lowercase__ :List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
633
1
import unittest from transformers import PegasusTokenizer, PegasusTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow from transformers.utils import cached_property from ...test_tokenization_common import TokenizerTesterMixin UpperCAmelCase_ = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''') @require_sentencepiece @require_tokenizers class __magic_name__ ( _UpperCamelCase , unittest.TestCase ): """simple docstring""" lowerCAmelCase : Dict = PegasusTokenizer lowerCAmelCase : Any = PegasusTokenizerFast lowerCAmelCase : Optional[int] = True lowerCAmelCase : Tuple = True def lowerCAmelCase ( self : str ): """simple docstring""" super().setUp() # We have a SentencePiece fixture for testing _UpperCamelCase: Any = PegasusTokenizer(_lowercase ) tokenizer.save_pretrained(self.tmpdirname ) @cached_property def lowerCAmelCase ( self : List[Any] ): """simple docstring""" return PegasusTokenizer.from_pretrained('''google/pegasus-large''' ) def lowerCAmelCase ( self : Dict , **_lowercase : Dict ): """simple docstring""" return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowercase ) def lowerCAmelCase ( self : str , _lowercase : Tuple ): """simple docstring""" return ("This is a test", "This is a test") def lowerCAmelCase ( self : Tuple ): """simple docstring""" _UpperCamelCase: List[str] = "</s>" _UpperCamelCase: Optional[Any] = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase ) def lowerCAmelCase ( self : int ): """simple docstring""" _UpperCamelCase: Optional[int] = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , '''<pad>''' ) self.assertEqual(vocab_keys[1] , '''</s>''' ) self.assertEqual(vocab_keys[-1] , '''v''' ) self.assertEqual(len(_lowercase ) , 1_103 ) def lowerCAmelCase ( self : Optional[Any] ): """simple docstring""" self.assertEqual(self.get_tokenizer().vocab_size , 1_103 ) def lowerCAmelCase ( self : Union[str, Any] ): """simple docstring""" _UpperCamelCase: Union[str, Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname ) _UpperCamelCase: str = self.tokenizer_class.from_pretrained(self.tmpdirname ) _UpperCamelCase: int = ( "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important" " </s> <pad> <pad> <pad>" ) _UpperCamelCase: Optional[Any] = rust_tokenizer([raw_input_str] , return_tensors=_lowercase , add_special_tokens=_lowercase ).input_ids[0] _UpperCamelCase: Tuple = py_tokenizer([raw_input_str] , return_tensors=_lowercase , add_special_tokens=_lowercase ).input_ids[0] self.assertListEqual(_lowercase , _lowercase ) def lowerCAmelCase ( self : Optional[Any] ): """simple docstring""" _UpperCamelCase: Union[str, Any] = self._large_tokenizer # <mask_1> masks whole sentence while <mask_2> masks single word _UpperCamelCase: int = "<mask_1> To ensure a <mask_2> flow of bank resolutions." _UpperCamelCase: str = [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1] _UpperCamelCase: str = tokenizer([raw_input_str] , return_tensors=_lowercase ).input_ids[0] self.assertListEqual(_lowercase , _lowercase ) def lowerCAmelCase ( self : str ): """simple docstring""" _UpperCamelCase: Dict = self._large_tokenizer # The tracebacks for the following asserts are **better** without messages or self.assertEqual assert tokenizer.vocab_size == 96_103 assert tokenizer.pad_token_id == 0 assert tokenizer.eos_token_id == 1 assert tokenizer.offset == 103 assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105 assert tokenizer.unk_token == "<unk>" assert tokenizer.model_max_length == 1_024 _UpperCamelCase: Dict = "To ensure a smooth flow of bank resolutions." _UpperCamelCase: Dict = [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1] _UpperCamelCase: List[Any] = tokenizer([raw_input_str] , return_tensors=_lowercase ).input_ids[0] self.assertListEqual(_lowercase , _lowercase ) assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"] @require_torch def lowerCAmelCase ( self : Optional[Any] ): """simple docstring""" _UpperCamelCase: List[str] = ["This is going to be way too long." * 150, "short example"] _UpperCamelCase: Any = ["not super long but more than 5 tokens", "tiny"] _UpperCamelCase: Optional[int] = self._large_tokenizer(_lowercase , padding=_lowercase , truncation=_lowercase , return_tensors='''pt''' ) _UpperCamelCase: Dict = self._large_tokenizer( text_target=_lowercase , max_length=5 , padding=_lowercase , truncation=_lowercase , return_tensors='''pt''' ) assert batch.input_ids.shape == (2, 1_024) assert batch.attention_mask.shape == (2, 1_024) assert targets["input_ids"].shape == (2, 5) assert len(_lowercase ) == 2 # input_ids, attention_mask. @slow def lowerCAmelCase ( self : Tuple ): """simple docstring""" _UpperCamelCase: Tuple = {"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 # fmt: on self.tokenizer_integration_test_util( expected_encoding=_lowercase , model_name='''google/bigbird-pegasus-large-arxiv''' , revision='''ba85d0851d708441f91440d509690f1ab6353415''' , ) @require_sentencepiece @require_tokenizers class __magic_name__ ( _UpperCamelCase , unittest.TestCase ): """simple docstring""" lowerCAmelCase : List[Any] = PegasusTokenizer lowerCAmelCase : List[str] = PegasusTokenizerFast lowerCAmelCase : int = True lowerCAmelCase : Dict = True def lowerCAmelCase ( self : Any ): """simple docstring""" super().setUp() # We have a SentencePiece fixture for testing _UpperCamelCase: int = PegasusTokenizer(_lowercase , offset=0 , mask_token_sent=_lowercase , mask_token='''[MASK]''' ) tokenizer.save_pretrained(self.tmpdirname ) @cached_property def lowerCAmelCase ( self : Optional[int] ): """simple docstring""" return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' ) def lowerCAmelCase ( self : List[Any] , **_lowercase : int ): """simple docstring""" return PegasusTokenizer.from_pretrained(self.tmpdirname , **_lowercase ) def lowerCAmelCase ( self : str , _lowercase : Dict ): """simple docstring""" return ("This is a test", "This is a test") def lowerCAmelCase ( self : Union[str, Any] ): """simple docstring""" _UpperCamelCase: Tuple = self.rust_tokenizer_class.from_pretrained(self.tmpdirname ) _UpperCamelCase: int = self.tokenizer_class.from_pretrained(self.tmpdirname ) _UpperCamelCase: Optional[Any] = ( "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>" " <pad> <pad> <pad>" ) _UpperCamelCase: Tuple = rust_tokenizer([raw_input_str] , return_tensors=_lowercase , add_special_tokens=_lowercase ).input_ids[0] _UpperCamelCase: Any = py_tokenizer([raw_input_str] , return_tensors=_lowercase , add_special_tokens=_lowercase ).input_ids[0] self.assertListEqual(_lowercase , _lowercase ) @require_torch def lowerCAmelCase ( self : Tuple ): """simple docstring""" _UpperCamelCase: Optional[int] = ["This is going to be way too long." * 1_000, "short example"] _UpperCamelCase: List[Any] = ["not super long but more than 5 tokens", "tiny"] _UpperCamelCase: Dict = self._large_tokenizer(_lowercase , padding=_lowercase , truncation=_lowercase , return_tensors='''pt''' ) _UpperCamelCase: List[Any] = self._large_tokenizer( text_target=_lowercase , max_length=5 , padding=_lowercase , truncation=_lowercase , return_tensors='''pt''' ) assert batch.input_ids.shape == (2, 4_096) assert batch.attention_mask.shape == (2, 4_096) assert targets["input_ids"].shape == (2, 5) assert len(_lowercase ) == 2 # input_ids, attention_mask. def lowerCAmelCase ( self : Dict ): """simple docstring""" _UpperCamelCase: Tuple = ( "This is an example string that is used to test the original TF implementation against the HF" " implementation" ) _UpperCamelCase: str = self._large_tokenizer(_lowercase ).input_ids self.assertListEqual( _lowercase , [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] , )
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import tempfile import torch from diffusers import IPNDMScheduler from .test_schedulers import SchedulerCommonTest class snake_case__(_UpperCamelCase ): """simple docstring""" lowercase_ = (IPNDMScheduler,) lowercase_ = (("""num_inference_steps""", 5_0),) def snake_case ( self : Dict , **SCREAMING_SNAKE_CASE : Union[str, Any] ): lowercase__ : str = {"num_train_timesteps": 1_000} config.update(**SCREAMING_SNAKE_CASE ) return config def snake_case ( self : Any , SCREAMING_SNAKE_CASE : int=0 , **SCREAMING_SNAKE_CASE : int ): lowercase__ : Any = dict(self.forward_default_kwargs ) lowercase__ : int = kwargs.pop("num_inference_steps" , SCREAMING_SNAKE_CASE ) lowercase__ : Optional[int] = self.dummy_sample lowercase__ : str = 0.1 * sample lowercase__ : Dict = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: lowercase__ : Optional[int] = self.get_scheduler_config(**SCREAMING_SNAKE_CASE ) lowercase__ : int = scheduler_class(**SCREAMING_SNAKE_CASE ) scheduler.set_timesteps(SCREAMING_SNAKE_CASE ) # copy over dummy past residuals lowercase__ : Optional[Any] = dummy_past_residuals[:] if time_step is None: lowercase__ : Optional[Any] = scheduler.timesteps[len(scheduler.timesteps ) // 2] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(SCREAMING_SNAKE_CASE ) lowercase__ : Any = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE ) new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE ) # copy over dummy past residuals lowercase__ : Union[str, Any] = dummy_past_residuals[:] lowercase__ : int = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample lowercase__ : Optional[Any] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" lowercase__ : str = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample lowercase__ : Optional[int] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def snake_case ( self : List[str] ): pass def snake_case ( self : int , SCREAMING_SNAKE_CASE : Dict=0 , **SCREAMING_SNAKE_CASE : List[Any] ): lowercase__ : Optional[int] = dict(self.forward_default_kwargs ) lowercase__ : Dict = kwargs.pop("num_inference_steps" , SCREAMING_SNAKE_CASE ) lowercase__ : List[str] = self.dummy_sample lowercase__ : Tuple = 0.1 * sample lowercase__ : Any = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] for scheduler_class in self.scheduler_classes: lowercase__ : Any = self.get_scheduler_config() lowercase__ : Any = scheduler_class(**SCREAMING_SNAKE_CASE ) scheduler.set_timesteps(SCREAMING_SNAKE_CASE ) # copy over dummy past residuals (must be after setting timesteps) lowercase__ : List[Any] = dummy_past_residuals[:] if time_step is None: lowercase__ : str = scheduler.timesteps[len(scheduler.timesteps ) // 2] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(SCREAMING_SNAKE_CASE ) lowercase__ : Tuple = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE ) # copy over dummy past residuals new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE ) # copy over dummy past residual (must be after setting timesteps) lowercase__ : Tuple = dummy_past_residuals[:] lowercase__ : Tuple = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample lowercase__ : List[Any] = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" lowercase__ : List[Any] = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample lowercase__ : int = new_scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def snake_case ( self : Tuple , **SCREAMING_SNAKE_CASE : List[Any] ): lowercase__ : List[str] = self.scheduler_classes[0] lowercase__ : Tuple = self.get_scheduler_config(**SCREAMING_SNAKE_CASE ) lowercase__ : str = scheduler_class(**SCREAMING_SNAKE_CASE ) lowercase__ : Any = 10 lowercase__ : Any = self.dummy_model() lowercase__ : Dict = self.dummy_sample_deter scheduler.set_timesteps(SCREAMING_SNAKE_CASE ) for i, t in enumerate(scheduler.timesteps ): lowercase__ : List[str] = model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) lowercase__ : Tuple = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample for i, t in enumerate(scheduler.timesteps ): lowercase__ : str = model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) lowercase__ : str = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).prev_sample return sample def snake_case ( self : Tuple ): lowercase__ : str = dict(self.forward_default_kwargs ) lowercase__ : Dict = kwargs.pop("num_inference_steps" , SCREAMING_SNAKE_CASE ) for scheduler_class in self.scheduler_classes: lowercase__ : Any = self.get_scheduler_config() lowercase__ : Optional[Any] = scheduler_class(**SCREAMING_SNAKE_CASE ) lowercase__ : int = self.dummy_sample lowercase__ : List[Any] = 0.1 * sample if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE , "set_timesteps" ): scheduler.set_timesteps(SCREAMING_SNAKE_CASE ) elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE , "set_timesteps" ): lowercase__ : Union[str, Any] = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) lowercase__ : str = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05] lowercase__ : str = dummy_past_residuals[:] lowercase__ : List[str] = scheduler.timesteps[5] lowercase__ : Tuple = scheduler.timesteps[6] lowercase__ : Any = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample lowercase__ : str = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) lowercase__ : Dict = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample lowercase__ : Dict = scheduler.step(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def snake_case ( self : int ): for timesteps in [100, 1_000]: self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE , time_step=SCREAMING_SNAKE_CASE ) def snake_case ( self : Any ): for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ): self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE , time_step=SCREAMING_SNAKE_CASE ) def snake_case ( self : Optional[int] ): lowercase__ : int = self.full_loop() lowercase__ : Optional[int] = torch.mean(torch.abs(SCREAMING_SNAKE_CASE ) ) assert abs(result_mean.item() - 2_540_529 ) < 10
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"""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
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"""simple docstring""" import argparse from pathlib import Path import torch from transformers import OPTConfig, OPTModel from transformers.utils import logging logging.set_verbosity_info() lowerCamelCase : Tuple =logging.get_logger(__name__) def _lowercase ( _SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]: '''simple docstring''' __A : List[str] = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' ) if "model" in sd.keys(): __A : int = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' )['model'] # pop unnecessary weights __A : str = [ 'decoder.version', 'decoder.output_projection.weight', ] for key in keys_to_delete: if key in sd: sd.pop(_SCREAMING_SNAKE_CASE ) __A : List[str] = { 'decoder.project_in_dim.weight': 'decoder.project_in.weight', 'decoder.project_out_dim.weight': 'decoder.project_out.weight', 'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight', 'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias', } for old_key, new_key in keys_to_rename.items(): if old_key in sd: __A : Any = sd.pop(_SCREAMING_SNAKE_CASE ) __A : Union[str, Any] = list(sd.keys() ) for key in keys: if ".qkv_proj." in key: __A : Tuple = sd[key] # We split QKV in separate Q,K,V __A : Any = key.replace('.qkv_proj.' , '.q_proj.' ) __A : Any = key.replace('.qkv_proj.' , '.k_proj.' ) __A : Any = key.replace('.qkv_proj.' , '.v_proj.' ) __A : List[Any] = value.shape[0] assert depth % 3 == 0 # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming: # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97 __A , __A , __A : List[str] = torch.split(_SCREAMING_SNAKE_CASE , depth // 3 , dim=0 ) __A : Optional[int] = q __A : int = k __A : List[str] = v del sd[key] return sd @torch.no_grad() def _lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[int]=None ) -> List[str]: '''simple docstring''' __A : Dict = load_checkpoint(_SCREAMING_SNAKE_CASE ) if config is not None: __A : Any = OPTConfig.from_pretrained(_SCREAMING_SNAKE_CASE ) else: __A : Tuple = OPTConfig() __A : Any = OPTModel(_SCREAMING_SNAKE_CASE ).half().eval() model.load_state_dict(_SCREAMING_SNAKE_CASE ) # Check results Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE ) model.save_pretrained(_SCREAMING_SNAKE_CASE ) if __name__ == "__main__": lowerCamelCase : Optional[Any] =argparse.ArgumentParser() # Required parameters parser.add_argument( '''--fairseq_path''', type=str, help=( '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:''' ''' https://huggingface.co/models?other=opt_metasq''' ), ) parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''') parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''') lowerCamelCase : Dict =parser.parse_args() convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
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0
import math def __lowerCAmelCase ( a__ , a__ ) -> int: __a = len(_UpperCamelCase ) __a = int(math.floor(math.sqrt(_UpperCamelCase ) ) ) __a = 0 while arr[min(_UpperCamelCase , _UpperCamelCase ) - 1] < x: __a = step step += int(math.floor(math.sqrt(_UpperCamelCase ) ) ) if prev >= n: return -1 while arr[prev] < x: __a = prev + 1 if prev == min(_UpperCamelCase , _UpperCamelCase ): return -1 if arr[prev] == x: return prev return -1 if __name__ == "__main__": A : Optional[int] = input('Enter numbers separated by a comma:\n').strip() A : Dict = [int(item) for item in user_input.split(',')] A : Any = int(input('Enter the number to be searched:\n')) A : Tuple = jump_search(arr, x) if res == -1: print('Number not found!') else: print(F"Number {x} is at index {res}")
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'''simple docstring''' import json import os from pathlib import Path from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple, Union import sentencepiece from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging lowerCamelCase : Union[str, Any] = logging.get_logger(__name__) lowerCamelCase : Dict = "▁" lowerCamelCase : Union[str, Any] = { "vocab_file": "vocab.json", "spm_file": "sentencepiece.bpe.model", } lowerCamelCase : Union[str, Any] = { "vocab_file": { "facebook/s2t-small-librispeech-asr": ( "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json" ), }, "spm_file": { "facebook/s2t-small-librispeech-asr": ( "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model" ) }, } lowerCamelCase : List[str] = { "facebook/s2t-small-librispeech-asr": 1_0_2_4, } lowerCamelCase : str = ["pt", "fr", "ru", "nl", "ro", "it", "es", "de"] lowerCamelCase : List[Any] = {"mustc": MUSTC_LANGS} class A__ ( A__ ): A__ = VOCAB_FILES_NAMES A__ = PRETRAINED_VOCAB_FILES_MAP A__ = MAX_MODEL_INPUT_SIZES A__ = ['input_ids', 'attention_mask'] A__ = [] def __init__( self : List[str] , _a : Tuple , _a : Optional[Any] , _a : Tuple="<s>" , _a : List[Any]="</s>" , _a : Union[str, Any]="<pad>" , _a : List[Any]="<unk>" , _a : Optional[int]=False , _a : Optional[Any]=False , _a : List[str]=None , _a : Any=None , _a : Optional[Dict[str, Any]] = None , **_a : str , ) -> None: '''simple docstring''' _SCREAMING_SNAKE_CASE ={} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=_a , eos_token=_a , unk_token=_a , pad_token=_a , do_upper_case=_a , do_lower_case=_a , tgt_lang=_a , lang_codes=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , ) _SCREAMING_SNAKE_CASE =do_upper_case _SCREAMING_SNAKE_CASE =do_lower_case _SCREAMING_SNAKE_CASE =load_json(_a ) _SCREAMING_SNAKE_CASE ={v: k for k, v in self.encoder.items()} _SCREAMING_SNAKE_CASE =spm_file _SCREAMING_SNAKE_CASE =load_spm(_a , self.sp_model_kwargs ) if lang_codes is not None: _SCREAMING_SNAKE_CASE =lang_codes _SCREAMING_SNAKE_CASE =LANGUAGES[lang_codes] _SCREAMING_SNAKE_CASE =[f"<lang:{lang}>" for lang in self.langs] _SCREAMING_SNAKE_CASE ={lang: self.sp_model.PieceToId(f"<lang:{lang}>" ) for lang in self.langs} _SCREAMING_SNAKE_CASE =self.lang_tokens _SCREAMING_SNAKE_CASE =tgt_lang if tgt_lang is not None else self.langs[0] self.set_tgt_lang_special_tokens(self._tgt_lang ) else: _SCREAMING_SNAKE_CASE ={} @property def A ( self : Union[str, Any] ) -> int: '''simple docstring''' return len(self.encoder ) @property def A ( self : str ) -> str: '''simple docstring''' return self._tgt_lang @tgt_lang.setter def A ( self : Dict , _a : Optional[int] ) -> None: '''simple docstring''' _SCREAMING_SNAKE_CASE =new_tgt_lang self.set_tgt_lang_special_tokens(_a ) def A ( self : Any , _a : str ) -> None: '''simple docstring''' _SCREAMING_SNAKE_CASE =self.lang_code_to_id[tgt_lang] _SCREAMING_SNAKE_CASE =[lang_code_id] def A ( self : Any , _a : str ) -> List[str]: '''simple docstring''' return self.sp_model.encode(_a , out_type=_a ) def A ( self : List[str] , _a : Optional[Any] ) -> Dict: '''simple docstring''' return self.encoder.get(_a , self.encoder[self.unk_token] ) def A ( self : str , _a : int ) -> str: '''simple docstring''' return self.decoder.get(_a , self.unk_token ) def A ( self : Any , _a : List[str] ) -> str: '''simple docstring''' _SCREAMING_SNAKE_CASE =[] _SCREAMING_SNAKE_CASE ='' for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: _SCREAMING_SNAKE_CASE =self.sp_model.decode(_a ) out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " " _SCREAMING_SNAKE_CASE =[] else: current_sub_tokens.append(_a ) _SCREAMING_SNAKE_CASE =self.sp_model.decode(_a ) out_string += decoded.upper() if self.do_upper_case else decoded return out_string.strip() def A ( self : Union[str, Any] , _a : List[Any] , _a : List[str]=None ) -> List[int]: '''simple docstring''' if token_ids_a is None: return self.prefix_tokens + token_ids_a + [self.eos_token_id] # We don't expect to process pairs, but leave the pair logic for API consistency return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id] def A ( self : Dict , _a : List[int] , _a : Optional[List[int]] = None , _a : bool = False ) -> List[int]: '''simple docstring''' if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a ) _SCREAMING_SNAKE_CASE =[1] * len(self.prefix_tokens ) _SCREAMING_SNAKE_CASE =[1] if token_ids_a is None: return prefix_ones + ([0] * len(_a )) + suffix_ones return prefix_ones + ([0] * len(_a )) + ([0] * len(_a )) + suffix_ones def A ( self : str ) -> Dict: '''simple docstring''' _SCREAMING_SNAKE_CASE =self.encoder.copy() vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Any ) -> Dict: '''simple docstring''' _SCREAMING_SNAKE_CASE =self.__dict__.copy() _SCREAMING_SNAKE_CASE =None return state def __setstate__( self : List[Any] , _a : Dict ) -> None: '''simple docstring''' _SCREAMING_SNAKE_CASE =d # for backward compatibility if not hasattr(self , 'sp_model_kwargs' ): _SCREAMING_SNAKE_CASE ={} _SCREAMING_SNAKE_CASE =load_spm(self.spm_file , self.sp_model_kwargs ) def A ( self : Any , _a : str , _a : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' _SCREAMING_SNAKE_CASE =Path(_a ) assert save_dir.is_dir(), f"{save_directory} should be a directory" _SCREAMING_SNAKE_CASE =save_dir / ( (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file'] ) _SCREAMING_SNAKE_CASE =save_dir / ( (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file'] ) save_json(self.encoder , _a ) if os.path.abspath(self.spm_file ) != os.path.abspath(_a ) and os.path.isfile(self.spm_file ): copyfile(self.spm_file , _a ) elif not os.path.isfile(self.spm_file ): with open(_a , 'wb' ) as fi: _SCREAMING_SNAKE_CASE =self.sp_model.serialized_model_proto() fi.write(_a ) return (str(_a ), str(_a )) def _lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor: """simple docstring""" _SCREAMING_SNAKE_CASE =sentencepiece.SentencePieceProcessor(**_UpperCamelCase ) spm.Load(str(_UpperCamelCase ) ) return spm def _lowerCAmelCase ( _UpperCamelCase : str ) -> Union[Dict, List]: """simple docstring""" with open(_UpperCamelCase , 'r' ) as f: return json.load(_UpperCamelCase ) def _lowerCAmelCase ( _UpperCamelCase : Dict , _UpperCamelCase : str ) -> None: """simple docstring""" with open(_UpperCamelCase , 'w' ) as f: json.dump(_UpperCamelCase , _UpperCamelCase , indent=2 )
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import json import os from functools import lru_cache from typing import Dict, List, Optional, Tuple, Union import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...tokenization_utils_base import BatchEncoding, EncodedInput from ...utils import PaddingStrategy, logging lowercase__ = logging.get_logger(__name__) lowercase__ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""} # See all LED models at https://huggingface.co/models?filter=LED lowercase__ = { """vocab_file""": { """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""", }, """merges_file""": { """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""", }, """tokenizer_file""": { """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""", }, } lowercase__ = { """allenai/led-base-16384""": 1_6384, } @lru_cache() # Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode def _snake_case ( ): _lowerCamelCase : List[str] = ( list(range(ord('!' ) , ord('~' ) + 1 ) ) + list(range(ord('¡' ) , ord('¬' ) + 1 ) ) + list(range(ord('®' ) , ord('ÿ' ) + 1 ) ) ) _lowerCamelCase : Any = bs[:] _lowerCamelCase : Optional[Any] = 0 for b in range(2**8 ): if b not in bs: bs.append(lowercase__ ) cs.append(2**8 + n ) n += 1 _lowerCamelCase : Dict = [chr(lowercase__ ) for n in cs] return dict(zip(lowercase__ , lowercase__ ) ) def _snake_case ( lowercase__ ): _lowerCamelCase : Union[str, Any] = set() _lowerCamelCase : Any = word[0] for char in word[1:]: pairs.add((prev_char, char) ) _lowerCamelCase : Any = char return pairs class lowerCAmelCase__ ( lowercase ): '''simple docstring''' lowerCamelCase__ = VOCAB_FILES_NAMES lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase__ = ["""input_ids""", """attention_mask"""] def __init__( self , lowercase , lowercase , lowercase="replace" , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase="<mask>" , lowercase=False , **lowercase , ): _lowerCamelCase : Dict = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else bos_token _lowerCamelCase : List[str] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else eos_token _lowerCamelCase : Optional[int] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else sep_token _lowerCamelCase : Union[str, Any] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else cls_token _lowerCamelCase : Union[str, Any] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else unk_token _lowerCamelCase : List[str] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else pad_token # Mask token behave like a normal word, i.e. include the space before it _lowerCamelCase : Union[str, Any] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token super().__init__( errors=lowercase , bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , add_prefix_space=lowercase , **lowercase , ) with open(lowercase , encoding='utf-8' ) as vocab_handle: _lowerCamelCase : List[str] = json.load(lowercase ) _lowerCamelCase : Union[str, Any] = {v: k for k, v in self.encoder.items()} _lowerCamelCase : Dict = errors # how to handle errors in decoding _lowerCamelCase : Tuple = bytes_to_unicode() _lowerCamelCase : List[Any] = {v: k for k, v in self.byte_encoder.items()} with open(lowercase , encoding='utf-8' ) as merges_handle: _lowerCamelCase : int = merges_handle.read().split('\n' )[1:-1] _lowerCamelCase : Tuple = [tuple(merge.split() ) for merge in bpe_merges] _lowerCamelCase : Tuple = dict(zip(lowercase , range(len(lowercase ) ) ) ) _lowerCamelCase : Tuple = {} _lowerCamelCase : Dict = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions _lowerCamelCase : str = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+' ) @property # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size def A_ ( self ): return len(self.encoder ) def A_ ( self ): return dict(self.encoder , **self.added_tokens_encoder ) def A_ ( self , lowercase ): if token in self.cache: return self.cache[token] _lowerCamelCase : List[Any] = tuple(lowercase ) _lowerCamelCase : List[Any] = get_pairs(lowercase ) if not pairs: return token while True: _lowerCamelCase : Union[str, Any] = min(lowercase , key=lambda lowercase : self.bpe_ranks.get(lowercase , float('inf' ) ) ) if bigram not in self.bpe_ranks: break _lowerCamelCase : Optional[int] = bigram _lowerCamelCase : Any = [] _lowerCamelCase : List[Any] = 0 while i < len(lowercase ): try: _lowerCamelCase : str = word.index(lowercase , lowercase ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) _lowerCamelCase : str = j if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 _lowerCamelCase : Tuple = tuple(lowercase ) _lowerCamelCase : Tuple = new_word if len(lowercase ) == 1: break else: _lowerCamelCase : List[str] = get_pairs(lowercase ) _lowerCamelCase : List[Any] = ' '.join(lowercase ) _lowerCamelCase : str = word return word def A_ ( self , lowercase ): _lowerCamelCase : Optional[int] = [] for token in re.findall(self.pat , lowercase ): _lowerCamelCase : int = ''.join( self.byte_encoder[b] for b in token.encode('utf-8' ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase ).split(' ' ) ) return bpe_tokens def A_ ( self , lowercase ): return self.encoder.get(lowercase , self.encoder.get(self.unk_token ) ) def A_ ( self , lowercase ): return self.decoder.get(lowercase ) def A_ ( self , lowercase ): _lowerCamelCase : List[str] = ''.join(lowercase ) _lowerCamelCase : Dict = bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8' , errors=self.errors ) return text def A_ ( self , lowercase , lowercase = None ): if not os.path.isdir(lowercase ): logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' ) return _lowerCamelCase : Dict = os.path.join( lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) _lowerCamelCase : Any = os.path.join( lowercase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] ) with open(lowercase , 'w' , encoding='utf-8' ) as f: f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowercase , ensure_ascii=lowercase ) + '\n' ) _lowerCamelCase : List[Any] = 0 with open(lowercase , 'w' , encoding='utf-8' ) as writer: writer.write('#version: 0.2\n' ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowercase : kv[1] ): if index != token_index: logger.warning( F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' ' Please check that the tokenizer is not corrupted!' ) _lowerCamelCase : Optional[Any] = token_index writer.write(' '.join(lowercase ) + '\n' ) index += 1 return vocab_file, merge_file def A_ ( self , lowercase , lowercase = None ): if token_ids_a is None: return [self.cls_token_id] + token_ids_a + [self.sep_token_id] _lowerCamelCase : Any = [self.cls_token_id] _lowerCamelCase : int = [self.sep_token_id] return cls + token_ids_a + sep + sep + token_ids_a + sep def A_ ( self , lowercase , lowercase = None , lowercase = False ): if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase ) if token_ids_a is None: return [1] + ([0] * len(lowercase )) + [1] return [1] + ([0] * len(lowercase )) + [1, 1] + ([0] * len(lowercase )) + [1] def A_ ( self , lowercase , lowercase = None ): _lowerCamelCase : Dict = [self.sep_token_id] _lowerCamelCase : int = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def A_ ( self , lowercase , lowercase=False , **lowercase ): _lowerCamelCase : Union[str, Any] = kwargs.pop('add_prefix_space' , self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(lowercase ) > 0 and not text[0].isspace()): _lowerCamelCase : int = ' ' + text return (text, kwargs) def A_ ( self , lowercase , lowercase = None , lowercase = PaddingStrategy.DO_NOT_PAD , lowercase = None , lowercase = None , ): _lowerCamelCase : Dict = super()._pad( encoded_inputs=lowercase , max_length=lowercase , padding_strategy=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , ) # Load from model defaults if return_attention_mask is None: _lowerCamelCase : List[str] = 'attention_mask' in self.model_input_names if return_attention_mask and "global_attention_mask" in encoded_inputs: _lowerCamelCase : List[str] = encoded_inputs[self.model_input_names[0]] # `global_attention_mask` need to have the same length as other (sequential) inputs. _lowerCamelCase : Any = len(encoded_inputs['global_attention_mask'] ) != len(lowercase ) if needs_to_be_padded: _lowerCamelCase : int = len(lowercase ) - len(encoded_inputs['global_attention_mask'] ) if self.padding_side == "right": # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend` _lowerCamelCase : str = ( encoded_inputs['global_attention_mask'] + [-1] * difference ) elif self.padding_side == "left": _lowerCamelCase : Union[str, Any] = [-1] * difference + encoded_inputs[ 'global_attention_mask' ] else: raise ValueError('Invalid padding strategy:' + str(self.padding_side ) ) return encoded_inputs
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging lowercase__ = logging.get_logger(__name__) lowercase__ = { """google/realm-cc-news-pretrained-embedder""": ( """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json""" ), """google/realm-cc-news-pretrained-encoder""": ( """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json""" ), """google/realm-cc-news-pretrained-scorer""": ( """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json""" ), """google/realm-cc-news-pretrained-openqa""": ( """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json""" ), """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""", """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""", """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""", """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""", # See all REALM models at https://huggingface.co/models?filter=realm } class lowerCAmelCase__ ( lowercase ): '''simple docstring''' lowerCamelCase__ = """realm""" def __init__( self , lowercase=30522 , lowercase=768 , lowercase=128 , lowercase=12 , lowercase=12 , lowercase=8 , lowercase=3072 , lowercase="gelu_new" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=2 , lowercase=0.02 , lowercase=1E-12 , lowercase=256 , lowercase=10 , lowercase=1E-3 , lowercase=5 , lowercase=320 , lowercase=13353718 , lowercase=5000 , lowercase=1 , lowercase=0 , lowercase=2 , **lowercase , ): super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase ) # Common config _lowerCamelCase : str = vocab_size _lowerCamelCase : Dict = max_position_embeddings _lowerCamelCase : int = hidden_size _lowerCamelCase : Optional[Any] = retriever_proj_size _lowerCamelCase : Dict = num_hidden_layers _lowerCamelCase : Any = num_attention_heads _lowerCamelCase : int = num_candidates _lowerCamelCase : List[Any] = intermediate_size _lowerCamelCase : int = hidden_act _lowerCamelCase : Union[str, Any] = hidden_dropout_prob _lowerCamelCase : Dict = attention_probs_dropout_prob _lowerCamelCase : Union[str, Any] = initializer_range _lowerCamelCase : List[Any] = type_vocab_size _lowerCamelCase : int = layer_norm_eps # Reader config _lowerCamelCase : Tuple = span_hidden_size _lowerCamelCase : int = max_span_width _lowerCamelCase : Tuple = reader_layer_norm_eps _lowerCamelCase : Union[str, Any] = reader_beam_size _lowerCamelCase : Union[str, Any] = reader_seq_len # Retrieval config _lowerCamelCase : Optional[Any] = num_block_records _lowerCamelCase : str = searcher_beam_size
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"""simple docstring""" from tempfile import TemporaryDirectory from unittest import TestCase from unittest.mock import MagicMock, patch from transformers import AutoModel, TFAutoModel from transformers.onnx import FeaturesManager from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch @require_torch @require_tf class lowerCAmelCase ( a ): """simple docstring""" def _lowerCAmelCase ( self ) -> Dict: '''simple docstring''' lowerCamelCase_ = SMALL_MODEL_IDENTIFIER lowerCamelCase_ = '''pt''' lowerCamelCase_ = '''tf''' def _lowerCAmelCase ( self , UpperCamelCase__ ) -> Optional[Any]: '''simple docstring''' lowerCamelCase_ = AutoModel.from_pretrained(self.test_model ) model_pt.save_pretrained(UpperCamelCase__ ) def _lowerCAmelCase ( self , UpperCamelCase__ ) -> List[str]: '''simple docstring''' lowerCamelCase_ = TFAutoModel.from_pretrained(self.test_model , from_pt=UpperCamelCase__ ) model_tf.save_pretrained(UpperCamelCase__ ) def _lowerCAmelCase ( self ) -> Optional[Any]: '''simple docstring''' lowerCamelCase_ = '''mock_framework''' # Framework provided - return whatever the user provides lowerCamelCase_ = FeaturesManager.determine_framework(self.test_model , UpperCamelCase__ ) self.assertEqual(UpperCamelCase__ , UpperCamelCase__ ) # Local checkpoint and framework provided - return provided framework # PyTorch checkpoint with TemporaryDirectory() as local_pt_ckpt: self._setup_pt_ckpt(UpperCamelCase__ ) lowerCamelCase_ = FeaturesManager.determine_framework(UpperCamelCase__ , UpperCamelCase__ ) self.assertEqual(UpperCamelCase__ , UpperCamelCase__ ) # TensorFlow checkpoint with TemporaryDirectory() as local_tf_ckpt: self._setup_tf_ckpt(UpperCamelCase__ ) lowerCamelCase_ = FeaturesManager.determine_framework(UpperCamelCase__ , UpperCamelCase__ ) self.assertEqual(UpperCamelCase__ , UpperCamelCase__ ) def _lowerCAmelCase ( self ) -> Dict: '''simple docstring''' with TemporaryDirectory() as local_pt_ckpt: self._setup_pt_ckpt(UpperCamelCase__ ) lowerCamelCase_ = FeaturesManager.determine_framework(UpperCamelCase__ ) self.assertEqual(UpperCamelCase__ , self.framework_pt ) # TensorFlow checkpoint with TemporaryDirectory() as local_tf_ckpt: self._setup_tf_ckpt(UpperCamelCase__ ) lowerCamelCase_ = FeaturesManager.determine_framework(UpperCamelCase__ ) self.assertEqual(UpperCamelCase__ , self.framework_tf ) # Invalid local checkpoint with TemporaryDirectory() as local_invalid_ckpt: with self.assertRaises(UpperCamelCase__ ): lowerCamelCase_ = FeaturesManager.determine_framework(UpperCamelCase__ ) def _lowerCAmelCase ( self ) -> Any: '''simple docstring''' lowerCamelCase_ = MagicMock(return_value=UpperCamelCase__ ) with patch('''transformers.onnx.features.is_tf_available''' , UpperCamelCase__ ): lowerCamelCase_ = FeaturesManager.determine_framework(self.test_model ) self.assertEqual(UpperCamelCase__ , self.framework_pt ) # PyTorch not in environment -> use TensorFlow lowerCamelCase_ = MagicMock(return_value=UpperCamelCase__ ) with patch('''transformers.onnx.features.is_torch_available''' , UpperCamelCase__ ): lowerCamelCase_ = FeaturesManager.determine_framework(self.test_model ) self.assertEqual(UpperCamelCase__ , self.framework_tf ) # Both in environment -> use PyTorch lowerCamelCase_ = MagicMock(return_value=UpperCamelCase__ ) lowerCamelCase_ = MagicMock(return_value=UpperCamelCase__ ) with patch('''transformers.onnx.features.is_tf_available''' , UpperCamelCase__ ), patch( '''transformers.onnx.features.is_torch_available''' , UpperCamelCase__ ): lowerCamelCase_ = FeaturesManager.determine_framework(self.test_model ) self.assertEqual(UpperCamelCase__ , self.framework_pt ) # Both not in environment -> raise error lowerCamelCase_ = MagicMock(return_value=UpperCamelCase__ ) lowerCamelCase_ = MagicMock(return_value=UpperCamelCase__ ) with patch('''transformers.onnx.features.is_tf_available''' , UpperCamelCase__ ), patch( '''transformers.onnx.features.is_torch_available''' , UpperCamelCase__ ): with self.assertRaises(UpperCamelCase__ ): lowerCamelCase_ = FeaturesManager.determine_framework(self.test_model )
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"""simple docstring""" import os def lowerCamelCase_ ( ): lowerCamelCase_ = os.path.dirname(os.path.realpath(_lowerCamelCase ) ) lowerCamelCase_ = os.path.join(_lowerCamelCase , '''triangle.txt''' ) with open(_lowerCamelCase ) as f: lowerCamelCase_ = f.readlines() lowerCamelCase_ = [] for line in triangle: lowerCamelCase_ = [] for number in line.strip().split(''' ''' ): numbers_from_line.append(int(_lowerCamelCase ) ) a.append(_lowerCamelCase ) for i in range(1 , len(_lowerCamelCase ) ): for j in range(len(a[i] ) ): lowerCamelCase_ = a[i - 1][j] if j != len(a[i - 1] ) else 0 lowerCamelCase_ = a[i - 1][j - 1] if j > 0 else 0 a[i][j] += max(_lowerCamelCase , _lowerCamelCase ) return max(a[-1] ) if __name__ == "__main__": print(solution())
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"""simple docstring""" import argparse import random import joblib import numpy as np import torch from igf.igf import ( SecondaryLearner, collect_objective_set, compute_perplexity, generate_datasets, load_gpta, recopy_gpta, set_seed, train_secondary_learner, ) from torch.utils.data import DataLoader, RandomSampler from transformers import GPTaLMHeadModel def __UpperCamelCase ( SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE=1_00 , SCREAMING_SNAKE_CASE=10_26 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl" , SCREAMING_SNAKE_CASE="igf_context_pairs.jbl" , ) -> Optional[Any]: """simple docstring""" set_seed(3 ) # generate train_data and objective_set __snake_case , __snake_case = generate_datasets( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , number=SCREAMING_SNAKE_CASE , min_len=10_26 , trim=SCREAMING_SNAKE_CASE ) # keeps model same across runs set_seed(4 ) # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights # can we train on GPU? __snake_case = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" ) # load pretrained model __snake_case = load_gpta("gpt2" ).to(SCREAMING_SNAKE_CASE ) print("computing perplexity on objective set" ) __snake_case = compute_perplexity(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ).item() print("perplexity on objective set:" , SCREAMING_SNAKE_CASE ) # collect igf pairs and save to file demo.jbl collect_objective_set(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # clean up, delete model and data we don't need anymore del model, train_data, objective_set torch.cuda.empty_cache() def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=15 , SCREAMING_SNAKE_CASE=1_28 , SCREAMING_SNAKE_CASE=1_00 , SCREAMING_SNAKE_CASE="igf_model.pt" , ) -> int: """simple docstring""" set_seed(42 ) # Load pre-trained model __snake_case = GPTaLMHeadModel.from_pretrained("gpt2" ) # Initialize secondary learner to use embedding weights of model __snake_case = SecondaryLearner(SCREAMING_SNAKE_CASE ) # Train secondary learner __snake_case = train_secondary_learner( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , max_epochs=SCREAMING_SNAKE_CASE , batch_size=SCREAMING_SNAKE_CASE , eval_freq=1_00 , igf_model_path=SCREAMING_SNAKE_CASE , ) del model, secondary_learner_train_data torch.cuda.empty_cache() return secondary_learner def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=32 , SCREAMING_SNAKE_CASE=10_00 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=recopy_gpta , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=10 , SCREAMING_SNAKE_CASE="gpt2_finetuned.pt" , ) -> Union[str, Any]: """simple docstring""" __snake_case = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" ) __snake_case = RandomSampler(SCREAMING_SNAKE_CASE ) __snake_case = DataLoader(SCREAMING_SNAKE_CASE , sampler=SCREAMING_SNAKE_CASE ) __snake_case = max_steps // (len(SCREAMING_SNAKE_CASE )) + 1 __snake_case = 0 __snake_case = torch.zeros((1, context_len) , dtype=torch.long , device=SCREAMING_SNAKE_CASE ) __snake_case , __snake_case , __snake_case = recopy_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) model.train() if secondary_learner is not None: secondary_learner.to(SCREAMING_SNAKE_CASE ) secondary_learner.eval() __snake_case = [] __snake_case = 0 __snake_case = [] __snake_case = [] # Compute the performance of the transformer model at the beginning __snake_case = compute_perplexity(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) test_perps.append(SCREAMING_SNAKE_CASE ) print("Test perplexity, step" , SCREAMING_SNAKE_CASE , ":" , SCREAMING_SNAKE_CASE ) for epoch in range(int(SCREAMING_SNAKE_CASE ) ): for step, example in enumerate(SCREAMING_SNAKE_CASE ): torch.cuda.empty_cache() __snake_case = random.randint(0 , example.size(2 ) - context_len - 1 ) __snake_case = example[0, 0, start : start + context_len] lm_optimizer.zero_grad() __snake_case = model(SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE ) __snake_case = True if secondary_learner is not None: __snake_case = secondary_learner.forward( torch.tensor(SCREAMING_SNAKE_CASE , dtype=torch.long , device=SCREAMING_SNAKE_CASE ).unsqueeze(0 ) )[0].item() observed_qs.append(float(SCREAMING_SNAKE_CASE ) ) # Here we implement the simple non-constant threshold for the predicted IG(X) value # We will decay the selectivity of our secondary learner filter from # 1 standard deviation above average to 1 below average after 10 batches. if global_step == 10: __snake_case = -1 if predicted_q < threshold: __snake_case = False # If we passed the filter, add the context to the batch! if do_backprop: contexts.append(np.array(context.cpu() ) ) __snake_case = outputs[0] lm_loss.backward() examples += 1 del outputs # Once the batch is filled with enough contexts, backprop on the batch. if examples == batch_size: torch.cuda.empty_cache() __snake_case = 0 # Do LM backprop torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 ) lm_optimizer.step() lm_scheduler.step() # Update learning rate schedule global_step += 1 # Compute the performance of the transformer model at this batch if global_step % eval_interval == 0: __snake_case = compute_perplexity(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) test_perps.append(SCREAMING_SNAKE_CASE ) print("Test perplexity, step" , SCREAMING_SNAKE_CASE , ":" , SCREAMING_SNAKE_CASE ) # Break out of the loop after 60 batches if max_steps > 0 and global_step > 60: break if max_steps > 0 and global_step > 60: break # save finetuned transformer model torch.save(model.state_dict() , SCREAMING_SNAKE_CASE ) torch.cuda.empty_cache() # Do some cleaning up so we can reinitialize for the next run of this function del lm_optimizer del lm_scheduler return model def __UpperCamelCase ( ) -> Any: """simple docstring""" __snake_case = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" ) # Required parameters parser.add_argument( "--data_dir" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help="The input data dir. Should contain data files for WikiText." , ) parser.add_argument( "--model_name_or_path" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help="Path to pretrained model or model identifier from huggingface.co/models" , ) parser.add_argument( "--data_file" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help=( "A jbl file containing tokenized data which can be split as objective dataset, " "train_dataset and test_dataset." ) , ) parser.add_argument( "--igf_data_file" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help="A jbl file containing the context and information gain pairs to train secondary learner." , ) parser.add_argument( "--output_dir" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , required=SCREAMING_SNAKE_CASE , help="The output directory where the final fine-tuned model is stored." , ) parser.add_argument( "--tokenizer_name" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , help="Pretrained tokenizer name or path if not the same as model_name" , ) parser.add_argument("--seed" , type=SCREAMING_SNAKE_CASE , default=SCREAMING_SNAKE_CASE , help="A seed for reproducible training." ) parser.add_argument( "--context_len" , default=32 , type=SCREAMING_SNAKE_CASE , help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) , ) parser.add_argument( "--size_objective_set" , default=1_00 , type=SCREAMING_SNAKE_CASE , help="number of articles that are long enough to be used as our objective set" , ) parser.add_argument( "--eval_freq" , default=1_00 , type=SCREAMING_SNAKE_CASE , help="secondary model evaluation is triggered at eval_freq" ) parser.add_argument("--max_steps" , default=10_00 , type=SCREAMING_SNAKE_CASE , help="To calculate training epochs" ) parser.add_argument( "--secondary_learner_batch_size" , default=1_28 , type=SCREAMING_SNAKE_CASE , help="batch size of training data for secondary learner" , ) parser.add_argument( "--batch_size" , default=16 , type=SCREAMING_SNAKE_CASE , help="batch size of training data of language model(gpt2) " ) parser.add_argument( "--eval_interval" , default=10 , type=SCREAMING_SNAKE_CASE , help=( "decay the selectivity of our secondary learner filter from" "1 standard deviation above average to 1 below average after 10 batches" ) , ) parser.add_argument( "--number" , default=1_00 , type=SCREAMING_SNAKE_CASE , help="The number of examples split to be used as objective_set/test_data" ) parser.add_argument( "--min_len" , default=10_26 , type=SCREAMING_SNAKE_CASE , help="The minimum length of the article to be used as objective set" ) parser.add_argument( "--secondary_learner_max_epochs" , default=15 , type=SCREAMING_SNAKE_CASE , help="number of epochs to train secondary learner" ) parser.add_argument("--trim" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , help="truncate the example if it exceeds context length" ) parser.add_argument( "--threshold" , default=1.0 , type=SCREAMING_SNAKE_CASE , help=( "The threshold value used by secondary learner to filter the train_data and allow only" " informative data as input to the model" ) , ) parser.add_argument("--finetuned_model_name" , default="gpt2_finetuned.pt" , type=SCREAMING_SNAKE_CASE , help="finetuned_model_name" ) parser.add_argument( "--recopy_model" , default=SCREAMING_SNAKE_CASE , type=SCREAMING_SNAKE_CASE , help="Reset the model to the original pretrained GPT-2 weights after each iteration" , ) # function calls # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner generate_n_pairs( context_len=32 , max_steps=10 , size_objective_set=1_00 , min_len=10_26 , trim=SCREAMING_SNAKE_CASE , data_file="data/tokenized_stories_train_wikitext103.jbl" , igf_data_file="igf_context_pairs.jbl" , ) # Load train data for secondary learner __snake_case = joblib.load("data/IGF_values.jbl" ) # Train secondary learner __snake_case = training_secondary_learner( SCREAMING_SNAKE_CASE , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="igf_model.pt" , ) # load pretrained gpt2 model __snake_case = GPTaLMHeadModel.from_pretrained("gpt2" ) set_seed(42 ) # Generate train and test data to train and evaluate gpt2 model __snake_case , __snake_case = generate_datasets( context_len=32 , file="data/tokenized_stories_train_wikitext103.jbl" , number=1_00 , min_len=10_26 , trim=SCREAMING_SNAKE_CASE ) # fine-tuning of the gpt2 model using igf (Information Gain Filtration) finetune( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=SCREAMING_SNAKE_CASE , secondary_learner=SCREAMING_SNAKE_CASE , eval_interval=10 , finetuned_model_name="gpt2_finetuned.pt" , ) if __name__ == "__main__": main()
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"""simple docstring""" import argparse from transformers import ( TapasConfig, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasTokenizer, load_tf_weights_in_tapas, ) from transformers.utils import logging logging.set_verbosity_info() def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]: """simple docstring""" __snake_case = TapasConfig.from_json_file(SCREAMING_SNAKE_CASE ) # set absolute/relative position embeddings parameter __snake_case = reset_position_index_per_cell # set remaining parameters of TapasConfig as well as the model based on the task if task == "SQA": __snake_case = TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE ) elif task == "WTQ": # run_task_main.py hparams __snake_case = 4 __snake_case = True # hparam_utils.py hparams __snake_case = 0.664_694 __snake_case = 0.207_951 __snake_case = 0.121_194 __snake_case = True __snake_case = True __snake_case = False __snake_case = 0.0_352_513 __snake_case = TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE ) elif task == "WIKISQL_SUPERVISED": # run_task_main.py hparams __snake_case = 4 __snake_case = False # hparam_utils.py hparams __snake_case = 36.4_519 __snake_case = 0.903_421 __snake_case = 222.088 __snake_case = True __snake_case = True __snake_case = True __snake_case = 0.763_141 __snake_case = TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE ) elif task == "TABFACT": __snake_case = TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE ) elif task == "MLM": __snake_case = TapasForMaskedLM(config=SCREAMING_SNAKE_CASE ) elif task == "INTERMEDIATE_PRETRAINING": __snake_case = TapasModel(config=SCREAMING_SNAKE_CASE ) else: raise ValueError(F'''Task {task} not supported.''' ) print(F'''Building PyTorch model from configuration: {config}''' ) # Load weights from tf checkpoint load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) # Save pytorch-model (weights and configuration) print(F'''Save PyTorch model to {pytorch_dump_path}''' ) model.save_pretrained(SCREAMING_SNAKE_CASE ) # Save tokenizer files print(F'''Save tokenizer files to {pytorch_dump_path}''' ) __snake_case = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt" , model_max_length=5_12 ) tokenizer.save_pretrained(SCREAMING_SNAKE_CASE ) print("Used relative position embeddings:" , model.config.reset_position_index_per_cell ) if __name__ == "__main__": _SCREAMING_SNAKE_CASE = argparse.ArgumentParser() # Required parameters parser.add_argument( """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA.""" ) parser.add_argument( """--reset_position_index_per_cell""", default=False, action="""store_true""", help="""Whether to use relative position embeddings or not. Defaults to True.""", ) parser.add_argument( """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path.""" ) parser.add_argument( """--tapas_config_file""", default=None, type=str, required=True, help=( """The config json file corresponding to the pre-trained TAPAS 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.""" ) _SCREAMING_SNAKE_CASE = parser.parse_args() convert_tf_checkpoint_to_pytorch( args.task, args.reset_position_index_per_cell, args.tf_checkpoint_path, args.tapas_config_file, args.pytorch_dump_path, )
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available, ) _lowerCAmelCase : List[str] = { "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"], "feature_extraction_whisper": ["WhisperFeatureExtractor"], "processing_whisper": ["WhisperProcessor"], "tokenization_whisper": ["WhisperTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : List[Any] = ["WhisperTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : Optional[Any] = [ "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "WhisperForConditionalGeneration", "WhisperModel", "WhisperPreTrainedModel", "WhisperForAudioClassification", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : Optional[Any] = [ "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFWhisperForConditionalGeneration", "TFWhisperModel", "TFWhisperPreTrainedModel", ] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCAmelCase : List[str] = [ "FlaxWhisperForConditionalGeneration", "FlaxWhisperModel", "FlaxWhisperPreTrainedModel", "FlaxWhisperForAudioClassification", ] if TYPE_CHECKING: from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig from .feature_extraction_whisper import WhisperFeatureExtractor from .processing_whisper import WhisperProcessor from .tokenization_whisper import WhisperTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_whisper_fast import WhisperTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_whisper import ( WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, WhisperForAudioClassification, WhisperForConditionalGeneration, WhisperModel, WhisperPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_whisper import ( TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST, TFWhisperForConditionalGeneration, TFWhisperModel, TFWhisperPreTrainedModel, ) try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_whisper import ( FlaxWhisperForAudioClassification, FlaxWhisperForConditionalGeneration, FlaxWhisperModel, FlaxWhisperPreTrainedModel, ) else: import sys _lowerCAmelCase : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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'''simple docstring''' from __future__ import annotations import unittest from transformers import LEDConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFLEDForConditionalGeneration, TFLEDModel @require_tf class snake_case : """simple docstring""" _lowerCAmelCase = LEDConfig _lowerCAmelCase = {} _lowerCAmelCase = 'gelu' def __init__( self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=99 , lowerCamelCase=32 , lowerCamelCase=2 , lowerCamelCase=4 , lowerCamelCase=37 , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=20 , lowerCamelCase=2 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=4 , ) -> Dict: """simple docstring""" snake_case__ : int = parent snake_case__ : Union[str, Any] = batch_size snake_case__ : Optional[Any] = seq_length snake_case__ : Any = is_training snake_case__ : Any = use_labels snake_case__ : Optional[Any] = vocab_size snake_case__ : List[Any] = hidden_size snake_case__ : Optional[int] = num_hidden_layers snake_case__ : List[str] = num_attention_heads snake_case__ : Tuple = intermediate_size snake_case__ : List[str] = hidden_dropout_prob snake_case__ : str = attention_probs_dropout_prob snake_case__ : Optional[Any] = max_position_embeddings snake_case__ : List[Any] = eos_token_id snake_case__ : Optional[Any] = pad_token_id snake_case__ : Tuple = bos_token_id snake_case__ : Optional[Any] = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window` and one before and one after snake_case__ : Optional[Any] = self.attention_window + 2 # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for # the `test_attention_outputs` and `test_hidden_states_output` tests snake_case__ : Optional[Any] = ( self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window ) def lowercase__ ( self ) -> int: """simple docstring""" snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) snake_case__ : Union[str, Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 ) snake_case__ : List[str] = tf.concat([input_ids, eos_tensor] , axis=1 ) snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) snake_case__ : Union[str, Any] = self.config_cls( vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , ) snake_case__ : str = prepare_led_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase ) snake_case__ : str = tf.concat( [tf.zeros_like(lowerCamelCase )[:, :-1], tf.ones_like(lowerCamelCase )[:, -1:]] , axis=-1 , ) snake_case__ : List[str] = global_attention_mask return config, inputs_dict def lowercase__ ( self , lowerCamelCase , lowerCamelCase ) -> Any: """simple docstring""" snake_case__ : Optional[Any] = TFLEDModel(config=lowerCamelCase ).get_decoder() snake_case__ : str = inputs_dict['''input_ids'''] snake_case__ : Tuple = input_ids[:1, :] snake_case__ : List[str] = inputs_dict['''attention_mask'''][:1, :] snake_case__ : Union[str, Any] = 1 # first forward pass snake_case__ : Dict = model(lowerCamelCase , attention_mask=lowerCamelCase , use_cache=lowerCamelCase ) snake_case__ ,snake_case__ : List[str] = outputs.to_tuple() # create hypothetical next token and extent to next_input_ids snake_case__ : Any = ids_tensor((self.batch_size, 3) , config.vocab_size ) snake_case__ : Optional[int] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta ) # append to next input_ids and snake_case__ : Optional[int] = tf.concat([input_ids, next_tokens] , axis=-1 ) snake_case__ : Dict = tf.concat([attention_mask, next_attn_mask] , axis=-1 ) snake_case__ : List[str] = model(lowerCamelCase , attention_mask=lowerCamelCase )[0] snake_case__ : int = model(lowerCamelCase , attention_mask=lowerCamelCase , past_key_values=lowerCamelCase )[0] self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] ) # select random slice snake_case__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) ) snake_case__ : List[str] = output_from_no_past[:, -3:, random_slice_idx] snake_case__ : Tuple = output_from_past[:, :, random_slice_idx] # test that outputs are equal for slice tf.debugging.assert_near(lowerCamelCase , lowerCamelCase , rtol=1E-3 ) def _A ( snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : Tuple=None , snake_case__ : List[str]=None , snake_case__ : Union[str, Any]=None , snake_case__ : List[str]=None , ): if attention_mask is None: snake_case__ : Dict = tf.cast(tf.math.not_equal(snake_case__ , config.pad_token_id ) , tf.inta ) if decoder_attention_mask is None: snake_case__ : Tuple = tf.concat( [ tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ), tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ), ] , axis=-1 , ) if head_mask is None: snake_case__ : List[str] = tf.ones((config.encoder_layers, config.encoder_attention_heads) ) if decoder_head_mask is None: snake_case__ : Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) ) return { "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask, } @require_tf class snake_case ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ): """simple docstring""" _lowerCAmelCase = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else () _lowerCAmelCase = (TFLEDForConditionalGeneration,) if is_tf_available() else () _lowerCAmelCase = ( { 'conversational': TFLEDForConditionalGeneration, 'feature-extraction': TFLEDModel, 'summarization': TFLEDForConditionalGeneration, 'text2text-generation': TFLEDForConditionalGeneration, 'translation': TFLEDForConditionalGeneration, } if is_tf_available() else {} ) _lowerCAmelCase = True _lowerCAmelCase = False _lowerCAmelCase = False _lowerCAmelCase = False def lowercase__ ( self ) -> Optional[Any]: """simple docstring""" snake_case__ : Union[str, Any] = TFLEDModelTester(self ) snake_case__ : int = ConfigTester(self , config_class=lowerCamelCase ) def lowercase__ ( self ) -> Any: """simple docstring""" self.config_tester.run_common_tests() def lowercase__ ( self ) -> List[Any]: """simple docstring""" snake_case__ : str = self.model_tester.prepare_config_and_inputs_for_common() self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase ) def lowercase__ ( self ) -> Optional[Any]: """simple docstring""" snake_case__ ,snake_case__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() snake_case__ : Dict = tf.zeros_like(inputs_dict['''attention_mask'''] ) snake_case__ : Union[str, Any] = 2 snake_case__ : Tuple = tf.where( tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , ) snake_case__ : Optional[Any] = True snake_case__ : Optional[int] = self.model_tester.seq_length snake_case__ : str = self.model_tester.encoder_seq_length def check_decoder_attentions_output(lowerCamelCase ): snake_case__ : Optional[Any] = outputs.decoder_attentions self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , ) def check_encoder_attentions_output(lowerCamelCase ): snake_case__ : Union[str, Any] = [t.numpy() for t in outputs.encoder_attentions] snake_case__ : Optional[Any] = [t.numpy() for t in outputs.encoder_global_attentions] self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_hidden_layers ) self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , ) self.assertListEqual( list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , ) for model_class in self.all_model_classes: snake_case__ : Dict = True snake_case__ : int = False snake_case__ : Optional[int] = False snake_case__ : Optional[int] = model_class(lowerCamelCase ) snake_case__ : Any = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) ) snake_case__ : Dict = len(lowerCamelCase ) self.assertEqual(config.output_hidden_states , lowerCamelCase ) check_encoder_attentions_output(lowerCamelCase ) if self.is_encoder_decoder: snake_case__ : List[str] = model_class(lowerCamelCase ) snake_case__ : Optional[int] = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) ) self.assertEqual(config.output_hidden_states , lowerCamelCase ) check_decoder_attentions_output(lowerCamelCase ) # Check that output attentions can also be changed via the config del inputs_dict["output_attentions"] snake_case__ : Union[str, Any] = True snake_case__ : str = model_class(lowerCamelCase ) snake_case__ : Union[str, Any] = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) ) self.assertEqual(config.output_hidden_states , lowerCamelCase ) check_encoder_attentions_output(lowerCamelCase ) # Check attention is always last and order is fine snake_case__ : Tuple = True snake_case__ : Any = True snake_case__ : Any = model_class(lowerCamelCase ) snake_case__ : Any = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) ) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(lowerCamelCase ) ) self.assertEqual(model.config.output_hidden_states , lowerCamelCase ) check_encoder_attentions_output(lowerCamelCase ) @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' ) def lowercase__ ( self ) -> List[Any]: """simple docstring""" pass def lowercase__ ( self ) -> int: """simple docstring""" pass def _A ( snake_case__ : Optional[int] ): return tf.constant(snake_case__ , dtype=tf.intaa ) _lowerCAmelCase : Tuple = 1E-4 @slow @require_tf class snake_case ( unittest.TestCase ): """simple docstring""" def lowercase__ ( self ) -> Union[str, Any]: """simple docstring""" snake_case__ : Union[str, Any] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led # change to intended input here snake_case__ : Dict = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] ) snake_case__ : str = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] ) snake_case__ : str = prepare_led_inputs_dict(model.config , lowerCamelCase , lowerCamelCase ) snake_case__ : List[str] = model(**lowerCamelCase )[0] snake_case__ : str = (1, 1024, 768) self.assertEqual(output.shape , lowerCamelCase ) # change to expected output here snake_case__ : Optional[Any] = tf.convert_to_tensor( [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]] , ) tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase , atol=1E-3 ) def lowercase__ ( self ) -> Union[str, Any]: """simple docstring""" snake_case__ : Optional[int] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ) # change to intended input here snake_case__ : Optional[int] = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] ) snake_case__ : str = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] ) snake_case__ : str = prepare_led_inputs_dict(model.config , lowerCamelCase , lowerCamelCase ) snake_case__ : Tuple = model(**lowerCamelCase )[0] snake_case__ : List[str] = (1, 1024, model.config.vocab_size) self.assertEqual(output.shape , lowerCamelCase ) # change to expected output here snake_case__ : Any = tf.convert_to_tensor( [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]] , ) tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase , atol=1E-3 , rtol=1E-3 )
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'''simple docstring''' from __future__ import annotations import math import random from typing import Any class a_ : def __init__( self ): a_ = [] a_ = 0 a_ = 0 def lowerCAmelCase__ ( self ): return self.head == self.tail def lowerCAmelCase__ ( self , UpperCAmelCase ): self.data.append(__UpperCamelCase ) a_ = self.tail + 1 def lowerCAmelCase__ ( self ): a_ = self.data[self.head] a_ = self.head + 1 return ret def lowerCAmelCase__ ( self ): return self.tail - self.head def lowerCAmelCase__ ( self ): print(self.data ) print("""**************""" ) print(self.data[self.head : self.tail] ) class a_ : def __init__( self , UpperCAmelCase ): a_ = data a_ = None a_ = None a_ = 1 def lowerCAmelCase__ ( self ): return self.data def lowerCAmelCase__ ( self ): return self.left def lowerCAmelCase__ ( self ): return self.right def lowerCAmelCase__ ( self ): return self.height def lowerCAmelCase__ ( self , UpperCAmelCase ): a_ = data def lowerCAmelCase__ ( self , UpperCAmelCase ): a_ = node def lowerCAmelCase__ ( self , UpperCAmelCase ): a_ = node def lowerCAmelCase__ ( self , UpperCAmelCase ): a_ = height def UpperCamelCase_ ( A__ ): if node is None: return 0 return node.get_height() def UpperCamelCase_ ( A__ , A__ ): if a > b: return a return b def UpperCamelCase_ ( A__ ): print("""left rotation node:""" , node.get_data() ) a_ = node.get_left() assert ret is not None node.set_left(ret.get_right() ) ret.set_right(UpperCAmelCase__ ) a_ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1 node.set_height(UpperCAmelCase__ ) a_ = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1 ret.set_height(UpperCAmelCase__ ) return ret def UpperCamelCase_ ( A__ ): print("""right rotation node:""" , node.get_data() ) a_ = node.get_right() assert ret is not None node.set_right(ret.get_left() ) ret.set_left(UpperCAmelCase__ ) a_ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1 node.set_height(UpperCAmelCase__ ) a_ = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1 ret.set_height(UpperCAmelCase__ ) return ret def UpperCamelCase_ ( A__ ): a_ = node.get_left() assert left_child is not None node.set_left(left_rotation(UpperCAmelCase__ ) ) return right_rotation(UpperCAmelCase__ ) def UpperCamelCase_ ( A__ ): a_ = node.get_right() assert right_child is not None node.set_right(right_rotation(UpperCAmelCase__ ) ) return left_rotation(UpperCAmelCase__ ) def UpperCamelCase_ ( A__ , A__ ): if node is None: return MyNode(UpperCAmelCase__ ) if data < node.get_data(): node.set_left(insert_node(node.get_left() , UpperCAmelCase__ ) ) if ( get_height(node.get_left() ) - get_height(node.get_right() ) == 2 ): # an unbalance detected a_ = node.get_left() assert left_child is not None if ( data < left_child.get_data() ): # new node is the left child of the left child a_ = right_rotation(UpperCAmelCase__ ) else: a_ = lr_rotation(UpperCAmelCase__ ) else: node.set_right(insert_node(node.get_right() , UpperCAmelCase__ ) ) if get_height(node.get_right() ) - get_height(node.get_left() ) == 2: a_ = node.get_right() assert right_child is not None if data < right_child.get_data(): a_ = rl_rotation(UpperCAmelCase__ ) else: a_ = left_rotation(UpperCAmelCase__ ) a_ = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1 node.set_height(UpperCAmelCase__ ) return node def UpperCamelCase_ ( A__ ): while True: a_ = root.get_right() if right_child is None: break a_ = right_child return root.get_data() def UpperCamelCase_ ( A__ ): while True: a_ = root.get_left() if left_child is None: break a_ = left_child return root.get_data() def UpperCamelCase_ ( A__ , A__ ): a_ = root.get_left() a_ = root.get_right() if root.get_data() == data: if left_child is not None and right_child is not None: a_ = get_left_most(UpperCAmelCase__ ) root.set_data(UpperCAmelCase__ ) root.set_right(del_node(UpperCAmelCase__ , UpperCAmelCase__ ) ) elif left_child is not None: a_ = left_child elif right_child is not None: a_ = right_child else: return None elif root.get_data() > data: if left_child is None: print("""No such data""" ) return root else: root.set_left(del_node(UpperCAmelCase__ , UpperCAmelCase__ ) ) else: # root.get_data() < data if right_child is None: return root else: root.set_right(del_node(UpperCAmelCase__ , UpperCAmelCase__ ) ) if get_height(UpperCAmelCase__ ) - get_height(UpperCAmelCase__ ) == 2: assert right_child is not None if get_height(right_child.get_right() ) > get_height(right_child.get_left() ): a_ = left_rotation(UpperCAmelCase__ ) else: a_ = rl_rotation(UpperCAmelCase__ ) elif get_height(UpperCAmelCase__ ) - get_height(UpperCAmelCase__ ) == -2: assert left_child is not None if get_height(left_child.get_left() ) > get_height(left_child.get_right() ): a_ = right_rotation(UpperCAmelCase__ ) else: a_ = lr_rotation(UpperCAmelCase__ ) a_ = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1 root.set_height(UpperCAmelCase__ ) return root class a_ : def __init__( self ): a_ = None def lowerCAmelCase__ ( self ): return get_height(self.root ) def lowerCAmelCase__ ( self , UpperCAmelCase ): print("""insert:""" + str(__UpperCamelCase ) ) a_ = insert_node(self.root , __UpperCamelCase ) def lowerCAmelCase__ ( self , UpperCAmelCase ): print("""delete:""" + str(__UpperCamelCase ) ) if self.root is None: print("""Tree is empty!""" ) return a_ = del_node(self.root , __UpperCamelCase ) def __str__( self , ): # a level traversale, gives a more intuitive look on the tree a_ = """""" a_ = MyQueue() q.push(self.root ) a_ = self.get_height() if layer == 0: return output a_ = 0 while not q.is_empty(): a_ = q.pop() a_ = """ """ * int(math.pow(2 , layer - 1 ) ) output += space if node is None: output += "*" q.push(__UpperCamelCase ) q.push(__UpperCamelCase ) else: output += str(node.get_data() ) q.push(node.get_left() ) q.push(node.get_right() ) output += space a_ = cnt + 1 for i in range(1_00 ): if cnt == math.pow(2 , __UpperCamelCase ) - 1: a_ = layer - 1 if layer == 0: output += "\n*************************************" return output output += "\n" break output += "\n*************************************" return output def UpperCamelCase_ ( ): import doctest doctest.testmod() if __name__ == "__main__": _test() lowercase__ =AVLtree() lowercase__ =list(range(10)) random.shuffle(lst) for i in lst: t.insert(i) print(str(t)) random.shuffle(lst) for i in lst: t.del_node(i) print(str(t))
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'''simple docstring''' import math def UpperCamelCase_ ( A__ ): a_ = [True] * n a_ = False a_ = False a_ = True for i in range(3 , int(n**0.5 + 1 ) , 2 ): a_ = i * 2 while index < n: a_ = False a_ = index + i a_ = [2] for i in range(3 , A__ , 2 ): if is_prime[i]: primes.append(A__ ) return primes def UpperCamelCase_ ( A__ = 99_99_66_66_33_33 ): a_ = math.floor(math.sqrt(A__ ) ) + 1_00 a_ = prime_sieve(A__ ) a_ = 0 a_ = 0 a_ = primes[prime_index] while (last_prime**2) <= limit: a_ = primes[prime_index + 1] a_ = last_prime**2 a_ = next_prime**2 # Get numbers divisible by lps(current) a_ = lower_bound + last_prime while upper_bound > current <= limit: matches_sum += current current += last_prime # Reset the upper_bound while (upper_bound - next_prime) > limit: upper_bound -= next_prime # Add the numbers divisible by ups(current) a_ = upper_bound - next_prime while current > lower_bound: matches_sum += current current -= next_prime # Remove the numbers divisible by both ups and lps a_ = 0 while upper_bound > current <= limit: if current <= lower_bound: # Increment the current number current += last_prime * next_prime continue if current > limit: break # Remove twice since it was added by both ups and lps matches_sum -= current * 2 # Increment the current number current += last_prime * next_prime # Setup for next pair a_ = next_prime prime_index += 1 return matches_sum if __name__ == "__main__": print(solution())
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import contextlib from multiprocessing import Pool, RLock from tqdm.auto import tqdm from ..utils import experimental, logging a_ = logging.get_logger(__name__) class lowercase__ : a_ =None @experimental def _a ( UpperCamelCase_ : str , UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : int , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Any ) -> List[Any]: """simple docstring""" if ParallelBackendConfig.backend_name is None: return _map_with_multiprocessing_pool( snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) return _map_with_joblib(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) def _a ( UpperCamelCase_ : str , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : int , UpperCamelCase_ : Tuple , UpperCamelCase_ : List[str] ) -> List[str]: """simple docstring""" lowerCAmelCase__ = num_proc if num_proc <= len(snake_case_ ) else len(snake_case_ ) lowerCAmelCase__ = [] # We organize the splits ourselve (contiguous splits) for index in range(snake_case_ ): lowerCAmelCase__ = len(snake_case_ ) // num_proc lowerCAmelCase__ = len(snake_case_ ) % num_proc lowerCAmelCase__ = div * index + min(snake_case_ , snake_case_ ) lowerCAmelCase__ = start + div + (1 if index < mod else 0) split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) ) if len(snake_case_ ) != sum(len(i[1] ) for i in split_kwds ): raise ValueError( F"Error dividing inputs iterable among processes. " F"Total number of objects {len(snake_case_ )}, " F"length: {sum(len(i[1] ) for i in split_kwds )}" ) logger.info( F"Spawning {num_proc} processes for {len(snake_case_ )} objects in slices of {[len(i[1] ) for i in split_kwds]}" ) lowerCAmelCase__ = None, None if not disable_tqdm: lowerCAmelCase__ = (RLock(),), tqdm.set_lock with Pool(snake_case_ , initargs=snake_case_ , initializer=snake_case_ ) as pool: lowerCAmelCase__ = pool.map(snake_case_ , snake_case_ ) logger.info(F"Finished {num_proc} processes" ) lowerCAmelCase__ = [obj for proc_res in mapped for obj in proc_res] logger.info(F"Unpacked {len(snake_case_ )} objects" ) return mapped def _a ( UpperCamelCase_ : str , UpperCamelCase_ : str , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Any , UpperCamelCase_ : List[str] ) -> List[str]: """simple docstring""" import joblib with joblib.parallel_backend(ParallelBackendConfig.backend_name , n_jobs=snake_case_ ): return joblib.Parallel()( joblib.delayed(snake_case_ )((function, obj, types, None, True, None) ) for obj in iterable ) @experimental @contextlib.contextmanager def _a ( UpperCamelCase_ : Tuple ) -> int: """simple docstring""" lowerCAmelCase__ = backend_name if backend_name == "spark": from joblibspark import register_spark register_spark() # TODO: call create_cache_and_write_probe if "download" in steps # TODO: raise NotImplementedError when Dataset.map etc is called try: yield finally: lowerCAmelCase__ = None
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import warnings from typing import List import numpy as np from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding from ...utils import is_flax_available, is_tf_available, is_torch_available class lowercase ( UpperCamelCase__ ): _a = ["image_processor", "tokenizer"] _a = "OwlViTImageProcessor" _a = ("CLIPTokenizer", "CLIPTokenizerFast") def __init__( self , _a=None , _a=None , **_a ) -> List[str]: _A : int = None if "feature_extractor" in kwargs: warnings.warn( """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`""" """ instead.""" , _a , ) _A : Optional[Any] = kwargs.pop("""feature_extractor""" ) _A : Union[str, Any] = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("""You need to specify an `image_processor`.""" ) if tokenizer is None: raise ValueError("""You need to specify a `tokenizer`.""" ) super().__init__(_a , _a ) def __call__( self , _a=None , _a=None , _a=None , _a="max_length" , _a="np" , **_a ) -> int: if text is None and query_images is None and images is None: raise ValueError( """You have to specify at least one text or query image or image. All three cannot be none.""" ) if text is not None: if isinstance(_a , _a ) or (isinstance(_a , _a ) and not isinstance(text[0] , _a )): _A : Optional[int] = [self.tokenizer(_a , padding=_a , return_tensors=_a , **_a )] elif isinstance(_a , _a ) and isinstance(text[0] , _a ): _A : Tuple = [] # Maximum number of queries across batch _A : Optional[int] = max([len(_a ) for t in text] ) # Pad all batch samples to max number of text queries for t in text: if len(_a ) != max_num_queries: _A : Optional[Any] = t + [""" """] * (max_num_queries - len(_a )) _A : Union[str, Any] = self.tokenizer(_a , padding=_a , return_tensors=_a , **_a ) encodings.append(_a ) else: raise TypeError("""Input text should be a string, a list of strings or a nested list of strings""" ) if return_tensors == "np": _A : Union[str, Any] = np.concatenate([encoding["""input_ids"""] for encoding in encodings] , axis=0 ) _A : Optional[Any] = np.concatenate([encoding["""attention_mask"""] for encoding in encodings] , axis=0 ) elif return_tensors == "jax" and is_flax_available(): import jax.numpy as jnp _A : Optional[Any] = jnp.concatenate([encoding["""input_ids"""] for encoding in encodings] , axis=0 ) _A : int = jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings] , axis=0 ) elif return_tensors == "pt" and is_torch_available(): import torch _A : Any = torch.cat([encoding["""input_ids"""] for encoding in encodings] , dim=0 ) _A : List[str] = torch.cat([encoding["""attention_mask"""] for encoding in encodings] , dim=0 ) elif return_tensors == "tf" and is_tf_available(): import tensorflow as tf _A : Optional[Any] = tf.stack([encoding["""input_ids"""] for encoding in encodings] , axis=0 ) _A : List[Any] = tf.stack([encoding["""attention_mask"""] for encoding in encodings] , axis=0 ) else: raise ValueError("""Target return tensor type could not be returned""" ) _A : List[Any] = BatchEncoding() _A : Optional[Any] = input_ids _A : str = attention_mask if query_images is not None: _A : Tuple = BatchEncoding() _A : Dict = self.image_processor( _a , return_tensors=_a , **_a ).pixel_values _A : Optional[Any] = query_pixel_values if images is not None: _A : Dict = self.image_processor(_a , return_tensors=_a , **_a ) if text is not None and images is not None: _A : Optional[int] = image_features.pixel_values return encoding elif query_images is not None and images is not None: _A : Union[str, Any] = image_features.pixel_values return encoding elif text is not None or query_images is not None: return encoding else: return BatchEncoding(data=dict(**_a ) , tensor_type=_a ) def a__ ( self , *_a , **_a ) -> List[Any]: return self.image_processor.post_process(*_a , **_a ) def a__ ( self , *_a , **_a ) -> List[str]: return self.image_processor.post_process_object_detection(*_a , **_a ) def a__ ( self , *_a , **_a ) -> Optional[Any]: return self.image_processor.post_process_image_guided_detection(*_a , **_a ) def a__ ( self , *_a , **_a ) -> str: return self.tokenizer.batch_decode(*_a , **_a ) def a__ ( self , *_a , **_a ) -> Tuple: return self.tokenizer.decode(*_a , **_a ) @property def a__ ( self ) -> Tuple: warnings.warn( """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , _a , ) return self.image_processor_class @property def a__ ( self ) -> Union[str, Any]: warnings.warn( """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , _a , ) return self.image_processor
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def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Tuple ) -> list[int]: if num <= 0: raise ValueError('Input must be a positive integer' ) __lowercase = [True] * (num + 1) __lowercase = 2 while p * p <= num: if primes[p]: for i in range(p * p , num + 1 , lowerCamelCase__ ): __lowercase = False p += 1 return [prime for prime in range(2 , num + 1 ) if primes[prime]] if __name__ == "__main__": import doctest doctest.testmod() SCREAMING_SNAKE_CASE__ = int(input("""Enter a positive integer: """).strip()) print(prime_sieve_eratosthenes(user_num))
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from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxSeqaSeqConfigWithPast from ...utils import logging SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) SCREAMING_SNAKE_CASE__ = { """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 A__ ( lowerCAmelCase__ ): lowerCAmelCase__ : Optional[int] = "umt5" lowerCAmelCase__ : Tuple = ["past_key_values"] def __init__( self : str , _UpperCAmelCase : int=25_01_12 , _UpperCAmelCase : Optional[int]=5_12 , _UpperCAmelCase : List[str]=64 , _UpperCAmelCase : Union[str, Any]=10_24 , _UpperCAmelCase : str=8 , _UpperCAmelCase : Tuple=None , _UpperCAmelCase : List[str]=6 , _UpperCAmelCase : str=32 , _UpperCAmelCase : Optional[int]=1_28 , _UpperCAmelCase : List[str]=0.1 , _UpperCAmelCase : str=1e-6 , _UpperCAmelCase : Dict=1.0 , _UpperCAmelCase : str="gated-gelu" , _UpperCAmelCase : str=True , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : Tuple="T5Tokenizer" , _UpperCAmelCase : Union[str, Any]=True , _UpperCAmelCase : List[str]=0 , _UpperCAmelCase : int=1 , _UpperCAmelCase : List[str]=0 , **_UpperCAmelCase : Union[str, Any] , ) -> Union[str, Any]: """simple docstring""" super().__init__( is_encoder_decoder=_UpperCAmelCase , tokenizer_class=_UpperCAmelCase , tie_word_embeddings=_UpperCAmelCase , pad_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase , decoder_start_token_id=_UpperCAmelCase , **_UpperCAmelCase , ) __lowercase = vocab_size __lowercase = d_model __lowercase = d_kv __lowercase = d_ff __lowercase = num_layers __lowercase = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry __lowercase = num_heads __lowercase = relative_attention_num_buckets __lowercase = relative_attention_max_distance __lowercase = dropout_rate __lowercase = layer_norm_epsilon __lowercase = initializer_factor __lowercase = feed_forward_proj __lowercase = use_cache __lowercase = self.feed_forward_proj.split('-' ) __lowercase = act_info[-1] __lowercase = act_info[0] == 'gated' if len(_UpperCAmelCase ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase ) > 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": __lowercase = 'gelu_new' @property def a__ ( self : Tuple ) -> Any: """simple docstring""" return self.d_model @property def a__ ( self : List[str] ) -> List[Any]: """simple docstring""" return self.num_heads @property def a__ ( self : Union[str, Any] ) -> str: """simple docstring""" return self.num_layers class A__ ( lowerCAmelCase__ ): @property # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs def a__ ( self : str ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" __lowercase = { 'input_ids': {0: 'batch', 1: 'encoder_sequence'}, 'attention_mask': {0: 'batch', 1: 'encoder_sequence'}, } if self.use_past: __lowercase = 'past_encoder_sequence + sequence' __lowercase = {0: 'batch'} __lowercase = {0: 'batch', 1: 'past_decoder_sequence + sequence'} else: __lowercase = {0: 'batch', 1: 'decoder_sequence'} __lowercase = {0: 'batch', 1: 'decoder_sequence'} if self.use_past: self.fill_with_past_key_values_(_UpperCAmelCase , direction='inputs' ) return common_inputs @property # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset def a__ ( self : List[str] ) -> int: """simple docstring""" return 13 @property def a__ ( self : Dict ) -> float: """simple docstring""" return 5e-4
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from __future__ import annotations import time a_ = list[tuple[int, int]] a_ = [ [0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], ] a_ = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right class _UpperCamelCase : '''simple docstring''' def __init__( self : Optional[Any] , a : int , a : int , a : int , a : int , a : Node | None ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE : List[Any] = pos_x SCREAMING_SNAKE_CASE : Dict = pos_y SCREAMING_SNAKE_CASE : int = (pos_y, pos_x) SCREAMING_SNAKE_CASE : Any = goal_x SCREAMING_SNAKE_CASE : Dict = goal_y SCREAMING_SNAKE_CASE : str = parent class _UpperCamelCase : '''simple docstring''' def __init__( self : Dict , a : tuple[int, int] , a : tuple[int, int] ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE : int = Node(start[1] , start[0] , goal[1] , goal[0] , a ) SCREAMING_SNAKE_CASE : Tuple = Node(goal[1] , goal[0] , goal[1] , goal[0] , a ) SCREAMING_SNAKE_CASE : str = [self.start] SCREAMING_SNAKE_CASE : Any = False def __UpperCamelCase ( self : str ) -> Path | None: """simple docstring""" while self.node_queue: SCREAMING_SNAKE_CASE : List[str] = self.node_queue.pop(0 ) if current_node.pos == self.target.pos: SCREAMING_SNAKE_CASE : Tuple = True return self.retrace_path(a ) SCREAMING_SNAKE_CASE : str = self.get_successors(a ) for node in successors: self.node_queue.append(a ) if not self.reached: return [self.start.pos] return None def __UpperCamelCase ( self : Dict , a : Node ) -> list[Node]: """simple docstring""" SCREAMING_SNAKE_CASE : Any = [] for action in delta: SCREAMING_SNAKE_CASE : int = parent.pos_x + action[1] SCREAMING_SNAKE_CASE : Dict = parent.pos_y + action[0] if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(a ) - 1): continue if grid[pos_y][pos_x] != 0: continue successors.append( Node(a , a , self.target.pos_y , self.target.pos_x , a ) ) return successors def __UpperCamelCase ( self : Union[str, Any] , a : Node | None ) -> Path: """simple docstring""" SCREAMING_SNAKE_CASE : Tuple = node SCREAMING_SNAKE_CASE : List[Any] = [] while current_node is not None: path.append((current_node.pos_y, current_node.pos_x) ) SCREAMING_SNAKE_CASE : Dict = current_node.parent path.reverse() return path class _UpperCamelCase : '''simple docstring''' def __init__( self : str , a : Optional[int] , a : Optional[Any] ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Optional[int] = BreadthFirstSearch(a , a ) SCREAMING_SNAKE_CASE : Any = BreadthFirstSearch(a , a ) SCREAMING_SNAKE_CASE : Dict = False def __UpperCamelCase ( self : List[Any] ) -> Path | None: """simple docstring""" while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue: SCREAMING_SNAKE_CASE : Optional[Any] = self.fwd_bfs.node_queue.pop(0 ) SCREAMING_SNAKE_CASE : Any = self.bwd_bfs.node_queue.pop(0 ) if current_bwd_node.pos == current_fwd_node.pos: SCREAMING_SNAKE_CASE : int = True return self.retrace_bidirectional_path( a , a ) SCREAMING_SNAKE_CASE : Union[str, Any] = current_bwd_node SCREAMING_SNAKE_CASE : str = current_fwd_node SCREAMING_SNAKE_CASE : Union[str, Any] = { self.fwd_bfs: self.fwd_bfs.get_successors(a ), self.bwd_bfs: self.bwd_bfs.get_successors(a ), } for bfs in [self.fwd_bfs, self.bwd_bfs]: for node in successors[bfs]: bfs.node_queue.append(a ) if not self.reached: return [self.fwd_bfs.start.pos] return None def __UpperCamelCase ( self : int , a : Node , a : Node ) -> Path: """simple docstring""" SCREAMING_SNAKE_CASE : Tuple = self.fwd_bfs.retrace_path(a ) SCREAMING_SNAKE_CASE : str = self.bwd_bfs.retrace_path(a ) bwd_path.pop() bwd_path.reverse() SCREAMING_SNAKE_CASE : Any = fwd_path + bwd_path return path if __name__ == "__main__": # all coordinates are given in format [y,x] import doctest doctest.testmod() a_ = (0, 0) a_ = (len(grid) - 1, len(grid[0]) - 1) for elem in grid: print(elem) a_ = time.time() a_ = BreadthFirstSearch(init, goal) a_ = bfs.search() a_ = time.time() - start_bfs_time print('Unidirectional BFS computation time : ', bfs_time) a_ = time.time() a_ = BidirectionalBreadthFirstSearch(init, goal) a_ = bd_bfs.search() a_ = time.time() - start_bd_bfs_time print('Bidirectional BFS computation time : ', bd_bfs_time)
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"""simple docstring""" import inspect import jax import jax.lax as lax import jax.numpy as jnp from ..utils import add_start_docstrings from ..utils.logging import get_logger A : Dict = get_logger(__name__) A : Dict = R"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input sequence tokens in the vocabulary.\n\n Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n [`PreTrainedTokenizer.__call__`] for details.\n\n [What are input IDs?](../glossary#input-ids)\n scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n search or log softmax for each vocabulary token when using beam search\n kwargs (`Dict[str, Any]`, *optional*):\n Additional logits processor specific kwargs.\n\n Return:\n `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n" class _UpperCamelCase : '''simple docstring''' @add_start_docstrings(__a ) def __call__( self , __a , __a ): raise NotImplementedError( f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." ) class _UpperCamelCase : '''simple docstring''' @add_start_docstrings(__a ) def __call__( self , __a , __a ): raise NotImplementedError( f"{self.__class__} is an abstract class. Only classes inheriting this class can be called." ) class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' @add_start_docstrings(__a ) def __call__( self , __a , __a , __a , **__a ): for processor in self: __lowerCAmelCase = inspect.signature(processor.__call__ ).parameters if len(__a ) > 3: if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ): raise ValueError( f"Make sure that all the required parameters: {list(function_args.keys() )} for " f"{processor.__class__} are passed to the logits processor." ) __lowerCAmelCase = processor(__a , __a , __a , **__a ) else: __lowerCAmelCase = processor(__a , __a , __a ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a ): if not isinstance(__a , __a ) or not (temperature > 0): raise ValueError(f"`temperature` has to be a strictly positive float, but is {temperature}" ) __lowerCAmelCase = temperature def __call__( self , __a , __a , __a ): __lowerCAmelCase = scores / self.temperature return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a = -float("Inf" ) , __a = 1 ): if not isinstance(__a , __a ) or (top_p < 0 or top_p > 1.0): raise ValueError(f"`top_p` has to be a float > 0 and < 1, but is {top_p}" ) if not isinstance(__a , __a ) or (min_tokens_to_keep < 1): raise ValueError(f"`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}" ) __lowerCAmelCase = top_p __lowerCAmelCase = filter_value __lowerCAmelCase = min_tokens_to_keep def __call__( self , __a , __a , __a ): __lowerCAmelCase , __lowerCAmelCase = lax.top_k(__a , scores.shape[-1] ) __lowerCAmelCase = jnp.full_like(__a , self.filter_value ) __lowerCAmelCase = jax.nn.softmax(__a , axis=-1 ).cumsum(axis=-1 ) __lowerCAmelCase = cumulative_probs < self.top_p # include the token that is higher than top_p as well __lowerCAmelCase = jnp.roll(__a , 1 ) score_mask |= score_mask.at[:, 0].set(__a ) # min tokens to keep __lowerCAmelCase = score_mask.at[:, : self.min_tokens_to_keep].set(__a ) __lowerCAmelCase = jnp.where(__a , __a , __a ) __lowerCAmelCase = jax.lax.sort_key_val(__a , __a )[-1] return next_scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a = -float("Inf" ) , __a = 1 ): if not isinstance(__a , __a ) or top_k <= 0: raise ValueError(f"`top_k` has to be a strictly positive integer, but is {top_k}" ) __lowerCAmelCase = max(__a , __a ) __lowerCAmelCase = filter_value def __call__( self , __a , __a , __a ): __lowerCAmelCase , __lowerCAmelCase = scores.shape __lowerCAmelCase = jnp.full(batch_size * vocab_size , self.filter_value ) __lowerCAmelCase = min(self.top_k , scores.shape[-1] ) # Safety check __lowerCAmelCase , __lowerCAmelCase = lax.top_k(__a , __a ) __lowerCAmelCase = jnp.broadcast_to((jnp.arange(__a ) * vocab_size)[:, None] , (batch_size, topk) ).flatten() __lowerCAmelCase = topk_scores.flatten() __lowerCAmelCase = topk_indices.flatten() + shift __lowerCAmelCase = next_scores_flat.at[topk_indices_flat].set(__a ) __lowerCAmelCase = next_scores_flat.reshape(__a , __a ) return next_scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a ): __lowerCAmelCase = bos_token_id def __call__( self , __a , __a , __a ): __lowerCAmelCase = jnp.full(scores.shape , -float("inf" ) ) __lowerCAmelCase = 1 - jnp.bool_(cur_len - 1 ) __lowerCAmelCase = jnp.where(__a , new_scores.at[:, self.bos_token_id].set(0 ) , __a ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a ): __lowerCAmelCase = max_length __lowerCAmelCase = eos_token_id def __call__( self , __a , __a , __a ): __lowerCAmelCase = jnp.full(scores.shape , -float("inf" ) ) __lowerCAmelCase = 1 - jnp.bool_(cur_len - self.max_length + 1 ) __lowerCAmelCase = jnp.where(__a , new_scores.at[:, self.eos_token_id].set(0 ) , __a ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a ): if not isinstance(__a , __a ) or min_length < 0: raise ValueError(f"`min_length` has to be a positive integer, but is {min_length}" ) if not isinstance(__a , __a ) or eos_token_id < 0: raise ValueError(f"`eos_token_id` has to be a positive integer, but is {eos_token_id}" ) __lowerCAmelCase = min_length __lowerCAmelCase = eos_token_id def __call__( self , __a , __a , __a ): # create boolean flag to decide if min length penalty should be applied __lowerCAmelCase = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 ) __lowerCAmelCase = jnp.where(__a , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , __a ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a ): __lowerCAmelCase = list(__a ) __lowerCAmelCase = begin_index def __call__( self , __a , __a , __a ): __lowerCAmelCase = 1 - jnp.bool_(cur_len - self.begin_index ) __lowerCAmelCase = jnp.where(__a , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , __a ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a ): __lowerCAmelCase = list(__a ) def __call__( self , __a , __a , __a ): __lowerCAmelCase = scores.at[..., self.suppress_tokens].set(-float("inf" ) ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a ): __lowerCAmelCase = dict(__a ) # Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the # index of the array corresponds to the index of the token to be forced, for XLA compatibility. # Indexes without forced tokens will have a negative value. __lowerCAmelCase = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1 for index, token in force_token_map.items(): if token is not None: __lowerCAmelCase = force_token_array.at[index].set(__a ) __lowerCAmelCase = jnp.intaa(__a ) def __call__( self , __a , __a , __a ): def _force_token(__a ): __lowerCAmelCase = scores.shape[0] __lowerCAmelCase = self.force_token_array[generation_idx] __lowerCAmelCase = jnp.ones_like(__a , dtype=scores.dtype ) * -float("inf" ) __lowerCAmelCase = jnp.zeros((batch_size, 1) , dtype=scores.dtype ) __lowerCAmelCase = lax.dynamic_update_slice(__a , __a , (0, current_token) ) return new_scores __lowerCAmelCase = lax.cond( cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond( self.force_token_array[cur_len] >= 0 , lambda: _force_token(__a ) , lambda: scores , ) , ) return scores class _UpperCamelCase ( lowerCAmelCase__ ): '''simple docstring''' def __init__( self , __a , __a , __a ): __lowerCAmelCase = generate_config.eos_token_id __lowerCAmelCase = generate_config.no_timestamps_token_id __lowerCAmelCase = generate_config.no_timestamps_token_id + 1 __lowerCAmelCase = decoder_input_length + 1 if generate_config.is_multilingual: # room for language token and task token self.begin_index += 2 if hasattr(__a , "max_initial_timestamp_index" ): __lowerCAmelCase = generate_config.max_initial_timestamp_index else: __lowerCAmelCase = model_config.vocab_size if self.max_initial_timestamp_index is None: __lowerCAmelCase = model_config.vocab_size def __call__( self , __a , __a , __a ): # suppress <|notimestamps|> which is handled by without_timestamps __lowerCAmelCase = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) ) def handle_pairs(__a , __a ): __lowerCAmelCase = jnp.where((cur_len - self.begin_index) >= 1 , __a , __a ) __lowerCAmelCase = jnp.where( input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , __a , ) __lowerCAmelCase = jnp.where((cur_len - self.begin_index) < 2 , __a , __a ) __lowerCAmelCase = jnp.where( input_ids_k[cur_len - 2] >= self.timestamp_begin , __a , __a , ) return jnp.where( __a , jnp.where( penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , __a , ) __lowerCAmelCase = jax.vmap(__a )(__a , __a ) __lowerCAmelCase = jnp.where(cur_len == self.begin_index , __a , __a ) __lowerCAmelCase = jnp.where( self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , __a , ) __lowerCAmelCase = self.timestamp_begin + self.max_initial_timestamp_index __lowerCAmelCase = jnp.where( __a , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , __a , ) # if sum of probability over timestamps is above any other token, sample timestamp __lowerCAmelCase = jax.nn.log_softmax(__a , axis=-1 ) def handle_cumulative_probs(__a , __a ): __lowerCAmelCase = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 ) __lowerCAmelCase = jnp.max(logprobs_k[: self.timestamp_begin] ) return jnp.where( timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , __a , ) __lowerCAmelCase = jax.vmap(__a )(__a , __a ) return scores
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from collections.abc import Callable import numpy as np def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> np.ndarray: lowerCamelCase : List[Any] =int(np.ceil((x_end - xa) / step_size ) ) lowerCamelCase : Tuple =np.zeros((n + 1,) ) lowerCamelCase : Dict =ya lowerCamelCase : Any =xa for k in range(SCREAMING_SNAKE_CASE_ ): lowerCamelCase : List[str] =y[k] + step_size * ode_func(SCREAMING_SNAKE_CASE_ , y[k] ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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import re import string from collections import Counter import sacrebleu import sacremoses from packaging import version import datasets snake_case_ = ''' @inproceedings{xu-etal-2016-optimizing, title = {Optimizing Statistical Machine Translation for Text Simplification}, authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris}, journal = {Transactions of the Association for Computational Linguistics}, volume = {4}, year={2016}, url = {https://www.aclweb.org/anthology/Q16-1029}, pages = {401--415 }, @inproceedings{post-2018-call, title = "A Call for Clarity in Reporting {BLEU} Scores", author = "Post, Matt", booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers", month = oct, year = "2018", address = "Belgium, Brussels", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W18-6319", pages = "186--191", } ''' snake_case_ = '''\ WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU It can be used to evaluate the quality of machine-generated texts. ''' snake_case_ = ''' Calculates sari score (between 0 and 100) given a list of source and predicted sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score. Args: sources: list of source sentences where each sentence should be a string. predictions: list of predicted sentences where each sentence should be a string. references: list of lists of reference sentences where each sentence should be a string. Returns: sari: sari score sacrebleu: sacrebleu score exact: exact score Examples: >>> sources=["About 95 species are currently accepted ."] >>> predictions=["About 95 you now get in ."] >>> references=[["About 95 species are currently known ."]] >>> wiki_split = datasets.load_metric("wiki_split") >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references) >>> print(results) {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0} ''' def A__ ( SCREAMING_SNAKE_CASE_ ) -> Tuple: def remove_articles(SCREAMING_SNAKE_CASE_ ): lowerCamelCase : Tuple =re.compile(R'''\b(a|an|the)\b''' , re.UNICODE ) return re.sub(SCREAMING_SNAKE_CASE_ , ''' ''' , SCREAMING_SNAKE_CASE_ ) def white_space_fix(SCREAMING_SNAKE_CASE_ ): return " ".join(text.split() ) def remove_punc(SCREAMING_SNAKE_CASE_ ): lowerCamelCase : int =set(string.punctuation ) return "".join(ch for ch in text if ch not in exclude ) def lower(SCREAMING_SNAKE_CASE_ ): return text.lower() return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_ ) ) ) ) def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]: return int(normalize_answer(SCREAMING_SNAKE_CASE_ ) == normalize_answer(SCREAMING_SNAKE_CASE_ ) ) def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]: lowerCamelCase : Union[str, Any] =[any(compute_exact(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for ref in refs ) for pred, refs in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )] return (sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING_SNAKE_CASE_ )) * 1_0_0 def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str: lowerCamelCase : Any =[rgram for rgrams in rgramslist for rgram in rgrams] lowerCamelCase : int =Counter(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : Dict =Counter(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : Any =Counter() for sgram, scount in sgramcounter.items(): lowerCamelCase : Tuple =scount * numref lowerCamelCase : Optional[int] =Counter(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : Tuple =Counter() for cgram, ccount in cgramcounter.items(): lowerCamelCase : Tuple =ccount * numref # KEEP lowerCamelCase : str =sgramcounter_rep & cgramcounter_rep lowerCamelCase : Union[str, Any] =keepgramcounter_rep & rgramcounter lowerCamelCase : Optional[Any] =sgramcounter_rep & rgramcounter lowerCamelCase : Optional[Any] =0 lowerCamelCase : List[Any] =0 for keepgram in keepgramcountergood_rep: keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram] # Fix an alleged bug [2] in the keep score computation. # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram] keeptmpscorea += keepgramcountergood_rep[keepgram] # Define 0/0=1 instead of 0 to give higher scores for predictions that match # a target exactly. lowerCamelCase : Tuple =1 lowerCamelCase : int =1 if len(SCREAMING_SNAKE_CASE_ ) > 0: lowerCamelCase : Tuple =keeptmpscorea / len(SCREAMING_SNAKE_CASE_ ) if len(SCREAMING_SNAKE_CASE_ ) > 0: # Fix an alleged bug [2] in the keep score computation. # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep) lowerCamelCase : Any =keeptmpscorea / sum(keepgramcounterall_rep.values() ) lowerCamelCase : Optional[Any] =0 if keepscore_precision > 0 or keepscore_recall > 0: lowerCamelCase : Optional[int] =2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall) # DELETION lowerCamelCase : int =sgramcounter_rep - cgramcounter_rep lowerCamelCase : Dict =delgramcounter_rep - rgramcounter lowerCamelCase : Dict =sgramcounter_rep - rgramcounter lowerCamelCase : Optional[int] =0 lowerCamelCase : List[Any] =0 for delgram in delgramcountergood_rep: deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram] deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram] # Define 0/0=1 instead of 0 to give higher scores for predictions that match # a target exactly. lowerCamelCase : str =1 if len(SCREAMING_SNAKE_CASE_ ) > 0: lowerCamelCase : Optional[int] =deltmpscorea / len(SCREAMING_SNAKE_CASE_ ) # ADDITION lowerCamelCase : List[Any] =set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : int =set(SCREAMING_SNAKE_CASE_ ) & set(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : Optional[Any] =set(SCREAMING_SNAKE_CASE_ ) - set(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : int =0 for addgram in addgramcountergood: addtmpscore += 1 # Define 0/0=1 instead of 0 to give higher scores for predictions that match # a target exactly. lowerCamelCase : int =1 lowerCamelCase : List[Any] =1 if len(SCREAMING_SNAKE_CASE_ ) > 0: lowerCamelCase : str =addtmpscore / len(SCREAMING_SNAKE_CASE_ ) if len(SCREAMING_SNAKE_CASE_ ) > 0: lowerCamelCase : List[str] =addtmpscore / len(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : Optional[Any] =0 if addscore_precision > 0 or addscore_recall > 0: lowerCamelCase : Optional[Any] =2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall) return (keepscore, delscore_precision, addscore) def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict: lowerCamelCase : Optional[int] =len(SCREAMING_SNAKE_CASE_ ) lowerCamelCase : Dict =ssent.split(''' ''' ) lowerCamelCase : Any =csent.split(''' ''' ) lowerCamelCase : str =[] lowerCamelCase : Optional[Any] =[] lowerCamelCase : List[Any] =[] lowerCamelCase : List[str] =[] lowerCamelCase : Tuple =[] lowerCamelCase : Optional[Any] =[] lowerCamelCase : int =[] lowerCamelCase : List[str] =[] lowerCamelCase : Dict =[] lowerCamelCase : Any =[] for rsent in rsents: lowerCamelCase : Any =rsent.split(''' ''' ) lowerCamelCase : int =[] lowerCamelCase : Optional[Any] =[] lowerCamelCase : List[Any] =[] ragramslist.append(SCREAMING_SNAKE_CASE_ ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ): if i < len(SCREAMING_SNAKE_CASE_ ) - 1: lowerCamelCase : Optional[int] =ragrams[i] + ''' ''' + ragrams[i + 1] ragrams.append(SCREAMING_SNAKE_CASE_ ) if i < len(SCREAMING_SNAKE_CASE_ ) - 2: lowerCamelCase : str =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] ragrams.append(SCREAMING_SNAKE_CASE_ ) if i < len(SCREAMING_SNAKE_CASE_ ) - 3: lowerCamelCase : int =ragrams[i] + ''' ''' + ragrams[i + 1] + ''' ''' + ragrams[i + 2] + ''' ''' + ragrams[i + 3] ragrams.append(SCREAMING_SNAKE_CASE_ ) ragramslist.append(SCREAMING_SNAKE_CASE_ ) ragramslist.append(SCREAMING_SNAKE_CASE_ ) ragramslist.append(SCREAMING_SNAKE_CASE_ ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ): if i < len(SCREAMING_SNAKE_CASE_ ) - 1: lowerCamelCase : Optional[int] =sagrams[i] + ''' ''' + sagrams[i + 1] sagrams.append(SCREAMING_SNAKE_CASE_ ) if i < len(SCREAMING_SNAKE_CASE_ ) - 2: lowerCamelCase : List[Any] =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] sagrams.append(SCREAMING_SNAKE_CASE_ ) if i < len(SCREAMING_SNAKE_CASE_ ) - 3: lowerCamelCase : Optional[int] =sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3] sagrams.append(SCREAMING_SNAKE_CASE_ ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ): if i < len(SCREAMING_SNAKE_CASE_ ) - 1: lowerCamelCase : Optional[int] =cagrams[i] + ''' ''' + cagrams[i + 1] cagrams.append(SCREAMING_SNAKE_CASE_ ) if i < len(SCREAMING_SNAKE_CASE_ ) - 2: lowerCamelCase : List[str] =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] cagrams.append(SCREAMING_SNAKE_CASE_ ) if i < len(SCREAMING_SNAKE_CASE_ ) - 3: lowerCamelCase : str =cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3] cagrams.append(SCREAMING_SNAKE_CASE_ ) ((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : Any =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : Optional[Any] =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : List[Any] =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ((lowerCamelCase) , (lowerCamelCase) , (lowerCamelCase)) : List[str] =SARIngram(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) lowerCamelCase : List[Any] =sum([keepascore, keepascore, keepascore, keepascore] ) / 4 lowerCamelCase : List[str] =sum([delascore, delascore, delascore, delascore] ) / 4 lowerCamelCase : int =sum([addascore, addascore, addascore, addascore] ) / 4 lowerCamelCase : Any =(avgkeepscore + avgdelscore + avgaddscore) / 3 return finalscore def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = "13a" , SCREAMING_SNAKE_CASE_ = True ) -> Any: # Normalization is requried for the ASSET dataset (one of the primary # datasets in sentence simplification) to allow using space # to split the sentence. Even though Wiki-Auto and TURK datasets, # do not require normalization, we do it for consistency. # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset. # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7 if lowercase: lowerCamelCase : Union[str, Any] =sentence.lower() if tokenizer in ["13a", "intl"]: if version.parse(sacrebleu.__version__ ).major >= 2: lowerCamelCase : List[Any] =sacrebleu.metrics.bleu._get_tokenizer(SCREAMING_SNAKE_CASE_ )()(SCREAMING_SNAKE_CASE_ ) else: lowerCamelCase : Any =sacrebleu.TOKENIZERS[tokenizer]()(SCREAMING_SNAKE_CASE_ ) elif tokenizer == "moses": lowerCamelCase : int =sacremoses.MosesTokenizer().tokenize(SCREAMING_SNAKE_CASE_ , return_str=SCREAMING_SNAKE_CASE_ , escape=SCREAMING_SNAKE_CASE_ ) elif tokenizer == "penn": lowerCamelCase : Any =sacremoses.MosesTokenizer().penn_tokenize(SCREAMING_SNAKE_CASE_ , return_str=SCREAMING_SNAKE_CASE_ ) else: lowerCamelCase : Optional[int] =sentence if not return_str: lowerCamelCase : Union[str, Any] =normalized_sent.split() return normalized_sent def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str: if not (len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ ) == len(SCREAMING_SNAKE_CASE_ )): raise ValueError('''Sources length must match predictions and references lengths.''' ) lowerCamelCase : Dict =0 for src, pred, refs in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): sari_score += SARIsent(normalize(SCREAMING_SNAKE_CASE_ ) , normalize(SCREAMING_SNAKE_CASE_ ) , [normalize(SCREAMING_SNAKE_CASE_ ) for sent in refs] ) lowerCamelCase : str =sari_score / len(SCREAMING_SNAKE_CASE_ ) return 1_0_0 * sari_score def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="exp" , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , ) -> Dict: lowerCamelCase : Optional[int] =len(references[0] ) if any(len(SCREAMING_SNAKE_CASE_ ) != references_per_prediction for refs in references ): raise ValueError('''Sacrebleu requires the same number of references for each prediction''' ) lowerCamelCase : Optional[int] =[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_ )] lowerCamelCase : Union[str, Any] =sacrebleu.corpus_bleu( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , smooth_method=SCREAMING_SNAKE_CASE_ , smooth_value=SCREAMING_SNAKE_CASE_ , force=SCREAMING_SNAKE_CASE_ , lowercase=SCREAMING_SNAKE_CASE_ , use_effective_order=SCREAMING_SNAKE_CASE_ , ) return output.score @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION) class snake_case_ ( datasets.Metric): def __lowercase ( self ) -> List[str]: return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { '''predictions''': datasets.Value('''string''' , id='''sequence''' ), '''references''': datasets.Sequence(datasets.Value('''string''' , id='''sequence''' ) , id='''references''' ), } ) , codebase_urls=[ '''https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py''', '''https://github.com/cocoxu/simplification/blob/master/SARI.py''', '''https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py''', '''https://github.com/mjpost/sacreBLEU''', ] , reference_urls=[ '''https://www.aclweb.org/anthology/Q16-1029.pdf''', '''https://github.com/mjpost/sacreBLEU''', '''https://en.wikipedia.org/wiki/BLEU''', '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''', ] , ) def __lowercase ( self , __lowercase , __lowercase , __lowercase ) -> Tuple: lowerCamelCase : str ={} result.update({'''sari''': compute_sari(sources=__lowercase , predictions=__lowercase , references=__lowercase )} ) result.update({'''sacrebleu''': compute_sacrebleu(predictions=__lowercase , references=__lowercase )} ) result.update({'''exact''': compute_em(predictions=__lowercase , references=__lowercase )} ) return result
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"""simple docstring""" from typing import Any def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,): """simple docstring""" _validation( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,) # Creates data structures and fill initial step _UpperCAmelCase = {} _UpperCAmelCase = {} for state in states_space: _UpperCAmelCase = observations_space[0] _UpperCAmelCase = ( initial_probabilities[state] * emission_probabilities[state][observation] ) _UpperCAmelCase = None # Fills the data structure with the probabilities of # different transitions and pointers to previous states for o in range(1 ,len(lowercase ) ): _UpperCAmelCase = observations_space[o] _UpperCAmelCase = observations_space[o - 1] for state in states_space: # Calculates the argmax for probability function _UpperCAmelCase = """""" _UpperCAmelCase = -1 for k_state in states_space: _UpperCAmelCase = ( probabilities[(k_state, prior_observation)] * transition_probabilities[k_state][state] * emission_probabilities[state][observation] ) if probability > max_probability: _UpperCAmelCase = probability _UpperCAmelCase = k_state # Update probabilities and pointers dicts _UpperCAmelCase = ( probabilities[(arg_max, prior_observation)] * transition_probabilities[arg_max][state] * emission_probabilities[state][observation] ) _UpperCAmelCase = arg_max # The final observation _UpperCAmelCase = observations_space[len(lowercase ) - 1] # argmax for given final observation _UpperCAmelCase = """""" _UpperCAmelCase = -1 for k_state in states_space: _UpperCAmelCase = probabilities[(k_state, final_observation)] if probability > max_probability: _UpperCAmelCase = probability _UpperCAmelCase = k_state _UpperCAmelCase = arg_max # Process pointers backwards _UpperCAmelCase = last_state _UpperCAmelCase = [] for o in range(len(lowercase ) - 1 ,-1 ,-1 ): result.append(lowercase ) _UpperCAmelCase = pointers[previous, observations_space[o]] result.reverse() return result def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,): """simple docstring""" _validate_not_empty( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,) _validate_lists(lowercase ,lowercase ) _validate_dicts( lowercase ,lowercase ,lowercase ) def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,): """simple docstring""" if not all( [ observations_space, states_space, initial_probabilities, transition_probabilities, emission_probabilities, ] ): raise ValueError("""There's an empty parameter""" ) def __UpperCAmelCase ( lowercase ,lowercase ): """simple docstring""" _validate_list(lowercase ,"""observations_space""" ) _validate_list(lowercase ,"""states_space""" ) def __UpperCAmelCase ( lowercase ,lowercase ): """simple docstring""" if not isinstance(_object ,lowercase ): _UpperCAmelCase = f'''{var_name} must be a list''' raise ValueError(lowercase ) else: for x in _object: if not isinstance(lowercase ,lowercase ): _UpperCAmelCase = f'''{var_name} must be a list of strings''' raise ValueError(lowercase ) def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,): """simple docstring""" _validate_dict(lowercase ,"""initial_probabilities""" ,lowercase ) _validate_nested_dict(lowercase ,"""transition_probabilities""" ) _validate_nested_dict(lowercase ,"""emission_probabilities""" ) def __UpperCAmelCase ( lowercase ,lowercase ): """simple docstring""" _validate_dict(_object ,lowercase ,lowercase ) for x in _object.values(): _validate_dict(lowercase ,lowercase ,lowercase ,lowercase ) def __UpperCAmelCase ( lowercase ,lowercase ,lowercase ,lowercase = False ): """simple docstring""" if not isinstance(_object ,lowercase ): _UpperCAmelCase = f'''{var_name} must be a dict''' raise ValueError(lowercase ) if not all(isinstance(lowercase ,lowercase ) for x in _object ): _UpperCAmelCase = f'''{var_name} all keys must be strings''' raise ValueError(lowercase ) if not all(isinstance(lowercase ,lowercase ) for x in _object.values() ): _UpperCAmelCase = """nested dictionary """ if nested else """""" _UpperCAmelCase = f'''{var_name} {nested_text}all values must be {value_type.__name__}''' raise ValueError(lowercase ) if __name__ == "__main__": from doctest import testmod testmod()
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"""simple docstring""" from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL import torch from transformers import CLIPImageProcessor, CLIPVisionModel from ...models import PriorTransformer from ...pipelines import DiffusionPipeline from ...schedulers import HeunDiscreteScheduler from ...utils import ( BaseOutput, is_accelerate_available, logging, randn_tensor, replace_example_docstring, ) from .renderer import ShapERenderer UpperCAmelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name UpperCAmelCase__ = """ Examples: ```py >>> from PIL import Image >>> import torch >>> from diffusers import DiffusionPipeline >>> from diffusers.utils import export_to_gif, load_image >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\") >>> repo = \"openai/shap-e-img2img\" >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16) >>> pipe = pipe.to(device) >>> guidance_scale = 3.0 >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\" >>> image = load_image(image_url).convert(\"RGB\") >>> images = pipe( ... image, ... guidance_scale=guidance_scale, ... num_inference_steps=64, ... frame_size=256, ... ).images >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\") ``` """ @dataclass class a ( lowerCAmelCase_ ): _snake_case : Union[PIL.Image.Image, np.ndarray] class a ( lowerCAmelCase_ ): def __init__( self : Dict , __lowerCAmelCase : PriorTransformer , __lowerCAmelCase : CLIPVisionModel , __lowerCAmelCase : CLIPImageProcessor , __lowerCAmelCase : HeunDiscreteScheduler , __lowerCAmelCase : ShapERenderer , ): super().__init__() self.register_modules( prior=__lowerCAmelCase , image_encoder=__lowerCAmelCase , image_processor=__lowerCAmelCase , scheduler=__lowerCAmelCase , renderer=__lowerCAmelCase , ) def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] ): if latents is None: _UpperCAmelCase = randn_tensor(__lowerCAmelCase , generator=__lowerCAmelCase , device=__lowerCAmelCase , dtype=__lowerCAmelCase ) else: if latents.shape != shape: raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' ) _UpperCAmelCase = latents.to(__lowerCAmelCase ) _UpperCAmelCase = latents * scheduler.init_noise_sigma return latents def lowerCAmelCase_ ( self : str , __lowerCAmelCase : List[str]=0 ): if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError("""Please install accelerate via `pip install accelerate`""" ) _UpperCAmelCase = torch.device(f'''cuda:{gpu_id}''' ) _UpperCAmelCase = [self.image_encoder, self.prior] for cpu_offloaded_model in models: if cpu_offloaded_model is not None: cpu_offload(__lowerCAmelCase , __lowerCAmelCase ) @property def lowerCAmelCase_ ( self : List[str] ): if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ): return self.device for module in self.image_encoder.modules(): if ( hasattr(__lowerCAmelCase , """_hf_hook""" ) and hasattr(module._hf_hook , """execution_device""" ) and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device ) return self.device def lowerCAmelCase_ ( self : int , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : Any , ): if isinstance(__lowerCAmelCase , __lowerCAmelCase ) and isinstance(image[0] , torch.Tensor ): _UpperCAmelCase = torch.cat(__lowerCAmelCase , axis=0 ) if image[0].ndim == 4 else torch.stack(__lowerCAmelCase , axis=0 ) if not isinstance(__lowerCAmelCase , torch.Tensor ): _UpperCAmelCase = self.image_processor(__lowerCAmelCase , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 ) _UpperCAmelCase = image.to(dtype=self.image_encoder.dtype , device=__lowerCAmelCase ) _UpperCAmelCase = self.image_encoder(__lowerCAmelCase )["""last_hidden_state"""] _UpperCAmelCase = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256 _UpperCAmelCase = image_embeds.repeat_interleave(__lowerCAmelCase , dim=0 ) if do_classifier_free_guidance: _UpperCAmelCase = torch.zeros_like(__lowerCAmelCase ) # 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([negative_image_embeds, image_embeds] ) return image_embeds @torch.no_grad() @replace_example_docstring(__lowerCAmelCase ) def __call__( self : Union[str, Any] , __lowerCAmelCase : Union[PIL.Image.Image, List[PIL.Image.Image]] , __lowerCAmelCase : int = 1 , __lowerCAmelCase : int = 25 , __lowerCAmelCase : Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCAmelCase : Optional[torch.FloatTensor] = None , __lowerCAmelCase : float = 4.0 , __lowerCAmelCase : int = 64 , __lowerCAmelCase : Optional[str] = "pil" , __lowerCAmelCase : bool = True , ): if isinstance(__lowerCAmelCase , PIL.Image.Image ): _UpperCAmelCase = 1 elif isinstance(__lowerCAmelCase , torch.Tensor ): _UpperCAmelCase = image.shape[0] elif isinstance(__lowerCAmelCase , __lowerCAmelCase ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ): _UpperCAmelCase = len(__lowerCAmelCase ) else: raise ValueError( f'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__lowerCAmelCase )}''' ) _UpperCAmelCase = self._execution_device _UpperCAmelCase = batch_size * num_images_per_prompt _UpperCAmelCase = guidance_scale > 1.0 _UpperCAmelCase = self._encode_image(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) # prior self.scheduler.set_timesteps(__lowerCAmelCase , device=__lowerCAmelCase ) _UpperCAmelCase = self.scheduler.timesteps _UpperCAmelCase = self.prior.config.num_embeddings _UpperCAmelCase = self.prior.config.embedding_dim _UpperCAmelCase = self.prepare_latents( (batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , self.scheduler , ) # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim _UpperCAmelCase = latents.reshape(latents.shape[0] , __lowerCAmelCase , __lowerCAmelCase ) for i, t in enumerate(self.progress_bar(__lowerCAmelCase ) ): # 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(__lowerCAmelCase , __lowerCAmelCase ) _UpperCAmelCase = self.prior( __lowerCAmelCase , timestep=__lowerCAmelCase , proj_embedding=__lowerCAmelCase , ).predicted_image_embedding # remove the variance _UpperCAmelCase , _UpperCAmelCase = noise_pred.split( scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim if do_classifier_free_guidance is not None: _UpperCAmelCase , _UpperCAmelCase = noise_pred.chunk(2 ) _UpperCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond) _UpperCAmelCase = self.scheduler.step( __lowerCAmelCase , timestep=__lowerCAmelCase , sample=__lowerCAmelCase , ).prev_sample if output_type == "latent": return ShapEPipelineOutput(images=__lowerCAmelCase ) _UpperCAmelCase = [] for i, latent in enumerate(__lowerCAmelCase ): print() _UpperCAmelCase = self.renderer.decode( latent[None, :] , __lowerCAmelCase , size=__lowerCAmelCase , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , ) images.append(__lowerCAmelCase ) _UpperCAmelCase = torch.stack(__lowerCAmelCase ) if output_type not in ["np", "pil"]: raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' ) _UpperCAmelCase = images.cpu().numpy() if output_type == "pil": _UpperCAmelCase = [self.numpy_to_pil(__lowerCAmelCase ) for image in images] # Offload last model to CPU if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None: self.final_offload_hook.offload() if not return_dict: return (images,) return ShapEPipelineOutput(images=__lowerCAmelCase )
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from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices lowerCamelCase__ = logging.get_logger(__name__) lowerCamelCase__ = { "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json", } class snake_case__ ( lowercase_ , lowercase_): '''simple docstring''' lowerCamelCase : Optional[Any] = "bit" lowerCamelCase : Optional[int] = ["preactivation", "bottleneck"] lowerCamelCase : str = ["SAME", "VALID"] def __init__( self , a__=3 , a__=64 , a__=[2_56, 5_12, 10_24, 20_48] , a__=[3, 4, 6, 3] , a__="preactivation" , a__="relu" , a__=None , a__=32 , a__=0.0 , a__=False , a__=32 , a__=1 , a__=None , a__=None , **a__ , ) -> List[Any]: '''simple docstring''' super().__init__(**__UpperCamelCase ) if layer_type not in self.layer_types: raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types )}''' ) if global_padding is not None: if global_padding.upper() in self.supported_padding: __snake_case :List[str] = global_padding.upper() else: raise ValueError(F'''Padding strategy {global_padding} not supported''' ) __snake_case :int = num_channels __snake_case :Optional[int] = embedding_size __snake_case :str = hidden_sizes __snake_case :str = depths __snake_case :Any = layer_type __snake_case :str = hidden_act __snake_case :int = global_padding __snake_case :Any = num_groups __snake_case :List[Any] = drop_path_rate __snake_case :Dict = embedding_dynamic_padding __snake_case :str = output_stride __snake_case :List[Any] = width_factor __snake_case :Any = ["""stem"""] + [F'''stage{idx}''' for idx in range(1 , len(__UpperCamelCase ) + 1 )] __snake_case , __snake_case :Tuple = get_aligned_output_features_output_indices( out_features=__UpperCamelCase , out_indices=__UpperCamelCase , stage_names=self.stage_names )
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# Copyright 2023 The HuggingFace Inc. 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 import torch from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor from ..utils import requires_backends from .base import PipelineTool if TYPE_CHECKING: from PIL import Image class snake_case__ ( lowercase_): '''simple docstring''' lowerCamelCase : Tuple = "dandelin/vilt-b32-finetuned-vqa" lowerCamelCase : List[str] = ( "This is a tool that answers a question about an image. It takes an input named `image` which should be the " "image containing the information, as well as a `question` which should be the question in English. It " "returns a text that is the answer to the question." ) lowerCamelCase : Optional[Any] = "image_qa" lowerCamelCase : str = AutoProcessor lowerCamelCase : Union[str, Any] = AutoModelForVisualQuestionAnswering lowerCamelCase : Any = ["image", "text"] lowerCamelCase : Dict = ["text"] def __init__( self , *a__ , **a__ ) -> Any: '''simple docstring''' requires_backends(self , ["""vision"""] ) super().__init__(*a__ , **a__ ) def __lowercase ( self , a__ , a__ ) -> int: '''simple docstring''' return self.pre_processor(a__ , a__ , return_tensors="""pt""" ) def __lowercase ( self , a__ ) -> Union[str, Any]: '''simple docstring''' with torch.no_grad(): return self.model(**a__ ).logits def __lowercase ( self , a__ ) -> Tuple: '''simple docstring''' __snake_case :str = outputs.argmax(-1 ).item() return self.model.config.idalabel[idx]
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import string def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> List[Any]: '''simple docstring''' for key in range(len(string.ascii_uppercase)): __UpperCamelCase : int = "" for symbol in message: if symbol in string.ascii_uppercase: __UpperCamelCase : List[Any] = string.ascii_uppercase.find(_lowercase) __UpperCamelCase : Optional[int] = num - key if num < 0: __UpperCamelCase : Union[str, Any] = num + len(string.ascii_uppercase) __UpperCamelCase : str = translated + string.ascii_uppercase[num] else: __UpperCamelCase : List[Any] = translated + symbol print(F'Decryption using Key #{key}: {translated}') def _SCREAMING_SNAKE_CASE ( ) -> Any: '''simple docstring''' __UpperCamelCase : Union[str, Any] = input("Encrypted message: ") __UpperCamelCase : List[Any] = message.upper() decrypt(_lowercase) if __name__ == "__main__": import doctest doctest.testmod() main()
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"""simple docstring""" # tests directory-specific settings - this file is run automatically # by pytest before any tests are run import sys import warnings from os.path import abspath, dirname, join # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. SCREAMING_SNAKE_CASE_ = abspath(join(dirname(dirname(__file__)), 'src')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='ignore', category=FutureWarning) def __snake_case ( _lowercase ): """simple docstring""" from diffusers.utils.testing_utils import pytest_addoption_shared pytest_addoption_shared(_lowercase ) def __snake_case ( _lowercase ): """simple docstring""" from diffusers.utils.testing_utils import pytest_terminal_summary_main UpperCamelCase = terminalreporter.config.getoption('''--make-reports''' ) if make_reports: pytest_terminal_summary_main(_lowercase ,id=_lowercase )
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import inspect import unittest from transformers import ConvNextConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_backbone_common import BackboneTesterMixin 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 ConvNextBackbone, ConvNextForImageClassification, ConvNextModel from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class UpperCAmelCase_ : """simple docstring""" def __init__( self , _a , _a=1_3 , _a=3_2 , _a=3 , _a=4 , _a=[1_0, 2_0, 3_0, 4_0] , _a=[2, 2, 3, 2] , _a=True , _a=True , _a=3_7 , _a="gelu" , _a=1_0 , _a=0.02 , _a=["stage2", "stage3", "stage4"] , _a=[2, 3, 4] , _a=None , ) -> Union[str, Any]: _a : Optional[int] = parent _a : Optional[int] = batch_size _a : Any = image_size _a : Tuple = num_channels _a : str = num_stages _a : List[str] = hidden_sizes _a : str = depths _a : Dict = is_training _a : Optional[Any] = use_labels _a : List[str] = intermediate_size _a : List[str] = hidden_act _a : List[str] = num_labels _a : Union[str, Any] = initializer_range _a : List[Any] = out_features _a : Optional[Any] = out_indices _a : int = scope def __lowercase ( self ) -> Optional[Any]: _a : Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) _a : Dict = None if self.use_labels: _a : Dict = ids_tensor([self.batch_size] , self.num_labels ) _a : Any = self.get_config() return config, pixel_values, labels def __lowercase ( self ) -> Union[str, Any]: return ConvNextConfig( num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=__a , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , ) def __lowercase ( self , _a , _a , _a ) -> List[Any]: _a : Optional[int] = ConvNextModel(config=__a ) model.to(__a ) model.eval() _a : Any = model(__a ) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , ) def __lowercase ( self , _a , _a , _a ) -> Optional[int]: _a : str = ConvNextForImageClassification(__a ) model.to(__a ) model.eval() _a : Any = model(__a , labels=__a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def __lowercase ( self , _a , _a , _a ) -> List[str]: _a : List[str] = ConvNextBackbone(config=__a ) model.to(__a ) model.eval() _a : int = model(__a ) # verify hidden states self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) ) self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] ) # verify channels self.parent.assertEqual(len(model.channels ) , len(config.out_features ) ) self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] ) # verify backbone works with out_features=None _a : Tuple = None _a : List[str] = ConvNextBackbone(config=__a ) model.to(__a ) model.eval() _a : List[Any] = model(__a ) # verify feature maps self.parent.assertEqual(len(result.feature_maps ) , 1 ) self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] ) # verify channels self.parent.assertEqual(len(model.channels ) , 1 ) self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] ) def __lowercase ( self ) -> Any: _a : List[Any] = self.prepare_config_and_inputs() _a : Tuple = config_and_inputs _a : int = {"""pixel_values""": pixel_values} return config, inputs_dict @require_torch class UpperCAmelCase_ ( __lowercase , __lowercase , unittest.TestCase ): """simple docstring""" UpperCAmelCase__ : int = ( ( ConvNextModel, ConvNextForImageClassification, ConvNextBackbone, ) if is_torch_available() else () ) UpperCAmelCase__ : str = ( {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification} if is_torch_available() else {} ) UpperCAmelCase__ : Union[str, Any] = True UpperCAmelCase__ : Optional[int] = False UpperCAmelCase__ : Tuple = False UpperCAmelCase__ : Union[str, Any] = False UpperCAmelCase__ : Tuple = False def __lowercase ( self ) -> Union[str, Any]: _a : Tuple = ConvNextModelTester(self ) _a : List[Any] = ConfigTester(self , config_class=__a , has_text_modality=__a , hidden_size=3_7 ) def __lowercase ( self ) -> Dict: 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 __lowercase ( self ) -> Optional[Any]: return @unittest.skip(reason='''ConvNext does not use inputs_embeds''' ) def __lowercase ( self ) -> Dict: pass @unittest.skip(reason='''ConvNext does not support input and output embeddings''' ) def __lowercase ( self ) -> Optional[Any]: pass @unittest.skip(reason='''ConvNext does not use feedforward chunking''' ) def __lowercase ( self ) -> List[Any]: pass def __lowercase ( self ) -> int: _a : List[str] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _a : Any = model_class(__a ) _a : str = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic _a : Optional[Any] = [*signature.parameters.keys()] _a : List[str] = ["""pixel_values"""] self.assertListEqual(arg_names[:1] , __a ) def __lowercase ( self ) -> str: _a : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__a ) def __lowercase ( self ) -> int: _a : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*__a ) def __lowercase ( self ) -> Optional[int]: def check_hidden_states_output(_a , _a , _a ): _a : str = model_class(__a ) model.to(__a ) model.eval() with torch.no_grad(): _a : Tuple = model(**self._prepare_for_class(__a , __a ) ) _a : int = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states _a : Optional[int] = self.model_tester.num_stages self.assertEqual(len(__a ) , expected_num_stages + 1 ) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , ) _a : List[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _a : List[Any] = True check_hidden_states_output(__a , __a , __a ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _a : Tuple = True check_hidden_states_output(__a , __a , __a ) def __lowercase ( self ) -> Optional[Any]: _a : List[str] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__a ) @slow def __lowercase ( self ) -> Tuple: for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _a : str = ConvNextModel.from_pretrained(__a ) self.assertIsNotNone(__a ) def __UpperCAmelCase ( ) -> Optional[int]: """simple docstring""" _a : int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class UpperCAmelCase_ ( unittest.TestCase ): """simple docstring""" @cached_property def __lowercase ( self ) -> Union[str, Any]: return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224''' ) if is_vision_available() else None @slow def __lowercase ( self ) -> Dict: _a : Tuple = ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224''' ).to(__a ) _a : Dict = self.default_image_processor _a : Union[str, Any] = prepare_img() _a : Optional[Any] = image_processor(images=__a , return_tensors='''pt''' ).to(__a ) # forward pass with torch.no_grad(): _a : Any = model(**__a ) # verify the logits _a : Union[str, Any] = torch.Size((1, 1_0_0_0) ) self.assertEqual(outputs.logits.shape , __a ) _a : Tuple = torch.tensor([-0.0260, -0.4739, 0.1911] ).to(__a ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , __a , atol=1e-4 ) ) @require_torch class UpperCAmelCase_ ( unittest.TestCase , __lowercase ): """simple docstring""" UpperCAmelCase__ : Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else () UpperCAmelCase__ : Optional[Any] = ConvNextConfig UpperCAmelCase__ : Optional[Any] = False def __lowercase ( self ) -> int: _a : Dict = ConvNextModelTester(self )
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from __future__ import annotations a__ = 10 def __UpperCAmelCase ( __a : list[int] ) -> list[int]: """simple docstring""" _a : Union[str, Any] = 1 _a : str = max(__a ) while placement <= max_digit: # declare and initialize empty buckets _a : list[list] = [[] for _ in range(__a )] # split list_of_ints between the buckets for i in list_of_ints: _a : Optional[Any] = int((i / placement) % RADIX ) buckets[tmp].append(__a ) # put each buckets' contents into list_of_ints _a : int = 0 for b in range(__a ): for i in buckets[b]: _a : Dict = i a += 1 # move to next placement *= RADIX return list_of_ints if __name__ == "__main__": import doctest doctest.testmod()
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