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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available __A = { "configuration_clipseg": [ "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP", "CLIPSegConfig", "CLIPSegTextConfig", "CLIPSegVisionConfig", ], "processing_clipseg": ["CLIPSegProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __A = [ "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST", "CLIPSegModel", "CLIPSegPreTrainedModel", "CLIPSegTextModel", "CLIPSegVisionModel", "CLIPSegForImageSegmentation", ] if TYPE_CHECKING: from .configuration_clipseg import ( CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP, CLIPSegConfig, CLIPSegTextConfig, CLIPSegVisionConfig, ) from .processing_clipseg import CLIPSegProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_clipseg import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegPreTrainedModel, CLIPSegTextModel, CLIPSegVisionModel, ) else: import sys __A = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" from collections.abc import Callable import numpy as np def _snake_case ( UpperCamelCase : Callable , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ): UpperCAmelCase : Any = int(np.ceil((x_end - xa) / step_size ) ) UpperCAmelCase : Optional[Any] = np.zeros((n + 1,) ) UpperCAmelCase : Optional[int] = ya UpperCAmelCase : int = xa for k in range(UpperCamelCase ): UpperCAmelCase : Optional[int] = y[k] + step_size * ode_func(UpperCamelCase , y[k] ) UpperCAmelCase : Optional[int] = y[k] + ( (step_size / 2) * (ode_func(UpperCamelCase , y[k] ) + ode_func(x + step_size , UpperCamelCase )) ) x += step_size return y if __name__ == "__main__": import doctest doctest.testmod()
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from typing import Any class _UpperCamelCase : def __init__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Any ) -> str: """simple docstring""" UpperCamelCase_ = data UpperCamelCase_ = None def __repr__( self: int ) -> str: """simple docstring""" return f'''Node({self.data})''' class _UpperCamelCase : def __init__( self: Optional[Any] ) -> List[str]: """simple docstring""" UpperCamelCase_ = None def __iter__( self: Optional[Any] ) -> Any: """simple docstring""" UpperCamelCase_ = self.head while node: yield node.data UpperCamelCase_ = node.next def __len__( self: List[Any] ) -> int: """simple docstring""" return sum(1 for _ in self ) def __repr__( self: str ) -> str: """simple docstring""" return "->".join([str(_SCREAMING_SNAKE_CASE ) for item in self] ) def __getitem__( self: Any , _SCREAMING_SNAKE_CASE: int ) -> Any: """simple docstring""" if not 0 <= index < len(self ): raise ValueError("list index out of range." ) for i, node in enumerate(self ): if i == index: return node return None def __setitem__( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Any ) -> None: """simple docstring""" if not 0 <= index < len(self ): raise ValueError("list index out of range." ) UpperCamelCase_ = self.head for _ in range(_SCREAMING_SNAKE_CASE ): UpperCamelCase_ = current.next UpperCamelCase_ = data def lowercase ( self: Union[str, Any] , _SCREAMING_SNAKE_CASE: Any ) -> None: """simple docstring""" self.insert_nth(len(self ) , _SCREAMING_SNAKE_CASE ) def lowercase ( self: List[str] , _SCREAMING_SNAKE_CASE: Any ) -> None: """simple docstring""" self.insert_nth(0 , _SCREAMING_SNAKE_CASE ) def lowercase ( self: List[Any] , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: Any ) -> None: """simple docstring""" if not 0 <= index <= len(self ): raise IndexError("list index out of range" ) UpperCamelCase_ = Node(_SCREAMING_SNAKE_CASE ) if self.head is None: UpperCamelCase_ = new_node elif index == 0: UpperCamelCase_ = self.head # link new_node to head UpperCamelCase_ = new_node else: UpperCamelCase_ = self.head for _ in range(index - 1 ): UpperCamelCase_ = temp.next UpperCamelCase_ = temp.next UpperCamelCase_ = new_node def lowercase ( self: Tuple ) -> None: # print every node data """simple docstring""" print(self ) def lowercase ( self: List[str] ) -> Any: """simple docstring""" return self.delete_nth(0 ) def lowercase ( self: List[str] ) -> Any: # delete from tail """simple docstring""" return self.delete_nth(len(self ) - 1 ) def lowercase ( self: Optional[int] , _SCREAMING_SNAKE_CASE: int = 0 ) -> Any: """simple docstring""" if not 0 <= index <= len(self ) - 1: # test if index is valid raise IndexError("List index out of range." ) UpperCamelCase_ = self.head # default first node if index == 0: UpperCamelCase_ = self.head.next else: UpperCamelCase_ = self.head for _ in range(index - 1 ): UpperCamelCase_ = temp.next UpperCamelCase_ = temp.next UpperCamelCase_ = temp.next.next return delete_node.data def lowercase ( self: Optional[Any] ) -> bool: """simple docstring""" return self.head is None def lowercase ( self: List[str] ) -> None: """simple docstring""" UpperCamelCase_ = None UpperCamelCase_ = self.head while current: # Store the current node's next node. UpperCamelCase_ = current.next # Make the current node's next point backwards UpperCamelCase_ = prev # Make the previous node be the current node UpperCamelCase_ = current # Make the current node the next node (to progress iteration) UpperCamelCase_ = next_node # Return prev in order to put the head at the end UpperCamelCase_ = prev def lowerCAmelCase_ ( ) -> None: UpperCamelCase_ = LinkedList() assert linked_list.is_empty() is True assert str(UpperCamelCase_ ) == "" try: linked_list.delete_head() raise AssertionError # This should not happen. except IndexError: assert True # This should happen. try: linked_list.delete_tail() raise AssertionError # This should not happen. except IndexError: assert True # This should happen. for i in range(10 ): assert len(UpperCamelCase_ ) == i linked_list.insert_nth(UpperCamelCase_ , i + 1 ) assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(1 , 11 ) ) linked_list.insert_head(0 ) linked_list.insert_tail(11 ) assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(0 , 12 ) ) assert linked_list.delete_head() == 0 assert linked_list.delete_nth(9 ) == 10 assert linked_list.delete_tail() == 11 assert len(UpperCamelCase_ ) == 9 assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(1 , 10 ) ) assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True for i in range(0 , 9 ): UpperCamelCase_ = -i assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True linked_list.reverse() assert str(UpperCamelCase_ ) == "->".join(str(UpperCamelCase_ ) for i in range(-8 , 1 ) ) def lowerCAmelCase_ ( ) -> None: UpperCamelCase_ = [ -9, 100, Node(77345112 ), "dlrow olleH", 7, 5555, 0, -1_92.5_55_55, "Hello, world!", 77.9, Node(10 ), None, None, 12.20, ] UpperCamelCase_ = LinkedList() for i in test_input: linked_list.insert_tail(UpperCamelCase_ ) # Check if it's empty or not assert linked_list.is_empty() is False assert ( str(UpperCamelCase_ ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->" "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2" ) # Delete the head UpperCamelCase_ = linked_list.delete_head() assert result == -9 assert ( str(UpperCamelCase_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->" "Hello, world!->77.9->Node(10)->None->None->12.2" ) # Delete the tail UpperCamelCase_ = linked_list.delete_tail() assert result == 12.2 assert ( str(UpperCamelCase_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->" "Hello, world!->77.9->Node(10)->None->None" ) # Delete a node in specific location in linked list UpperCamelCase_ = linked_list.delete_nth(10 ) assert result is None assert ( str(UpperCamelCase_ ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->" "Hello, world!->77.9->Node(10)->None" ) # Add a Node instance to its head linked_list.insert_head(Node("Hello again, world!" ) ) assert ( str(UpperCamelCase_ ) == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->" "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None" ) # Add None to its tail linked_list.insert_tail(UpperCamelCase_ ) assert ( str(UpperCamelCase_ ) == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->" "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None" ) # Reverse the linked list linked_list.reverse() assert ( str(UpperCamelCase_ ) == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->" "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)" ) def lowerCAmelCase_ ( ) -> Any: from doctest import testmod testmod() UpperCamelCase_ = LinkedList() linked_list.insert_head(input("Inserting 1st at head " ).strip() ) linked_list.insert_head(input("Inserting 2nd at head " ).strip() ) print("\nPrint list:" ) linked_list.print_list() linked_list.insert_tail(input("\nInserting 1st at tail " ).strip() ) linked_list.insert_tail(input("Inserting 2nd at tail " ).strip() ) print("\nPrint list:" ) linked_list.print_list() print("\nDelete head" ) linked_list.delete_head() print("Delete tail" ) linked_list.delete_tail() print("\nPrint list:" ) linked_list.print_list() print("\nReverse linked list" ) linked_list.reverse() print("\nPrint list:" ) linked_list.print_list() print("\nString representation of linked list:" ) print(UpperCamelCase_ ) print("\nReading/changing Node data using indexing:" ) print(F'''Element at Position 1: {linked_list[1]}''' ) UpperCamelCase_ = input("Enter New Value: " ).strip() print("New list:" ) print(UpperCamelCase_ ) print(F'''length of linked_list is : {len(UpperCamelCase_ )}''' ) if __name__ == "__main__": main()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available _UpperCAmelCase = { 'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'], 'tokenization_tapas': ['TapasTokenizer'], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _UpperCAmelCase = [ 'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST', 'TapasForMaskedLM', 'TapasForQuestionAnswering', 'TapasForSequenceClassification', 'TapasModel', 'TapasPreTrainedModel', 'load_tf_weights_in_tapas', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _UpperCAmelCase = [ 'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFTapasForMaskedLM', 'TFTapasForQuestionAnswering', 'TFTapasForSequenceClassification', 'TFTapasModel', 'TFTapasPreTrainedModel', ] if TYPE_CHECKING: from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig from .tokenization_tapas import TapasTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tapas import ( TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasPreTrainedModel, load_tf_weights_in_tapas, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_tapas import ( TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST, TFTapasForMaskedLM, TFTapasForQuestionAnswering, TFTapasForSequenceClassification, TFTapasModel, TFTapasPreTrainedModel, ) else: import sys _UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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from ...configuration_utils import PretrainedConfig from ...utils import logging __a = logging.get_logger(__name__) class __SCREAMING_SNAKE_CASE ( A__ ): A : Tuple = 'timm_backbone' def __init__( self , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=3 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=None , **SCREAMING_SNAKE_CASE__ , ): super().__init__(**SCREAMING_SNAKE_CASE__ ) lowercase : Dict = backbone lowercase : List[Any] = num_channels lowercase : List[Any] = features_only lowercase : str = use_pretrained_backbone lowercase : str = True lowercase : List[str] = out_indices if out_indices is not None else (-1,)
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def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->Union[str, Any]: """simple docstring""" lowercase : Union[str, Any] = [False] * len(_UpperCamelCase ) lowercase : Optional[int] = [] queue.append(_UpperCamelCase ) lowercase : Union[str, Any] = True while queue: lowercase : List[str] = queue.pop(0 ) for ind in range(len(graph[u] ) ): if visited[ind] is False and graph[u][ind] > 0: queue.append(_UpperCamelCase ) lowercase : Tuple = True lowercase : Optional[Any] = u return visited[t] def __lowercase ( _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ) ->List[str]: """simple docstring""" lowercase : List[str] = [-1] * (len(_UpperCamelCase )) lowercase : int = 0 while bfs(_UpperCamelCase, _UpperCamelCase, _UpperCamelCase, _UpperCamelCase ): lowercase : List[str] = float('''Inf''' ) lowercase : int = sink while s != source: # Find the minimum value in select path lowercase : List[Any] = min(_UpperCamelCase, graph[parent[s]][s] ) lowercase : Union[str, Any] = parent[s] max_flow += path_flow lowercase : Optional[int] = sink while v != source: lowercase : Any = parent[v] graph[u][v] -= path_flow graph[v][u] += path_flow lowercase : Union[str, Any] = parent[v] return max_flow __a = [ [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], ] __a , __a = 0, 5 print(ford_fulkerson(graph, source, sink))
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'''simple docstring''' import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class lowerCAmelCase_ : '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = None lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : Optional[Any] = False lowerCAmelCase_ : int = None lowerCAmelCase_ : Optional[Any] = None lowerCAmelCase_ : Dict = False lowerCAmelCase_ : List[str] = False lowerCAmelCase_ : Tuple = False lowerCAmelCase_ : Dict = True lowerCAmelCase_ : Tuple = None lowerCAmelCase_ : int = 1 lowerCAmelCase_ : Union[str, Any] = None lowerCAmelCase_ : int = False lowerCAmelCase_ : Optional[int] = None lowerCAmelCase_ : Optional[int] = None def SCREAMING_SNAKE_CASE__ ( self : Tuple ): """simple docstring""" return self.__class__(**{k: copy.deepcopy(lowercase_ ) for k, v in self.__dict__.items()} )
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'''simple docstring''' from typing import Dict, List, Optional from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = { 'nielsr/canine-s': 2_0_4_8, } # Unicode defines 1,114,112 total “codepoints” UpperCAmelCase_ = 1_1_1_4_1_1_2 # Below: Constants defining canonical codepoints for special, pseudo-characters. # Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py UpperCAmelCase_ = 0 UpperCAmelCase_ = 0Xe000 UpperCAmelCase_ = 0Xe001 UpperCAmelCase_ = 0Xe002 UpperCAmelCase_ = 0Xe003 UpperCAmelCase_ = 0Xe004 # Maps special codepoints to human-readable names. UpperCAmelCase_ = { # Special symbols are represented using codepoints values that are valid, # but designated as "Private Use", meaning that they will never be assigned # characters by the Unicode Consortium, and are thus safe for use here. # # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly # excluded and should fail with a hard error. CLS: "[CLS]", SEP: "[SEP]", BOS: "[BOS]", MASK: "[MASK]", PAD: "[PAD]", RESERVED: "[RESERVED]", } # Maps special codepoint human-readable names to their codepoint values. UpperCAmelCase_ = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()} class lowerCAmelCase_ ( lowerCamelCase_ ): '''simple docstring''' lowerCAmelCase_ : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self : Tuple , _UpperCAmelCase : Dict=chr(_UpperCAmelCase ) , _UpperCAmelCase : int=chr(_UpperCAmelCase ) , _UpperCAmelCase : List[Any]=chr(_UpperCAmelCase ) , _UpperCAmelCase : Any=chr(_UpperCAmelCase ) , _UpperCAmelCase : Dict=chr(_UpperCAmelCase ) , _UpperCAmelCase : List[Any]=chr(_UpperCAmelCase ) , _UpperCAmelCase : Dict=False , _UpperCAmelCase : Dict=20_48 , **_UpperCAmelCase : Union[str, Any] , ): """simple docstring""" UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else bos_token UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else eos_token UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else sep_token UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else cls_token UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else pad_token # Mask token behave like a normal word, i.e. include the space before it UpperCAmelCase__ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token super().__init__( bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , model_max_length=_UpperCAmelCase , **_UpperCAmelCase , ) # Creates a mapping for looking up the IDs of special symbols. UpperCAmelCase__ = {} for codepoint, name in SPECIAL_CODEPOINTS.items(): UpperCAmelCase__ = codepoint # Creates a mapping for looking up the string forms of special symbol IDs. UpperCAmelCase__ = { codepoint: name for name, codepoint in self._special_codepoints.items() } UpperCAmelCase__ = UNICODE_VOCAB_SIZE UpperCAmelCase__ = len(self._special_codepoints ) @property def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ): """simple docstring""" return self._unicode_vocab_size def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] , _UpperCAmelCase : str ): """simple docstring""" return list(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : str , _UpperCAmelCase : str ): """simple docstring""" try: return ord(_UpperCAmelCase ) except TypeError: raise ValueError(f'''invalid token: \'{token}\'''' ) def SCREAMING_SNAKE_CASE__ ( self : int , _UpperCAmelCase : int ): """simple docstring""" try: if index in SPECIAL_CODEPOINTS: return SPECIAL_CODEPOINTS[index] return chr(_UpperCAmelCase ) except TypeError: raise ValueError(f'''invalid id: {index}''' ) def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , _UpperCAmelCase : List[Any] ): """simple docstring""" return "".join(_UpperCAmelCase ) def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] UpperCAmelCase__ = cls + token_ids_a + sep if token_ids_a is not None: result += token_ids_a + sep return result def SCREAMING_SNAKE_CASE__ ( self : List[Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ): """simple docstring""" if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase ) UpperCAmelCase__ = [1] + ([0] * len(_UpperCAmelCase )) + [1] if token_ids_a is not None: result += ([0] * len(_UpperCAmelCase )) + [1] return result def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ): """simple docstring""" UpperCAmelCase__ = [self.sep_token_id] UpperCAmelCase__ = [self.cls_token_id] UpperCAmelCase__ = len(cls + token_ids_a + sep ) * [0] if token_ids_a is not None: result += len(token_ids_a + sep ) * [1] return result def SCREAMING_SNAKE_CASE__ ( self : Any , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ): """simple docstring""" return ()
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"""simple docstring""" import gc import unittest from parameterized import parameterized from diffusers import FlaxUNetaDConditionModel from diffusers.utils import is_flax_available from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow if is_flax_available(): import jax import jax.numpy as jnp @slow @require_flax class _lowerCamelCase ( unittest.TestCase ): def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : Any , UpperCamelCase : Optional[Any] ) -> int: """simple docstring""" return f"""gaussian_noise_s={seed}_shape={'_'.join([str(UpperCamelCase ) for s in shape] )}.npy""" def _lowerCAmelCase ( self : Tuple ) -> Optional[int]: """simple docstring""" # clean up the VRAM after each test super().tearDown() gc.collect() def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Optional[Any]=0 , UpperCamelCase : List[Any]=(4, 4, 64, 64) , UpperCamelCase : str=False ) -> List[Any]: """simple docstring""" lowerCAmelCase__ : Optional[int] = jnp.bfloataa if fpaa else jnp.floataa lowerCAmelCase__ : Any = jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase , UpperCamelCase ) ) , dtype=UpperCamelCase ) return image def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Tuple=False , UpperCamelCase : str="CompVis/stable-diffusion-v1-4" ) -> int: """simple docstring""" lowerCAmelCase__ : int = jnp.bfloataa if fpaa else jnp.floataa lowerCAmelCase__ : Tuple = """bf16""" if fpaa else None lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = FlaxUNetaDConditionModel.from_pretrained( UpperCamelCase , subfolder="""unet""" , dtype=UpperCamelCase , revision=UpperCamelCase ) return model, params def _lowerCAmelCase ( self : Any , UpperCamelCase : List[Any]=0 , UpperCamelCase : str=(4, 77, 7_68) , UpperCamelCase : List[Any]=False ) -> str: """simple docstring""" lowerCAmelCase__ : Optional[Any] = jnp.bfloataa if fpaa else jnp.floataa lowerCAmelCase__ : Optional[int] = jnp.array(load_hf_numpy(self.get_file_format(UpperCamelCase , UpperCamelCase ) ) , dtype=UpperCamelCase ) return hidden_states @parameterized.expand( [ # fmt: off [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]], [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]], [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]], [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]], # fmt: on ] ) def _lowerCAmelCase ( self : str , UpperCamelCase : int , UpperCamelCase : Optional[Any] , UpperCamelCase : str ) -> str: """simple docstring""" lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4""" , fpaa=UpperCamelCase ) lowerCAmelCase__ : Optional[Any] = self.get_latents(UpperCamelCase , fpaa=UpperCamelCase ) lowerCAmelCase__ : Optional[int] = self.get_encoder_hidden_states(UpperCamelCase , fpaa=UpperCamelCase ) lowerCAmelCase__ : Optional[Any] = model.apply( {"""params""": params} , UpperCamelCase , jnp.array(UpperCamelCase , dtype=jnp.intaa ) , encoder_hidden_states=UpperCamelCase , ).sample assert sample.shape == latents.shape lowerCAmelCase__ : Optional[Any] = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa ) lowerCAmelCase__ : Union[str, Any] = jnp.array(UpperCamelCase , dtype=jnp.floataa ) # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware assert jnp.allclose(UpperCamelCase , UpperCamelCase , atol=1E-2 ) @parameterized.expand( [ # fmt: off [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]], [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]], [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]], [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]], # fmt: on ] ) def _lowerCAmelCase ( self : List[Any] , UpperCamelCase : int , UpperCamelCase : Optional[int] , UpperCamelCase : int ) -> List[str]: """simple docstring""" lowerCAmelCase__ , lowerCAmelCase__ : Optional[Any] = self.get_unet_model(model_id="""stabilityai/stable-diffusion-2""" , fpaa=UpperCamelCase ) lowerCAmelCase__ : List[Any] = self.get_latents(UpperCamelCase , shape=(4, 4, 96, 96) , fpaa=UpperCamelCase ) lowerCAmelCase__ : List[Any] = self.get_encoder_hidden_states(UpperCamelCase , shape=(4, 77, 10_24) , fpaa=UpperCamelCase ) lowerCAmelCase__ : List[str] = model.apply( {"""params""": params} , UpperCamelCase , jnp.array(UpperCamelCase , dtype=jnp.intaa ) , encoder_hidden_states=UpperCamelCase , ).sample assert sample.shape == latents.shape lowerCAmelCase__ : str = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) ) , dtype=jnp.floataa ) lowerCAmelCase__ : Optional[Any] = jnp.array(UpperCamelCase , dtype=jnp.floataa ) # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware assert jnp.allclose(UpperCamelCase , UpperCamelCase , atol=1E-2 )
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"""simple docstring""" import numpy as np from sklearn.datasets import fetch_california_housing from sklearn.metrics import mean_absolute_error, mean_squared_error from sklearn.model_selection import train_test_split from xgboost import XGBRegressor def lowercase_ ( __UpperCAmelCase ) -> tuple: return (data["data"], data["target"]) def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> np.ndarray: lowerCAmelCase__ : List[Any] = XGBRegressor(verbosity=0 , random_state=42 ) xgb.fit(__UpperCAmelCase , __UpperCAmelCase ) # Predict target for test data lowerCAmelCase__ : Dict = xgb.predict(__UpperCAmelCase ) lowerCAmelCase__ : Any = predictions.reshape(len(__UpperCAmelCase ) , 1 ) return predictions def lowercase_ ( ) -> None: lowerCAmelCase__ : Optional[Any] = fetch_california_housing() lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = data_handling(__UpperCAmelCase ) lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = train_test_split( __UpperCAmelCase , __UpperCAmelCase , test_size=0.25 , random_state=1 ) lowerCAmelCase__ : Optional[Any] = xgboost(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) # Error printing print(f"""Mean Absolute Error : {mean_absolute_error(__UpperCAmelCase , __UpperCAmelCase )}""" ) print(f"""Mean Square Error : {mean_squared_error(__UpperCAmelCase , __UpperCAmelCase )}""" ) if __name__ == "__main__": import doctest doctest.testmod(verbose=True) main()
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices __SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__) __SCREAMING_SNAKE_CASE :List[str] = { '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''', } class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ ): _lowerCamelCase : str = """focalnet""" def __init__( self : List[Any] , snake_case_ : str=2_2_4 , snake_case_ : int=4 , snake_case_ : Tuple=3 , snake_case_ : Optional[int]=9_6 , snake_case_ : Optional[Any]=False , snake_case_ : Tuple=[1_9_2, 3_8_4, 7_6_8, 7_6_8] , snake_case_ : Tuple=[2, 2, 6, 2] , snake_case_ : str=[2, 2, 2, 2] , snake_case_ : List[Any]=[3, 3, 3, 3] , snake_case_ : str="gelu" , snake_case_ : List[str]=4.0 , snake_case_ : int=0.0 , snake_case_ : List[str]=0.1 , snake_case_ : Tuple=False , snake_case_ : str=1e-4 , snake_case_ : Optional[Any]=False , snake_case_ : Union[str, Any]=False , snake_case_ : List[str]=False , snake_case_ : str=0.0_2 , snake_case_ : Union[str, Any]=1e-5 , snake_case_ : str=3_2 , snake_case_ : Any=None , snake_case_ : Optional[Any]=None , **snake_case_ : Optional[Any] , ): super().__init__(**snake_case_ ) _UpperCAmelCase = image_size _UpperCAmelCase = patch_size _UpperCAmelCase = num_channels _UpperCAmelCase = embed_dim _UpperCAmelCase = use_conv_embed _UpperCAmelCase = hidden_sizes _UpperCAmelCase = depths _UpperCAmelCase = focal_levels _UpperCAmelCase = focal_windows _UpperCAmelCase = hidden_act _UpperCAmelCase = mlp_ratio _UpperCAmelCase = hidden_dropout_prob _UpperCAmelCase = drop_path_rate _UpperCAmelCase = use_layerscale _UpperCAmelCase = layerscale_value _UpperCAmelCase = use_post_layernorm _UpperCAmelCase = use_post_layernorm_in_modulation _UpperCAmelCase = normalize_modulator _UpperCAmelCase = initializer_range _UpperCAmelCase = layer_norm_eps _UpperCAmelCase = encoder_stride _UpperCAmelCase = ["stem"] + [f'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )] _UpperCAmelCase , _UpperCAmelCase = get_aligned_output_features_output_indices( out_features=snake_case_ , out_indices=snake_case_ , stage_names=self.stage_names )
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'''simple docstring''' def UpperCAmelCase_ ( __lowercase : list ) -> list: '''simple docstring''' for i in range(len(__lowercase ) - 1 , 0 , -1 ): _UpperCAmelCase = False for j in range(__lowercase , 0 , -1 ): if unsorted[j] < unsorted[j - 1]: _UpperCAmelCase , _UpperCAmelCase = unsorted[j - 1], unsorted[j] _UpperCAmelCase = True for j in range(__lowercase ): if unsorted[j] > unsorted[j + 1]: _UpperCAmelCase , _UpperCAmelCase = unsorted[j + 1], unsorted[j] _UpperCAmelCase = True if not swapped: break return unsorted if __name__ == "__main__": import doctest doctest.testmod() __SCREAMING_SNAKE_CASE :List[str] = input('''Enter numbers separated by a comma:\n''').strip() __SCREAMING_SNAKE_CASE :Any = [int(item) for item in user_input.split(''',''')] print(F"{cocktail_shaker_sort(unsorted) = }")
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import gc import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, StableDiffusionPanoramaPipeline, UNetaDConditionModel, ) from diffusers.utils import slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() @skip_mps class A ( __snake_case , __snake_case , unittest.TestCase ): '''simple docstring''' A__ = StableDiffusionPanoramaPipeline A__ = TEXT_TO_IMAGE_PARAMS A__ = TEXT_TO_IMAGE_BATCH_PARAMS A__ = TEXT_TO_IMAGE_IMAGE_PARAMS A__ = TEXT_TO_IMAGE_IMAGE_PARAMS def lowerCamelCase__ (self : List[Any] ) -> int: """simple docstring""" torch.manual_seed(0 ) lowercase__ = UNetaDConditionModel( block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , ) lowercase__ = DDIMScheduler() torch.manual_seed(0 ) lowercase__ = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , ) torch.manual_seed(0 ) lowercase__ = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) lowercase__ = CLIPTextModel(A_ ) lowercase__ = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" ) lowercase__ = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Tuple , _UpperCAmelCase : Optional[Any]=0 ) -> int: """simple docstring""" lowercase__ = torch.manual_seed(A_ ) lowercase__ = { "prompt": "a photo of the dolomites", "generator": generator, # Setting height and width to None to prevent OOMs on CPU. "height": None, "width": None, "num_inference_steps": 1, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def lowerCamelCase__ (self : str ) -> Optional[Any]: """simple docstring""" lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator lowercase__ = self.get_dummy_components() lowercase__ = StableDiffusionPanoramaPipeline(**A_ ) lowercase__ = sd_pipe.to(A_ ) sd_pipe.set_progress_bar_config(disable=A_ ) lowercase__ = self.get_dummy_inputs(A_ ) lowercase__ = sd_pipe(**A_ ).images lowercase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ = np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase__ (self : List[str] ) -> List[str]: """simple docstring""" super().test_inference_batch_consistent(batch_sizes=[1, 2] ) def lowerCamelCase__ (self : Tuple ) -> Union[str, Any]: """simple docstring""" super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.2_5E-3 ) def lowerCamelCase__ (self : Dict ) -> List[Any]: """simple docstring""" lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator lowercase__ = self.get_dummy_components() lowercase__ = StableDiffusionPanoramaPipeline(**A_ ) lowercase__ = sd_pipe.to(A_ ) sd_pipe.set_progress_bar_config(disable=A_ ) lowercase__ = self.get_dummy_inputs(A_ ) lowercase__ = "french fries" lowercase__ = sd_pipe(**A_ , negative_prompt=A_ ) lowercase__ = output.images lowercase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ = np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase__ (self : Union[str, Any] ) -> Optional[Any]: """simple docstring""" lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator lowercase__ = self.get_dummy_components() lowercase__ = StableDiffusionPanoramaPipeline(**A_ ) lowercase__ = sd_pipe.to(A_ ) sd_pipe.set_progress_bar_config(disable=A_ ) lowercase__ = self.get_dummy_inputs(A_ ) lowercase__ = sd_pipe(**A_ , view_batch_size=2 ) lowercase__ = output.images lowercase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ = np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase__ (self : Any ) -> Optional[int]: """simple docstring""" lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator lowercase__ = self.get_dummy_components() lowercase__ = EulerAncestralDiscreteScheduler( beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" ) lowercase__ = StableDiffusionPanoramaPipeline(**A_ ) lowercase__ = sd_pipe.to(A_ ) sd_pipe.set_progress_bar_config(disable=A_ ) lowercase__ = self.get_dummy_inputs(A_ ) lowercase__ = sd_pipe(**A_ ).images lowercase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ = np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 def lowerCamelCase__ (self : Optional[int] ) -> Any: """simple docstring""" lowercase__ = "cpu" # ensure determinism for the device-dependent torch.Generator lowercase__ = self.get_dummy_components() lowercase__ = PNDMScheduler( beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , skip_prk_steps=A_ ) lowercase__ = StableDiffusionPanoramaPipeline(**A_ ) lowercase__ = sd_pipe.to(A_ ) sd_pipe.set_progress_bar_config(disable=A_ ) lowercase__ = self.get_dummy_inputs(A_ ) lowercase__ = sd_pipe(**A_ ).images lowercase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) lowercase__ = np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 @slow @require_torch_gpu class A ( unittest.TestCase ): '''simple docstring''' def lowerCamelCase__ (self : Optional[int] ) -> Any: """simple docstring""" super().tearDown() gc.collect() torch.cuda.empty_cache() def lowerCamelCase__ (self : List[Any] , _UpperCAmelCase : str=0 ) -> Optional[int]: """simple docstring""" lowercase__ = torch.manual_seed(A_ ) lowercase__ = { "prompt": "a photo of the dolomites", "generator": generator, "num_inference_steps": 3, "guidance_scale": 7.5, "output_type": "numpy", } return inputs def lowerCamelCase__ (self : Any ) -> Optional[int]: """simple docstring""" lowercase__ = "stabilityai/stable-diffusion-2-base" lowercase__ = DDIMScheduler.from_pretrained(A_ , subfolder="""scheduler""" ) lowercase__ = StableDiffusionPanoramaPipeline.from_pretrained(A_ , scheduler=A_ , safety_checker=A_ ) pipe.to(A_ ) pipe.set_progress_bar_config(disable=A_ ) pipe.enable_attention_slicing() lowercase__ = self.get_inputs() lowercase__ = pipe(**A_ ).images lowercase__ = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 2048, 3) lowercase__ = np.array( [ 0.36_968_392, 0.27_025_372, 0.32_446_766, 0.28_379_387, 0.36_363_274, 0.30_733_347, 0.27_100_027, 0.27_054_125, 0.25_536_096, ] ) assert np.abs(expected_slice - image_slice ).max() < 1E-2 def lowerCamelCase__ (self : Any ) -> List[str]: """simple docstring""" lowercase__ = StableDiffusionPanoramaPipeline.from_pretrained( """stabilityai/stable-diffusion-2-base""" , safety_checker=A_ ) lowercase__ = LMSDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.to(A_ ) pipe.set_progress_bar_config(disable=A_ ) pipe.enable_attention_slicing() lowercase__ = self.get_inputs() lowercase__ = pipe(**A_ ).images lowercase__ = image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 512, 2048, 3) lowercase__ = np.array( [ [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ] ] ) assert np.abs(expected_slice - image_slice ).max() < 1E-3 def lowerCamelCase__ (self : Optional[int] ) -> int: """simple docstring""" lowercase__ = 0 def callback_fn(_UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any] ) -> None: lowercase__ = True nonlocal number_of_steps number_of_steps += 1 if step == 1: lowercase__ = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 256) lowercase__ = latents[0, -3:, -3:, -1] lowercase__ = np.array( [ 0.18_681_869, 0.33_907_816, 0.5_361_276, 0.14_432_865, -0.02_856_611, -0.73_941_123, 0.23_397_987, 0.47_322_682, -0.37_823_164, ] ) assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2 elif step == 2: lowercase__ = latents.detach().cpu().numpy() assert latents.shape == (1, 4, 64, 256) lowercase__ = latents[0, -3:, -3:, -1] lowercase__ = np.array( [ 0.18_539_645, 0.33_987_248, 0.5_378_559, 0.14_437_142, -0.02_455_261, -0.7_338_317, 0.23_990_755, 0.47_356_272, -0.3_786_505, ] ) assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5E-2 lowercase__ = False lowercase__ = "stabilityai/stable-diffusion-2-base" lowercase__ = DDIMScheduler.from_pretrained(A_ , subfolder="""scheduler""" ) lowercase__ = StableDiffusionPanoramaPipeline.from_pretrained(A_ , scheduler=A_ , safety_checker=A_ ) lowercase__ = pipe.to(A_ ) pipe.set_progress_bar_config(disable=A_ ) pipe.enable_attention_slicing() lowercase__ = self.get_inputs() pipe(**A_ , callback=A_ , callback_steps=1 ) assert callback_fn.has_been_called assert number_of_steps == 3 def lowerCamelCase__ (self : int ) -> Tuple: """simple docstring""" torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() lowercase__ = "stabilityai/stable-diffusion-2-base" lowercase__ = DDIMScheduler.from_pretrained(A_ , subfolder="""scheduler""" ) lowercase__ = StableDiffusionPanoramaPipeline.from_pretrained(A_ , scheduler=A_ , safety_checker=A_ ) lowercase__ = pipe.to(A_ ) pipe.set_progress_bar_config(disable=A_ ) pipe.enable_attention_slicing(1 ) pipe.enable_sequential_cpu_offload() lowercase__ = self.get_inputs() lowercase__ = pipe(**A_ ) lowercase__ = torch.cuda.max_memory_allocated() # make sure that less than 5.2 GB is allocated assert mem_bytes < 5.5 * 10**9
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def A_ ( _lowerCAmelCase ) -> str: UpperCamelCase : Optional[int] = int(_lowerCAmelCase ) if decimal in (0, 1): # Exit cases for the recursion return str(_lowerCAmelCase ) UpperCamelCase , UpperCamelCase : Dict = divmod(_lowerCAmelCase , 2 ) return binary_recursive(_lowerCAmelCase ) + str(_lowerCAmelCase ) def A_ ( _lowerCAmelCase ) -> str: UpperCamelCase : Tuple = str(_lowerCAmelCase ).strip() if not number: raise ValueError("No input value was provided" ) UpperCamelCase : Optional[int] = "-" if number.startswith("-" ) else "" UpperCamelCase : Any = number.lstrip("-" ) if not number.isnumeric(): raise ValueError("Input value is not an integer" ) return F"""{negative}0b{binary_recursive(int(_lowerCAmelCase ) )}""" if __name__ == "__main__": from doctest import testmod testmod()
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def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: list ) -> list: _UpperCAmelCase : Dict = len(lowerCAmelCase ) for _ in range(lowerCAmelCase ): for i in range(_ % 2 , arr_size - 1 , 2 ): if arr[i + 1] < arr[i]: _UpperCAmelCase , _UpperCAmelCase : Tuple = arr[i + 1], arr[i] return arr if __name__ == "__main__": SCREAMING_SNAKE_CASE_ = list(range(10, 0, -1)) print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')
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from typing import Any class a : def __init__( self , A_ ): '''simple docstring''' _UpperCAmelCase : List[Any] = data _UpperCAmelCase : Any = None class a : def __init__( self ): '''simple docstring''' _UpperCAmelCase : Union[str, Any] = None def _UpperCAmelCase ( self ): '''simple docstring''' _UpperCAmelCase : str = self.head while temp is not None: print(temp.data , end=" " ) _UpperCAmelCase : str = temp.next print() def _UpperCAmelCase ( self , A_ ): '''simple docstring''' _UpperCAmelCase : Optional[int] = Node(A_ ) _UpperCAmelCase : Tuple = self.head _UpperCAmelCase : Tuple = new_node def _UpperCAmelCase ( self , A_ , A_ ): '''simple docstring''' if node_data_a == node_data_a: return else: _UpperCAmelCase : int = self.head while node_a is not None and node_a.data != node_data_a: _UpperCAmelCase : Tuple = node_a.next _UpperCAmelCase : Dict = self.head while node_a is not None and node_a.data != node_data_a: _UpperCAmelCase : List[Any] = node_a.next if node_a is None or node_a is None: return _UpperCAmelCase , _UpperCAmelCase : Optional[int] = node_a.data, node_a.data if __name__ == "__main__": SCREAMING_SNAKE_CASE_ = LinkedList() for i in range(5, 0, -1): ll.push(i) ll.print_list() ll.swap_nodes(1, 4) print('After swapping') ll.print_list()
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import sys from .dependency_versions_table import deps from .utils.versions import require_version, require_version_core # define which module versions we always want to check at run time # (usually the ones defined in `install_requires` in setup.py) # # order specific notes: # - tqdm must be checked before tokenizers a : Any = 'python tqdm regex requests packaging filelock numpy tokenizers'.split() if sys.version_info < (3, 7): pkgs_to_check_at_runtime.append('dataclasses') if sys.version_info < (3, 8): pkgs_to_check_at_runtime.append('importlib_metadata') for pkg in pkgs_to_check_at_runtime: if pkg in deps: if pkg == "tokenizers": # must be loaded here, or else tqdm check may fail from .utils import is_tokenizers_available if not is_tokenizers_available(): continue # not required, check version only if installed require_version_core(deps[pkg]) else: raise ValueError(F'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''') def lowerCAmelCase_ (lowerCAmelCase__: Dict , lowerCAmelCase__: List[str]=None ): """simple docstring""" require_version(deps[pkg] , _lowerCAmelCase )
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available __a = { "configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __a = ["BloomTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __a = [ "BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST", "BloomForCausalLM", "BloomModel", "BloomPreTrainedModel", "BloomForSequenceClassification", "BloomForTokenClassification", "BloomForQuestionAnswering", ] if TYPE_CHECKING: from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_bloom_fast import BloomTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_bloom import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, BloomPreTrainedModel, ) else: import sys __a = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" from __future__ import annotations from typing import TypedDict class snake_case ( _UpperCamelCase): __UpperCamelCase = 42 __UpperCamelCase = 42 def a__ ( __lowercase ) -> list[str]: if not isinstance(__lowercase , __lowercase ): raise TypeError("The parameter s type must be str." ) return [s[i:] + s[:i] for i in range(len(__lowercase ) )] def a__ ( __lowercase ) -> BWTTransformDict: if not isinstance(__lowercase , __lowercase ): raise TypeError("The parameter s type must be str." ) if not s: raise ValueError("The parameter s must not be empty." ) _A = all_rotations(__lowercase ) rotations.sort() # sort the list of rotations in alphabetically order # make a string composed of the last char of each rotation _A = { "bwt_string": "".join([word[-1] for word in rotations] ), "idx_original_string": rotations.index(__lowercase ), } return response def a__ ( __lowercase , __lowercase ) -> str: if not isinstance(__lowercase , __lowercase ): raise TypeError("The parameter bwt_string type must be str." ) if not bwt_string: raise ValueError("The parameter bwt_string must not be empty." ) try: _A = int(__lowercase ) except ValueError: raise TypeError( "The parameter idx_original_string type must be int or passive" " of cast to int." ) if idx_original_string < 0: raise ValueError("The parameter idx_original_string must not be lower than 0." ) if idx_original_string >= len(__lowercase ): raise ValueError( "The parameter idx_original_string must be lower than" " len(bwt_string)." ) _A = [""] * len(__lowercase ) for _ in range(len(__lowercase ) ): for i in range(len(__lowercase ) ): _A = bwt_string[i] + ordered_rotations[i] ordered_rotations.sort() return ordered_rotations[idx_original_string] if __name__ == "__main__": a_ = "Provide a string that I will generate its BWT transform: " a_ = input(entry_msg).strip() a_ = bwt_transform(s) print( f'''Burrows Wheeler transform for string \'{s}\' results ''' f'''in \'{result["bwt_string"]}\'''' ) a_ = reverse_bwt(result["bwt_string"], result["idx_original_string"]) print( f'''Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' ''' f'''we get original string \'{original_string}\'''' )
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"""simple docstring""" import contextlib import copy import random from typing import Any, Dict, Iterable, Optional, Union import numpy as np import torch from .utils import deprecate, is_transformers_available if is_transformers_available(): import transformers def a__ ( __lowercase ) -> Any: random.seed(__lowercase ) np.random.seed(__lowercase ) torch.manual_seed(__lowercase ) torch.cuda.manual_seed_all(__lowercase ) # ^^ safe to call this function even if cuda is not available class snake_case : def __init__( self : str , a__ : Iterable[torch.nn.Parameter] , a__ : float = 0.9_9_9_9 , a__ : float = 0.0 , a__ : int = 0 , a__ : bool = False , a__ : Union[float, int] = 1.0 , a__ : Union[float, int] = 2 / 3 , a__ : Optional[Any] = None , a__ : Dict[str, Any] = None , **a__ : Optional[Any] , ) -> Optional[Any]: '''simple docstring''' if isinstance(a__ , torch.nn.Module ): _A = ( "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. " "Please pass the parameters of the module instead." ) deprecate( "passing a `torch.nn.Module` to `ExponentialMovingAverage`" , "1.0.0" , a__ , standard_warn=a__ , ) _A = parameters.parameters() # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility _A = True if kwargs.get("max_value" , a__ ) is not None: _A = "The `max_value` argument is deprecated. Please use `decay` instead." deprecate("max_value" , "1.0.0" , a__ , standard_warn=a__ ) _A = kwargs["max_value"] if kwargs.get("min_value" , a__ ) is not None: _A = "The `min_value` argument is deprecated. Please use `min_decay` instead." deprecate("min_value" , "1.0.0" , a__ , standard_warn=a__ ) _A = kwargs["min_value"] _A = list(a__ ) _A = [p.clone().detach() for p in parameters] if kwargs.get("device" , a__ ) is not None: _A = "The `device` argument is deprecated. Please use `to` instead." deprecate("device" , "1.0.0" , a__ , standard_warn=a__ ) self.to(device=kwargs["device"] ) _A = None _A = decay _A = min_decay _A = update_after_step _A = use_ema_warmup _A = inv_gamma _A = power _A = 0 _A = None # set in `step()` _A = model_cls _A = model_config @classmethod def a_ ( cls : Dict , a__ : str , a__ : str ) -> "EMAModel": '''simple docstring''' _A , _A = model_cls.load_config(a__ , return_unused_kwargs=a__ ) _A = model_cls.from_pretrained(a__ ) _A = cls(model.parameters() , model_cls=a__ , model_config=model.config ) ema_model.load_state_dict(a__ ) return ema_model def a_ ( self : List[Any] , a__ : List[str] ) -> int: '''simple docstring''' if self.model_cls is None: raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__." ) if self.model_config is None: raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__." ) _A = self.model_cls.from_config(self.model_config ) _A = self.state_dict() state_dict.pop("shadow_params" , a__ ) model.register_to_config(**a__ ) self.copy_to(model.parameters() ) model.save_pretrained(a__ ) def a_ ( self : str , a__ : int ) -> float: '''simple docstring''' _A = max(0 , optimization_step - self.update_after_step - 1 ) if step <= 0: return 0.0 if self.use_ema_warmup: _A = 1 - (1 + step / self.inv_gamma) ** -self.power else: _A = (1 + step) / (10 + step) _A = min(a__ , self.decay ) # make sure decay is not smaller than min_decay _A = max(a__ , self.min_decay ) return cur_decay_value @torch.no_grad() def a_ ( self : List[Any] , a__ : Iterable[torch.nn.Parameter] ) -> Optional[int]: '''simple docstring''' if isinstance(a__ , torch.nn.Module ): _A = ( "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. " "Please pass the parameters of the module instead." ) deprecate( "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`" , "1.0.0" , a__ , standard_warn=a__ , ) _A = parameters.parameters() _A = list(a__ ) self.optimization_step += 1 # Compute the decay factor for the exponential moving average. _A = self.get_decay(self.optimization_step ) _A = decay _A = 1 - decay _A = contextlib.nullcontext if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled(): import deepspeed for s_param, param in zip(self.shadow_params , a__ ): if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled(): _A = deepspeed.zero.GatheredParameters(a__ , modifier_rank=a__ ) with context_manager(): if param.requires_grad: s_param.sub_(one_minus_decay * (s_param - param) ) else: s_param.copy_(a__ ) def a_ ( self : Dict , a__ : Iterable[torch.nn.Parameter] ) -> None: '''simple docstring''' _A = list(a__ ) for s_param, param in zip(self.shadow_params , a__ ): param.data.copy_(s_param.to(param.device ).data ) def a_ ( self : List[str] , a__ : int=None , a__ : List[Any]=None ) -> None: '''simple docstring''' _A = [ p.to(device=a__ , dtype=a__ ) if p.is_floating_point() else p.to(device=a__ ) for p in self.shadow_params ] def a_ ( self : Tuple ) -> dict: '''simple docstring''' return { "decay": self.decay, "min_decay": self.min_decay, "optimization_step": self.optimization_step, "update_after_step": self.update_after_step, "use_ema_warmup": self.use_ema_warmup, "inv_gamma": self.inv_gamma, "power": self.power, "shadow_params": self.shadow_params, } def a_ ( self : Union[str, Any] , a__ : Iterable[torch.nn.Parameter] ) -> None: '''simple docstring''' _A = [param.detach().cpu().clone() for param in parameters] def a_ ( self : Union[str, Any] , a__ : Iterable[torch.nn.Parameter] ) -> None: '''simple docstring''' if self.temp_stored_params is None: raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`" ) for c_param, param in zip(self.temp_stored_params , a__ ): param.data.copy_(c_param.data ) # Better memory-wise. _A = None def a_ ( self : Optional[Any] , a__ : dict ) -> None: '''simple docstring''' _A = copy.deepcopy(a__ ) _A = state_dict.get("decay" , self.decay ) if self.decay < 0.0 or self.decay > 1.0: raise ValueError("Decay must be between 0 and 1" ) _A = state_dict.get("min_decay" , self.min_decay ) if not isinstance(self.min_decay , a__ ): raise ValueError("Invalid min_decay" ) _A = state_dict.get("optimization_step" , self.optimization_step ) if not isinstance(self.optimization_step , a__ ): raise ValueError("Invalid optimization_step" ) _A = state_dict.get("update_after_step" , self.update_after_step ) if not isinstance(self.update_after_step , a__ ): raise ValueError("Invalid update_after_step" ) _A = state_dict.get("use_ema_warmup" , self.use_ema_warmup ) if not isinstance(self.use_ema_warmup , a__ ): raise ValueError("Invalid use_ema_warmup" ) _A = state_dict.get("inv_gamma" , self.inv_gamma ) if not isinstance(self.inv_gamma , (float, int) ): raise ValueError("Invalid inv_gamma" ) _A = state_dict.get("power" , self.power ) if not isinstance(self.power , (float, int) ): raise ValueError("Invalid power" ) _A = state_dict.get("shadow_params" , a__ ) if shadow_params is not None: _A = shadow_params if not isinstance(self.shadow_params , a__ ): raise ValueError("shadow_params must be a list" ) if not all(isinstance(a__ , torch.Tensor ) for p in self.shadow_params ): raise ValueError("shadow_params must all be Tensors" )
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import math class A : def _A (self , lowerCAmelCase , lowerCAmelCase ): __lowercase= 0.0 __lowercase= 0.0 for i in range(len(lowerCAmelCase ) ): da += math.pow((sample[i] - weights[0][i]) , 2 ) da += math.pow((sample[i] - weights[1][i]) , 2 ) return 0 if da > da else 1 return 0 def _A (self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ): for i in range(len(lowerCAmelCase ) ): weights[j][i] += alpha * (sample[i] - weights[j][i]) return weights def _lowerCamelCase( ) -> None: '''simple docstring''' __lowercase= [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]] # weight initialization ( n, C ) __lowercase= [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]] # training __lowercase= SelfOrganizingMap() __lowercase= 3 __lowercase= 0.5 for _ in range(lowercase__ ): for j in range(len(lowercase__ ) ): # training sample __lowercase= training_samples[j] # Compute the winning vector __lowercase= self_organizing_map.get_winner(lowercase__ , lowercase__ ) # Update the winning vector __lowercase= self_organizing_map.update(lowercase__ , lowercase__ , lowercase__ , lowercase__ ) # classify test sample __lowercase= [0, 0, 0, 1] __lowercase= self_organizing_map.get_winner(lowercase__ , lowercase__ ) # results print(F'Clusters that the test sample belongs to : {winner}' ) print(F'Weights that have been trained : {weights}' ) # running the main() function if __name__ == "__main__": main()
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import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class A ( nn.Module ): def __init__(self ): super().__init__() __lowercase= nn.Linear(3 , 4 ) __lowercase= nn.BatchNormad(4 ) __lowercase= nn.Linear(4 , 5 ) def _A (self , lowerCAmelCase ): return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase ) ) ) class A ( A_ ): def _A (self , lowerCAmelCase , *lowerCAmelCase , **lowerCAmelCase ): return (args[0] + 1,) + args[1:], kwargs class A ( A_ ): def _A (self , lowerCAmelCase , lowerCAmelCase ): return output + 1 class A ( unittest.TestCase ): def _A (self ): __lowercase= ModelForTest() __lowercase= ModelHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) self.assertEqual(test_model._hf_hook , lowerCAmelCase ) self.assertTrue(hasattr(lowerCAmelCase , '_old_forward' ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , 'forward' ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] ) remove_hook_from_module(lowerCAmelCase ) self.assertFalse(hasattr(lowerCAmelCase , '_hf_hook' ) ) self.assertFalse(hasattr(lowerCAmelCase , '_old_forward' ) ) def _A (self ): __lowercase= ModelForTest() __lowercase= ModelHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) add_hook_to_module(lowerCAmelCase , lowerCAmelCase , append=lowerCAmelCase ) self.assertEqual(isinstance(test_model._hf_hook , lowerCAmelCase ) , lowerCAmelCase ) self.assertEqual(len(test_model._hf_hook.hooks ) , 2 ) self.assertTrue(hasattr(lowerCAmelCase , '_old_forward' ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__ , 'forward' ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ) , ['x'] ) remove_hook_from_module(lowerCAmelCase ) self.assertFalse(hasattr(lowerCAmelCase , '_hf_hook' ) ) self.assertFalse(hasattr(lowerCAmelCase , '_old_forward' ) ) def _A (self ): __lowercase= ModelForTest() __lowercase= torch.randn(2 , 3 ) __lowercase= test_model(x + 1 ) __lowercase= test_model(x + 2 ) __lowercase= PreForwardHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) self.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __lowercase= PreForwardHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) self.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __lowercase= SequentialHook(PreForwardHook() , PreForwardHook() ) add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) assert torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1E-5 ) def _A (self ): __lowercase= ModelForTest() __lowercase= torch.randn(2 , 3 ) __lowercase= test_model(lowerCAmelCase ) __lowercase= PostForwardHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) self.assertTrue(torch.allclose(lowerCAmelCase , output + 1 , atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain __lowercase= PostForwardHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) self.assertTrue(torch.allclose(lowerCAmelCase , output + 1 , atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks __lowercase= SequentialHook(PostForwardHook() , PostForwardHook() ) add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) assert torch.allclose(lowerCAmelCase , output + 2 , atol=1E-5 ) def _A (self ): __lowercase= ModelForTest() __lowercase= torch.randn(2 , 3 ) __lowercase= test_model(lowerCAmelCase ) __lowercase= PostForwardHook() add_hook_to_module(lowerCAmelCase , lowerCAmelCase ) __lowercase= test_model(lowerCAmelCase ) self.assertTrue(torch.allclose(lowerCAmelCase , output + 1 ) ) self.assertTrue(outputa.requires_grad ) __lowercase= True __lowercase= test_model(lowerCAmelCase ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def _A (self ): __lowercase= ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara , AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device , torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device(0 ) ) self.assertEqual(model.lineara.weight.device , torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(lowerCAmelCase , AlignDevicesHook(io_same_device=lowerCAmelCase ) ) __lowercase= torch.randn(2 , 3 ).to(0 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , torch.device(0 ) ) def _A (self ): __lowercase= ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # This will move each submodule on different devices __lowercase= {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True} add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**lowerCAmelCase ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) ) self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) # Buffers are not included in the offload by default, so are on the execution device __lowercase= torch.device(hook_kwargs['execution_device'] ) self.assertEqual(model.batchnorm.running_mean.device , lowerCAmelCase ) __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , lowerCAmelCase ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # Now test with buffers included in the offload __lowercase= { 'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True, 'offload_buffers': True, } add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) ) add_hook_to_module(model.batchnorm , AlignDevicesHook(**lowerCAmelCase ) ) add_hook_to_module(model.lineara , AlignDevicesHook(**lowerCAmelCase ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) ) self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) ) __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , lowerCAmelCase ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) def _A (self ): __lowercase= ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # This will move each submodule on different devices __lowercase= 0 if torch.cuda.is_available() else 'cpu' attach_align_device_hook(lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) ) self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) # Buffers are not included in the offload by default, so are on the execution device __lowercase= torch.device(lowerCAmelCase ) self.assertEqual(model.batchnorm.running_mean.device , lowerCAmelCase ) __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , lowerCAmelCase ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(lowerCAmelCase ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # Now test with buffers included in the offload attach_align_device_hook(lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase , offload_buffers=lowerCAmelCase ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) ) self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) ) __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , lowerCAmelCase ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(lowerCAmelCase ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) def _A (self ): __lowercase= ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # This will move each submodule on different devices __lowercase= 0 if torch.cuda.is_available() else 'cpu' attach_align_device_hook( lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase , weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) ) self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) # Buffers are not included in the offload by default, so are on the execution device __lowercase= torch.device(lowerCAmelCase ) self.assertEqual(model.batchnorm.running_mean.device , lowerCAmelCase ) __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , lowerCAmelCase ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(lowerCAmelCase ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) # Now test with buffers included in the offload attach_align_device_hook( lowerCAmelCase , execution_device=lowerCAmelCase , offload=lowerCAmelCase , weights_map=model.state_dict() , offload_buffers=lowerCAmelCase , ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('meta' ) ) self.assertEqual(model.lineara.weight.device , torch.device('meta' ) ) self.assertEqual(model.batchnorm.running_mean.device , torch.device('meta' ) ) __lowercase= torch.randn(2 , 3 ) __lowercase= model(lowerCAmelCase ) self.assertEqual(output.device , lowerCAmelCase ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(lowerCAmelCase ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) ) self.assertEqual(model.batchnorm.weight.device , torch.device('cpu' ) ) self.assertEqual(model.lineara.weight.device , torch.device('cpu' ) )
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'''simple docstring''' from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices lowerCamelCase = logging.get_logger(__name__) lowerCamelCase = { """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""", } class _UpperCamelCase ( A , A ): '''simple docstring''' lowerCAmelCase__ = """resnet""" lowerCAmelCase__ = ["""basic""", """bottleneck"""] def __init__( self : Any , _lowerCAmelCase : List[str]=3 , _lowerCAmelCase : Optional[int]=6_4 , _lowerCAmelCase : str=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , _lowerCAmelCase : Any=[3, 4, 6, 3] , _lowerCAmelCase : List[Any]="bottleneck" , _lowerCAmelCase : List[str]="relu" , _lowerCAmelCase : int=False , _lowerCAmelCase : int=None , _lowerCAmelCase : Any=None , **_lowerCAmelCase : Any , ): '''simple docstring''' super().__init__(**_lowerCAmelCase) if layer_type not in self.layer_types: raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types)}""") __lowercase =num_channels __lowercase =embedding_size __lowercase =hidden_sizes __lowercase =depths __lowercase =layer_type __lowercase =hidden_act __lowercase =downsample_in_first_stage __lowercase =['stem'] + [f"""stage{idx}""" for idx in range(1 , len(_lowerCAmelCase) + 1)] __lowercase , __lowercase =get_aligned_output_features_output_indices( out_features=_lowerCAmelCase , out_indices=_lowerCAmelCase , stage_names=self.stage_names) class _UpperCamelCase ( A ): '''simple docstring''' lowerCAmelCase__ = version.parse("""1.11""" ) @property def __lowerCamelCase ( self : List[Any]): '''simple docstring''' return OrderedDict( [ ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}), ]) @property def __lowerCamelCase ( self : Tuple): '''simple docstring''' return 1e-3
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'''simple docstring''' from __future__ import annotations from math import pi, sqrt def _A ( _lowerCAmelCase , _lowerCAmelCase ): """simple docstring""" if inductance <= 0: raise ValueError('Inductance cannot be 0 or negative' ) elif capacitance <= 0: raise ValueError('Capacitance cannot be 0 or negative' ) else: return ( "Resonant frequency", float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ), ) if __name__ == "__main__": import doctest doctest.testmod()
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import unittest import numpy as np from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision 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 DPTImageProcessor class A ( unittest.TestCase ): def __init__( self, UpperCamelCase__, UpperCamelCase__=7, UpperCamelCase__=3, UpperCamelCase__=18, UpperCamelCase__=30, UpperCamelCase__=400, UpperCamelCase__=True, UpperCamelCase__=None, UpperCamelCase__=True, UpperCamelCase__=[0.5, 0.5, 0.5], UpperCamelCase__=[0.5, 0.5, 0.5], ): """simple docstring""" lowerCAmelCase_ = size if size is not None else {'''height''': 18, '''width''': 18} lowerCAmelCase_ = parent lowerCAmelCase_ = batch_size lowerCAmelCase_ = num_channels lowerCAmelCase_ = image_size lowerCAmelCase_ = min_resolution lowerCAmelCase_ = max_resolution lowerCAmelCase_ = do_resize lowerCAmelCase_ = size lowerCAmelCase_ = do_normalize lowerCAmelCase_ = image_mean lowerCAmelCase_ = image_std def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, } @require_torch @require_vision class A ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ): __snake_case = DPTImageProcessor if is_vision_available() else None def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" lowerCAmelCase_ = DPTImageProcessingTester(self ) @property def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" return self.image_processor_tester.prepare_image_processor_dict() def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(__UpperCAmelCase, '''image_mean''' ) ) self.assertTrue(hasattr(__UpperCAmelCase, '''image_std''' ) ) self.assertTrue(hasattr(__UpperCAmelCase, '''do_normalize''' ) ) self.assertTrue(hasattr(__UpperCAmelCase, '''do_resize''' ) ) self.assertTrue(hasattr(__UpperCAmelCase, '''size''' ) ) def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size, {'''height''': 18, '''width''': 18} ) lowerCAmelCase_ = self.image_processing_class.from_dict(self.image_processor_dict, size=42 ) self.assertEqual(image_processor.size, {'''height''': 42, '''width''': 42} ) def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict ) # create random PIL images lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester, equal_resolution=__UpperCAmelCase ) for image in image_inputs: self.assertIsInstance(__UpperCAmelCase, Image.Image ) # Test not batched input lowerCAmelCase_ = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) # Test batched lowerCAmelCase_ = image_processing(__UpperCAmelCase, return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester, equal_resolution=__UpperCAmelCase, numpify=__UpperCAmelCase ) for image in image_inputs: self.assertIsInstance(__UpperCAmelCase, np.ndarray ) # Test not batched input lowerCAmelCase_ = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) # Test batched lowerCAmelCase_ = image_processing(__UpperCAmelCase, return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) def SCREAMING_SNAKE_CASE__ ( self ): """simple docstring""" lowerCAmelCase_ = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors lowerCAmelCase_ = prepare_image_inputs(self.image_processor_tester, equal_resolution=__UpperCAmelCase, torchify=__UpperCAmelCase ) for image in image_inputs: self.assertIsInstance(__UpperCAmelCase, torch.Tensor ) # Test not batched input lowerCAmelCase_ = image_processing(image_inputs[0], return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) # Test batched lowerCAmelCase_ = image_processing(__UpperCAmelCase, return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), )
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"""simple docstring""" import math import os import sys def UpperCamelCase ( UpperCAmelCase ) ->str: """simple docstring""" a_ = "" try: with open(UpperCAmelCase , "rb" ) as binary_file: a_ = binary_file.read() for dat in data: a_ = F'''{dat:08b}''' result += curr_byte return result except OSError: print("File not accessible" ) sys.exit() def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->None: """simple docstring""" lexicon.pop(UpperCAmelCase ) a_ = last_match_id if math.loga(UpperCAmelCase ).is_integer(): for curr_key in lexicon: a_ = "0" + lexicon[curr_key] a_ = bin(UpperCAmelCase )[2:] def UpperCamelCase ( UpperCAmelCase ) ->str: """simple docstring""" a_ = {"0": "0", "1": "1"} a_ , a_ = "", "" a_ = len(UpperCAmelCase ) for i in range(len(UpperCAmelCase ) ): curr_string += data_bits[i] if curr_string not in lexicon: continue a_ = lexicon[curr_string] result += last_match_id add_key_to_lexicon(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) index += 1 a_ = "" while curr_string != "" and curr_string not in lexicon: curr_string += "0" if curr_string != "": a_ = lexicon[curr_string] result += last_match_id return result def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->str: """simple docstring""" a_ = os.path.getsize(UpperCAmelCase ) a_ = bin(UpperCAmelCase )[2:] a_ = len(UpperCAmelCase ) return "0" * (length_length - 1) + file_length_binary + compressed def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->None: """simple docstring""" a_ = 8 try: with open(UpperCAmelCase , "wb" ) as opened_file: a_ = [ to_write[i : i + byte_length] for i in range(0 , len(UpperCAmelCase ) , UpperCAmelCase ) ] if len(result_byte_array[-1] ) % byte_length == 0: result_byte_array.append("10000000" ) else: result_byte_array[-1] += "1" + "0" * ( byte_length - len(result_byte_array[-1] ) - 1 ) for elem in result_byte_array: opened_file.write(int(UpperCAmelCase , 2 ).to_bytes(1 , byteorder="big" ) ) except OSError: print("File not accessible" ) sys.exit() def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ) ->None: """simple docstring""" a_ = read_file_binary(UpperCAmelCase ) a_ = compress_data(UpperCAmelCase ) a_ = add_file_length(UpperCAmelCase , UpperCAmelCase ) write_file_binary(UpperCAmelCase , UpperCAmelCase ) if __name__ == "__main__": compress(sys.argv[1], sys.argv[2])
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import argparse import logging import os import sys import numpy as np import onnxruntime import torch from bart_onnx.generation_onnx import BARTBeamSearchGenerator from bart_onnx.reduce_onnx_size import remove_dup_initializers import transformers from transformers import BartForConditionalGeneration, BartTokenizer logging.basicConfig( format="%(asctime)s | %(levelname)s | %(name)s | [%(filename)s:%(lineno)d] %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=os.environ.get("LOGLEVEL", "INFO").upper(), stream=sys.stdout, ) __UpperCAmelCase : Optional[int] = logging.getLogger(__name__) __UpperCAmelCase : List[Any] = {"facebook/bart-base": BartForConditionalGeneration} __UpperCAmelCase : int = {"facebook/bart-base": BartTokenizer} def A__ ( ) -> Optional[Any]: __snake_case: Tuple = argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph.""") parser.add_argument( """--validation_file""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""A csv or a json file containing the validation data.""") parser.add_argument( """--max_length""" , type=SCREAMING_SNAKE_CASE__ , default=5 , help="""The maximum total input sequence length after tokenization.""" , ) parser.add_argument( """--num_beams""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help=( """Number of beams to use for evaluation. This argument will be """ """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.""" ) , ) parser.add_argument( """--model_name_or_path""" , type=SCREAMING_SNAKE_CASE__ , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=SCREAMING_SNAKE_CASE__ , ) parser.add_argument( """--config_name""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""Pretrained config name or path if not the same as model_name""" , ) parser.add_argument( """--device""" , type=SCREAMING_SNAKE_CASE__ , default="""cpu""" , help="""Device where the model will be run""" , ) parser.add_argument("""--output_file_path""" , type=SCREAMING_SNAKE_CASE__ , default=SCREAMING_SNAKE_CASE__ , help="""Where to store the final ONNX file.""") __snake_case: List[Any] = parser.parse_args() return args def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="cpu") -> Dict: __snake_case: List[str] = model_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__).to(SCREAMING_SNAKE_CASE__) __snake_case: Optional[Any] = tokenizer_dict[model_name].from_pretrained(SCREAMING_SNAKE_CASE__) if model_name in ["facebook/bart-base"]: __snake_case: Any = 0 __snake_case: Any = None __snake_case: str = 0 return huggingface_model, tokenizer def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Optional[int]: model.eval() __snake_case: Tuple = None __snake_case: List[str] = torch.jit.script(BARTBeamSearchGenerator(SCREAMING_SNAKE_CASE__)) with torch.no_grad(): __snake_case: Tuple = """My friends are cool but they eat too many carbs.""" __snake_case: Union[str, Any] = tokenizer([ARTICLE_TO_SUMMARIZE] , max_length=1024 , return_tensors="""pt""").to(model.device) __snake_case: Tuple = model.generate( inputs["""input_ids"""] , attention_mask=inputs["""attention_mask"""] , num_beams=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , early_stopping=SCREAMING_SNAKE_CASE__ , decoder_start_token_id=model.config.decoder_start_token_id , ) torch.onnx.export( SCREAMING_SNAKE_CASE__ , ( inputs["""input_ids"""], inputs["""attention_mask"""], num_beams, max_length, model.config.decoder_start_token_id, ) , SCREAMING_SNAKE_CASE__ , opset_version=14 , input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""] , output_names=["""output_ids"""] , dynamic_axes={ """input_ids""": {0: """batch""", 1: """seq"""}, """output_ids""": {0: """batch""", 1: """seq_out"""}, } , example_outputs=SCREAMING_SNAKE_CASE__ , ) logger.info("""Model exported to {}""".format(SCREAMING_SNAKE_CASE__)) __snake_case: Any = remove_dup_initializers(os.path.abspath(SCREAMING_SNAKE_CASE__)) logger.info("""Deduplicated and optimized model written to {}""".format(SCREAMING_SNAKE_CASE__)) __snake_case: int = onnxruntime.InferenceSession(SCREAMING_SNAKE_CASE__) __snake_case: Dict = ort_sess.run( SCREAMING_SNAKE_CASE__ , { """input_ids""": inputs["""input_ids"""].cpu().numpy(), """attention_mask""": inputs["""attention_mask"""].cpu().numpy(), """num_beams""": np.array(SCREAMING_SNAKE_CASE__), """max_length""": np.array(SCREAMING_SNAKE_CASE__), """decoder_start_token_id""": np.array(model.config.decoder_start_token_id), } , ) np.testing.assert_allclose(summary_ids.cpu().numpy() , ort_out[0] , rtol=1e-3 , atol=1e-3) logger.info("""Model outputs from torch and ONNX Runtime are similar.""") logger.info("""Success.""") def A__ ( ) -> Dict: __snake_case: Dict = parse_args() __snake_case: Any = 5 __snake_case: List[Any] = 4 # Make one log on every process with the configuration for debugging. logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO , ) logger.setLevel(logging.INFO) transformers.utils.logging.set_verbosity_error() __snake_case: List[str] = torch.device(args.device) __snake_case: Tuple = load_model_tokenizer(args.model_name_or_path , SCREAMING_SNAKE_CASE__) if model.config.decoder_start_token_id is None: raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined""") model.to(SCREAMING_SNAKE_CASE__) if args.max_length: __snake_case: Any = args.max_length if args.num_beams: __snake_case: List[str] = args.num_beams if args.output_file_path: __snake_case: Union[str, Any] = args.output_file_path else: __snake_case: Tuple = """BART.onnx""" logger.info("""Exporting model to ONNX""") export_and_validate_model(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) if __name__ == "__main__": main()
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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, ) __UpperCAmelCase : List[str] = { "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"], "tokenization_roberta": ["RobertaTokenizer"], } try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __UpperCAmelCase : Optional[Any] = ["RobertaTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __UpperCAmelCase : Tuple = [ "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: __UpperCAmelCase : Optional[int] = [ "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: __UpperCAmelCase : List[Any] = [ "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 __UpperCAmelCase : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import unittest import numpy as np from transformers import AlbertConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask if is_flax_available(): import jax.numpy as jnp from transformers.models.albert.modeling_flax_albert import ( FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForPreTraining, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertModel, ) class UpperCamelCase__ ( unittest.TestCase ): def __init__(self : Dict , snake_case_ : str , snake_case_ : Union[str, Any]=1_3 , snake_case_ : Optional[int]=7 , snake_case_ : Tuple=True , snake_case_ : int=True , snake_case_ : Dict=True , snake_case_ : int=True , snake_case_ : int=9_9 , snake_case_ : Optional[Any]=3_2 , snake_case_ : Optional[int]=5 , snake_case_ : Optional[int]=4 , snake_case_ : Optional[int]=3_7 , snake_case_ : Tuple="gelu" , snake_case_ : Dict=0.1 , snake_case_ : Dict=0.1 , snake_case_ : int=5_1_2 , snake_case_ : int=1_6 , snake_case_ : int=2 , snake_case_ : str=0.02 , snake_case_ : Optional[int]=4 , ): __a : Any = parent __a : str = batch_size __a : List[str] = seq_length __a : Dict = is_training __a : int = use_attention_mask __a : Tuple = use_token_type_ids __a : int = use_labels __a : List[str] = vocab_size __a : Tuple = hidden_size __a : int = num_hidden_layers __a : Dict = num_attention_heads __a : List[str] = intermediate_size __a : str = hidden_act __a : int = hidden_dropout_prob __a : List[str] = attention_probs_dropout_prob __a : List[str] = max_position_embeddings __a : Union[str, Any] = type_vocab_size __a : Any = type_sequence_label_size __a : Dict = initializer_range __a : str = num_choices def lowerCAmelCase (self : Optional[int] ): __a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __a : Optional[Any] = None if self.use_attention_mask: __a : Tuple = random_attention_mask([self.batch_size, self.seq_length] ) __a : Dict = None if self.use_token_type_ids: __a : str = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) __a : Optional[Any] = AlbertConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , ) return config, input_ids, token_type_ids, attention_mask def lowerCAmelCase (self : str ): __a : List[Any] = self.prepare_config_and_inputs() __a , __a , __a , __a : Union[str, Any] = config_and_inputs __a : List[str] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask} return config, inputs_dict @require_flax class UpperCamelCase__ ( __lowercase ,unittest.TestCase ): _SCREAMING_SNAKE_CASE : Dict = ( ( FlaxAlbertModel, FlaxAlbertForPreTraining, FlaxAlbertForMaskedLM, FlaxAlbertForMultipleChoice, FlaxAlbertForQuestionAnswering, FlaxAlbertForSequenceClassification, FlaxAlbertForTokenClassification, FlaxAlbertForQuestionAnswering, ) if is_flax_available() else () ) def lowerCAmelCase (self : Union[str, Any] ): __a : Union[str, Any] = FlaxAlbertModelTester(self ) @slow def lowerCAmelCase (self : Optional[Any] ): for model_class_name in self.all_model_classes: __a : Tuple = model_class_name.from_pretrained('''albert-base-v2''' ) __a : Any = model(np.ones((1, 1) ) ) self.assertIsNotNone(snake_case_ ) @require_flax class UpperCamelCase__ ( unittest.TestCase ): @slow def lowerCAmelCase (self : Optional[int] ): __a : Dict = FlaxAlbertModel.from_pretrained('''albert-base-v2''' ) __a : Tuple = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] ) __a : Any = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) __a : List[str] = model(snake_case_ , attention_mask=snake_case_ )[0] __a : Any = (1, 1_1, 7_6_8) self.assertEqual(output.shape , snake_case_ ) __a : str = np.array( [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] ) self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , snake_case_ , atol=1E-4 ) )
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import gc import unittest from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline from diffusers.utils import is_flax_available, slow from diffusers.utils.testing_utils import require_flax if is_flax_available(): import jax import jax.numpy as jnp from flax.jax_utils import replicate from flax.training.common_utils import shard @slow @require_flax class UpperCamelCase__ ( unittest.TestCase ): def lowerCAmelCase (self : Optional[Any] ): # clean up the VRAM after each test super().tearDown() gc.collect() def lowerCAmelCase (self : int ): __a , __a : Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained( '''stabilityai/stable-diffusion-2''' , revision='''bf16''' , dtype=jnp.bfloataa , ) __a : Any = '''A painting of a squirrel eating a burger''' __a : Dict = jax.device_count() __a : Optional[int] = num_samples * [prompt] __a : Optional[Any] = sd_pipe.prepare_inputs(snake_case_ ) __a : Optional[Any] = replicate(snake_case_ ) __a : Optional[int] = shard(snake_case_ ) __a : int = jax.random.PRNGKey(0 ) __a : str = jax.random.split(snake_case_ , jax.device_count() ) __a : int = sd_pipe(snake_case_ , snake_case_ , snake_case_ , num_inference_steps=2_5 , jit=snake_case_ )[0] assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3) __a : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] ) __a : List[Any] = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1] __a : Dict = jnp.asarray(jax.device_get(image_slice.flatten() ) ) __a : str = jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512] ) print(f"output_slice: {output_slice}" ) assert jnp.abs(output_slice - expected_slice ).max() < 1E-2 def lowerCAmelCase (self : Tuple ): __a : Optional[Any] = '''stabilityai/stable-diffusion-2''' __a , __a : Dict = FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_ , subfolder='''scheduler''' ) __a , __a : List[Any] = FlaxStableDiffusionPipeline.from_pretrained( snake_case_ , scheduler=snake_case_ , revision='''bf16''' , dtype=jnp.bfloataa , ) __a : Union[str, Any] = scheduler_params __a : List[Any] = '''A painting of a squirrel eating a burger''' __a : Any = jax.device_count() __a : Any = num_samples * [prompt] __a : List[Any] = sd_pipe.prepare_inputs(snake_case_ ) __a : Tuple = replicate(snake_case_ ) __a : Dict = shard(snake_case_ ) __a : Dict = jax.random.PRNGKey(0 ) __a : Dict = jax.random.split(snake_case_ , jax.device_count() ) __a : str = sd_pipe(snake_case_ , snake_case_ , snake_case_ , num_inference_steps=2_5 , jit=snake_case_ )[0] assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3) __a : List[Any] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] ) __a : Any = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1] __a : List[Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) ) __a : List[Any] = jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297] ) print(f"output_slice: {output_slice}" ) assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
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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 torch from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer from .base import PipelineTool class A ( UpperCAmelCase__ ): '''simple docstring''' A__ = '''facebook/bart-large-mnli''' A__ = ( '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which ''' '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. ''' '''It returns the most likely label in the list of provided `labels` for the input text.''' ) A__ = '''text_classifier''' A__ = AutoTokenizer A__ = AutoModelForSequenceClassification A__ = ['''text''', ['''text''']] A__ = ['''text'''] def lowerCamelCase__ (self : Optional[Any] ) -> Optional[Any]: """simple docstring""" super().setup() lowercase__ = self.model.config lowercase__ = -1 for idx, label in config.idalabel.items(): if label.lower().startswith("""entail""" ): lowercase__ = int(_UpperCAmelCase ) if self.entailment_id == -1: raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""" ) def lowerCamelCase__ (self : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] ) -> Union[str, Any]: """simple docstring""" lowercase__ = labels return self.pre_processor( [text] * len(_UpperCAmelCase ) , [f'''This example is {label}''' for label in labels] , return_tensors="""pt""" , padding="""max_length""" , ) def lowerCamelCase__ (self : List[str] , _UpperCAmelCase : Optional[int] ) -> Tuple: """simple docstring""" lowercase__ = outputs.logits lowercase__ = torch.argmax(logits[:, 2] ).item() return self._labels[label_id]
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import json import os import unittest from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class A ( UpperCAmelCase__ , unittest.TestCase ): '''simple docstring''' A__ = OpenAIGPTTokenizer A__ = OpenAIGPTTokenizerFast A__ = True A__ = False def lowerCamelCase__ (self : List[str] ) -> int: """simple docstring""" super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt lowercase__ = [ """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """w</w>""", """r</w>""", """t</w>""", """lo""", """low""", """er</w>""", """low</w>""", """lowest</w>""", """newer</w>""", """wider</w>""", """<unk>""", ] lowercase__ = dict(zip(_UpperCAmelCase , range(len(_UpperCAmelCase ) ) ) ) lowercase__ = ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""] lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] ) with open(self.vocab_file , """w""" ) as fp: fp.write(json.dumps(_UpperCAmelCase ) ) with open(self.merges_file , """w""" ) as fp: fp.write("""\n""".join(_UpperCAmelCase ) ) def lowerCamelCase__ (self : Dict , _UpperCAmelCase : List[Any] ) -> Any: """simple docstring""" return "lower newer", "lower newer" def lowerCamelCase__ (self : Dict ) -> Tuple: """simple docstring""" lowercase__ = OpenAIGPTTokenizer(self.vocab_file , self.merges_file ) lowercase__ = """lower""" lowercase__ = ["""low""", """er</w>"""] lowercase__ = tokenizer.tokenize(_UpperCAmelCase ) self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase ) lowercase__ = tokens + ["""<unk>"""] lowercase__ = [14, 15, 20] self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) , _UpperCAmelCase ) def lowerCamelCase__ (self : Optional[int] , _UpperCAmelCase : Union[str, Any]=15 ) -> Optional[int]: """simple docstring""" 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(_UpperCAmelCase , **_UpperCAmelCase ) # Simple input lowercase__ = """This is a simple input""" lowercase__ = ["""This is a simple input 1""", """This is a simple input 2"""] lowercase__ = ("""This is a simple input""", """This is a pair""") lowercase__ = [ ("""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(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" ) # Simple input self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" ) # Simple input self.assertRaises( _UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" , ) # Pair input self.assertRaises(_UpperCAmelCase , tokenizer_r.encode , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" ) # Pair input self.assertRaises(_UpperCAmelCase , tokenizer_r.encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" ) # Pair input self.assertRaises( _UpperCAmelCase , tokenizer_r.batch_encode_plus , _UpperCAmelCase , max_length=_UpperCAmelCase , padding="""max_length""" , ) def lowerCamelCase__ (self : Tuple ) -> Any: """simple docstring""" pass @require_ftfy @require_spacy @require_tokenizers class A ( UpperCAmelCase__ ): '''simple docstring''' pass
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'''simple docstring''' import shutil import tempfile import unittest import numpy as np import pytest from transformers import is_speech_available, is_vision_available from transformers.testing_utils import require_torch if is_vision_available(): from transformers import TvltImageProcessor if is_speech_available(): from transformers import TvltFeatureExtractor from transformers import TvltProcessor @require_torch class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" def __lowercase ( self : Tuple ): '''simple docstring''' _a : str = 'ZinengTang/tvlt-base' _a : Optional[Any] = tempfile.mkdtemp() def __lowercase ( self : List[str] ,**_a : int ): '''simple docstring''' return TvltImageProcessor.from_pretrained(self.checkpoint ,**_a ) def __lowercase ( self : List[str] ,**_a : Optional[Any] ): '''simple docstring''' return TvltFeatureExtractor.from_pretrained(self.checkpoint ,**_a ) def __lowercase ( self : List[Any] ): '''simple docstring''' shutil.rmtree(self.tmpdirname ) def __lowercase ( self : Union[str, Any] ): '''simple docstring''' _a : int = self.get_image_processor() _a : Optional[Any] = self.get_feature_extractor() _a : Any = TvltProcessor(image_processor=_a ,feature_extractor=_a ) processor.save_pretrained(self.tmpdirname ) _a : Any = TvltProcessor.from_pretrained(self.tmpdirname ) self.assertIsInstance(processor.feature_extractor ,_a ) self.assertIsInstance(processor.image_processor ,_a ) def __lowercase ( self : Tuple ): '''simple docstring''' _a : str = self.get_image_processor() _a : Any = self.get_feature_extractor() _a : List[str] = TvltProcessor(image_processor=_a ,feature_extractor=_a ) _a : Tuple = np.ones([1_2000] ) _a : Optional[Any] = feature_extractor(_a ,return_tensors='np' ) _a : str = processor(audio=_a ,return_tensors='np' ) for key in audio_dict.keys(): self.assertAlmostEqual(audio_dict[key].sum() ,input_processor[key].sum() ,delta=1E-2 ) def __lowercase ( self : str ): '''simple docstring''' _a : Tuple = self.get_image_processor() _a : Tuple = self.get_feature_extractor() _a : List[str] = TvltProcessor(image_processor=_a ,feature_extractor=_a ) _a : Dict = np.ones([3, 224, 224] ) _a : List[str] = image_processor(_a ,return_tensors='np' ) _a : Tuple = processor(images=_a ,return_tensors='np' ) for key in image_dict.keys(): self.assertAlmostEqual(image_dict[key].sum() ,input_processor[key].sum() ,delta=1E-2 ) def __lowercase ( self : Optional[int] ): '''simple docstring''' _a : int = self.get_image_processor() _a : List[str] = self.get_feature_extractor() _a : Optional[Any] = TvltProcessor(image_processor=_a ,feature_extractor=_a ) _a : int = np.ones([1_2000] ) _a : int = np.ones([3, 224, 224] ) _a : str = processor(audio=_a ,images=_a ) self.assertListEqual(list(inputs.keys() ) ,['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask'] ) # test if it raises when no input is passed with pytest.raises(_a ): processor() def __lowercase ( self : Optional[Any] ): '''simple docstring''' _a : int = self.get_image_processor() _a : Tuple = self.get_feature_extractor() _a : str = TvltProcessor(image_processor=_a ,feature_extractor=_a ) self.assertListEqual( processor.model_input_names ,image_processor.model_input_names + feature_extractor.model_input_names ,msg='`processor` and `image_processor`+`feature_extractor` model input names do not match' ,)
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'''simple docstring''' import doctest import logging import os import unittest from pathlib import Path from typing import List, Union import transformers from transformers.testing_utils import require_tf, require_torch, slow __lowerCAmelCase = logging.getLogger() @unittest.skip('''Temporarily disable the doc tests.''' ) @require_torch @require_tf @slow class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" def __lowercase ( self : str ,_a : Path ,_a : Union[str, None] = None ,_a : Union[List[str], None] = None ,_a : Union[str, List[str], None] = None ,_a : bool = True ,): '''simple docstring''' _a : Optional[int] = [file for file in os.listdir(_a ) if os.path.isfile(os.path.join(_a ,_a ) )] if identifier is not None: _a : List[str] = [file for file in files if identifier in file] if n_identifier is not None: if isinstance(_a ,_a ): for n_ in n_identifier: _a : Tuple = [file for file in files if n_ not in file] else: _a : Optional[Any] = [file for file in files if n_identifier not in file] _a : List[str] = ignore_files or [] ignore_files.append('__init__.py' ) _a : Tuple = [file for file in files if file not in ignore_files] for file in files: # Open all files print('Testing' ,_a ) if only_modules: _a : Any = file.split('.' )[0] try: _a : List[str] = getattr(_a ,_a ) _a : int = doctest.DocTestSuite(_a ) _a : Any = unittest.TextTestRunner().run(_a ) self.assertIs(len(result.failures ) ,0 ) except AttributeError: logger.info(F"""{module_identifier} is not a module.""" ) else: _a : Union[str, Any] = doctest.testfile(str('..' / directory / file ) ,optionflags=doctest.ELLIPSIS ) self.assertIs(result.failed ,0 ) def __lowercase ( self : Any ): '''simple docstring''' _a : int = Path('src/transformers' ) _a : List[Any] = 'modeling' _a : Optional[Any] = [ 'modeling_ctrl.py', 'modeling_tf_ctrl.py', ] self.analyze_directory(_a ,identifier=_a ,ignore_files=_a ) def __lowercase ( self : Optional[int] ): '''simple docstring''' _a : Optional[Any] = Path('src/transformers' ) _a : Optional[Any] = 'tokenization' self.analyze_directory(_a ,identifier=_a ) def __lowercase ( self : List[str] ): '''simple docstring''' _a : Dict = Path('src/transformers' ) _a : str = 'configuration' self.analyze_directory(_a ,identifier=_a ) def __lowercase ( self : Any ): '''simple docstring''' _a : Tuple = Path('src/transformers' ) _a : List[Any] = ['configuration', 'modeling', 'tokenization'] self.analyze_directory(_a ,n_identifier=_a ) def __lowercase ( self : Tuple ): '''simple docstring''' _a : List[Any] = Path('docs/source' ) _a : List[str] = ['favicon.ico'] self.analyze_directory(_a ,ignore_files=_a ,only_modules=_a )
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from __future__ import annotations def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool: snake_case : List[Any] = str(lowercase ) return len(lowercase ) == 9 and set(lowercase ) == set("""123456789""" ) def SCREAMING_SNAKE_CASE__ ( ) -> int | None: for base_num in range(9999 ,4999 ,-1 ): snake_case : List[str] = 100002 * base_num if is_9_pandigital(lowercase ): return candidate for base_num in range(333 ,99 ,-1 ): snake_case : List[Any] = 1002003 * base_num if is_9_pandigital(lowercase ): return candidate return None if __name__ == "__main__": print(f"""{solution() = }""")
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import inspect from typing import Optional, Union import numpy as np import PIL import torch from torch.nn import functional as F from torchvision import transforms from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput from diffusers.utils import ( PIL_INTERPOLATION, randn_tensor, ) def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ) -> Union[str, Any]: if isinstance(lowercase ,torch.Tensor ): return image elif isinstance(lowercase ,PIL.Image.Image ): snake_case : str = [image] if isinstance(image[0] ,PIL.Image.Image ): snake_case : List[Any] = [np.array(i.resize((w, h) ,resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image] snake_case : Optional[int] = np.concatenate(lowercase ,axis=0 ) snake_case : str = np.array(lowercase ).astype(np.floataa ) / 255.0 snake_case : List[str] = image.transpose(0 ,3 ,1 ,2 ) snake_case : Any = 2.0 * image - 1.0 snake_case : Optional[Any] = torch.from_numpy(lowercase ) elif isinstance(image[0] ,torch.Tensor ): snake_case : Optional[int] = torch.cat(lowercase ,dim=0 ) return image def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,lowercase ,lowercase=0.9995 ) -> Optional[int]: if not isinstance(lowercase ,np.ndarray ): snake_case : Any = True snake_case : str = va.device snake_case : Optional[Any] = va.cpu().numpy() snake_case : str = va.cpu().numpy() snake_case : Tuple = np.sum(va * va / (np.linalg.norm(lowercase ) * np.linalg.norm(lowercase )) ) if np.abs(lowercase ) > DOT_THRESHOLD: snake_case : Optional[int] = (1 - t) * va + t * va else: snake_case : List[Any] = np.arccos(lowercase ) snake_case : str = np.sin(lowercase ) snake_case : int = theta_a * t snake_case : Dict = np.sin(lowercase ) snake_case : Optional[Any] = np.sin(theta_a - theta_t ) / sin_theta_a snake_case : Union[str, Any] = sin_theta_t / sin_theta_a snake_case : Union[str, Any] = sa * va + sa * va if inputs_are_torch: snake_case : List[Any] = torch.from_numpy(lowercase ).to(lowercase ) return va def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> Tuple: snake_case : Dict = F.normalize(lowercase ,dim=-1 ) snake_case : Optional[Any] = F.normalize(lowercase ,dim=-1 ) return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 ) def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> str: for param in model.parameters(): snake_case : Tuple = value class __lowercase (UpperCamelCase__ ): """simple docstring""" def __init__( self , A , A , A , A , A , A , A , A=None , A=None , A=None , ) -> List[Any]: super().__init__() self.register_modules( vae=A , text_encoder=A , clip_model=A , tokenizer=A , unet=A , scheduler=A , feature_extractor=A , coca_model=A , coca_tokenizer=A , coca_transform=A , ) snake_case : Optional[int] = ( feature_extractor.size if isinstance(feature_extractor.size , A ) else feature_extractor.size["""shortest_edge"""] ) snake_case : Union[str, Any] = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std ) set_requires_grad(self.text_encoder , A ) set_requires_grad(self.clip_model , A ) def UpperCAmelCase ( self , A = "auto" ) -> Tuple: if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory snake_case : List[str] = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(A ) def UpperCAmelCase ( self ) -> Optional[int]: self.enable_attention_slicing(A ) def UpperCAmelCase ( self ) -> Any: set_requires_grad(self.vae , A ) def UpperCAmelCase ( self ) -> List[Any]: set_requires_grad(self.vae , A ) def UpperCAmelCase ( self ) -> Union[str, Any]: set_requires_grad(self.unet , A ) def UpperCAmelCase ( self ) -> Tuple: set_requires_grad(self.unet , A ) def UpperCAmelCase ( self , A , A , A ) -> Dict: # get the original timestep using init_timestep snake_case : Tuple = min(int(num_inference_steps * strength ) , A ) snake_case : List[str] = max(num_inference_steps - init_timestep , 0 ) snake_case : List[str] = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start def UpperCAmelCase ( self , A , A , A , A , A , A=None ) -> List[str]: if not isinstance(A , torch.Tensor ): raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(A )}""" ) snake_case : str = image.to(device=A , dtype=A ) if isinstance(A , A ): snake_case : int = [ self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(A ) ] snake_case : str = torch.cat(A , dim=0 ) else: snake_case : List[Any] = self.vae.encode(A ).latent_dist.sample(A ) # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor snake_case : Dict = 0.1_82_15 * init_latents snake_case : Tuple = init_latents.repeat_interleave(A , dim=0 ) snake_case : Optional[int] = randn_tensor(init_latents.shape , generator=A , device=A , dtype=A ) # get latents snake_case : Union[str, Any] = self.scheduler.add_noise(A , A , A ) snake_case : List[Any] = init_latents return latents def UpperCAmelCase ( self , A ) -> int: snake_case : Optional[Any] = self.coca_transform(A ).unsqueeze(0 ) with torch.no_grad(), torch.cuda.amp.autocast(): snake_case : Optional[Any] = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) ) snake_case : int = self.coca_tokenizer.decode(generated[0].cpu().numpy() ) return generated.split("""<end_of_text>""" )[0].replace("""<start_of_text>""" , """""" ).rstrip(""" .,""" ) def UpperCAmelCase ( self , A , A ) -> List[Any]: snake_case : Tuple = self.feature_extractor.preprocess(A ) snake_case : List[Any] = torch.from_numpy(clip_image_input["""pixel_values"""][0] ).unsqueeze(0 ).to(self.device ).half() snake_case : Optional[int] = self.clip_model.get_image_features(A ) snake_case : List[str] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A ) snake_case : Tuple = image_embeddings_clip.repeat_interleave(A , dim=0 ) return image_embeddings_clip @torch.enable_grad() def UpperCAmelCase ( self , A , A , A , A , A , A , A , ) -> Any: snake_case : Dict = latents.detach().requires_grad_() snake_case : str = self.scheduler.scale_model_input(A , A ) # predict the noise residual snake_case : str = self.unet(A , A , encoder_hidden_states=A ).sample if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ): snake_case : int = self.scheduler.alphas_cumprod[timestep] snake_case : Tuple = 1 - alpha_prod_t # compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf snake_case : Any = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5 snake_case : str = torch.sqrt(A ) snake_case : str = pred_original_sample * (fac) + latents * (1 - fac) elif isinstance(self.scheduler , A ): snake_case : int = self.scheduler.sigmas[index] snake_case : List[Any] = latents - sigma * noise_pred else: raise ValueError(f"""scheduler type {type(self.scheduler )} not supported""" ) # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor snake_case : List[str] = 1 / 0.1_82_15 * sample snake_case : str = self.vae.decode(A ).sample snake_case : Any = (image / 2 + 0.5).clamp(0 , 1 ) snake_case : str = transforms.Resize(self.feature_extractor_size )(A ) snake_case : Dict = self.normalize(A ).to(latents.dtype ) snake_case : Union[str, Any] = self.clip_model.get_image_features(A ) snake_case : List[str] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=A ) snake_case : Optional[int] = spherical_dist_loss(A , A ).mean() * clip_guidance_scale snake_case : int = -torch.autograd.grad(A , A )[0] if isinstance(self.scheduler , A ): snake_case : Union[str, Any] = latents.detach() + grads * (sigma**2) snake_case : Union[str, Any] = noise_pred_original else: snake_case : List[str] = noise_pred_original - torch.sqrt(A ) * grads return noise_pred, latents @torch.no_grad() def __call__( self , A , A , A = None , A = None , A = 5_1_2 , A = 5_1_2 , A = 0.6 , A = 5_0 , A = 7.5 , A = 1 , A = 0.0 , A = 1_0_0 , A = None , A = "pil" , A = True , A = 0.8 , A = 0.1 , A = 0.1 , ) -> Union[str, Any]: if isinstance(A , A ) and len(A ) != batch_size: raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(A )} generators.""" ) if height % 8 != 0 or width % 8 != 0: raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" ) if isinstance(A , torch.Generator ) and batch_size > 1: snake_case : Dict = [generator] + [None] * (batch_size - 1) snake_case : Tuple = [ ("""model""", self.coca_model is None), ("""tokenizer""", self.coca_tokenizer is None), ("""transform""", self.coca_transform is None), ] snake_case : List[str] = [x[0] for x in coca_is_none if x[1]] snake_case : Optional[int] = """, """.join(A ) # generate prompts with coca model if prompt is None if content_prompt is None: if len(A ): raise ValueError( f"""Content prompt is None and CoCa [{coca_is_none_str}] is None.""" f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" ) snake_case : Tuple = self.get_image_description(A ) if style_prompt is None: if len(A ): raise ValueError( f"""Style prompt is None and CoCa [{coca_is_none_str}] is None.""" f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.""" ) snake_case : List[Any] = self.get_image_description(A ) # get prompt text embeddings for content and style snake_case : Dict = self.tokenizer( A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors="""pt""" , ) snake_case : str = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0] snake_case : Dict = self.tokenizer( A , padding="""max_length""" , max_length=self.tokenizer.model_max_length , truncation=A , return_tensors="""pt""" , ) snake_case : Tuple = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0] snake_case : List[str] = slerp(A , A , A ) # duplicate text embeddings for each generation per prompt snake_case : List[Any] = text_embeddings.repeat_interleave(A , dim=0 ) # set timesteps snake_case : Union[str, Any] = """offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() ) snake_case : Optional[Any] = {} if accepts_offset: snake_case : Dict = 1 self.scheduler.set_timesteps(A , **A ) # Some schedulers like PNDM have timesteps as arrays # It's more optimized to move all timesteps to correct device beforehand self.scheduler.timesteps.to(self.device ) snake_case , snake_case : List[Any] = self.get_timesteps(A , A , self.device ) snake_case : List[str] = timesteps[:1].repeat(A ) # Preprocess image snake_case : Dict = preprocess(A , A , A ) snake_case : List[Any] = self.prepare_latents( A , A , A , text_embeddings.dtype , self.device , A ) snake_case : Optional[int] = preprocess(A , A , A ) snake_case : Optional[Any] = self.prepare_latents( A , A , A , text_embeddings.dtype , self.device , A ) snake_case : str = slerp(A , A , A ) if clip_guidance_scale > 0: snake_case : List[Any] = self.get_clip_image_embeddings(A , A ) snake_case : Any = self.get_clip_image_embeddings(A , A ) snake_case : Tuple = slerp( A , A , A ) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. snake_case : Optional[Any] = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: snake_case : List[str] = content_text_input.input_ids.shape[-1] snake_case : Any = self.tokenizer([""""""] , padding="""max_length""" , max_length=A , return_tensors="""pt""" ) snake_case : Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0] # duplicate unconditional embeddings for each generation per prompt snake_case : Tuple = uncond_embeddings.repeat_interleave(A , dim=0 ) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes snake_case : str = torch.cat([uncond_embeddings, text_embeddings] ) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. snake_case : Tuple = (batch_size, self.unet.config.in_channels, height // 8, width // 8) snake_case : List[Any] = text_embeddings.dtype if latents is None: if self.device.type == "mps": # randn does not work reproducibly on mps snake_case : List[Any] = torch.randn(A , generator=A , device="""cpu""" , dtype=A ).to( self.device ) else: snake_case : Optional[int] = torch.randn(A , generator=A , device=self.device , dtype=A ) else: if latents.shape != latents_shape: raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" ) snake_case : Union[str, Any] = latents.to(self.device ) # scale the initial noise by the standard deviation required by the scheduler snake_case : Dict = latents * self.scheduler.init_noise_sigma # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] snake_case : Tuple = """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() ) snake_case : Dict = {} if accepts_eta: snake_case : Union[str, Any] = eta # check if the scheduler accepts generator snake_case : List[Any] = """generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() ) if accepts_generator: snake_case : List[str] = generator with self.progress_bar(total=A ): for i, t in enumerate(A ): # expand the latents if we are doing classifier free guidance snake_case : Optional[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents snake_case : List[str] = self.scheduler.scale_model_input(A , A ) # predict the noise residual snake_case : Any = self.unet(A , A , encoder_hidden_states=A ).sample # perform classifier free guidance if do_classifier_free_guidance: snake_case , snake_case : int = noise_pred.chunk(2 ) snake_case : Tuple = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # perform clip guidance if clip_guidance_scale > 0: snake_case : Any = ( text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings ) snake_case , snake_case : List[Any] = self.cond_fn( A , A , A , A , A , A , A , ) # compute the previous noisy sample x_t -> x_t-1 snake_case : Tuple = self.scheduler.step(A , A , A , **A ).prev_sample # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor snake_case : str = 1 / 0.1_82_15 * latents snake_case : Optional[Any] = self.vae.decode(A ).sample snake_case : Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 ) snake_case : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": snake_case : Tuple = self.numpy_to_pil(A ) if not return_dict: return (image, None) return StableDiffusionPipelineOutput(images=A , nsfw_content_detected=A )
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"""simple docstring""" import requests _a = '' # <-- Put your OpenWeatherMap appid here! _a = 'https://api.openweathermap.org/data/2.5/' def __a ( __lowerCamelCase = "Chicago", __lowerCamelCase = APPID ): return requests.get(URL_BASE + "weather", params=locals() ).json() def __a ( __lowerCamelCase = "Kolkata, India", __lowerCamelCase = APPID ): return requests.get(URL_BASE + "forecast", params=locals() ).json() def __a ( __lowerCamelCase = 55.68, __lowerCamelCase = 12.57, __lowerCamelCase = APPID ): return requests.get(URL_BASE + "onecall", params=locals() ).json() if __name__ == "__main__": from pprint import pprint while True: _a = input('Enter a location:').strip() if location: pprint(current_weather(location)) else: break
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"""simple docstring""" from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def __a ( ): UpperCAmelCase_ : List[Any] = { "repo_name": ["test_repo1", "test_repo2", "test_repo3"], "path": ["test_1.py", "test_2.py", "unit_test.py"], "content": ["a " * 20, "a " * 30, "b " * 7], } UpperCAmelCase_ : Optional[int] = Dataset.from_dict(__lowerCamelCase ) return dataset class A_ (lowercase__ ): '''simple docstring''' def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Union[str, Any] = get_dataset() UpperCAmelCase_ : Any = make_duplicate_clusters(lowercase_ , 0.85 ) self.assertEqual(len(duplicate_clusters[0] ) , 2 ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : List[str] = get_dataset() UpperCAmelCase_ , UpperCAmelCase_ : List[str] = deduplicate_dataset(lowercase_ ) self.assertEqual(len(lowercase_ ) , 2 ) print(lowercase_ ) self.assertEqual(duplicate_clusters[0][0]["copies"] , 2 ) self.assertEqual(duplicate_clusters[0][0]["is_extreme"] , lowercase_ )
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import unittest from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_tf, require_torch, require_vision, slow, ) from .test_pipelines_common import ANY if is_vision_available(): from PIL import Image else: class A__ : """simple docstring""" @staticmethod def a_ ( *__snake_case , **__snake_case ): pass @is_pipeline_test @require_vision @require_torch class A__ ( unittest.TestCase ): """simple docstring""" __magic_name__ = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING def a_ ( self , __snake_case , __snake_case , __snake_case ): snake_case = pipeline( '''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' ) snake_case = [ { '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''', '''candidate_labels''': ['''cat''', '''remote''', '''couch'''], } ] return object_detector, examples def a_ ( self , __snake_case , __snake_case ): snake_case = object_detector(examples[0] , threshold=0.0 ) snake_case = len(__snake_case ) self.assertGreater(__snake_case , 0 ) self.assertEqual( __snake_case , [ { '''score''': ANY(__snake_case ), '''label''': ANY(__snake_case ), '''box''': {'''xmin''': ANY(__snake_case ), '''ymin''': ANY(__snake_case ), '''xmax''': ANY(__snake_case ), '''ymax''': ANY(__snake_case )}, } for i in range(__snake_case ) ] , ) @require_tf @unittest.skip('''Zero Shot Object Detection not implemented in TF''' ) def a_ ( self ): pass @require_torch def a_ ( self ): snake_case = pipeline( '''zero-shot-object-detection''' , model='''hf-internal-testing/tiny-random-owlvit-object-detection''' ) snake_case = object_detector( '''./tests/fixtures/tests_samples/COCO/000000039769.png''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=0.64 , ) self.assertEqual( nested_simplify(__snake_case , decimals=4 ) , [ {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}}, {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}}, {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}}, {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}}, {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}}, {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}}, {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}}, {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}}, {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}}, ] , ) snake_case = object_detector( [ { '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''', '''candidate_labels''': ['''cat''', '''remote''', '''couch'''], } ] , threshold=0.64 , ) self.assertEqual( nested_simplify(__snake_case , decimals=4 ) , [ [ {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}}, {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}}, {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 2_0_4, '''ymin''': 1_6_7, '''xmax''': 2_3_2, '''ymax''': 1_9_0}}, {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}}, {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}}, {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 5_7_1, '''ymin''': 8_3, '''xmax''': 5_9_8, '''ymax''': 1_0_3}}, {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}}, {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 6_7, '''ymin''': 2_7_4, '''xmax''': 9_3, '''ymax''': 2_9_7}}, {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 4_9_4, '''ymin''': 1_0_5, '''xmax''': 5_2_1, '''ymax''': 1_2_7}}, ] ] , ) @require_torch @slow def a_ ( self ): snake_case = pipeline('''zero-shot-object-detection''' ) snake_case = object_detector( '''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , ) self.assertEqual( nested_simplify(__snake_case , decimals=4 ) , [ {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}}, {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}}, {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}}, {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}}, {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}}, ] , ) snake_case = object_detector( [ { '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''', '''candidate_labels''': ['''cat''', '''remote''', '''couch'''], }, { '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''', '''candidate_labels''': ['''cat''', '''remote''', '''couch'''], }, ] , ) self.assertEqual( nested_simplify(__snake_case , decimals=4 ) , [ [ {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}}, {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}}, {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}}, {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}}, {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}}, ], [ {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}}, {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}}, {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}}, {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 3_3_5, '''ymin''': 7_4, '''xmax''': 3_7_1, '''ymax''': 1_8_7}}, {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_4_2, '''ymax''': 4_7_6}}, ], ] , ) @require_tf @unittest.skip('''Zero Shot Object Detection not implemented in TF''' ) def a_ ( self ): pass @require_torch @slow def a_ ( self ): snake_case = 0.2 snake_case = pipeline('''zero-shot-object-detection''' ) snake_case = object_detector( '''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , threshold=__snake_case , ) self.assertEqual( nested_simplify(__snake_case , decimals=4 ) , [ {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}}, {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}}, {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 5_5, '''xmax''': 3_1_5, '''ymax''': 4_7_2}}, ] , ) @require_torch @slow def a_ ( self ): snake_case = 2 snake_case = pipeline('''zero-shot-object-detection''' ) snake_case = object_detector( '''http://images.cocodataset.org/val2017/000000039769.jpg''' , candidate_labels=['''cat''', '''remote''', '''couch'''] , top_k=__snake_case , ) self.assertEqual( nested_simplify(__snake_case , decimals=4 ) , [ {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 3_2_4, '''ymin''': 2_0, '''xmax''': 6_4_0, '''ymax''': 3_7_3}}, {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 4_0, '''ymin''': 7_2, '''xmax''': 1_7_7, '''ymax''': 1_1_5}}, ] , )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _SCREAMING_SNAKE_CASE : int = { "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _SCREAMING_SNAKE_CASE : Dict = [ "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TimesformerModel", "TimesformerForVideoClassification", "TimesformerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_timesformer import ( TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TimesformerForVideoClassification, TimesformerModel, TimesformerPreTrainedModel, ) else: import sys _SCREAMING_SNAKE_CASE : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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import argparse import io import requests import torch from omegaconf import OmegaConf from diffusers import AutoencoderKL from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( assign_to_checkpoint, conv_attn_to_linear, create_vae_diffusers_config, renew_vae_attention_paths, renew_vae_resnet_paths, ) def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> Any: '''simple docstring''' lowerCAmelCase : Optional[int] = checkpoint lowerCAmelCase : Optional[int] = {} lowerCAmelCase : List[str] = vae_state_dict["encoder.conv_in.weight"] lowerCAmelCase : int = vae_state_dict["encoder.conv_in.bias"] lowerCAmelCase : Dict = vae_state_dict["encoder.conv_out.weight"] lowerCAmelCase : Any = vae_state_dict["encoder.conv_out.bias"] lowerCAmelCase : Optional[Any] = vae_state_dict["encoder.norm_out.weight"] lowerCAmelCase : int = vae_state_dict["encoder.norm_out.bias"] lowerCAmelCase : Optional[Any] = vae_state_dict["decoder.conv_in.weight"] lowerCAmelCase : List[str] = vae_state_dict["decoder.conv_in.bias"] lowerCAmelCase : str = vae_state_dict["decoder.conv_out.weight"] lowerCAmelCase : str = vae_state_dict["decoder.conv_out.bias"] lowerCAmelCase : int = vae_state_dict["decoder.norm_out.weight"] lowerCAmelCase : List[str] = vae_state_dict["decoder.norm_out.bias"] lowerCAmelCase : int = vae_state_dict["quant_conv.weight"] lowerCAmelCase : int = vae_state_dict["quant_conv.bias"] lowerCAmelCase : List[str] = vae_state_dict["post_quant_conv.weight"] lowerCAmelCase : Tuple = vae_state_dict["post_quant_conv.bias"] # Retrieves the keys for the encoder down blocks only lowerCAmelCase : str = len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'encoder.down' in layer} ) lowerCAmelCase : Optional[Any] = { layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(_UpperCAmelCase ) } # Retrieves the keys for the decoder up blocks only lowerCAmelCase : Union[str, Any] = len({'.'.join(layer.split('.' )[:3] ) for layer in vae_state_dict if 'decoder.up' in layer} ) lowerCAmelCase : Dict = { layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(_UpperCAmelCase ) } for i in range(_UpperCAmelCase ): lowerCAmelCase : int = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: lowerCAmelCase : Optional[Any] = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.weight" ) lowerCAmelCase : Dict = vae_state_dict.pop( f"encoder.down.{i}.downsample.conv.bias" ) lowerCAmelCase : Optional[Any] = renew_vae_resnet_paths(_UpperCAmelCase ) lowerCAmelCase : str = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} assign_to_checkpoint(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, additional_replacements=[meta_path], config=_UpperCAmelCase ) lowerCAmelCase : int = [key for key in vae_state_dict if "encoder.mid.block" in key] lowerCAmelCase : str = 2 for i in range(1, num_mid_res_blocks + 1 ): lowerCAmelCase : Dict = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] lowerCAmelCase : Dict = renew_vae_resnet_paths(_UpperCAmelCase ) lowerCAmelCase : Dict = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} assign_to_checkpoint(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, additional_replacements=[meta_path], config=_UpperCAmelCase ) lowerCAmelCase : List[str] = [key for key in vae_state_dict if "encoder.mid.attn" in key] lowerCAmelCase : Union[str, Any] = renew_vae_attention_paths(_UpperCAmelCase ) lowerCAmelCase : Union[str, Any] = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} assign_to_checkpoint(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, additional_replacements=[meta_path], config=_UpperCAmelCase ) conv_attn_to_linear(_UpperCAmelCase ) for i in range(_UpperCAmelCase ): lowerCAmelCase : Union[str, Any] = num_up_blocks - 1 - i lowerCAmelCase : List[Any] = [ key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key ] if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: lowerCAmelCase : Optional[Any] = vae_state_dict[ f"decoder.up.{block_id}.upsample.conv.weight" ] lowerCAmelCase : Any = vae_state_dict[ f"decoder.up.{block_id}.upsample.conv.bias" ] lowerCAmelCase : int = renew_vae_resnet_paths(_UpperCAmelCase ) lowerCAmelCase : Union[str, Any] = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} assign_to_checkpoint(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, additional_replacements=[meta_path], config=_UpperCAmelCase ) lowerCAmelCase : Any = [key for key in vae_state_dict if "decoder.mid.block" in key] lowerCAmelCase : Union[str, Any] = 2 for i in range(1, num_mid_res_blocks + 1 ): lowerCAmelCase : Tuple = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] lowerCAmelCase : Tuple = renew_vae_resnet_paths(_UpperCAmelCase ) lowerCAmelCase : List[str] = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} assign_to_checkpoint(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, additional_replacements=[meta_path], config=_UpperCAmelCase ) lowerCAmelCase : str = [key for key in vae_state_dict if "decoder.mid.attn" in key] lowerCAmelCase : Tuple = renew_vae_attention_paths(_UpperCAmelCase ) lowerCAmelCase : Optional[Any] = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} assign_to_checkpoint(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, additional_replacements=[meta_path], config=_UpperCAmelCase ) conv_attn_to_linear(_UpperCAmelCase ) return new_checkpoint def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, ) -> Any: '''simple docstring''' lowerCAmelCase : List[str] = requests.get( ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml' ) lowerCAmelCase : Optional[int] = io.BytesIO(r.content ) lowerCAmelCase : Optional[Any] = OmegaConf.load(_UpperCAmelCase ) lowerCAmelCase : Optional[int] = 512 lowerCAmelCase : str = "cuda" if torch.cuda.is_available() else "cpu" if checkpoint_path.endswith('safetensors' ): from safetensors import safe_open lowerCAmelCase : Dict = {} with safe_open(_UpperCAmelCase, framework='pt', device='cpu' ) as f: for key in f.keys(): lowerCAmelCase : int = f.get_tensor(_UpperCAmelCase ) else: lowerCAmelCase : Union[str, Any] = torch.load(_UpperCAmelCase, map_location=_UpperCAmelCase )["state_dict"] # Convert the VAE model. lowerCAmelCase : str = create_vae_diffusers_config(_UpperCAmelCase, image_size=_UpperCAmelCase ) lowerCAmelCase : str = custom_convert_ldm_vae_checkpoint(_UpperCAmelCase, _UpperCAmelCase ) lowerCAmelCase : Optional[int] = AutoencoderKL(**_UpperCAmelCase ) vae.load_state_dict(_UpperCAmelCase ) vae.save_pretrained(_UpperCAmelCase ) if __name__ == "__main__": __A : Any = argparse.ArgumentParser() parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''') parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''') __A : List[str] = parser.parse_args() vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
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'''simple docstring''' def A_ ( snake_case ): if not all(x.isalpha() for x in string ): raise ValueError("String must only contain alphabetic characters." ) SCREAMING_SNAKE_CASE:Optional[int] = sorted(string.lower() ) return len(snake_case ) == len(set(snake_case ) ) if __name__ == "__main__": A_ = input("Enter a string ").strip() A_ = is_isogram(input_str) print(f'''{input_str} is {"an" if isogram else "not an"} isogram.''')
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging __UpperCamelCase : str = logging.get_logger(__name__) __UpperCamelCase : Optional[Any] = { "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json", } class a ( SCREAMING_SNAKE_CASE__ ): snake_case__ = 'gpt_neox_japanese' def __init__( self , _snake_case=3_20_00 , _snake_case=25_60 , _snake_case=32 , _snake_case=32 , _snake_case=4 , _snake_case="gelu" , _snake_case=1.00 , _snake_case=1_00_00 , _snake_case=20_48 , _snake_case=0.02 , _snake_case=1E-5 , _snake_case=True , _snake_case=3_19_96 , _snake_case=3_19_99 , _snake_case=0.1 , _snake_case=0.0 , **_snake_case , ): """simple docstring""" super().__init__(bos_token_id=a_ , eos_token_id=a_ , **a_ ) lowerCAmelCase = vocab_size lowerCAmelCase = max_position_embeddings lowerCAmelCase = hidden_size lowerCAmelCase = num_hidden_layers lowerCAmelCase = num_attention_heads lowerCAmelCase = intermediate_multiple_size lowerCAmelCase = hidden_act lowerCAmelCase = rotary_pct lowerCAmelCase = rotary_emb_base lowerCAmelCase = initializer_range lowerCAmelCase = layer_norm_eps lowerCAmelCase = use_cache lowerCAmelCase = attention_dropout lowerCAmelCase = hidden_dropout
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"""simple docstring""" import os from collections.abc import Iterator def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ): for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ): lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._'] for filename in filenames: if filename == "__init__.py": continue if os.path.splitext(_UpperCAmelCase )[1] in (".py", ".ipynb"): yield os.path.join(_UpperCAmelCase , _UpperCAmelCase ).lstrip('./' ) def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ): return F'{i * " "}*' if i else "\n##" def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : str ): lowerCAmelCase = old_path.split(os.sep ) for i, new_part in enumerate(new_path.split(os.sep ) ): if (i + 1 > len(_UpperCAmelCase ) or old_parts[i] != new_part) and new_part: print(F'{md_prefix(_UpperCAmelCase )} {new_part.replace("_" , " " ).title()}' ) return new_path def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ): lowerCAmelCase = '' for filepath in sorted(good_file_paths(_UpperCAmelCase ) ): lowerCAmelCase ,lowerCAmelCase = os.path.split(_UpperCAmelCase ) if filepath != old_path: lowerCAmelCase = print_path(_UpperCAmelCase , _UpperCAmelCase ) lowerCAmelCase = (filepath.count(os.sep ) + 1) if filepath else 0 lowerCAmelCase = F'{filepath}/{filename}'.replace(' ' , '%20' ) lowerCAmelCase = os.path.splitext(filename.replace('_' , ' ' ).title() )[0] print(F'{md_prefix(_UpperCAmelCase )} [{filename}]({url})' ) if __name__ == "__main__": print_directory_md('''.''')
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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() and is_transformers_version('''>=''', '''4.25.0''')): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import ( VersatileDiffusionDualGuidedPipeline, VersatileDiffusionImageVariationPipeline, VersatileDiffusionPipeline, VersatileDiffusionTextToImagePipeline, ) else: from .modeling_text_unet import UNetFlatConditionModel from .pipeline_versatile_diffusion import VersatileDiffusionPipeline from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
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"""simple docstring""" from __future__ import annotations def UpperCAmelCase__ (snake_case__ : list[float] ): """simple docstring""" _snake_case : int = 0.00 _snake_case : int = 0 for resistor in resistors: if resistor <= 0: _snake_case : Dict = F"Resistor at index {index} has a negative or zero value!" raise ValueError(snake_case__ ) first_sum += 1 / float(snake_case__ ) index += 1 return 1 / first_sum def UpperCAmelCase__ (snake_case__ : list[float] ): """simple docstring""" _snake_case : Union[str, Any] = 0.00 _snake_case : Any = 0 for resistor in resistors: sum_r += resistor if resistor < 0: _snake_case : Any = F"Resistor at index {index} has a negative value!" raise ValueError(snake_case__ ) index += 1 return sum_r if __name__ == "__main__": import doctest doctest.testmod()
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1
"""simple docstring""" from math import pow, sqrt def _A (*__a ) -> bool: """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = len(__a ) > 0 and all(value > 0.0 for value in values ) return result def _A (__a , __a ) -> float | ValueError: """simple docstring""" return ( round(sqrt(molar_mass_a / molar_mass_a ) , 6 ) if validate(__a , __a ) else ValueError('''Input Error: Molar mass values must greater than 0.''' ) ) def _A (__a , __a , __a ) -> float | ValueError: """simple docstring""" return ( round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 ) if validate(__a , __a , __a ) else ValueError( '''Input Error: Molar mass and effusion rate values must greater than 0.''' ) ) def _A (__a , __a , __a ) -> float | ValueError: """simple docstring""" return ( round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 ) if validate(__a , __a , __a ) else ValueError( '''Input Error: Molar mass and effusion rate values must greater than 0.''' ) ) def _A (__a , __a , __a ) -> float | ValueError: """simple docstring""" return ( round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 ) if validate(__a , __a , __a ) else ValueError( '''Input Error: Molar mass and effusion rate values must greater than 0.''' ) ) def _A (__a , __a , __a ) -> float | ValueError: """simple docstring""" return ( round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 ) if validate(__a , __a , __a ) else ValueError( '''Input Error: Molar mass and effusion rate values must greater than 0.''' ) )
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"""simple docstring""" from __future__ import annotations UpperCAmelCase_ : List[str] = list[list[int]] # assigning initial values to the grid UpperCAmelCase_ : Matrix = [ [3, 0, 6, 5, 0, 8, 4, 0, 0], [5, 2, 0, 0, 0, 0, 0, 0, 0], [0, 8, 7, 0, 0, 0, 0, 3, 1], [0, 0, 3, 0, 1, 0, 0, 8, 0], [9, 0, 0, 8, 6, 3, 0, 0, 5], [0, 5, 0, 0, 9, 0, 6, 0, 0], [1, 3, 0, 0, 0, 0, 2, 5, 0], [0, 0, 0, 0, 0, 0, 0, 7, 4], [0, 0, 5, 2, 0, 6, 3, 0, 0], ] # a grid with no solution UpperCAmelCase_ : Matrix = [ [5, 0, 6, 5, 0, 8, 4, 0, 3], [5, 2, 0, 0, 0, 0, 0, 0, 2], [1, 8, 7, 0, 0, 0, 0, 3, 1], [0, 0, 3, 0, 1, 0, 0, 8, 0], [9, 0, 0, 8, 6, 3, 0, 0, 5], [0, 5, 0, 0, 9, 0, 6, 0, 0], [1, 3, 0, 0, 0, 0, 2, 5, 0], [0, 0, 0, 0, 0, 0, 0, 7, 4], [0, 0, 5, 2, 0, 6, 3, 0, 0], ] def _A (__a , __a , __a , __a ) -> bool: """simple docstring""" for i in range(9 ): if grid[row][i] == n or grid[i][column] == n: return False for i in range(3 ): for j in range(3 ): if grid[(row - row % 3) + i][(column - column % 3) + j] == n: return False return True def _A (__a ) -> tuple[int, int] | None: """simple docstring""" for i in range(9 ): for j in range(9 ): if grid[i][j] == 0: return i, j return None def _A (__a ) -> Matrix | None: """simple docstring""" if location := find_empty_location(__a ): SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict = location else: # If the location is ``None``, then the grid is solved. return grid for digit in range(1 , 10 ): if is_safe(__a , __a , __a , __a ): SCREAMING_SNAKE_CASE_ : Tuple = digit if sudoku(__a ) is not None: return grid SCREAMING_SNAKE_CASE_ : Any = 0 return None def _A (__a ) -> None: """simple docstring""" for row in grid: for cell in row: print(__a , end=''' ''' ) print() if __name__ == "__main__": # make a copy of grid so that you can compare with the unmodified grid for example_grid in (initial_grid, no_solution): print("""\nExample grid:\n""" + """=""" * 20) print_solution(example_grid) print("""\nExample grid solution:""") UpperCAmelCase_ : str = sudoku(example_grid) if solution is not None: print_solution(solution) else: print("""Cannot find a solution.""")
318
1
'''simple docstring''' import unittest import numpy as np from transformers.testing_utils import require_pytesseract, require_torch from transformers.utils import is_pytesseract_available, is_torch_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_pytesseract_available(): from PIL import Image from transformers import LayoutLMvaImageProcessor class UpperCAmelCase_ ( unittest.TestCase ): def __init__( self : Dict , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Union[str, Any]=7 , UpperCAmelCase__ : int=3 , UpperCAmelCase__ : Tuple=1_8 , UpperCAmelCase__ : Optional[int]=3_0 , UpperCAmelCase__ : Union[str, Any]=4_0_0 , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Any=None , UpperCAmelCase__ : Optional[Any]=True , ) -> int: lowerCAmelCase = size if size is not None else {'height': 1_8, 'width': 1_8} lowerCAmelCase = parent lowerCAmelCase = batch_size lowerCAmelCase = num_channels lowerCAmelCase = image_size lowerCAmelCase = min_resolution lowerCAmelCase = max_resolution lowerCAmelCase = do_resize lowerCAmelCase = size lowerCAmelCase = apply_ocr def __UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]: return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr} @require_torch @require_pytesseract class UpperCAmelCase_ ( __lowercase , unittest.TestCase ): lowerCamelCase : Optional[int] = LayoutLMvaImageProcessor if is_pytesseract_available() else None def __UpperCAmelCase ( self : Tuple ) -> int: lowerCAmelCase = LayoutLMvaImageProcessingTester(self ) @property def __UpperCAmelCase ( self : Any ) -> List[Any]: return self.image_processor_tester.prepare_image_processor_dict() def __UpperCAmelCase ( self : int ) -> Union[str, Any]: lowerCAmelCase = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(UpperCAmelCase__ , 'do_resize' ) ) self.assertTrue(hasattr(UpperCAmelCase__ , 'size' ) ) self.assertTrue(hasattr(UpperCAmelCase__ , 'apply_ocr' ) ) def __UpperCAmelCase ( self : List[Any] ) -> Union[str, Any]: lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {'height': 1_8, 'width': 1_8} ) lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 ) self.assertEqual(image_processor.size , {'height': 4_2, 'width': 4_2} ) def __UpperCAmelCase ( self : Dict ) -> str: pass def __UpperCAmelCase ( self : Optional[Any] ) -> Union[str, Any]: # Initialize image_processing lowerCAmelCase = self.image_processing_class(**self.image_processor_dict ) # create random PIL images lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ ) for image in image_inputs: self.assertIsInstance(UpperCAmelCase__ , Image.Image ) # Test not batched input lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ) self.assertEqual( encoding.pixel_values.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['height'], self.image_processor_tester.size['width'], ) , ) self.assertIsInstance(encoding.words , UpperCAmelCase__ ) self.assertIsInstance(encoding.boxes , UpperCAmelCase__ ) # Test batched lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['height'], self.image_processor_tester.size['width'], ) , ) def __UpperCAmelCase ( self : Optional[int] ) -> Dict: # Initialize image_processing lowerCAmelCase = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ ) for image in image_inputs: self.assertIsInstance(UpperCAmelCase__ , np.ndarray ) # Test not batched input lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['height'], self.image_processor_tester.size['width'], ) , ) # Test batched lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['height'], self.image_processor_tester.size['width'], ) , ) def __UpperCAmelCase ( self : List[Any] ) -> Any: # Initialize image_processing lowerCAmelCase = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ ) for image in image_inputs: self.assertIsInstance(UpperCAmelCase__ , torch.Tensor ) # Test not batched input lowerCAmelCase = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['height'], self.image_processor_tester.size['width'], ) , ) # Test batched lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['height'], self.image_processor_tester.size['width'], ) , ) def __UpperCAmelCase ( self : str ) -> Optional[int]: # with apply_OCR = True lowerCAmelCase = LayoutLMvaImageProcessor() from datasets import load_dataset lowerCAmelCase = load_dataset('hf-internal-testing/fixtures_docvqa' , split='test' ) lowerCAmelCase = Image.open(ds[0]['file'] ).convert('RGB' ) lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ) self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) ) self.assertEqual(len(encoding.words ) , len(encoding.boxes ) ) # fmt: off # the words and boxes were obtained with Tesseract 4.1.1 lowerCAmelCase = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the', 'lobby', 'adjacent', 'to', 'exhibit', 'area.', 'Please', 'move', 'into', 'exhibit', 'area.', '(Exhibits', 'Open)', 'TRRF', 'GENERAL', 'SESSION', '(PART', '|)', 'Presiding:', 'Lee', 'A.', 'Waller', 'TRRF', 'Vice', 'President', '“Introductory', 'Remarks”', 'Lee', 'A.', 'Waller,', 'TRRF', 'Vice', 'Presi-', 'dent', 'Individual', 'Interviews', 'with', 'TRRF', 'Public', 'Board', 'Members', 'and', 'Sci-', 'entific', 'Advisory', 'Council', 'Mem-', 'bers', 'Conducted', 'by', 'TRRF', 'Treasurer', 'Philip', 'G.', 'Kuehn', 'to', 'get', 'answers', 'which', 'the', 'public', 'refrigerated', 'warehousing', 'industry', 'is', 'looking', 'for.', 'Plus', 'questions', 'from', 'the', 'floor.', 'Dr.', 'Emil', 'M.', 'Mrak,', 'University', 'of', 'Cal-', 'ifornia,', 'Chairman,', 'TRRF', 'Board;', 'Sam', 'R.', 'Cecil,', 'University', 'of', 'Georgia', 'College', 'of', 'Agriculture;', 'Dr.', 'Stanley', 'Charm,', 'Tufts', 'University', 'School', 'of', 'Medicine;', 'Dr.', 'Robert', 'H.', 'Cotton,', 'ITT', 'Continental', 'Baking', 'Company;', 'Dr.', 'Owen', 'Fennema,', 'University', 'of', 'Wis-', 'consin;', 'Dr.', 'Robert', 'E.', 'Hardenburg,', 'USDA.', 'Questions', 'and', 'Answers', 'Exhibits', 'Open', 'Capt.', 'Jack', 'Stoney', 'Room', 'TRRF', 'Scientific', 'Advisory', 'Council', 'Meeting', 'Ballroom', 'Foyer']] # noqa: E231 lowerCAmelCase = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231 # fmt: on self.assertListEqual(encoding.words , UpperCAmelCase__ ) self.assertListEqual(encoding.boxes , UpperCAmelCase__ ) # with apply_OCR = False lowerCAmelCase = LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__ ) lowerCAmelCase = image_processing(UpperCAmelCase__ , return_tensors='pt' ) self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
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"""simple docstring""" from arguments import InitializationArguments from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser # Configuration SCREAMING_SNAKE_CASE__ = HfArgumentParser(InitializationArguments) SCREAMING_SNAKE_CASE__ = parser.parse_args() # Load codeparrot tokenizer trained for Python code tokenization SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained(args.tokenizer_name) # Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks SCREAMING_SNAKE_CASE__ = { "vocab_size": len(tokenizer), "scale_attn_by_inverse_layer_idx": True, "reorder_and_upcast_attn": True, } # Load model config (GPT-2 large in this case) SCREAMING_SNAKE_CASE__ = AutoConfig.from_pretrained(args.config_name, **config_kwargs) # Initialize new model with config SCREAMING_SNAKE_CASE__ = AutoModelForCausalLM.from_config(config) # Save model to the hub model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
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"""simple docstring""" import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class __snake_case ( unittest.TestCase ): @property def lowerCamelCase_ ( self) -> Dict: '''simple docstring''' torch.manual_seed(0) a__: str = UNetaDModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , ) return model @property def lowerCamelCase_ ( self) -> int: '''simple docstring''' torch.manual_seed(0) a__: List[Any] = VQModel( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=3 , ) return model @property def lowerCamelCase_ ( self) -> Optional[Any]: '''simple docstring''' torch.manual_seed(0) a__: Optional[int] = 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=10_00 , ) return CLIPTextModel(lowercase) def lowerCamelCase_ ( self) -> str: '''simple docstring''' a__: Union[str, Any] = self.dummy_uncond_unet a__: Optional[int] = DDIMScheduler() a__: Optional[int] = self.dummy_vq_model a__: Union[str, Any] = LDMPipeline(unet=lowercase , vqvae=lowercase , scheduler=lowercase) ldm.to(lowercase) ldm.set_progress_bar_config(disable=lowercase) a__: str = torch.manual_seed(0) a__: Dict = ldm(generator=lowercase , num_inference_steps=2 , output_type='numpy').images a__: Union[str, Any] = torch.manual_seed(0) a__: int = ldm(generator=lowercase , num_inference_steps=2 , output_type='numpy' , return_dict=lowercase)[0] a__: Union[str, Any] = image[0, -3:, -3:, -1] a__: int = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) a__: int = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]) a__: Optional[Any] = 1e-2 if torch_device != 'mps' else 3e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance @slow @require_torch class __snake_case ( unittest.TestCase ): def lowerCamelCase_ ( self) -> Optional[Any]: '''simple docstring''' a__: Union[str, Any] = LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256') ldm.to(lowercase) ldm.set_progress_bar_config(disable=lowercase) a__: List[str] = torch.manual_seed(0) a__: Optional[int] = ldm(generator=lowercase , num_inference_steps=5 , output_type='numpy').images a__: Optional[int] = image[0, -3:, -3:, -1] assert image.shape == (1, 2_56, 2_56, 3) a__: int = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]) a__: Any = 1e-2 if torch_device != 'mps' else 3e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
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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 lowercase__ = logging.get_logger(__name__) lowercase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'} lowercase__ = { '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' ), }, } lowercase__ = { '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' ), }, } lowercase__ = { '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' ), }, } lowercase__ = { 'facebook/dpr-ctx_encoder-single-nq-base': 512, 'facebook/dpr-ctx_encoder-multiset-base': 512, } lowercase__ = { 'facebook/dpr-question_encoder-single-nq-base': 512, 'facebook/dpr-question_encoder-multiset-base': 512, } lowercase__ = { 'facebook/dpr-reader-single-nq-base': 512, 'facebook/dpr-reader-multiset-base': 512, } lowercase__ = { 'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True}, 'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True}, } lowercase__ = { 'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True}, 'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True}, } lowercase__ = { 'facebook/dpr-reader-single-nq-base': {'do_lower_case': True}, 'facebook/dpr-reader-multiset-base': {'do_lower_case': True}, } class __snake_case ( __lowerCAmelCase ): a__ = VOCAB_FILES_NAMES a__ = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP a__ = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a__ = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION class __snake_case ( __lowerCAmelCase ): a__ = VOCAB_FILES_NAMES a__ = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP a__ = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a__ = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION lowercase__ = collections.namedtuple( 'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text'] ) lowercase__ = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits']) lowercase__ = r'\n Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n with the format:\n\n ```\n [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n ```\n\n Args:\n questions (`str` or `List[str]`):\n The questions to be encoded. You can specify one question for many passages. In this case, the question\n will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n `titles` or `texts`.\n titles (`str` or `List[str]`):\n The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n texts (`str` or `List[str]`):\n The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n Activates and controls padding. Accepts the following values:\n\n - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n if provided).\n - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided.\n - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n lengths).\n truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n Activates and controls truncation. Accepts the following values:\n\n - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n the maximum acceptable input length for the model if that argument is not provided. This will truncate\n token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n of pairs) is provided.\n - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the first\n sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n acceptable input length for the model if that argument is not provided. This will only truncate the\n second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n greater than the model maximum admissible input size).\n max_length (`int`, *optional*):\n Controls the maximum length to use by one of the truncation/padding parameters.\n\n If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n is required by one of the truncation/padding parameters. If the model has no specific maximum input\n length (like XLNet) truncation/padding to a maximum length will be deactivated.\n return_tensors (`str` or [`~utils.TensorType`], *optional*):\n If set, will return tensors instead of list of python integers. Acceptable values are:\n\n - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n - `\'np\'`: Return Numpy `np.ndarray` objects.\n return_attention_mask (`bool`, *optional*):\n Whether or not to return the attention mask. If not set, will return the attention mask according to the\n specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n [What are attention masks?](../glossary#attention-mask)\n\n Returns:\n `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n - `input_ids`: List of token ids to be fed to a model.\n - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n ' @add_start_docstrings(__lowerCAmelCase ) class __snake_case : def __call__( self , lowercase , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = None , lowercase = None , lowercase = None , **lowercase , ) -> BatchEncoding: '''simple docstring''' if titles is None and texts is None: return super().__call__( lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , return_tensors=lowercase , return_attention_mask=lowercase , **lowercase , ) elif titles is None or texts is None: a__: str = titles if texts is None else texts return super().__call__( lowercase , lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , return_tensors=lowercase , return_attention_mask=lowercase , **lowercase , ) a__: Tuple = titles if not isinstance(lowercase , lowercase) else [titles] a__: Optional[int] = texts if not isinstance(lowercase , lowercase) else [texts] a__: Dict = len(lowercase) a__: Dict = questions if not isinstance(lowercase , lowercase) else [questions] * n_passages if len(lowercase) != len(lowercase): raise ValueError( f'There should be as many titles than texts but got {len(lowercase)} titles and {len(lowercase)} texts.') a__: List[str] = super().__call__(lowercase , lowercase , padding=lowercase , truncation=lowercase)['input_ids'] a__: List[Any] = super().__call__(lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase)['input_ids'] a__: Optional[Any] = { '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(lowercase , lowercase) ] } if return_attention_mask is not False: a__: Dict = [] 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__: int = attention_mask return self.pad(lowercase , padding=lowercase , max_length=lowercase , return_tensors=lowercase) def lowerCamelCase_ ( self , lowercase , lowercase , lowercase = 16 , lowercase = 64 , lowercase = 4 , ) -> List[DPRSpanPrediction]: '''simple docstring''' a__: Dict = reader_input['input_ids'] a__ , a__ , a__: Tuple = reader_output[:3] a__: Tuple = len(lowercase) a__: Optional[Any] = sorted(range(lowercase) , reverse=lowercase , key=relevance_logits.__getitem__) a__: List[DPRReaderOutput] = [] for doc_id in sorted_docs: a__: Tuple = list(input_ids[doc_id]) # assuming question & title information is at the beginning of the sequence a__: Dict = sequence_ids.index(self.sep_token_id , 2) + 1 # second sep id if sequence_ids[-1] == self.pad_token_id: a__: Any = sequence_ids.index(self.pad_token_id) else: a__: Optional[Any] = len(lowercase) a__: Optional[int] = self._get_best_spans( start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=lowercase , top_spans=lowercase , ) 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=lowercase , start_index=lowercase , end_index=lowercase , text=self.decode(sequence_ids[start_index : end_index + 1]) , )) if len(lowercase) >= num_spans: break return nbest_spans_predictions[:num_spans] def lowerCamelCase_ ( self , lowercase , lowercase , lowercase , lowercase , ) -> List[DPRSpanPrediction]: '''simple docstring''' a__: Optional[Any] = [] for start_index, start_score in enumerate(lowercase): 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__: str = sorted(lowercase , key=lambda lowercase: x[1] , reverse=lowercase) a__: Optional[Any] = [] for (start_index, end_index), score in scores: if start_index > end_index: raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]') a__: str = 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(lowercase) == top_spans: break return chosen_span_intervals @add_end_docstrings(__lowerCAmelCase ) class __snake_case ( __lowerCAmelCase , __lowerCAmelCase ): a__ = VOCAB_FILES_NAMES a__ = READER_PRETRAINED_VOCAB_FILES_MAP a__ = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a__ = READER_PRETRAINED_INIT_CONFIGURATION a__ = ["""input_ids""", """attention_mask"""]
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"""simple docstring""" def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> list: A__ = int(lowercase_ ) if n_element < 1: A__ = ValueError("a should be a positive number" ) raise my_error A__ = [1] A__, A__, A__ = (0, 0, 0) A__ = 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 = 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 = hamming(int(n)) print("-----------------------------------------------------") print(f'The list with nth numbers is: {hamming_numbers}') print("-----------------------------------------------------")
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"""simple docstring""" import logging import os import sys from dataclasses import dataclass, field from itertools import chain from typing import Optional, Union import datasets import numpy as np import torch from datasets import load_dataset import transformers from transformers import ( AutoConfig, AutoModelForMultipleChoice, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.tokenization_utils_base import PreTrainedTokenizerBase from transformers.trainer_utils import get_last_checkpoint from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") SCREAMING_SNAKE_CASE = logging.getLogger(__name__) @dataclass class UpperCAmelCase_ : lowercase__ = field( metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ) lowercase__ = field( default=A_, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} ) lowercase__ = field( default=A_, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} ) lowercase__ = field( default=A_, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, ) lowercase__ = field( default=A_, metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}, ) lowercase__ = field( default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, ) lowercase__ = field( default=A_, metadata={ '''help''': ( '''Will use the token generated when running `huggingface-cli login` (necessary to use this script ''' '''with private models).''' ) }, ) @dataclass class UpperCAmelCase_ : lowercase__ = field(default=A_, metadata={'''help''': '''The input training data file (a text file).'''} ) lowercase__ = field( default=A_, metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''}, ) lowercase__ = field( default=A_, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} ) lowercase__ = field( default=A_, metadata={'''help''': '''The number of processes to use for the preprocessing.'''}, ) lowercase__ = field( default=A_, metadata={ '''help''': ( '''The maximum total input sequence length after tokenization. If passed, sequences longer ''' '''than this will be truncated, sequences shorter will be padded.''' ) }, ) lowercase__ = field( default=A_, metadata={ '''help''': ( '''Whether to pad all samples to the maximum sentence length. ''' '''If False, will pad the samples dynamically when batching to the maximum length in the batch. More ''' '''efficient on GPU but very bad for TPU.''' ) }, ) lowercase__ = field( default=A_, metadata={ '''help''': ( '''For debugging purposes or quicker training, truncate the number of training examples to this ''' '''value if set.''' ) }, ) lowercase__ = field( default=A_, metadata={ '''help''': ( '''For debugging purposes or quicker training, truncate the number of evaluation examples to this ''' '''value if set.''' ) }, ) def __magic_name__ ( self : Union[str, Any] ) -> Optional[int]: '''simple docstring''' if self.train_file is not None: A__ = self.train_file.split("." )[-1] assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." if self.validation_file is not None: A__ = self.validation_file.split("." )[-1] assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." @dataclass class UpperCAmelCase_ : lowercase__ = 42 lowercase__ = True lowercase__ = None lowercase__ = None def __call__( self : Optional[Any] , snake_case_ : List[str] ) -> Union[str, Any]: '''simple docstring''' A__ = "label" if "label" in features[0].keys() else "labels" A__ = [feature.pop(snake_case_ ) for feature in features] A__ = len(snake_case_ ) A__ = len(features[0]["input_ids"] ) A__ = [ [{k: v[i] for k, v in feature.items()} for i in range(snake_case_ )] for feature in features ] A__ = list(chain(*snake_case_ ) ) A__ = self.tokenizer.pad( snake_case_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" , ) # Un-flatten A__ = {k: v.view(snake_case_ , snake_case_ , -1 ) for k, v in batch.items()} # Add back labels A__ = torch.tensor(snake_case_ , dtype=torch.intaa ) return batch def _SCREAMING_SNAKE_CASE ( ) -> int: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. A__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. A__, A__, A__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: A__, A__, A__ = parser.parse_args_into_dataclasses() # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The # information sent is the one passed as arguments along with your Python/PyTorch versions. send_example_telemetry("run_swag" , lowercase_ , lowercase_ ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , ) if training_args.should_log: # The default of training_args.log_level is passive, so we set log level at info here to have that default. transformers.utils.logging.set_verbosity_info() A__ = training_args.get_process_log_level() logger.setLevel(lowercase_ ) datasets.utils.logging.set_verbosity(lowercase_ ) transformers.utils.logging.set_verbosity(lowercase_ ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}""" + f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" ) logger.info(f"""Training/evaluation parameters {training_args}""" ) # Detecting last checkpoint. A__ = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: A__ = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( f"""Output directory ({training_args.output_dir}) already exists and is not empty. """ "Use --overwrite_output_dir to overcome." ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """ "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." ) # Set seed before initializing model. set_seed(training_args.seed ) # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ # (the dataset will be downloaded automatically from the datasets Hub). # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called # 'text' is found. You can easily tweak this behavior (see below). # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.train_file is not None or data_args.validation_file is not None: A__ = {} if data_args.train_file is not None: A__ = data_args.train_file if data_args.validation_file is not None: A__ = data_args.validation_file A__ = data_args.train_file.split("." )[-1] A__ = load_dataset( lowercase_ , data_files=lowercase_ , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) else: # Downloading and loading the swag dataset from the hub. A__ = load_dataset( "swag" , "regular" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , ) # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at # https://huggingface.co/docs/datasets/loading_datasets.html. # Load pretrained model and tokenizer # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. A__ = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) A__ = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) A__ = AutoModelForMultipleChoice.from_pretrained( model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) # When using your own dataset or a different dataset from swag, you will probably need to change this. A__ = [f"""ending{i}""" for i in range(4 )] A__ = "sent1" A__ = "sent2" if data_args.max_seq_length is None: A__ = tokenizer.model_max_length if max_seq_length > 10_24: logger.warning( "The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value" " of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can" " override this default with `--block_size xxx`." ) A__ = 10_24 else: if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( f"""The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the""" f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.""" ) A__ = min(data_args.max_seq_length , tokenizer.model_max_length ) # Preprocessing the datasets. def preprocess_function(lowercase_ ): A__ = [[context] * 4 for context in examples[context_name]] A__ = examples[question_header_name] A__ = [ [f"""{header} {examples[end][i]}""" for end in ending_names] for i, header in enumerate(lowercase_ ) ] # Flatten out A__ = list(chain(*lowercase_ ) ) A__ = list(chain(*lowercase_ ) ) # Tokenize A__ = tokenizer( lowercase_ , lowercase_ , truncation=lowercase_ , max_length=lowercase_ , padding="max_length" if data_args.pad_to_max_length else False , ) # Un-flatten return {k: [v[i : i + 4] for i in range(0 , len(lowercase_ ) , 4 )] for k, v in tokenized_examples.items()} if training_args.do_train: if "train" not in raw_datasets: raise ValueError("--do_train requires a train dataset" ) A__ = raw_datasets["train"] if data_args.max_train_samples is not None: A__ = min(len(lowercase_ ) , data_args.max_train_samples ) A__ = train_dataset.select(range(lowercase_ ) ) with training_args.main_process_first(desc="train dataset map pre-processing" ): A__ = train_dataset.map( lowercase_ , batched=lowercase_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , ) if training_args.do_eval: if "validation" not in raw_datasets: raise ValueError("--do_eval requires a validation dataset" ) A__ = raw_datasets["validation"] if data_args.max_eval_samples is not None: A__ = min(len(lowercase_ ) , data_args.max_eval_samples ) A__ = eval_dataset.select(range(lowercase_ ) ) with training_args.main_process_first(desc="validation dataset map pre-processing" ): A__ = eval_dataset.map( lowercase_ , batched=lowercase_ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , ) # Data collator A__ = ( default_data_collator if data_args.pad_to_max_length else DataCollatorForMultipleChoice(tokenizer=lowercase_ , pad_to_multiple_of=8 if training_args.fpaa else None ) ) # Metric def compute_metrics(lowercase_ ): A__, A__ = eval_predictions A__ = np.argmax(lowercase_ , axis=1 ) return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()} # Initialize our Trainer A__ = Trainer( model=lowercase_ , args=lowercase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=lowercase_ , data_collator=lowercase_ , compute_metrics=lowercase_ , ) # Training if training_args.do_train: A__ = None if training_args.resume_from_checkpoint is not None: A__ = training_args.resume_from_checkpoint elif last_checkpoint is not None: A__ = last_checkpoint A__ = trainer.train(resume_from_checkpoint=lowercase_ ) trainer.save_model() # Saves the tokenizer too for easy upload A__ = train_result.metrics A__ = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_ ) ) A__ = min(lowercase_ , len(lowercase_ ) ) trainer.log_metrics("train" , lowercase_ ) trainer.save_metrics("train" , lowercase_ ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("*** Evaluate ***" ) A__ = trainer.evaluate() A__ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_ ) A__ = min(lowercase_ , len(lowercase_ ) ) trainer.log_metrics("eval" , lowercase_ ) trainer.save_metrics("eval" , lowercase_ ) A__ = { "finetuned_from": model_args.model_name_or_path, "tasks": "multiple-choice", "dataset_tags": "swag", "dataset_args": "regular", "dataset": "SWAG", "language": "en", } if training_args.push_to_hub: trainer.push_to_hub(**lowercase_ ) else: trainer.create_model_card(**lowercase_ ) def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any: # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
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'''simple docstring''' import unittest from transformers import ( MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, logging, pipeline, ) from transformers.testing_utils import ( CaptureLogger, is_pipeline_test, require_accelerate, require_tf, require_torch, require_torch_gpu, require_torch_or_tf, ) from .test_pipelines_common import ANY @is_pipeline_test @require_torch_or_tf class _UpperCAmelCase ( unittest.TestCase ): """simple docstring""" snake_case = MODEL_FOR_CAUSAL_LM_MAPPING snake_case = TF_MODEL_FOR_CAUSAL_LM_MAPPING @require_torch def lowerCAmelCase ( self : Any ): '''simple docstring''' _A = pipeline(task="text-generation" , model="sshleifer/tiny-ctrl" , framework="pt" ) # Using `do_sample=False` to force deterministic output _A = text_generator("This is a test" , do_sample=__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [ { "generated_text": ( "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope." " oscope. FiliFili@@" ) } ] , ) _A = text_generator(["This is a test", "This is a second test"] ) self.assertEqual( __UpperCAmelCase , [ [ { "generated_text": ( "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope." " oscope. FiliFili@@" ) } ], [ { "generated_text": ( "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy" " oscope. oscope. FiliFili@@" ) } ], ] , ) _A = text_generator("This is a test" , do_sample=__UpperCAmelCase , num_return_sequences=2 , return_tensors=__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [ {"generated_token_ids": ANY(__UpperCAmelCase )}, {"generated_token_ids": ANY(__UpperCAmelCase )}, ] , ) _A = text_generator.model.config.eos_token_id _A = "<pad>" _A = text_generator( ["This is a test", "This is a second test"] , do_sample=__UpperCAmelCase , num_return_sequences=2 , batch_size=2 , return_tensors=__UpperCAmelCase , ) self.assertEqual( __UpperCAmelCase , [ [ {"generated_token_ids": ANY(__UpperCAmelCase )}, {"generated_token_ids": ANY(__UpperCAmelCase )}, ], [ {"generated_token_ids": ANY(__UpperCAmelCase )}, {"generated_token_ids": ANY(__UpperCAmelCase )}, ], ] , ) @require_tf def lowerCAmelCase ( self : Union[str, Any] ): '''simple docstring''' _A = pipeline(task="text-generation" , model="sshleifer/tiny-ctrl" , framework="tf" ) # Using `do_sample=False` to force deterministic output _A = text_generator("This is a test" , do_sample=__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [ { "generated_text": ( "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵" " please," ) } ] , ) _A = text_generator(["This is a test", "This is a second test"] , do_sample=__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [ [ { "generated_text": ( "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵" " please," ) } ], [ { "generated_text": ( "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes" " Cannes 閲閲Cannes Cannes Cannes 攵 please," ) } ], ] , ) def lowerCAmelCase ( self : str , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[Any] ): '''simple docstring''' _A = TextGenerationPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase ) return text_generator, ["This is a test", "Another test"] def lowerCAmelCase ( self : int ): '''simple docstring''' _A = "Hello I believe in" _A = pipeline("text-generation" , model="hf-internal-testing/tiny-random-gpt2" ) _A = text_generator(__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}] , ) _A = text_generator(__UpperCAmelCase , stop_sequence=" fe" ) self.assertEqual(__UpperCAmelCase , [{"generated_text": "Hello I believe in fe"}] ) def lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : Dict ): '''simple docstring''' _A = text_generator.model _A = text_generator.tokenizer _A = text_generator("This is a test" ) self.assertEqual(__UpperCAmelCase , [{"generated_text": ANY(__UpperCAmelCase )}] ) self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) ) _A = text_generator("This is a test" , return_full_text=__UpperCAmelCase ) self.assertEqual(__UpperCAmelCase , [{"generated_text": ANY(__UpperCAmelCase )}] ) self.assertNotIn("This is a test" , outputs[0]["generated_text"] ) _A = pipeline(task="text-generation" , model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , return_full_text=__UpperCAmelCase ) _A = text_generator("This is a test" ) self.assertEqual(__UpperCAmelCase , [{"generated_text": ANY(__UpperCAmelCase )}] ) self.assertNotIn("This is a test" , outputs[0]["generated_text"] ) _A = text_generator("This is a test" , return_full_text=__UpperCAmelCase ) self.assertEqual(__UpperCAmelCase , [{"generated_text": ANY(__UpperCAmelCase )}] ) self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) ) _A = text_generator(["This is great !", "Something else"] , num_return_sequences=2 , do_sample=__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [ [{"generated_text": ANY(__UpperCAmelCase )}, {"generated_text": ANY(__UpperCAmelCase )}], [{"generated_text": ANY(__UpperCAmelCase )}, {"generated_text": ANY(__UpperCAmelCase )}], ] , ) if text_generator.tokenizer.pad_token is not None: _A = text_generator( ["This is great !", "Something else"] , num_return_sequences=2 , batch_size=2 , do_sample=__UpperCAmelCase ) self.assertEqual( __UpperCAmelCase , [ [{"generated_text": ANY(__UpperCAmelCase )}, {"generated_text": ANY(__UpperCAmelCase )}], [{"generated_text": ANY(__UpperCAmelCase )}, {"generated_text": ANY(__UpperCAmelCase )}], ] , ) with self.assertRaises(__UpperCAmelCase ): _A = text_generator("test" , return_full_text=__UpperCAmelCase , return_text=__UpperCAmelCase ) with self.assertRaises(__UpperCAmelCase ): _A = text_generator("test" , return_full_text=__UpperCAmelCase , return_tensors=__UpperCAmelCase ) with self.assertRaises(__UpperCAmelCase ): _A = text_generator("test" , return_text=__UpperCAmelCase , return_tensors=__UpperCAmelCase ) # Empty prompt is slighly special # it requires BOS token to exist. # Special case for Pegasus which will always append EOS so will # work even without BOS. if ( text_generator.tokenizer.bos_token_id is not None or "Pegasus" in tokenizer.__class__.__name__ or "Git" in model.__class__.__name__ ): _A = text_generator("" ) self.assertEqual(__UpperCAmelCase , [{"generated_text": ANY(__UpperCAmelCase )}] ) else: with self.assertRaises((ValueError, AssertionError) ): _A = text_generator("" ) if text_generator.framework == "tf": # TF generation does not support max_new_tokens, and it's impossible # to control long generation with only max_length without # fancy calculation, dismissing tests for now. return # We don't care about infinite range models. # They already work. # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly. _A = ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"] if ( tokenizer.model_max_length < 10000 and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS ): # Handling of large generations with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ): text_generator("This is a test" * 500 , max_new_tokens=20 ) _A = text_generator("This is a test" * 500 , handle_long_generation="hole" , max_new_tokens=20 ) # Hole strategy cannot work with self.assertRaises(__UpperCAmelCase ): text_generator( "This is a test" * 500 , handle_long_generation="hole" , max_new_tokens=tokenizer.model_max_length + 10 , ) @require_torch @require_accelerate @require_torch_gpu def lowerCAmelCase ( self : Tuple ): '''simple docstring''' import torch # Classic `model_kwargs` _A = pipeline( model="hf-internal-testing/tiny-random-bloom" , model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa} , ) self.assertEqual(pipe.model.device , torch.device(0 ) ) self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa ) _A = pipe("This is a test" ) self.assertEqual( __UpperCAmelCase , [ { "generated_text": ( "This is a test test test test test test test test test test test test test test test test" " test" ) } ] , ) # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.) _A = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" , torch_dtype=torch.bfloataa ) self.assertEqual(pipe.model.device , torch.device(0 ) ) self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa ) _A = pipe("This is a test" ) self.assertEqual( __UpperCAmelCase , [ { "generated_text": ( "This is a test test test test test test test test test test test test test test test test" " test" ) } ] , ) # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602 _A = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" ) self.assertEqual(pipe.model.device , torch.device(0 ) ) self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa ) _A = pipe("This is a test" ) self.assertEqual( __UpperCAmelCase , [ { "generated_text": ( "This is a test test test test test test test test test test test test test test test test" " test" ) } ] , ) @require_torch @require_torch_gpu def lowerCAmelCase ( self : Optional[int] ): '''simple docstring''' import torch _A = pipeline(model="hf-internal-testing/tiny-random-bloom" , device=0 , torch_dtype=torch.floataa ) pipe("This is a test" ) @require_torch @require_accelerate @require_torch_gpu def lowerCAmelCase ( self : Optional[Any] ): '''simple docstring''' import torch _A = pipeline(model="hf-internal-testing/tiny-random-bloom" , device_map="auto" , torch_dtype=torch.floataa ) pipe("This is a test" , do_sample=__UpperCAmelCase , top_p=0.5 ) def lowerCAmelCase ( self : int ): '''simple docstring''' _A = "Hello world" _A = pipeline("text-generation" , model="hf-internal-testing/tiny-random-gpt2" ) if text_generator.model.framework == "tf": _A = logging.get_logger("transformers.generation.tf_utils" ) else: _A = logging.get_logger("transformers.generation.utils" ) _A = "Both `max_new_tokens`" # The beggining of the message to be checked in this test # Both are set by the user -> log warning with CaptureLogger(__UpperCAmelCase ) as cl: _A = text_generator(__UpperCAmelCase , max_length=10 , max_new_tokens=1 ) self.assertIn(__UpperCAmelCase , cl.out ) # The user only sets one -> no warning with CaptureLogger(__UpperCAmelCase ) as cl: _A = text_generator(__UpperCAmelCase , max_new_tokens=1 ) self.assertNotIn(__UpperCAmelCase , cl.out ) with CaptureLogger(__UpperCAmelCase ) as cl: _A = text_generator(__UpperCAmelCase , max_length=10 ) self.assertNotIn(__UpperCAmelCase , cl.out )
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'''simple docstring''' import os lowerCamelCase_ = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00} def __lowercase ( __lowercase ) -> int: '''simple docstring''' _A = 0 _A = 0 while index < len(__lowercase ) - 1: _A = SYMBOLS[numerals[index]] _A = SYMBOLS[numerals[index + 1]] if current_value < next_value: total_value -= current_value else: total_value += current_value index += 1 total_value += SYMBOLS[numerals[index]] return total_value def __lowercase ( __lowercase ) -> str: '''simple docstring''' _A = "" _A = num // 1000 numerals += m_count * "M" num %= 1000 _A = num // 100 if c_count == 9: numerals += "CM" c_count -= 9 elif c_count == 4: numerals += "CD" c_count -= 4 if c_count >= 5: numerals += "D" c_count -= 5 numerals += c_count * "C" num %= 100 _A = num // 10 if x_count == 9: numerals += "XC" x_count -= 9 elif x_count == 4: numerals += "XL" x_count -= 4 if x_count >= 5: numerals += "L" x_count -= 5 numerals += x_count * "X" num %= 10 if num == 9: numerals += "IX" num -= 9 elif num == 4: numerals += "IV" num -= 4 if num >= 5: numerals += "V" num -= 5 numerals += num * "I" return numerals def __lowercase ( __lowercase = "/p089_roman.txt" ) -> int: '''simple docstring''' _A = 0 with open(os.path.dirname(__lowercase ) + roman_numerals_filename ) as filea: _A = filea.readlines() for line in lines: _A = line.strip() _A = parse_roman_numerals(__lowercase ) _A = generate_roman_numerals(__lowercase ) savings += len(__lowercase ) - len(__lowercase ) return savings if __name__ == "__main__": print(F"""{solution() = }""")
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import argparse import json from collections import OrderedDict from functools import partial from pathlib import Path import timm import torch from huggingface_hub import hf_hub_download from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor from transformers.utils import logging logging.set_verbosity_info() A : List[Any] = logging.get_logger() def lowercase_ ( _A : int , _A : str , _A : LevitConfig , _A : Path , _A : bool = True ): """simple docstring""" print(F"Converting {name}..." ) with torch.no_grad(): if hidden_sizes == 128: if name[-1] == "S": lowerCamelCase__ : int = timm.create_model("levit_128s" , pretrained=_A ) else: lowerCamelCase__ : Tuple = timm.create_model("levit_128" , pretrained=_A ) if hidden_sizes == 192: lowerCamelCase__ : Optional[Any] = timm.create_model("levit_192" , pretrained=_A ) if hidden_sizes == 256: lowerCamelCase__ : Optional[Any] = timm.create_model("levit_256" , pretrained=_A ) if hidden_sizes == 384: lowerCamelCase__ : Optional[int] = timm.create_model("levit_384" , pretrained=_A ) from_model.eval() lowerCamelCase__ : Union[str, Any] = LevitForImageClassificationWithTeacher(_A ).eval() lowerCamelCase__ : Optional[Any] = OrderedDict() lowerCamelCase__ : Optional[Any] = from_model.state_dict() lowerCamelCase__ : List[str] = list(from_model.state_dict().keys() ) lowerCamelCase__ : int = list(our_model.state_dict().keys() ) print(len(_A ) , len(_A ) ) for i in range(len(_A ) ): lowerCamelCase__ : str = weights[og_keys[i]] our_model.load_state_dict(_A ) lowerCamelCase__ : Union[str, Any] = torch.randn((2, 3, 224, 224) ) lowerCamelCase__ : Optional[int] = from_model(_A ) lowerCamelCase__ : str = our_model(_A ).logits assert torch.allclose(_A , _A ), "The model logits don't match the original one." lowerCamelCase__ : Dict = name print(_A ) if push_to_hub: our_model.save_pretrained(save_directory / checkpoint_name ) lowerCamelCase__ : Dict = LevitImageProcessor() image_processor.save_pretrained(save_directory / checkpoint_name ) print(F"Pushed {checkpoint_name}" ) def lowercase_ ( _A : Path , _A : str = None , _A : bool = True ): """simple docstring""" lowerCamelCase__ : Union[str, Any] = "imagenet-1k-id2label.json" lowerCamelCase__ : List[str] = 1000 lowerCamelCase__ : Tuple = (1, num_labels) lowerCamelCase__ : Optional[int] = "huggingface/label-files" lowerCamelCase__ : List[str] = num_labels lowerCamelCase__ : List[str] = json.load(open(hf_hub_download(_A , _A , repo_type="dataset" ) , "r" ) ) lowerCamelCase__ : List[str] = {int(_A ): v for k, v in idalabel.items()} lowerCamelCase__ : Optional[Any] = idalabel lowerCamelCase__ : int = {v: k for k, v in idalabel.items()} lowerCamelCase__ : Tuple = partial(_A , num_labels=_A , idalabel=_A , labelaid=_A ) lowerCamelCase__ : int = { "levit-128S": 128, "levit-128": 128, "levit-192": 192, "levit-256": 256, "levit-384": 384, } lowerCamelCase__ : Any = { "levit-128S": ImageNetPreTrainedConfig( hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ), "levit-128": ImageNetPreTrainedConfig( hidden_sizes=[128, 256, 384] , num_attention_heads=[4, 8, 12] , depths=[4, 4, 4] , key_dim=[16, 16, 16] , drop_path_rate=0 , ), "levit-192": ImageNetPreTrainedConfig( hidden_sizes=[192, 288, 384] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ), "levit-256": ImageNetPreTrainedConfig( hidden_sizes=[256, 384, 512] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0 , ), "levit-384": ImageNetPreTrainedConfig( hidden_sizes=[384, 512, 768] , num_attention_heads=[6, 9, 12] , depths=[4, 4, 4] , key_dim=[32, 32, 32] , drop_path_rate=0.1 , ), } if model_name: convert_weight_and_push( names_to_hidden_sizes[model_name] , _A , names_to_config[model_name] , _A , _A ) else: for model_name, config in names_to_config.items(): convert_weight_and_push(names_to_hidden_sizes[model_name] , _A , _A , _A , _A ) return config, expected_shape if __name__ == "__main__": A : Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--model_name", default=None, type=str, help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,", ) parser.add_argument( "--pytorch_dump_folder_path", default="levit-dump-folder/", type=Path, required=False, help="Path to the output PyTorch model directory.", ) parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub") parser.add_argument( "--no-push_to_hub", dest="push_to_hub", action="store_false", help="Do not push model and image processor to the hub", ) A : int = parser.parse_args() A : Path = args.pytorch_dump_folder_path pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True) convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
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from ...configuration_utils import PretrainedConfig from ...utils import logging A : str = logging.get_logger(__name__) A : int = { "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json", } class _lowercase ( lowercase__): """simple docstring""" A__ = "mgp-str" def __init__( self : List[str] , __lowerCamelCase : List[Any]=[32, 128] , __lowerCamelCase : Optional[int]=4 , __lowerCamelCase : str=3 , __lowerCamelCase : Optional[Any]=27 , __lowerCamelCase : List[str]=38 , __lowerCamelCase : Dict=50257 , __lowerCamelCase : List[Any]=30522 , __lowerCamelCase : Optional[Any]=768 , __lowerCamelCase : str=12 , __lowerCamelCase : Optional[int]=12 , __lowerCamelCase : List[str]=4.0 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Any=False , __lowerCamelCase : Dict=1E-5 , __lowerCamelCase : int=0.0 , __lowerCamelCase : Optional[int]=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : Union[str, Any]=False , __lowerCamelCase : str=0.0_2 , **__lowerCamelCase : Dict , ): '''simple docstring''' super().__init__(**__lowerCamelCase ) lowerCamelCase__ : int = image_size lowerCamelCase__ : Union[str, Any] = patch_size lowerCamelCase__ : Dict = num_channels lowerCamelCase__ : Union[str, Any] = max_token_length lowerCamelCase__ : Optional[int] = num_character_labels lowerCamelCase__ : Union[str, Any] = num_bpe_labels lowerCamelCase__ : Optional[int] = num_wordpiece_labels lowerCamelCase__ : List[str] = hidden_size lowerCamelCase__ : Dict = num_hidden_layers lowerCamelCase__ : Any = num_attention_heads lowerCamelCase__ : Any = mlp_ratio lowerCamelCase__ : List[Any] = distilled lowerCamelCase__ : Optional[Any] = layer_norm_eps lowerCamelCase__ : Union[str, Any] = drop_rate lowerCamelCase__ : List[Any] = qkv_bias lowerCamelCase__ : int = attn_drop_rate lowerCamelCase__ : List[Any] = drop_path_rate lowerCamelCase__ : List[str] = output_aa_attentions lowerCamelCase__ : Dict = initializer_range
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1
import copy from dataclasses import dataclass from pathlib import Path from typing import Dict, Optional, Union @dataclass class lowerCamelCase__ : '''simple docstring''' snake_case_ =None snake_case_ =False snake_case_ =False snake_case_ =False snake_case_ =None snake_case_ =None snake_case_ =False snake_case_ =False snake_case_ =False snake_case_ =True snake_case_ =None snake_case_ =1 snake_case_ =None snake_case_ =False snake_case_ =None snake_case_ =None def lowerCAmelCase__ (self ) -> "DownloadConfig": """simple docstring""" return self.__class__(**{k: copy.deepcopy(__lowerCamelCase ) for k, v in self.__dict__.items()} )
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def lowerCAmelCase__ ( lowerCamelCase_ : str = "The quick brown fox jumps over the lazy dog" ,): '''simple docstring''' lowerCAmelCase__ : Any = set() # Replace all the whitespace in our sentence lowerCAmelCase__ : List[Any] = input_str.replace(''' ''' ,'''''') for alpha in input_str: if "a" <= alpha.lower() <= "z": frequency.add(alpha.lower()) return len(lowerCamelCase_) == 26 def lowerCAmelCase__ ( lowerCamelCase_ : str = "The quick brown fox jumps over the lazy dog" ,): '''simple docstring''' lowerCAmelCase__ : List[str] = [False] * 26 for char in input_str: if char.islower(): lowerCAmelCase__ : Union[str, Any] = True elif char.isupper(): lowerCAmelCase__ : str = True return all(lowerCamelCase_) def lowerCAmelCase__ ( lowerCamelCase_ : str = "The quick brown fox jumps over the lazy dog" ,): '''simple docstring''' return len({char for char in input_str.lower() if char.isalpha()}) == 26 def lowerCAmelCase__ ( ): '''simple docstring''' from timeit import timeit lowerCAmelCase__ : Optional[Any] = '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest''' print(timeit('''is_pangram()''' ,setup=lowerCamelCase_)) print(timeit('''is_pangram_faster()''' ,setup=lowerCamelCase_)) print(timeit('''is_pangram_fastest()''' ,setup=lowerCamelCase_)) # 5.348480500048026, 2.6477354579837993, 1.8470395830227062 # 5.036091582966037, 2.644472333951853, 1.8869528750656173 if __name__ == "__main__": import doctest doctest.testmod() benchmark()
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0
import inspect from typing import Optional, Union import numpy as np import PIL import torch from torch.nn import functional as F from torchvision import transforms from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput from diffusers.utils import ( PIL_INTERPOLATION, randn_tensor, ) def UpperCAmelCase ( lowercase , lowercase , lowercase ): """simple docstring""" if isinstance(lowercase , torch.Tensor ): return image elif isinstance(lowercase , PIL.Image.Image ): __lowercase = [image] if isinstance(image[0] , PIL.Image.Image ): __lowercase = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] ) )[None, :] for i in image] __lowercase = np.concatenate(lowercase , axis=0 ) __lowercase = np.array(lowercase ).astype(np.floataa ) / 255.0 __lowercase = image.transpose(0 , 3 , 1 , 2 ) __lowercase = 2.0 * image - 1.0 __lowercase = torch.from_numpy(lowercase ) elif isinstance(image[0] , torch.Tensor ): __lowercase = torch.cat(lowercase , dim=0 ) return image def UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase=0.9995 ): """simple docstring""" if not isinstance(lowercase , np.ndarray ): __lowercase = True __lowercase = va.device __lowercase = va.cpu().numpy() __lowercase = va.cpu().numpy() __lowercase = np.sum(va * va / (np.linalg.norm(lowercase ) * np.linalg.norm(lowercase )) ) if np.abs(lowercase ) > DOT_THRESHOLD: __lowercase = (1 - t) * va + t * va else: __lowercase = np.arccos(lowercase ) __lowercase = np.sin(lowercase ) __lowercase = theta_a * t __lowercase = np.sin(lowercase ) __lowercase = np.sin(theta_a - theta_t ) / sin_theta_a __lowercase = sin_theta_t / sin_theta_a __lowercase = sa * va + sa * va if inputs_are_torch: __lowercase = torch.from_numpy(lowercase ).to(lowercase ) return va def UpperCAmelCase ( lowercase , lowercase ): """simple docstring""" __lowercase = F.normalize(lowercase , dim=-1 ) __lowercase = F.normalize(lowercase , dim=-1 ) return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 ) def UpperCAmelCase ( lowercase , lowercase ): """simple docstring""" for param in model.parameters(): __lowercase = value class _UpperCamelCase ( _UpperCAmelCase ): """simple docstring""" def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , ) -> str: '''simple docstring''' super().__init__() self.register_modules( vae=lowerCAmelCase__ , text_encoder=lowerCAmelCase__ , clip_model=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ , unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , feature_extractor=lowerCAmelCase__ , coca_model=lowerCAmelCase__ , coca_tokenizer=lowerCAmelCase__ , coca_transform=lowerCAmelCase__ , ) __lowercase = ( feature_extractor.size if isinstance(feature_extractor.size , lowerCAmelCase__ ) else feature_extractor.size['''shortest_edge'''] ) __lowercase = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std ) set_requires_grad(self.text_encoder , lowerCAmelCase__ ) set_requires_grad(self.clip_model , lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ = "auto" ) -> Optional[Any]: '''simple docstring''' if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory __lowercase = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self ) -> str: '''simple docstring''' self.enable_attention_slicing(lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]: '''simple docstring''' set_requires_grad(self.vae , lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self ) -> List[Any]: '''simple docstring''' set_requires_grad(self.vae , lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self ) -> int: '''simple docstring''' set_requires_grad(self.unet , lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self ) -> Optional[int]: '''simple docstring''' set_requires_grad(self.unet , lowerCAmelCase__ ) def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any: '''simple docstring''' __lowercase = min(int(num_inference_steps * strength ) , lowerCAmelCase__ ) __lowercase = max(num_inference_steps - init_timestep , 0 ) __lowercase = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=None ) -> Optional[Any]: '''simple docstring''' if not isinstance(lowerCAmelCase__ , torch.Tensor ): raise ValueError(F"`image` has to be of type `torch.Tensor` but is {type(lowerCAmelCase__ )}" ) __lowercase = image.to(device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): __lowercase = [ self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCAmelCase__ ) ] __lowercase = torch.cat(lowerCAmelCase__ , dim=0 ) else: __lowercase = self.vae.encode(lowerCAmelCase__ ).latent_dist.sample(lowerCAmelCase__ ) # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor __lowercase = 0.1_8215 * init_latents __lowercase = init_latents.repeat_interleave(lowerCAmelCase__ , dim=0 ) __lowercase = randn_tensor(init_latents.shape , generator=lowerCAmelCase__ , device=lowerCAmelCase__ , dtype=lowerCAmelCase__ ) # get latents __lowercase = self.scheduler.add_noise(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) __lowercase = init_latents return latents def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ ) -> Optional[Any]: '''simple docstring''' __lowercase = self.coca_transform(lowerCAmelCase__ ).unsqueeze(0 ) with torch.no_grad(), torch.cuda.amp.autocast(): __lowercase = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) ) __lowercase = self.coca_tokenizer.decode(generated[0].cpu().numpy() ) return generated.split('''<end_of_text>''' )[0].replace('''<start_of_text>''' , '''''' ).rstrip(''' .,''' ) def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any: '''simple docstring''' __lowercase = self.feature_extractor.preprocess(lowerCAmelCase__ ) __lowercase = torch.from_numpy(clip_image_input['''pixel_values'''][0] ).unsqueeze(0 ).to(self.device ).half() __lowercase = self.clip_model.get_image_features(lowerCAmelCase__ ) __lowercase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowerCAmelCase__ ) __lowercase = image_embeddings_clip.repeat_interleave(lowerCAmelCase__ , dim=0 ) return image_embeddings_clip @torch.enable_grad() def _SCREAMING_SNAKE_CASE ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> List[Any]: '''simple docstring''' __lowercase = latents.detach().requires_grad_() __lowercase = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ ) # predict the noise residual __lowercase = self.unet(lowerCAmelCase__ , lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ ).sample if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ): __lowercase = self.scheduler.alphas_cumprod[timestep] __lowercase = 1 - alpha_prod_t # compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf __lowercase = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5 __lowercase = torch.sqrt(lowerCAmelCase__ ) __lowercase = pred_original_sample * (fac) + latents * (1 - fac) elif isinstance(self.scheduler , lowerCAmelCase__ ): __lowercase = self.scheduler.sigmas[index] __lowercase = latents - sigma * noise_pred else: raise ValueError(F"scheduler type {type(self.scheduler )} not supported" ) # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor __lowercase = 1 / 0.1_8215 * sample __lowercase = self.vae.decode(lowerCAmelCase__ ).sample __lowercase = (image / 2 + 0.5).clamp(0 , 1 ) __lowercase = transforms.Resize(self.feature_extractor_size )(lowerCAmelCase__ ) __lowercase = self.normalize(lowerCAmelCase__ ).to(latents.dtype ) __lowercase = self.clip_model.get_image_features(lowerCAmelCase__ ) __lowercase = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowerCAmelCase__ ) __lowercase = spherical_dist_loss(lowerCAmelCase__ , lowerCAmelCase__ ).mean() * clip_guidance_scale __lowercase = -torch.autograd.grad(lowerCAmelCase__ , lowerCAmelCase__ )[0] if isinstance(self.scheduler , lowerCAmelCase__ ): __lowercase = latents.detach() + grads * (sigma**2) __lowercase = noise_pred_original else: __lowercase = noise_pred_original - torch.sqrt(lowerCAmelCase__ ) * grads return noise_pred, latents @torch.no_grad() def __call__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 5_12 , lowerCAmelCase__ = 5_12 , lowerCAmelCase__ = 0.6 , lowerCAmelCase__ = 50 , lowerCAmelCase__ = 7.5 , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 1_00 , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , lowerCAmelCase__ = 0.8 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , ) -> List[Any]: '''simple docstring''' if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and len(lowerCAmelCase__ ) != batch_size: raise ValueError(F"You have passed {batch_size} batch_size, but only {len(lowerCAmelCase__ )} generators." ) if height % 8 != 0 or width % 8 != 0: raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}." ) if isinstance(lowerCAmelCase__ , torch.Generator ) and batch_size > 1: __lowercase = [generator] + [None] * (batch_size - 1) __lowercase = [ ('''model''', self.coca_model is None), ('''tokenizer''', self.coca_tokenizer is None), ('''transform''', self.coca_transform is None), ] __lowercase = [x[0] for x in coca_is_none if x[1]] __lowercase = ''', '''.join(lowerCAmelCase__ ) # generate prompts with coca model if prompt is None if content_prompt is None: if len(lowerCAmelCase__ ): raise ValueError( F"Content prompt is None and CoCa [{coca_is_none_str}] is None." F"Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." ) __lowercase = self.get_image_description(lowerCAmelCase__ ) if style_prompt is None: if len(lowerCAmelCase__ ): raise ValueError( F"Style prompt is None and CoCa [{coca_is_none_str}] is None." F" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline." ) __lowercase = self.get_image_description(lowerCAmelCase__ ) # get prompt text embeddings for content and style __lowercase = self.tokenizer( lowerCAmelCase__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=lowerCAmelCase__ , return_tensors='''pt''' , ) __lowercase = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0] __lowercase = self.tokenizer( lowerCAmelCase__ , padding='''max_length''' , max_length=self.tokenizer.model_max_length , truncation=lowerCAmelCase__ , return_tensors='''pt''' , ) __lowercase = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0] __lowercase = slerp(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) # duplicate text embeddings for each generation per prompt __lowercase = text_embeddings.repeat_interleave(lowerCAmelCase__ , dim=0 ) # set timesteps __lowercase = '''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() ) __lowercase = {} if accepts_offset: __lowercase = 1 self.scheduler.set_timesteps(lowerCAmelCase__ , **lowerCAmelCase__ ) # Some schedulers like PNDM have timesteps as arrays # It's more optimized to move all timesteps to correct device beforehand self.scheduler.timesteps.to(self.device ) __lowercase , __lowercase = self.get_timesteps(lowerCAmelCase__ , lowerCAmelCase__ , self.device ) __lowercase = timesteps[:1].repeat(lowerCAmelCase__ ) # Preprocess image __lowercase = preprocess(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) __lowercase = self.prepare_latents( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , text_embeddings.dtype , self.device , lowerCAmelCase__ ) __lowercase = preprocess(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) __lowercase = self.prepare_latents( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , text_embeddings.dtype , self.device , lowerCAmelCase__ ) __lowercase = slerp(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) if clip_guidance_scale > 0: __lowercase = self.get_clip_image_embeddings(lowerCAmelCase__ , lowerCAmelCase__ ) __lowercase = self.get_clip_image_embeddings(lowerCAmelCase__ , lowerCAmelCase__ ) __lowercase = slerp( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. __lowercase = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: __lowercase = content_text_input.input_ids.shape[-1] __lowercase = self.tokenizer([''''''] , padding='''max_length''' , max_length=lowerCAmelCase__ , return_tensors='''pt''' ) __lowercase = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0] # duplicate unconditional embeddings for each generation per prompt __lowercase = uncond_embeddings.repeat_interleave(lowerCAmelCase__ , dim=0 ) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes __lowercase = torch.cat([uncond_embeddings, text_embeddings] ) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. __lowercase = (batch_size, self.unet.config.in_channels, height // 8, width // 8) __lowercase = text_embeddings.dtype if latents is None: if self.device.type == "mps": # randn does not work reproducibly on mps __lowercase = torch.randn(lowerCAmelCase__ , generator=lowerCAmelCase__ , device='''cpu''' , dtype=lowerCAmelCase__ ).to( self.device ) else: __lowercase = torch.randn(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=self.device , dtype=lowerCAmelCase__ ) else: if latents.shape != latents_shape: raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" ) __lowercase = latents.to(self.device ) # scale the initial noise by the standard deviation required by the scheduler __lowercase = latents * self.scheduler.init_noise_sigma # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] __lowercase = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() ) __lowercase = {} if accepts_eta: __lowercase = eta # check if the scheduler accepts generator __lowercase = '''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() ) if accepts_generator: __lowercase = generator with self.progress_bar(total=lowerCAmelCase__ ): for i, t in enumerate(lowerCAmelCase__ ): # expand the latents if we are doing classifier free guidance __lowercase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents __lowercase = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ ) # predict the noise residual __lowercase = self.unet(lowerCAmelCase__ , lowerCAmelCase__ , encoder_hidden_states=lowerCAmelCase__ ).sample # perform classifier free guidance if do_classifier_free_guidance: __lowercase , __lowercase = noise_pred.chunk(2 ) __lowercase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # perform clip guidance if clip_guidance_scale > 0: __lowercase = ( text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings ) __lowercase , __lowercase = self.cond_fn( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) # compute the previous noisy sample x_t -> x_t-1 __lowercase = self.scheduler.step(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , **lowerCAmelCase__ ).prev_sample # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor __lowercase = 1 / 0.1_8215 * latents __lowercase = self.vae.decode(lowerCAmelCase__ ).sample __lowercase = (image / 2 + 0.5).clamp(0 , 1 ) __lowercase = image.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": __lowercase = self.numpy_to_pil(lowerCAmelCase__ ) if not return_dict: return (image, None) return StableDiffusionPipelineOutput(images=lowerCAmelCase__ , nsfw_content_detected=lowerCAmelCase__ )
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from typing import List, Optional, TypeVar from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets from .dataset_dict import DatasetDict, IterableDatasetDict from .info import DatasetInfo from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets from .splits import NamedSplit from .utils import logging from .utils.py_utils import Literal __a : Optional[Any] = logging.get_logger(__name__) __a : List[str] = TypeVar("""DatasetType""", Dataset, IterableDataset) def UpperCAmelCase ( lowercase , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = "first_exhausted" , ): """simple docstring""" from .arrow_dataset import Dataset from .iterable_dataset import IterableDataset if not datasets: raise ValueError('''Unable to interleave an empty list of datasets.''' ) for i, dataset in enumerate(lowercase ): if not isinstance(lowercase , (Dataset, IterableDataset) ): if isinstance(lowercase , (DatasetDict, IterableDatasetDict) ): if not dataset: raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} " '''is an empty dataset dictionary.''' ) raise ValueError( F"Dataset at position {i} has at least one split: {list(lowercase )}\n" F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase ) )}']" ) raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase ).__name__}." ) if i == 0: __lowercase , __lowercase = ( (Dataset, IterableDataset) if isinstance(lowercase , lowercase ) else (IterableDataset, Dataset) ) elif not isinstance(lowercase , lowercase ): raise ValueError( F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." ) if stopping_strategy not in ["first_exhausted", "all_exhausted"]: raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." ) if dataset_type is Dataset: return _interleave_map_style_datasets( lowercase , lowercase , lowercase , info=lowercase , split=lowercase , stopping_strategy=lowercase ) else: return _interleave_iterable_datasets( lowercase , lowercase , lowercase , info=lowercase , split=lowercase , stopping_strategy=lowercase ) def UpperCAmelCase ( lowercase , lowercase = None , lowercase = None , lowercase = 0 , ): """simple docstring""" if not dsets: raise ValueError('''Unable to concatenate an empty list of datasets.''' ) for i, dataset in enumerate(lowercase ): if not isinstance(lowercase , (Dataset, IterableDataset) ): if isinstance(lowercase , (DatasetDict, IterableDatasetDict) ): if not dataset: raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} " '''is an empty dataset dictionary.''' ) raise ValueError( F"Dataset at position {i} has at least one split: {list(lowercase )}\n" F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase ) )}']" ) raise ValueError( F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase ).__name__}." ) if i == 0: __lowercase , __lowercase = ( (Dataset, IterableDataset) if isinstance(lowercase , lowercase ) else (IterableDataset, Dataset) ) elif not isinstance(lowercase , lowercase ): raise ValueError( F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." ) if dataset_type is Dataset: return _concatenate_map_style_datasets(lowercase , info=lowercase , split=lowercase , axis=lowercase ) else: return _concatenate_iterable_datasets(lowercase , info=lowercase , split=lowercase , axis=lowercase )
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase : Union[str, Any] = logging.get_logger(__name__) UpperCamelCase : Optional[Any] = { "facebook/levit-128S": "https://huggingface.co/facebook/levit-128S/resolve/main/config.json", # See all LeViT models at https://huggingface.co/models?filter=levit } class __lowerCAmelCase ( _UpperCAmelCase ): lowercase = "levit" def __init__( self , __UpperCAmelCase=224 , __UpperCAmelCase=3 , __UpperCAmelCase=3 , __UpperCAmelCase=2 , __UpperCAmelCase=1 , __UpperCAmelCase=16 , __UpperCAmelCase=[128, 256, 384] , __UpperCAmelCase=[4, 8, 12] , __UpperCAmelCase=[4, 4, 4] , __UpperCAmelCase=[16, 16, 16] , __UpperCAmelCase=0 , __UpperCAmelCase=[2, 2, 2] , __UpperCAmelCase=[2, 2, 2] , __UpperCAmelCase=0.0_2 , **__UpperCAmelCase , ): '''simple docstring''' super().__init__(**lowercase_ ) __UpperCamelCase = image_size __UpperCamelCase = num_channels __UpperCamelCase = kernel_size __UpperCamelCase = stride __UpperCamelCase = padding __UpperCamelCase = hidden_sizes __UpperCamelCase = num_attention_heads __UpperCamelCase = depths __UpperCamelCase = key_dim __UpperCamelCase = drop_path_rate __UpperCamelCase = patch_size __UpperCamelCase = attention_ratio __UpperCamelCase = mlp_ratio __UpperCamelCase = initializer_range __UpperCamelCase = [ ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2], ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2], ] class __lowerCAmelCase ( _UpperCAmelCase ): lowercase = version.parse("1.11" ) @property def UpperCAmelCase ( self ): '''simple docstring''' return OrderedDict( [ ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}), ] ) @property def UpperCAmelCase ( self ): '''simple docstring''' return 1E-4
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"""simple docstring""" import multiprocessing import os from typing import BinaryIO, Optional, Union import fsspec from .. import Dataset, Features, NamedSplit, config from ..formatting import query_table from ..packaged_modules.json.json import Json from ..utils import logging from ..utils.typing import NestedDataStructureLike, PathLike from .abc import AbstractDatasetReader class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ): def __init__( self , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = False , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ): '''simple docstring''' super().__init__( __UpperCAmelCase , split=__UpperCAmelCase , features=__UpperCAmelCase , cache_dir=__UpperCAmelCase , keep_in_memory=__UpperCAmelCase , streaming=__UpperCAmelCase , num_proc=__UpperCAmelCase , **__UpperCAmelCase , ) __UpperCamelCase = field __UpperCamelCase = path_or_paths if isinstance(__UpperCAmelCase , __UpperCAmelCase ) else {self.split: path_or_paths} __UpperCamelCase = Json( cache_dir=__UpperCAmelCase , data_files=__UpperCAmelCase , features=__UpperCAmelCase , field=__UpperCAmelCase , **__UpperCAmelCase , ) def UpperCAmelCase ( self ): '''simple docstring''' if self.streaming: __UpperCamelCase = self.builder.as_streaming_dataset(split=self.split ) # Build regular (map-style) dataset else: __UpperCamelCase = None __UpperCamelCase = None __UpperCamelCase = None __UpperCamelCase = None self.builder.download_and_prepare( download_config=__UpperCAmelCase , download_mode=__UpperCAmelCase , verification_mode=__UpperCAmelCase , base_path=__UpperCAmelCase , num_proc=self.num_proc , ) __UpperCamelCase = self.builder.as_dataset( split=self.split , verification_mode=__UpperCAmelCase , in_memory=self.keep_in_memory ) return dataset class __lowerCAmelCase : def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = None , **__UpperCAmelCase , ): '''simple docstring''' if num_proc is not None and num_proc <= 0: raise ValueError(F'num_proc {num_proc} must be an integer > 0.' ) __UpperCamelCase = dataset __UpperCamelCase = path_or_buf __UpperCamelCase = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE __UpperCamelCase = num_proc __UpperCamelCase = 'utf-8' __UpperCamelCase = to_json_kwargs def UpperCAmelCase ( self ): '''simple docstring''' __UpperCamelCase = self.to_json_kwargs.pop('path_or_buf' , __UpperCAmelCase ) __UpperCamelCase = self.to_json_kwargs.pop('orient' , 'records' ) __UpperCamelCase = self.to_json_kwargs.pop('lines' , True if orient == 'records' else False ) __UpperCamelCase = self.to_json_kwargs.pop('index' , False if orient in ['split', 'table'] else True ) __UpperCamelCase = self.to_json_kwargs.pop('compression' , __UpperCAmelCase ) if compression not in [None, "infer", "gzip", "bz2", "xz"]: raise NotImplementedError(F'`datasets` currently does not support {compression} compression' ) if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ): with fsspec.open(self.path_or_buf , 'wb' , compression=__UpperCAmelCase ) as buffer: __UpperCamelCase = self._write(file_obj=__UpperCAmelCase , orient=__UpperCAmelCase , lines=__UpperCAmelCase , index=__UpperCAmelCase , **self.to_json_kwargs ) else: if compression: raise NotImplementedError( F'The compression parameter is not supported when writing to a buffer, but compression={compression}' ' was passed. Please provide a local path instead.' ) __UpperCamelCase = self._write( file_obj=self.path_or_buf , orient=__UpperCAmelCase , lines=__UpperCAmelCase , index=__UpperCAmelCase , **self.to_json_kwargs ) return written def UpperCAmelCase ( self , __UpperCAmelCase ): '''simple docstring''' __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = args __UpperCamelCase = query_table( table=self.dataset.data , key=slice(__UpperCAmelCase , offset + self.batch_size ) , indices=self.dataset._indices , ) __UpperCamelCase = batch.to_pandas().to_json( path_or_buf=__UpperCAmelCase , orient=__UpperCAmelCase , lines=__UpperCAmelCase , index=__UpperCAmelCase , **__UpperCAmelCase ) if not json_str.endswith('\n' ): json_str += "\n" return json_str.encode(self.encoding ) def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , **__UpperCAmelCase , ): '''simple docstring''' __UpperCamelCase = 0 if self.num_proc is None or self.num_proc == 1: for offset in logging.tqdm( range(0 , len(self.dataset ) , self.batch_size ) , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating json from Arrow format' , ): __UpperCamelCase = self._batch_json((offset, orient, lines, index, to_json_kwargs) ) written += file_obj.write(__UpperCAmelCase ) else: __UpperCamelCase , __UpperCamelCase = len(self.dataset ), self.batch_size with multiprocessing.Pool(self.num_proc ) as pool: for json_str in logging.tqdm( pool.imap( self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , __UpperCAmelCase , __UpperCAmelCase )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit='ba' , disable=not logging.is_progress_bar_enabled() , desc='Creating json from Arrow format' , ): written += file_obj.write(__UpperCAmelCase ) return written
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0
'''simple docstring''' import unittest import numpy as np from transformers import RobertaConfig, is_flax_available from transformers.testing_utils import require_flax, slow from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask if is_flax_available(): from transformers.models.roberta.modeling_flax_roberta import ( FlaxRobertaForCausalLM, FlaxRobertaForMaskedLM, FlaxRobertaForMultipleChoice, FlaxRobertaForQuestionAnswering, FlaxRobertaForSequenceClassification, FlaxRobertaForTokenClassification, FlaxRobertaModel, ) class snake_case ( unittest.TestCase ): """simple docstring""" def __init__( self , UpperCamelCase , UpperCamelCase=13 , UpperCamelCase=7 , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=True , UpperCamelCase=99 , UpperCamelCase=32 , UpperCamelCase=5 , UpperCamelCase=4 , UpperCamelCase=37 , UpperCamelCase="gelu" , UpperCamelCase=0.1 , UpperCamelCase=0.1 , UpperCamelCase=512 , UpperCamelCase=16 , UpperCamelCase=2 , UpperCamelCase=0.02 , UpperCamelCase=4 , ): """simple docstring""" lowerCamelCase_ = parent lowerCamelCase_ = batch_size lowerCamelCase_ = seq_length lowerCamelCase_ = is_training lowerCamelCase_ = use_attention_mask lowerCamelCase_ = use_token_type_ids lowerCamelCase_ = use_labels lowerCamelCase_ = vocab_size lowerCamelCase_ = hidden_size lowerCamelCase_ = num_hidden_layers lowerCamelCase_ = num_attention_heads lowerCamelCase_ = intermediate_size lowerCamelCase_ = hidden_act 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_choices def snake_case ( self ): """simple docstring""" lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) lowerCamelCase_ = None if self.use_attention_mask: lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] ) lowerCamelCase_ = None if self.use_token_type_ids: lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) lowerCamelCase_ = RobertaConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase , initializer_range=self.initializer_range , ) return config, input_ids, token_type_ids, attention_mask def snake_case ( self ): """simple docstring""" lowerCamelCase_ = self.prepare_config_and_inputs() lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = config_and_inputs lowerCamelCase_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask} return config, inputs_dict def snake_case ( self ): """simple docstring""" lowerCamelCase_ = self.prepare_config_and_inputs() lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = config_and_inputs lowerCamelCase_ = True lowerCamelCase_ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, encoder_hidden_states, encoder_attention_mask, ) @require_flax class snake_case ( lowercase , unittest.TestCase ): """simple docstring""" _lowerCamelCase = True _lowerCamelCase = ( ( FlaxRobertaModel, FlaxRobertaForCausalLM, FlaxRobertaForMaskedLM, FlaxRobertaForSequenceClassification, FlaxRobertaForTokenClassification, FlaxRobertaForMultipleChoice, FlaxRobertaForQuestionAnswering, ) if is_flax_available() else () ) def snake_case ( self ): """simple docstring""" lowerCamelCase_ = FlaxRobertaModelTester(self ) @slow def snake_case ( self ): """simple docstring""" for model_class_name in self.all_model_classes: lowerCamelCase_ = model_class_name.from_pretrained("roberta-base" , from_pt=UpperCamelCase ) lowerCamelCase_ = model(np.ones((1, 1) ) ) self.assertIsNotNone(UpperCamelCase )
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'''simple docstring''' # Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from argparse import ArgumentParser from accelerate.commands.config import get_config_parser from accelerate.commands.env import env_command_parser from accelerate.commands.launch import launch_command_parser from accelerate.commands.test import test_command_parser from accelerate.commands.tpu import tpu_command_parser def __snake_case ( ): lowerCamelCase_ = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=UpperCAmelCase_ ) lowerCamelCase_ = parser.add_subparsers(help="accelerate command helpers" ) # Register commands get_config_parser(subparsers=UpperCAmelCase_ ) env_command_parser(subparsers=UpperCAmelCase_ ) launch_command_parser(subparsers=UpperCAmelCase_ ) tpu_command_parser(subparsers=UpperCAmelCase_ ) test_command_parser(subparsers=UpperCAmelCase_ ) # Let's go lowerCamelCase_ = parser.parse_args() if not hasattr(UpperCAmelCase_ , "func" ): parser.print_help() exit(1 ) # Run args.func(UpperCAmelCase_ ) if __name__ == "__main__": main()
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1
"""simple docstring""" def _lowerCAmelCase ( lowercase_ ): return str(lowercase_ ) == str(lowercase_ )[::-1] def _lowerCAmelCase ( lowercase_ ): return int(lowercase_ ) + int(str(lowercase_ )[::-1] ) def _lowerCAmelCase ( lowercase_ = 10000 ): UpperCAmelCase = [] for num in range(1 , lowercase_ ): UpperCAmelCase = 0 UpperCAmelCase = num while iterations < 50: UpperCAmelCase = sum_reverse(lowercase_ ) iterations += 1 if is_palindrome(lowercase_ ): break else: lychrel_nums.append(lowercase_ ) return len(lowercase_ ) if __name__ == "__main__": print(f'''{solution() = }''')
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"""simple docstring""" from typing import Callable, List, Optional, Union import PIL import torch from transformers import ( CLIPImageProcessor, CLIPSegForImageSegmentation, CLIPSegProcessor, CLIPTextModel, CLIPTokenizer, ) from diffusers import DiffusionPipeline from diffusers.configuration_utils import FrozenDict from diffusers.models import AutoencoderKL, UNetaDConditionModel from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler from diffusers.utils import deprecate, is_accelerate_available, logging snake_case_ = logging.get_logger(__name__) # pylint: disable=invalid-name class A_ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" def __init__( self :Optional[Any] , lowercase_ :CLIPSegForImageSegmentation , lowercase_ :CLIPSegProcessor , lowercase_ :AutoencoderKL , lowercase_ :CLIPTextModel , lowercase_ :CLIPTokenizer , lowercase_ :UNetaDConditionModel , lowercase_ :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowercase_ :StableDiffusionSafetyChecker , lowercase_ :CLIPImageProcessor , ) -> List[str]: super().__init__() if hasattr(scheduler.config , 'steps_offset' ) and scheduler.config.steps_offset != 1: UpperCAmelCase = ( f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`""" f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """ 'to update the config accordingly as leaving `steps_offset` might led to incorrect results' ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,' ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`' ' file' ) deprecate('steps_offset!=1' , '1.0.0' , lowercase_ , standard_warn=lowercase_ ) UpperCAmelCase = dict(scheduler.config ) UpperCAmelCase = 1 UpperCAmelCase = FrozenDict(lowercase_ ) if hasattr(scheduler.config , 'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False: UpperCAmelCase = ( f"""The configuration file of this scheduler: {scheduler} has not set the configuration""" ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make' ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to' ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face' ' Hub, it would be very nice if you could open a Pull request for the' ' `scheduler/scheduler_config.json` file' ) deprecate('skip_prk_steps not set' , '1.0.0' , lowercase_ , standard_warn=lowercase_ ) UpperCAmelCase = dict(scheduler.config ) UpperCAmelCase = True UpperCAmelCase = FrozenDict(lowercase_ ) if safety_checker is None: logger.warning( f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure""" ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered' ' results in services or applications open to the public. Both the diffusers team and Hugging Face' ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling' ' it only for use-cases that involve analyzing network behavior or auditing its results. For more' ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' ) self.register_modules( segmentation_model=lowercase_ , segmentation_processor=lowercase_ , vae=lowercase_ , text_encoder=lowercase_ , tokenizer=lowercase_ , unet=lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , feature_extractor=lowercase_ , ) def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :Optional[Union[str, int]] = "auto" ) -> int: if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory UpperCAmelCase = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(lowercase_ ) def UpperCAmelCase__ ( self :Optional[int] ) -> List[str]: self.enable_attention_slicing(lowercase_ ) def UpperCAmelCase__ ( self :int ) -> int: if is_accelerate_available(): from accelerate import cpu_offload else: raise ImportError('Please install accelerate via `pip install accelerate`' ) UpperCAmelCase = torch.device('cuda' ) for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]: if cpu_offloaded_model is not None: cpu_offload(lowercase_ , lowercase_ ) @property # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device def UpperCAmelCase__ ( self :Optional[Any] ) -> List[str]: if self.device != torch.device('meta' ) or not hasattr(self.unet , '_hf_hook' ): return self.device for module in self.unet.modules(): if ( hasattr(lowercase_ , '_hf_hook' ) and hasattr(module._hf_hook , 'execution_device' ) and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device ) return self.device @torch.no_grad() def __call__( self :Optional[Any] , lowercase_ :Union[str, List[str]] , lowercase_ :Union[torch.FloatTensor, PIL.Image.Image] , lowercase_ :str , lowercase_ :int = 5_12 , lowercase_ :int = 5_12 , lowercase_ :int = 50 , lowercase_ :float = 7.5 , lowercase_ :Optional[Union[str, List[str]]] = None , lowercase_ :Optional[int] = 1 , lowercase_ :float = 0.0 , lowercase_ :Optional[torch.Generator] = None , lowercase_ :Optional[torch.FloatTensor] = None , lowercase_ :Optional[str] = "pil" , lowercase_ :bool = True , lowercase_ :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowercase_ :int = 1 , **lowercase_ :int , ) -> int: UpperCAmelCase = self.segmentation_processor( text=[text] , images=[image] , padding='max_length' , return_tensors='pt' ).to(self.device ) UpperCAmelCase = self.segmentation_model(**lowercase_ ) UpperCAmelCase = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy() UpperCAmelCase = self.numpy_to_pil(lowercase_ )[0].resize(image.size ) # Run inpainting pipeline with the generated mask UpperCAmelCase = StableDiffusionInpaintPipeline( vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , ) return inpainting_pipeline( prompt=lowercase_ , image=lowercase_ , mask_image=lowercase_ , height=lowercase_ , width=lowercase_ , num_inference_steps=lowercase_ , guidance_scale=lowercase_ , negative_prompt=lowercase_ , num_images_per_prompt=lowercase_ , eta=lowercase_ , generator=lowercase_ , latents=lowercase_ , output_type=lowercase_ , return_dict=lowercase_ , callback=lowercase_ , callback_steps=lowercase_ , )
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1
from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCAmelCase_ ( )-> int: '''simple docstring''' UpperCAmelCase : str ={ '''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''], '''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''], '''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7], } UpperCAmelCase : Union[str, Any] =Dataset.from_dict(__lowerCAmelCase ) return dataset class __snake_case ( lowerCamelCase__ ): def UpperCAmelCase__ ( self ) -> Tuple: '''simple docstring''' UpperCAmelCase : List[str] =get_dataset() UpperCAmelCase : Optional[int] =make_duplicate_clusters(snake_case__ , 0.85 ) self.assertEqual(len(duplicate_clusters[0] ) , 2 ) def UpperCAmelCase__ ( self ) -> Tuple: '''simple docstring''' UpperCAmelCase : str =get_dataset() UpperCAmelCase , UpperCAmelCase : Tuple =deduplicate_dataset(snake_case__ ) self.assertEqual(len(snake_case__ ) , 2 ) print(snake_case__ ) self.assertEqual(duplicate_clusters[0][0]['''copies'''] , 2 ) self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''] , snake_case__ )
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__snake_case = '''Input must be a string of 8 numbers plus letter''' __snake_case = '''TRWAGMYFPDXBNJZSQVHLCKE''' def lowerCAmelCase_ ( __lowerCAmelCase )-> bool: '''simple docstring''' if not isinstance(__lowerCAmelCase , __lowerCAmelCase ): UpperCAmelCase : Optional[Any] =f'''Expected string as input, found {type(__lowerCAmelCase ).__name__}''' raise TypeError(__lowerCAmelCase ) UpperCAmelCase : List[Any] =spanish_id.replace('''-''' , '''''' ).upper() if len(__lowerCAmelCase ) != 9: raise ValueError(__lowerCAmelCase ) try: UpperCAmelCase : int =int(spanish_id_clean[0:8] ) UpperCAmelCase : Optional[int] =spanish_id_clean[8] except ValueError as ex: raise ValueError(__lowerCAmelCase ) from ex if letter.isdigit(): raise ValueError(__lowerCAmelCase ) return letter == LOOKUP_LETTERS[number % 23] if __name__ == "__main__": import doctest doctest.testmod()
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import inspect import unittest from transformers import SegformerConfig, is_torch_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_MAPPING, SegformerForImageClassification, SegformerForSemanticSegmentation, SegformerModel, ) from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import SegformerImageProcessor class A_ ( _lowerCamelCase ): def _lowerCAmelCase (self :Union[str, Any] )-> str: __A = self.config_class(**self.inputs_dict ) self.parent.assertTrue(hasattr(_UpperCamelCase , '''hidden_sizes''' ) ) self.parent.assertTrue(hasattr(_UpperCamelCase , '''num_attention_heads''' ) ) self.parent.assertTrue(hasattr(_UpperCamelCase , '''num_encoder_blocks''' ) ) class A_ : def __init__(self :int , _UpperCamelCase :List[Any] , _UpperCamelCase :Optional[Any]=13 , _UpperCamelCase :str=64 , _UpperCamelCase :Optional[int]=3 , _UpperCamelCase :List[Any]=4 , _UpperCamelCase :int=[2, 2, 2, 2] , _UpperCamelCase :Dict=[8, 4, 2, 1] , _UpperCamelCase :Optional[int]=[16, 32, 64, 128] , _UpperCamelCase :List[Any]=[1, 4, 8, 16] , _UpperCamelCase :List[Any]=[1, 2, 4, 8] , _UpperCamelCase :Dict=True , _UpperCamelCase :int=True , _UpperCamelCase :str="gelu" , _UpperCamelCase :Tuple=0.1 , _UpperCamelCase :Tuple=0.1 , _UpperCamelCase :Tuple=0.0_2 , _UpperCamelCase :int=3 , _UpperCamelCase :int=None , )-> Optional[Any]: __A = parent __A = batch_size __A = image_size __A = num_channels __A = num_encoder_blocks __A = sr_ratios __A = depths __A = hidden_sizes __A = downsampling_rates __A = num_attention_heads __A = is_training __A = use_labels __A = hidden_act __A = hidden_dropout_prob __A = attention_probs_dropout_prob __A = initializer_range __A = num_labels __A = scope def _lowerCAmelCase (self :Optional[int] )-> Optional[int]: __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.image_size, self.image_size] , self.num_labels ) __A = self.get_config() return config, pixel_values, labels def _lowerCAmelCase (self :List[str] )-> Optional[Any]: return SegformerConfig( image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , ) def _lowerCAmelCase (self :Optional[Any] , _UpperCamelCase :Tuple , _UpperCamelCase :List[Any] , _UpperCamelCase :List[Any] )-> Optional[int]: __A = SegformerModel(config=_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() __A = model(_UpperCamelCase ) __A = __A = self.image_size // (self.downsampling_rates[-1] * 2) self.parent.assertEqual( result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def _lowerCAmelCase (self :Dict , _UpperCamelCase :int , _UpperCamelCase :Tuple , _UpperCamelCase :int )-> str: __A = self.num_labels __A = SegformerForSemanticSegmentation(_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() __A = model(_UpperCamelCase ) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) __A = model(_UpperCamelCase , labels=_UpperCamelCase ) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) self.parent.assertGreater(result.loss , 0.0 ) def _lowerCAmelCase (self :Optional[Any] , _UpperCamelCase :Any , _UpperCamelCase :Dict , _UpperCamelCase :Optional[int] )-> List[Any]: __A = 1 __A = SegformerForSemanticSegmentation(config=_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() __A = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(_UpperCamelCase ) __A = model(_UpperCamelCase , labels=_UpperCamelCase ) self.parent.assertGreater(result.loss , 0.0 ) def _lowerCAmelCase (self :str )-> Dict: __A = self.prepare_config_and_inputs() __A , __A , __A = config_and_inputs __A = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class A_ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ): lowerCAmelCase__ = ( ( SegformerModel, SegformerForSemanticSegmentation, SegformerForImageClassification, ) if is_torch_available() else () ) lowerCAmelCase__ = ( { """feature-extraction""": SegformerModel, """image-classification""": SegformerForImageClassification, """image-segmentation""": SegformerForSemanticSegmentation, } if is_torch_available() else {} ) lowerCAmelCase__ = True lowerCAmelCase__ = False lowerCAmelCase__ = False lowerCAmelCase__ = False def _lowerCAmelCase (self :List[str] )-> int: __A = SegformerModelTester(self ) __A = SegformerConfigTester(self , config_class=_UpperCamelCase ) def _lowerCAmelCase (self :Any )-> int: self.config_tester.run_common_tests() def _lowerCAmelCase (self :int )-> List[Any]: __A = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_UpperCamelCase ) def _lowerCAmelCase (self :Optional[Any] )-> Dict: __A = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_binary_image_segmentation(*_UpperCamelCase ) def _lowerCAmelCase (self :List[str] )-> Optional[Any]: __A = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_segmentation(*_UpperCamelCase ) @unittest.skip('''SegFormer does not use inputs_embeds''' ) def _lowerCAmelCase (self :Union[str, Any] )-> Optional[int]: pass @unittest.skip('''SegFormer does not have get_input_embeddings method and get_output_embeddings methods''' ) def _lowerCAmelCase (self :Optional[Any] )-> str: pass def _lowerCAmelCase (self :Optional[int] )-> List[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 _lowerCAmelCase (self :Tuple )-> str: __A , __A = self.model_tester.prepare_config_and_inputs_for_common() __A = True for model_class in self.all_model_classes: __A = True __A = False __A = True __A = model_class(_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() with torch.no_grad(): __A = model(**self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) ) __A = outputs.attentions __A = sum(self.model_tester.depths ) self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase ) # check that output_attentions also work using config del inputs_dict["output_attentions"] __A = True __A = model_class(_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() with torch.no_grad(): __A = model(**self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) ) __A = outputs.attentions self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase ) # verify the first attentions (first block, first layer) __A = (self.model_tester.image_size // 4) ** 2 __A = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , ) # verify the last attentions (last block, last layer) __A = (self.model_tester.image_size // 32) ** 2 __A = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2 self.assertListEqual( list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , ) __A = len(_UpperCamelCase ) # Check attention is always last and order is fine __A = True __A = True __A = model_class(_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() with torch.no_grad(): __A = model(**self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) ) self.assertEqual(out_len + 1 , len(_UpperCamelCase ) ) __A = outputs.attentions self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase ) # verify the first attentions (first block, first layer) __A = (self.model_tester.image_size // 4) ** 2 __A = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , ) def _lowerCAmelCase (self :List[str] )-> Optional[int]: def check_hidden_states_output(_UpperCamelCase :Tuple , _UpperCamelCase :Any , _UpperCamelCase :Tuple ): __A = model_class(_UpperCamelCase ) model.to(_UpperCamelCase ) model.eval() with torch.no_grad(): __A = model(**self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) ) __A = outputs.hidden_states __A = self.model_tester.num_encoder_blocks self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase ) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:] ) , [ self.model_tester.hidden_sizes[0], self.model_tester.image_size // 4, self.model_tester.image_size // 4, ] , ) __A , __A = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: __A = True check_hidden_states_output(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] __A = True check_hidden_states_output(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) def _lowerCAmelCase (self :Dict )-> str: if not self.model_tester.is_training: return __A , __A = self.model_tester.prepare_config_and_inputs_for_common() __A = True for model_class in self.all_model_classes: if model_class in get_values(_UpperCamelCase ): continue __A = model_class(_UpperCamelCase ) model.to(_UpperCamelCase ) model.train() __A = self._prepare_for_class(_UpperCamelCase , _UpperCamelCase , return_labels=_UpperCamelCase ) __A = model(**_UpperCamelCase ).loss loss.backward() @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' ) def _lowerCAmelCase (self :Union[str, Any] )-> Any: pass @slow def _lowerCAmelCase (self :Tuple )-> List[Any]: for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: __A = SegformerModel.from_pretrained(_UpperCamelCase ) self.assertIsNotNone(_UpperCamelCase ) def _a ( ) -> Union[str, Any]: '''simple docstring''' __A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch class A_ ( unittest.TestCase ): @slow def _lowerCAmelCase (self :Optional[Any] )-> List[Any]: # only resize + normalize __A = SegformerImageProcessor( image_scale=(512, 512) , keep_ratio=_UpperCamelCase , align=_UpperCamelCase , do_random_crop=_UpperCamelCase ) __A = SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''' ).to( _UpperCamelCase ) __A = prepare_img() __A = image_processor(images=_UpperCamelCase , return_tensors='''pt''' ) __A = encoded_inputs.pixel_values.to(_UpperCamelCase ) with torch.no_grad(): __A = model(_UpperCamelCase ) __A = torch.Size((1, model.config.num_labels, 128, 128) ) self.assertEqual(outputs.logits.shape , _UpperCamelCase ) __A = torch.tensor( [ [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]], [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]], [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]], ] ).to(_UpperCamelCase ) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , _UpperCamelCase , atol=1e-4 ) ) @slow def _lowerCAmelCase (self :List[Any] )-> List[str]: # only resize + normalize __A = SegformerImageProcessor( image_scale=(512, 512) , keep_ratio=_UpperCamelCase , align=_UpperCamelCase , do_random_crop=_UpperCamelCase ) __A = SegformerForSemanticSegmentation.from_pretrained( '''nvidia/segformer-b1-finetuned-cityscapes-1024-1024''' ).to(_UpperCamelCase ) __A = prepare_img() __A = image_processor(images=_UpperCamelCase , return_tensors='''pt''' ) __A = encoded_inputs.pixel_values.to(_UpperCamelCase ) with torch.no_grad(): __A = model(_UpperCamelCase ) __A = torch.Size((1, model.config.num_labels, 128, 128) ) self.assertEqual(outputs.logits.shape , _UpperCamelCase ) __A = torch.tensor( [ [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]], [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]], [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]], ] ).to(_UpperCamelCase ) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , _UpperCamelCase , atol=1e-1 ) ) @slow def _lowerCAmelCase (self :Any )-> Any: # only resize + normalize __A = SegformerImageProcessor( image_scale=(512, 512) , keep_ratio=_UpperCamelCase , align=_UpperCamelCase , do_random_crop=_UpperCamelCase ) __A = SegformerForSemanticSegmentation.from_pretrained('''nvidia/segformer-b0-finetuned-ade-512-512''' ).to( _UpperCamelCase ) __A = prepare_img() __A = image_processor(images=_UpperCamelCase , return_tensors='''pt''' ) __A = encoded_inputs.pixel_values.to(_UpperCamelCase ) with torch.no_grad(): __A = model(_UpperCamelCase ) __A = outputs.logits.detach().cpu() __A = image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase , target_sizes=[(500, 300)] ) __A = torch.Size((500, 300) ) self.assertEqual(segmentation[0].shape , _UpperCamelCase ) __A = image_processor.post_process_semantic_segmentation(outputs=_UpperCamelCase ) __A = torch.Size((128, 128) ) self.assertEqual(segmentation[0].shape , _UpperCamelCase )
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import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...utils import logging snake_case__ : str = logging.get_logger(__name__) snake_case__ : Optional[int] = { 'google/pix2struct-textcaps-base': ( 'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json' ), } class A_ ( _lowerCamelCase ): lowerCAmelCase__ = """pix2struct_text_model""" lowerCAmelCase__ = ["""past_key_values"""] lowerCAmelCase__ = { """hidden_size""": """hidden_size""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers""", } def __init__(self :Any , _UpperCamelCase :int=5_0244 , _UpperCamelCase :Optional[Any]=768 , _UpperCamelCase :Optional[Any]=64 , _UpperCamelCase :Dict=2048 , _UpperCamelCase :int=12 , _UpperCamelCase :Optional[int]=12 , _UpperCamelCase :Optional[int]=32 , _UpperCamelCase :Dict=128 , _UpperCamelCase :Tuple=0.1 , _UpperCamelCase :List[str]=1e-6 , _UpperCamelCase :Optional[Any]=1.0 , _UpperCamelCase :Union[str, Any]="gelu_new" , _UpperCamelCase :int=0 , _UpperCamelCase :int=False , _UpperCamelCase :int=0 , _UpperCamelCase :Dict=1 , _UpperCamelCase :Any=False , _UpperCamelCase :Optional[Any]=True , **_UpperCamelCase :Tuple , )-> Dict: __A = vocab_size __A = hidden_size __A = d_kv __A = d_ff __A = num_layers __A = num_heads __A = relative_attention_num_buckets __A = relative_attention_max_distance __A = dropout_rate __A = layer_norm_epsilon __A = initializer_factor __A = use_cache __A = eos_token_id __A = decoder_start_token_id # for backwards compatibility __A = dense_act_fn super().__init__( pad_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , decoder_start_token_id=_UpperCamelCase , tie_word_embeddings=_UpperCamelCase , is_decoder=_UpperCamelCase , **_UpperCamelCase , ) @classmethod def _lowerCAmelCase (cls :List[str] , _UpperCamelCase :Union[str, os.PathLike] , **_UpperCamelCase :List[Any] )-> "PretrainedConfig": cls._set_token_in_kwargs(_UpperCamelCase ) __A , __A = cls.get_config_dict(_UpperCamelCase , **_UpperCamelCase ) # get the text config dict if we are loading from Pix2StructConfig if config_dict.get('''model_type''' ) == "pix2struct": __A = config_dict['''text_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """ f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" ) return cls.from_dict(_UpperCamelCase , **_UpperCamelCase ) class A_ ( _lowerCamelCase ): lowerCAmelCase__ = """pix2struct_vision_model""" def __init__(self :Dict , _UpperCamelCase :Optional[Any]=768 , _UpperCamelCase :List[str]=768 , _UpperCamelCase :Any=2048 , _UpperCamelCase :Tuple=64 , _UpperCamelCase :int=12 , _UpperCamelCase :Optional[int]=12 , _UpperCamelCase :Tuple="gelu_new" , _UpperCamelCase :Dict=1e-6 , _UpperCamelCase :int=0.0 , _UpperCamelCase :int=0.0 , _UpperCamelCase :Union[str, Any]=1e-10 , _UpperCamelCase :Tuple=1.0 , _UpperCamelCase :Tuple=4096 , _UpperCamelCase :List[str]=32 , _UpperCamelCase :Optional[Any]=128 , **_UpperCamelCase :List[str] , )-> Any: super().__init__(**_UpperCamelCase ) __A = hidden_size __A = patch_embed_hidden_size __A = d_ff __A = dropout_rate __A = num_hidden_layers __A = num_attention_heads __A = initializer_range __A = initializer_factor __A = attention_dropout __A = layer_norm_eps __A = dense_act_fn __A = seq_len __A = relative_attention_num_buckets __A = relative_attention_max_distance __A = d_kv @classmethod def _lowerCAmelCase (cls :List[str] , _UpperCamelCase :Union[str, os.PathLike] , **_UpperCamelCase :List[str] )-> "PretrainedConfig": cls._set_token_in_kwargs(_UpperCamelCase ) __A , __A = cls.get_config_dict(_UpperCamelCase , **_UpperCamelCase ) # get the vision config dict if we are loading from Pix2StructConfig if config_dict.get('''model_type''' ) == "pix2struct": __A = config_dict['''vision_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """ f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" ) return cls.from_dict(_UpperCamelCase , **_UpperCamelCase ) class A_ ( _lowerCamelCase ): lowerCAmelCase__ = """pix2struct""" lowerCAmelCase__ = True def __init__(self :List[Any] , _UpperCamelCase :str=None , _UpperCamelCase :int=None , _UpperCamelCase :List[Any]=1.0 , _UpperCamelCase :int=0.0_2 , _UpperCamelCase :List[str]=False , _UpperCamelCase :Optional[Any]=False , _UpperCamelCase :int=True , **_UpperCamelCase :Any , )-> Optional[Any]: super().__init__(tie_word_embeddings=_UpperCamelCase , is_encoder_decoder=_UpperCamelCase , **_UpperCamelCase ) if text_config is None: __A = {} logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.''' ) if vision_config is None: __A = {} logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.''' ) __A = PixaStructTextConfig(**_UpperCamelCase ) __A = PixaStructVisionConfig(**_UpperCamelCase ) __A = self.text_config.decoder_start_token_id __A = self.text_config.pad_token_id __A = self.text_config.eos_token_id __A = initializer_factor __A = initializer_range __A = self.initializer_range __A = self.initializer_range __A = is_vqa @classmethod def _lowerCAmelCase (cls :str , _UpperCamelCase :PixaStructTextConfig , _UpperCamelCase :PixaStructVisionConfig , **_UpperCamelCase :Union[str, Any] )-> List[str]: return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **_UpperCamelCase ) def _lowerCAmelCase (self :Union[str, Any] )-> int: __A = copy.deepcopy(self.__dict__ ) __A = self.text_config.to_dict() __A = self.vision_config.to_dict() __A = self.__class__.model_type return output
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'''simple docstring''' from ...processing_utils import ProcessorMixin class A__ ( _snake_case ): lowercase = "WhisperFeatureExtractor" lowercase = "WhisperTokenizer" def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> int: '''simple docstring''' super().__init__(UpperCamelCase__ , UpperCamelCase__ ) A_ = self.feature_extractor A_ = False def snake_case_ ( self , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=True ) -> List[Any]: '''simple docstring''' return self.tokenizer.get_decoder_prompt_ids(task=UpperCamelCase__ , language=UpperCamelCase__ , no_timestamps=UpperCamelCase__ ) def __call__( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> str: '''simple docstring''' # For backward compatibility if self._in_target_context_manager: return self.current_processor(*UpperCamelCase__ , **UpperCamelCase__ ) A_ = kwargs.pop("""audio""" , UpperCamelCase__ ) A_ = kwargs.pop("""sampling_rate""" , UpperCamelCase__ ) A_ = kwargs.pop("""text""" , UpperCamelCase__ ) if len(UpperCamelCase__ ) > 0: A_ = args[0] A_ = args[1:] if audio is None and text is None: raise ValueError("""You need to specify either an `audio` or `text` input to process.""" ) if audio is not None: A_ = self.feature_extractor(UpperCamelCase__ , *UpperCamelCase__ , sampling_rate=UpperCamelCase__ , **UpperCamelCase__ ) if text is not None: A_ = self.tokenizer(UpperCamelCase__ , **UpperCamelCase__ ) if text is None: return inputs elif audio is None: return encodings else: A_ = encodings["""input_ids"""] return inputs def snake_case_ ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]: '''simple docstring''' return self.tokenizer.batch_decode(*UpperCamelCase__ , **UpperCamelCase__ ) def snake_case_ ( self , *UpperCamelCase__ , **UpperCamelCase__ ) -> Optional[Any]: '''simple docstring''' return self.tokenizer.decode(*UpperCamelCase__ , **UpperCamelCase__ ) def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__="np" ) -> List[Any]: '''simple docstring''' return self.tokenizer.get_prompt_ids(UpperCamelCase__ , return_tensors=UpperCamelCase__ )
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'''simple docstring''' def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[list[float]]: A_ = [] for data in source_data: for i, el in enumerate(UpperCAmelCase__ ): if len(UpperCAmelCase__ ) < i + 1: data_lists.append([] ) data_lists[i].append(float(UpperCAmelCase__ ) ) return data_lists def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> list[list[float]]: A_ = [] for dlist, weight in zip(UpperCAmelCase__, UpperCAmelCase__ ): A_ = min(UpperCAmelCase__ ) A_ = max(UpperCAmelCase__ ) A_ = [] # for weight 0 score is 1 - actual score if weight == 0: for item in dlist: try: score.append(1 - ((item - mind) / (maxd - mind)) ) except ZeroDivisionError: score.append(1 ) elif weight == 1: for item in dlist: try: score.append((item - mind) / (maxd - mind) ) except ZeroDivisionError: score.append(0 ) # weight not 0 or 1 else: A_ = F'''Invalid weight of {weight:f} provided''' raise ValueError(UpperCAmelCase__ ) score_lists.append(UpperCAmelCase__ ) return score_lists def UpperCAmelCase__ ( UpperCAmelCase__ ) -> list[float]: A_ = [0 for i in range(len(score_lists[0] ) )] for slist in score_lists: for j, ele in enumerate(UpperCAmelCase__ ): A_ = final_scores[j] + ele return final_scores def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__ ) -> list[list[float]]: A_ = get_data(UpperCAmelCase__ ) A_ = calculate_each_score(UpperCAmelCase__, UpperCAmelCase__ ) A_ = generate_final_scores(UpperCAmelCase__ ) # append scores to source data for i, ele in enumerate(UpperCAmelCase__ ): source_data[i].append(UpperCAmelCase__ ) return source_data
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1
def A ( _lowercase , _lowercase = False ): if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): SCREAMING_SNAKE_CASE : Dict = f"""Expected string as input, found {type(SCREAMING_SNAKE_CASE__ )}""" raise ValueError(SCREAMING_SNAKE_CASE__ ) if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ): SCREAMING_SNAKE_CASE : int = f"""Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE__ )}""" raise ValueError(SCREAMING_SNAKE_CASE__ ) SCREAMING_SNAKE_CASE : Any = input_str.split('''_''' ) SCREAMING_SNAKE_CASE : Optional[Any] = 0 if use_pascal else 1 SCREAMING_SNAKE_CASE : Optional[Any] = words[start_index:] SCREAMING_SNAKE_CASE : Union[str, Any] = [word[0].upper() + word[1:] for word in words_to_capitalize] SCREAMING_SNAKE_CASE : str = '''''' if use_pascal else words[0] return "".join([initial_word, *capitalized_words] ) if __name__ == "__main__": from doctest import testmod testmod()
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from __future__ import annotations import numpy as np def A ( _lowercase ): return np.maximum(0 , _lowercase ) if __name__ == "__main__": print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
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import os import unittest from transformers import LxmertTokenizer, LxmertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class lowerCamelCase__ ( lowerCAmelCase , unittest.TestCase): SCREAMING_SNAKE_CASE__ = LxmertTokenizer SCREAMING_SNAKE_CASE__ = LxmertTokenizerFast SCREAMING_SNAKE_CASE__ = True SCREAMING_SNAKE_CASE__ = True def __A (self ) -> Any: super().setUp() _lowercase =[ '''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''', ] _lowercase =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 , UpperCAmelCase ) -> str: _lowercase ='''UNwant\u00E9d,running''' _lowercase ='''unwanted, running''' return input_text, output_text def __A (self ) -> Tuple: _lowercase =self.tokenizer_class(self.vocab_file ) _lowercase =tokenizer.tokenize('''UNwant\u00E9d,running''' ) self.assertListEqual(UpperCAmelCase , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase ) , [7, 4, 5, 1_0, 8, 9] ) def __A (self ) -> int: 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(UpperCAmelCase ) _lowercase =rust_tokenizer.tokenize(UpperCAmelCase ) self.assertListEqual(UpperCAmelCase , UpperCAmelCase ) _lowercase =tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) _lowercase =rust_tokenizer.encode(UpperCAmelCase , add_special_tokens=UpperCAmelCase ) self.assertListEqual(UpperCAmelCase , UpperCAmelCase ) _lowercase =self.get_rust_tokenizer() _lowercase =tokenizer.encode(UpperCAmelCase ) _lowercase =rust_tokenizer.encode(UpperCAmelCase ) self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
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from typing import Any def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> list: """simple docstring""" _validation( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) # Creates data structures and fill initial step _lowercase ={} _lowercase ={} for state in states_space: _lowercase =observations_space[0] _lowercase =( initial_probabilities[state] * emission_probabilities[state][observation] ) _lowercase =None # Fills the data structure with the probabilities of # different transitions and pointers to previous states for o in range(1 , len(__snake_case ) ): _lowercase =observations_space[o] _lowercase =observations_space[o - 1] for state in states_space: # Calculates the argmax for probability function _lowercase ='''''' _lowercase =-1 for k_state in states_space: _lowercase =( probabilities[(k_state, prior_observation)] * transition_probabilities[k_state][state] * emission_probabilities[state][observation] ) if probability > max_probability: _lowercase =probability _lowercase =k_state # Update probabilities and pointers dicts _lowercase =( probabilities[(arg_max, prior_observation)] * transition_probabilities[arg_max][state] * emission_probabilities[state][observation] ) _lowercase =arg_max # The final observation _lowercase =observations_space[len(__snake_case ) - 1] # argmax for given final observation _lowercase ='''''' _lowercase =-1 for k_state in states_space: _lowercase =probabilities[(k_state, final_observation)] if probability > max_probability: _lowercase =probability _lowercase =k_state _lowercase =arg_max # Process pointers backwards _lowercase =last_state _lowercase =[] for o in range(len(__snake_case ) - 1 , -1 , -1 ): result.append(__snake_case ) _lowercase =pointers[previous, observations_space[o]] result.reverse() return result def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None: """simple docstring""" _validate_not_empty( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) _validate_lists(__snake_case , __snake_case ) _validate_dicts( __snake_case , __snake_case , __snake_case ) def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None: """simple docstring""" if not all( [ observations_space, states_space, initial_probabilities, transition_probabilities, emission_probabilities, ] ): raise ValueError('''There\'s an empty parameter''' ) def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None: """simple docstring""" _validate_list(__snake_case , '''observations_space''' ) _validate_list(__snake_case , '''states_space''' ) def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None: """simple docstring""" if not isinstance(_object , __snake_case ): _lowercase =F"{var_name} must be a list" raise ValueError(__snake_case ) else: for x in _object: if not isinstance(__snake_case , __snake_case ): _lowercase =F"{var_name} must be a list of strings" raise ValueError(__snake_case ) def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , ) -> None: """simple docstring""" _validate_dict(__snake_case , '''initial_probabilities''' , __snake_case ) _validate_nested_dict(__snake_case , '''transition_probabilities''' ) _validate_nested_dict(__snake_case , '''emission_probabilities''' ) def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None: """simple docstring""" _validate_dict(_object , __snake_case , __snake_case ) for x in _object.values(): _validate_dict(__snake_case , __snake_case , __snake_case , __snake_case ) def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case = False ) -> None: """simple docstring""" if not isinstance(_object , __snake_case ): _lowercase =F"{var_name} must be a dict" raise ValueError(__snake_case ) if not all(isinstance(__snake_case , __snake_case ) for x in _object ): _lowercase =F"{var_name} all keys must be strings" raise ValueError(__snake_case ) if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ): _lowercase ='''nested dictionary ''' if nested else '''''' _lowercase =F"{var_name} {nested_text}all values must be {value_type.__name__}" raise ValueError(__snake_case ) if __name__ == "__main__": from doctest import testmod testmod()
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1
'''simple docstring''' import logging import os import sys from dataclasses import dataclass, field from importlib import import_module from typing import Dict, List, Optional, Tuple import numpy as np from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score from torch import nn from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask import transformers from transformers import ( AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import is_main_process lowerCAmelCase : Optional[int] = logging.getLogger(__name__) @dataclass class UpperCamelCase__ : """simple docstring""" __magic_name__ = field( metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ) __magic_name__ = field( default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Pretrained config name or path if not the same as model_name"} ) __magic_name__ = field( default="NER" , metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} ) __magic_name__ = field( default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} ) __magic_name__ = field(default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Set this flag to use fast tokenization."} ) # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script, # or just modify its tokenizer_config.json. __magic_name__ = field( default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , ) @dataclass class UpperCamelCase__ : """simple docstring""" __magic_name__ = field( metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} ) __magic_name__ = field( default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."} , ) __magic_name__ = field( default=1_2_8 , metadata={ "help": ( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) } , ) __magic_name__ = field( default=SCREAMING_SNAKE_CASE_ , metadata={"help": "Overwrite the cached training and evaluation sets"} ) def lowercase (): """simple docstring""" _lowerCAmelCase : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir ) and os.listdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( f'Output directory ({training_args.output_dir}) already exists and is not empty. Use' ' --overwrite_output_dir to overcome.' ) _lowerCAmelCase : Tuple = import_module('tasks' ) try: _lowerCAmelCase : Dict = getattr(_A , model_args.task_type ) _lowerCAmelCase : TokenClassificationTask = token_classification_task_clazz() except AttributeError: raise ValueError( f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. ' f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}' ) # Setup logging logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , ) logger.warning( 'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank ): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info('Training/evaluation parameters %s' , _A ) # Set seed set_seed(training_args.seed ) # Prepare CONLL-2003 task _lowerCAmelCase : str = token_classification_task.get_labels(data_args.labels ) _lowerCAmelCase : Dict[int, str] = dict(enumerate(_A ) ) _lowerCAmelCase : Optional[int] = len(_A ) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. _lowerCAmelCase : Union[str, Any] = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_A , idalabel=_A , labelaid={label: i for i, label in enumerate(_A )} , cache_dir=model_args.cache_dir , ) _lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , ) _lowerCAmelCase : List[Any] = AutoModelForTokenClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_A , cache_dir=model_args.cache_dir , ) # Get datasets _lowerCAmelCase : Optional[int] = ( TokenClassificationDataset( token_classification_task=_A , data_dir=data_args.data_dir , tokenizer=_A , labels=_A , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , ) if training_args.do_train else None ) _lowerCAmelCase : Optional[int] = ( TokenClassificationDataset( token_classification_task=_A , data_dir=data_args.data_dir , tokenizer=_A , labels=_A , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , ) if training_args.do_eval else None ) def align_predictions(_A , _A ) -> Tuple[List[int], List[int]]: _lowerCAmelCase : Any = np.argmax(_A , axis=2 ) _lowerCAmelCase , _lowerCAmelCase : Tuple = preds.shape _lowerCAmelCase : Any = [[] for _ in range(_A )] _lowerCAmelCase : List[Any] = [[] for _ in range(_A )] for i in range(_A ): for j in range(_A ): if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index: out_label_list[i].append(label_map[label_ids[i][j]] ) preds_list[i].append(label_map[preds[i][j]] ) return preds_list, out_label_list def compute_metrics(_A ) -> Dict: _lowerCAmelCase , _lowerCAmelCase : List[Any] = align_predictions(p.predictions , p.label_ids ) return { "accuracy_score": accuracy_score(_A , _A ), "precision": precision_score(_A , _A ), "recall": recall_score(_A , _A ), "f1": fa_score(_A , _A ), } # Data collator _lowerCAmelCase : Dict = DataCollatorWithPadding(_A , pad_to_multiple_of=8 ) if training_args.fpaa else None # Initialize our Trainer _lowerCAmelCase : Dict = Trainer( model=_A , args=_A , train_dataset=_A , eval_dataset=_A , compute_metrics=_A , data_collator=_A , ) # Training if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None ) trainer.save_model() # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) if trainer.is_world_process_zero(): tokenizer.save_pretrained(training_args.output_dir ) # Evaluation _lowerCAmelCase : Dict = {} if training_args.do_eval: logger.info('*** Evaluate ***' ) _lowerCAmelCase : Optional[Any] = trainer.evaluate() _lowerCAmelCase : Optional[int] = os.path.join(training_args.output_dir , 'eval_results.txt' ) if trainer.is_world_process_zero(): with open(_A , 'w' ) as writer: logger.info('***** Eval results *****' ) for key, value in result.items(): logger.info(' %s = %s' , _A , _A ) writer.write('%s = %s\n' % (key, value) ) results.update(_A ) # Predict if training_args.do_predict: _lowerCAmelCase : str = TokenClassificationDataset( token_classification_task=_A , data_dir=data_args.data_dir , tokenizer=_A , labels=_A , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , ) _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = trainer.predict(_A ) _lowerCAmelCase , _lowerCAmelCase : Tuple = align_predictions(_A , _A ) _lowerCAmelCase : Tuple = os.path.join(training_args.output_dir , 'test_results.txt' ) if trainer.is_world_process_zero(): with open(_A , 'w' ) as writer: for key, value in metrics.items(): logger.info(' %s = %s' , _A , _A ) writer.write('%s = %s\n' % (key, value) ) # Save predictions _lowerCAmelCase : Optional[Any] = os.path.join(training_args.output_dir , 'test_predictions.txt' ) if trainer.is_world_process_zero(): with open(_A , 'w' ) as writer: with open(os.path.join(data_args.data_dir , 'test.txt' ) , 'r' ) as f: token_classification_task.write_predictions_to_file(_A , _A , _A ) return results def lowercase (_A ): """simple docstring""" main() if __name__ == "__main__": main()
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'''simple docstring''' from ....configuration_utils import PretrainedConfig from ....utils import logging lowerCAmelCase : Optional[Any] = logging.get_logger(__name__) lowerCAmelCase : Optional[Any] = { """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": ( """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json""" ), # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer } class UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ ): """simple docstring""" __magic_name__ = "trajectory_transformer" __magic_name__ = ["past_key_values"] __magic_name__ = { "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self , snake_case__=100 , snake_case__=5 , snake_case__=1 , snake_case__=1 , snake_case__=249 , snake_case__=6 , snake_case__=17 , snake_case__=25 , snake_case__=4 , snake_case__=4 , snake_case__=128 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.1 , snake_case__=0.0006 , snake_case__=512 , snake_case__=0.02 , snake_case__=1E-12 , snake_case__=1 , snake_case__=True , snake_case__=1 , snake_case__=5_0256 , snake_case__=5_0256 , **snake_case__ , ): '''simple docstring''' _lowerCAmelCase : List[Any] = vocab_size _lowerCAmelCase : Any = action_weight _lowerCAmelCase : Optional[int] = reward_weight _lowerCAmelCase : Union[str, Any] = value_weight _lowerCAmelCase : List[str] = max_position_embeddings _lowerCAmelCase : Tuple = block_size _lowerCAmelCase : List[Any] = action_dim _lowerCAmelCase : List[Any] = observation_dim _lowerCAmelCase : Union[str, Any] = transition_dim _lowerCAmelCase : Tuple = learning_rate _lowerCAmelCase : int = n_layer _lowerCAmelCase : Any = n_head _lowerCAmelCase : Tuple = n_embd _lowerCAmelCase : Optional[Any] = embd_pdrop _lowerCAmelCase : Union[str, Any] = attn_pdrop _lowerCAmelCase : Any = resid_pdrop _lowerCAmelCase : Optional[Any] = initializer_range _lowerCAmelCase : List[Any] = layer_norm_eps _lowerCAmelCase : Union[str, Any] = kaiming_initializer_range _lowerCAmelCase : List[Any] = use_cache super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ )
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1
"""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 snake_case ( _lowerCAmelCase ): '''simple docstring''' def _SCREAMING_SNAKE_CASE ( self : Any ): '''simple docstring''' __A = SMALL_MODEL_IDENTIFIER __A = '''pt''' __A = '''tf''' def _SCREAMING_SNAKE_CASE ( self : Optional[int], _lowerCamelCase : int ): '''simple docstring''' __A = AutoModel.from_pretrained(self.test_model ) model_pt.save_pretrained(_lowerCamelCase ) def _SCREAMING_SNAKE_CASE ( self : Dict, _lowerCamelCase : Optional[Any] ): '''simple docstring''' __A = TFAutoModel.from_pretrained(self.test_model, from_pt=_lowerCamelCase ) model_tf.save_pretrained(_lowerCamelCase ) def _SCREAMING_SNAKE_CASE ( self : Tuple ): '''simple docstring''' __A = '''mock_framework''' # Framework provided - return whatever the user provides __A = FeaturesManager.determine_framework(self.test_model, _lowerCamelCase ) self.assertEqual(_lowerCamelCase, _lowerCamelCase ) # Local checkpoint and framework provided - return provided framework # PyTorch checkpoint with TemporaryDirectory() as local_pt_ckpt: self._setup_pt_ckpt(_lowerCamelCase ) __A = FeaturesManager.determine_framework(_lowerCamelCase, _lowerCamelCase ) self.assertEqual(_lowerCamelCase, _lowerCamelCase ) # TensorFlow checkpoint with TemporaryDirectory() as local_tf_ckpt: self._setup_tf_ckpt(_lowerCamelCase ) __A = FeaturesManager.determine_framework(_lowerCamelCase, _lowerCamelCase ) self.assertEqual(_lowerCamelCase, _lowerCamelCase ) def _SCREAMING_SNAKE_CASE ( self : Tuple ): '''simple docstring''' # PyTorch checkpoint with TemporaryDirectory() as local_pt_ckpt: self._setup_pt_ckpt(_lowerCamelCase ) __A = FeaturesManager.determine_framework(_lowerCamelCase ) self.assertEqual(_lowerCamelCase, self.framework_pt ) # TensorFlow checkpoint with TemporaryDirectory() as local_tf_ckpt: self._setup_tf_ckpt(_lowerCamelCase ) __A = FeaturesManager.determine_framework(_lowerCamelCase ) self.assertEqual(_lowerCamelCase, self.framework_tf ) # Invalid local checkpoint with TemporaryDirectory() as local_invalid_ckpt: with self.assertRaises(_lowerCamelCase ): __A = FeaturesManager.determine_framework(_lowerCamelCase ) def _SCREAMING_SNAKE_CASE ( self : str ): '''simple docstring''' __A = MagicMock(return_value=_lowerCamelCase ) with patch('''transformers.onnx.features.is_tf_available''', _lowerCamelCase ): __A = FeaturesManager.determine_framework(self.test_model ) self.assertEqual(_lowerCamelCase, self.framework_pt ) # PyTorch not in environment -> use TensorFlow __A = MagicMock(return_value=_lowerCamelCase ) with patch('''transformers.onnx.features.is_torch_available''', _lowerCamelCase ): __A = FeaturesManager.determine_framework(self.test_model ) self.assertEqual(_lowerCamelCase, self.framework_tf ) # Both in environment -> use PyTorch __A = MagicMock(return_value=_lowerCamelCase ) __A = MagicMock(return_value=_lowerCamelCase ) with patch('''transformers.onnx.features.is_tf_available''', _lowerCamelCase ), patch( '''transformers.onnx.features.is_torch_available''', _lowerCamelCase ): __A = FeaturesManager.determine_framework(self.test_model ) self.assertEqual(_lowerCamelCase, self.framework_pt ) # Both not in environment -> raise error __A = MagicMock(return_value=_lowerCamelCase ) __A = MagicMock(return_value=_lowerCamelCase ) with patch('''transformers.onnx.features.is_tf_available''', _lowerCamelCase ), patch( '''transformers.onnx.features.is_torch_available''', _lowerCamelCase ): with self.assertRaises(_lowerCamelCase ): __A = FeaturesManager.determine_framework(self.test_model )
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"""simple docstring""" import unittest import numpy as np from transformers.testing_utils import require_pytesseract, require_torch from transformers.utils import is_pytesseract_available, is_torch_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_pytesseract_available(): from PIL import Image from transformers import LayoutLMvaImageProcessor class snake_case ( unittest.TestCase ): '''simple docstring''' def __init__( self : List[Any], _lowerCamelCase : int, _lowerCamelCase : List[Any]=7, _lowerCamelCase : int=3, _lowerCamelCase : Optional[Any]=18, _lowerCamelCase : Any=30, _lowerCamelCase : str=4_00, _lowerCamelCase : int=True, _lowerCamelCase : Union[str, Any]=None, _lowerCamelCase : str=True, ): '''simple docstring''' __A = size if 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 = apply_ocr def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ): '''simple docstring''' return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr} @require_torch @require_pytesseract class snake_case ( _lowerCAmelCase , unittest.TestCase ): '''simple docstring''' A_ : Optional[int] = LayoutLMvaImageProcessor if is_pytesseract_available() else None def _SCREAMING_SNAKE_CASE ( self : Dict ): '''simple docstring''' __A = LayoutLMvaImageProcessingTester(self ) @property def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ): '''simple docstring''' return self.image_processor_tester.prepare_image_processor_dict() def _SCREAMING_SNAKE_CASE ( self : int ): '''simple docstring''' __A = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(_lowerCamelCase, '''do_resize''' ) ) self.assertTrue(hasattr(_lowerCamelCase, '''size''' ) ) self.assertTrue(hasattr(_lowerCamelCase, '''apply_ocr''' ) ) def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ): '''simple docstring''' __A = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size, {'''height''': 18, '''width''': 18} ) __A = self.image_processing_class.from_dict(self.image_processor_dict, size=42 ) self.assertEqual(image_processor.size, {'''height''': 42, '''width''': 42} ) def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ): '''simple docstring''' pass def _SCREAMING_SNAKE_CASE ( 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=_lowerCamelCase ) for image in image_inputs: self.assertIsInstance(_lowerCamelCase, Image.Image ) # Test not batched input __A = image_processing(image_inputs[0], return_tensors='''pt''' ) self.assertEqual( encoding.pixel_values.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) self.assertIsInstance(encoding.words, _lowerCamelCase ) self.assertIsInstance(encoding.boxes, _lowerCamelCase ) # Test batched __A = image_processing(_lowerCamelCase, return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) def _SCREAMING_SNAKE_CASE ( self : 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=_lowerCamelCase, numpify=_lowerCamelCase ) for image in image_inputs: self.assertIsInstance(_lowerCamelCase, 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.size['''height'''], self.image_processor_tester.size['''width'''], ), ) # Test batched __A = image_processing(_lowerCamelCase, return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ): '''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=_lowerCamelCase, torchify=_lowerCamelCase ) for image in image_inputs: self.assertIsInstance(_lowerCamelCase, 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.size['''height'''], self.image_processor_tester.size['''width'''], ), ) # Test batched __A = image_processing(_lowerCamelCase, return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ), ) def _SCREAMING_SNAKE_CASE ( self : List[str] ): '''simple docstring''' # with apply_OCR = True __A = LayoutLMvaImageProcessor() from datasets import load_dataset __A = load_dataset('''hf-internal-testing/fixtures_docvqa''', split='''test''' ) __A = Image.open(ds[0]['''file'''] ).convert('''RGB''' ) __A = image_processing(_lowerCamelCase, return_tensors='''pt''' ) self.assertEqual(encoding.pixel_values.shape, (1, 3, 2_24, 2_24) ) self.assertEqual(len(encoding.words ), len(encoding.boxes ) ) # fmt: off # the words and boxes were obtained with Tesseract 4.1.1 __A = [['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']] # noqa: E231 __A = [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]] # noqa: E231 # fmt: on self.assertListEqual(encoding.words, _lowerCamelCase ) self.assertListEqual(encoding.boxes, _lowerCamelCase ) # with apply_OCR = False __A = LayoutLMvaImageProcessor(apply_ocr=_lowerCamelCase ) __A = image_processing(_lowerCamelCase, return_tensors='''pt''' ) self.assertEqual(encoding.pixel_values.shape, (1, 3, 2_24, 2_24) )
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import unittest from transformers import load_tool from .test_tools_common import ToolTesterMixin class _a ( unittest.TestCase , _lowerCAmelCase ): def __snake_case (self ) -> Optional[int]: UpperCAmelCase_: Optional[int] = load_tool("""text-classification""" ) self.tool.setup() UpperCAmelCase_: str = load_tool("""text-classification""", remote=SCREAMING_SNAKE_CASE_ ) def __snake_case (self ) -> Optional[Any]: UpperCAmelCase_: Any = self.tool("""That's quite cool""", ["""positive""", """negative"""] ) self.assertEqual(SCREAMING_SNAKE_CASE_, """positive""" ) def __snake_case (self ) -> Optional[int]: UpperCAmelCase_: List[str] = self.remote_tool("""That's quite cool""", ["""positive""", """negative"""] ) self.assertEqual(SCREAMING_SNAKE_CASE_, """positive""" ) def __snake_case (self ) -> Any: UpperCAmelCase_: Tuple = self.tool(text="""That's quite cool""", labels=["""positive""", """negative"""] ) self.assertEqual(SCREAMING_SNAKE_CASE_, """positive""" ) def __snake_case (self ) -> int: UpperCAmelCase_: Dict = self.remote_tool(text="""That's quite cool""", labels=["""positive""", """negative"""] ) self.assertEqual(SCREAMING_SNAKE_CASE_, """positive""" )
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_electra import ElectraTokenizer a : List[str] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'} a : str = { 'vocab_file': { 'google/electra-small-generator': ( 'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt' ), 'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt', 'google/electra-large-generator': ( 'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt' ), 'google/electra-small-discriminator': ( 'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt' ), 'google/electra-base-discriminator': ( 'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt' ), 'google/electra-large-discriminator': ( 'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt' ), }, 'tokenizer_file': { 'google/electra-small-generator': ( 'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json' ), 'google/electra-base-generator': ( 'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json' ), 'google/electra-large-generator': ( 'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json' ), 'google/electra-small-discriminator': ( 'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json' ), 'google/electra-base-discriminator': ( 'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json' ), 'google/electra-large-discriminator': ( 'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json' ), }, } a : Dict = { 'google/electra-small-generator': 512, 'google/electra-base-generator': 512, 'google/electra-large-generator': 512, 'google/electra-small-discriminator': 512, 'google/electra-base-discriminator': 512, 'google/electra-large-discriminator': 512, } a : Optional[Any] = { 'google/electra-small-generator': {'do_lower_case': True}, 'google/electra-base-generator': {'do_lower_case': True}, 'google/electra-large-generator': {'do_lower_case': True}, 'google/electra-small-discriminator': {'do_lower_case': True}, 'google/electra-base-discriminator': {'do_lower_case': True}, 'google/electra-large-discriminator': {'do_lower_case': True}, } class _a ( _lowerCAmelCase ): A = VOCAB_FILES_NAMES A = PRETRAINED_VOCAB_FILES_MAP A = PRETRAINED_INIT_CONFIGURATION A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES A = ElectraTokenizer def __init__(self, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="[UNK]", SCREAMING_SNAKE_CASE_="[SEP]", SCREAMING_SNAKE_CASE_="[PAD]", SCREAMING_SNAKE_CASE_="[CLS]", SCREAMING_SNAKE_CASE_="[MASK]", SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=None, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]: super().__init__( SCREAMING_SNAKE_CASE_, tokenizer_file=SCREAMING_SNAKE_CASE_, do_lower_case=SCREAMING_SNAKE_CASE_, unk_token=SCREAMING_SNAKE_CASE_, sep_token=SCREAMING_SNAKE_CASE_, pad_token=SCREAMING_SNAKE_CASE_, cls_token=SCREAMING_SNAKE_CASE_, mask_token=SCREAMING_SNAKE_CASE_, tokenize_chinese_chars=SCREAMING_SNAKE_CASE_, strip_accents=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, ) UpperCAmelCase_: List[str] = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get("""lowercase""", SCREAMING_SNAKE_CASE_ ) != do_lower_case or normalizer_state.get("""strip_accents""", SCREAMING_SNAKE_CASE_ ) != strip_accents or normalizer_state.get("""handle_chinese_chars""", SCREAMING_SNAKE_CASE_ ) != tokenize_chinese_chars ): UpperCAmelCase_: Optional[int] = getattr(SCREAMING_SNAKE_CASE_, normalizer_state.pop("""type""" ) ) UpperCAmelCase_: Union[str, Any] = do_lower_case UpperCAmelCase_: Dict = strip_accents UpperCAmelCase_: List[Any] = tokenize_chinese_chars UpperCAmelCase_: int = normalizer_class(**SCREAMING_SNAKE_CASE_ ) UpperCAmelCase_: Tuple = do_lower_case def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=None ) -> Optional[Any]: UpperCAmelCase_: Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> List[int]: UpperCAmelCase_: Optional[int] = [self.sep_token_id] UpperCAmelCase_: Optional[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 ) * [0] + len(token_ids_a + sep ) * [1] def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]: UpperCAmelCase_: Tuple = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_, name=SCREAMING_SNAKE_CASE_ ) return tuple(SCREAMING_SNAKE_CASE_ )
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'''simple docstring''' from __future__ import annotations from typing import Any def UpperCamelCase ( _lowerCamelCase : list[Any] ): create_state_space_tree(_lowerCamelCase , [] , 0 ) def UpperCamelCase ( _lowerCamelCase : list[Any] , _lowerCamelCase : list[Any] , _lowerCamelCase : int ): if index == len(_lowerCamelCase ): print(_lowerCamelCase ) return create_state_space_tree(_lowerCamelCase , _lowerCamelCase , index + 1 ) current_subsequence.append(sequence[index] ) create_state_space_tree(_lowerCamelCase , _lowerCamelCase , index + 1 ) current_subsequence.pop() if __name__ == "__main__": __lowerCAmelCase : list[Any] =[3, 1, 2, 4] generate_all_subsequences(seq) seq.clear() seq.extend(["A", "B", "C"]) generate_all_subsequences(seq)
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'''simple docstring''' import argparse from typing import List import evaluate import numpy as np import torch from datasets import DatasetDict, load_dataset # New Code # # We'll be using StratifiedKFold for this example from sklearn.model_selection import StratifiedKFold 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, # specifically showcasing how to perform Cross Validation, # and builds off the `nlp_example.py` script. # # 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 help focus on the differences in the code, building `DataLoaders` # was refactored into its own function. # New additions from the base script can be found quickly by # looking for the # New Code # tags # # 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[int] =16 __lowerCAmelCase : Tuple =32 def UpperCamelCase ( _lowerCamelCase : Accelerator , _lowerCamelCase : DatasetDict , _lowerCamelCase : List[int] , _lowerCamelCase : List[int] , _lowerCamelCase : int = 16 ): A__ = AutoTokenizer.from_pretrained("bert-base-cased" ) A__ = DatasetDict( { "train": dataset["train"].select(_lowerCamelCase ), "validation": dataset["train"].select(_lowerCamelCase ), "test": dataset["validation"], } ) def tokenize_function(_lowerCamelCase : Dict ): # max_length=None => use the model max length (it's actually the default) A__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCamelCase , max_length=_lowerCamelCase ) 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(): A__ = datasets.map( _lowerCamelCase , batched=_lowerCamelCase , 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 A__ = tokenized_datasets.rename_column("label" , "labels" ) def collate_fn(_lowerCamelCase : Any ): # On TPU it's best to pad everything to the same length or training will be very slow. A__ = 1_28 if accelerator.distributed_type == DistributedType.TPU else None # When using mixed precision we want round multiples of 8/16 if accelerator.mixed_precision == "fp8": A__ = 16 elif accelerator.mixed_precision != "no": A__ = 8 else: A__ = None return tokenizer.pad( _lowerCamelCase , padding="longest" , max_length=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_tensors="pt" , ) # Instantiate dataloaders. A__ = DataLoader( tokenized_datasets["train"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase ) A__ = DataLoader( tokenized_datasets["validation"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase ) A__ = DataLoader( tokenized_datasets["test"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase ) return train_dataloader, eval_dataloader, test_dataloader def UpperCamelCase ( _lowerCamelCase : List[Any] , _lowerCamelCase : str ): # New Code # A__ = [] # Download the dataset A__ = load_dataset("glue" , "mrpc" ) # Create our splits A__ = StratifiedKFold(n_splits=int(args.num_folds ) ) # Initialize accelerator A__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision ) # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs A__ = config["lr"] A__ = int(config["num_epochs"] ) A__ = int(config["seed"] ) A__ = int(config["batch_size"] ) A__ = evaluate.load("glue" , "mrpc" ) # If the batch size is too big we use gradient accumulation A__ = 1 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU: A__ = batch_size // MAX_GPU_BATCH_SIZE A__ = MAX_GPU_BATCH_SIZE set_seed(_lowerCamelCase ) # New Code # # Create our folds: A__ = kfold.split(np.zeros(datasets["train"].num_rows ) , datasets["train"]["label"] ) A__ = [] # Iterate over them for i, (train_idxs, valid_idxs) in enumerate(_lowerCamelCase ): A__, A__, A__ = get_fold_dataloaders( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ) # Instantiate the model (we build the model here so that the seed also control new weights initialization) A__ = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=_lowerCamelCase ) # 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). A__ = model.to(accelerator.device ) # Instantiate optimizer A__ = AdamW(params=model.parameters() , lr=_lowerCamelCase ) # Instantiate scheduler A__ = get_linear_schedule_with_warmup( optimizer=_lowerCamelCase , num_warmup_steps=1_00 , num_training_steps=(len(_lowerCamelCase ) * 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. A__, A__, A__, A__, A__ = accelerator.prepare( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) # Now we train the model for epoch in range(_lowerCamelCase ): model.train() for step, batch in enumerate(_lowerCamelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) A__ = model(**_lowerCamelCase ) A__ = outputs.loss A__ = loss / gradient_accumulation_steps accelerator.backward(_lowerCamelCase ) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() model.eval() for step, batch in enumerate(_lowerCamelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) with torch.no_grad(): A__ = model(**_lowerCamelCase ) A__ = outputs.logits.argmax(dim=-1 ) A__, A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) ) metric.add_batch( predictions=_lowerCamelCase , references=_lowerCamelCase , ) A__ = metric.compute() # Use accelerator.print to print only on the main process. accelerator.print(F"epoch {epoch}:" , _lowerCamelCase ) # New Code # # We also run predictions on the test set at the very end A__ = [] for step, batch in enumerate(_lowerCamelCase ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) with torch.no_grad(): A__ = model(**_lowerCamelCase ) A__ = outputs.logits A__, A__ = accelerator.gather_for_metrics((predictions, batch["labels"]) ) fold_predictions.append(predictions.cpu() ) if i == 0: # We need all of the test predictions test_references.append(references.cpu() ) # Use accelerator.print to print only on the main process. test_predictions.append(torch.cat(_lowerCamelCase , dim=0 ) ) # We now need to release all our memory and get rid of the current model, optimizer, etc accelerator.free_memory() # New Code # # Finally we check the accuracy of our folded results: A__ = torch.cat(_lowerCamelCase , dim=0 ) A__ = torch.stack(_lowerCamelCase , dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 ) A__ = metric.compute(predictions=_lowerCamelCase , references=_lowerCamelCase ) accelerator.print("Average test metrics from all folds:" , _lowerCamelCase ) def UpperCamelCase ( ): A__ = argparse.ArgumentParser(description="Simple example of training script." ) parser.add_argument( "--mixed_precision" , type=_lowerCamelCase , default=_lowerCamelCase , 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." ) # New Code # parser.add_argument("--num_folds" , type=_lowerCamelCase , default=3 , help="The number of splits to perform across the dataset" ) A__ = parser.parse_args() A__ = {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16} training_function(_lowerCamelCase , _lowerCamelCase ) if __name__ == "__main__": main()
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"""simple docstring""" def lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[int]] ) -> int: '''simple docstring''' def update_area_of_max_square(_UpperCamelCase : int , _UpperCamelCase : int ) -> int: # BASE CASE if row >= rows or col >= cols: return 0 __UpperCAmelCase : Optional[int] = update_area_of_max_square(_UpperCamelCase , col + 1 ) __UpperCAmelCase : int = update_area_of_max_square(row + 1 , col + 1 ) __UpperCAmelCase : Optional[Any] = update_area_of_max_square(row + 1 , _UpperCamelCase ) if mat[row][col]: __UpperCAmelCase : List[Any] = 1 + min([right, diagonal, down] ) __UpperCAmelCase : Any = max(largest_square_area[0] , _UpperCamelCase ) return sub_problem_sol else: return 0 __UpperCAmelCase : Optional[int] = [0] update_area_of_max_square(0 , 0 ) return largest_square_area[0] def lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[int]] ) -> int: '''simple docstring''' def update_area_of_max_square_using_dp_array( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[int]] ) -> int: if row >= rows or col >= cols: return 0 if dp_array[row][col] != -1: return dp_array[row][col] __UpperCAmelCase : List[str] = update_area_of_max_square_using_dp_array(_UpperCamelCase , col + 1 , _UpperCamelCase ) __UpperCAmelCase : Optional[Any] = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , _UpperCamelCase ) __UpperCAmelCase : str = update_area_of_max_square_using_dp_array(row + 1 , _UpperCamelCase , _UpperCamelCase ) if mat[row][col]: __UpperCAmelCase : List[Any] = 1 + min([right, diagonal, down] ) __UpperCAmelCase : Optional[Any] = max(largest_square_area[0] , _UpperCamelCase ) __UpperCAmelCase : Tuple = sub_problem_sol return sub_problem_sol else: return 0 __UpperCAmelCase : Dict = [0] __UpperCAmelCase : Any = [[-1] * cols for _ in range(_UpperCamelCase )] update_area_of_max_square_using_dp_array(0 , 0 , _UpperCamelCase ) return largest_square_area[0] def lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[int]] ) -> int: '''simple docstring''' __UpperCAmelCase : List[Any] = [[0] * (cols + 1) for _ in range(rows + 1 )] __UpperCAmelCase : Any = 0 for row in range(rows - 1 , -1 , -1 ): for col in range(cols - 1 , -1 , -1 ): __UpperCAmelCase : Union[str, Any] = dp_array[row][col + 1] __UpperCAmelCase : Optional[int] = dp_array[row + 1][col + 1] __UpperCAmelCase : Optional[Any] = dp_array[row + 1][col] if mat[row][col] == 1: __UpperCAmelCase : Optional[int] = 1 + min(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) __UpperCAmelCase : Optional[Any] = max(dp_array[row][col] , _UpperCamelCase ) else: __UpperCAmelCase : int = 0 return largest_square_area def lowerCamelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[int]] ) -> int: '''simple docstring''' __UpperCAmelCase : int = [0] * (cols + 1) __UpperCAmelCase : Tuple = [0] * (cols + 1) __UpperCAmelCase : int = 0 for row in range(rows - 1 , -1 , -1 ): for col in range(cols - 1 , -1 , -1 ): __UpperCAmelCase : int = current_row[col + 1] __UpperCAmelCase : List[Any] = next_row[col + 1] __UpperCAmelCase : str = next_row[col] if mat[row][col] == 1: __UpperCAmelCase : Any = 1 + min(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) __UpperCAmelCase : Union[str, Any] = max(current_row[col] , _UpperCamelCase ) else: __UpperCAmelCase : Tuple = 0 __UpperCAmelCase : Any = current_row return largest_square_area if __name__ == "__main__": import doctest doctest.testmod() print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
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"""simple docstring""" UpperCAmelCase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/' def lowerCamelCase ( _UpperCamelCase : bytes ) -> bytes: '''simple docstring''' if not isinstance(_UpperCamelCase , _UpperCamelCase ): __UpperCAmelCase : Any = f'''a bytes-like object is required, not \'{data.__class__.__name__}\'''' raise TypeError(_UpperCamelCase ) __UpperCAmelCase : str = """""".join(bin(_UpperCamelCase )[2:].zfill(8 ) for byte in data ) __UpperCAmelCase : int = len(_UpperCamelCase ) % 6 != 0 if padding_needed: # The padding that will be added later __UpperCAmelCase : Dict = b"""=""" * ((6 - len(_UpperCamelCase ) % 6) // 2) # Append binary_stream with arbitrary binary digits (0's by default) to make its # length a multiple of 6. binary_stream += "0" * (6 - len(_UpperCamelCase ) % 6) else: __UpperCAmelCase : List[str] = b"""""" # Encode every 6 binary digits to their corresponding Base64 character return ( "".join( B64_CHARSET[int(binary_stream[index : index + 6] , 2 )] for index in range(0 , len(_UpperCamelCase ) , 6 ) ).encode() + padding ) def lowerCamelCase ( _UpperCamelCase : str ) -> bytes: '''simple docstring''' if not isinstance(_UpperCamelCase , _UpperCamelCase ) and not isinstance(_UpperCamelCase , _UpperCamelCase ): __UpperCAmelCase : Tuple = ( """argument should be a bytes-like object or ASCII string, """ f'''not \'{encoded_data.__class__.__name__}\'''' ) raise TypeError(_UpperCamelCase ) # In case encoded_data is a bytes-like object, make sure it contains only # ASCII characters so we convert it to a string object if isinstance(_UpperCamelCase , _UpperCamelCase ): try: __UpperCAmelCase : Optional[Any] = encoded_data.decode("""utf-8""" ) except UnicodeDecodeError: raise ValueError("""base64 encoded data should only contain ASCII characters""" ) __UpperCAmelCase : str = encoded_data.count("""=""" ) # Check if the encoded string contains non base64 characters if padding: assert all( char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found." else: assert all( char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found." # Check the padding assert len(_UpperCamelCase ) % 4 == 0 and padding < 3, "Incorrect padding" if padding: # Remove padding if there is one __UpperCAmelCase : List[str] = encoded_data[:-padding] __UpperCAmelCase : int = """""".join( bin(B64_CHARSET.index(_UpperCamelCase ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2] else: __UpperCAmelCase : Optional[Any] = """""".join( bin(B64_CHARSET.index(_UpperCamelCase ) )[2:].zfill(6 ) for char in encoded_data ) __UpperCAmelCase : List[Any] = [ int(binary_stream[index : index + 8] , 2 ) for index in range(0 , len(_UpperCamelCase ) , 8 ) ] return bytes(_UpperCamelCase ) if __name__ == "__main__": import doctest doctest.testmod()
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import math def UpperCamelCase__ ( A__ ) -> bool: if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(math.sqrt(A__ ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def UpperCamelCase__ ( A__ = 1_0001 ) -> int: try: snake_case__ : Tuple = int(A__ ) except (TypeError, ValueError): raise TypeError('Parameter nth must be int or castable to int.' ) from None if nth <= 0: raise ValueError('Parameter nth must be greater than or equal to one.' ) snake_case__ : list[int] = [] snake_case__ : int = 2 while len(A__ ) < nth: if is_prime(A__ ): primes.append(A__ ) num += 1 else: num += 1 return primes[len(A__ ) - 1] if __name__ == "__main__": print(F'''{solution() = }''')
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from abc import ABC, abstractmethod from argparse import ArgumentParser class __snake_case ( _lowerCamelCase ): @staticmethod @abstractmethod def __a ( __UpperCamelCase ) -> Dict: '''simple docstring''' raise NotImplementedError() @abstractmethod def __a ( self ) -> Optional[int]: '''simple docstring''' raise NotImplementedError()
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging _lowercase = logging.get_logger(__name__) _lowercase = { 'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json', } class UpperCAmelCase_ ( _lowerCamelCase ): '''simple docstring''' _lowercase : Tuple = '''roc_bert''' def __init__( self , _lowercase=30_522 , _lowercase=768 , _lowercase=12 , _lowercase=12 , _lowercase=3_072 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=2 , _lowercase=0.02 , _lowercase=1e-12 , _lowercase=True , _lowercase=0 , _lowercase="absolute" , _lowercase=None , _lowercase=True , _lowercase=True , _lowercase=768 , _lowercase=910 , _lowercase=512 , _lowercase=24_858 , _lowercase=True , **_lowercase , ): """simple docstring""" _lowerCAmelCase = vocab_size _lowerCAmelCase = max_position_embeddings _lowerCAmelCase = hidden_size _lowerCAmelCase = num_hidden_layers _lowerCAmelCase = num_attention_heads _lowerCAmelCase = intermediate_size _lowerCAmelCase = hidden_act _lowerCAmelCase = hidden_dropout_prob _lowerCAmelCase = attention_probs_dropout_prob _lowerCAmelCase = initializer_range _lowerCAmelCase = type_vocab_size _lowerCAmelCase = layer_norm_eps _lowerCAmelCase = use_cache _lowerCAmelCase = enable_pronunciation _lowerCAmelCase = enable_shape _lowerCAmelCase = pronunciation_embed_dim _lowerCAmelCase = pronunciation_vocab_size _lowerCAmelCase = shape_embed_dim _lowerCAmelCase = shape_vocab_size _lowerCAmelCase = concat_input _lowerCAmelCase = position_embedding_type _lowerCAmelCase = classifier_dropout super().__init__(pad_token_id=_lowercase , **_lowercase )
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) _lowercase = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]} try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = ["""DeiTFeatureExtractor"""] _lowercase = ["""DeiTImageProcessor"""] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ """DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""", """DeiTForImageClassification""", """DeiTForImageClassificationWithTeacher""", """DeiTForMaskedImageModeling""", """DeiTModel""", """DeiTPreTrainedModel""", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowercase = [ """TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""", """TFDeiTForImageClassification""", """TFDeiTForImageClassificationWithTeacher""", """TFDeiTForMaskedImageModeling""", """TFDeiTModel""", """TFDeiTPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_deit import DeiTFeatureExtractor from .image_processing_deit import DeiTImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deit import ( DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, DeiTModel, DeiTPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_deit import ( TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, TFDeiTPreTrainedModel, ) else: import sys _lowercase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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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 A_ : List[str] = logging.get_logger(__name__) A_ : str = '▁' A_ : int = { 'vocab_file': 'vocab.json', 'spm_file': 'sentencepiece.bpe.model', } A_ : str = { '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' ) }, } A_ : Optional[Any] = { 'facebook/s2t-small-librispeech-asr': 1024, } A_ : List[Any] = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de'] A_ : str = {'mustc': MUSTC_LANGS} class A_ ( _a ): '''simple docstring''' a__ = VOCAB_FILES_NAMES a__ = PRETRAINED_VOCAB_FILES_MAP a__ = MAX_MODEL_INPUT_SIZES a__ = ["input_ids", "attention_mask"] a__ = [] def __init__(self , lowercase__ , lowercase__ , lowercase__="<s>" , lowercase__="</s>" , lowercase__="<pad>" , lowercase__="<unk>" , lowercase__=False , lowercase__=False , lowercase__=None , lowercase__=None , lowercase__ = None , **lowercase__ , ) -> None: __UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=lowercase__ , eos_token=lowercase__ , unk_token=lowercase__ , pad_token=lowercase__ , do_upper_case=lowercase__ , do_lower_case=lowercase__ , tgt_lang=lowercase__ , lang_codes=lowercase__ , sp_model_kwargs=self.sp_model_kwargs , **lowercase__ , ) __UpperCAmelCase = do_upper_case __UpperCAmelCase = do_lower_case __UpperCAmelCase = load_json(lowercase__ ) __UpperCAmelCase = {v: k for k, v in self.encoder.items()} __UpperCAmelCase = spm_file __UpperCAmelCase = load_spm(lowercase__ , self.sp_model_kwargs ) if lang_codes is not None: __UpperCAmelCase = lang_codes __UpperCAmelCase = LANGUAGES[lang_codes] __UpperCAmelCase = [F'''<lang:{lang}>''' for lang in self.langs] __UpperCAmelCase = {lang: self.sp_model.PieceToId(F'''<lang:{lang}>''' ) for lang in self.langs} __UpperCAmelCase = self.lang_tokens __UpperCAmelCase = tgt_lang if tgt_lang is not None else self.langs[0] self.set_tgt_lang_special_tokens(self._tgt_lang ) else: __UpperCAmelCase = {} @property def lowerCAmelCase_ (self ) -> int: return len(self.encoder ) @property def lowerCAmelCase_ (self ) -> str: return self._tgt_lang @tgt_lang.setter def lowerCAmelCase_ (self , lowercase__ ) -> None: __UpperCAmelCase = new_tgt_lang self.set_tgt_lang_special_tokens(lowercase__ ) def lowerCAmelCase_ (self , lowercase__ ) -> None: __UpperCAmelCase = self.lang_code_to_id[tgt_lang] __UpperCAmelCase = [lang_code_id] def lowerCAmelCase_ (self , lowercase__ ) -> List[str]: return self.sp_model.encode(lowercase__ , out_type=lowercase__ ) def lowerCAmelCase_ (self , lowercase__ ) -> Union[str, Any]: return self.encoder.get(lowercase__ , self.encoder[self.unk_token] ) def lowerCAmelCase_ (self , lowercase__ ) -> str: return self.decoder.get(lowercase__ , self.unk_token ) def lowerCAmelCase_ (self , lowercase__ ) -> str: __UpperCAmelCase = [] __UpperCAmelCase = '''''' for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: __UpperCAmelCase = self.sp_model.decode(lowercase__ ) out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " " __UpperCAmelCase = [] else: current_sub_tokens.append(lowercase__ ) __UpperCAmelCase = self.sp_model.decode(lowercase__ ) out_string += decoded.upper() if self.do_upper_case else decoded return out_string.strip() def lowerCAmelCase_ (self , lowercase__ , lowercase__=None ) -> List[int]: 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 lowerCAmelCase_ (self , lowercase__ , lowercase__ = None , lowercase__ = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=lowercase__ , token_ids_a=lowercase__ , already_has_special_tokens=lowercase__ ) __UpperCAmelCase = [1] * len(self.prefix_tokens ) __UpperCAmelCase = [1] if token_ids_a is None: return prefix_ones + ([0] * len(lowercase__ )) + suffix_ones return prefix_ones + ([0] * len(lowercase__ )) + ([0] * len(lowercase__ )) + suffix_ones def lowerCAmelCase_ (self ) -> Dict: __UpperCAmelCase = self.encoder.copy() vocab.update(self.added_tokens_encoder ) return vocab def __getstate__(self ) -> Dict: __UpperCAmelCase = self.__dict__.copy() __UpperCAmelCase = None return state def __setstate__(self , lowercase__ ) -> None: __UpperCAmelCase = d # for backward compatibility if not hasattr(self , '''sp_model_kwargs''' ): __UpperCAmelCase = {} __UpperCAmelCase = load_spm(self.spm_file , self.sp_model_kwargs ) def lowerCAmelCase_ (self , lowercase__ , lowercase__ = None ) -> Tuple[str]: __UpperCAmelCase = Path(lowercase__ ) assert save_dir.is_dir(), F'''{save_directory} should be a directory''' __UpperCAmelCase = save_dir / ( (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file'''] ) __UpperCAmelCase = save_dir / ( (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file'''] ) save_json(self.encoder , lowercase__ ) if os.path.abspath(self.spm_file ) != os.path.abspath(lowercase__ ) and os.path.isfile(self.spm_file ): copyfile(self.spm_file , lowercase__ ) elif not os.path.isfile(self.spm_file ): with open(lowercase__ , '''wb''' ) as fi: __UpperCAmelCase = self.sp_model.serialized_model_proto() fi.write(lowercase__ ) return (str(lowercase__ ), str(lowercase__ )) def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> sentencepiece.SentencePieceProcessor: '''simple docstring''' __UpperCAmelCase = sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE ) spm.Load(str(SCREAMING_SNAKE_CASE ) ) return spm def __a ( SCREAMING_SNAKE_CASE ) -> Union[Dict, List]: '''simple docstring''' with open(SCREAMING_SNAKE_CASE , '''r''' ) as f: return json.load(SCREAMING_SNAKE_CASE ) def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> None: '''simple docstring''' with open(SCREAMING_SNAKE_CASE , '''w''' ) as f: json.dump(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , indent=2 )
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from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import rescale, resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_vision_available, logging if is_vision_available(): import PIL A_ : Tuple = logging.get_logger(__name__) def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Any: '''simple docstring''' __UpperCAmelCase = b.T __UpperCAmelCase = np.sum(np.square(SCREAMING_SNAKE_CASE ) , axis=1 ) __UpperCAmelCase = np.sum(np.square(SCREAMING_SNAKE_CASE ) , axis=0 ) __UpperCAmelCase = np.matmul(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) __UpperCAmelCase = aa[:, None] - 2 * ab + ba[None, :] return d def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Dict: '''simple docstring''' __UpperCAmelCase = x.reshape(-1 , 3 ) __UpperCAmelCase = squared_euclidean_distance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) return np.argmin(SCREAMING_SNAKE_CASE , axis=1 ) class A_ ( _a ): '''simple docstring''' a__ = ["pixel_values"] def __init__(self , lowercase__ = None , lowercase__ = True , lowercase__ = None , lowercase__ = PILImageResampling.BILINEAR , lowercase__ = True , lowercase__ = True , **lowercase__ , ) -> None: super().__init__(**lowercase__ ) __UpperCAmelCase = size if size is not None else {'''height''': 256, '''width''': 256} __UpperCAmelCase = get_size_dict(lowercase__ ) __UpperCAmelCase = np.array(lowercase__ ) if clusters is not None else None __UpperCAmelCase = do_resize __UpperCAmelCase = size __UpperCAmelCase = resample __UpperCAmelCase = do_normalize __UpperCAmelCase = do_color_quantize def lowerCAmelCase_ (self , lowercase__ , lowercase__ , lowercase__ = PILImageResampling.BILINEAR , lowercase__ = None , **lowercase__ , ) -> np.ndarray: __UpperCAmelCase = get_size_dict(lowercase__ ) if "height" not in size or "width" not in size: raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}''' ) return resize( lowercase__ , size=(size['''height'''], size['''width''']) , resample=lowercase__ , data_format=lowercase__ , **lowercase__ ) def lowerCAmelCase_ (self , lowercase__ , lowercase__ = None , ) -> np.ndarray: __UpperCAmelCase = rescale(image=lowercase__ , scale=1 / 127.5 , data_format=lowercase__ ) __UpperCAmelCase = image - 1 return image def lowerCAmelCase_ (self , lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = None , lowercase__ = ChannelDimension.FIRST , **lowercase__ , ) -> PIL.Image.Image: __UpperCAmelCase = do_resize if do_resize is not None else self.do_resize __UpperCAmelCase = size if size is not None else self.size __UpperCAmelCase = get_size_dict(lowercase__ ) __UpperCAmelCase = resample if resample is not None else self.resample __UpperCAmelCase = do_normalize if do_normalize is not None else self.do_normalize __UpperCAmelCase = do_color_quantize if do_color_quantize is not None else self.do_color_quantize __UpperCAmelCase = clusters if clusters is not None else self.clusters __UpperCAmelCase = np.array(lowercase__ ) __UpperCAmelCase = make_list_of_images(lowercase__ ) if not valid_images(lowercase__ ): 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 or resample is None: raise ValueError('''Size and resample must be specified if do_resize is True.''' ) if do_color_quantize and clusters is None: raise ValueError('''Clusters must be specified if do_color_quantize is True.''' ) # All transformations expect numpy arrays. __UpperCAmelCase = [to_numpy_array(lowercase__ ) for image in images] if do_resize: __UpperCAmelCase = [self.resize(image=lowercase__ , size=lowercase__ , resample=lowercase__ ) for image in images] if do_normalize: __UpperCAmelCase = [self.normalize(image=lowercase__ ) for image in images] if do_color_quantize: __UpperCAmelCase = [to_channel_dimension_format(lowercase__ , ChannelDimension.LAST ) for image in images] # color quantize from (batch_size, height, width, 3) to (batch_size, height, width) __UpperCAmelCase = np.array(lowercase__ ) __UpperCAmelCase = color_quantize(lowercase__ , lowercase__ ).reshape(images.shape[:-1] ) # flatten to (batch_size, height*width) __UpperCAmelCase = images.shape[0] __UpperCAmelCase = images.reshape(lowercase__ , -1 ) # We need to convert back to a list of images to keep consistent behaviour across processors. __UpperCAmelCase = list(lowercase__ ) else: __UpperCAmelCase = [to_channel_dimension_format(lowercase__ , lowercase__ ) for image in images] __UpperCAmelCase = {'''input_ids''': images} return BatchFeature(data=lowercase__ , tensor_type=lowercase__ )
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"""simple docstring""" 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 UpperCamelCase ( lowercase_ ): lowercase = 42 lowercase = 42 if is_transformers_available() and is_torch_available(): from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
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"""simple docstring""" class UpperCamelCase : def __init__( self ,__UpperCamelCase ,__UpperCamelCase ) -> int: '''simple docstring''' lowercase_ : List[Any] = name lowercase_ : int = val def __str__( self ) -> Tuple: '''simple docstring''' return f'''{self.__class__.__name__}({self.name}, {self.val})''' def __lt__( self ,__UpperCamelCase ) -> Tuple: '''simple docstring''' return self.val < other.val class UpperCamelCase : def __init__( self ,__UpperCamelCase ) -> Dict: '''simple docstring''' lowercase_ : Optional[int] = {} lowercase_ : Tuple = {} lowercase_ : Union[str, Any] = self.build_heap(__UpperCamelCase ) def __getitem__( self ,__UpperCamelCase ) -> int: '''simple docstring''' return self.get_value(__UpperCamelCase ) def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[str]: '''simple docstring''' return (idx - 1) // 2 def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Optional[Any]: '''simple docstring''' return idx * 2 + 1 def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Tuple: '''simple docstring''' return idx * 2 + 2 def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[Any]: '''simple docstring''' return self.heap_dict[key] def _UpperCAmelCase ( self ,__UpperCamelCase ) -> List[str]: '''simple docstring''' lowercase_ : Optional[int] = len(__UpperCamelCase ) - 1 lowercase_ : Optional[int] = self.get_parent_idx(__UpperCamelCase ) for idx, i in enumerate(__UpperCamelCase ): lowercase_ : Any = idx lowercase_ : str = i.val for i in range(__UpperCamelCase ,-1 ,-1 ): self.sift_down(__UpperCamelCase ,__UpperCamelCase ) return array def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> Tuple: '''simple docstring''' while True: lowercase_ : List[str] = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741 lowercase_ : List[str] = self.get_right_child_idx(__UpperCamelCase ) lowercase_ : List[str] = idx if l < len(__UpperCamelCase ) and array[l] < array[idx]: lowercase_ : List[str] = l if r < len(__UpperCamelCase ) and array[r] < array[smallest]: lowercase_ : Dict = r if smallest != idx: lowercase_ , lowercase_ : Union[str, Any] = array[smallest], array[idx] ( ( lowercase_ ) , ( lowercase_ ) , ) : str = ( self.idx_of_element[array[smallest]], self.idx_of_element[array[idx]], ) lowercase_ : Any = smallest else: break def _UpperCAmelCase ( self ,__UpperCamelCase ) -> int: '''simple docstring''' lowercase_ : Dict = self.get_parent_idx(__UpperCamelCase ) while p >= 0 and self.heap[p] > self.heap[idx]: lowercase_ , lowercase_ : Any = self.heap[idx], self.heap[p] lowercase_ , lowercase_ : Tuple = ( self.idx_of_element[self.heap[idx]], self.idx_of_element[self.heap[p]], ) lowercase_ : int = p lowercase_ : str = self.get_parent_idx(__UpperCamelCase ) def _UpperCAmelCase ( self ) -> str: '''simple docstring''' return self.heap[0] def _UpperCAmelCase ( self ) -> Optional[int]: '''simple docstring''' lowercase_ , lowercase_ : Optional[Any] = self.heap[-1], self.heap[0] lowercase_ , lowercase_ : Tuple = ( self.idx_of_element[self.heap[-1]], self.idx_of_element[self.heap[0]], ) lowercase_ : Tuple = self.heap.pop() del self.idx_of_element[x] self.sift_down(0 ,self.heap ) return x def _UpperCAmelCase ( self ,__UpperCamelCase ) -> Dict: '''simple docstring''' self.heap.append(__UpperCamelCase ) lowercase_ : Tuple = len(self.heap ) - 1 lowercase_ : Optional[int] = node.val self.sift_up(len(self.heap ) - 1 ) def _UpperCAmelCase ( self ) -> Optional[Any]: '''simple docstring''' return len(self.heap ) == 0 def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ) -> List[Any]: '''simple docstring''' assert ( self.heap[self.idx_of_element[node]].val > new_value ), "newValue must be less that current value" lowercase_ : Any = new_value lowercase_ : List[str] = new_value self.sift_up(self.idx_of_element[node] ) __SCREAMING_SNAKE_CASE =Node("R", -1) __SCREAMING_SNAKE_CASE =Node("B", 6) __SCREAMING_SNAKE_CASE =Node("A", 3) __SCREAMING_SNAKE_CASE =Node("X", 1) __SCREAMING_SNAKE_CASE =Node("E", 4) # Use one of these two ways to generate Min-Heap # Generating Min-Heap from array __SCREAMING_SNAKE_CASE =MinHeap([r, b, a, x, e]) # Generating Min-Heap by Insert method # myMinHeap.insert(a) # myMinHeap.insert(b) # myMinHeap.insert(x) # myMinHeap.insert(r) # myMinHeap.insert(e) # Before print("Min Heap - before decrease key") for i in my_min_heap.heap: print(i) print("Min Heap - After decrease key of node [B -> -17]") my_min_heap.decrease_key(b, -17) # After for i in my_min_heap.heap: print(i) if __name__ == "__main__": import doctest doctest.testmod()
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import unittest import numpy as np from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision 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 DPTImageProcessor class lowercase__( unittest.TestCase ): """simple docstring""" def __init__( self : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Optional[Any]=7 , SCREAMING_SNAKE_CASE_ : Optional[int]=3 , SCREAMING_SNAKE_CASE_ : Any=1_8 , SCREAMING_SNAKE_CASE_ : Tuple=3_0 , SCREAMING_SNAKE_CASE_ : int=4_0_0 , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : str=None , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Optional[Any]=[0.5, 0.5, 0.5] , SCREAMING_SNAKE_CASE_ : List[str]=[0.5, 0.5, 0.5] , ) -> Optional[Any]: lowercase_ = size if size is not None else {'''height''': 1_8, '''width''': 1_8} lowercase_ = parent lowercase_ = batch_size lowercase_ = num_channels lowercase_ = image_size lowercase_ = min_resolution lowercase_ = max_resolution lowercase_ = do_resize lowercase_ = size lowercase_ = do_normalize lowercase_ = image_mean lowercase_ = image_std def _lowercase ( self : Tuple ) -> List[Any]: return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, } @require_torch @require_vision class lowercase__( UpperCAmelCase , unittest.TestCase ): """simple docstring""" a :Optional[Any] = DPTImageProcessor if is_vision_available() else None def _lowercase ( self : Any ) -> Union[str, Any]: lowercase_ = DPTImageProcessingTester(self ) @property def _lowercase ( self : Any ) -> Optional[int]: return self.image_processor_tester.prepare_image_processor_dict() def _lowercase ( self : int ) -> Dict: lowercase_ = self.image_processing_class(**self.image_processor_dict ) self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''image_mean''' ) ) self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''image_std''' ) ) self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_normalize''' ) ) self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''do_resize''' ) ) self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , '''size''' ) ) def _lowercase ( self : Optional[Any] ) -> Optional[int]: lowercase_ = self.image_processing_class.from_dict(self.image_processor_dict ) self.assertEqual(image_processor.size , {'''height''': 1_8, '''width''': 1_8} ) lowercase_ = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 ) self.assertEqual(image_processor.size , {'''height''': 4_2, '''width''': 4_2} ) def _lowercase ( self : Tuple ) -> List[Any]: # Initialize image_processing lowercase_ = self.image_processing_class(**self.image_processor_dict ) # create random PIL images lowercase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ ) for image in image_inputs: self.assertIsInstance(SCREAMING_SNAKE_CASE_ , Image.Image ) # Test not batched input lowercase_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ) , ) # Test batched lowercase_ = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ) , ) def _lowercase ( self : str ) -> Optional[int]: # Initialize image_processing lowercase_ = self.image_processing_class(**self.image_processor_dict ) # create random numpy tensors lowercase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , numpify=SCREAMING_SNAKE_CASE_ ) for image in image_inputs: self.assertIsInstance(SCREAMING_SNAKE_CASE_ , np.ndarray ) # Test not batched input lowercase_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ) , ) # Test batched lowercase_ = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ) , ) def _lowercase ( self : List[str] ) -> str: # Initialize image_processing lowercase_ = self.image_processing_class(**self.image_processor_dict ) # create random PyTorch tensors lowercase_ = prepare_image_inputs(self.image_processor_tester , equal_resolution=SCREAMING_SNAKE_CASE_ , torchify=SCREAMING_SNAKE_CASE_ ) for image in image_inputs: self.assertIsInstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ) # Test not batched input lowercase_ = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ) , ) # Test batched lowercase_ = image_processing(SCREAMING_SNAKE_CASE_ , return_tensors='''pt''' ).pixel_values self.assertEqual( encoded_images.shape , ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size['''height'''], self.image_processor_tester.size['''width'''], ) , )
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available, is_vision_available, ) __a = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']} try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __a = ['DeiTFeatureExtractor'] __a = ['DeiTImageProcessor'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __a = [ 'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST', 'DeiTForImageClassification', 'DeiTForImageClassificationWithTeacher', 'DeiTForMaskedImageModeling', 'DeiTModel', 'DeiTPreTrainedModel', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __a = [ 'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFDeiTForImageClassification', 'TFDeiTForImageClassificationWithTeacher', 'TFDeiTForMaskedImageModeling', 'TFDeiTModel', 'TFDeiTPreTrainedModel', ] if TYPE_CHECKING: from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_deit import DeiTFeatureExtractor from .image_processing_deit import DeiTImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_deit import ( DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, DeiTModel, DeiTPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_deit import ( TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, TFDeiTPreTrainedModel, ) else: import sys __a = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_torch_available, ) __A ={ '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''], '''processing_trocr''': ['''TrOCRProcessor'''], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __A =[ '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TrOCRForCausalLM''', '''TrOCRPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig from .processing_trocr import TrOCRProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel else: import sys __A =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __A ={ '''configuration_bridgetower''': [ '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BridgeTowerConfig''', '''BridgeTowerTextConfig''', '''BridgeTowerVisionConfig''', ], '''processing_bridgetower''': ['''BridgeTowerProcessor'''], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __A =['''BridgeTowerImageProcessor'''] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __A =[ '''BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST''', '''BridgeTowerForContrastiveLearning''', '''BridgeTowerForImageAndTextRetrieval''', '''BridgeTowerForMaskedLM''', '''BridgeTowerModel''', '''BridgeTowerPreTrainedModel''', ] if TYPE_CHECKING: from .configuration_bridgetower import ( BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP, BridgeTowerConfig, BridgeTowerTextConfig, BridgeTowerVisionConfig, ) from .processing_bridgetower import BridgeTowerProcessor try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_bridgetower import BridgeTowerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_bridgetower import ( BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST, BridgeTowerForContrastiveLearning, BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerModel, BridgeTowerPreTrainedModel, ) else: import sys __A =_LazyModule(__name__, globals()['''__file__'''], _import_structure)
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import argparse import json import os import fairseq import torch from fairseq.data import Dictionary from transformers import ( WavaVecaConformerConfig, WavaVecaConformerForCTC, WavaVecaConformerForPreTraining, WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor, logging, ) logging.set_verbosity_info() UpperCAmelCase__ : str = logging.get_logger(__name__) UpperCAmelCase__ : int = { """post_extract_proj""": """feature_projection.projection""", """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""", """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""", """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""", """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""", """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""", """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""", """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""", """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""", """self_attn.rotary_emb""": """encoder.embed_positions""", """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""", """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""", """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""", """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""", """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""", """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""", """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""", """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""", """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""", """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""", """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""", """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""", """final_layer_norm""": """encoder.layers.*.final_layer_norm""", """encoder.layer_norm""": """encoder.layer_norm""", """w2v_model.layer_norm""": """feature_projection.layer_norm""", """quantizer.weight_proj""": """quantizer.weight_proj""", """quantizer.vars""": """quantizer.codevectors""", """project_q""": """project_q""", """final_proj""": """project_hid""", """w2v_encoder.proj""": """lm_head""", """mask_emb""": """masked_spec_embed""", } UpperCAmelCase__ : str = [ """lm_head""", """quantizer.weight_proj""", """quantizer.codevectors""", """project_q""", """project_hid""", ] def __lowercase ( _A , _A , _A , _A , _A ) -> Optional[Any]: for attribute in key.split(""".""" ): SCREAMING_SNAKE_CASE : int = getattr(_A , _A ) if weight_type is not None: SCREAMING_SNAKE_CASE : Any = getattr(_A , _A ).shape else: SCREAMING_SNAKE_CASE : List[str] = 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 : List[str] = value elif weight_type == "weight_g": SCREAMING_SNAKE_CASE : List[str] = value elif weight_type == "weight_v": SCREAMING_SNAKE_CASE : Union[str, Any] = value elif weight_type == "bias": SCREAMING_SNAKE_CASE : str = value elif weight_type == "running_mean": SCREAMING_SNAKE_CASE : Optional[Any] = value elif weight_type == "running_var": SCREAMING_SNAKE_CASE : int = value elif weight_type == "num_batches_tracked": SCREAMING_SNAKE_CASE : List[Any] = value elif weight_type == "inv_freq": SCREAMING_SNAKE_CASE : List[str] = value else: SCREAMING_SNAKE_CASE : List[str] = value logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." ) def __lowercase ( _A , _A , _A ) -> str: SCREAMING_SNAKE_CASE : List[str] = [] SCREAMING_SNAKE_CASE : Optional[Any] = fairseq_model.state_dict() SCREAMING_SNAKE_CASE : Tuple = hf_model.wavaveca_conformer.feature_extractor for name, value in fairseq_dict.items(): SCREAMING_SNAKE_CASE : List[str] = False if "conv_layers" in name: load_conv_layer( _A , _A , _A , _A , hf_model.config.feat_extract_norm == """group""" , ) SCREAMING_SNAKE_CASE : List[Any] = True else: for key, mapped_key in MAPPING.items(): SCREAMING_SNAKE_CASE : str = """wav2vec2_conformer.""" + 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 : Optional[int] = True if "*" in mapped_key: SCREAMING_SNAKE_CASE : Union[str, Any] = name.split(_A )[0].split(""".""" )[-2] SCREAMING_SNAKE_CASE : List[str] = mapped_key.replace("""*""" , _A ) if "pos_bias_u" in name: SCREAMING_SNAKE_CASE : Optional[int] = None elif "pos_bias_v" in name: SCREAMING_SNAKE_CASE : List[str] = None elif "weight_g" in name: SCREAMING_SNAKE_CASE : List[Any] = """weight_g""" elif "weight_v" in name: SCREAMING_SNAKE_CASE : Optional[int] = """weight_v""" elif "bias" in name: SCREAMING_SNAKE_CASE : Optional[Any] = """bias""" elif "weight" in name: # TODO: don't match quantizer.weight_proj SCREAMING_SNAKE_CASE : int = """weight""" elif "running_mean" in name: SCREAMING_SNAKE_CASE : Tuple = """running_mean""" elif "inv_freq" in name: SCREAMING_SNAKE_CASE : Optional[Any] = """inv_freq""" elif "running_var" in name: SCREAMING_SNAKE_CASE : str = """running_var""" elif "num_batches_tracked" in name: SCREAMING_SNAKE_CASE : str = """num_batches_tracked""" else: SCREAMING_SNAKE_CASE : Optional[int] = None set_recursively(_A , _A , _A , _A , _A ) continue if not is_used: unused_weights.append(_A ) logger.warning(F"Unused weights: {unused_weights}" ) def __lowercase ( _A , _A , _A , _A , _A ) -> Optional[Any]: SCREAMING_SNAKE_CASE : Optional[int] = full_name.split("""conv_layers.""" )[-1] SCREAMING_SNAKE_CASE : Dict = name.split(""".""" ) SCREAMING_SNAKE_CASE : Dict = int(items[0] ) SCREAMING_SNAKE_CASE : Optional[Any] = 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 : Union[str, Any] = 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 : Union[str, Any] = 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 : Union[str, Any] = 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 : Tuple = 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 __lowercase ( _A , _A , _A=None , _A=None , _A=True ) -> List[Any]: if config_path is not None: SCREAMING_SNAKE_CASE : Optional[int] = WavaVecaConformerConfig.from_pretrained(_A , hidden_act="""swish""" ) else: SCREAMING_SNAKE_CASE : Any = WavaVecaConformerConfig() if "rope" in checkpoint_path: SCREAMING_SNAKE_CASE : Dict = """rotary""" if is_finetuned: if dict_path: SCREAMING_SNAKE_CASE : List[str] = Dictionary.load(_A ) # important change bos & pad token id since CTC symbol is <pad> and # not <s> as in fairseq SCREAMING_SNAKE_CASE : str = target_dict.pad_index SCREAMING_SNAKE_CASE : List[Any] = target_dict.bos_index SCREAMING_SNAKE_CASE : Optional[int] = target_dict.eos_index SCREAMING_SNAKE_CASE : Optional[int] = len(target_dict.symbols ) SCREAMING_SNAKE_CASE : Optional[Any] = 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 : Optional[int] = target_dict.indices # fairseq has the <pad> and <s> switched SCREAMING_SNAKE_CASE : Optional[Any] = 0 SCREAMING_SNAKE_CASE : Optional[Any] = 1 with open(_A , """w""" , encoding="""utf-8""" ) as vocab_handle: json.dump(_A , _A ) SCREAMING_SNAKE_CASE : Optional[Any] = 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 : Optional[Any] = True if config.feat_extract_norm == """layer""" else False SCREAMING_SNAKE_CASE : Optional[int] = WavaVecaFeatureExtractor( feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_A , return_attention_mask=_A , ) SCREAMING_SNAKE_CASE : Optional[Any] = WavaVecaProcessor(feature_extractor=_A , tokenizer=_A ) processor.save_pretrained(_A ) SCREAMING_SNAKE_CASE : Any = WavaVecaConformerForCTC(_A ) else: SCREAMING_SNAKE_CASE : Dict = WavaVecaConformerForPreTraining(_A ) if is_finetuned: SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = fairseq.checkpoint_utils.load_model_ensemble_and_task( [checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} ) else: SCREAMING_SNAKE_CASE : List[Any] = argparse.Namespace(task="""audio_pretraining""" ) SCREAMING_SNAKE_CASE : Any = fairseq.tasks.setup_task(_A ) SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_A ) SCREAMING_SNAKE_CASE : Optional[int] = model[0].eval() recursively_load_weights(_A , _A , not is_finetuned ) hf_wavavec.save_pretrained(_A ) if __name__ == "__main__": UpperCAmelCase__ : List[Any] = 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""" ) UpperCAmelCase__ : Optional[int] = parser.parse_args() convert_wavaveca_conformer_checkpoint( args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned )
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import argparse import glob import logging import os from argparse import Namespace from importlib import import_module import numpy as np import torch from lightning_base import BaseTransformer, add_generic_args, generic_train from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score from torch.nn import CrossEntropyLoss from torch.utils.data import DataLoader, TensorDataset from utils_ner import TokenClassificationTask UpperCAmelCase__ : Tuple = logging.getLogger(__name__) class a__ ( UpperCAmelCase ): """simple docstring""" UpperCAmelCase__ : List[str] ="""token-classification""" def __init__( self : Tuple , UpperCAmelCase__ : Tuple ) ->Optional[Any]: """simple docstring""" if type(UpperCAmelCase__ ) == dict: SCREAMING_SNAKE_CASE : List[str] = Namespace(**UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : List[str] = import_module("""tasks""" ) try: SCREAMING_SNAKE_CASE : str = getattr(UpperCAmelCase__ , hparams.task_type ) SCREAMING_SNAKE_CASE : TokenClassificationTask = token_classification_task_clazz() except AttributeError: raise ValueError( f"Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. " f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" ) SCREAMING_SNAKE_CASE : List[Any] = self.token_classification_task.get_labels(hparams.labels ) SCREAMING_SNAKE_CASE : List[Any] = CrossEntropyLoss().ignore_index super().__init__(UpperCAmelCase__ , len(self.labels ) , self.mode ) def _lowercase ( self : List[str] , **UpperCAmelCase__ : Optional[int] ) ->Any: """simple docstring""" return self.model(**UpperCAmelCase__ ) def _lowercase ( self : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) ->List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Optional[Any] = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]} if self.config.model_type != "distilbert": SCREAMING_SNAKE_CASE : List[str] = ( batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None ) # XLM and RoBERTa don"t use token_type_ids SCREAMING_SNAKE_CASE : Union[str, Any] = self(**UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : int = outputs[0] # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]} return {"loss": loss} def _lowercase ( self : Optional[Any] ) ->List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE : Tuple = self.hparams for mode in ["train", "dev", "test"]: SCREAMING_SNAKE_CASE : Any = self._feature_file(UpperCAmelCase__ ) if os.path.exists(UpperCAmelCase__ ) and not args.overwrite_cache: logger.info("""Loading features from cached file %s""" , UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : Tuple = torch.load(UpperCAmelCase__ ) else: logger.info("""Creating features from dataset file at %s""" , args.data_dir ) SCREAMING_SNAKE_CASE : Optional[int] = self.token_classification_task.read_examples_from_file(args.data_dir , UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : List[str] = self.token_classification_task.convert_examples_to_features( UpperCAmelCase__ , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=UpperCAmelCase__ , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , ) logger.info("""Saving features into cached file %s""" , UpperCAmelCase__ ) torch.save(UpperCAmelCase__ , UpperCAmelCase__ ) def _lowercase ( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : bool = False ) ->DataLoader: """simple docstring""" SCREAMING_SNAKE_CASE : Any = self._feature_file(UpperCAmelCase__ ) logger.info("""Loading features from cached file %s""" , UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : List[Any] = torch.load(UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([f.input_ids for f in features] , dtype=torch.long ) SCREAMING_SNAKE_CASE : int = torch.tensor([f.attention_mask for f in features] , dtype=torch.long ) if features[0].token_type_ids is not None: SCREAMING_SNAKE_CASE : Any = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long ) else: SCREAMING_SNAKE_CASE : Tuple = torch.tensor([0 for f in features] , dtype=torch.long ) # HACK(we will not use this anymore soon) SCREAMING_SNAKE_CASE : Dict = torch.tensor([f.label_ids for f in features] , dtype=torch.long ) return DataLoader( TensorDataset(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) , batch_size=UpperCAmelCase__ ) def _lowercase ( self : Tuple , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[Any] ) ->Tuple: """simple docstring""" """Compute validation""" "" SCREAMING_SNAKE_CASE : Tuple = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]} if self.config.model_type != "distilbert": SCREAMING_SNAKE_CASE : str = ( batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None ) # XLM and RoBERTa don"t use token_type_ids SCREAMING_SNAKE_CASE : Dict = self(**UpperCAmelCase__ ) SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = outputs[:2] SCREAMING_SNAKE_CASE : Optional[Any] = logits.detach().cpu().numpy() SCREAMING_SNAKE_CASE : Tuple = inputs["""labels"""].detach().cpu().numpy() return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids} def _lowercase ( self : int , UpperCAmelCase__ : List[str] ) ->Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE : List[str] = torch.stack([x["""val_loss"""] for x in outputs] ).mean() SCREAMING_SNAKE_CASE : str = np.concatenate([x["""pred"""] for x in outputs] , axis=0 ) SCREAMING_SNAKE_CASE : Tuple = np.argmax(UpperCAmelCase__ , axis=2 ) SCREAMING_SNAKE_CASE : Optional[Any] = np.concatenate([x["""target"""] for x in outputs] , axis=0 ) SCREAMING_SNAKE_CASE : Union[str, Any] = dict(enumerate(self.labels ) ) SCREAMING_SNAKE_CASE : int = [[] for _ in range(out_label_ids.shape[0] )] SCREAMING_SNAKE_CASE : Optional[int] = [[] for _ in range(out_label_ids.shape[0] )] for i in range(out_label_ids.shape[0] ): for j in range(out_label_ids.shape[1] ): if out_label_ids[i, j] != self.pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]] ) preds_list[i].append(label_map[preds[i][j]] ) SCREAMING_SNAKE_CASE : Tuple = { """val_loss""": val_loss_mean, """accuracy_score""": accuracy_score(UpperCAmelCase__ , UpperCAmelCase__ ), """precision""": precision_score(UpperCAmelCase__ , UpperCAmelCase__ ), """recall""": recall_score(UpperCAmelCase__ , UpperCAmelCase__ ), """f1""": fa_score(UpperCAmelCase__ , UpperCAmelCase__ ), } SCREAMING_SNAKE_CASE : Optional[int] = dict(results.items() ) SCREAMING_SNAKE_CASE : Optional[Any] = results return ret, preds_list, out_label_list def _lowercase ( self : Dict , UpperCAmelCase__ : Union[str, Any] ) ->Optional[Any]: """simple docstring""" SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = self._eval_end(UpperCAmelCase__ ) SCREAMING_SNAKE_CASE : Optional[int] = ret["""log"""] return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs} def _lowercase ( self : List[str] , UpperCAmelCase__ : Union[str, Any] ) ->Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = self._eval_end(UpperCAmelCase__ ) # Converting to the dict required by pl # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\ # pytorch_lightning/trainer/logging.py#L139 SCREAMING_SNAKE_CASE : Dict = ret["""log"""] # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss` return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs} @staticmethod def _lowercase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : int ) ->List[Any]: """simple docstring""" BaseTransformer.add_model_specific_args(UpperCAmelCase__ , UpperCAmelCase__ ) parser.add_argument( """--task_type""" , default="""NER""" , type=UpperCAmelCase__ , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" ) parser.add_argument( """--max_seq_length""" , default=1_2_8 , type=UpperCAmelCase__ , help=( """The maximum total input sequence length after tokenization. Sequences longer """ """than this will be truncated, sequences shorter will be padded.""" ) , ) parser.add_argument( """--labels""" , default="""""" , type=UpperCAmelCase__ , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , ) parser.add_argument( """--gpus""" , default=0 , type=UpperCAmelCase__ , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , ) parser.add_argument( """--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" ) return parser if __name__ == "__main__": UpperCAmelCase__ : str = argparse.ArgumentParser() add_generic_args(parser, os.getcwd()) UpperCAmelCase__ : Tuple = NERTransformer.add_model_specific_args(parser, os.getcwd()) UpperCAmelCase__ : int = parser.parse_args() UpperCAmelCase__ : Union[str, Any] = NERTransformer(args) UpperCAmelCase__ : str = generic_train(model, args) if args.do_predict: # See https://github.com/huggingface/transformers/issues/3159 # pl use this default format to create a checkpoint: # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\ # /pytorch_lightning/callbacks/model_checkpoint.py#L322 UpperCAmelCase__ : Union[str, Any] = sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True)) UpperCAmelCase__ : Any = model.load_from_checkpoint(checkpoints[-1]) trainer.test(model)
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"""simple docstring""" import os import time import numpy as np import onnxruntime as ort _lowercase : Union[str, Any] = '1' _lowercase : str = '0' _lowercase : List[Any] = '1' _lowercase : Dict = ort.SessionOptions() _lowercase : int = ort.GraphOptimizationLevel.ORT_DISABLE_ALL print('Create inference session...') _lowercase : Optional[Any] = ['TensorrtExecutionProvider', 'CUDAExecutionProvider'] _lowercase : Optional[int] = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider) _lowercase : str = ort.RunOptions() _lowercase : Optional[Any] = 1_28 _lowercase : str = 1 _lowercase : Optional[int] = np.ones((batch, sequence), dtype=np.intaa) _lowercase : Dict = np.ones((batch, sequence), dtype=np.intaa) _lowercase : Dict = np.ones((batch, sequence), dtype=np.intaa) print('Warm up phase...') sess.run( None, { sess.get_inputs()[0].name: input_ids, sess.get_inputs()[1].name: attention_mask, sess.get_inputs()[2].name: token_type_ids, }, run_options=run_opt, ) print('Start inference...') _lowercase : Any = time.time() _lowercase : List[str] = 20_00 _lowercase : List[Any] = {} for iter in range(max_iters): _lowercase : Any = sess.run( None, { sess.get_inputs()[0].name: input_ids, sess.get_inputs()[1].name: attention_mask, sess.get_inputs()[2].name: token_type_ids, }, run_options=run_opt, ) print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 10_00 / max_iters))
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"""simple docstring""" from __future__ import annotations import sys from collections import deque from typing import Generic, TypeVar _lowercase : List[Any] = TypeVar('T') class _UpperCAmelCase ( Generic[T] ): a__ : deque[T] # Cache store of keys a__ : set[T] # References of the keys in cache a__ : int = 10 # Maximum capacity of cache def __init__( self : Optional[Any] , _lowercase : int ): __UpperCAmelCase = deque() __UpperCAmelCase = set() if not n: __UpperCAmelCase = sys.maxsize elif n < 0: raise ValueError('''n should be an integer greater than 0.''' ) else: __UpperCAmelCase = n def a ( self : Optional[Any] , _lowercase : T ): if x not in self.key_reference: if len(self.dq_store ) == LRUCache._MAX_CAPACITY: __UpperCAmelCase = self.dq_store.pop() self.key_reference.remove(_lowercase ) else: self.dq_store.remove(_lowercase ) self.dq_store.appendleft(_lowercase ) self.key_reference.add(_lowercase ) def a ( self : str ): for k in self.dq_store: print(_lowercase ) def __repr__( self : Dict ): return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}''' if __name__ == "__main__": import doctest doctest.testmod() _lowercase : LRUCache[str | int] = LRUCache(4) lru_cache.refer('A') lru_cache.refer(2) lru_cache.refer(3) lru_cache.refer('A') lru_cache.refer(4) lru_cache.refer(5) lru_cache.display() print(lru_cache) assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
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import fire from torch.utils.data import DataLoader from tqdm import tqdm from transformers import AutoTokenizer from utils import SeqaSeqDataset, pickle_save def A (__A : Optional[int] , __A : Any , __A : str=1024 , __A : Tuple=1024 , __A : int=False , **__A : Any ) -> List[Any]: """simple docstring""" UpperCAmelCase_ = AutoTokenizer.from_pretrained(__A ) UpperCAmelCase_ = SeqaSeqDataset(__A , __A , __A , __A , type_path='''train''' , **__A ) UpperCAmelCase_ = tok.pad_token_id def get_lens(__A : Optional[int] ): UpperCAmelCase_ = tqdm( DataLoader(__A , batch_size=512 , num_workers=8 , shuffle=__A , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , ) UpperCAmelCase_ = [] for batch in dl: UpperCAmelCase_ = batch['''input_ids'''].ne(__A ).sum(1 ).tolist() UpperCAmelCase_ = batch['''labels'''].ne(__A ).sum(1 ).tolist() if consider_target: for src, tgt in zip(__A , __A ): max_lens.append(max(__A , __A ) ) else: max_lens.extend(__A ) return max_lens UpperCAmelCase_ = get_lens(__A ) UpperCAmelCase_ = SeqaSeqDataset(__A , __A , __A , __A , type_path='''val''' , **__A ) UpperCAmelCase_ = get_lens(__A ) pickle_save(__A , train_ds.len_file ) pickle_save(__A , val_ds.len_file ) if __name__ == "__main__": fire.Fire(save_len_file)
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import math def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> bool: assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and ( number >= 0 ), "'number' must been an int and positive" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or not number % 2: # Negatives, 0, 1 and all even numbers are not primes return False __lowercase = range(3 , int(math.sqrt(SCREAMING_SNAKE_CASE ) + 1 ) , 2 ) return not any(not number % i for i in odd_numbers ) def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Tuple=1 , **SCREAMING_SNAKE_CASE : Tuple ) -> Dict: __lowercase = factor * value __lowercase = value while not is_prime(SCREAMING_SNAKE_CASE ): value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1 if value == first_value_val: return next_prime(value + 1 , **SCREAMING_SNAKE_CASE ) return value
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'''simple docstring''' def a__ ( lowercase : list, lowercase : int = 0 ) -> list: """simple docstring""" _UpperCamelCase = length or len(lowercase ) _UpperCamelCase = False for i in range(length - 1 ): if list_data[i] > list_data[i + 1]: _UpperCamelCase , _UpperCamelCase = list_data[i + 1], list_data[i] _UpperCamelCase = True return list_data if not swapped else bubble_sort(lowercase, length - 1 ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import unittest from diffusers.pipelines.pipeline_utils import is_safetensors_compatible class __lowerCAmelCase ( unittest.TestCase ): """simple docstring""" def snake_case__ ( self : Tuple ) -> int: '''simple docstring''' _UpperCamelCase = [ '''safety_checker/pytorch_model.bin''', '''safety_checker/model.safetensors''', '''vae/diffusion_pytorch_model.bin''', '''vae/diffusion_pytorch_model.safetensors''', '''text_encoder/pytorch_model.bin''', '''text_encoder/model.safetensors''', '''unet/diffusion_pytorch_model.bin''', '''unet/diffusion_pytorch_model.safetensors''', ] self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ ) ) def snake_case__ ( self : int ) -> Tuple: '''simple docstring''' _UpperCamelCase = [ '''unet/diffusion_pytorch_model.bin''', '''unet/diffusion_pytorch_model.safetensors''', ] self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ ) ) def snake_case__ ( self : Optional[Any] ) -> List[str]: '''simple docstring''' _UpperCamelCase = [ '''safety_checker/pytorch_model.bin''', '''safety_checker/model.safetensors''', '''vae/diffusion_pytorch_model.bin''', '''vae/diffusion_pytorch_model.safetensors''', '''text_encoder/pytorch_model.bin''', '''text_encoder/model.safetensors''', '''unet/diffusion_pytorch_model.bin''', # Removed: 'unet/diffusion_pytorch_model.safetensors', ] self.assertFalse(is_safetensors_compatible(lowerCAmelCase__ ) ) def snake_case__ ( self : List[Any] ) -> List[Any]: '''simple docstring''' _UpperCamelCase = [ '''text_encoder/pytorch_model.bin''', '''text_encoder/model.safetensors''', ] self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ ) ) def snake_case__ ( self : Dict ) -> Dict: '''simple docstring''' _UpperCamelCase = [ '''safety_checker/pytorch_model.bin''', '''safety_checker/model.safetensors''', '''vae/diffusion_pytorch_model.bin''', '''vae/diffusion_pytorch_model.safetensors''', '''text_encoder/pytorch_model.bin''', # Removed: 'text_encoder/model.safetensors', '''unet/diffusion_pytorch_model.bin''', '''unet/diffusion_pytorch_model.safetensors''', ] self.assertFalse(is_safetensors_compatible(lowerCAmelCase__ ) ) def snake_case__ ( self : Any ) -> Optional[int]: '''simple docstring''' _UpperCamelCase = [ '''safety_checker/pytorch_model.fp16.bin''', '''safety_checker/model.fp16.safetensors''', '''vae/diffusion_pytorch_model.fp16.bin''', '''vae/diffusion_pytorch_model.fp16.safetensors''', '''text_encoder/pytorch_model.fp16.bin''', '''text_encoder/model.fp16.safetensors''', '''unet/diffusion_pytorch_model.fp16.bin''', '''unet/diffusion_pytorch_model.fp16.safetensors''', ] _UpperCamelCase = '''fp16''' self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) ) def snake_case__ ( self : Optional[Any] ) -> Dict: '''simple docstring''' _UpperCamelCase = [ '''unet/diffusion_pytorch_model.fp16.bin''', '''unet/diffusion_pytorch_model.fp16.safetensors''', ] _UpperCamelCase = '''fp16''' self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) ) def snake_case__ ( self : Tuple ) -> Optional[Any]: '''simple docstring''' _UpperCamelCase = [ '''unet/diffusion_pytorch_model.bin''', '''unet/diffusion_pytorch_model.safetensors''', ] _UpperCamelCase = '''fp16''' self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) ) def snake_case__ ( self : Tuple ) -> Union[str, Any]: '''simple docstring''' _UpperCamelCase = [ '''safety_checker/pytorch_model.fp16.bin''', '''safety_checker/model.fp16.safetensors''', '''vae/diffusion_pytorch_model.fp16.bin''', '''vae/diffusion_pytorch_model.fp16.safetensors''', '''text_encoder/pytorch_model.fp16.bin''', '''text_encoder/model.fp16.safetensors''', '''unet/diffusion_pytorch_model.fp16.bin''', # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors', ] _UpperCamelCase = '''fp16''' self.assertFalse(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) ) def snake_case__ ( self : Optional[int] ) -> Dict: '''simple docstring''' _UpperCamelCase = [ '''text_encoder/pytorch_model.fp16.bin''', '''text_encoder/model.fp16.safetensors''', ] _UpperCamelCase = '''fp16''' self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) ) def snake_case__ ( self : Optional[Any] ) -> str: '''simple docstring''' _UpperCamelCase = [ '''text_encoder/pytorch_model.bin''', '''text_encoder/model.safetensors''', ] _UpperCamelCase = '''fp16''' self.assertTrue(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) ) def snake_case__ ( self : List[str] ) -> Optional[int]: '''simple docstring''' _UpperCamelCase = [ '''safety_checker/pytorch_model.fp16.bin''', '''safety_checker/model.fp16.safetensors''', '''vae/diffusion_pytorch_model.fp16.bin''', '''vae/diffusion_pytorch_model.fp16.safetensors''', '''text_encoder/pytorch_model.fp16.bin''', # 'text_encoder/model.fp16.safetensors', '''unet/diffusion_pytorch_model.fp16.bin''', '''unet/diffusion_pytorch_model.fp16.safetensors''', ] _UpperCamelCase = '''fp16''' self.assertFalse(is_safetensors_compatible(lowerCAmelCase__ , variant=lowerCAmelCase__ ) )
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'''simple docstring''' import tempfile import torch from diffusers import ( DEISMultistepScheduler, DPMSolverMultistepScheduler, DPMSolverSinglestepScheduler, UniPCMultistepScheduler, ) from .test_schedulers import SchedulerCommonTest class _a ( __a ): __a : Optional[Any] = (DEISMultistepScheduler,) __a : Any = (("""num_inference_steps""", 25),) def A ( self : Any , **lowercase : Optional[int] ): '''simple docstring''' UpperCAmelCase = { '''num_train_timesteps''': 1_000, '''beta_start''': 0.0001, '''beta_end''': 0.02, '''beta_schedule''': '''linear''', '''solver_order''': 2, } config.update(**lowercase ) return config def A ( self : Union[str, Any] , lowercase : Optional[Any]=0 , **lowercase : Union[str, Any] ): '''simple docstring''' UpperCAmelCase = dict(self.forward_default_kwargs ) UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase ) UpperCAmelCase = self.dummy_sample UpperCAmelCase = 0.1 * sample UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: UpperCAmelCase = self.get_scheduler_config(**lowercase ) UpperCAmelCase = scheduler_class(**lowercase ) scheduler.set_timesteps(lowercase ) # copy over dummy past residuals UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(lowercase ) UpperCAmelCase = scheduler_class.from_pretrained(lowercase ) new_scheduler.set_timesteps(lowercase ) # copy over dummy past residuals UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order] UpperCAmelCase , UpperCAmelCase = sample, sample for t in range(lowercase , time_step + scheduler.config.solver_order + 1 ): UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample UpperCAmelCase = new_scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def A ( self : int ): '''simple docstring''' pass def A ( self : str , lowercase : Any=0 , **lowercase : Tuple ): '''simple docstring''' UpperCAmelCase = dict(self.forward_default_kwargs ) UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase ) UpperCAmelCase = self.dummy_sample UpperCAmelCase = 0.1 * sample UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10] for scheduler_class in self.scheduler_classes: UpperCAmelCase = self.get_scheduler_config() UpperCAmelCase = scheduler_class(**lowercase ) scheduler.set_timesteps(lowercase ) # copy over dummy past residuals (must be after setting timesteps) UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order] with tempfile.TemporaryDirectory() as tmpdirname: scheduler.save_config(lowercase ) UpperCAmelCase = scheduler_class.from_pretrained(lowercase ) # copy over dummy past residuals new_scheduler.set_timesteps(lowercase ) # copy over dummy past residual (must be after setting timesteps) UpperCAmelCase = dummy_past_residuals[: new_scheduler.config.solver_order] UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample UpperCAmelCase = new_scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical" def A ( self : Any , lowercase : List[str]=None , **lowercase : List[Any] ): '''simple docstring''' if scheduler is None: UpperCAmelCase = self.scheduler_classes[0] UpperCAmelCase = self.get_scheduler_config(**lowercase ) UpperCAmelCase = scheduler_class(**lowercase ) UpperCAmelCase = self.scheduler_classes[0] UpperCAmelCase = self.get_scheduler_config(**lowercase ) UpperCAmelCase = scheduler_class(**lowercase ) UpperCAmelCase = 10 UpperCAmelCase = self.dummy_model() UpperCAmelCase = self.dummy_sample_deter scheduler.set_timesteps(lowercase ) for i, t in enumerate(scheduler.timesteps ): UpperCAmelCase = model(lowercase , lowercase ) UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase ).prev_sample return sample def A ( self : List[str] ): '''simple docstring''' UpperCAmelCase = dict(self.forward_default_kwargs ) UpperCAmelCase = kwargs.pop('''num_inference_steps''' , lowercase ) for scheduler_class in self.scheduler_classes: UpperCAmelCase = self.get_scheduler_config() UpperCAmelCase = scheduler_class(**lowercase ) UpperCAmelCase = self.dummy_sample UpperCAmelCase = 0.1 * sample if num_inference_steps is not None and hasattr(lowercase , '''set_timesteps''' ): scheduler.set_timesteps(lowercase ) elif num_inference_steps is not None and not hasattr(lowercase , '''set_timesteps''' ): UpperCAmelCase = num_inference_steps # copy over dummy past residuals (must be done after set_timesteps) UpperCAmelCase = [residual + 0.2, residual + 0.15, residual + 0.10] UpperCAmelCase = dummy_past_residuals[: scheduler.config.solver_order] UpperCAmelCase = scheduler.timesteps[5] UpperCAmelCase = scheduler.timesteps[6] UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample self.assertEqual(output_a.shape , sample.shape ) self.assertEqual(output_a.shape , output_a.shape ) def A ( self : str ): '''simple docstring''' UpperCAmelCase = DEISMultistepScheduler(**self.get_scheduler_config() ) UpperCAmelCase = self.full_loop(scheduler=lowercase ) UpperCAmelCase = torch.mean(torch.abs(lowercase ) ) assert abs(result_mean.item() - 0.2_3916 ) < 1E-3 UpperCAmelCase = DPMSolverSinglestepScheduler.from_config(scheduler.config ) UpperCAmelCase = DPMSolverMultistepScheduler.from_config(scheduler.config ) UpperCAmelCase = UniPCMultistepScheduler.from_config(scheduler.config ) UpperCAmelCase = DEISMultistepScheduler.from_config(scheduler.config ) UpperCAmelCase = self.full_loop(scheduler=lowercase ) UpperCAmelCase = torch.mean(torch.abs(lowercase ) ) assert abs(result_mean.item() - 0.2_3916 ) < 1E-3 def A ( self : Dict ): '''simple docstring''' for timesteps in [25, 50, 100, 999, 1_000]: self.check_over_configs(num_train_timesteps=lowercase ) def A ( self : int ): '''simple docstring''' self.check_over_configs(thresholding=lowercase ) for order in [1, 2, 3]: for solver_type in ["logrho"]: for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample"]: self.check_over_configs( thresholding=lowercase , prediction_type=lowercase , sample_max_value=lowercase , algorithm_type='''deis''' , solver_order=lowercase , solver_type=lowercase , ) def A ( self : Optional[int] ): '''simple docstring''' for prediction_type in ["epsilon", "v_prediction"]: self.check_over_configs(prediction_type=lowercase ) def A ( self : Tuple ): '''simple docstring''' for algorithm_type in ["deis"]: for solver_type in ["logrho"]: for order in [1, 2, 3]: for prediction_type in ["epsilon", "sample"]: self.check_over_configs( solver_order=lowercase , solver_type=lowercase , prediction_type=lowercase , algorithm_type=lowercase , ) UpperCAmelCase = self.full_loop( solver_order=lowercase , solver_type=lowercase , prediction_type=lowercase , algorithm_type=lowercase , ) assert not torch.isnan(lowercase ).any(), "Samples have nan numbers" def A ( self : int ): '''simple docstring''' self.check_over_configs(lower_order_final=lowercase ) self.check_over_configs(lower_order_final=lowercase ) def A ( self : List[Any] ): '''simple docstring''' for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]: self.check_over_forward(num_inference_steps=lowercase , time_step=0 ) def A ( self : Optional[int] ): '''simple docstring''' UpperCAmelCase = self.full_loop() UpperCAmelCase = torch.mean(torch.abs(lowercase ) ) assert abs(result_mean.item() - 0.2_3916 ) < 1E-3 def A ( self : Optional[int] ): '''simple docstring''' UpperCAmelCase = self.full_loop(prediction_type='''v_prediction''' ) UpperCAmelCase = torch.mean(torch.abs(lowercase ) ) assert abs(result_mean.item() - 0.091 ) < 1E-3 def A ( self : List[str] ): '''simple docstring''' UpperCAmelCase = self.scheduler_classes[0] UpperCAmelCase = self.get_scheduler_config(thresholding=lowercase , dynamic_thresholding_ratio=0 ) UpperCAmelCase = scheduler_class(**lowercase ) UpperCAmelCase = 10 UpperCAmelCase = self.dummy_model() UpperCAmelCase = self.dummy_sample_deter.half() scheduler.set_timesteps(lowercase ) for i, t in enumerate(scheduler.timesteps ): UpperCAmelCase = model(lowercase , lowercase ) UpperCAmelCase = scheduler.step(lowercase , lowercase , lowercase ).prev_sample assert sample.dtype == torch.floataa
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"""simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float] SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float] def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ): A__ = end_pointa[0] - end_pointa[0] A__ = end_pointa[1] - end_pointa[1] A__ = end_pointa[2] - end_pointa[2] return (x, y, z) def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : Vectorad ): A__ = ab[1] * ac[2] - ab[2] * ac[1] # *i A__ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j A__ = ab[0] * ac[1] - ab[1] * ac[0] # *k return (x, y, z) def _snake_case ( UpperCAmelCase_ : Vectorad , UpperCAmelCase_ : int ): return tuple(round(UpperCAmelCase_ , UpperCAmelCase_ ) for x in vector ) == (0, 0, 0) def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad , UpperCAmelCase_ : int = 10 ): A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ ) A__ = create_vector(UpperCAmelCase_ , UpperCAmelCase_ ) return is_zero_vector(get_ad_vectors_cross(UpperCAmelCase_ , UpperCAmelCase_ ) , UpperCAmelCase_ )
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'''simple docstring''' import logging import os import random import sys from dataclasses import dataclass, field from typing import Optional import datasets import numpy as np import pandas as pd from datasets import load_dataset import transformers from transformers import ( AutoConfig, BartForSequenceClassification, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, TapexTokenizer, Trainer, TrainingArguments, default_data_collator, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version from transformers.utils.versions import require_version # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.17.0.dev0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") _lowercase : Any = logging.getLogger(__name__) @dataclass class __magic_name__ : UpperCamelCase__ = field( default='''tab_fact''', metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}) UpperCamelCase__ = field( default='''tab_fact''', metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}, ) UpperCamelCase__ = field( default=1024, metadata={ '''help''': ( '''The maximum total input sequence length after tokenization. Sequences longer ''' '''than this will be truncated, sequences shorter will be padded.''' ) }, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={ '''help''': ( '''Whether to pad all samples to `max_seq_length`. ''' '''If False, will pad the samples dynamically when batching to the maximum length in the batch.''' ) }, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={ '''help''': ( '''For debugging purposes or quicker training, truncate the number of training examples to this ''' '''value if set.''' ) }, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={ '''help''': ( '''For debugging purposes or quicker training, truncate the number of evaluation examples to this ''' '''value if set.''' ) }, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={ '''help''': ( '''For debugging purposes or quicker training, truncate the number of prediction examples to this ''' '''value if set.''' ) }, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''A csv or a json file containing the training data.'''}) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''A csv or a json file containing the validation data.'''}) UpperCamelCase__ = field(default=_UpperCAmelCase, metadata={'''help''': '''A csv or a json file containing the test data.'''}) def SCREAMING_SNAKE_CASE_ ( self : Dict ): if self.dataset_name is not None: pass elif self.train_file is None or self.validation_file is None: raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" ) else: lowercase_ : List[str] = self.train_file.split(""".""" )[-1] assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file." lowercase_ : Optional[int] = self.validation_file.split(""".""" )[-1] assert ( validation_extension == train_extension ), "`validation_file` should have the same extension (csv or json) as `train_file`." @dataclass class __magic_name__ : UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}, ) UpperCamelCase__ = field( default='''main''', metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}, ) UpperCamelCase__ = field( default=_UpperCAmelCase, metadata={ '''help''': ( '''Will use the token generated when running `huggingface-cli login` (necessary to use this script ''' '''with private models).''' ) }, ) def lowerCamelCase ( ) -> Optional[Any]: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. lowercase_ : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. lowercase_ , lowercase_ , lowercase_ : List[str] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: lowercase_ , lowercase_ , lowercase_ : Any = parser.parse_args_into_dataclasses() # Setup logging logging.basicConfig( format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , ) lowercase_ : Tuple = training_args.get_process_log_level() logger.setLevel(UpperCAmelCase__ ) datasets.utils.logging.set_verbosity(UpperCAmelCase__ ) transformers.utils.logging.set_verbosity(UpperCAmelCase__ ) transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() # Log on each process the small summary: logger.warning( F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}''' + F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' ) logger.info(F'''Training/evaluation parameters {training_args}''' ) # Detecting last checkpoint. lowercase_ : str = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: lowercase_ : Tuple = get_last_checkpoint(training_args.output_dir ) if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0: raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. ''' """Use --overwrite_output_dir to overcome.""" ) elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: logger.info( F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change ''' """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" ) # Set seed before initializing model. set_seed(training_args.seed ) # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). # # For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table. # # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this # single column. You can easily tweak this behavior (see below) # # In distributed training, the load_dataset function guarantee that only one local process can concurrently # download the dataset. if data_args.dataset_name is not None: # Downloading and loading a dataset from the hub. lowercase_ : List[str] = load_dataset( data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir ) else: # Loading a dataset from your local files. # CSV/JSON training and evaluation files are needed. lowercase_ : List[str] = {"""train""": data_args.train_file, """validation""": data_args.validation_file} # Get the test dataset: you can provide your own CSV/JSON test file (see below) # when you use `do_predict` without specifying a GLUE benchmark task. if training_args.do_predict: if data_args.test_file is not None: lowercase_ : int = data_args.train_file.split(""".""" )[-1] lowercase_ : Any = data_args.test_file.split(""".""" )[-1] assert ( test_extension == train_extension ), "`test_file` should have the same extension (csv or json) as `train_file`." lowercase_ : List[str] = data_args.test_file else: raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" ) for key in data_files.keys(): logger.info(F'''load a local file for {key}: {data_files[key]}''' ) if data_args.train_file.endswith(""".csv""" ): # Loading a dataset from local csv files lowercase_ : List[str] = load_dataset("""csv""" , data_files=UpperCAmelCase__ , cache_dir=model_args.cache_dir ) else: # Loading a dataset from local json files lowercase_ : Optional[Any] = load_dataset("""json""" , data_files=UpperCAmelCase__ , cache_dir=model_args.cache_dir ) # See more about loading any type of standard or custom dataset at # https://huggingface.co/docs/datasets/loading_datasets.html. # Labels lowercase_ : List[Any] = raw_datasets["""train"""].features["""label"""].names lowercase_ : Dict = len(UpperCAmelCase__ ) # Load pretrained model and tokenizer # # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. lowercase_ : Dict = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) # load tapex tokenizer lowercase_ : str = TapexTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=UpperCAmelCase__ , ) lowercase_ : List[str] = BartForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=UpperCAmelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) # Padding strategy if data_args.pad_to_max_length: lowercase_ : Any = """max_length""" else: # We will pad later, dynamically at batch creation, to the max sequence length in each batch lowercase_ : int = False # Some models have set the order of the labels to use, so let's make sure we do use it. lowercase_ : Dict = {"""Refused""": 0, """Entailed""": 1} lowercase_ : Optional[int] = {0: """Refused""", 1: """Entailed"""} if data_args.max_seq_length > tokenizer.model_max_length: logger.warning( F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the''' F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' ) lowercase_ : List[Any] = min(data_args.max_seq_length , tokenizer.model_max_length ) def preprocess_tabfact_function(UpperCAmelCase__ : List[str] ): # Tokenize the texts def _convert_table_text_to_pandas(UpperCAmelCase__ : Dict ): lowercase_ : Optional[int] = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )] lowercase_ : str = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] ) return _table_pd lowercase_ : List[Any] = examples["""statement"""] lowercase_ : Dict = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) ) lowercase_ : Optional[int] = tokenizer(UpperCAmelCase__ , UpperCAmelCase__ , padding=UpperCAmelCase__ , max_length=UpperCAmelCase__ , truncation=UpperCAmelCase__ ) lowercase_ : Union[str, Any] = examples["""label"""] return result with training_args.main_process_first(desc="""dataset map pre-processing""" ): lowercase_ : Optional[Any] = raw_datasets.map( UpperCAmelCase__ , batched=UpperCAmelCase__ , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , ) if training_args.do_train: if "train" not in raw_datasets: raise ValueError("""--do_train requires a train dataset""" ) lowercase_ : List[Any] = raw_datasets["""train"""] if data_args.max_train_samples is not None: lowercase_ : Union[str, Any] = train_dataset.select(range(data_args.max_train_samples ) ) if training_args.do_eval: if "validation" not in raw_datasets and "validation_matched" not in raw_datasets: raise ValueError("""--do_eval requires a validation dataset""" ) lowercase_ : Union[str, Any] = raw_datasets["""validation"""] if data_args.max_eval_samples is not None: lowercase_ : str = eval_dataset.select(range(data_args.max_eval_samples ) ) if training_args.do_predict or data_args.test_file is not None: if "test" not in raw_datasets and "test_matched" not in raw_datasets: raise ValueError("""--do_predict requires a test dataset""" ) lowercase_ : Optional[Any] = raw_datasets["""test"""] if data_args.max_predict_samples is not None: lowercase_ : Any = predict_dataset.select(range(data_args.max_predict_samples ) ) # Log a few random samples from the training set: if training_args.do_train: for index in random.sample(range(len(UpperCAmelCase__ ) ) , 3 ): logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' ) # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a # predictions and label_ids field) and has to return a dictionary string to float. def compute_metrics(UpperCAmelCase__ : EvalPrediction ): lowercase_ : Optional[Any] = p.predictions[0] if isinstance(p.predictions , UpperCAmelCase__ ) else p.predictions lowercase_ : Optional[int] = np.argmax(UpperCAmelCase__ , axis=1 ) return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()} # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding. if data_args.pad_to_max_length: lowercase_ : Tuple = default_data_collator elif training_args.fpaa: lowercase_ : int = DataCollatorWithPadding(UpperCAmelCase__ , pad_to_multiple_of=8 ) else: lowercase_ : Union[str, Any] = None # Initialize our Trainer lowercase_ : Any = Trainer( model=UpperCAmelCase__ , args=UpperCAmelCase__ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=UpperCAmelCase__ , tokenizer=UpperCAmelCase__ , data_collator=UpperCAmelCase__ , ) # Training if training_args.do_train: lowercase_ : int = None if training_args.resume_from_checkpoint is not None: lowercase_ : str = training_args.resume_from_checkpoint elif last_checkpoint is not None: lowercase_ : Tuple = last_checkpoint lowercase_ : Tuple = trainer.train(resume_from_checkpoint=UpperCAmelCase__ ) lowercase_ : Optional[Any] = train_result.metrics lowercase_ : int = ( data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase__ ) ) lowercase_ : int = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) ) trainer.save_model() # Saves the tokenizer too for easy upload trainer.log_metrics("""train""" , UpperCAmelCase__ ) trainer.save_metrics("""train""" , UpperCAmelCase__ ) trainer.save_state() # Evaluation if training_args.do_eval: logger.info("""*** Evaluate ***""" ) lowercase_ : Union[str, Any] = trainer.evaluate(eval_dataset=UpperCAmelCase__ ) lowercase_ : Optional[int] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase__ ) lowercase_ : Union[str, Any] = min(UpperCAmelCase__ , len(UpperCAmelCase__ ) ) trainer.log_metrics("""eval""" , UpperCAmelCase__ ) trainer.save_metrics("""eval""" , UpperCAmelCase__ ) if training_args.do_predict: logger.info("""*** Predict ***""" ) # Removing the `label` columns because it contains -1 and Trainer won't like that. lowercase_ : Optional[Any] = predict_dataset.remove_columns("""label""" ) lowercase_ : Any = trainer.predict(UpperCAmelCase__ , metric_key_prefix="""predict""" ).predictions lowercase_ : str = np.argmax(UpperCAmelCase__ , axis=1 ) lowercase_ : Union[str, Any] = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" ) if trainer.is_world_process_zero(): with open(UpperCAmelCase__ , """w""" ) as writer: logger.info("""***** Predict Results *****""" ) writer.write("""index\tprediction\n""" ) for index, item in enumerate(UpperCAmelCase__ ): lowercase_ : List[Any] = label_list[item] writer.write(F'''{index}\t{item}\n''' ) lowercase_ : Optional[int] = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""} if training_args.push_to_hub: trainer.push_to_hub(**UpperCAmelCase__ ) else: trainer.create_model_card(**UpperCAmelCase__ ) def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[Any]: # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
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'''simple docstring''' import os import numpy import onnx def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str ) -> Tuple: lowercase_ : Tuple = a.name lowercase_ : Tuple = b.name lowercase_ : Any = """""" lowercase_ : List[Any] = """""" lowercase_ : List[Any] = a == b lowercase_ : Union[str, Any] = name_a lowercase_ : Optional[Any] = name_b return res def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Optional[Any] ) -> Union[str, Any]: for i, input_name in enumerate(node_proto.input ): if input_name == name: node_proto.input.insert(UpperCAmelCase__ , UpperCAmelCase__ ) node_proto.input.pop(i + 1 ) if node_proto.op_type == "If": _graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ ) _graph_replace_input_with(node_proto.attribute[1].g , UpperCAmelCase__ , UpperCAmelCase__ ) if node_proto.op_type == "Loop": _graph_replace_input_with(node_proto.attribute[0].g , UpperCAmelCase__ , UpperCAmelCase__ ) def lowerCamelCase ( UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : str ) -> int: for n in graph_proto.node: _node_replace_input_with(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Union[str, Any] , UpperCAmelCase__ : Dict ) -> List[str]: lowercase_ : int = list(model.graph.initializer ) lowercase_ : List[str] = list(model_without_ext.graph.initializer ) for i, ref_i in ind_to_replace: assert inits_with_data[i].name == inits[i].name assert inits_with_data[ref_i].name == inits[ref_i].name assert i > ref_i lowercase_ : Optional[Any] = inits[i].name lowercase_ : List[str] = inits[ref_i].name model_without_ext.graph.initializer.remove(inits[i] ) # for n in model.graph.node: _graph_replace_input_with(model_without_ext.graph , UpperCAmelCase__ , UpperCAmelCase__ ) def lowerCamelCase ( UpperCAmelCase__ : int ) -> List[str]: lowercase_ : Dict = os.path.dirname(UpperCAmelCase__ ) lowercase_ : Optional[Any] = os.path.basename(UpperCAmelCase__ ) lowercase_ : str = onnx.load(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) ) lowercase_ : List[Any] = list(model.graph.initializer ) lowercase_ : int = set() lowercase_ : int = {} lowercase_ : str = [] lowercase_ : int = 0 for i in range(len(UpperCAmelCase__ ) ): if i in dup_set: continue for j in range(i + 1 , len(UpperCAmelCase__ ) ): if j in dup_set: continue if _is_equal_tensor_proto(inits[i] , inits[j] ): dup_set.add(UpperCAmelCase__ ) dup_set.add(UpperCAmelCase__ ) lowercase_ : Dict = inits[j].data_type lowercase_ : List[str] = numpy.prod(inits[j].dims ) if dtype == 1: mem_size *= 4 elif dtype == 6: mem_size *= 4 elif dtype == 7 or dtype == 11: mem_size *= 8 else: print("""unexpected data type: """ , UpperCAmelCase__ ) total_reduced_size += mem_size lowercase_ : int = inits[i].name lowercase_ : List[str] = inits[j].name if name_i in dup_map: dup_map[name_i].append(UpperCAmelCase__ ) else: lowercase_ : Optional[int] = [name_j] ind_to_replace.append((j, i) ) print("""total reduced size: """ , total_reduced_size / 1024 / 1024 / 1024 , """GB""" ) lowercase_ : Tuple = sorted(UpperCAmelCase__ ) _remove_dup_initializers_from_model(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) lowercase_ : Union[str, Any] = """optimized_""" + model_file_name lowercase_ : Optional[int] = os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) onnx.save(UpperCAmelCase__ , UpperCAmelCase__ ) return new_model
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1
UpperCamelCase__ = frozenset( [ """prompt""", """height""", """width""", """guidance_scale""", """negative_prompt""", """prompt_embeds""", """negative_prompt_embeds""", """cross_attention_kwargs""", ] ) UpperCamelCase__ = frozenset(["""prompt""", """negative_prompt"""]) UpperCamelCase__ = frozenset([]) UpperCamelCase__ = frozenset(["""image"""]) UpperCamelCase__ = frozenset( [ """image""", """height""", """width""", """guidance_scale""", ] ) UpperCamelCase__ = frozenset(["""image"""]) UpperCamelCase__ = frozenset( [ """prompt""", """image""", """height""", """width""", """guidance_scale""", """negative_prompt""", """prompt_embeds""", """negative_prompt_embeds""", ] ) UpperCamelCase__ = frozenset(["""prompt""", """image""", """negative_prompt"""]) UpperCamelCase__ = frozenset( [ # Text guided image variation with an image mask """prompt""", """image""", """mask_image""", """height""", """width""", """guidance_scale""", """negative_prompt""", """prompt_embeds""", """negative_prompt_embeds""", ] ) UpperCamelCase__ = frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""]) UpperCamelCase__ = frozenset( [ # image variation with an image mask """image""", """mask_image""", """height""", """width""", """guidance_scale""", ] ) UpperCamelCase__ = frozenset(["""image""", """mask_image"""]) UpperCamelCase__ = frozenset( [ """example_image""", """image""", """mask_image""", """height""", """width""", """guidance_scale""", ] ) UpperCamelCase__ = frozenset(["""example_image""", """image""", """mask_image"""]) UpperCamelCase__ = frozenset(["""class_labels"""]) UpperCamelCase__ = frozenset(["""class_labels"""]) UpperCamelCase__ = frozenset(["""batch_size"""]) UpperCamelCase__ = frozenset([]) UpperCamelCase__ = frozenset(["""batch_size"""]) UpperCamelCase__ = frozenset([]) UpperCamelCase__ = frozenset( [ """prompt""", """audio_length_in_s""", """guidance_scale""", """negative_prompt""", """prompt_embeds""", """negative_prompt_embeds""", """cross_attention_kwargs""", ] ) UpperCamelCase__ = frozenset(["""prompt""", """negative_prompt"""]) UpperCamelCase__ = frozenset(["""input_tokens"""]) UpperCamelCase__ = frozenset(["""input_tokens"""])
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from typing import TYPE_CHECKING from ...utils import _LazyModule UpperCamelCase__ = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]} if TYPE_CHECKING: from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer else: import sys UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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1
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : list ) -> list: if len(_snake_case ) <= 1: return lst UpperCAmelCase_ = 1 while i < len(_snake_case ): if lst[i - 1] <= lst[i]: i += 1 else: UpperCAmelCase_ = lst[i], lst[i - 1] i -= 1 if i == 0: UpperCAmelCase_ = 1 return lst if __name__ == "__main__": _lowerCamelCase = input('Enter numbers separated by a comma:\n').strip() _lowerCamelCase = [int(item) for item in user_input.split(',')] print(gnome_sort(unsorted))
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# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/ import gc import random import tempfile import unittest import numpy as np import torch from PIL import Image from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, ControlNetModel, DDIMScheduler, StableDiffusionControlNetImgaImgPipeline, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS, ) from ..test_pipelines_common import ( PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() class a ( _A , _A , _A , unittest.TestCase ): '''simple docstring''' lowerCAmelCase : List[Any] = StableDiffusionControlNetImgaImgPipeline lowerCAmelCase : List[str] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'} lowerCAmelCase : Union[str, Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS lowerCAmelCase : Optional[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} ) lowerCAmelCase : List[Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS def lowerCamelCase_ ( self : Dict ): 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 , ) torch.manual_seed(0 ) UpperCAmelCase_ = ControlNetModel( block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , ) torch.manual_seed(0 ) UpperCAmelCase_ = DDIMScheduler( beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase_ = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) torch.manual_seed(0 ) UpperCAmelCase_ = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) UpperCAmelCase_ = CLIPTextModel(__snake_case ) UpperCAmelCase_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ) UpperCAmelCase_ = { '''unet''': unet, '''controlnet''': controlnet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def lowerCamelCase_ ( self : Union[str, Any] , __snake_case : Optional[int] , __snake_case : Any=0 ): if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase_ = torch.manual_seed(__snake_case ) else: UpperCAmelCase_ = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase_ = 2 UpperCAmelCase_ = randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__snake_case , device=torch.device(__snake_case ) , ) UpperCAmelCase_ = floats_tensor(control_image.shape , rng=random.Random(__snake_case ) ).to(__snake_case ) UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0] UpperCAmelCase_ = Image.fromarray(np.uinta(__snake_case ) ).convert('''RGB''' ).resize((64, 64) ) UpperCAmelCase_ = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', '''image''': image, '''control_image''': control_image, } return inputs def lowerCamelCase_ ( self : Union[str, Any] ): return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 ) @unittest.skipIf( torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , ) def lowerCamelCase_ ( self : Optional[Any] ): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 ) def lowerCamelCase_ ( self : Dict ): self._test_inference_batch_single_identical(expected_max_diff=2E-3 ) class a ( _A , _A , unittest.TestCase ): '''simple docstring''' lowerCAmelCase : Dict = StableDiffusionControlNetImgaImgPipeline lowerCAmelCase : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'} lowerCAmelCase : Any = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS lowerCAmelCase : Optional[int] = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess def lowerCamelCase_ ( self : Optional[Any] ): 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 , ) torch.manual_seed(0 ) def init_weights(__snake_case : Tuple ): if isinstance(__snake_case , torch.nn.Convad ): torch.nn.init.normal(m.weight ) m.bias.data.fill_(1.0 ) UpperCAmelCase_ = ControlNetModel( block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , ) controlneta.controlnet_down_blocks.apply(__snake_case ) torch.manual_seed(0 ) UpperCAmelCase_ = ControlNetModel( block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , ) controlneta.controlnet_down_blocks.apply(__snake_case ) torch.manual_seed(0 ) UpperCAmelCase_ = DDIMScheduler( beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , ) torch.manual_seed(0 ) UpperCAmelCase_ = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) torch.manual_seed(0 ) UpperCAmelCase_ = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , ) UpperCAmelCase_ = CLIPTextModel(__snake_case ) UpperCAmelCase_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ) UpperCAmelCase_ = MultiControlNetModel([controlneta, controlneta] ) UpperCAmelCase_ = { '''unet''': unet, '''controlnet''': controlnet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def lowerCamelCase_ ( self : Optional[Any] , __snake_case : Any , __snake_case : Optional[Any]=0 ): if str(__snake_case ).startswith('''mps''' ): UpperCAmelCase_ = torch.manual_seed(__snake_case ) else: UpperCAmelCase_ = torch.Generator(device=__snake_case ).manual_seed(__snake_case ) UpperCAmelCase_ = 2 UpperCAmelCase_ = [ randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__snake_case , device=torch.device(__snake_case ) , ), randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__snake_case , device=torch.device(__snake_case ) , ), ] UpperCAmelCase_ = floats_tensor(control_image[0].shape , rng=random.Random(__snake_case ) ).to(__snake_case ) UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 )[0] UpperCAmelCase_ = Image.fromarray(np.uinta(__snake_case ) ).convert('''RGB''' ).resize((64, 64) ) UpperCAmelCase_ = { '''prompt''': '''A painting of a squirrel eating a burger''', '''generator''': generator, '''num_inference_steps''': 2, '''guidance_scale''': 6.0, '''output_type''': '''numpy''', '''image''': image, '''control_image''': control_image, } return inputs def lowerCamelCase_ ( self : List[Any] ): UpperCAmelCase_ = self.get_dummy_components() UpperCAmelCase_ = self.pipeline_class(**__snake_case ) pipe.to(__snake_case ) UpperCAmelCase_ = 10.0 UpperCAmelCase_ = 4 UpperCAmelCase_ = self.get_dummy_inputs(__snake_case ) UpperCAmelCase_ = steps UpperCAmelCase_ = scale UpperCAmelCase_ = pipe(**__snake_case )[0] UpperCAmelCase_ = self.get_dummy_inputs(__snake_case ) UpperCAmelCase_ = steps UpperCAmelCase_ = scale UpperCAmelCase_ = pipe(**__snake_case , control_guidance_start=0.1 , control_guidance_end=0.2 )[0] UpperCAmelCase_ = self.get_dummy_inputs(__snake_case ) UpperCAmelCase_ = steps UpperCAmelCase_ = scale UpperCAmelCase_ = pipe(**__snake_case , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0] UpperCAmelCase_ = self.get_dummy_inputs(__snake_case ) UpperCAmelCase_ = steps UpperCAmelCase_ = scale UpperCAmelCase_ = pipe(**__snake_case , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0] # make sure that all outputs are different assert np.sum(np.abs(output_a - output_a ) ) > 1E-3 assert np.sum(np.abs(output_a - output_a ) ) > 1E-3 assert np.sum(np.abs(output_a - output_a ) ) > 1E-3 def lowerCamelCase_ ( self : Optional[int] ): return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 ) @unittest.skipIf( torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , ) def lowerCamelCase_ ( self : List[Any] ): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 ) def lowerCamelCase_ ( self : List[Any] ): self._test_inference_batch_single_identical(expected_max_diff=2E-3 ) def lowerCamelCase_ ( self : Optional[Any] ): UpperCAmelCase_ = self.get_dummy_components() UpperCAmelCase_ = self.pipeline_class(**__snake_case ) pipe.to(__snake_case ) pipe.set_progress_bar_config(disable=__snake_case ) with tempfile.TemporaryDirectory() as tmpdir: try: # save_pretrained is not implemented for Multi-ControlNet pipe.save_pretrained(__snake_case ) except NotImplementedError: pass @slow @require_torch_gpu class a ( unittest.TestCase ): '''simple docstring''' def lowerCamelCase_ ( self : Optional[Any] ): super().tearDown() gc.collect() torch.cuda.empty_cache() def lowerCamelCase_ ( self : Union[str, Any] ): UpperCAmelCase_ = ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''' ) UpperCAmelCase_ = StableDiffusionControlNetImgaImgPipeline.from_pretrained( '''runwayml/stable-diffusion-v1-5''' , safety_checker=__snake_case , controlnet=__snake_case ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=__snake_case ) UpperCAmelCase_ = torch.Generator(device='''cpu''' ).manual_seed(0 ) UpperCAmelCase_ = '''evil space-punk bird''' UpperCAmelCase_ = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''' ).resize((5_12, 5_12) ) UpperCAmelCase_ = load_image( '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png''' ).resize((5_12, 5_12) ) UpperCAmelCase_ = pipe( __snake_case , __snake_case , control_image=__snake_case , generator=__snake_case , output_type='''np''' , num_inference_steps=50 , strength=0.6 , ) UpperCAmelCase_ = output.images[0] assert image.shape == (5_12, 5_12, 3) UpperCAmelCase_ = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy''' ) assert np.abs(expected_image - image ).max() < 9E-2
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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 _lowerCamelCase : Dict = """\ @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} } """ _lowerCamelCase : Union[str, Any] = """\ 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 """ _lowerCamelCase : Optional[Any] = """ 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 UpperCamelCase_ ( datasets.Metric ): '''simple docstring''' def SCREAMING_SNAKE_CASE ( self : Any) ->Tuple: '''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 SCREAMING_SNAKE_CASE ( self : Union[str, Any] , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Optional[Any]="auto" , UpperCAmelCase__ : Tuple=-1 , UpperCAmelCase__ : Any=0.9 , UpperCAmelCase__ : Optional[Any]=5 , UpperCAmelCase__ : Any=500 , UpperCAmelCase__ : str="gpt2-large" , UpperCAmelCase__ : Dict=-1 , UpperCAmelCase__ : int=1_024 , UpperCAmelCase__ : Any=25 , UpperCAmelCase__ : Optional[int]=5 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : List[Any]=25 , ) ->Any: '''simple docstring''' A__ = compute_mauve( p_text=UpperCAmelCase__ , q_text=UpperCAmelCase__ , p_features=UpperCAmelCase__ , q_features=UpperCAmelCase__ , p_tokens=UpperCAmelCase__ , q_tokens=UpperCAmelCase__ , num_buckets=UpperCAmelCase__ , pca_max_data=UpperCAmelCase__ , kmeans_explained_var=UpperCAmelCase__ , kmeans_num_redo=UpperCAmelCase__ , kmeans_max_iter=UpperCAmelCase__ , featurize_model_name=UpperCAmelCase__ , device_id=UpperCAmelCase__ , max_text_length=UpperCAmelCase__ , divergence_curve_discretization_size=UpperCAmelCase__ , mauve_scaling_factor=UpperCAmelCase__ , verbose=UpperCAmelCase__ , seed=UpperCAmelCase__ , ) return out
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"""simple docstring""" import random from typing import Any def _A (__a ) -> list[Any]: """simple docstring""" for _ in range(len(__a ) ): SCREAMING_SNAKE_CASE_ : Optional[int] = random.randint(0 , len(__a ) - 1 ) SCREAMING_SNAKE_CASE_ : Tuple = random.randint(0 , len(__a ) - 1 ) SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Union[str, Any] = data[b], data[a] return data if __name__ == "__main__": UpperCAmelCase_ : Dict = [0, 1, 2, 3, 4, 5, 6, 7] UpperCAmelCase_ : Dict = ["""python""", """says""", """hello""", """!"""] print("""Fisher-Yates Shuffle:""") print("""List""", integers, strings) print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
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from collections import namedtuple import requests from lxml import html # type: ignore A_ : int = namedtuple("covid_data", "cases deaths recovered") def lowerCamelCase_ ( _lowerCamelCase = "https://www.worldometers.info/coronavirus/" ): lowerCamelCase__ : Any = '//div[@class = "maincounter-number"]/span/text()' return covid_data(*html.fromstring(requests.get(_lowerCamelCase ).content ).xpath(_lowerCamelCase ) ) A_ : Dict = "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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"""simple docstring""" def lowerCamelCase_ ( _lowerCamelCase ): lowerCamelCase__ : Union[str, Any] = [] lowerCamelCase__ : List[str] = [] lowerCamelCase__ : Tuple = { '^': 3, '*': 2, '/': 2, '%': 2, '+': 1, '-': 1, } # Priority of each operator lowerCamelCase__ : List[str] = len(_lowerCamelCase ) if (len(_lowerCamelCase ) > 7) else 7 # Print table header for output print( 'Symbol'.center(8 ) , 'Stack'.center(_lowerCamelCase ) , 'Postfix'.center(_lowerCamelCase ) , sep=' | ' , ) print('-' * (print_width * 3 + 7) ) for x in infix: if x.isalpha() or x.isdigit(): post_fix.append(_lowerCamelCase ) # if x is Alphabet / Digit, add it to Postfix elif x == "(": stack.append(_lowerCamelCase ) # if x is "(" push to Stack elif x == ")": # if x is ")" pop stack until "(" is encountered while stack[-1] != "(": post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix stack.pop() else: if len(_lowerCamelCase ) == 0: stack.append(_lowerCamelCase ) # If stack is empty, push x to stack else: # while priority of x is not > priority of element in the stack while len(_lowerCamelCase ) > 0 and priority[x] <= priority[stack[-1]]: post_fix.append(stack.pop() ) # pop stack & add to Postfix stack.append(_lowerCamelCase ) # push x to stack print( x.center(8 ) , (''.join(_lowerCamelCase )).ljust(_lowerCamelCase ) , (''.join(_lowerCamelCase )).ljust(_lowerCamelCase ) , sep=' | ' , ) # Output in tabular format while len(_lowerCamelCase ) > 0: # while stack is not empty post_fix.append(stack.pop() ) # pop stack & add to Postfix print( ' '.center(8 ) , (''.join(_lowerCamelCase )).ljust(_lowerCamelCase ) , (''.join(_lowerCamelCase )).ljust(_lowerCamelCase ) , sep=' | ' , ) # Output in tabular format return "".join(_lowerCamelCase ) # return Postfix as str def lowerCamelCase_ ( _lowerCamelCase ): lowerCamelCase__ : Union[str, Any] = list(infix[::-1] ) # reverse the infix equation for i in range(len(_lowerCamelCase ) ): if infix[i] == "(": lowerCamelCase__ : List[Any] = ')' # change "(" to ")" elif infix[i] == ")": lowerCamelCase__ : Tuple = '(' # change ")" to "(" return (infix_2_postfix(''.join(_lowerCamelCase ) ))[ ::-1 ] # call infix_2_postfix on Infix, return reverse of Postfix if __name__ == "__main__": A_ : Tuple = input("\nEnter an Infix Equation = ") # Input an Infix equation A_ : List[str] = "".join(Infix.split()) # Remove spaces from the input print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
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"""simple docstring""" from math import acos, sin from typing import List, Tuple, Union import numpy as np import torch from PIL import Image from ...models import AutoencoderKL, UNetaDConditionModel from ...schedulers import DDIMScheduler, DDPMScheduler from ...utils import randn_tensor from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput from .mel import Mel class __snake_case ( __SCREAMING_SNAKE_CASE ): a__ = ["""vqvae"""] def __init__( self , lowercase , lowercase , lowercase , lowercase , ) -> str: '''simple docstring''' super().__init__() self.register_modules(unet=__UpperCAmelCase , scheduler=__UpperCAmelCase , mel=__UpperCAmelCase , vqvae=__UpperCAmelCase) def lowerCamelCase_ ( self) -> Optional[int]: '''simple docstring''' return 50 if isinstance(self.scheduler , __UpperCAmelCase) else 10_00 @torch.no_grad() def __call__( self , lowercase = 1 , lowercase = None , lowercase = None , lowercase = 0 , lowercase = 0 , lowercase = None , lowercase = None , lowercase = 0 , lowercase = 0 , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase=True , ) -> int: '''simple docstring''' a__: Dict = steps or self.get_default_steps() self.scheduler.set_timesteps(__UpperCAmelCase) a__: str = step_generator or generator # For backwards compatibility if type(self.unet.config.sample_size) == int: a__: List[str] = (self.unet.config.sample_size, self.unet.config.sample_size) if noise is None: a__: str = randn_tensor( ( batch_size, self.unet.config.in_channels, self.unet.config.sample_size[0], self.unet.config.sample_size[1], ) , generator=__UpperCAmelCase , device=self.device , ) a__: List[str] = noise a__: Optional[Any] = None if audio_file is not None or raw_audio is not None: self.mel.load_audio(__UpperCAmelCase , __UpperCAmelCase) a__: str = self.mel.audio_slice_to_image(__UpperCAmelCase) a__: Optional[int] = np.frombuffer(input_image.tobytes() , dtype='uint8').reshape( (input_image.height, input_image.width)) a__: Any = (input_image / 2_55) * 2 - 1 a__: int = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float).to(self.device) if self.vqvae is not None: a__: Tuple = self.vqvae.encode(torch.unsqueeze(__UpperCAmelCase , 0)).latent_dist.sample( generator=__UpperCAmelCase)[0] a__: Union[str, Any] = self.vqvae.config.scaling_factor * input_images if start_step > 0: a__: List[str] = self.scheduler.add_noise(__UpperCAmelCase , __UpperCAmelCase , self.scheduler.timesteps[start_step - 1]) a__: Optional[int] = ( self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length ) a__: Any = int(mask_start_secs * pixels_per_second) a__: Optional[Any] = int(mask_end_secs * pixels_per_second) a__: int = self.scheduler.add_noise(__UpperCAmelCase , __UpperCAmelCase , torch.tensor(self.scheduler.timesteps[start_step:])) for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:])): if isinstance(self.unet , __UpperCAmelCase): a__: Optional[Any] = self.unet(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase)['sample'] else: a__: int = self.unet(__UpperCAmelCase , __UpperCAmelCase)['sample'] if isinstance(self.scheduler , __UpperCAmelCase): a__: int = self.scheduler.step( model_output=__UpperCAmelCase , timestep=__UpperCAmelCase , sample=__UpperCAmelCase , eta=__UpperCAmelCase , generator=__UpperCAmelCase , )['prev_sample'] else: a__: List[Any] = self.scheduler.step( model_output=__UpperCAmelCase , timestep=__UpperCAmelCase , sample=__UpperCAmelCase , generator=__UpperCAmelCase , )['prev_sample'] if mask is not None: if mask_start > 0: a__: str = mask[:, step, :, :mask_start] if mask_end > 0: a__: List[str] = mask[:, step, :, -mask_end:] if self.vqvae is not None: # 0.18215 was scaling factor used in training to ensure unit variance a__: List[Any] = 1 / self.vqvae.config.scaling_factor * images a__: Optional[int] = self.vqvae.decode(__UpperCAmelCase)['sample'] a__: Optional[Any] = (images / 2 + 0.5).clamp(0 , 1) a__: Union[str, Any] = images.cpu().permute(0 , 2 , 3 , 1).numpy() a__: Optional[int] = (images * 2_55).round().astype('uint8') a__: Optional[Any] = list( (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 else (Image.fromarray(__UpperCAmelCase , mode='RGB').convert('L') for _ in images)) a__: Optional[int] = [self.mel.image_to_audio(__UpperCAmelCase) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) return BaseOutput(**AudioPipelineOutput(np.array(__UpperCAmelCase)[:, np.newaxis, :]) , **ImagePipelineOutput(__UpperCAmelCase)) @torch.no_grad() def lowerCamelCase_ ( self , lowercase , lowercase = 50) -> Tuple: '''simple docstring''' assert isinstance(self.scheduler , __UpperCAmelCase) self.scheduler.set_timesteps(__UpperCAmelCase) a__: Dict = np.array( [np.frombuffer(image.tobytes() , dtype='uint8').reshape((1, image.height, image.width)) for image in images]) a__: Optional[int] = (sample / 2_55) * 2 - 1 a__: Optional[Any] = torch.Tensor(__UpperCAmelCase).to(self.device) for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,))): a__: str = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps a__: Optional[int] = self.scheduler.alphas_cumprod[t] a__: str = ( self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod ) a__: Any = 1 - alpha_prod_t a__: List[str] = self.unet(__UpperCAmelCase , __UpperCAmelCase)['sample'] a__: Union[str, Any] = (1 - alpha_prod_t_prev) ** 0.5 * model_output a__: str = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5) a__: Any = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output return sample @staticmethod def lowerCamelCase_ ( lowercase , lowercase , lowercase) -> Tuple: '''simple docstring''' a__: int = acos(torch.dot(torch.flatten(__UpperCAmelCase) , torch.flatten(__UpperCAmelCase)) / torch.norm(__UpperCAmelCase) / torch.norm(__UpperCAmelCase)) return sin((1 - alpha) * theta) * xa / sin(__UpperCAmelCase) + sin(alpha * theta) * xa / sin(__UpperCAmelCase)
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"""simple docstring""" 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 __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ): lowercase = ["image_processor", "tokenizer"] lowercase = "OwlViTImageProcessor" lowercase = ("CLIPTokenizer", "CLIPTokenizerFast") def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ): '''simple docstring''' __UpperCamelCase = None if "feature_extractor" in kwargs: warnings.warn( 'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`' ' instead.' , __UpperCAmelCase , ) __UpperCamelCase = kwargs.pop('feature_extractor' ) __UpperCamelCase = 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__(__UpperCAmelCase , __UpperCAmelCase ) def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase="max_length" , __UpperCAmelCase="np" , **__UpperCAmelCase ): '''simple docstring''' 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(__UpperCAmelCase , __UpperCAmelCase ) or (isinstance(__UpperCAmelCase , __UpperCAmelCase ) and not isinstance(text[0] , __UpperCAmelCase )): __UpperCamelCase = [self.tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )] elif isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(text[0] , __UpperCAmelCase ): __UpperCamelCase = [] # Maximum number of queries across batch __UpperCamelCase = max([len(__UpperCAmelCase ) for t in text] ) # Pad all batch samples to max number of text queries for t in text: if len(__UpperCAmelCase ) != max_num_queries: __UpperCamelCase = t + [' '] * (max_num_queries - len(__UpperCAmelCase )) __UpperCamelCase = self.tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase ) encodings.append(__UpperCAmelCase ) else: raise TypeError('Input text should be a string, a list of strings or a nested list of strings' ) if return_tensors == "np": __UpperCamelCase = np.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 ) __UpperCamelCase = np.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 ) elif return_tensors == "jax" and is_flax_available(): import jax.numpy as jnp __UpperCamelCase = jnp.concatenate([encoding['input_ids'] for encoding in encodings] , axis=0 ) __UpperCamelCase = jnp.concatenate([encoding['attention_mask'] for encoding in encodings] , axis=0 ) elif return_tensors == "pt" and is_torch_available(): import torch __UpperCamelCase = torch.cat([encoding['input_ids'] for encoding in encodings] , dim=0 ) __UpperCamelCase = torch.cat([encoding['attention_mask'] for encoding in encodings] , dim=0 ) elif return_tensors == "tf" and is_tf_available(): import tensorflow as tf __UpperCamelCase = tf.stack([encoding['input_ids'] for encoding in encodings] , axis=0 ) __UpperCamelCase = tf.stack([encoding['attention_mask'] for encoding in encodings] , axis=0 ) else: raise ValueError('Target return tensor type could not be returned' ) __UpperCamelCase = BatchEncoding() __UpperCamelCase = input_ids __UpperCamelCase = attention_mask if query_images is not None: __UpperCamelCase = BatchEncoding() __UpperCamelCase = self.image_processor( __UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase ).pixel_values __UpperCamelCase = query_pixel_values if images is not None: __UpperCamelCase = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase ) if text is not None and images is not None: __UpperCamelCase = image_features.pixel_values return encoding elif query_images is not None and images is not None: __UpperCamelCase = image_features.pixel_values return encoding elif text is not None or query_images is not None: return encoding else: return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase ) def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ): '''simple docstring''' return self.image_processor.post_process(*__UpperCAmelCase , **__UpperCAmelCase ) def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ): '''simple docstring''' return self.image_processor.post_process_object_detection(*__UpperCAmelCase , **__UpperCAmelCase ) def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ): '''simple docstring''' return self.image_processor.post_process_image_guided_detection(*__UpperCAmelCase , **__UpperCAmelCase ) def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ): '''simple docstring''' return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase ) def UpperCAmelCase ( self , *__UpperCAmelCase , **__UpperCAmelCase ): '''simple docstring''' return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase ) @property def UpperCAmelCase ( self ): '''simple docstring''' warnings.warn( '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , __UpperCAmelCase , ) return self.image_processor_class @property def UpperCAmelCase ( self ): '''simple docstring''' warnings.warn( '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , __UpperCAmelCase , ) return self.image_processor
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def __lowerCAmelCase ( ): return [ a * b * (1000 - a - b) for a in range(1 , 999 ) for b in range(_snake_case , 999 ) if (a * a + b * b == (1000 - a - b) ** 2) ][0] if __name__ == "__main__": print(F'{solution() = }')
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from __future__ import annotations from typing import TypedDict class _snake_case ( lowercase__): UpperCamelCase__ : str UpperCamelCase__ : int def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): raise TypeError("The parameter s type must be str." ) return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE_ ) )] def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ): if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): raise TypeError("The parameter s type must be str." ) if not s: raise ValueError("The parameter s must not be empty." ) lowercase__ = all_rotations(SCREAMING_SNAKE_CASE_ ) rotations.sort() # sort the list of rotations in alphabetically order # make a string composed of the last char of each rotation lowercase__ = { "bwt_string": "".join([word[-1] for word in rotations] ), "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE_ ), } return response def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ): raise TypeError("The parameter bwt_string type must be str." ) if not bwt_string: raise ValueError("The parameter bwt_string must not be empty." ) try: lowercase__ = int(SCREAMING_SNAKE_CASE_ ) except ValueError: raise TypeError( "The parameter idx_original_string type must be int or passive" " of cast to int." ) if idx_original_string < 0: raise ValueError("The parameter idx_original_string must not be lower than 0." ) if idx_original_string >= len(SCREAMING_SNAKE_CASE_ ): raise ValueError( "The parameter idx_original_string must be lower than" " len(bwt_string)." ) lowercase__ = [""] * len(SCREAMING_SNAKE_CASE_ ) for _ in range(len(SCREAMING_SNAKE_CASE_ ) ): for i in range(len(SCREAMING_SNAKE_CASE_ ) ): lowercase__ = bwt_string[i] + ordered_rotations[i] ordered_rotations.sort() return ordered_rotations[idx_original_string] if __name__ == "__main__": lowercase_ = """Provide a string that I will generate its BWT transform: """ lowercase_ = input(entry_msg).strip() lowercase_ = bwt_transform(s) print( F'Burrows Wheeler transform for string \'{s}\' results ' F'in \'{result["bwt_string"]}\'' ) lowercase_ = reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""]) print( F'Reversing Burrows Wheeler transform for entry \'{result["bwt_string"]}\' ' F'we get original string \'{original_string}\'' )
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"""simple docstring""" import argparse import json from pathlib import Path import requests import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel from transformers.utils import logging logging.set_verbosity_info() __snake_case = logging.get_logger(__name__) def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : List[str]=False ): """simple docstring""" _a = [] for i in range(config.num_hidden_layers ): # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight') ) rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias') ) rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight') ) rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias') ) rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight') ) rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias') ) rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight') ) rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias') ) rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight') ) rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias') ) # projection layer + position embeddings rename_keys.extend( [ ('''cls_token''', '''vit.embeddings.cls_token'''), ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''), ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''), ('''pos_embed''', '''vit.embeddings.position_embeddings'''), ] ) if base_model: # layernorm + pooler rename_keys.extend( [ ('''norm.weight''', '''layernorm.weight'''), ('''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 A_ ( _lowerCAmelCase : Dict, _lowerCAmelCase : Dict, _lowerCAmelCase : Tuple=False ): """simple docstring""" 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'blocks.{i}.attn.qkv.weight' ) _a = state_dict.pop(f'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 A_ ( _lowerCAmelCase : str ): """simple docstring""" _a = ["""head.weight""", """head.bias"""] for k in ignore_keys: state_dict.pop(_lowerCAmelCase, _lowerCAmelCase ) def A_ ( _lowerCAmelCase : int, _lowerCAmelCase : Optional[Any], _lowerCAmelCase : int ): """simple docstring""" _a = dct.pop(_lowerCAmelCase ) _a = val def A_ ( ): """simple docstring""" _a = """http://images.cocodataset.org/val2017/000000039769.jpg""" _a = Image.open(requests.get(_lowerCAmelCase, stream=_lowerCAmelCase ).raw ) return im @torch.no_grad() def A_ ( _lowerCAmelCase : List[str], _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Union[str, Any]=True ): """simple docstring""" _a = ViTConfig() # patch_size if model_name[-1] == "8": _a = 8 # set labels if required if not base_model: _a = 10_00 _a = """huggingface/label-files""" _a = """imagenet-1k-id2label.json""" _a = json.load(open(hf_hub_download(_lowerCAmelCase, _lowerCAmelCase, repo_type='''dataset''' ), '''r''' ) ) _a = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} _a = idalabel _a = {v: k for k, v in idalabel.items()} # size of the architecture if model_name in ["dino_vits8", "dino_vits16"]: _a = 3_84 _a = 15_36 _a = 12 _a = 6 # load original model from torch hub _a = torch.hub.load('''facebookresearch/dino:main''', _lowerCAmelCase ) original_model.eval() # load state_dict of original model, remove and rename some keys _a = original_model.state_dict() if base_model: remove_classification_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, _lowerCAmelCase ) # load HuggingFace model if base_model: _a = ViTModel(_lowerCAmelCase, add_pooling_layer=_lowerCAmelCase ).eval() else: _a = ViTForImageClassification(_lowerCAmelCase ).eval() model.load_state_dict(_lowerCAmelCase ) # Check outputs on an image, prepared by ViTImageProcessor _a = ViTImageProcessor() _a = image_processor(images=prepare_img(), return_tensors='''pt''' ) _a = encoding["""pixel_values"""] _a = model(_lowerCAmelCase ) if base_model: _a = original_model(_lowerCAmelCase ) assert torch.allclose(_lowerCAmelCase, outputs.last_hidden_state[:, 0, :], atol=1e-1 ) else: _a = original_model(_lowerCAmelCase ) assert logits.shape == outputs.logits.shape assert torch.allclose(_lowerCAmelCase, outputs.logits, atol=1e-3 ) Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) print(f'Saving model {model_name} to {pytorch_dump_folder_path}' ) model.save_pretrained(_lowerCAmelCase ) print(f'Saving image processor to {pytorch_dump_folder_path}' ) image_processor.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": __snake_case = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default='''dino_vitb16''', type=str, help='''Name of the model trained with DINO 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( '''--base_model''', action='''store_true''', help='''Whether to only convert the base model (no projection head weights).''', ) parser.set_defaults(base_model=True) __snake_case = parser.parse_args() convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
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from scipy.stats import pearsonr import datasets _lowerCamelCase =""" Pearson correlation coefficient and p-value for testing non-correlation. The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases. The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. """ _lowerCamelCase =""" Args: predictions (`list` of `int`): Predicted class labels, as returned by a model. references (`list` of `int`): Ground truth labels. return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`. Returns: pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation. p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities. Examples: Example 1-A simple example using only predictions and references. >>> pearsonr_metric = datasets.load_metric(\"pearsonr\") >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5]) >>> print(round(results['pearsonr'], 2)) -0.74 Example 2-The same as Example 1, but that also returns the `p-value`. >>> pearsonr_metric = datasets.load_metric(\"pearsonr\") >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True) >>> print(sorted(list(results.keys()))) ['p-value', 'pearsonr'] >>> print(round(results['pearsonr'], 2)) -0.74 >>> print(round(results['p-value'], 2)) 0.15 """ _lowerCamelCase =""" @article{2020SciPy-NMeth, author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, Ilhan and Feng, Yu and Moore, Eric W. and {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Antonio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}}, title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific Computing in Python}}, journal = {Nature Methods}, year = {2020}, volume = {17}, pages = {261--272}, adsurl = {https://rdcu.be/b08Wh}, doi = {10.1038/s41592-019-0686-2}, } """ @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION) class A__ ( datasets.Metric): def UpperCamelCase__ ( self ): 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.pearsonr.html"""] , ) def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ , __magic_name__=False ): if return_pvalue: lowerCamelCase : Optional[Any] = pearsonr(__magic_name__ , __magic_name__ ) return {"pearsonr": results[0], "p-value": results[1]} else: return {"pearsonr": float(pearsonr(__magic_name__ , __magic_name__ )[0] )}
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'''simple docstring''' import copy import os from typing import Union from ...configuration_utils import PretrainedConfig from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from ...utils import logging from ..auto import CONFIG_MAPPING _snake_case : str = logging.get_logger(__name__) _snake_case : Optional[Any] = { 'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json', } class A ( __lowerCAmelCase ): lowercase_ = '''instructblip_vision_model''' def __init__( self : Any , lowerCAmelCase_ : Union[str, Any]=14_08 , lowerCAmelCase_ : Tuple=61_44 , lowerCAmelCase_ : Optional[int]=39 , lowerCAmelCase_ : int=16 , lowerCAmelCase_ : Optional[int]=2_24 , lowerCAmelCase_ : int=14 , lowerCAmelCase_ : Tuple="gelu" , lowerCAmelCase_ : Dict=1e-6 , lowerCAmelCase_ : Any=0.0 , lowerCAmelCase_ : Tuple=1e-10 , lowerCAmelCase_ : Optional[int]=True , **lowerCAmelCase_ : List[Any] , ) -> Any: """simple docstring""" super().__init__(**lowerCAmelCase_ ) _a = hidden_size _a = intermediate_size _a = num_hidden_layers _a = num_attention_heads _a = patch_size _a = image_size _a = initializer_range _a = attention_dropout _a = layer_norm_eps _a = hidden_act _a = qkv_bias @classmethod def __lowerCAmelCase ( cls : List[str] , lowerCAmelCase_ : List[Any] , **lowerCAmelCase_ : Any ) -> "PretrainedConfig": """simple docstring""" cls._set_token_in_kwargs(lowerCAmelCase_ ) _a , _a = cls.get_config_dict(lowerCAmelCase_ , **lowerCAmelCase_ ) # get the vision config dict if we are loading from InstructBlipConfig if config_dict.get('''model_type''' ) == "instructblip": _a = config_dict['''vision_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(lowerCAmelCase_ , **lowerCAmelCase_ ) class A ( __lowerCAmelCase ): lowercase_ = '''instructblip_qformer''' def __init__( self : int , lowerCAmelCase_ : List[str]=3_05_22 , lowerCAmelCase_ : List[str]=7_68 , lowerCAmelCase_ : Union[str, Any]=12 , lowerCAmelCase_ : List[Any]=12 , lowerCAmelCase_ : str=30_72 , lowerCAmelCase_ : Optional[Any]="gelu" , lowerCAmelCase_ : List[Any]=0.1 , lowerCAmelCase_ : str=0.1 , lowerCAmelCase_ : Optional[Any]=5_12 , lowerCAmelCase_ : Tuple=0.0_2 , lowerCAmelCase_ : Tuple=1e-12 , lowerCAmelCase_ : Dict=0 , lowerCAmelCase_ : Optional[int]="absolute" , lowerCAmelCase_ : List[Any]=2 , lowerCAmelCase_ : Optional[int]=14_08 , **lowerCAmelCase_ : Any , ) -> Dict: """simple docstring""" super().__init__(pad_token_id=lowerCAmelCase_ , **lowerCAmelCase_ ) _a = vocab_size _a = hidden_size _a = num_hidden_layers _a = num_attention_heads _a = hidden_act _a = intermediate_size _a = hidden_dropout_prob _a = attention_probs_dropout_prob _a = max_position_embeddings _a = initializer_range _a = layer_norm_eps _a = position_embedding_type _a = cross_attention_frequency _a = encoder_hidden_size @classmethod def __lowerCAmelCase ( cls : Optional[Any] , lowerCAmelCase_ : Tuple , **lowerCAmelCase_ : Optional[int] ) -> "PretrainedConfig": """simple docstring""" cls._set_token_in_kwargs(lowerCAmelCase_ ) _a , _a = cls.get_config_dict(lowerCAmelCase_ , **lowerCAmelCase_ ) # get the qformer config dict if we are loading from InstructBlipConfig if config_dict.get('''model_type''' ) == "instructblip": _a = config_dict['''qformer_config'''] if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type: logger.warning( F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type ' F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' ) return cls.from_dict(lowerCAmelCase_ , **lowerCAmelCase_ ) class A ( __lowerCAmelCase ): lowercase_ = '''instructblip''' lowercase_ = True def __init__( self : Optional[int] , lowerCAmelCase_ : str=None , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : Optional[Any]=None , lowerCAmelCase_ : List[str]=32 , **lowerCAmelCase_ : Tuple ) -> Tuple: """simple docstring""" super().__init__(**lowerCAmelCase_ ) if vision_config is None: _a = {} logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' ) if qformer_config is None: _a = {} logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' ) if text_config is None: _a = {} logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' ) _a = InstructBlipVisionConfig(**lowerCAmelCase_ ) _a = InstructBlipQFormerConfig(**lowerCAmelCase_ ) _a = text_config['''model_type'''] if '''model_type''' in text_config else '''opt''' _a = CONFIG_MAPPING[text_model_type](**lowerCAmelCase_ ) _a = self.text_config.tie_word_embeddings _a = self.text_config.is_encoder_decoder _a = num_query_tokens _a = self.vision_config.hidden_size _a = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES _a = 1.0 _a = 0.0_2 @classmethod def __lowerCAmelCase ( cls : List[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : int , lowerCAmelCase_ : Any , **lowerCAmelCase_ : List[str] , ) -> Any: """simple docstring""" return cls( vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **lowerCAmelCase_ , ) def __lowerCAmelCase ( self : Union[str, Any] ) -> Any: """simple docstring""" _a = copy.deepcopy(self.__dict__ ) _a = self.vision_config.to_dict() _a = self.qformer_config.to_dict() _a = self.text_config.to_dict() _a = self.__class__.model_type return output
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'''simple docstring''' import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_bert import BertTokenizer _snake_case : Any = logging.get_logger(__name__) _snake_case : Any = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'} _snake_case : List[str] = { 'vocab_file': { 'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt', 'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt', 'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt', 'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt', 'bert-base-multilingual-uncased': ( 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt' ), 'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt', 'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt', 'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt', 'bert-large-uncased-whole-word-masking': ( 'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt' ), 'bert-large-cased-whole-word-masking': ( 'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt' ), 'bert-large-uncased-whole-word-masking-finetuned-squad': ( 'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt' ), 'bert-large-cased-whole-word-masking-finetuned-squad': ( 'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt' ), 'bert-base-cased-finetuned-mrpc': ( 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt' ), 'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt', 'bert-base-german-dbmdz-uncased': ( 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt' ), 'TurkuNLP/bert-base-finnish-cased-v1': ( 'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt' ), 'TurkuNLP/bert-base-finnish-uncased-v1': ( 'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt' ), 'wietsedv/bert-base-dutch-cased': ( 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt' ), }, 'tokenizer_file': { 'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json', 'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json', 'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json', 'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json', 'bert-base-multilingual-uncased': ( 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json' ), 'bert-base-multilingual-cased': ( 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json' ), 'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json', 'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json', 'bert-large-uncased-whole-word-masking': ( 'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json' ), 'bert-large-cased-whole-word-masking': ( 'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json' ), 'bert-large-uncased-whole-word-masking-finetuned-squad': ( 'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json' ), 'bert-large-cased-whole-word-masking-finetuned-squad': ( 'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json' ), 'bert-base-cased-finetuned-mrpc': ( 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json' ), 'bert-base-german-dbmdz-cased': ( 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json' ), 'bert-base-german-dbmdz-uncased': ( 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json' ), 'TurkuNLP/bert-base-finnish-cased-v1': ( 'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json' ), 'TurkuNLP/bert-base-finnish-uncased-v1': ( 'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json' ), 'wietsedv/bert-base-dutch-cased': ( 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json' ), }, } _snake_case : int = { 'bert-base-uncased': 512, 'bert-large-uncased': 512, 'bert-base-cased': 512, 'bert-large-cased': 512, 'bert-base-multilingual-uncased': 512, 'bert-base-multilingual-cased': 512, 'bert-base-chinese': 512, 'bert-base-german-cased': 512, 'bert-large-uncased-whole-word-masking': 512, 'bert-large-cased-whole-word-masking': 512, 'bert-large-uncased-whole-word-masking-finetuned-squad': 512, 'bert-large-cased-whole-word-masking-finetuned-squad': 512, 'bert-base-cased-finetuned-mrpc': 512, 'bert-base-german-dbmdz-cased': 512, 'bert-base-german-dbmdz-uncased': 512, 'TurkuNLP/bert-base-finnish-cased-v1': 512, 'TurkuNLP/bert-base-finnish-uncased-v1': 512, 'wietsedv/bert-base-dutch-cased': 512, } _snake_case : int = { 'bert-base-uncased': {'do_lower_case': True}, 'bert-large-uncased': {'do_lower_case': True}, 'bert-base-cased': {'do_lower_case': False}, 'bert-large-cased': {'do_lower_case': False}, 'bert-base-multilingual-uncased': {'do_lower_case': True}, 'bert-base-multilingual-cased': {'do_lower_case': False}, 'bert-base-chinese': {'do_lower_case': False}, 'bert-base-german-cased': {'do_lower_case': False}, 'bert-large-uncased-whole-word-masking': {'do_lower_case': True}, 'bert-large-cased-whole-word-masking': {'do_lower_case': False}, 'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True}, 'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False}, 'bert-base-cased-finetuned-mrpc': {'do_lower_case': False}, 'bert-base-german-dbmdz-cased': {'do_lower_case': False}, 'bert-base-german-dbmdz-uncased': {'do_lower_case': True}, 'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False}, 'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True}, 'wietsedv/bert-base-dutch-cased': {'do_lower_case': False}, } class A ( _a ): lowercase_ = VOCAB_FILES_NAMES lowercase_ = PRETRAINED_VOCAB_FILES_MAP lowercase_ = PRETRAINED_INIT_CONFIGURATION lowercase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase_ = BertTokenizer def __init__( self : str , lowerCAmelCase_ : Union[str, Any]=None , lowerCAmelCase_ : str=None , lowerCAmelCase_ : List[Any]=True , lowerCAmelCase_ : Any="[UNK]" , lowerCAmelCase_ : Union[str, Any]="[SEP]" , lowerCAmelCase_ : Tuple="[PAD]" , lowerCAmelCase_ : Tuple="[CLS]" , lowerCAmelCase_ : Optional[int]="[MASK]" , lowerCAmelCase_ : Dict=True , lowerCAmelCase_ : Union[str, Any]=None , **lowerCAmelCase_ : Union[str, Any] , ) -> Union[str, Any]: """simple docstring""" super().__init__( lowerCAmelCase_ , tokenizer_file=lowerCAmelCase_ , do_lower_case=lowerCAmelCase_ , unk_token=lowerCAmelCase_ , sep_token=lowerCAmelCase_ , pad_token=lowerCAmelCase_ , cls_token=lowerCAmelCase_ , mask_token=lowerCAmelCase_ , tokenize_chinese_chars=lowerCAmelCase_ , strip_accents=lowerCAmelCase_ , **lowerCAmelCase_ , ) _a = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get('''lowercase''' , lowerCAmelCase_ ) != do_lower_case or normalizer_state.get('''strip_accents''' , lowerCAmelCase_ ) != strip_accents or normalizer_state.get('''handle_chinese_chars''' , lowerCAmelCase_ ) != tokenize_chinese_chars ): _a = getattr(lowerCAmelCase_ , normalizer_state.pop('''type''' ) ) _a = do_lower_case _a = strip_accents _a = tokenize_chinese_chars _a = normalizer_class(**lowerCAmelCase_ ) _a = do_lower_case def __lowerCAmelCase ( self : str , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int=None ) -> List[str]: """simple docstring""" _a = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def __lowerCAmelCase ( self : Any , lowerCAmelCase_ : List[int] , lowerCAmelCase_ : Optional[List[int]] = None ) -> List[int]: """simple docstring""" _a = [self.sep_token_id] _a = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def __lowerCAmelCase ( self : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : Optional[str] = None ) -> Tuple[str]: """simple docstring""" _a = self._tokenizer.model.save(lowerCAmelCase_ , name=lowerCAmelCase_ ) return tuple(lowerCAmelCase_ )
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import os def a__ ( __UpperCamelCase = "input.txt" ): with open(os.path.join(os.path.dirname(__UpperCamelCase ) , __UpperCamelCase ) ) as input_file: SCREAMING_SNAKE_CASE_ = [ [int(__UpperCamelCase ) for element in line.split("," )] for line in input_file.readlines() ] SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) SCREAMING_SNAKE_CASE_ = len(matrix[0] ) SCREAMING_SNAKE_CASE_ = [[-1 for _ in range(__UpperCamelCase )] for _ in range(__UpperCamelCase )] for i in range(__UpperCamelCase ): SCREAMING_SNAKE_CASE_ = matrix[i][0] for j in range(1 , __UpperCamelCase ): for i in range(__UpperCamelCase ): SCREAMING_SNAKE_CASE_ = minimal_path_sums[i][j - 1] + matrix[i][j] for i in range(1 , __UpperCamelCase ): SCREAMING_SNAKE_CASE_ = min( minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] ) for i in range(rows - 2 , -1 , -1 ): SCREAMING_SNAKE_CASE_ = min( minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j] ) return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums ) if __name__ == "__main__": print(f"{solution() = }")
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def a__ ( __UpperCamelCase ): if not head: return True # split the list to two parts SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = head.next, head while fast and fast.next: SCREAMING_SNAKE_CASE_ = fast.next.next SCREAMING_SNAKE_CASE_ = slow.next SCREAMING_SNAKE_CASE_ = slow.next SCREAMING_SNAKE_CASE_ = None # Don't forget here! But forget still works! # reverse the second part SCREAMING_SNAKE_CASE_ = None while second: SCREAMING_SNAKE_CASE_ = second.next SCREAMING_SNAKE_CASE_ = node SCREAMING_SNAKE_CASE_ = second SCREAMING_SNAKE_CASE_ = nxt # compare two parts # second part has the same or one less node while node: if node.val != head.val: return False SCREAMING_SNAKE_CASE_ = node.next SCREAMING_SNAKE_CASE_ = head.next return True def a__ ( __UpperCamelCase ): if not head or not head.next: return True # 1. Get the midpoint (slow) SCREAMING_SNAKE_CASE_ = SCREAMING_SNAKE_CASE_ = SCREAMING_SNAKE_CASE_ = head while fast and fast.next: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = fast.next.next, slow.next # 2. Push the second half into the stack SCREAMING_SNAKE_CASE_ = [slow.val] while slow.next: SCREAMING_SNAKE_CASE_ = slow.next stack.append(slow.val ) # 3. Comparison while stack: if stack.pop() != cur.val: return False SCREAMING_SNAKE_CASE_ = cur.next return True def a__ ( __UpperCamelCase ): if not head or not head.next: return True SCREAMING_SNAKE_CASE_ = {} SCREAMING_SNAKE_CASE_ = 0 while head: if head.val in d: d[head.val].append(__UpperCamelCase ) else: SCREAMING_SNAKE_CASE_ = [pos] SCREAMING_SNAKE_CASE_ = head.next pos += 1 SCREAMING_SNAKE_CASE_ = pos - 1 SCREAMING_SNAKE_CASE_ = 0 for v in d.values(): if len(__UpperCamelCase ) % 2 != 0: middle += 1 else: SCREAMING_SNAKE_CASE_ = 0 for i in range(0 , len(__UpperCamelCase ) ): if v[i] + v[len(__UpperCamelCase ) - 1 - step] != checksum: return False step += 1 if middle > 1: return False return True
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1
def A (__A : list , __A : list ) -> float: """simple docstring""" _validate_point(__A ) _validate_point(__A ) if len(__A ) != len(__A ): raise ValueError('''Both points must be in the same n-dimensional space''' ) return float(sum(abs(a - b ) for a, b in zip(__A , __A ) ) ) def A (__A : list[float] ) -> None: """simple docstring""" if point: if isinstance(__A , __A ): for item in point: if not isinstance(__A , (int, float) ): UpperCAmelCase_ = ( '''Expected a list of numbers as input, found ''' F"""{type(__A ).__name__}""" ) raise TypeError(__A ) else: UpperCAmelCase_ = F"""Expected a list of numbers as input, found {type(__A ).__name__}""" raise TypeError(__A ) else: raise ValueError('''Missing an input''' ) def A (__A : list , __A : list ) -> float: """simple docstring""" _validate_point(__A ) _validate_point(__A ) if len(__A ) != len(__A ): raise ValueError('''Both points must be in the same n-dimensional space''' ) return float(sum(abs(x - y ) for x, y in zip(__A , __A ) ) ) if __name__ == "__main__": import doctest doctest.testmod()
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import unittest import numpy as np import torch from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad class __snake_case ( unittest.TestCase ): def lowerCamelCase ( self : Dict): """simple docstring""" UpperCAmelCase_ = 10 def lowerCamelCase ( self : Tuple): """simple docstring""" UpperCAmelCase_ = [1, 2, 3, 4] UpperCAmelCase_ = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0] self.assertEqual(truncate_or_pad(_snake_case , self.block_size , 0) , _snake_case) def lowerCamelCase ( self : Optional[int]): """simple docstring""" UpperCAmelCase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] UpperCAmelCase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(truncate_or_pad(_snake_case , self.block_size , 0) , _snake_case) def lowerCamelCase ( self : int): """simple docstring""" UpperCAmelCase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] UpperCAmelCase_ = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] self.assertEqual(truncate_or_pad(_snake_case , self.block_size , 0) , _snake_case) def lowerCamelCase ( self : List[str]): """simple docstring""" UpperCAmelCase_ = '''It was the year of Our Lord one thousand seven hundred and seventy-five.\n\nSpiritual revelations were conceded to England at that favoured period, as at this.''' UpperCAmelCase_ , UpperCAmelCase_ = process_story(_snake_case) self.assertEqual(_snake_case , []) def lowerCamelCase ( self : Optional[Any]): """simple docstring""" UpperCAmelCase_ = '''''' UpperCAmelCase_ , UpperCAmelCase_ = process_story(_snake_case) self.assertEqual(_snake_case , []) self.assertEqual(_snake_case , []) def lowerCamelCase ( self : Dict): """simple docstring""" UpperCAmelCase_ = ( '''It was the year of Our Lord one thousand seven hundred and ''' '''seventy-five\n\nSpiritual revelations were conceded to England ''' '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times''' ) UpperCAmelCase_ , UpperCAmelCase_ = process_story(_snake_case) UpperCAmelCase_ = [ '''It was the year of Our Lord one thousand seven hundred and seventy-five.''', '''Spiritual revelations were conceded to England at that favoured period, as at this.''', ] self.assertEqual(_snake_case , _snake_case) UpperCAmelCase_ = ['''It was the best of times.'''] self.assertEqual(_snake_case , _snake_case) def lowerCamelCase ( self : str): """simple docstring""" UpperCAmelCase_ = torch.tensor([1, 2, 3, 4]) UpperCAmelCase_ = torch.tensor([1, 1, 1, 1]) np.testing.assert_array_equal(build_mask(_snake_case , 0).numpy() , expected.numpy()) def lowerCamelCase ( self : Dict): """simple docstring""" UpperCAmelCase_ = torch.tensor([1, 2, 3, 4, 23, 23, 23]) UpperCAmelCase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0]) np.testing.assert_array_equal(build_mask(_snake_case , 23).numpy() , expected.numpy()) def lowerCamelCase ( self : int): """simple docstring""" UpperCAmelCase_ = torch.tensor([8, 2, 3, 4, 1, 1, 1]) UpperCAmelCase_ = torch.tensor([1, 1, 1, 1, 0, 0, 0]) np.testing.assert_array_equal(build_mask(_snake_case , 1).numpy() , expected.numpy()) def lowerCamelCase ( self : List[Any]): """simple docstring""" UpperCAmelCase_ = 101 UpperCAmelCase_ = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]) UpperCAmelCase_ = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]) UpperCAmelCase_ = compute_token_type_ids(_snake_case , _snake_case) np.testing.assert_array_equal(_snake_case , _snake_case)
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1
from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __snake_case = { """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""], """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __snake_case = ["""MaskFormerFeatureExtractor"""] __snake_case = ["""MaskFormerImageProcessor"""] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __snake_case = [ """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""", """MaskFormerForInstanceSegmentation""", """MaskFormerModel""", """MaskFormerPreTrainedModel""", ] __snake_case = [ """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 __snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
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'''simple docstring''' from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available UpperCamelCase_ = { "configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"], "configuration_maskformer_swin": ["MaskFormerSwinConfig"], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase_ = ["MaskFormerFeatureExtractor"] UpperCamelCase_ = ["MaskFormerImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase_ = [ "MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "MaskFormerForInstanceSegmentation", "MaskFormerModel", "MaskFormerPreTrainedModel", ] UpperCamelCase_ = [ "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 UpperCamelCase_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
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0
"""simple docstring""" from __future__ import annotations def lowerCAmelCase (__UpperCamelCase : int , __UpperCamelCase : int ): """simple docstring""" __UpperCamelCase =[] create_all_state(1 , __UpperCamelCase , __UpperCamelCase , [] , __UpperCamelCase ) return result def lowerCAmelCase (__UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : int , __UpperCamelCase : list[int] , __UpperCamelCase : list[list[int]] , ): """simple docstring""" if level == 0: total_list.append(current_list[:] ) return for i in range(__UpperCamelCase , total_number - level + 2 ): current_list.append(__UpperCamelCase ) create_all_state(i + 1 , __UpperCamelCase , level - 1 , __UpperCamelCase , __UpperCamelCase ) current_list.pop() def lowerCAmelCase (__UpperCamelCase : list[list[int]] ): """simple docstring""" for i in total_list: print(*__UpperCamelCase ) if __name__ == "__main__": __lowercase = 4 __lowercase = 2 __lowercase = generate_all_combinations(n, k) print_all_state(total_list)
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"""simple docstring""" import json from typing import TYPE_CHECKING, List, Optional, Tuple from tokenizers import pre_tokenizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation __lowercase = logging.get_logger(__name__) __lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''} __lowercase = { '''tokenizer_file''': { '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''', }, } __lowercase = { '''gpt-neox-20b''': 2_048, } class _lowercase ( __a ): """simple docstring""" lowercase__ = VOCAB_FILES_NAMES lowercase__ = PRETRAINED_VOCAB_FILES_MAP lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase__ = ['''input_ids''', '''attention_mask'''] def __init__( self : Optional[Any] , UpperCamelCase__ : str=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Tuple=None , UpperCamelCase__ : Optional[int]="<|endoftext|>" , UpperCamelCase__ : Optional[int]="<|endoftext|>" , UpperCamelCase__ : Optional[int]="<|endoftext|>" , UpperCamelCase__ : Optional[int]=False , **UpperCamelCase__ : str , ) -> Union[str, Any]: '''simple docstring''' super().__init__( UpperCamelCase__ , UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , unk_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , add_prefix_space=UpperCamelCase__ , **UpperCamelCase__ , ) __UpperCamelCase =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() ) if pre_tok_state.get('''add_prefix_space''' , UpperCamelCase__ ) != add_prefix_space: __UpperCamelCase =getattr(UpperCamelCase__ , pre_tok_state.pop('''type''' ) ) __UpperCamelCase =add_prefix_space __UpperCamelCase =pre_tok_class(**UpperCamelCase__ ) __UpperCamelCase =add_prefix_space def UpperCAmelCase_ ( self : List[str] , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ) -> Tuple[str]: '''simple docstring''' __UpperCamelCase =self._tokenizer.model.save(UpperCamelCase__ , name=UpperCamelCase__ ) return tuple(UpperCamelCase__ ) def UpperCAmelCase_ ( self : int , UpperCamelCase__ : "Conversation" ) -> List[int]: '''simple docstring''' __UpperCamelCase =[] for is_user, text in conversation.iter_texts(): input_ids.extend(self.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ ) + [self.eos_token_id] ) if len(UpperCamelCase__ ) > self.model_max_length: __UpperCamelCase =input_ids[-self.model_max_length :] return input_ids
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'''simple docstring''' from typing import Optional, Tuple, Union import tensorflow as tf from ...activations_tf import ACTaFN from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward from ...modeling_tf_outputs import ( TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFSequenceClassifierOutput, ) from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs from ...tf_utils import shape_list from ...utils import logging from .configuration_regnet import RegNetConfig A__ : Any =logging.get_logger(__name__) # General docstring A__ : Optional[int] ='''RegNetConfig''' # Base docstring A__ : Union[str, Any] ='''facebook/regnet-y-040''' A__ : Optional[Any] =[1, 10_88, 7, 7] # Image classification docstring A__ : int ='''facebook/regnet-y-040''' A__ : List[Any] ='''tabby, tabby cat''' A__ : Any =[ '''facebook/regnet-y-040''', # See all regnet models at https://huggingface.co/models?filter=regnet ] class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Dict , __snake_case : List[Any] , __snake_case : Optional[Any] = 3 , __snake_case : Optional[int] = 1 , __snake_case : str = 1 , __snake_case : Optional[Any] = "relu" , **__snake_case : Optional[Any] , ) -> Optional[Any]: super().__init__(**__snake_case ) # The padding and conv has been verified in # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb _lowerCAmelCase = tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 ) _lowerCAmelCase = tf.keras.layers.ConvaD( filters=__snake_case , kernel_size=__snake_case , strides=__snake_case , padding="""VALID""" , groups=__snake_case , use_bias=__snake_case , name="""convolution""" , ) _lowerCAmelCase = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="""normalization""" ) _lowerCAmelCase = ACTaFN[activation] if activation is not None else tf.identity def lowercase__ ( self : Dict , __snake_case : Tuple ) -> str: _lowerCAmelCase = self.convolution(self.padding(__snake_case ) ) _lowerCAmelCase = self.normalization(__snake_case ) _lowerCAmelCase = self.activation(__snake_case ) return hidden_state class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Tuple , __snake_case : Optional[Any] , **__snake_case : Optional[int] ) -> Optional[int]: super().__init__(**__snake_case ) _lowerCAmelCase = config.num_channels _lowerCAmelCase = TFRegNetConvLayer( out_channels=config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act , name="""embedder""" , ) def lowercase__ ( self : Any , __snake_case : str ) -> Union[str, Any]: _lowerCAmelCase = shape_list(__snake_case )[1] if tf.executing_eagerly() and num_channels != self.num_channels: raise ValueError( """Make sure that the channel dimension of the pixel values match with the one set in the configuration.""" ) # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format. # So change the input format from `NCHW` to `NHWC`. # shape = (batch_size, in_height, in_width, in_channels=num_channels) _lowerCAmelCase = tf.transpose(__snake_case , perm=(0, 2, 3, 1) ) _lowerCAmelCase = self.embedder(__snake_case ) return hidden_state class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Dict , __snake_case : List[str] , __snake_case : int = 2 , **__snake_case : Any ) -> List[str]: super().__init__(**__snake_case ) _lowerCAmelCase = tf.keras.layers.ConvaD( filters=__snake_case , kernel_size=1 , strides=__snake_case , use_bias=__snake_case , name="""convolution""" ) _lowerCAmelCase = tf.keras.layers.BatchNormalization(epsilon=1E-5 , momentum=0.9 , name="""normalization""" ) def lowercase__ ( self : Tuple , __snake_case : Union[str, Any] , __snake_case : List[str] = False ) -> tf.Tensor: return self.normalization(self.convolution(__snake_case ) , training=__snake_case ) class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Tuple , __snake_case : Optional[Any] , __snake_case : Optional[int] , **__snake_case : Tuple ) -> str: super().__init__(**__snake_case ) _lowerCAmelCase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case , name="""pooler""" ) _lowerCAmelCase = [ tf.keras.layers.ConvaD(filters=__snake_case , kernel_size=1 , activation="""relu""" , name="""attention.0""" ), tf.keras.layers.ConvaD(filters=__snake_case , kernel_size=1 , activation="""sigmoid""" , name="""attention.2""" ), ] def lowercase__ ( self : int , __snake_case : Optional[Any] ) -> Dict: # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels] _lowerCAmelCase = self.pooler(__snake_case ) for layer_module in self.attention: _lowerCAmelCase = layer_module(__snake_case ) _lowerCAmelCase = hidden_state * pooled return hidden_state class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Any , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Tuple = 1 , **__snake_case : Optional[Any] ) -> int: super().__init__(**__snake_case ) _lowerCAmelCase = in_channels != out_channels or stride != 1 _lowerCAmelCase = max(1 , out_channels // config.groups_width ) _lowerCAmelCase = ( TFRegNetShortCut(__snake_case , stride=__snake_case , name="""shortcut""" ) if should_apply_shortcut else tf.keras.layers.Activation("""linear""" , name="""shortcut""" ) ) # `self.layers` instead of `self.layer` because that is a reserved argument. _lowerCAmelCase = [ TFRegNetConvLayer(__snake_case , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ), TFRegNetConvLayer( __snake_case , stride=__snake_case , groups=__snake_case , activation=config.hidden_act , name="""layer.1""" ), TFRegNetConvLayer(__snake_case , kernel_size=1 , activation=__snake_case , name="""layer.2""" ), ] _lowerCAmelCase = ACTaFN[config.hidden_act] def lowercase__ ( self : int , __snake_case : List[Any] ) -> Union[str, Any]: _lowerCAmelCase = hidden_state for layer_module in self.layers: _lowerCAmelCase = layer_module(__snake_case ) _lowerCAmelCase = self.shortcut(__snake_case ) hidden_state += residual _lowerCAmelCase = self.activation(__snake_case ) return hidden_state class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Optional[int] , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict = 1 , **__snake_case : List[Any] ) -> Tuple: super().__init__(**__snake_case ) _lowerCAmelCase = in_channels != out_channels or stride != 1 _lowerCAmelCase = max(1 , out_channels // config.groups_width ) _lowerCAmelCase = ( TFRegNetShortCut(__snake_case , stride=__snake_case , name="""shortcut""" ) if should_apply_shortcut else tf.keras.layers.Activation("""linear""" , name="""shortcut""" ) ) _lowerCAmelCase = [ TFRegNetConvLayer(__snake_case , kernel_size=1 , activation=config.hidden_act , name="""layer.0""" ), TFRegNetConvLayer( __snake_case , stride=__snake_case , groups=__snake_case , activation=config.hidden_act , name="""layer.1""" ), TFRegNetSELayer(__snake_case , reduced_channels=int(round(in_channels / 4 ) ) , name="""layer.2""" ), TFRegNetConvLayer(__snake_case , kernel_size=1 , activation=__snake_case , name="""layer.3""" ), ] _lowerCAmelCase = ACTaFN[config.hidden_act] def lowercase__ ( self : Dict , __snake_case : int ) -> Optional[int]: _lowerCAmelCase = hidden_state for layer_module in self.layers: _lowerCAmelCase = layer_module(__snake_case ) _lowerCAmelCase = self.shortcut(__snake_case ) hidden_state += residual _lowerCAmelCase = self.activation(__snake_case ) return hidden_state class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Any , __snake_case : List[Any] , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Optional[Any] = 2 , __snake_case : List[Any] = 2 , **__snake_case : Tuple ) -> int: super().__init__(**__snake_case ) _lowerCAmelCase = TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer _lowerCAmelCase = [ # downsampling is done in the first layer with stride of 2 layer(__snake_case , __snake_case , __snake_case , stride=__snake_case , name="""layers.0""" ), *[layer(__snake_case , __snake_case , __snake_case , name=f"layers.{i+1}" ) for i in range(depth - 1 )], ] def lowercase__ ( self : Tuple , __snake_case : Tuple ) -> Optional[Any]: for layer_module in self.layers: _lowerCAmelCase = layer_module(__snake_case ) return hidden_state class UpperCAmelCase ( tf.keras.layers.Layer ): def __init__( self : Union[str, Any] , __snake_case : List[Any] , **__snake_case : Tuple ) -> Tuple: super().__init__(**__snake_case ) _lowerCAmelCase = [] # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input self.stages.append( TFRegNetStage( __snake_case , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , name="""stages.0""" , ) ) _lowerCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] ) for i, ((in_channels, out_channels), depth) in enumerate(zip(__snake_case , config.depths[1:] ) ): self.stages.append(TFRegNetStage(__snake_case , __snake_case , __snake_case , depth=__snake_case , name=f"stages.{i+1}" ) ) def lowercase__ ( self : Dict , __snake_case : Optional[Any] , __snake_case : int = False , __snake_case : int = True ) -> TFBaseModelOutputWithNoAttention: _lowerCAmelCase = () if output_hidden_states else None for stage_module in self.stages: if output_hidden_states: _lowerCAmelCase = hidden_states + (hidden_state,) _lowerCAmelCase = stage_module(__snake_case ) if output_hidden_states: _lowerCAmelCase = hidden_states + (hidden_state,) if not return_dict: return tuple(v for v in [hidden_state, hidden_states] if v is not None ) return TFBaseModelOutputWithNoAttention(last_hidden_state=__snake_case , hidden_states=__snake_case ) @keras_serializable class UpperCAmelCase ( tf.keras.layers.Layer ): _lowercase: str = RegNetConfig def __init__( self : Optional[Any] , __snake_case : Any , **__snake_case : List[Any] ) -> Any: super().__init__(**__snake_case ) _lowerCAmelCase = config _lowerCAmelCase = TFRegNetEmbeddings(__snake_case , name="""embedder""" ) _lowerCAmelCase = TFRegNetEncoder(__snake_case , name="""encoder""" ) _lowerCAmelCase = tf.keras.layers.GlobalAveragePoolingaD(keepdims=__snake_case , name="""pooler""" ) @unpack_inputs def lowercase__ ( self : int , __snake_case : List[str] , __snake_case : Any = None , __snake_case : List[Any] = None , __snake_case : List[str] = False , ) -> TFBaseModelOutputWithPoolingAndNoAttention: _lowerCAmelCase = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) _lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict _lowerCAmelCase = self.embedder(__snake_case , training=__snake_case ) _lowerCAmelCase = self.encoder( __snake_case , output_hidden_states=__snake_case , return_dict=__snake_case , training=__snake_case ) _lowerCAmelCase = encoder_outputs[0] _lowerCAmelCase = self.pooler(__snake_case ) # Change to NCHW output format have uniformity in the modules _lowerCAmelCase = tf.transpose(__snake_case , perm=(0, 3, 1, 2) ) _lowerCAmelCase = tf.transpose(__snake_case , perm=(0, 3, 1, 2) ) # Change the other hidden state outputs to NCHW as well if output_hidden_states: _lowerCAmelCase = tuple([tf.transpose(__snake_case , perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] ) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return TFBaseModelOutputWithPoolingAndNoAttention( last_hidden_state=__snake_case , pooler_output=__snake_case , hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states , ) class UpperCAmelCase ( snake_case_ ): _lowercase: List[str] = RegNetConfig _lowercase: str = '''regnet''' _lowercase: Optional[int] = '''pixel_values''' @property def lowercase__ ( self : str ) -> str: return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) , dtype=tf.floataa )} A__ : Dict =r''' Parameters: This model is a Tensorflow [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and behavior. config ([`RegNetConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights. ''' A__ : Any =r''' Args: pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConveNextImageProcessor.__call__`] for details. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. ''' @add_start_docstrings( '''The bare RegNet model outputting raw features without any specific head on top.''' , snake_case_ , ) class UpperCAmelCase ( snake_case_ ): def __init__( self : List[Any] , __snake_case : List[Any] , *__snake_case : str , **__snake_case : Optional[int] ) -> Any: super().__init__(__snake_case , *__snake_case , **__snake_case ) _lowerCAmelCase = TFRegNetMainLayer(__snake_case , name="""regnet""" ) @unpack_inputs @add_start_docstrings_to_model_forward(__snake_case ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC , output_type=__snake_case , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , ) def lowercase__ ( self : Dict , __snake_case : Optional[Any] , __snake_case : Optional[int] = None , __snake_case : Optional[int] = None , __snake_case : List[str]=False , ) -> Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]: _lowerCAmelCase = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) _lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict _lowerCAmelCase = self.regnet( pixel_values=__snake_case , output_hidden_states=__snake_case , return_dict=__snake_case , training=__snake_case , ) if not return_dict: return (outputs[0],) + outputs[1:] return TFBaseModelOutputWithPoolingAndNoAttention( last_hidden_state=outputs.last_hidden_state , pooler_output=outputs.pooler_output , hidden_states=outputs.hidden_states , ) @add_start_docstrings( ''' RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for ImageNet. ''' , snake_case_ , ) class UpperCAmelCase ( snake_case_ , snake_case_ ): def __init__( self : str , __snake_case : Tuple , *__snake_case : int , **__snake_case : Optional[int] ) -> str: super().__init__(__snake_case , *__snake_case , **__snake_case ) _lowerCAmelCase = config.num_labels _lowerCAmelCase = TFRegNetMainLayer(__snake_case , name="""regnet""" ) # classification head _lowerCAmelCase = [ tf.keras.layers.Flatten(), tf.keras.layers.Dense(config.num_labels , name="""classifier.1""" ) if config.num_labels > 0 else tf.identity, ] @unpack_inputs @add_start_docstrings_to_model_forward(__snake_case ) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__snake_case , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , ) def lowercase__ ( self : Tuple , __snake_case : Optional[int] = None , __snake_case : int = None , __snake_case : Tuple = None , __snake_case : int = None , __snake_case : int=False , ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: _lowerCAmelCase = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) _lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict _lowerCAmelCase = self.regnet( __snake_case , output_hidden_states=__snake_case , return_dict=__snake_case , training=__snake_case ) _lowerCAmelCase = outputs.pooler_output if return_dict else outputs[1] _lowerCAmelCase = self.classifier[0](__snake_case ) _lowerCAmelCase = self.classifier[1](__snake_case ) _lowerCAmelCase = None if labels is None else self.hf_compute_loss(labels=__snake_case , logits=__snake_case ) if not return_dict: _lowerCAmelCase = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TFSequenceClassifierOutput(loss=__snake_case , logits=__snake_case , hidden_states=outputs.hidden_states )
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'''simple docstring''' import collections.abc from typing import Optional, Tuple, Union import torch import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ...activations import ACTaFN from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention from ...modeling_utils import PreTrainedModel from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging from .configuration_poolformer import PoolFormerConfig lowercase_ = logging.get_logger(__name__) # General docstring lowercase_ = """PoolFormerConfig""" # Base docstring lowercase_ = """sail/poolformer_s12""" lowercase_ = [1, 512, 7, 7] # Image classification docstring lowercase_ = """sail/poolformer_s12""" lowercase_ = """tabby, tabby cat""" lowercase_ = [ """sail/poolformer_s12""", # See all PoolFormer models at https://huggingface.co/models?filter=poolformer ] def lowerCamelCase ( __lowerCamelCase : List[Any] , __lowerCamelCase : float = 0.0 , __lowerCamelCase : bool = False ) ->int: if drop_prob == 0.0 or not training: return input _SCREAMING_SNAKE_CASE = 1 - drop_prob _SCREAMING_SNAKE_CASE = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets _SCREAMING_SNAKE_CASE = keep_prob + torch.rand(__lowerCamelCase , dtype=input.dtype , device=input.device ) random_tensor.floor_() # binarize _SCREAMING_SNAKE_CASE = input.div(__lowerCamelCase ) * random_tensor return output class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A = None ) -> None: super().__init__() _SCREAMING_SNAKE_CASE = drop_prob def snake_case_( self , A ) -> torch.Tensor: return drop_path(A , self.drop_prob , self.training ) def snake_case_( self ) -> str: return "p={}".format(self.drop_prob ) class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A , A , A , A , A , A=None ) -> Union[str, Any]: super().__init__() _SCREAMING_SNAKE_CASE = patch_size if isinstance(A , collections.abc.Iterable ) else (patch_size, patch_size) _SCREAMING_SNAKE_CASE = stride if isinstance(A , collections.abc.Iterable ) else (stride, stride) _SCREAMING_SNAKE_CASE = padding if isinstance(A , collections.abc.Iterable ) else (padding, padding) _SCREAMING_SNAKE_CASE = nn.Convad(A , A , kernel_size=A , stride=A , padding=A ) _SCREAMING_SNAKE_CASE = norm_layer(A ) if norm_layer else nn.Identity() def snake_case_( self , A ) -> Optional[Any]: _SCREAMING_SNAKE_CASE = self.projection(A ) _SCREAMING_SNAKE_CASE = self.norm(A ) return embeddings class a_ ( nn.GroupNorm ): '''simple docstring''' def __init__( self , A , **A ) -> Union[str, Any]: super().__init__(1 , A , **A ) class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A ) -> Union[str, Any]: super().__init__() _SCREAMING_SNAKE_CASE = nn.AvgPoolad(A , stride=1 , padding=pool_size // 2 , count_include_pad=A ) def snake_case_( self , A ) -> Union[str, Any]: return self.pool(A ) - hidden_states class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A , A , A , A ) -> List[Any]: super().__init__() _SCREAMING_SNAKE_CASE = nn.Convad(A , A , 1 ) _SCREAMING_SNAKE_CASE = nn.Convad(A , A , 1 ) _SCREAMING_SNAKE_CASE = PoolFormerDropPath(A ) if isinstance(config.hidden_act , A ): _SCREAMING_SNAKE_CASE = ACTaFN[config.hidden_act] else: _SCREAMING_SNAKE_CASE = config.hidden_act def snake_case_( self , A ) -> Optional[int]: _SCREAMING_SNAKE_CASE = self.conva(A ) _SCREAMING_SNAKE_CASE = self.act_fn(A ) _SCREAMING_SNAKE_CASE = self.drop(A ) _SCREAMING_SNAKE_CASE = self.conva(A ) _SCREAMING_SNAKE_CASE = self.drop(A ) return hidden_states class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A , A , A , A , A , A ) -> Union[str, Any]: super().__init__() _SCREAMING_SNAKE_CASE = PoolFormerPooling(A ) _SCREAMING_SNAKE_CASE = PoolFormerOutput(A , A , A , A ) _SCREAMING_SNAKE_CASE = PoolFormerGroupNorm(A ) _SCREAMING_SNAKE_CASE = PoolFormerGroupNorm(A ) # Useful for training neural nets _SCREAMING_SNAKE_CASE = PoolFormerDropPath(A ) if drop_path > 0.0 else nn.Identity() _SCREAMING_SNAKE_CASE = config.use_layer_scale if config.use_layer_scale: _SCREAMING_SNAKE_CASE = nn.Parameter( config.layer_scale_init_value * torch.ones((A) ) , requires_grad=A ) _SCREAMING_SNAKE_CASE = nn.Parameter( config.layer_scale_init_value * torch.ones((A) ) , requires_grad=A ) def snake_case_( self , A ) -> Optional[Any]: if self.use_layer_scale: _SCREAMING_SNAKE_CASE = self.pooling(self.before_norm(A ) ) _SCREAMING_SNAKE_CASE = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * pooling_output # First residual connection _SCREAMING_SNAKE_CASE = hidden_states + self.drop_path(A ) _SCREAMING_SNAKE_CASE = () _SCREAMING_SNAKE_CASE = self.output(self.after_norm(A ) ) _SCREAMING_SNAKE_CASE = self.layer_scale_a.unsqueeze(-1 ).unsqueeze(-1 ) * layer_output # Second residual connection _SCREAMING_SNAKE_CASE = hidden_states + self.drop_path(A ) _SCREAMING_SNAKE_CASE = (output,) + outputs return outputs else: _SCREAMING_SNAKE_CASE = self.drop_path(self.pooling(self.before_norm(A ) ) ) # First residual connection _SCREAMING_SNAKE_CASE = pooling_output + hidden_states _SCREAMING_SNAKE_CASE = () # Second residual connection inside the PoolFormerOutput block _SCREAMING_SNAKE_CASE = self.drop_path(self.output(self.after_norm(A ) ) ) _SCREAMING_SNAKE_CASE = hidden_states + layer_output _SCREAMING_SNAKE_CASE = (output,) + outputs return outputs class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A ) -> Any: super().__init__() _SCREAMING_SNAKE_CASE = config # stochastic depth decay rule _SCREAMING_SNAKE_CASE = [x.item() for x in torch.linspace(0 , config.drop_path_rate , sum(config.depths ) )] # patch embeddings _SCREAMING_SNAKE_CASE = [] for i in range(config.num_encoder_blocks ): embeddings.append( PoolFormerEmbeddings( patch_size=config.patch_sizes[i] , stride=config.strides[i] , padding=config.padding[i] , num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] , hidden_size=config.hidden_sizes[i] , ) ) _SCREAMING_SNAKE_CASE = nn.ModuleList(A ) # Transformer blocks _SCREAMING_SNAKE_CASE = [] _SCREAMING_SNAKE_CASE = 0 for i in range(config.num_encoder_blocks ): # each block consists of layers _SCREAMING_SNAKE_CASE = [] if i != 0: cur += config.depths[i - 1] for j in range(config.depths[i] ): layers.append( PoolFormerLayer( A , num_channels=config.hidden_sizes[i] , pool_size=config.pool_size , hidden_size=config.hidden_sizes[i] , intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio ) , drop_path=dpr[cur + j] , ) ) blocks.append(nn.ModuleList(A ) ) _SCREAMING_SNAKE_CASE = nn.ModuleList(A ) def snake_case_( self , A , A=False , A=True ) -> List[Any]: _SCREAMING_SNAKE_CASE = () if output_hidden_states else None _SCREAMING_SNAKE_CASE = pixel_values for idx, layers in enumerate(zip(self.patch_embeddings , self.block ) ): _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = layers # Get patch embeddings from hidden_states _SCREAMING_SNAKE_CASE = embedding_layer(A ) # Send the embeddings through the blocks for _, blk in enumerate(A ): _SCREAMING_SNAKE_CASE = blk(A ) _SCREAMING_SNAKE_CASE = layer_outputs[0] if output_hidden_states: _SCREAMING_SNAKE_CASE = all_hidden_states + (hidden_states,) if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states] if v is not None ) return BaseModelOutputWithNoAttention(last_hidden_state=A , hidden_states=A ) class a_ ( snake_case_ ): '''simple docstring''' UpperCamelCase = PoolFormerConfig UpperCamelCase = '''poolformer''' UpperCamelCase = '''pixel_values''' UpperCamelCase = True def snake_case_( self , A ) -> int: if isinstance(A , (nn.Linear, nn.Convad) ): module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range ) if module.bias is not None: module.bias.data.zero_() elif isinstance(A , nn.LayerNorm ): module.bias.data.zero_() module.weight.data.fill_(1.0 ) def snake_case_( self , A , A=False ) -> Dict: if isinstance(A , A ): _SCREAMING_SNAKE_CASE = value lowercase_ = R""" This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ lowercase_ = R""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`PoolFormerImageProcessor.__call__`] for details. """ @add_start_docstrings( '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.''' , snake_case_ , ) class a_ ( snake_case_ ): '''simple docstring''' def __init__( self , A ) -> int: super().__init__(A ) _SCREAMING_SNAKE_CASE = config _SCREAMING_SNAKE_CASE = PoolFormerEncoder(A ) # Initialize weights and apply final processing self.post_init() def snake_case_( self ) -> Any: return self.embeddings.patch_embeddings @add_start_docstrings_to_model_forward(A ) @add_code_sample_docstrings( checkpoint=_CHECKPOINT_FOR_DOC , output_type=A , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , ) def snake_case_( self , A = None , A = None , A = None , ) -> Union[Tuple, BaseModelOutputWithNoAttention]: _SCREAMING_SNAKE_CASE = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) _SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict if pixel_values is None: raise ValueError("""You have to specify pixel_values""" ) _SCREAMING_SNAKE_CASE = self.encoder( A , output_hidden_states=A , return_dict=A , ) _SCREAMING_SNAKE_CASE = encoder_outputs[0] if not return_dict: return (sequence_output, None) + encoder_outputs[1:] return BaseModelOutputWithNoAttention( last_hidden_state=A , hidden_states=encoder_outputs.hidden_states , ) class a_ ( nn.Module ): '''simple docstring''' def __init__( self , A ) -> Dict: super().__init__() _SCREAMING_SNAKE_CASE = nn.Linear(config.hidden_size , config.hidden_size ) def snake_case_( self , A ) -> str: _SCREAMING_SNAKE_CASE = self.dense(A ) return output @add_start_docstrings( ''' PoolFormer Model transformer with an image classification head on top ''' , snake_case_ , ) class a_ ( snake_case_ ): '''simple docstring''' def __init__( self , A ) -> Optional[Any]: super().__init__(A ) _SCREAMING_SNAKE_CASE = config.num_labels _SCREAMING_SNAKE_CASE = PoolFormerModel(A ) # Final norm _SCREAMING_SNAKE_CASE = PoolFormerGroupNorm(config.hidden_sizes[-1] ) # Classifier head _SCREAMING_SNAKE_CASE = ( nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() ) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(A ) @add_code_sample_docstrings( checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=A , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , ) def snake_case_( self , A = None , A = None , A = None , A = None , ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]: _SCREAMING_SNAKE_CASE = return_dict if return_dict is not None else self.config.use_return_dict _SCREAMING_SNAKE_CASE = self.poolformer( A , output_hidden_states=A , return_dict=A , ) _SCREAMING_SNAKE_CASE = outputs[0] _SCREAMING_SNAKE_CASE = self.classifier(self.norm(A ).mean([-2, -1] ) ) _SCREAMING_SNAKE_CASE = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: _SCREAMING_SNAKE_CASE = """regression""" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): _SCREAMING_SNAKE_CASE = """single_label_classification""" else: _SCREAMING_SNAKE_CASE = """multi_label_classification""" if self.config.problem_type == "regression": _SCREAMING_SNAKE_CASE = MSELoss() if self.num_labels == 1: _SCREAMING_SNAKE_CASE = loss_fct(logits.squeeze() , labels.squeeze() ) else: _SCREAMING_SNAKE_CASE = loss_fct(A , A ) elif self.config.problem_type == "single_label_classification": _SCREAMING_SNAKE_CASE = CrossEntropyLoss() _SCREAMING_SNAKE_CASE = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) ) elif self.config.problem_type == "multi_label_classification": _SCREAMING_SNAKE_CASE = BCEWithLogitsLoss() _SCREAMING_SNAKE_CASE = loss_fct(A , A ) if not return_dict: _SCREAMING_SNAKE_CASE = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return ImageClassifierOutputWithNoAttention(loss=A , logits=A , hidden_states=outputs.hidden_states )
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"""simple docstring""" def _lowercase ( __lowerCAmelCase ) -> Dict: SCREAMING_SNAKE_CASE__ : Dict = [] SCREAMING_SNAKE_CASE__ : Optional[Any] = [] SCREAMING_SNAKE_CASE__ : int = { """^""": 3, """*""": 2, """/""": 2, """%""": 2, """+""": 1, """-""": 1, } # Priority of each operator SCREAMING_SNAKE_CASE__ : List[Any] = len(__lowerCAmelCase ) if (len(__lowerCAmelCase ) > 7) else 7 # Print table header for output print( """Symbol""".center(8 ) , """Stack""".center(__lowerCAmelCase ) , """Postfix""".center(__lowerCAmelCase ) , sep=""" | """ , ) print("""-""" * (print_width * 3 + 7) ) for x in infix: if x.isalpha() or x.isdigit(): post_fix.append(__lowerCAmelCase ) # if x is Alphabet / Digit, add it to Postfix elif x == "(": stack.append(__lowerCAmelCase ) # if x is "(" push to Stack elif x == ")": # if x is ")" pop stack until "(" is encountered while stack[-1] != "(": post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix stack.pop() else: if len(__lowerCAmelCase ) == 0: stack.append(__lowerCAmelCase ) # If stack is empty, push x to stack else: # while priority of x is not > priority of element in the stack while len(__lowerCAmelCase ) > 0 and priority[x] <= priority[stack[-1]]: post_fix.append(stack.pop() ) # pop stack & add to Postfix stack.append(__lowerCAmelCase ) # push x to stack print( x.center(8 ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , sep=""" | """ , ) # Output in tabular format while len(__lowerCAmelCase ) > 0: # while stack is not empty post_fix.append(stack.pop() ) # pop stack & add to Postfix print( """ """.center(8 ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , ("""""".join(__lowerCAmelCase )).ljust(__lowerCAmelCase ) , sep=""" | """ , ) # Output in tabular format return "".join(__lowerCAmelCase ) # return Postfix as str def _lowercase ( __lowerCAmelCase ) -> str: SCREAMING_SNAKE_CASE__ : List[str] = list(infix[::-1] ) # reverse the infix equation for i in range(len(__lowerCAmelCase ) ): if infix[i] == "(": SCREAMING_SNAKE_CASE__ : Optional[int] = """)""" # change "(" to ")" elif infix[i] == ")": SCREAMING_SNAKE_CASE__ : Optional[Any] = """(""" # change ")" to "(" return (infix_2_postfix("""""".join(__lowerCAmelCase ) ))[ ::-1 ] # call infix_2_postfix on Infix, return reverse of Postfix if __name__ == "__main__": a :Optional[int] = input("\nEnter an Infix Equation = ") # Input an Infix equation a :Dict = "".join(Infix.split()) # Remove spaces from the input print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
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"""simple docstring""" def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: while b: SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b return a def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int: return a if b == 0 else euclidean_gcd_recursive(__lowerCAmelCase , a % b ) def _lowercase ( ) -> Union[str, Any]: print(F'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' ) print(F'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' ) print(F'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' ) print(F'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' ) print(F'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' ) print(F'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' ) print(F'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' ) print(F'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' ) print(F'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' ) print(F'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' ) if __name__ == "__main__": main()
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"""simple docstring""" def a_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ): '''simple docstring''' lowercase__ : List[str] = len(_lowerCAmelCase ) + 1 lowercase__ : Any = len(_lowerCAmelCase ) + 1 # dp is a 2d matrix where dp[i][j] denotes whether prefix string of # length i of input_string matches with prefix string of length j of # given pattern. # "dp" stands for dynamic programming. lowercase__ : List[str] = [[0 for i in range(_lowerCAmelCase )] for j in range(_lowerCAmelCase )] # since string of zero length match pattern of zero length lowercase__ : Any = 1 # since pattern of zero length will never match with string of non-zero length for i in range(1 , _lowerCAmelCase ): lowercase__ : Tuple = 0 # since string of zero length will match with pattern where there # is at least one * alternatively for j in range(1 , _lowerCAmelCase ): lowercase__ : Union[str, Any] = dp[0][j - 2] if pattern[j - 1] == '*' else 0 # now using bottom-up approach to find for all remaining lengths for i in range(1 , _lowerCAmelCase ): for j in range(1 , _lowerCAmelCase ): if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".": lowercase__ : List[Any] = dp[i - 1][j - 1] elif pattern[j - 1] == "*": if dp[i][j - 2] == 1: lowercase__ : Union[str, Any] = 1 elif pattern[j - 2] in (input_string[i - 1], "."): lowercase__ : Tuple = dp[i - 1][j] else: lowercase__ : Tuple = 0 else: lowercase__ : List[Any] = 0 return bool(dp[-1][-1] ) if __name__ == "__main__": import doctest doctest.testmod() # inputing the strings # input_string = input("input a string :") # pattern = input("input a pattern :") _UpperCamelCase : Any = "aab" _UpperCamelCase : int = "c*a*b" # using function to check whether given string matches the given pattern if match_pattern(input_string, pattern): print(f'''{input_string} matches the given pattern {pattern}''') else: print(f'''{input_string} does not match with the given pattern {pattern}''')
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'''simple docstring''' def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return number | (1 << position) def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return number & ~(1 << position) def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return number ^ (1 << position) def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return ((number >> position) & 1) == 1 def __A ( lowerCamelCase_ , lowerCamelCase_ ): """simple docstring""" return int((number & (1 << position)) != 0 ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from __future__ import annotations from typing import Any class SCREAMING_SNAKE_CASE__ : def __init__( self , A_ = 6 )-> None: '''simple docstring''' UpperCamelCase = None UpperCamelCase = None self.create_linked_list(A_ ) def UpperCAmelCase_ ( self , A_ )-> None: '''simple docstring''' UpperCamelCase = Node() UpperCamelCase = current_node UpperCamelCase = current_node UpperCamelCase = current_node for _ in range(1 , A_ ): UpperCamelCase = Node() UpperCamelCase = current_node UpperCamelCase = previous_node UpperCamelCase = current_node UpperCamelCase = self.front UpperCamelCase = previous_node def UpperCAmelCase_ ( self )-> bool: '''simple docstring''' return ( self.front == self.rear and self.front is not None and self.front.data is None ) def UpperCAmelCase_ ( self )-> Any | None: '''simple docstring''' self.check_can_perform_operation() return self.front.data if self.front else None def UpperCAmelCase_ ( self , A_ )-> None: '''simple docstring''' if self.rear is None: return self.check_is_full() if not self.is_empty(): UpperCamelCase = self.rear.next if self.rear: UpperCamelCase = data def UpperCAmelCase_ ( self )-> Any: '''simple docstring''' self.check_can_perform_operation() if self.rear is None or self.front is None: return None if self.front == self.rear: UpperCamelCase = self.front.data UpperCamelCase = None return data UpperCamelCase = self.front UpperCamelCase = old_front.next UpperCamelCase = old_front.data UpperCamelCase = None return data def UpperCAmelCase_ ( self )-> None: '''simple docstring''' if self.is_empty(): raise Exception('Empty Queue' ) def UpperCAmelCase_ ( self )-> None: '''simple docstring''' if self.rear and self.rear.next == self.front: raise Exception('Full Queue' ) class SCREAMING_SNAKE_CASE__ : def __init__( self )-> None: '''simple docstring''' UpperCamelCase = None UpperCamelCase = None UpperCamelCase = None if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from ... import PretrainedConfig lowerCAmelCase : List[str] = { 'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json', } class SCREAMING_SNAKE_CASE__ ( snake_case_): lowerCAmelCase_ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP lowerCAmelCase_ = """nezha""" def __init__( self , A_=21128 , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=64 , A_=2 , A_=0.02 , A_=1e-12 , A_=0.1 , A_=0 , A_=2 , A_=3 , A_=True , **A_ , )-> List[str]: '''simple docstring''' super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ ) 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 = max_relative_position UpperCamelCase = type_vocab_size UpperCamelCase = initializer_range UpperCamelCase = layer_norm_eps UpperCamelCase = classifier_dropout UpperCamelCase = use_cache
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from __future__ import annotations import collections import pprint from pathlib import Path def lowerCAmelCase_ ( snake_case_ ): return "".join(sorted(_snake_case ) ) def lowerCAmelCase_ ( snake_case_ ): return word_by_signature[signature(_snake_case )] _snake_case = Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8") _snake_case = sorted({word.strip().lower() for word in data.splitlines()}) _snake_case = collections.defaultdict(list) for word in word_list: word_by_signature[signature(word)].append(word) if __name__ == "__main__": _snake_case = {word: anagram(word) for word in word_list if len(anagram(word)) > 1} with open("anagrams.txt", "w") as file: file.write("all_anagrams = \n ") file.write(pprint.pformat(all_anagrams))
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"""simple docstring""" from scipy.stats import spearmanr import datasets a = ''' The Spearman rank-order correlation coefficient is a measure of the relationship between two datasets. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Positive correlations imply that as data in dataset x increases, so does data in dataset y. Negative correlations imply that as x increases, y decreases. Correlations of -1 or +1 imply an exact monotonic relationship. Unlike the Pearson correlation, the Spearman correlation does not assume that both datasets are normally distributed. The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Spearman correlation at least as extreme as the one computed from these datasets. The p-values are not entirely reliable but are probably reasonable for datasets larger than 500 or so. ''' a = ''' Args: predictions (`List[float]`): Predicted labels, as returned by a model. references (`List[float]`): Ground truth labels. return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns only the spearmanr score. Defaults to `False`. Returns: spearmanr (`float`): Spearman correlation coefficient. p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input. Examples: Example 1: >>> spearmanr_metric = datasets.load_metric("spearmanr") >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4]) >>> print(results) {\'spearmanr\': -0.7} Example 2: >>> spearmanr_metric = datasets.load_metric("spearmanr") >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], ... predictions=[10, 9, 2.5, 6, 4], ... return_pvalue=True) >>> print(results[\'spearmanr\']) -0.7 >>> print(round(results[\'spearmanr_pvalue\'], 2)) 0.19 ''' a = r'''\ @book{kokoska2000crc, title={CRC standard probability and statistics tables and formulae}, author={Kokoska, Stephen and Zwillinger, Daniel}, year={2000}, publisher={Crc Press} } @article{2020SciPy-NMeth, author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}}, title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific Computing in Python}}, journal = {Nature Methods}, year = {2020}, volume = {17}, pages = {261--272}, adsurl = {https://rdcu.be/b08Wh}, doi = {10.1038/s41592-019-0686-2}, } ''' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class lowercase_ ( datasets.Metric ): '''simple docstring''' def lowerCAmelCase_ ( self : Optional[int] ): 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 lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : int , _UpperCAmelCase : Optional[int]=False ): _A = spearmanr(_UpperCAmelCase , _UpperCAmelCase ) if return_pvalue: return {"spearmanr": results[0], "spearmanr_pvalue": results[1]} else: return {"spearmanr": results[0]}
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig lowercase : Optional[int] = { """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""", """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""", """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""", """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""", """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""", """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""", """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""", """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""", } class A__ ( __a ): """simple docstring""" __A : Tuple = '''albert''' def __init__( self , lowercase=3_0000 , lowercase=128 , lowercase=4096 , lowercase=12 , lowercase=1 , lowercase=64 , lowercase=1_6384 , lowercase=1 , lowercase="gelu_new" , lowercase=0 , lowercase=0 , lowercase=512 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=0.1 , lowercase="absolute" , lowercase=0 , lowercase=2 , lowercase=3 , **lowercase , ) -> Dict: '''simple docstring''' super().__init__(pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , **UpperCamelCase__) a__ : Optional[int] = vocab_size a__ : str = embedding_size a__ : List[str] = hidden_size a__ : Optional[Any] = num_hidden_layers a__ : Optional[Any] = num_hidden_groups a__ : Any = num_attention_heads a__ : List[Any] = inner_group_num a__ : str = hidden_act a__ : Union[str, Any] = intermediate_size a__ : Optional[int] = hidden_dropout_prob a__ : Optional[int] = attention_probs_dropout_prob a__ : List[Any] = max_position_embeddings a__ : Union[str, Any] = type_vocab_size a__ : Tuple = initializer_range a__ : Optional[int] = layer_norm_eps a__ : str = classifier_dropout_prob a__ : int = position_embedding_type class A__ ( __a ): """simple docstring""" @property def __lowercase ( self) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' if self.task == "multiple-choice": a__ : str = {0: 'batch', 1: 'choice', 2: 'sequence'} else: a__ : Any = {0: 'batch', 1: 'sequence'} return OrderedDict( [ ('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis), ])
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import logging import os import sys from dataclasses import dataclass, field from importlib import import_module from typing import Dict, List, Optional, Tuple import numpy as np from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score from torch import nn from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask import transformers from transformers import ( AutoConfig, AutoModelForTokenClassification, AutoTokenizer, DataCollatorWithPadding, EvalPrediction, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import is_main_process lowercase : Union[str, Any] = logging.getLogger(__name__) @dataclass class A__ : """simple docstring""" __A : str = field( metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ) __A : Optional[str] = field( default=__UpperCAmelCase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} ) __A : Optional[str] = field( default='''NER''' , metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} ) __A : Optional[str] = field( default=__UpperCAmelCase , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} ) __A : bool = field(default=__UpperCAmelCase , metadata={'''help''': '''Set this flag to use fast tokenization.'''} ) # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script, # or just modify its tokenizer_config.json. __A : Optional[str] = field( default=__UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , ) @dataclass class A__ : """simple docstring""" __A : str = field( metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} ) __A : Optional[str] = field( default=__UpperCAmelCase , metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} , ) __A : int = field( default=1_2_8 , metadata={ '''help''': ( '''The maximum total input sequence length after tokenization. Sequences longer ''' '''than this will be truncated, sequences shorter will be padded.''' ) } , ) __A : bool = field( default=__UpperCAmelCase , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} ) def A_ ( ) -> Dict: # See all possible arguments in src/transformers/training_args.py # or by passing the --help flag to this script. # We now keep distinct sets of args, for a cleaner separation of concerns. a__ : List[str] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. a__ , a__ , a__ : int = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: a__ , a__ , a__ : List[Any] = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir ) and os.listdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( F'Output directory ({training_args.output_dir}) already exists and is not empty. Use' ' --overwrite_output_dir to overcome.' ) a__ : Optional[Any] = import_module('tasks' ) try: a__ : List[Any] = getattr(A__ , model_args.task_type ) a__ : TokenClassificationTask = token_classification_task_clazz() except AttributeError: raise ValueError( F'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. ' F'Available tasks classes are: {TokenClassificationTask.__subclasses__()}' ) # Setup logging logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , ) logger.warning( 'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank ): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info('Training/evaluation parameters %s' , A__ ) # Set seed set_seed(training_args.seed ) # Prepare CONLL-2003 task a__ : Tuple = token_classification_task.get_labels(data_args.labels ) a__ : Dict[int, str] = dict(enumerate(A__ ) ) a__ : Union[str, Any] = len(A__ ) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. a__ : Optional[Any] = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=A__ , idalabel=A__ , labelaid={label: i for i, label in enumerate(A__ )} , cache_dir=model_args.cache_dir , ) a__ : str = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , ) a__ : List[Any] = AutoModelForTokenClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=A__ , cache_dir=model_args.cache_dir , ) # Get datasets a__ : int = ( TokenClassificationDataset( token_classification_task=A__ , data_dir=data_args.data_dir , tokenizer=A__ , labels=A__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , ) if training_args.do_train else None ) a__ : Optional[int] = ( TokenClassificationDataset( token_classification_task=A__ , data_dir=data_args.data_dir , tokenizer=A__ , labels=A__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , ) if training_args.do_eval else None ) def align_predictions(A__ , A__ ) -> Tuple[List[int], List[int]]: a__ : Union[str, Any] = np.argmax(A__ , axis=2 ) a__ , a__ : Dict = preds.shape a__ : Union[str, Any] = [[] for _ in range(A__ )] a__ : Optional[int] = [[] for _ in range(A__ )] for i in range(A__ ): for j in range(A__ ): if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index: out_label_list[i].append(label_map[label_ids[i][j]] ) preds_list[i].append(label_map[preds[i][j]] ) return preds_list, out_label_list def compute_metrics(A__ ) -> Dict: a__ , a__ : Union[str, Any] = align_predictions(p.predictions , p.label_ids ) return { "accuracy_score": accuracy_score(A__ , A__ ), "precision": precision_score(A__ , A__ ), "recall": recall_score(A__ , A__ ), "f1": fa_score(A__ , A__ ), } # Data collator a__ : Union[str, Any] = DataCollatorWithPadding(A__ , pad_to_multiple_of=8 ) if training_args.fpaa else None # Initialize our Trainer a__ : List[str] = Trainer( model=A__ , args=A__ , train_dataset=A__ , eval_dataset=A__ , compute_metrics=A__ , data_collator=A__ , ) # Training if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None ) trainer.save_model() # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) if trainer.is_world_process_zero(): tokenizer.save_pretrained(training_args.output_dir ) # Evaluation a__ : Any = {} if training_args.do_eval: logger.info('*** Evaluate ***' ) a__ : Optional[Any] = trainer.evaluate() a__ : List[Any] = os.path.join(training_args.output_dir , 'eval_results.txt' ) if trainer.is_world_process_zero(): with open(A__ , 'w' ) as writer: logger.info('***** Eval results *****' ) for key, value in result.items(): logger.info(' %s = %s' , A__ , A__ ) writer.write('%s = %s\n' % (key, value) ) results.update(A__ ) # Predict if training_args.do_predict: a__ : Optional[Any] = TokenClassificationDataset( token_classification_task=A__ , data_dir=data_args.data_dir , tokenizer=A__ , labels=A__ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , ) a__ , a__ , a__ : Any = trainer.predict(A__ ) a__ , a__ : Union[str, Any] = align_predictions(A__ , A__ ) a__ : Optional[int] = os.path.join(training_args.output_dir , 'test_results.txt' ) if trainer.is_world_process_zero(): with open(A__ , 'w' ) as writer: for key, value in metrics.items(): logger.info(' %s = %s' , A__ , A__ ) writer.write('%s = %s\n' % (key, value) ) # Save predictions a__ : Tuple = os.path.join(training_args.output_dir , 'test_predictions.txt' ) if trainer.is_world_process_zero(): with open(A__ , 'w' ) as writer: with open(os.path.join(data_args.data_dir , 'test.txt' ) , 'r' ) as f: token_classification_task.write_predictions_to_file(A__ , A__ , A__ ) return results def A_ ( A__ ) -> Tuple: # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
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import os import unittest from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer from ...test_tokenization_common import TokenizerTesterMixin class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase , unittest.TestCase ): __lowerCAmelCase = PhobertTokenizer __lowerCAmelCase = False def lowerCamelCase_ ( self : Dict ): """simple docstring""" super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt UpperCamelCase = ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""] UpperCamelCase = dict(zip(lowerCamelCase_ , range(len(lowerCamelCase_ ) ) ) ) UpperCamelCase = ["""#version: 0.2""", """l à</w>"""] UpperCamelCase = {"""unk_token""": """<unk>"""} UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: for token in vocab_tokens: fp.write(f"""{token} {vocab_tokens[token]}\n""" ) with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp: fp.write("""\n""".join(lowerCamelCase_ ) ) def lowerCamelCase_ ( self : str , **lowerCamelCase_ : List[str] ): """simple docstring""" kwargs.update(self.special_tokens_map ) return PhobertTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase_ ) def lowerCamelCase_ ( self : Optional[Any] , lowerCamelCase_ : str ): """simple docstring""" UpperCamelCase = """Tôi là VinAI Research""" UpperCamelCase = """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>""" return input_text, output_text def lowerCamelCase_ ( self : int ): """simple docstring""" UpperCamelCase = PhobertTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map ) UpperCamelCase = """Tôi là VinAI Research""" UpperCamelCase = """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split() UpperCamelCase = tokenizer.tokenize(lowerCamelCase_ ) print(lowerCamelCase_ ) self.assertListEqual(lowerCamelCase_ , lowerCamelCase_ ) UpperCamelCase = tokens + [tokenizer.unk_token] UpperCamelCase = [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_ ) , lowerCamelCase_ )
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from __future__ import annotations from typing import Any class SCREAMING_SNAKE_CASE_ ( __lowerCAmelCase ): pass class SCREAMING_SNAKE_CASE_ : def __init__( self : List[Any] , lowerCamelCase_ : Any ): """simple docstring""" UpperCamelCase = data UpperCamelCase = None def __iter__( self : Optional[int] ): """simple docstring""" UpperCamelCase = self UpperCamelCase = [] while node: if node in visited: raise ContainsLoopError visited.append(lowerCamelCase_ ) yield node.data UpperCamelCase = node.next_node @property def lowerCamelCase_ ( self : List[Any] ): """simple docstring""" try: list(self ) return False except ContainsLoopError: return True if __name__ == "__main__": _SCREAMING_SNAKE_CASE = Node(1) _SCREAMING_SNAKE_CASE = Node(2) _SCREAMING_SNAKE_CASE = Node(3) _SCREAMING_SNAKE_CASE = Node(4) print(root_node.has_loop) # False _SCREAMING_SNAKE_CASE = root_node.next_node print(root_node.has_loop) # True _SCREAMING_SNAKE_CASE = Node(5) _SCREAMING_SNAKE_CASE = Node(6) _SCREAMING_SNAKE_CASE = Node(5) _SCREAMING_SNAKE_CASE = Node(6) print(root_node.has_loop) # False _SCREAMING_SNAKE_CASE = Node(1) print(root_node.has_loop) # False
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"""simple docstring""" import math import random from typing import Any from .hill_climbing import SearchProblem def UpperCAmelCase__ (snake_case__ : List[str] , snake_case__ : bool = True , snake_case__ : float = math.inf , snake_case__ : float = -math.inf , snake_case__ : float = math.inf , snake_case__ : float = -math.inf , snake_case__ : bool = False , snake_case__ : float = 1_00 , snake_case__ : float = 0.01 , snake_case__ : float = 1 , ): """simple docstring""" _snake_case : Dict = False _snake_case : int = search_prob _snake_case : Dict = start_temperate _snake_case : int = [] _snake_case : str = 0 _snake_case : Tuple = None while not search_end: _snake_case : List[str] = current_state.score() if best_state is None or current_score > best_state.score(): _snake_case : Tuple = current_state scores.append(snake_case__ ) iterations += 1 _snake_case : Tuple = None _snake_case : str = current_state.get_neighbors() while ( next_state is None and neighbors ): # till we do not find a neighbor that we can move to _snake_case : Union[str, Any] = random.randint(0 , len(snake_case__ ) - 1 ) # picking a random neighbor _snake_case : int = neighbors.pop(snake_case__ ) _snake_case : Tuple = picked_neighbor.score() - current_score if ( picked_neighbor.x > max_x or picked_neighbor.x < min_x or picked_neighbor.y > max_y or picked_neighbor.y < min_y ): continue # neighbor outside our bounds if not find_max: _snake_case : List[Any] = change * -1 # in case we are finding minimum if change > 0: # improves the solution _snake_case : Optional[int] = picked_neighbor else: _snake_case : str = (math.e) ** ( change / current_temp ) # probability generation function if random.random() < probability: # random number within probability _snake_case : int = picked_neighbor _snake_case : List[Any] = current_temp - (current_temp * rate_of_decrease) if current_temp < threshold_temp or next_state is None: # temperature below threshold, or could not find a suitable neighbor _snake_case : List[str] = True else: _snake_case : List[Any] = next_state if visualization: from matplotlib import pyplot as plt plt.plot(range(snake_case__ ) , snake_case__ ) plt.xlabel("""Iterations""" ) plt.ylabel("""Function values""" ) plt.show() return best_state if __name__ == "__main__": def UpperCAmelCase__ (snake_case__ : List[str] , snake_case__ : Dict ): """simple docstring""" return (x**2) + (y**2) # starting the problem with initial coordinates (12, 47) A_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa) A_ = simulated_annealing( prob, find_max=False, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True ) print( '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 ''' F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}''' ) # starting the problem with initial coordinates (12, 47) A_ = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa) A_ = simulated_annealing( prob, find_max=True, max_x=1_00, min_x=5, max_y=50, min_y=-5, visualization=True ) print( '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 ''' F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}''' ) def UpperCAmelCase__ (snake_case__ : Any , snake_case__ : Optional[Any] ): """simple docstring""" return (3 * x**2) - (6 * y) A_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa) A_ = simulated_annealing(prob, find_max=False, visualization=True) print( '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: ''' F'''{local_min.score()}''' ) A_ = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa) A_ = simulated_annealing(prob, find_max=True, visualization=True) print( '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: ''' F'''{local_min.score()}''' )
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"""simple docstring""" import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class lowercase( nn.Module ): '''simple docstring''' def __init__( self: Optional[int] ): '''simple docstring''' super().__init__() _snake_case : List[str] = nn.Linear(3, 4 ) _snake_case : int = nn.BatchNormad(4 ) _snake_case : List[str] = nn.Linear(4, 5 ) def UpperCamelCase_ ( self: Any, a_: Union[str, Any] ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(a_ ) ) ) class lowercase( __a ): '''simple docstring''' def UpperCamelCase_ ( self: Any, a_: int, *a_: Dict, **a_: Dict ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class lowercase( __a ): '''simple docstring''' def UpperCamelCase_ ( self: str, a_: Tuple, a_: Union[str, Any] ): '''simple docstring''' return output + 1 class lowercase( unittest.TestCase ): '''simple docstring''' def UpperCamelCase_ ( self: Union[str, Any] ): '''simple docstring''' _snake_case : Tuple = ModelForTest() _snake_case : List[str] = ModelHook() add_hook_to_module(a_, a_ ) self.assertEqual(test_model._hf_hook, a_ ) self.assertTrue(hasattr(a_, """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__, """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ), ["""x"""] ) remove_hook_from_module(a_ ) self.assertFalse(hasattr(a_, """_hf_hook""" ) ) self.assertFalse(hasattr(a_, """_old_forward""" ) ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Dict = ModelForTest() _snake_case : List[Any] = ModelHook() add_hook_to_module(a_, a_ ) add_hook_to_module(a_, a_, append=a_ ) self.assertEqual(isinstance(test_model._hf_hook, a_ ), a_ ) self.assertEqual(len(test_model._hf_hook.hooks ), 2 ) self.assertTrue(hasattr(a_, """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__, """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ), ["""x"""] ) remove_hook_from_module(a_ ) self.assertFalse(hasattr(a_, """_hf_hook""" ) ) self.assertFalse(hasattr(a_, """_old_forward""" ) ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Optional[Any] = ModelForTest() _snake_case : Optional[Any] = torch.randn(2, 3 ) _snake_case : List[Any] = test_model(x + 1 ) _snake_case : List[str] = test_model(x + 2 ) _snake_case : Any = PreForwardHook() add_hook_to_module(a_, a_ ) _snake_case : List[Any] = test_model(a_ ) self.assertTrue(torch.allclose(a_, a_, atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain _snake_case : List[str] = PreForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Tuple = test_model(a_ ) self.assertTrue(torch.allclose(a_, a_, atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks _snake_case : str = SequentialHook(PreForwardHook(), PreForwardHook() ) add_hook_to_module(a_, a_ ) _snake_case : str = test_model(a_ ) assert torch.allclose(a_, a_, atol=1E-5 ) def UpperCamelCase_ ( self: Optional[int] ): '''simple docstring''' _snake_case : Optional[int] = ModelForTest() _snake_case : List[Any] = torch.randn(2, 3 ) _snake_case : List[str] = test_model(a_ ) _snake_case : List[Any] = PostForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Union[str, Any] = test_model(a_ ) self.assertTrue(torch.allclose(a_, output + 1, atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain _snake_case : Tuple = PostForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Optional[Any] = test_model(a_ ) self.assertTrue(torch.allclose(a_, output + 1, atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks _snake_case : Dict = SequentialHook(PostForwardHook(), PostForwardHook() ) add_hook_to_module(a_, a_ ) _snake_case : List[str] = test_model(a_ ) assert torch.allclose(a_, output + 2, atol=1E-5 ) def UpperCamelCase_ ( self: Optional[Any] ): '''simple docstring''' _snake_case : str = ModelForTest() _snake_case : Any = torch.randn(2, 3 ) _snake_case : List[str] = test_model(a_ ) _snake_case : List[Any] = PostForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Dict = test_model(a_ ) self.assertTrue(torch.allclose(a_, output + 1 ) ) self.assertTrue(outputa.requires_grad ) _snake_case : Union[str, Any] = True _snake_case : Dict = test_model(a_ ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Dict = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara, AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm, AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara, AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device, torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device, torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device(0 ) ) self.assertEqual(model.lineara.weight.device, torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device _snake_case : Any = torch.randn(2, 3 ) _snake_case : Any = model(a_ ) self.assertEqual(output.device, torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(a_, AlignDevicesHook(io_same_device=a_ ) ) _snake_case : int = torch.randn(2, 3 ).to(0 ) _snake_case : Optional[Any] = model(a_ ) self.assertEqual(output.device, torch.device(0 ) ) def UpperCamelCase_ ( self: Union[str, Any] ): '''simple docstring''' _snake_case : int = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices _snake_case : Optional[int] = {"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.batchnorm, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device _snake_case : Union[str, Any] = torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device, a_ ) _snake_case : str = torch.randn(2, 3 ) _snake_case : Optional[int] = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # Now test with buffers included in the offload _snake_case : Any = { """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.batchnorm, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device("""meta""" ) ) _snake_case : List[Any] = torch.randn(2, 3 ) _snake_case : Any = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) def UpperCamelCase_ ( self: str ): '''simple docstring''' _snake_case : Any = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices _snake_case : Tuple = 0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(a_, execution_device=a_, offload=a_ ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device _snake_case : Optional[int] = torch.device(a_ ) self.assertEqual(model.batchnorm.running_mean.device, a_ ) _snake_case : List[Any] = torch.randn(2, 3 ) _snake_case : List[Any] = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(a_, execution_device=a_, offload=a_, offload_buffers=a_ ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device("""meta""" ) ) _snake_case : str = torch.randn(2, 3 ) _snake_case : List[Any] = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) def UpperCamelCase_ ( self: Any ): '''simple docstring''' _snake_case : List[str] = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices _snake_case : Any = 0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( a_, execution_device=a_, offload=a_, weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device _snake_case : Optional[Any] = torch.device(a_ ) self.assertEqual(model.batchnorm.running_mean.device, a_ ) _snake_case : int = torch.randn(2, 3 ) _snake_case : str = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( a_, execution_device=a_, offload=a_, weights_map=model.state_dict(), offload_buffers=a_, ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device("""meta""" ) ) _snake_case : Optional[int] = torch.randn(2, 3 ) _snake_case : Any = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) )
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0
"""simple docstring""" def UpperCAmelCase__ (lowerCAmelCase_ ): '''simple docstring''' if upper_limit < 0: raise ValueError("Limit for the Catalan sequence must be ≥ 0" ) __SCREAMING_SNAKE_CASE = [0] * (upper_limit + 1) # Base case: C(0) = C(1) = 1 __SCREAMING_SNAKE_CASE = 1 if upper_limit > 0: __SCREAMING_SNAKE_CASE = 1 # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i for i in range(2 , upper_limit + 1 ): for j in range(lowerCAmelCase_ ): catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1] return catalan_list if __name__ == "__main__": print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''') print('''\n*** Enter -1 at any time to quit ***''') print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''') try: while True: a__ : List[str] = int(input().strip()) if N < 0: print('''\n********* Goodbye!! ************''') break else: print(F"The Catalan numbers from 0 through {N} are:") print(catalan_numbers(N)) print('''Try another upper limit for the sequence: ''', end='''''') except (NameError, ValueError): print('''\n********* Invalid input, goodbye! ************\n''') import doctest doctest.testmod()
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import argparse import json from pathlib import Path import torch import torchaudio from datasets import load_dataset from huggingface_hub import hf_hub_download from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification from transformers.utils import logging logging.set_verbosity_info() lowerCamelCase : List[str] = logging.get_logger(__name__) def snake_case_ ( lowerCAmelCase_ : List[Any] ): __lowercase : Optional[Any] = ASTConfig() if "10-10" in model_name: pass elif "speech-commands" in model_name: __lowercase : Union[str, Any] = 128 elif "12-12" in model_name: __lowercase : Tuple = 12 __lowercase : List[Any] = 12 elif "14-14" in model_name: __lowercase : int = 14 __lowercase : Dict = 14 elif "16-16" in model_name: __lowercase : str = 16 __lowercase : Dict = 16 else: raise ValueError("""Model not supported""" ) __lowercase : Union[str, Any] = """huggingface/label-files""" if "speech-commands" in model_name: __lowercase : List[Any] = 35 __lowercase : str = """speech-commands-v2-id2label.json""" else: __lowercase : Any = 527 __lowercase : Dict = """audioset-id2label.json""" __lowercase : Optional[Any] = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type="""dataset""" ) , """r""" ) ) __lowercase : List[str] = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()} __lowercase : Union[str, Any] = idalabel __lowercase : List[str] = {v: k for k, v in idalabel.items()} return config def snake_case_ ( lowerCAmelCase_ : Union[str, Any] ): if "module.v" in name: __lowercase : List[str] = name.replace("""module.v""" , """audio_spectrogram_transformer""" ) if "cls_token" in name: __lowercase : int = name.replace("""cls_token""" , """embeddings.cls_token""" ) if "dist_token" in name: __lowercase : str = name.replace("""dist_token""" , """embeddings.distillation_token""" ) if "pos_embed" in name: __lowercase : Any = name.replace("""pos_embed""" , """embeddings.position_embeddings""" ) if "patch_embed.proj" in name: __lowercase : Optional[Any] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" ) # transformer blocks if "blocks" in name: __lowercase : Union[str, Any] = name.replace("""blocks""" , """encoder.layer""" ) if "attn.proj" in name: __lowercase : int = name.replace("""attn.proj""" , """attention.output.dense""" ) if "attn" in name: __lowercase : Union[str, Any] = name.replace("""attn""" , """attention.self""" ) if "norm1" in name: __lowercase : Dict = name.replace("""norm1""" , """layernorm_before""" ) if "norm2" in name: __lowercase : Tuple = name.replace("""norm2""" , """layernorm_after""" ) if "mlp.fc1" in name: __lowercase : Any = name.replace("""mlp.fc1""" , """intermediate.dense""" ) if "mlp.fc2" in name: __lowercase : Optional[Any] = name.replace("""mlp.fc2""" , """output.dense""" ) # final layernorm if "audio_spectrogram_transformer.norm" in name: __lowercase : Tuple = name.replace("""audio_spectrogram_transformer.norm""" , """audio_spectrogram_transformer.layernorm""" ) # classifier head if "module.mlp_head.0" in name: __lowercase : Optional[Any] = name.replace("""module.mlp_head.0""" , """classifier.layernorm""" ) if "module.mlp_head.1" in name: __lowercase : List[str] = name.replace("""module.mlp_head.1""" , """classifier.dense""" ) return name def snake_case_ ( lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[str] ): for key in orig_state_dict.copy().keys(): __lowercase : str = orig_state_dict.pop(lowerCAmelCase_ ) if "qkv" in key: __lowercase : Optional[int] = key.split(""".""" ) __lowercase : Optional[int] = int(key_split[3] ) __lowercase : Any = config.hidden_size if "weight" in key: __lowercase : Union[str, Any] = val[:dim, :] __lowercase : Union[str, Any] = val[dim : dim * 2, :] __lowercase : Dict = val[-dim:, :] else: __lowercase : Optional[int] = val[:dim] __lowercase : Any = val[dim : dim * 2] __lowercase : int = val[-dim:] else: __lowercase : Any = val return orig_state_dict def snake_case_ ( lowerCAmelCase_ : Optional[int] ): __lowercase : int = [ """module.v.head.weight""", """module.v.head.bias""", """module.v.head_dist.weight""", """module.v.head_dist.bias""", ] for k in ignore_keys: state_dict.pop(lowerCAmelCase_ , lowerCAmelCase_ ) @torch.no_grad() def snake_case_ ( lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : int=False ): __lowercase : List[Any] = get_audio_spectrogram_transformer_config(lowerCAmelCase_ ) __lowercase : Tuple = { """ast-finetuned-audioset-10-10-0.4593""": ( """https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1""" ), """ast-finetuned-audioset-10-10-0.450""": ( """https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1""" ), """ast-finetuned-audioset-10-10-0.448""": ( """https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1""" ), """ast-finetuned-audioset-10-10-0.448-v2""": ( """https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1""" ), """ast-finetuned-audioset-12-12-0.447""": ( """https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1""" ), """ast-finetuned-audioset-14-14-0.443""": ( """https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1""" ), """ast-finetuned-audioset-16-16-0.442""": ( """https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1""" ), """ast-finetuned-speech-commands-v2""": ( """https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1""" ), } # load original state_dict __lowercase : str = model_name_to_url[model_name] __lowercase : Dict = torch.hub.load_state_dict_from_url(lowerCAmelCase_ , map_location="""cpu""" ) # remove some keys remove_keys(lowerCAmelCase_ ) # rename some keys __lowercase : Optional[Any] = convert_state_dict(lowerCAmelCase_ , lowerCAmelCase_ ) # load 🤗 model __lowercase : str = ASTForAudioClassification(lowerCAmelCase_ ) model.eval() model.load_state_dict(lowerCAmelCase_ ) # verify outputs on dummy input # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62 __lowercase : Tuple = -4.2_677_393 if """speech-commands""" not in model_name else -6.845_978 __lowercase : Optional[int] = 4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526 __lowercase : Union[str, Any] = 1024 if """speech-commands""" not in model_name else 128 __lowercase : Any = ASTFeatureExtractor(mean=lowerCAmelCase_ , std=lowerCAmelCase_ , max_length=lowerCAmelCase_ ) if "speech-commands" in model_name: __lowercase : Optional[int] = load_dataset("""speech_commands""" , """v0.02""" , split="""validation""" ) __lowercase : Union[str, Any] = dataset[0]["""audio"""]["""array"""] else: __lowercase : List[Any] = hf_hub_download( repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" , ) __lowercase , __lowercase : Union[str, Any] = torchaudio.load(lowerCAmelCase_ ) __lowercase : Union[str, Any] = waveform.squeeze().numpy() __lowercase : int = feature_extractor(lowerCAmelCase_ , sampling_rate=16000 , return_tensors="""pt""" ) # forward pass __lowercase : Tuple = model(**lowerCAmelCase_ ) __lowercase : int = outputs.logits if model_name == "ast-finetuned-audioset-10-10-0.4593": __lowercase : Union[str, Any] = torch.tensor([-0.8_760, -7.0_042, -8.6_602] ) elif model_name == "ast-finetuned-audioset-10-10-0.450": __lowercase : Optional[int] = torch.tensor([-1.1_986, -7.0_903, -8.2_718] ) elif model_name == "ast-finetuned-audioset-10-10-0.448": __lowercase : Union[str, Any] = torch.tensor([-2.6_128, -8.0_080, -9.4_344] ) elif model_name == "ast-finetuned-audioset-10-10-0.448-v2": __lowercase : Optional[Any] = torch.tensor([-1.5_080, -7.4_534, -8.8_917] ) elif model_name == "ast-finetuned-audioset-12-12-0.447": __lowercase : List[Any] = torch.tensor([-0.5_050, -6.5_833, -8.0_843] ) elif model_name == "ast-finetuned-audioset-14-14-0.443": __lowercase : str = torch.tensor([-0.3_826, -7.0_336, -8.2_413] ) elif model_name == "ast-finetuned-audioset-16-16-0.442": __lowercase : List[str] = torch.tensor([-1.2_113, -6.9_101, -8.3_470] ) elif model_name == "ast-finetuned-speech-commands-v2": __lowercase : List[Any] = torch.tensor([6.1_589, -8.0_566, -8.7_984] ) else: raise ValueError("""Unknown model name""" ) if not torch.allclose(logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ): raise ValueError("""Logits don't match""" ) print("""Looks ok!""" ) if pytorch_dump_folder_path is not None: Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ ) print(F"Saving model {model_name} to {pytorch_dump_folder_path}" ) model.save_pretrained(lowerCAmelCase_ ) print(F"Saving feature extractor to {pytorch_dump_folder_path}" ) feature_extractor.save_pretrained(lowerCAmelCase_ ) if push_to_hub: print("""Pushing model and feature extractor to the hub...""" ) model.push_to_hub(F"MIT/{model_name}" ) feature_extractor.push_to_hub(F"MIT/{model_name}" ) if __name__ == "__main__": lowerCamelCase : int = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--model_name''', default='''ast-finetuned-audioset-10-10-0.4593''', type=str, help='''Name of the Audio Spectrogram Transformer 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 or not to push the converted model to the 🤗 hub.''' ) lowerCamelCase : Tuple = parser.parse_args() convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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'''simple docstring''' import unittest import numpy as np import torch from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device enable_full_determinism() class a__ ( unittest.TestCase ): '''simple docstring''' @property def __SCREAMING_SNAKE_CASE ( self ) -> Any: torch.manual_seed(0 ) lowerCAmelCase__ = UNetaDModel( block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , ) return model def __SCREAMING_SNAKE_CASE ( self ) -> int: lowerCAmelCase__ = self.dummy_uncond_unet lowerCAmelCase__ = PNDMScheduler() lowerCAmelCase__ = PNDMPipeline(unet=lowerCamelCase_ , scheduler=lowerCamelCase_ ) pndm.to(lowerCamelCase_ ) pndm.set_progress_bar_config(disable=lowerCamelCase_ ) lowerCAmelCase__ = torch.manual_seed(0 ) lowerCAmelCase__ = pndm(generator=lowerCamelCase_ , num_inference_steps=20 , output_type='''numpy''' ).images lowerCAmelCase__ = torch.manual_seed(0 ) lowerCAmelCase__ = pndm(generator=lowerCamelCase_ , num_inference_steps=20 , output_type='''numpy''' , return_dict=lowerCamelCase_ )[0] lowerCAmelCase__ = image[0, -3:, -3:, -1] lowerCAmelCase__ = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase__ = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2 @slow @require_torch class a__ ( unittest.TestCase ): '''simple docstring''' def __SCREAMING_SNAKE_CASE ( self ) -> Tuple: lowerCAmelCase__ = '''google/ddpm-cifar10-32''' lowerCAmelCase__ = UNetaDModel.from_pretrained(lowerCamelCase_ ) lowerCAmelCase__ = PNDMScheduler() lowerCAmelCase__ = PNDMPipeline(unet=lowerCamelCase_ , scheduler=lowerCamelCase_ ) pndm.to(lowerCamelCase_ ) pndm.set_progress_bar_config(disable=lowerCamelCase_ ) lowerCAmelCase__ = torch.manual_seed(0 ) lowerCAmelCase__ = pndm(generator=lowerCamelCase_ , output_type='''numpy''' ).images lowerCAmelCase__ = image[0, -3:, -3:, -1] assert image.shape == (1, 32, 32, 3) lowerCAmelCase__ = np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
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'''simple docstring''' from sklearn.metrics import fa_score, matthews_corrcoef import datasets from .record_evaluation import evaluate as evaluate_record __UpperCAmelCase = '''\ @article{wang2019superglue, title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems}, author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R}, journal={arXiv preprint arXiv:1905.00537}, year={2019} } ''' __UpperCAmelCase = '''\ SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after GLUE with a new set of more difficult language understanding tasks, improved resources, and a new public leaderboard. ''' __UpperCAmelCase = ''' Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset. Args: predictions: list of predictions to score. Depending on the SuperGlUE subset: - for \'record\': list of question-answer dictionaries with the following keys: - \'idx\': index of the question as specified by the dataset - \'prediction_text\': the predicted answer text - for \'multirc\': list of question-answer dictionaries with the following keys: - \'idx\': index of the question-answer pair as specified by the dataset - \'prediction\': the predicted answer label - otherwise: list of predicted labels references: list of reference labels. Depending on the SuperGLUE subset: - for \'record\': list of question-answers dictionaries with the following keys: - \'idx\': index of the question as specified by the dataset - \'answers\': list of possible answers - otherwise: list of reference labels Returns: depending on the SuperGLUE subset: - for \'record\': - \'exact_match\': Exact match between answer and gold answer - \'f1\': F1 score - for \'multirc\': - \'exact_match\': Exact match between answer and gold answer - \'f1_m\': Per-question macro-F1 score - \'f1_a\': Average F1 score over all answers - for \'axb\': \'matthews_correlation\': Matthew Correlation - for \'cb\': - \'accuracy\': Accuracy - \'f1\': F1 score - for all others: - \'accuracy\': Accuracy Examples: >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\') # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"] >>> predictions = [0, 1] >>> references = [0, 1] >>> results = super_glue_metric.compute(predictions=predictions, references=references) >>> print(results) {\'accuracy\': 1.0} >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\') >>> predictions = [0, 1] >>> references = [0, 1] >>> results = super_glue_metric.compute(predictions=predictions, references=references) >>> print(results) {\'accuracy\': 1.0, \'f1\': 1.0} >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\') >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}] >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}] >>> results = super_glue_metric.compute(predictions=predictions, references=references) >>> print(results) {\'exact_match\': 1.0, \'f1\': 1.0} >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\') >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}] >>> references = [0, 1] >>> results = super_glue_metric.compute(predictions=predictions, references=references) >>> print(results) {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0} >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\') >>> references = [0, 1] >>> predictions = [0, 1] >>> results = super_glue_metric.compute(predictions=predictions, references=references) >>> print(results) {\'matthews_correlation\': 1.0} ''' def _snake_case ( A , A ) -> List[Any]: return float((preds == labels).mean() ) def _snake_case ( A , A , A="binary" ) -> int: lowerCAmelCase__ = simple_accuracy(A , A ) lowerCAmelCase__ = float(fa_score(y_true=A , y_pred=A , average=A ) ) return { "accuracy": acc, "f1": fa, } def _snake_case ( A , A ) -> List[Any]: lowerCAmelCase__ = {} for id_pred, label in zip(A , A ): lowerCAmelCase__ = F"""{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}""" lowerCAmelCase__ = id_pred['''prediction'''] if question_id in question_map: question_map[question_id].append((pred, label) ) else: lowerCAmelCase__ = [(pred, label)] lowerCAmelCase__ , lowerCAmelCase__ = [], [] for question, preds_labels in question_map.items(): lowerCAmelCase__ , lowerCAmelCase__ = zip(*A ) lowerCAmelCase__ = fa_score(y_true=A , y_pred=A , average='''macro''' ) fas.append(A ) lowerCAmelCase__ = int(sum(pred == label for pred, label in preds_labels ) == len(A ) ) ems.append(A ) lowerCAmelCase__ = float(sum(A ) / len(A ) ) lowerCAmelCase__ = sum(A ) / len(A ) lowerCAmelCase__ = float(fa_score(y_true=A , y_pred=[id_pred['''prediction'''] for id_pred in ids_preds] ) ) return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a} @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class a__ ( datasets.Metric ): '''simple docstring''' def __SCREAMING_SNAKE_CASE ( self ) -> Dict: if self.config_name not in [ "boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg", ]: raise KeyError( '''You should supply a configuration name selected in ''' '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' ) return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format='''numpy''' if not self.config_name == '''record''' and not self.config_name == '''multirc''' else None , ) def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]: if self.config_name == "record": return { "predictions": { "idx": { "passage": datasets.Value('''int64''' ), "query": datasets.Value('''int64''' ), }, "prediction_text": datasets.Value('''string''' ), }, "references": { "idx": { "passage": datasets.Value('''int64''' ), "query": datasets.Value('''int64''' ), }, "answers": datasets.Sequence(datasets.Value('''string''' ) ), }, } elif self.config_name == "multirc": return { "predictions": { "idx": { "answer": datasets.Value('''int64''' ), "paragraph": datasets.Value('''int64''' ), "question": datasets.Value('''int64''' ), }, "prediction": datasets.Value('''int64''' ), }, "references": datasets.Value('''int64''' ), } else: return { "predictions": datasets.Value('''int64''' ), "references": datasets.Value('''int64''' ), } def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ ) -> Dict: if self.config_name == "axb": return {"matthews_correlation": matthews_corrcoef(lowerCamelCase_ , lowerCamelCase_ )} elif self.config_name == "cb": return acc_and_fa(lowerCamelCase_ , lowerCamelCase_ , fa_avg='''macro''' ) elif self.config_name == "record": lowerCAmelCase__ = [ { '''qas''': [ {'''id''': ref['''idx''']['''query'''], '''answers''': [{'''text''': ans} for ans in ref['''answers''']]} for ref in references ] } ] lowerCAmelCase__ = {pred['''idx''']['''query''']: pred['''prediction_text'''] for pred in predictions} return evaluate_record(lowerCamelCase_ , lowerCamelCase_ )[0] elif self.config_name == "multirc": return evaluate_multirc(lowerCamelCase_ , lowerCamelCase_ ) elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]: return {"accuracy": simple_accuracy(lowerCamelCase_ , lowerCamelCase_ )} else: raise KeyError( '''You should supply a configuration name selected in ''' '''["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]''' )
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'''simple docstring''' import collections import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging a__ : Dict =logging.get_logger(__name__) a__ : Optional[Any] ='''▁''' a__ : List[str] ={'''vocab_file''': '''prophetnet.tokenizer'''} a__ : List[str] ={ '''vocab_file''': { '''microsoft/xprophetnet-large-wiki100-cased''': ( '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer''' ), } } a__ : List[Any] ={ '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False}, } a__ : Any ={ '''microsoft/xprophetnet-large-wiki100-cased''': 512, } def lowercase__ ( __lowercase : List[str] ) -> Optional[Any]: """simple docstring""" __UpperCamelCase = collections.OrderedDict() with open(__lowercase , 'r' , encoding='utf-8' ) as reader: __UpperCamelCase = reader.readlines() for index, token in enumerate(__lowercase ): __UpperCamelCase = token.rstrip('\n' ) __UpperCamelCase = index return vocab class snake_case ( __lowerCamelCase ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] =VOCAB_FILES_NAMES SCREAMING_SNAKE_CASE_ : Optional[Any] =PRETRAINED_VOCAB_FILES_MAP SCREAMING_SNAKE_CASE_ : Dict =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES SCREAMING_SNAKE_CASE_ : str =["input_ids", "attention_mask"] def __init__( self : Dict , __A : Union[str, Any] , __A : Any="[SEP]" , __A : Optional[Any]="[SEP]" , __A : int="[SEP]" , __A : Any="[UNK]" , __A : Union[str, Any]="[PAD]" , __A : Any="[CLS]" , __A : Tuple="[MASK]" , __A : Optional[Dict[str, Any]] = None , **__A : Tuple , ): __UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs super().__init__( bos_token=__A , eos_token=__A , sep_token=__A , unk_token=__A , pad_token=__A , cls_token=__A , mask_token=__A , sp_model_kwargs=self.sp_model_kwargs , **__A , ) try: import sentencepiece as spm except ImportError: logger.warning( 'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece' ' pip install sentencepiece' ) raise __UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__A ) ) __UpperCamelCase = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ---- # fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # put special tokens and [unused] tokens into the vocab __UpperCamelCase = {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4} for i in range(1_0 ): __UpperCamelCase = f'''[unused{i}]''' __UpperCamelCase = 5 + i # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab __UpperCamelCase = 1_2 __UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()} for k in self.fairseq_tokens_to_ids.keys(): self.unique_no_split_tokens.append(__A ) def __getstate__( self : Union[str, Any] ): __UpperCamelCase = self.__dict__.copy() __UpperCamelCase = None return state def __setstate__( self : Any , __A : Dict ): __UpperCamelCase = d try: import sentencepiece as spm except ImportError: logger.warning( 'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece' ' pip install sentencepiece' ) raise # for backward compatibility if not hasattr(self , 'sp_model_kwargs' ): __UpperCamelCase = {} __UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def _lowerCamelCase ( self : List[Any] , __A : List[int] , __A : Optional[List[int]] = None , __A : bool = False ): if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A ) if token_ids_a is None: return ([0] * len(__A )) + [1] return ([0] * len(__A )) + [1] + ([0] * len(__A )) + [1] def _lowerCamelCase ( self : int , __A : List[int] , __A : Optional[List[int]] = None ): __UpperCamelCase = [self.sep_token_id] if token_ids_a is None: return len(token_ids_a + sep ) * [0] return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0] @property def _lowerCamelCase ( self : Optional[Any] ): return len(self.sp_model ) + self.fairseq_offset def _lowerCamelCase ( self : Dict ): __UpperCamelCase = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def _lowerCamelCase ( self : int , __A : str ): return self.sp_model.encode(__A , out_type=__A ) def _lowerCamelCase ( self : Tuple , __A : Tuple ): if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] __UpperCamelCase = self.sp_model.PieceToId(__A ) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _lowerCamelCase ( self : Optional[Any] , __A : List[Any] ): if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset ) def _lowerCamelCase ( self : Union[str, Any] , __A : Optional[int] ): __UpperCamelCase = ''.join(__A ).replace(__A , ' ' ).strip() return out_string def _lowerCamelCase ( self : Tuple , __A : str , __A : Optional[str] = None ): if not os.path.isdir(__A ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return __UpperCamelCase = os.path.join( __A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __A ) elif not os.path.isfile(self.vocab_file ): with open(__A , 'wb' ) as fi: __UpperCamelCase = self.sp_model.serialized_model_proto() fi.write(__A ) return (out_vocab_file,) def _lowerCamelCase ( self : Optional[Any] , __A : List[int] , __A : Optional[List[int]] = None ): if token_ids_a is None: return token_ids_a + [self.sep_token_id] __UpperCamelCase = [self.sep_token_id] return token_ids_a + sep + token_ids_a + sep
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'''simple docstring''' import json import os import pickle import shutil import tempfile from unittest import TestCase from unittest.mock import patch import numpy as np from datasets import Dataset from transformers import is_faiss_available from transformers.models.bart.configuration_bart import BartConfig from transformers.models.bart.tokenization_bart import BartTokenizer from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES from transformers.models.dpr.configuration_dpr import DPRConfig from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer from transformers.models.rag.configuration_rag import RagConfig from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch if is_faiss_available(): import faiss @require_faiss class snake_case ( __lowerCamelCase ): """simple docstring""" def _lowerCamelCase ( self : Any ): __UpperCamelCase = tempfile.mkdtemp() __UpperCamelCase = 8 # DPR tok __UpperCamelCase = [ '[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest', ] __UpperCamelCase = os.path.join(self.tmpdirname , 'dpr_tokenizer' ) os.makedirs(__A , exist_ok=__A ) __UpperCamelCase = os.path.join(__A , DPR_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] ) ) # BART tok __UpperCamelCase = [ 'l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', '\u0120', '\u0120l', '\u0120n', '\u0120lo', '\u0120low', 'er', '\u0120lowest', '\u0120newer', '\u0120wider', '<unk>', ] __UpperCamelCase = dict(zip(__A , range(len(__A ) ) ) ) __UpperCamelCase = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', ''] __UpperCamelCase = {'unk_token': '<unk>'} __UpperCamelCase = os.path.join(self.tmpdirname , 'bart_tokenizer' ) os.makedirs(__A , exist_ok=__A ) __UpperCamelCase = os.path.join(__A , BART_VOCAB_FILES_NAMES['vocab_file'] ) __UpperCamelCase = os.path.join(__A , BART_VOCAB_FILES_NAMES['merges_file'] ) with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp: fp.write(json.dumps(__A ) + '\n' ) with open(self.merges_file , 'w' , encoding='utf-8' ) as fp: fp.write('\n'.join(__A ) ) def _lowerCamelCase ( self : Tuple ): return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) ) def _lowerCamelCase ( self : Optional[int] ): return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) ) def _lowerCamelCase ( self : Union[str, Any] ): return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'bart_tokenizer' ) ) def _lowerCamelCase ( self : str ): shutil.rmtree(self.tmpdirname ) def _lowerCamelCase ( self : Dict ): __UpperCamelCase = Dataset.from_dict( { 'id': ['0', '1'], 'text': ['foo', 'bar'], 'title': ['Foo', 'Bar'], 'embeddings': [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )], } ) dataset.add_faiss_index('embeddings' , string_factory='Flat' , metric_type=faiss.METRIC_INNER_PRODUCT ) return dataset def _lowerCamelCase ( self : Tuple ): __UpperCamelCase = self.get_dummy_dataset() __UpperCamelCase = RagConfig( retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , ) with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset: __UpperCamelCase = dataset __UpperCamelCase = RagRetriever( __A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , ) return retriever def _lowerCamelCase ( self : Any , __A : bool ): __UpperCamelCase = self.get_dummy_dataset() __UpperCamelCase = RagConfig( retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='custom' , ) if from_disk: __UpperCamelCase = os.path.join(self.tmpdirname , 'dataset' ) __UpperCamelCase = os.path.join(self.tmpdirname , 'index.faiss' ) dataset.get_index('embeddings' ).save(os.path.join(self.tmpdirname , 'index.faiss' ) ) dataset.drop_index('embeddings' ) dataset.save_to_disk(os.path.join(self.tmpdirname , 'dataset' ) ) del dataset __UpperCamelCase = RagRetriever( __A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , ) else: __UpperCamelCase = RagRetriever( __A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , __A ) , ) return retriever def _lowerCamelCase ( self : int ): __UpperCamelCase = Dataset.from_dict( { 'id': ['0', '1'], 'text': ['foo', 'bar'], 'title': ['Foo', 'Bar'], 'embeddings': [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )], } ) dataset.add_faiss_index('embeddings' , string_factory='Flat' , metric_type=faiss.METRIC_INNER_PRODUCT ) __UpperCamelCase = os.path.join(self.tmpdirname , 'hf_bert_base.hnswSQ8_correct_phi_128.c_index' ) dataset.save_faiss_index('embeddings' , index_file_name + '.index.dpr' ) pickle.dump(dataset['id'] , open(index_file_name + '.index_meta.dpr' , 'wb' ) ) __UpperCamelCase = os.path.join(self.tmpdirname , 'psgs_w100.tsv.pkl' ) __UpperCamelCase = {sample['id']: [sample['text'], sample['title']] for sample in dataset} pickle.dump(__A , open(__A , 'wb' ) ) __UpperCamelCase = RagConfig( retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name='legacy' , index_path=self.tmpdirname , ) __UpperCamelCase = RagRetriever( __A , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() ) return retriever def _lowerCamelCase ( self : List[str] ): __UpperCamelCase = 1 __UpperCamelCase = self.get_dummy_canonical_hf_index_retriever() __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = retriever.retrieve(__A , n_docs=__A ) self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) ) self.assertEqual(len(__A ) , 2 ) self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] ) self.assertEqual(len(doc_dicts[0]['id'] ) , __A ) self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc self.assertListEqual(doc_ids.tolist() , [[1], [0]] ) def _lowerCamelCase ( self : Optional[Any] ): __UpperCamelCase = self.get_dummy_canonical_hf_index_retriever() with tempfile.TemporaryDirectory() as tmp_dirname: with patch('transformers.models.rag.retrieval_rag.load_dataset' ) as mock_load_dataset: __UpperCamelCase = self.get_dummy_dataset() retriever.save_pretrained(__A ) __UpperCamelCase = RagRetriever.from_pretrained(__A ) self.assertIsInstance(__A , __A ) __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase = retriever.retrieve(__A , n_docs=1 ) self.assertTrue(out is not None ) def _lowerCamelCase ( self : Optional[int] ): __UpperCamelCase = 1 __UpperCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__A ) __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = retriever.retrieve(__A , n_docs=__A ) self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) ) self.assertEqual(len(__A ) , 2 ) self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] ) self.assertEqual(len(doc_dicts[0]['id'] ) , __A ) self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc self.assertListEqual(doc_ids.tolist() , [[1], [0]] ) def _lowerCamelCase ( self : str ): __UpperCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__A ) with tempfile.TemporaryDirectory() as tmp_dirname: retriever.save_pretrained(__A ) __UpperCamelCase = RagRetriever.from_pretrained(__A ) self.assertIsInstance(__A , __A ) __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase = retriever.retrieve(__A , n_docs=1 ) self.assertTrue(out is not None ) def _lowerCamelCase ( self : Optional[Any] ): __UpperCamelCase = 1 __UpperCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__A ) __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = retriever.retrieve(__A , n_docs=__A ) self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) ) self.assertEqual(len(__A ) , 2 ) self.assertEqual(sorted(doc_dicts[0] ) , ['embeddings', 'id', 'text', 'title'] ) self.assertEqual(len(doc_dicts[0]['id'] ) , __A ) self.assertEqual(doc_dicts[0]['id'][0] , '1' ) # max inner product is reached with second doc self.assertEqual(doc_dicts[1]['id'][0] , '0' ) # max inner product is reached with first doc self.assertListEqual(doc_ids.tolist() , [[1], [0]] ) def _lowerCamelCase ( self : Any ): __UpperCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__A ) with tempfile.TemporaryDirectory() as tmp_dirname: retriever.save_pretrained(__A ) __UpperCamelCase = RagRetriever.from_pretrained(__A ) self.assertIsInstance(__A , __A ) __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase = retriever.retrieve(__A , n_docs=1 ) self.assertTrue(out is not None ) def _lowerCamelCase ( self : Dict ): __UpperCamelCase = 1 __UpperCamelCase = self.get_dummy_legacy_index_retriever() __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = retriever.retrieve(__A , n_docs=__A ) self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) ) self.assertEqual(len(__A ) , 2 ) self.assertEqual(sorted(doc_dicts[0] ) , ['text', 'title'] ) self.assertEqual(len(doc_dicts[0]['text'] ) , __A ) self.assertEqual(doc_dicts[0]['text'][0] , 'bar' ) # max inner product is reached with second doc self.assertEqual(doc_dicts[1]['text'][0] , 'foo' ) # max inner product is reached with first doc self.assertListEqual(doc_ids.tolist() , [[1], [0]] ) def _lowerCamelCase ( self : str ): __UpperCamelCase = self.get_dummy_legacy_index_retriever() with tempfile.TemporaryDirectory() as tmp_dirname: retriever.save_pretrained(__A ) __UpperCamelCase = RagRetriever.from_pretrained(__A ) self.assertIsInstance(__A , __A ) __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase = retriever.retrieve(__A , n_docs=1 ) self.assertTrue(out is not None ) @require_torch @require_tokenizers @require_sentencepiece def _lowerCamelCase ( self : Optional[Any] ): import torch __UpperCamelCase = 1 __UpperCamelCase = self.get_dummy_canonical_hf_index_retriever() __UpperCamelCase = [[5, 7], [1_0, 1_1]] __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase = retriever(__A , __A , prefix=retriever.config.generator.prefix , n_docs=__A ) __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = ( out['context_input_ids'], out['context_attention_mask'], out['retrieved_doc_embeds'], ) self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) ) self.assertIsInstance(__A , __A ) self.assertIsInstance(__A , __A ) self.assertIsInstance(__A , np.ndarray ) __UpperCamelCase = retriever( __A , __A , prefix=retriever.config.generator.prefix , n_docs=__A , return_tensors='pt' , ) __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = ( # noqa: F841 out['context_input_ids'], out['context_attention_mask'], out['retrieved_doc_embeds'], out['doc_ids'], ) self.assertEqual(retrieved_doc_embeds.shape , (2, n_docs, self.retrieval_vector_size) ) self.assertIsInstance(__A , torch.Tensor ) self.assertIsInstance(__A , torch.Tensor ) self.assertIsInstance(__A , torch.Tensor ) @require_torch @require_tokenizers @require_sentencepiece def _lowerCamelCase ( self : Any ): __UpperCamelCase = self.get_dpr_ctx_encoder_tokenizer() __UpperCamelCase = 1 __UpperCamelCase = self.get_dummy_custom_hf_index_retriever(from_disk=__A ) retriever.set_ctx_encoder_tokenizer(__A ) __UpperCamelCase = [[5, 7], [1_0, 1_1]] __UpperCamelCase = np.array( [np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] , dtype=np.floataa ) __UpperCamelCase = retriever(__A , __A , prefix=retriever.config.generator.prefix , n_docs=__A ) self.assertEqual( len(__A ) , 6 ) # check whether the retriever output consist of 6 attributes including tokenized docs self.assertEqual( all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask') ) , __A ) # check for doc token related keys in dictionary.
53
1
"""simple docstring""" import unittest from pathlib import Path from shutil import copyfile from transformers import SPIECE_UNDERLINE, is_sentencepiece_available from transformers.models.speech_to_text import SpeechaTextTokenizer from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from ...test_tokenization_common import TokenizerTesterMixin __snake_case = get_tests_dir('''fixtures/test_sentencepiece.model''') if is_sentencepiece_available(): import sentencepiece as sp __snake_case = 5 __snake_case = 10 @require_sentencepiece @require_tokenizers class __lowerCamelCase ( _a , unittest.TestCase ): '''simple docstring''' A_ : str = SpeechaTextTokenizer A_ : Any = False A_ : Optional[int] = True def _UpperCAmelCase ( self ) -> Tuple: super().setUp() _a = sp.SentencePieceProcessor() spm_model.Load(__lowerCAmelCase ) _a = ['''<s>''', '''<pad>''', '''</s>''', '''<unk>'''] vocab += [spm_model.IdToPiece(id_ ) for id_ in range(len(__lowerCAmelCase ) )] _a = dict(zip(__lowerCAmelCase , range(len(__lowerCAmelCase ) ) ) ) _a = Path(self.tmpdirname ) save_json(__lowerCAmelCase , save_dir / VOCAB_FILES_NAMES['''vocab_file'''] ) if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists(): copyfile(__lowerCAmelCase , save_dir / VOCAB_FILES_NAMES['''spm_file'''] ) _a = SpeechaTextTokenizer.from_pretrained(self.tmpdirname ) tokenizer.save_pretrained(self.tmpdirname ) def _UpperCAmelCase ( self ) -> str: _a = '''<pad>''' _a = 1 self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase ) , __lowerCAmelCase ) self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase ) , __lowerCAmelCase ) def _UpperCAmelCase ( self ) -> int: _a = list(self.get_tokenizer().get_vocab().keys() ) self.assertEqual(vocab_keys[0] , '''<s>''' ) self.assertEqual(vocab_keys[1] , '''<pad>''' ) self.assertEqual(vocab_keys[-1] , '''j''' ) self.assertEqual(len(__lowerCAmelCase ) , 1001 ) def _UpperCAmelCase ( self ) -> Tuple: self.assertEqual(self.get_tokenizer().vocab_size , 1001 ) def _UpperCAmelCase ( self ) -> int: _a = SpeechaTextTokenizer.from_pretrained(self.tmpdirname ) _a = tokenizer.tokenize('''This is a test''' ) self.assertListEqual(__lowerCAmelCase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] ) self.assertListEqual( tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , [289, 50, 14, 174, 386] , ) _a = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' ) self.assertListEqual( __lowerCAmelCase , [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(__lowerCAmelCase ) self.assertListEqual(__lowerCAmelCase , [12, 25, 88, 59, 28, 23, 11, 4, 606, 351, 351, 351, 7, 16, 70, 50, 76, 84, 10, 4, 8] ) _a = tokenizer.convert_ids_to_tokens(__lowerCAmelCase ) self.assertListEqual( __lowerCAmelCase , [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>''', '''.'''] , ) @slow def _UpperCAmelCase ( self ) -> List[str]: # fmt: off _a = {'''input_ids''': [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64, 302, 548, 528, 423, 4, 509, 406, 423, 37, 601, 4, 777, 302, 548, 528, 423, 284, 4, 3388, 511, 459, 4, 3555, 40, 321, 302, 705, 4, 3388, 511, 583, 326, 5, 5, 5, 62, 3310, 560, 177, 2680, 217, 1508, 32, 31, 853, 418, 64, 583, 511, 1605, 62, 35, 93, 560, 177, 2680, 217, 1508, 1521, 64, 583, 511, 519, 62, 20, 1515, 764, 20, 149, 261, 5625, 7972, 20, 5540, 567, 1276, 93, 3925, 1675, 11, 15, 802, 7972, 576, 217, 1508, 11, 35, 93, 1253, 2441, 15, 289, 652, 31, 416, 321, 3842, 115, 40, 911, 8, 476, 619, 4, 380, 142, 423, 335, 240, 35, 93, 264, 8, 11, 335, 569, 420, 163, 5, 2], [260, 548, 528, 423, 20, 451, 20, 2681, 1153, 3434, 20, 5540, 37, 567, 126, 1253, 2441, 3376, 449, 210, 431, 1563, 177, 767, 5540, 11, 1203, 472, 11, 2953, 685, 285, 364, 706, 1153, 20, 6799, 20, 2869, 20, 4464, 126, 40, 2429, 20, 1040, 866, 2664, 418, 20, 318, 20, 1726, 186, 20, 265, 522, 35, 93, 2191, 4634, 20, 1040, 12, 6799, 15, 228, 2356, 142, 31, 11, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2575, 2666, 684, 1582, 1176, 12, 627, 149, 619, 20, 4902, 563, 11, 20, 149, 261, 3420, 2356, 174, 142, 4714, 131, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=__lowerCAmelCase , model_name='''facebook/s2t-small-mustc-en-de-st''' , revision='''a14f04cf0776c02f62a8cb800cf7909e15ea23ad''' , ) @require_sentencepiece class __lowerCamelCase ( unittest.TestCase ): '''simple docstring''' A_ : Any = """valhalla/s2t_mustc_multilinguial_medium""" A_ : List[Any] = """C'est trop cool""" A_ : Optional[Any] = """Esto es genial""" @classmethod def _UpperCAmelCase ( cls ) -> int: _a = SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name ) return cls def _UpperCAmelCase ( self ) -> Union[str, Any]: self.assertEqual(self.tokenizer.lang_code_to_id['''pt'''] , 4 ) self.assertEqual(self.tokenizer.lang_code_to_id['''ru'''] , 6 ) self.assertEqual(self.tokenizer.lang_code_to_id['''it'''] , 9 ) self.assertEqual(self.tokenizer.lang_code_to_id['''de'''] , 11 ) def _UpperCAmelCase ( self ) -> List[Any]: self.assertEqual(self.tokenizer.vocab_size , 10000 ) def _UpperCAmelCase ( self ) -> Optional[Any]: self.assertIn(__lowerCAmelCase , self.tokenizer.all_special_ids ) _a = [ES_CODE, 4, 1601, 47, 7647, 2] _a = self.tokenizer.decode(__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase ) _a = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowerCAmelCase ) self.assertEqual(__lowerCAmelCase , __lowerCAmelCase ) self.assertNotIn(self.tokenizer.eos_token , __lowerCAmelCase ) def _UpperCAmelCase ( self ) -> str: _a = '''fr''' _a = self.tokenizer(self.french_text ).input_ids self.assertEqual(encoded[0] , __lowerCAmelCase ) self.assertEqual(encoded[-1] , self.tokenizer.eos_token_id ) def _UpperCAmelCase ( self ) -> Tuple: _a = '''fr''' self.assertListEqual(self.tokenizer.prefix_tokens , [FR_CODE] ) _a = '''es''' self.assertListEqual(self.tokenizer.prefix_tokens , [ES_CODE] )
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"""simple docstring""" def A_ ( _lowerCAmelCase : str ): """simple docstring""" _a = '''''' for ch in key: if ch == " " or ch not in key_no_dups and ch.isalpha(): key_no_dups += ch return key_no_dups def A_ ( _lowerCAmelCase : str ): """simple docstring""" _a = [chr(i + 65 ) for i in range(26 )] # Remove duplicate characters from key _a = remove_duplicates(key.upper() ) _a = len(_lowerCAmelCase ) # First fill cipher with key characters _a = {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase )} # Then map remaining characters in alphabet to # the alphabet from the beginning for i in range(len(_lowerCAmelCase ), 26 ): _a = alphabet[i - offset] # Ensure we are not mapping letters to letters previously mapped while char in key: offset -= 1 _a = alphabet[i - offset] _a = char return cipher_alphabet def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : dict[str, str] ): """simple docstring""" return "".join(cipher_map.get(_lowerCAmelCase, _lowerCAmelCase ) for ch in message.upper() ) def A_ ( _lowerCAmelCase : str, _lowerCAmelCase : dict[str, str] ): """simple docstring""" _a = {v: k for k, v in cipher_map.items()} return "".join(rev_cipher_map.get(_lowerCAmelCase, _lowerCAmelCase ) for ch in message.upper() ) def A_ ( ): """simple docstring""" _a = input('''Enter message to encode or decode: ''' ).strip() _a = input('''Enter keyword: ''' ).strip() _a = input('''Encipher or decipher? E/D:''' ).strip()[0].lower() try: _a = {'''e''': encipher, '''d''': decipher}[option] except KeyError: raise KeyError('''invalid input option''' ) _a = create_cipher_map(_lowerCAmelCase ) print(func(_lowerCAmelCase, _lowerCAmelCase ) ) if __name__ == "__main__": import doctest doctest.testmod() main()
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0
'''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 lowercase : Optional[int] = logging.get_logger(__name__) lowercase : Optional[int] = { 'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json', # See all GPT-J models at https://huggingface.co/models?filter=gpt_j } class A ( __snake_case ): __magic_name__ = '''gptj''' __magic_name__ = { '''max_position_embeddings''': '''n_positions''', '''hidden_size''': '''n_embd''', '''num_attention_heads''': '''n_head''', '''num_hidden_layers''': '''n_layer''', } def __init__( self , SCREAMING_SNAKE_CASE=50400 , SCREAMING_SNAKE_CASE=2048 , SCREAMING_SNAKE_CASE=4096 , SCREAMING_SNAKE_CASE=28 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=64 , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE="gelu_new" , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE=0.02 , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=50256 , SCREAMING_SNAKE_CASE=50256 , SCREAMING_SNAKE_CASE=False , **SCREAMING_SNAKE_CASE , ) -> Optional[Any]: """simple docstring""" A : int = vocab_size A : Tuple = n_positions A : Optional[int] = n_embd A : List[Any] = n_layer A : Optional[Any] = n_head A : Tuple = n_inner A : Tuple = rotary_dim A : Tuple = activation_function A : Optional[int] = resid_pdrop A : Dict = embd_pdrop A : Tuple = attn_pdrop A : Tuple = layer_norm_epsilon A : Any = initializer_range A : Dict = use_cache A : Tuple = bos_token_id A : Any = 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 ( __snake_case ): def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = "default" , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = False , ) -> Optional[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? A : str = 0 @property def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]: """simple docstring""" A : str = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} ) if self.use_past: self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE , direction='''inputs''' ) A : Union[str, Any] = {0: '''batch''', 1: '''past_sequence + sequence'''} else: A : Optional[Any] = {0: '''batch''', 1: '''sequence'''} return common_inputs @property def __lowerCAmelCase ( self ) -> int: """simple docstring""" return self._config.n_layer @property def __lowerCAmelCase ( self ) -> int: """simple docstring""" return self._config.n_head def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = -1 , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , ) -> Mapping[str, Any]: """simple docstring""" A : Optional[int] = 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() A : Any = 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 A, A : Tuple = common_inputs['''input_ids'''].shape # Not using the same length for past_key_values A : Dict = seqlen + 2 A : Optional[Any] = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) A : Optional[Any] = [ (torch.zeros(SCREAMING_SNAKE_CASE ), torch.zeros(SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers ) ] A : Any = common_inputs['''attention_mask'''] if self.use_past: A : List[str] = ordered_inputs['''attention_mask'''].dtype A : int = 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 __lowerCAmelCase ( self ) -> int: """simple docstring""" return 13
3
"""simple docstring""" import warnings from contextlib import contextmanager from ...processing_utils import ProcessorMixin class SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ): lowercase__ = "Speech2TextFeatureExtractor" lowercase__ = "Speech2TextTokenizer" def __init__( self : Optional[Any] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Tuple): """simple docstring""" super().__init__(lowerCAmelCase_ , lowerCAmelCase_) lowercase_ = self.feature_extractor lowercase_ = False def __call__( self : Dict , *lowerCAmelCase_ : List[str] , **lowerCAmelCase_ : List[str]): """simple docstring""" if self._in_target_context_manager: return self.current_processor(*lowerCAmelCase_ , **lowerCAmelCase_) if "raw_speech" in kwargs: warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""") lowercase_ = kwargs.pop("""raw_speech""") else: lowercase_ = kwargs.pop("""audio""" , lowerCAmelCase_) lowercase_ = kwargs.pop("""sampling_rate""" , lowerCAmelCase_) lowercase_ = kwargs.pop("""text""" , lowerCAmelCase_) if len(lowerCAmelCase_) > 0: lowercase_ = args[0] lowercase_ = args[1:] if audio is None and text is None: raise ValueError("""You need to specify either an `audio` or `text` input to process.""") if audio is not None: lowercase_ = self.feature_extractor(lowerCAmelCase_ , *lowerCAmelCase_ , sampling_rate=lowerCAmelCase_ , **lowerCAmelCase_) if text is not None: lowercase_ = self.tokenizer(lowerCAmelCase_ , **lowerCAmelCase_) if text is None: return inputs elif audio is None: return encodings else: lowercase_ = encodings["""input_ids"""] return inputs def _UpperCAmelCase ( self : List[str] , *lowerCAmelCase_ : Dict , **lowerCAmelCase_ : Optional[Any]): """simple docstring""" return self.tokenizer.batch_decode(*lowerCAmelCase_ , **lowerCAmelCase_) def _UpperCAmelCase ( self : Optional[int] , *lowerCAmelCase_ : int , **lowerCAmelCase_ : str): """simple docstring""" return self.tokenizer.decode(*lowerCAmelCase_ , **lowerCAmelCase_) @contextmanager def _UpperCAmelCase ( self : Optional[int]): """simple docstring""" warnings.warn( """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """ """labels by using the argument `text` of the regular `__call__` method (either in the same call as """ """your audio inputs, or in a separate call.""") lowercase_ = True lowercase_ = self.tokenizer yield lowercase_ = self.feature_extractor lowercase_ = False
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from collections.abc import Iterable from typing import Generic, TypeVar snake_case = TypeVar("""_T""") class SCREAMING_SNAKE_CASE ( Generic[_T] ): '''simple docstring''' def __init__( self : Tuple , UpperCAmelCase_ : Iterable[_T] | None = None ): SCREAMING_SNAKE_CASE : list[_T] = list(iterable or [] ) SCREAMING_SNAKE_CASE : list[_T] = [] def __len__( self : Dict ): return len(self._stacka ) + len(self._stacka ) def __repr__( self : str ): return f'''Queue({tuple(self._stacka[::-1] + self._stacka )})''' def _A ( self : List[Any] , UpperCAmelCase_ : _T ): self._stacka.append(UpperCAmelCase_ ) def _A ( self : List[Any] ): SCREAMING_SNAKE_CASE : str = self._stacka.pop SCREAMING_SNAKE_CASE : Tuple = self._stacka.append if not self._stacka: while self._stacka: stacka_append(stacka_pop() ) if not self._stacka: raise IndexError("Queue is empty" ) return self._stacka.pop() if __name__ == "__main__": from doctest import testmod testmod()
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class SCREAMING_SNAKE_CASE : '''simple docstring''' def __init__( self : Union[str, Any] , UpperCAmelCase_ : list ): SCREAMING_SNAKE_CASE : Union[str, Any] = set_counts SCREAMING_SNAKE_CASE : Any = max(UpperCAmelCase_ ) SCREAMING_SNAKE_CASE : Any = len(UpperCAmelCase_ ) SCREAMING_SNAKE_CASE : List[str] = [1] * num_sets SCREAMING_SNAKE_CASE : List[str] = list(range(UpperCAmelCase_ ) ) def _A ( self : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ): SCREAMING_SNAKE_CASE : List[Any] = self.get_parent(UpperCAmelCase_ ) SCREAMING_SNAKE_CASE : List[str] = self.get_parent(UpperCAmelCase_ ) if src_parent == dst_parent: return False if self.ranks[dst_parent] >= self.ranks[src_parent]: self.set_counts[dst_parent] += self.set_counts[src_parent] SCREAMING_SNAKE_CASE : Dict = 0 SCREAMING_SNAKE_CASE : Union[str, Any] = dst_parent if self.ranks[dst_parent] == self.ranks[src_parent]: self.ranks[dst_parent] += 1 SCREAMING_SNAKE_CASE : List[str] = self.set_counts[dst_parent] else: self.set_counts[src_parent] += self.set_counts[dst_parent] SCREAMING_SNAKE_CASE : Optional[int] = 0 SCREAMING_SNAKE_CASE : Tuple = src_parent SCREAMING_SNAKE_CASE : Optional[int] = self.set_counts[src_parent] SCREAMING_SNAKE_CASE : Optional[Any] = max(self.max_set , UpperCAmelCase_ ) return True def _A ( self : Tuple , UpperCAmelCase_ : int ): if self.parents[disj_set] == disj_set: return disj_set SCREAMING_SNAKE_CASE : Tuple = self.get_parent(self.parents[disj_set] ) return self.parents[disj_set]
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging lowercase_ = logging.get_logger(__name__) lowercase_ = { "facebook/dpr-ctx_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-single-nq-base": ( "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json" ), "facebook/dpr-reader-single-nq-base": ( "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json" ), "facebook/dpr-ctx_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-question_encoder-multiset-base": ( "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json" ), "facebook/dpr-reader-multiset-base": ( "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json" ), } class __A ( A ): '''simple docstring''' __lowerCamelCase : Tuple = 'dpr' def __init__(self , A=30_522 , A=768 , A=12 , A=12 , A=3_072 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=2 , A=0.02 , A=1E-12 , A=0 , A="absolute" , A = 0 , **A , ) -> Optional[Any]: """simple docstring""" super().__init__(pad_token_id=A , **A ) _a = vocab_size _a = hidden_size _a = num_hidden_layers _a = num_attention_heads _a = hidden_act _a = intermediate_size _a = hidden_dropout_prob _a = attention_probs_dropout_prob _a = max_position_embeddings _a = type_vocab_size _a = initializer_range _a = layer_norm_eps _a = projection_dim _a = position_embedding_type
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'''simple docstring''' from __future__ import annotations from typing import TypedDict class __A ( A ): '''simple docstring''' __lowerCamelCase : str __lowerCamelCase : int def lowerCAmelCase (__A): """simple docstring""" if not isinstance(__A , __A): raise TypeError('''The parameter s type must be str.''') return [s[i:] + s[:i] for i in range(len(__A))] def lowerCAmelCase (__A): """simple docstring""" if not isinstance(__A , __A): raise TypeError('''The parameter s type must be str.''') if not s: raise ValueError('''The parameter s must not be empty.''') _a = all_rotations(__A) rotations.sort() # sort the list of rotations in alphabetically order # make a string composed of the last char of each rotation _a = { "bwt_string": "".join([word[-1] for word in rotations]), "idx_original_string": rotations.index(__A), } return response def lowerCAmelCase (__A , __A): """simple docstring""" if not isinstance(__A , __A): raise TypeError('''The parameter bwt_string type must be str.''') if not bwt_string: raise ValueError('''The parameter bwt_string must not be empty.''') try: _a = int(__A) except ValueError: raise TypeError( '''The parameter idx_original_string type must be int or passive''' ''' of cast to int.''') if idx_original_string < 0: raise ValueError('''The parameter idx_original_string must not be lower than 0.''') if idx_original_string >= len(__A): raise ValueError( '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).''') _a = [''''''] * len(__A) for _ in range(len(__A)): for i in range(len(__A)): _a = bwt_string[i] + ordered_rotations[i] ordered_rotations.sort() return ordered_rotations[idx_original_string] if __name__ == "__main__": lowercase_ = "Provide a string that I will generate its BWT transform: " lowercase_ = input(entry_msg).strip() lowercase_ = bwt_transform(s) print( F"""Burrows Wheeler transform for string '{s}' results """ F"""in '{result['bwt_string']}'""" ) lowercase_ = reverse_bwt(result["bwt_string"], result["idx_original_string"]) print( F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """ F"""we get original string '{original_string}'""" )
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices lowercase__ : Optional[int] = logging.get_logger(__name__) class UpperCamelCase__ ( lowercase_, lowercase_ ): """simple docstring""" _SCREAMING_SNAKE_CASE = """maskformer-swin""" _SCREAMING_SNAKE_CASE = { """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers""", } def __init__( self : Tuple , SCREAMING_SNAKE_CASE_ : Optional[Any]=2_2_4 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=4 , SCREAMING_SNAKE_CASE_ : int=3 , SCREAMING_SNAKE_CASE_ : Dict=9_6 , SCREAMING_SNAKE_CASE_ : Optional[int]=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE_ : List[Any]=[3, 6, 1_2, 2_4] , SCREAMING_SNAKE_CASE_ : List[str]=7 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=4.0 , SCREAMING_SNAKE_CASE_ : Optional[Any]=True , SCREAMING_SNAKE_CASE_ : Tuple=0.0 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.0 , SCREAMING_SNAKE_CASE_ : int=0.1 , SCREAMING_SNAKE_CASE_ : Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE_ : Dict=False , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.02 , SCREAMING_SNAKE_CASE_ : int=1E-5 , SCREAMING_SNAKE_CASE_ : Dict=None , SCREAMING_SNAKE_CASE_ : Optional[int]=None , **SCREAMING_SNAKE_CASE_ : str , ): super().__init__(**SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Dict = image_size lowerCAmelCase_ : Optional[Any] = patch_size lowerCAmelCase_ : Optional[int] = num_channels lowerCAmelCase_ : List[str] = embed_dim lowerCAmelCase_ : Dict = depths lowerCAmelCase_ : Optional[Any] = len(SCREAMING_SNAKE_CASE_ ) lowerCAmelCase_ : Tuple = num_heads lowerCAmelCase_ : List[str] = window_size lowerCAmelCase_ : Any = mlp_ratio lowerCAmelCase_ : Any = qkv_bias lowerCAmelCase_ : List[Any] = hidden_dropout_prob lowerCAmelCase_ : List[Any] = attention_probs_dropout_prob lowerCAmelCase_ : Tuple = drop_path_rate lowerCAmelCase_ : List[str] = hidden_act lowerCAmelCase_ : Any = use_absolute_embeddings lowerCAmelCase_ : Optional[Any] = layer_norm_eps lowerCAmelCase_ : str = initializer_range # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel # this indicates the channel dimension after the last stage of the model lowerCAmelCase_ : List[str] = int(embed_dim * 2 ** (len(SCREAMING_SNAKE_CASE_ ) - 1) ) lowerCAmelCase_ : List[Any] = ['stem'] + [F"stage{idx}" for idx in range(1 , len(SCREAMING_SNAKE_CASE_ ) + 1 )] lowerCAmelCase_ ,lowerCAmelCase_ : Tuple = get_aligned_output_features_output_indices( out_features=SCREAMING_SNAKE_CASE_ , out_indices=SCREAMING_SNAKE_CASE_ , stage_names=self.stage_names )
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"""simple docstring""" def UpperCamelCase_ ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : list[int] ) -> None: """simple docstring""" lowerCAmelCase_ : List[Any] = len(lowerCAmelCase__ ) print('The following activities are selected:' ) # The first activity is always selected lowerCAmelCase_ : str = 0 print(lowerCAmelCase__ , end=',' ) # Consider rest of the activities for j in range(lowerCAmelCase__ ): # If this activity has start time greater than # or equal to the finish time of previously # selected activity, then select it if start[j] >= finish[i]: print(lowerCAmelCase__ , end=',' ) lowerCAmelCase_ : Tuple = j if __name__ == "__main__": import doctest doctest.testmod() lowercase__ : List[str] = [1, 3, 0, 5, 8, 5] lowercase__ : Dict = [2, 4, 6, 7, 9, 9] print_max_activities(start, finish)
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from __future__ import annotations from math import pow, sqrt def __UpperCAmelCase ( a_ , a_ , a_): if (resistance, reactance, impedance).count(0) != 1: raise ValueError('One and only one argument must be 0') if resistance == 0: return {"resistance": sqrt(pow(a_ , 2) - pow(a_ , 2))} elif reactance == 0: return {"reactance": sqrt(pow(a_ , 2) - pow(a_ , 2))} elif impedance == 0: return {"impedance": sqrt(pow(a_ , 2) + pow(a_ , 2))} else: raise ValueError('Exactly one argument must be 0') if __name__ == "__main__": import doctest doctest.testmod()
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from typing import TYPE_CHECKING from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available from ...utils import OptionalDependencyNotAvailable lowercase = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]} try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase = ["GPTNeoXTokenizerFast"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase = [ "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST", "GPTNeoXForCausalLM", "GPTNeoXForQuestionAnswering", "GPTNeoXForSequenceClassification", "GPTNeoXForTokenClassification", "GPTNeoXLayer", "GPTNeoXModel", "GPTNeoXPreTrainedModel", ] if TYPE_CHECKING: from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_gpt_neox import ( GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST, GPTNeoXForCausalLM, GPTNeoXForQuestionAnswering, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification, GPTNeoXLayer, GPTNeoXModel, GPTNeoXPreTrainedModel, ) else: import sys lowercase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto import CONFIG_MAPPING lowerCAmelCase__ = logging.get_logger(__name__) lowerCAmelCase__ = { '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''', # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr } class SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ): """simple docstring""" a : Optional[Any] ='''deformable_detr''' a : Dict ={ '''hidden_size''': '''d_model''', '''num_attention_heads''': '''encoder_attention_heads''', } def __init__( self , snake_case__=True , snake_case__=None , snake_case__=3 , snake_case__=300 , snake_case__=1_024 , snake_case__=6 , snake_case__=1_024 , snake_case__=8 , snake_case__=6 , snake_case__=1_024 , snake_case__=8 , snake_case__=0.0 , snake_case__=True , snake_case__="relu" , snake_case__=256 , snake_case__=0.1 , snake_case__=0.0 , snake_case__=0.0 , snake_case__=0.02 , snake_case__=1.0 , snake_case__=True , snake_case__=False , snake_case__="sine" , snake_case__="resnet50" , snake_case__=True , snake_case__=False , snake_case__=4 , snake_case__=4 , snake_case__=4 , snake_case__=False , snake_case__=300 , snake_case__=False , snake_case__=1 , snake_case__=5 , snake_case__=2 , snake_case__=1 , snake_case__=1 , snake_case__=5 , snake_case__=2 , snake_case__=0.1 , snake_case__=0.25 , snake_case__=False , **snake_case__ , ): """simple docstring""" if backbone_config is not None and use_timm_backbone: raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." ) if not use_timm_backbone: if backbone_config is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." ) lowerCAmelCase : List[str] = CONFIG_MAPPING["resnet"](out_features=["stage4"] ) elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ): lowerCAmelCase : Optional[Any] = backbone_config.get("model_type" ) lowerCAmelCase : Optional[int] = CONFIG_MAPPING[backbone_model_type] lowerCAmelCase : Tuple = config_class.from_dict(lowerCAmelCase_ ) lowerCAmelCase : Tuple = use_timm_backbone lowerCAmelCase : Optional[int] = backbone_config lowerCAmelCase : List[str] = num_channels lowerCAmelCase : Any = num_queries lowerCAmelCase : Optional[Any] = max_position_embeddings lowerCAmelCase : Optional[int] = d_model lowerCAmelCase : List[str] = encoder_ffn_dim lowerCAmelCase : int = encoder_layers lowerCAmelCase : Dict = encoder_attention_heads lowerCAmelCase : List[str] = decoder_ffn_dim lowerCAmelCase : Tuple = decoder_layers lowerCAmelCase : Optional[Any] = decoder_attention_heads lowerCAmelCase : Optional[int] = dropout lowerCAmelCase : List[str] = attention_dropout lowerCAmelCase : List[Any] = activation_dropout lowerCAmelCase : List[str] = activation_function lowerCAmelCase : Optional[int] = init_std lowerCAmelCase : List[str] = init_xavier_std lowerCAmelCase : Any = encoder_layerdrop lowerCAmelCase : Tuple = auxiliary_loss lowerCAmelCase : List[Any] = position_embedding_type lowerCAmelCase : int = backbone lowerCAmelCase : str = use_pretrained_backbone lowerCAmelCase : Any = dilation # deformable attributes lowerCAmelCase : Dict = num_feature_levels lowerCAmelCase : Dict = encoder_n_points lowerCAmelCase : int = decoder_n_points lowerCAmelCase : int = two_stage lowerCAmelCase : Any = two_stage_num_proposals lowerCAmelCase : Dict = with_box_refine if two_stage is True and with_box_refine is False: raise ValueError("If two_stage is True, with_box_refine must be True." ) # Hungarian matcher lowerCAmelCase : Tuple = class_cost lowerCAmelCase : int = bbox_cost lowerCAmelCase : Dict = giou_cost # Loss coefficients lowerCAmelCase : Tuple = mask_loss_coefficient lowerCAmelCase : Tuple = dice_loss_coefficient lowerCAmelCase : Optional[int] = bbox_loss_coefficient lowerCAmelCase : Optional[int] = giou_loss_coefficient lowerCAmelCase : Any = eos_coefficient lowerCAmelCase : Union[str, Any] = focal_alpha lowerCAmelCase : int = disable_custom_kernels super().__init__(is_encoder_decoder=lowerCAmelCase_ , **lowerCAmelCase_ ) @property def lowercase__ ( self ): """simple docstring""" return self.encoder_attention_heads @property def lowercase__ ( self ): """simple docstring""" return self.d_model def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : Optional[int] = copy.deepcopy(self.__dict__ ) if self.backbone_config is not None: lowerCAmelCase : Any = self.backbone_config.to_dict() lowerCAmelCase : List[str] = self.__class__.model_type return output
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"""simple docstring""" import unittest from datasets import load_dataset from transformers import BloomTokenizerFast from transformers.testing_utils import require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_tokenizers class SCREAMING_SNAKE_CASE__ ( lowercase , unittest.TestCase ): """simple docstring""" a : List[str] =None a : List[Any] =BloomTokenizerFast a : Optional[int] =BloomTokenizerFast a : Optional[Any] =True a : Dict =False a : Optional[Any] ="tokenizer_file" a : Optional[int] ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"} def lowercase__ ( self ): """simple docstring""" super().setUp() lowerCAmelCase : Tuple = BloomTokenizerFast.from_pretrained("bigscience/tokenizer" ) tokenizer.save_pretrained(self.tmpdirname ) def lowercase__ ( self , **snake_case__ ): """simple docstring""" kwargs.update(self.special_tokens_map ) return BloomTokenizerFast.from_pretrained(self.tmpdirname , **snake_case__ ) def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : int = self.get_rust_tokenizer() lowerCAmelCase : List[Any] = ["The quick brown fox</s>", "jumps over the lazy dog</s>"] lowerCAmelCase : str = [[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]] lowerCAmelCase : Optional[int] = tokenizer.batch_encode_plus(snake_case__ )["input_ids"] self.assertListEqual(snake_case__ , snake_case__ ) lowerCAmelCase : Optional[int] = tokenizer.batch_decode(snake_case__ ) self.assertListEqual(snake_case__ , snake_case__ ) def lowercase__ ( self , snake_case__=6 ): """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ): lowerCAmelCase : Tuple = self.rust_tokenizer_class.from_pretrained(snake_case__ , **snake_case__ ) # tokenizer_r.pad_token = None # Hotfixing padding = None # Simple input lowerCAmelCase : str = "This is a simple input" lowerCAmelCase : Tuple = ["This is a simple input 1", "This is a simple input 2"] lowerCAmelCase : Any = ("This is a simple input", "This is a pair") lowerCAmelCase : Tuple = [ ("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 try: tokenizer_r.encode(snake_case__ , max_length=snake_case__ ) tokenizer_r.encode_plus(snake_case__ , max_length=snake_case__ ) tokenizer_r.batch_encode_plus(snake_case__ , max_length=snake_case__ ) tokenizer_r.encode(snake_case__ , max_length=snake_case__ ) tokenizer_r.batch_encode_plus(snake_case__ , max_length=snake_case__ ) except ValueError: self.fail("Bloom Tokenizer should be able to deal with padding" ) lowerCAmelCase : Tuple = None # Hotfixing padding = None self.assertRaises(snake_case__ , tokenizer_r.encode , snake_case__ , max_length=snake_case__ , padding="max_length" ) # Simple input self.assertRaises(snake_case__ , tokenizer_r.encode_plus , snake_case__ , max_length=snake_case__ , padding="max_length" ) # Simple input self.assertRaises( snake_case__ , tokenizer_r.batch_encode_plus , snake_case__ , max_length=snake_case__ , padding="max_length" , ) # Pair input self.assertRaises(snake_case__ , tokenizer_r.encode , snake_case__ , max_length=snake_case__ , padding="max_length" ) # Pair input self.assertRaises(snake_case__ , tokenizer_r.encode_plus , snake_case__ , max_length=snake_case__ , padding="max_length" ) # Pair input self.assertRaises( snake_case__ , tokenizer_r.batch_encode_plus , snake_case__ , max_length=snake_case__ , padding="max_length" , ) def lowercase__ ( self ): """simple docstring""" lowerCAmelCase : int = self.get_rust_tokenizer() lowerCAmelCase : int = load_dataset("xnli" , "all_languages" , split="test" , streaming=snake_case__ ) lowerCAmelCase : Tuple = next(iter(snake_case__ ) )["premise"] # pick up one data lowerCAmelCase : Optional[Any] = list(sample_data.values() ) lowerCAmelCase : int = list(map(tokenizer.encode , snake_case__ ) ) lowerCAmelCase : List[Any] = [tokenizer.decode(snake_case__ , clean_up_tokenization_spaces=snake_case__ ) for x in output_tokens] self.assertListEqual(snake_case__ , snake_case__ ) def lowercase__ ( self ): """simple docstring""" self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 ) self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
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import argparse import requests import torch # pip3 install salesforce-lavis # I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch) # also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml # same for Vicuna-13b from lavis.models import load_model_and_preprocess from PIL import Image from transformers import ( AutoTokenizer, BlipImageProcessor, InstructBlipConfig, InstructBlipForConditionalGeneration, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig, LlamaConfig, LlamaTokenizerFast, TaConfig, TaTokenizerFast, ) from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD def _SCREAMING_SNAKE_CASE ( ): '''simple docstring''' lowerCamelCase_ = 'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg' lowerCamelCase_ = Image.open(requests.get(lowercase , stream=lowercase ).raw ).convert('RGB' ) return image def _SCREAMING_SNAKE_CASE ( lowercase : Tuple ): '''simple docstring''' lowerCamelCase_ = [] # fmt: off # vision encoder rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') ) rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') ) rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') ) rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') ) rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') ) rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') ) for i in range(config.vision_config.num_hidden_layers ): rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) ) rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") ) rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") ) # QFormer rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight') ) rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias') ) # fmt: on return rename_keys def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : Dict , lowercase : List[str] ): '''simple docstring''' lowerCamelCase_ = dct.pop(lowercase ) lowerCamelCase_ = val def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] , lowercase : Tuple ): '''simple docstring''' for i in range(config.vision_config.num_hidden_layers ): # read in original q and v biases lowerCamelCase_ = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" ) lowerCamelCase_ = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" ) # next, set bias in the state dict lowerCamelCase_ = torch.cat((q_bias, torch.zeros_like(lowercase , requires_grad=lowercase ), v_bias) ) lowerCamelCase_ = qkv_bias def _SCREAMING_SNAKE_CASE ( lowercase : Any ): '''simple docstring''' lowerCamelCase_ = 3_64 if 'coco' in model_name else 2_24 lowerCamelCase_ = InstructBlipVisionConfig(image_size=lowercase ).to_dict() # make sure the models have proper bos_token_id and eos_token_id set (important for generation) # seems like flan-T5 models don't have bos_token_id properly set? if "t5-xl" in model_name: lowerCamelCase_ = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict() elif "t5-xxl" in model_name: lowerCamelCase_ = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict() elif "vicuna-7b" in model_name: lowerCamelCase_ = LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf' , vocab_size=3_20_01 ).to_dict() elif "vicuna-13b" in model_name: lowerCamelCase_ = LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf' , vocab_size=3_20_01 ).to_dict() else: raise ValueError('Model name not supported' ) # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1 lowerCamelCase_ = InstructBlipQFormerConfig(vocab_size=3_05_23 ).to_dict() lowerCamelCase_ = InstructBlipConfig(vision_config=lowercase , text_config=lowercase , qformer_config=lowercase ) return config, image_size @torch.no_grad() def _SCREAMING_SNAKE_CASE ( lowercase : List[str] , lowercase : Optional[int]=None , lowercase : Optional[Any]=False ): '''simple docstring''' lowerCamelCase_ = AutoTokenizer.from_pretrained('bert-base-uncased' , truncation_side='left' ) qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'} ) if "t5" in model_name: lowerCamelCase_ = TaTokenizerFast.from_pretrained('google/flan-t5-xl' , truncation_side='left' ) elif "vicuna" in model_name: # the following was used in the original implementation: # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left") # tokenizer.add_special_tokens({"pad_token": "[PAD]"}) # tokenizer.add_special_tokens({"bos_token": "</s>"}) # tokenizer.add_special_tokens({"eos_token": "</s>"}) # tokenizer.add_special_tokens({"unk_token": "</s>"}) lowerCamelCase_ = LlamaTokenizerFast.from_pretrained( 'huggyllama/llama-7b' , truncation_side='left' , bos_token='</s>' , unk_token='</s>' ) tokenizer.add_special_tokens({'pad_token': '[PAD]'} ) lowerCamelCase_ , lowerCamelCase_ = get_blipa_config(lowercase ) lowerCamelCase_ = InstructBlipForConditionalGeneration(lowercase ).eval() lowerCamelCase_ = { 'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'), 'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'), 'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'), 'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'), } lowerCamelCase_ , lowerCamelCase_ = model_name_to_original[model_name] # load original model print('Loading original model...' ) lowerCamelCase_ = 'cuda:1' if torch.cuda.is_available() else 'cpu' lowerCamelCase_ = 'cuda:2' if torch.cuda.is_available() else 'cpu' lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = load_model_and_preprocess( name=lowercase , model_type=lowercase , is_eval=lowercase , device=lowercase ) original_model.eval() print('Done!' ) # update state dict keys lowerCamelCase_ = original_model.state_dict() lowerCamelCase_ = create_rename_keys(lowercase ) for src, dest in rename_keys: rename_key(lowercase , lowercase , lowercase ) # some keys can be renamed efficiently for key, val in state_dict.copy().items(): lowerCamelCase_ = state_dict.pop(lowercase ) if key.startswith('Qformer.bert' ): lowerCamelCase_ = key.replace('Qformer.bert' , 'qformer' ) if "attention.self" in key: lowerCamelCase_ = key.replace('self' , 'attention' ) if "llm_proj" in key: lowerCamelCase_ = key.replace('llm_proj' , 'language_projection' ) if "t5_proj" in key: lowerCamelCase_ = key.replace('t5_proj' , 'language_projection' ) if key.startswith('llm_model' ): lowerCamelCase_ = key.replace('llm_model' , 'language_model' ) if key.startswith('t5' ): lowerCamelCase_ = key.replace('t5' , 'language' ) lowerCamelCase_ = val # read in qv biases read_in_q_v_bias(lowercase , lowercase ) # note: weights get loaded in torch.float32 by default hf_model.load_state_dict(lowercase , strict=lowercase ) lowerCamelCase_ = load_demo_image() lowerCamelCase_ = 'What is unusual about this image?' # create processor lowerCamelCase_ = BlipImageProcessor( size={'height': image_size, 'width': image_size} , image_mean=lowercase , image_std=lowercase ) lowerCamelCase_ = InstructBlipProcessor( image_processor=lowercase , tokenizer=lowercase , qformer_tokenizer=lowercase , ) lowerCamelCase_ = processor(images=lowercase , text=lowercase , return_tensors='pt' ).to(lowercase ) # make sure processor creates exact same pixel values lowerCamelCase_ = vis_processors['eval'](lowercase ).unsqueeze(0 ).to(lowercase ) lowerCamelCase_ = inputs.pixel_values assert torch.allclose(original_pixel_values.to(pixel_values.device ) , lowercase ) original_model.to(lowercase ) hf_model.to(lowercase ) with torch.no_grad(): if "vicuna" in model_name: lowerCamelCase_ = original_model({'image': original_pixel_values, 'text_input': [prompt]} ).logits lowerCamelCase_ = hf_model(**lowercase ).logits else: lowerCamelCase_ = original_model( {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']} ).logits lowerCamelCase_ = tokenizer('\n' , return_tensors='pt' ).input_ids.to(lowercase ) lowerCamelCase_ = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -1_00 ) lowerCamelCase_ = hf_model(**lowercase , labels=lowercase ).logits print('First values of original logits:' , original_logits[0, :3, :3] ) print('First values of HF logits:' , logits[0, :3, :3] ) # assert values assert original_logits.shape == logits.shape lowerCamelCase_ = 1e-4 if 'vicuna' in model_name else 1e-5 assert torch.allclose(original_logits.to(logits.device ) , lowercase , atol=lowercase ) print('Looks ok!' ) print('Generating with original model...' ) lowerCamelCase_ = original_model.generate({'image': original_pixel_values, 'prompt': prompt} , num_beams=5 ) # important: we need to cast the weights of the HF model to the appropriate type print('Generating with HF model...' ) lowerCamelCase_ = hf_model.generate( **lowercase , do_sample=lowercase , num_beams=5 , max_length=2_56 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , ) if "vicuna" in model_name: # convert output id 0 to 2 (eos_token_id) # TODO add this in the generate method? lowerCamelCase_ = 2 print('Original generation:' , lowercase ) lowerCamelCase_ = processor.batch_decode(lowercase , skip_special_tokens=lowercase ) lowerCamelCase_ = [text.strip() for text in output_text] print('HF generation:' , lowercase ) if pytorch_dump_folder_path is not None: processor.save_pretrained(lowercase ) hf_model.save_pretrained(lowercase ) if push_to_hub: processor.push_to_hub(f"""Salesforce/{model_name}""" ) hf_model.push_to_hub(f"""Salesforce/{model_name}""" ) if __name__ == "__main__": lowerCamelCase : Any = argparse.ArgumentParser() lowerCamelCase : Tuple = [ "instructblip-vicuna-7b", "instructblip-vicuna-13b", "instructblip-flan-t5-xl", "instructblip-flan-t5-xxl", ] parser.add_argument( "--model_name", default="instructblip-flan-t5-xl", choices=choices, type=str, help="Path to hf config.json of model to convert", ) parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument( "--push_to_hub", action="store_true", help="Whether to push the model and processor to the hub after converting", ) lowerCamelCase : Tuple = parser.parse_args() convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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from __future__ import annotations import unittest from transformers import is_tf_available from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy as np import tensorflow as tf from transformers import ( TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST, FlaubertConfig, TFFlaubertForMultipleChoice, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForSequenceClassification, TFFlaubertForTokenClassification, TFFlaubertModel, TFFlaubertWithLMHeadModel, ) class A: '''simple docstring''' def __init__( self : str , A_ : Optional[Any] , ) -> str: """simple docstring""" lowerCamelCase_ = parent lowerCamelCase_ = 13 lowerCamelCase_ = 7 lowerCamelCase_ = True lowerCamelCase_ = True lowerCamelCase_ = True lowerCamelCase_ = True lowerCamelCase_ = True lowerCamelCase_ = False lowerCamelCase_ = False lowerCamelCase_ = False lowerCamelCase_ = 2 lowerCamelCase_ = 99 lowerCamelCase_ = 0 lowerCamelCase_ = 32 lowerCamelCase_ = 2 lowerCamelCase_ = 4 lowerCamelCase_ = 0.1 lowerCamelCase_ = 0.1 lowerCamelCase_ = 512 lowerCamelCase_ = 16 lowerCamelCase_ = 2 lowerCamelCase_ = 0.02 lowerCamelCase_ = 3 lowerCamelCase_ = 4 lowerCamelCase_ = 'last' lowerCamelCase_ = True lowerCamelCase_ = None lowerCamelCase_ = 0 def a__ ( self : Dict ) -> Union[str, Any]: """simple docstring""" lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa ) 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 , dtype=tf.floataa ) lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices ) lowerCamelCase_ = 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 , bos_token_id=self.bos_token_id , ) return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def a__ ( self : int , A_ : List[str] , A_ : List[Any] , A_ : str , A_ : List[Any] , A_ : int , A_ : Tuple , A_ : Optional[int] , A_ : Optional[int] , A_ : str , ) -> Union[str, Any]: """simple docstring""" lowerCamelCase_ = TFFlaubertModel(config=A_ ) lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids} lowerCamelCase_ = model(A_ ) lowerCamelCase_ = [input_ids, input_mask] lowerCamelCase_ = model(A_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def a__ ( self : Tuple , A_ : List[str] , A_ : int , A_ : List[Any] , A_ : Any , A_ : Any , A_ : Dict , A_ : str , A_ : List[Any] , A_ : Union[str, Any] , ) -> List[str]: """simple docstring""" lowerCamelCase_ = TFFlaubertWithLMHeadModel(A_ ) lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids} lowerCamelCase_ = model(A_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def a__ ( self : str , A_ : Tuple , A_ : Any , A_ : Any , A_ : List[Any] , A_ : Dict , A_ : List[Any] , A_ : Union[str, Any] , A_ : Optional[int] , A_ : List[Any] , ) -> Union[str, Any]: """simple docstring""" lowerCamelCase_ = TFFlaubertForQuestionAnsweringSimple(A_ ) lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths} lowerCamelCase_ = model(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 a__ ( self : Optional[int] , A_ : List[Any] , A_ : str , A_ : List[str] , A_ : Dict , A_ : Optional[Any] , A_ : Tuple , A_ : str , A_ : Optional[int] , A_ : Tuple , ) -> List[Any]: """simple docstring""" lowerCamelCase_ = TFFlaubertForSequenceClassification(A_ ) lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths} lowerCamelCase_ = model(A_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def a__ ( self : Dict , A_ : Optional[Any] , A_ : List[Any] , A_ : int , A_ : Any , A_ : Union[str, Any] , A_ : str , A_ : Any , A_ : Union[str, Any] , A_ : List[str] , ) -> Union[str, Any]: """simple docstring""" lowerCamelCase_ = self.num_labels lowerCamelCase_ = TFFlaubertForTokenClassification(config=A_ ) lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids} lowerCamelCase_ = model(A_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def a__ ( self : List[Any] , A_ : Optional[int] , A_ : List[Any] , A_ : Optional[int] , A_ : Tuple , A_ : Union[str, Any] , A_ : int , A_ : str , A_ : Tuple , A_ : str , ) -> Optional[int]: """simple docstring""" lowerCamelCase_ = self.num_choices lowerCamelCase_ = TFFlaubertForMultipleChoice(config=A_ ) lowerCamelCase_ = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) ) lowerCamelCase_ = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) ) lowerCamelCase_ = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) ) lowerCamelCase_ = { 'input_ids': multiple_choice_inputs_ids, 'attention_mask': multiple_choice_input_mask, 'token_type_ids': multiple_choice_token_type_ids, } lowerCamelCase_ = model(A_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def a__ ( self : Union[str, Any] ) -> List[Any]: """simple docstring""" lowerCamelCase_ = self.prepare_config_and_inputs() ( ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ( lowerCamelCase_ ) , ) = config_and_inputs lowerCamelCase_ = { 'input_ids': input_ids, 'token_type_ids': token_type_ids, 'langs': token_type_ids, 'lengths': input_lengths, } return config, inputs_dict @require_tf class A( UpperCamelCase , UpperCamelCase , unittest.TestCase ): '''simple docstring''' UpperCamelCase = ( ( TFFlaubertModel, TFFlaubertWithLMHeadModel, TFFlaubertForSequenceClassification, TFFlaubertForQuestionAnsweringSimple, TFFlaubertForTokenClassification, TFFlaubertForMultipleChoice, ) if is_tf_available() else () ) UpperCamelCase = ( (TFFlaubertWithLMHeadModel,) if is_tf_available() else () ) # TODO (PVP): Check other models whether language generation is also applicable UpperCamelCase = ( { '''feature-extraction''': TFFlaubertModel, '''fill-mask''': TFFlaubertWithLMHeadModel, '''question-answering''': TFFlaubertForQuestionAnsweringSimple, '''text-classification''': TFFlaubertForSequenceClassification, '''token-classification''': TFFlaubertForTokenClassification, '''zero-shot''': TFFlaubertForSequenceClassification, } if is_tf_available() else {} ) UpperCamelCase = False UpperCamelCase = False def a__ ( self : Union[str, Any] , A_ : Any , A_ : List[Any] , A_ : Union[str, Any] , A_ : str , A_ : List[str] ) -> Optional[Any]: """simple docstring""" 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 a__ ( self : List[str] ) -> Optional[int]: """simple docstring""" lowerCamelCase_ = TFFlaubertModelTester(self ) lowerCamelCase_ = ConfigTester(self , config_class=A_ , emb_dim=37 ) def a__ ( self : List[str] ) -> str: """simple docstring""" self.config_tester.run_common_tests() def a__ ( self : int ) -> Optional[int]: """simple docstring""" lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_model(*A_ ) def a__ ( self : List[str] ) -> Any: """simple docstring""" lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_lm_head(*A_ ) def a__ ( self : Dict ) -> Tuple: """simple docstring""" lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_qa(*A_ ) def a__ ( self : Union[str, Any] ) -> Tuple: """simple docstring""" lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_sequence_classif(*A_ ) def a__ ( self : List[Any] ) -> Dict: """simple docstring""" lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_for_token_classification(*A_ ) def a__ ( self : int ) -> Optional[int]: """simple docstring""" lowerCamelCase_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_for_multiple_choice(*A_ ) @slow def a__ ( self : Union[str, Any] ) -> Optional[int]: """simple docstring""" for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: lowerCamelCase_ = TFFlaubertModel.from_pretrained(A_ ) self.assertIsNotNone(A_ ) @require_tf @require_sentencepiece @require_tokenizers class A( unittest.TestCase ): '''simple docstring''' @slow def a__ ( self : Dict ) -> str: """simple docstring""" lowerCamelCase_ = TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased' ) lowerCamelCase_ = tf.convert_to_tensor( [[0, 158, 735, 2592, 1424, 6727, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !" lowerCamelCase_ = model(A_ )[0] lowerCamelCase_ = tf.TensorShape((1, 8, 512) ) self.assertEqual(output.shape , A_ ) # compare the actual values for a slice. lowerCamelCase_ = tf.convert_to_tensor( [ [ [-1.8768773, -1.566555, 0.27072418], [-1.6920038, -0.5873505, 1.9329599], [-2.9563985, -1.6993835, 1.7972052], ] ] , dtype=tf.floataa , ) self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
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1
"""simple docstring""" def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> list: lowercase__: Optional[int] = [0] * len(__UpperCAmelCase ) for i in range(1 , len(__UpperCAmelCase ) ): # use last results for better performance - dynamic programming lowercase__: Any = prefix_result[i - 1] while j > 0 and input_string[i] != input_string[j]: lowercase__: List[Any] = prefix_result[j - 1] if input_string[i] == input_string[j]: j += 1 lowercase__: Union[str, Any] = j return prefix_result def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase ) -> int: return max(prefix_function(__UpperCAmelCase ) ) if __name__ == "__main__": import doctest doctest.testmod()
2
"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging __A = logging.get_logger(__name__) __A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"} class UpperCAmelCase (_UpperCAmelCase ): """simple docstring""" _UpperCAmelCase :Optional[Any] = "ctrl" _UpperCAmelCase :int = ["past_key_values"] _UpperCAmelCase :Dict = { "max_position_embeddings": "n_positions", "hidden_size": "n_embd", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self , _UpperCAmelCase=246534 , _UpperCAmelCase=256 , _UpperCAmelCase=1280 , _UpperCAmelCase=8192 , _UpperCAmelCase=48 , _UpperCAmelCase=16 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=1e-6 , _UpperCAmelCase=0.02 , _UpperCAmelCase=True , **_UpperCAmelCase , ): lowercase__: Union[str, Any] = vocab_size lowercase__: Optional[int] = n_positions lowercase__: Optional[int] = n_embd lowercase__: Any = n_layer lowercase__: Any = n_head lowercase__: int = dff lowercase__: Dict = resid_pdrop lowercase__: Any = embd_pdrop lowercase__: Any = layer_norm_epsilon lowercase__: Optional[int] = initializer_range lowercase__: Dict = use_cache super().__init__(**_UpperCAmelCase )
2
1
'''simple docstring''' from ..utils import DummyObject, requires_backends class a ( metaclass=_lowerCamelCase ): snake_case_ = ["torch", "transformers", "onnx"] def __init__( self : int , *lowercase_ : List[Any] , **lowercase_ : List[str] ): requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Optional[Any] , *lowercase_ : Tuple , **lowercase_ : str ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Optional[Any] , *lowercase_ : int , **lowercase_ : List[str] ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) class a ( metaclass=_lowerCamelCase ): snake_case_ = ["torch", "transformers", "onnx"] def __init__( self : Any , *lowercase_ : Tuple , **lowercase_ : str ): requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : List[str] , *lowercase_ : Any , **lowercase_ : int ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : List[Any] , *lowercase_ : List[str] , **lowercase_ : str ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) class a ( metaclass=_lowerCamelCase ): snake_case_ = ["torch", "transformers", "onnx"] def __init__( self : Union[str, Any] , *lowercase_ : Tuple , **lowercase_ : Tuple ): requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : List[str] , *lowercase_ : Optional[int] , **lowercase_ : Optional[int] ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : List[Any] ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) class a ( metaclass=_lowerCamelCase ): snake_case_ = ["torch", "transformers", "onnx"] def __init__( self : Dict , *lowercase_ : Tuple , **lowercase_ : Tuple ): requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Any , *lowercase_ : int , **lowercase_ : Any ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Optional[Any] , *lowercase_ : Optional[Any] , **lowercase_ : Any ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) class a ( metaclass=_lowerCamelCase ): snake_case_ = ["torch", "transformers", "onnx"] def __init__( self : Dict , *lowercase_ : str , **lowercase_ : Union[str, Any] ): requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Dict , *lowercase_ : List[Any] , **lowercase_ : str ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Dict , *lowercase_ : Dict , **lowercase_ : List[str] ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) class a ( metaclass=_lowerCamelCase ): snake_case_ = ["torch", "transformers", "onnx"] def __init__( self : Optional[Any] , *lowercase_ : Optional[int] , **lowercase_ : List[str] ): requires_backends(self , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : int , *lowercase_ : Dict , **lowercase_ : Optional[int] ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] ) @classmethod def A_ ( cls : Optional[Any] , *lowercase_ : Any , **lowercase_ : str ): requires_backends(cls , ['''torch''', '''transformers''', '''onnx'''] )
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'''simple docstring''' import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel 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, skip_mps from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS, ) from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin enable_full_determinism() class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ): snake_case_ = CycleDiffusionPipeline snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - { "negative_prompt", "height", "width", "negative_prompt_embeds", } snake_case_ = PipelineTesterMixin.required_optional_params - {"latents"} snake_case_ = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} ) snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS snake_case_ = IMAGE_TO_IMAGE_IMAGE_PARAMS def A_ ( self : Tuple ): torch.manual_seed(0 ) snake_case_ = 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 , ) snake_case_ = DDIMScheduler( beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , num_train_timesteps=1000 , clip_sample=lowercase_ , set_alpha_to_one=lowercase_ , ) torch.manual_seed(0 ) snake_case_ = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , ) torch.manual_seed(0 ) snake_case_ = 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 , ) snake_case_ = CLIPTextModel(lowercase_ ) snake_case_ = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' ) snake_case_ = { '''unet''': unet, '''scheduler''': scheduler, '''vae''': vae, '''text_encoder''': text_encoder, '''tokenizer''': tokenizer, '''safety_checker''': None, '''feature_extractor''': None, } return components def A_ ( self : Any , lowercase_ : int , lowercase_ : Optional[Any]=0 ): snake_case_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowercase_ ) ).to(lowercase_ ) snake_case_ = image / 2 + 0.5 if str(lowercase_ ).startswith('''mps''' ): snake_case_ = torch.manual_seed(lowercase_ ) else: snake_case_ = torch.Generator(device=lowercase_ ).manual_seed(lowercase_ ) snake_case_ = { '''prompt''': '''An astronaut riding an elephant''', '''source_prompt''': '''An astronaut riding a horse''', '''image''': image, '''generator''': generator, '''num_inference_steps''': 2, '''eta''': 0.1, '''strength''': 0.8, '''guidance_scale''': 3, '''source_guidance_scale''': 1, '''output_type''': '''numpy''', } return inputs def A_ ( self : Union[str, Any] ): snake_case_ = '''cpu''' # ensure determinism for the device-dependent torch.Generator snake_case_ = self.get_dummy_components() snake_case_ = CycleDiffusionPipeline(**lowercase_ ) snake_case_ = pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) snake_case_ = self.get_dummy_inputs(lowercase_ ) snake_case_ = pipe(**lowercase_ ) snake_case_ = output.images snake_case_ = images[0, -3:, -3:, -1] assert images.shape == (1, 32, 32, 3) snake_case_ = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 @unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' ) def A_ ( self : Union[str, Any] ): snake_case_ = self.get_dummy_components() for name, module in components.items(): if hasattr(lowercase_ , '''half''' ): snake_case_ = module.half() snake_case_ = CycleDiffusionPipeline(**lowercase_ ) snake_case_ = pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) snake_case_ = self.get_dummy_inputs(lowercase_ ) snake_case_ = pipe(**lowercase_ ) snake_case_ = output.images snake_case_ = images[0, -3:, -3:, -1] assert images.shape == (1, 32, 32, 3) snake_case_ = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 @skip_mps def A_ ( self : Optional[int] ): return super().test_save_load_local() @unittest.skip('''non-deterministic pipeline''' ) def A_ ( self : List[Any] ): return super().test_inference_batch_single_identical() @skip_mps def A_ ( self : Union[str, Any] ): return super().test_dict_tuple_outputs_equivalent() @skip_mps def A_ ( self : Union[str, Any] ): return super().test_save_load_optional_components() @skip_mps def A_ ( self : Union[str, Any] ): return super().test_attention_slicing_forward_pass() @slow @require_torch_gpu class a ( unittest.TestCase ): def A_ ( self : List[Any] ): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def A_ ( self : Union[str, Any] ): snake_case_ = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/cycle-diffusion/black_colored_car.png''' ) snake_case_ = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy''' ) snake_case_ = init_image.resize((512, 512) ) snake_case_ = '''CompVis/stable-diffusion-v1-4''' snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' ) snake_case_ = CycleDiffusionPipeline.from_pretrained( lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ , torch_dtype=torch.floataa , revision='''fp16''' ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) pipe.enable_attention_slicing() snake_case_ = '''A black colored car''' snake_case_ = '''A blue colored car''' snake_case_ = torch.manual_seed(0 ) snake_case_ = pipe( prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , ) snake_case_ = output.images # the values aren't exactly equal, but the images look the same visually assert np.abs(image - expected_image ).max() < 5e-1 def A_ ( self : List[str] ): snake_case_ = load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/cycle-diffusion/black_colored_car.png''' ) snake_case_ = load_numpy( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy''' ) snake_case_ = init_image.resize((512, 512) ) snake_case_ = '''CompVis/stable-diffusion-v1-4''' snake_case_ = DDIMScheduler.from_pretrained(lowercase_ , subfolder='''scheduler''' ) snake_case_ = CycleDiffusionPipeline.from_pretrained(lowercase_ , scheduler=lowercase_ , safety_checker=lowercase_ ) pipe.to(lowercase_ ) pipe.set_progress_bar_config(disable=lowercase_ ) pipe.enable_attention_slicing() snake_case_ = '''A black colored car''' snake_case_ = '''A blue colored car''' snake_case_ = torch.manual_seed(0 ) snake_case_ = pipe( prompt=lowercase_ , source_prompt=lowercase_ , image=lowercase_ , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=lowercase_ , output_type='''np''' , ) snake_case_ = output.images assert np.abs(image - expected_image ).max() < 2e-2
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import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device if is_torch_available(): from transformers import AutoModelForSeqaSeqLM, AutoTokenizer @require_torch @require_sentencepiece @require_tokenizers class __lowerCAmelCase ( unittest.TestCase ): """simple docstring""" @slow def lowercase_ ( self ) -> List[str]: '''simple docstring''' __lowerCamelCase = AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' , return_dict=lowerCamelCase__ ).to(lowerCamelCase__ ) __lowerCamelCase = AutoTokenizer.from_pretrained('google/mt5-small' ) __lowerCamelCase = tokenizer('Hello there' , return_tensors='pt' ).input_ids __lowerCamelCase = tokenizer('Hi I am' , return_tensors='pt' ).input_ids __lowerCamelCase = model(input_ids.to(lowerCamelCase__ ) , labels=labels.to(lowerCamelCase__ ) ).loss __lowerCamelCase = -(labels.shape[-1] * loss.item()) __lowerCamelCase = -84.91_27 self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
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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 ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer from .base import PipelineTool class __lowerCAmelCase ( __magic_name__ ): """simple docstring""" snake_case_ = '''philschmid/bart-large-cnn-samsum''' snake_case_ = ( '''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, ''' '''and returns a summary of the text.''' ) snake_case_ = '''summarizer''' snake_case_ = AutoTokenizer snake_case_ = AutoModelForSeqaSeqLM snake_case_ = ['''text'''] snake_case_ = ['''text'''] def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]: '''simple docstring''' return self.pre_processor(lowerCamelCase__ , return_tensors='pt' , truncation=lowerCamelCase__ ) def lowercase_ ( self , lowerCamelCase__ ) -> Dict: '''simple docstring''' return self.model.generate(**lowerCamelCase__ )[0] def lowercase_ ( self , lowerCamelCase__ ) -> Any: '''simple docstring''' return self.pre_processor.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
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'''simple docstring''' from __future__ import annotations import math import numpy as np from numpy.linalg import norm def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> float: return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(_lowerCAmelCase , _lowerCAmelCase ) ) ) def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> list[list[list[float] | float]]: if dataset.ndim != value_array.ndim: snake_case__ : Tuple = ( """Wrong input data's dimensions... """ f"dataset : {dataset.ndim}, value_array : {value_array.ndim}" ) raise ValueError(_lowerCAmelCase ) try: if dataset.shape[1] != value_array.shape[1]: snake_case__ : Any = ( """Wrong input data's shape... """ f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}" ) raise ValueError(_lowerCAmelCase ) except IndexError: if dataset.ndim != value_array.ndim: raise TypeError("""Wrong shape""" ) if dataset.dtype != value_array.dtype: snake_case__ : Tuple = ( """Input data have different datatype... """ f"dataset : {dataset.dtype}, value_array : {value_array.dtype}" ) raise TypeError(_lowerCAmelCase ) snake_case__ : Tuple = [] for value in value_array: snake_case__ : Any = euclidean(_lowerCAmelCase , dataset[0] ) snake_case__ : Any = dataset[0].tolist() for dataset_value in dataset[1:]: snake_case__ : Union[str, Any] = euclidean(_lowerCAmelCase , _lowerCAmelCase ) if dist > temp_dist: snake_case__ : Union[str, Any] = temp_dist snake_case__ : int = dataset_value.tolist() answer.append([vector, dist] ) return answer def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> float: return np.dot(_lowerCAmelCase , _lowerCAmelCase ) / (norm(_lowerCAmelCase ) * norm(_lowerCAmelCase )) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from typing import Any, Dict, List, Union from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends from .base import PIPELINE_INIT_ARGS, ChunkPipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): import torch from transformers.modeling_outputs import BaseModelOutput from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING _UpperCamelCase : Dict = logging.get_logger(__name__) @add_end_docstrings(_a) class UpperCAmelCase_ ( _a): def __init__( self , **a ) -> Dict: super().__init__(**a ) if self.framework == "tf": raise ValueError(f"""The {self.__class__} is only available in PyTorch.""" ) requires_backends(self , 'vision' ) self.check_model_type(a ) def __call__( self , a , a = None , **a , ) -> List[str]: if "text_queries" in kwargs: lowercase__ : Optional[Any] = kwargs.pop('text_queries' ) if isinstance(a , (str, Image.Image) ): lowercase__ : Optional[Any] = {'image': image, 'candidate_labels': candidate_labels} else: lowercase__ : List[str] = image lowercase__ : Optional[Any] = super().__call__(a , **a ) return results def _UpperCAmelCase ( self , **a ) -> Dict: lowercase__ : Optional[Any] = {} if "threshold" in kwargs: lowercase__ : Tuple = kwargs['threshold'] if "top_k" in kwargs: lowercase__ : List[Any] = kwargs['top_k'] return {}, {}, postprocess_params def _UpperCAmelCase ( self , a ) -> Dict: lowercase__ : Any = load_image(inputs['image'] ) lowercase__ : Optional[int] = inputs['candidate_labels'] if isinstance(a , a ): lowercase__ : Optional[int] = candidate_labels.split(',' ) lowercase__ : Optional[int] = torch.tensor([[image.height, image.width]] , dtype=torch.intaa ) for i, candidate_label in enumerate(a ): lowercase__ : List[str] = self.tokenizer(a , return_tensors=self.framework ) lowercase__ : List[Any] = self.image_processor(a , return_tensors=self.framework ) yield { "is_last": i == len(a ) - 1, "target_size": target_size, "candidate_label": candidate_label, **text_inputs, **image_features, } def _UpperCAmelCase ( self , a ) -> List[Any]: lowercase__ : List[Any] = model_inputs.pop('target_size' ) lowercase__ : Dict = model_inputs.pop('candidate_label' ) lowercase__ : Dict = model_inputs.pop('is_last' ) lowercase__ : Optional[int] = self.model(**a ) lowercase__ : Any = {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs} return model_outputs def _UpperCAmelCase ( self , a , a=0.1 , a=None ) -> Union[str, Any]: lowercase__ : Dict = [] for model_output in model_outputs: lowercase__ : List[Any] = model_output['candidate_label'] lowercase__ : Optional[int] = BaseModelOutput(a ) lowercase__ : Any = self.image_processor.post_process_object_detection( outputs=a , threshold=a , target_sizes=model_output['target_size'] )[0] for index in outputs["scores"].nonzero(): lowercase__ : Union[str, Any] = outputs['scores'][index].item() lowercase__ : Tuple = self._get_bounding_box(outputs['boxes'][index][0] ) lowercase__ : Tuple = {'score': score, 'label': label, 'box': box} results.append(a ) lowercase__ : Dict = sorted(a , key=lambda a : x["score"] , reverse=a ) if top_k: lowercase__ : Dict = results[:top_k] return results def _UpperCAmelCase ( self , a ) -> Dict[str, int]: if self.framework != "pt": raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.' ) lowercase__ , lowercase__ , lowercase__ , lowercase__ : Tuple = box.int().tolist() lowercase__ : Any = { 'xmin': xmin, 'ymin': ymin, 'xmax': xmax, 'ymax': ymax, } return bbox
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import math def A__ ( lowerCamelCase = 1_00 ) -> Tuple: UpperCamelCase_: Tuple = sum(i * i for i in range(1 , n + 1 ) ) UpperCamelCase_: Dict = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) ) return square_of_sum - sum_of_squares if __name__ == "__main__": print(F"""{solution() = }""")
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from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCamelCase_ : Optional[int] = logging.get_logger(__name__) lowerCamelCase_ : Dict = { """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""", """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""", } class _UpperCamelCase ( _A ): '''simple docstring''' __UpperCamelCase : Optional[int] = """luke""" def __init__( self : Tuple , snake_case_ : List[Any]=5_0267 , snake_case_ : Any=50_0000 , snake_case_ : str=768 , snake_case_ : int=256 , snake_case_ : str=12 , snake_case_ : int=12 , snake_case_ : Dict=3072 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Dict=0.1 , snake_case_ : List[str]=0.1 , snake_case_ : int=512 , snake_case_ : Dict=2 , snake_case_ : List[Any]=0.02 , snake_case_ : int=1e-12 , snake_case_ : Union[str, Any]=True , snake_case_ : Union[str, Any]=None , snake_case_ : Dict=1 , snake_case_ : Optional[int]=0 , snake_case_ : List[str]=2 , **snake_case_ : Union[str, Any] , ): super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ ) UpperCamelCase_: Dict = vocab_size UpperCamelCase_: Tuple = entity_vocab_size UpperCamelCase_: Optional[int] = hidden_size UpperCamelCase_: Any = entity_emb_size UpperCamelCase_: str = num_hidden_layers UpperCamelCase_: Union[str, Any] = num_attention_heads UpperCamelCase_: Dict = hidden_act UpperCamelCase_: Dict = intermediate_size UpperCamelCase_: str = hidden_dropout_prob UpperCamelCase_: List[str] = attention_probs_dropout_prob UpperCamelCase_: int = max_position_embeddings UpperCamelCase_: int = type_vocab_size UpperCamelCase_: List[Any] = initializer_range UpperCamelCase_: Union[str, Any] = layer_norm_eps UpperCamelCase_: Tuple = use_entity_aware_attention UpperCamelCase_: int = classifier_dropout
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available UpperCAmelCase : Optional[Any] = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase : int = [ """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""", """SwinForImageClassification""", """SwinForMaskedImageModeling""", """SwinModel""", """SwinPreTrainedModel""", """SwinBackbone""", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase : List[str] = [ """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""", """TFSwinForImageClassification""", """TFSwinForMaskedImageModeling""", """TFSwinModel""", """TFSwinPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_swin import ( SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel, SwinPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_swin import ( TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST, TFSwinForImageClassification, TFSwinForMaskedImageModeling, TFSwinModel, TFSwinPreTrainedModel, ) else: import sys UpperCAmelCase : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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'''simple docstring''' def _A ( lowercase__ , lowercase__ ): if a < 0 or b < 0: raise ValueError("""the value of both inputs must be positive""" ) lowercase__ = str(bin(lowercase__ ) )[2:] # remove the leading "0b" lowercase__ = str(bin(lowercase__ ) )[2:] lowercase__ = max(len(lowercase__ ) , len(lowercase__ ) ) return "0b" + "".join( str(int("""1""" in (char_a, char_b) ) ) for char_a, char_b in zip(a_binary.zfill(lowercase__ ) , b_binary.zfill(lowercase__ ) ) ) if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" def UpperCAmelCase__ (snake_case__ : str = "The quick brown fox jumps over the lazy dog" , ): """simple docstring""" _snake_case : Any = set() # Replace all the whitespace in our sentence _snake_case : List[Any] = input_str.replace(""" """ , """""" ) for alpha in input_str: if "a" <= alpha.lower() <= "z": frequency.add(alpha.lower() ) return len(lowerCamelCase_ ) == 26 def UpperCAmelCase__ (snake_case__ : str = "The quick brown fox jumps over the lazy dog" , ): """simple docstring""" _snake_case : List[str] = [False] * 26 for char in input_str: if char.islower(): _snake_case : Union[str, Any] = True elif char.isupper(): _snake_case : str = True return all(lowerCamelCase_ ) def UpperCAmelCase__ (snake_case__ : str = "The quick brown fox jumps over the lazy dog" , ): """simple docstring""" return len({char for char in input_str.lower() if char.isalpha()} ) == 26 def UpperCAmelCase__ (): """simple docstring""" from timeit import timeit _snake_case : Optional[Any] = '''from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest''' print(timeit("""is_pangram()""" , setup=lowerCamelCase_ ) ) print(timeit("""is_pangram_faster()""" , setup=lowerCamelCase_ ) ) print(timeit("""is_pangram_fastest()""" , setup=lowerCamelCase_ ) ) # 5.348480500048026, 2.6477354579837993, 1.8470395830227062 # 5.036091582966037, 2.644472333951853, 1.8869528750656173 if __name__ == "__main__": import doctest doctest.testmod() benchmark()
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"""simple docstring""" import inspect import unittest import torch import torch.nn as nn from accelerate.hooks import ( AlignDevicesHook, ModelHook, SequentialHook, add_hook_to_module, attach_align_device_hook, remove_hook_from_module, remove_hook_from_submodules, ) from accelerate.test_utils import require_multi_gpu class lowercase( nn.Module ): '''simple docstring''' def __init__( self: Optional[int] ): '''simple docstring''' super().__init__() _snake_case : List[str] = nn.Linear(3, 4 ) _snake_case : int = nn.BatchNormad(4 ) _snake_case : List[str] = nn.Linear(4, 5 ) def UpperCamelCase_ ( self: Any, a_: Union[str, Any] ): '''simple docstring''' return self.lineara(self.batchnorm(self.lineara(a_ ) ) ) class lowercase( __a ): '''simple docstring''' def UpperCamelCase_ ( self: Any, a_: int, *a_: Dict, **a_: Dict ): '''simple docstring''' return (args[0] + 1,) + args[1:], kwargs class lowercase( __a ): '''simple docstring''' def UpperCamelCase_ ( self: str, a_: Tuple, a_: Union[str, Any] ): '''simple docstring''' return output + 1 class lowercase( unittest.TestCase ): '''simple docstring''' def UpperCamelCase_ ( self: Union[str, Any] ): '''simple docstring''' _snake_case : Tuple = ModelForTest() _snake_case : List[str] = ModelHook() add_hook_to_module(a_, a_ ) self.assertEqual(test_model._hf_hook, a_ ) self.assertTrue(hasattr(a_, """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__, """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ), ["""x"""] ) remove_hook_from_module(a_ ) self.assertFalse(hasattr(a_, """_hf_hook""" ) ) self.assertFalse(hasattr(a_, """_old_forward""" ) ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Dict = ModelForTest() _snake_case : List[Any] = ModelHook() add_hook_to_module(a_, a_ ) add_hook_to_module(a_, a_, append=a_ ) self.assertEqual(isinstance(test_model._hf_hook, a_ ), a_ ) self.assertEqual(len(test_model._hf_hook.hooks ), 2 ) self.assertTrue(hasattr(a_, """_old_forward""" ) ) # Check adding the hook did not change the name or the signature self.assertEqual(test_model.forward.__name__, """forward""" ) self.assertListEqual(list(inspect.signature(test_model.forward ).parameters ), ["""x"""] ) remove_hook_from_module(a_ ) self.assertFalse(hasattr(a_, """_hf_hook""" ) ) self.assertFalse(hasattr(a_, """_old_forward""" ) ) def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Optional[Any] = ModelForTest() _snake_case : Optional[Any] = torch.randn(2, 3 ) _snake_case : List[Any] = test_model(x + 1 ) _snake_case : List[str] = test_model(x + 2 ) _snake_case : Any = PreForwardHook() add_hook_to_module(a_, a_ ) _snake_case : List[Any] = test_model(a_ ) self.assertTrue(torch.allclose(a_, a_, atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain _snake_case : List[str] = PreForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Tuple = test_model(a_ ) self.assertTrue(torch.allclose(a_, a_, atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks _snake_case : str = SequentialHook(PreForwardHook(), PreForwardHook() ) add_hook_to_module(a_, a_ ) _snake_case : str = test_model(a_ ) assert torch.allclose(a_, a_, atol=1E-5 ) def UpperCamelCase_ ( self: Optional[int] ): '''simple docstring''' _snake_case : Optional[int] = ModelForTest() _snake_case : List[Any] = torch.randn(2, 3 ) _snake_case : List[str] = test_model(a_ ) _snake_case : List[Any] = PostForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Union[str, Any] = test_model(a_ ) self.assertTrue(torch.allclose(a_, output + 1, atol=1E-5 ) ) # Attaching a hook to a model when it already has one replaces, does not chain _snake_case : Tuple = PostForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Optional[Any] = test_model(a_ ) self.assertTrue(torch.allclose(a_, output + 1, atol=1E-5 ) ) # You need to use the sequential hook to chain two or more hooks _snake_case : Dict = SequentialHook(PostForwardHook(), PostForwardHook() ) add_hook_to_module(a_, a_ ) _snake_case : List[str] = test_model(a_ ) assert torch.allclose(a_, output + 2, atol=1E-5 ) def UpperCamelCase_ ( self: Optional[Any] ): '''simple docstring''' _snake_case : str = ModelForTest() _snake_case : Any = torch.randn(2, 3 ) _snake_case : List[str] = test_model(a_ ) _snake_case : List[Any] = PostForwardHook() add_hook_to_module(a_, a_ ) _snake_case : Dict = test_model(a_ ) self.assertTrue(torch.allclose(a_, output + 1 ) ) self.assertTrue(outputa.requires_grad ) _snake_case : Union[str, Any] = True _snake_case : Dict = test_model(a_ ) self.assertFalse(outputa.requires_grad ) @require_multi_gpu def UpperCamelCase_ ( self: Tuple ): '''simple docstring''' _snake_case : Dict = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices add_hook_to_module(model.lineara, AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.batchnorm, AlignDevicesHook(execution_device=0 ) ) add_hook_to_module(model.lineara, AlignDevicesHook(execution_device=1 ) ) self.assertEqual(model.lineara.weight.device, torch.device(0 ) ) self.assertEqual(model.batchnorm.weight.device, torch.device(0 ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device(0 ) ) self.assertEqual(model.lineara.weight.device, torch.device(1 ) ) # We can still make a forward pass. The input does not need to be on any particular device _snake_case : Any = torch.randn(2, 3 ) _snake_case : Any = model(a_ ) self.assertEqual(output.device, torch.device(1 ) ) # We can add a general hook to put back output on same device as input. add_hook_to_module(a_, AlignDevicesHook(io_same_device=a_ ) ) _snake_case : int = torch.randn(2, 3 ).to(0 ) _snake_case : Optional[Any] = model(a_ ) self.assertEqual(output.device, torch.device(0 ) ) def UpperCamelCase_ ( self: Union[str, Any] ): '''simple docstring''' _snake_case : int = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices _snake_case : Optional[int] = {"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True} add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.batchnorm, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device _snake_case : Union[str, Any] = torch.device(hook_kwargs["""execution_device"""] ) self.assertEqual(model.batchnorm.running_mean.device, a_ ) _snake_case : str = torch.randn(2, 3 ) _snake_case : Optional[int] = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # Now test with buffers included in the offload _snake_case : Any = { """execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True, """offload_buffers""": True, } add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.batchnorm, AlignDevicesHook(**a_ ) ) add_hook_to_module(model.lineara, AlignDevicesHook(**a_ ) ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device("""meta""" ) ) _snake_case : List[Any] = torch.randn(2, 3 ) _snake_case : Any = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_module(model.lineara ) remove_hook_from_module(model.batchnorm ) remove_hook_from_module(model.lineara ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) def UpperCamelCase_ ( self: str ): '''simple docstring''' _snake_case : Any = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices _snake_case : Tuple = 0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook(a_, execution_device=a_, offload=a_ ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device _snake_case : Optional[int] = torch.device(a_ ) self.assertEqual(model.batchnorm.running_mean.device, a_ ) _snake_case : List[Any] = torch.randn(2, 3 ) _snake_case : List[Any] = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook(a_, execution_device=a_, offload=a_, offload_buffers=a_ ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device("""meta""" ) ) _snake_case : str = torch.randn(2, 3 ) _snake_case : List[Any] = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) def UpperCamelCase_ ( self: Any ): '''simple docstring''' _snake_case : List[str] = ModelForTest() # Everything is on CPU self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # This will move each submodule on different devices _snake_case : Any = 0 if torch.cuda.is_available() else """cpu""" attach_align_device_hook( a_, execution_device=a_, offload=a_, weights_map=model.state_dict() ) # Parameters have been offloaded, so on the meta device self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) # Buffers are not included in the offload by default, so are on the execution device _snake_case : Optional[Any] = torch.device(a_ ) self.assertEqual(model.batchnorm.running_mean.device, a_ ) _snake_case : int = torch.randn(2, 3 ) _snake_case : str = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) # Now test with buffers included in the offload attach_align_device_hook( a_, execution_device=a_, offload=a_, weights_map=model.state_dict(), offload_buffers=a_, ) # Parameters have been offloaded, so on the meta device, buffers included self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""meta""" ) ) self.assertEqual(model.batchnorm.running_mean.device, torch.device("""meta""" ) ) _snake_case : Optional[int] = torch.randn(2, 3 ) _snake_case : Any = model(a_ ) self.assertEqual(output.device, a_ ) # Removing hooks loads back the weights in the model. remove_hook_from_submodules(a_ ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.batchnorm.weight.device, torch.device("""cpu""" ) ) self.assertEqual(model.lineara.weight.device, torch.device("""cpu""" ) )
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0
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, flip_channel_order, get_resize_output_image_size, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging if is_vision_available(): import PIL if is_torch_available(): import torch snake_case_ = logging.get_logger(__name__) class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ): A_ : Dict = ["""pixel_values"""] def __init__(self : Tuple , a__ : Dict = True , a__ : Any = None , a__ : str = PILImageResampling.BILINEAR , a__ : int = True , a__ : Optional[Any] = 1 / 255 , a__ : Tuple = True , a__ : List[str] = None , a__ : List[str] = True , **a__ : Tuple , ): """simple docstring""" super().__init__(**_a ) __snake_case = size if size is not None else {"""shortest_edge""": 224} __snake_case = get_size_dict(_a , default_to_square=_a ) __snake_case = crop_size if crop_size is not None else {"""height""": 256, """width""": 256} __snake_case = get_size_dict(_a , param_name='''crop_size''' ) __snake_case = do_resize __snake_case = size __snake_case = resample __snake_case = do_rescale __snake_case = rescale_factor __snake_case = do_center_crop __snake_case = crop_size __snake_case = do_flip_channel_order def a (self : List[str] , a__ : Any , a__ : Dict , a__ : int = PIL.Image.BILINEAR , a__ : Union[str, Any] = None , **a__ : List[Any] , ): """simple docstring""" __snake_case = get_size_dict(_a , default_to_square=_a ) if "shortest_edge" not in size: raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""" ) __snake_case = get_resize_output_image_size(_a , size=size['''shortest_edge'''] , default_to_square=_a ) return resize(_a , size=_a , resample=_a , data_format=_a , **_a ) def a (self : List[str] , a__ : Tuple , a__ : Tuple , a__ : str = None , **a__ : Optional[Any] , ): """simple docstring""" __snake_case = get_size_dict(_a ) if "height" not in size or "width" not in size: raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" ) return center_crop(_a , size=(size['''height'''], size['''width''']) , data_format=_a , **_a ) def a (self : List[str] , a__ : Any , a__ : Dict , a__ : List[Any] = None , **a__ : List[str] , ): """simple docstring""" return rescale(_a , scale=_a , data_format=_a , **_a ) def a (self : List[str] , a__ : str , a__ : List[Any] = None ): """simple docstring""" return flip_channel_order(_a , data_format=_a ) def a (self : int , a__ : Optional[int] , a__ : Dict = None , a__ : Any = None , a__ : Tuple = None , a__ : str = None , a__ : List[Any] = None , a__ : Any = None , a__ : Tuple = None , a__ : Tuple = None , a__ : Any = None , a__ : Optional[int] = ChannelDimension.FIRST , **a__ : int , ): """simple docstring""" __snake_case = do_resize if do_resize is not None else self.do_resize __snake_case = resample if resample is not None else self.resample __snake_case = do_rescale if do_rescale is not None else self.do_rescale __snake_case = rescale_factor if rescale_factor is not None else self.rescale_factor __snake_case = do_center_crop if do_center_crop is not None else self.do_center_crop __snake_case = ( do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order ) __snake_case = size if size is not None else self.size __snake_case = get_size_dict(_a , default_to_square=_a ) __snake_case = crop_size if crop_size is not None else self.crop_size __snake_case = get_size_dict(_a , param_name='''crop_size''' ) __snake_case = 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_rescale and rescale_factor is None: raise ValueError('''Rescale factor must be specified if do_rescale is True.''' ) if do_center_crop and crop_size is None: raise ValueError('''Crop size must be specified if do_center_crop is True.''' ) # All transformations expect numpy arrays. __snake_case = [to_numpy_array(_a ) for image in images] if do_resize: __snake_case = [self.resize(image=_a , size=_a , resample=_a ) for image in images] if do_center_crop: __snake_case = [self.center_crop(image=_a , size=_a ) for image in images] if do_rescale: __snake_case = [self.rescale(image=_a , scale=_a ) for image in images] # the pretrained checkpoints assume images are BGR, not RGB if do_flip_channel_order: __snake_case = [self.flip_channel_order(image=_a ) for image in images] __snake_case = [to_channel_dimension_format(_a , _a ) for image in images] __snake_case = {"""pixel_values""": images} return BatchFeature(data=_a , tensor_type=_a ) def a (self : int , a__ : List[Any] , a__ : Optional[int] = None ): """simple docstring""" __snake_case = 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 ): __snake_case = target_sizes.numpy() __snake_case = [] for idx in range(len(_a ) ): __snake_case = torch.nn.functional.interpolate( logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_a ) __snake_case = resized_logits[0].argmax(dim=0 ) semantic_segmentation.append(_a ) else: __snake_case = logits.argmax(dim=1 ) __snake_case = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )] return semantic_segmentation
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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 __a : '''simple docstring''' def __init__( self , _a , _a=13 , _a=2 , _a=24 , _a=16 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=None , _a=2 , _a=2 , ) -> int: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = parent SCREAMING_SNAKE_CASE__ : Union[str, Any] = batch_size SCREAMING_SNAKE_CASE__ : Optional[Any] = patch_size SCREAMING_SNAKE_CASE__ : str = max_length SCREAMING_SNAKE_CASE__ : Optional[Any] = num_mel_bins SCREAMING_SNAKE_CASE__ : Optional[Any] = is_training SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_labels SCREAMING_SNAKE_CASE__ : Optional[int] = hidden_size SCREAMING_SNAKE_CASE__ : Tuple = num_hidden_layers SCREAMING_SNAKE_CASE__ : List[str] = num_attention_heads SCREAMING_SNAKE_CASE__ : int = intermediate_size SCREAMING_SNAKE_CASE__ : Tuple = hidden_act SCREAMING_SNAKE_CASE__ : Optional[Any] = hidden_dropout_prob SCREAMING_SNAKE_CASE__ : List[str] = attention_probs_dropout_prob SCREAMING_SNAKE_CASE__ : List[str] = type_sequence_label_size SCREAMING_SNAKE_CASE__ : Tuple = initializer_range SCREAMING_SNAKE_CASE__ : List[Any] = scope SCREAMING_SNAKE_CASE__ : List[str] = frequency_stride SCREAMING_SNAKE_CASE__ : Union[str, Any] = time_stride # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens) SCREAMING_SNAKE_CASE__ : Optional[int] = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1 SCREAMING_SNAKE_CASE__ : Any = (self.max_length - self.patch_size) // self.time_stride + 1 SCREAMING_SNAKE_CASE__ : Union[str, Any] = frequency_out_dimension * time_out_dimension SCREAMING_SNAKE_CASE__ : Any = num_patches + 2 def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : str = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] ) SCREAMING_SNAKE_CASE__ : int = None if self.use_labels: SCREAMING_SNAKE_CASE__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size ) SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_config() return config, input_values, labels def _a ( self ) -> Union[str, Any]: """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=_a , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , ) def _a ( self , _a , _a , _a ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ : Tuple = ASTModel(config=_a ) model.to(_a ) model.eval() SCREAMING_SNAKE_CASE__ : Union[str, Any] = model(_a ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def _a ( self ) -> List[Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.prepare_config_and_inputs() ( ( SCREAMING_SNAKE_CASE__ ) , ( SCREAMING_SNAKE_CASE__ ) , ( SCREAMING_SNAKE_CASE__ ) , ) : List[str] = config_and_inputs SCREAMING_SNAKE_CASE__ : Union[str, Any] = {"""input_values""": input_values} return config, inputs_dict @require_torch class __a (UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase): '''simple docstring''' _SCREAMING_SNAKE_CASE :Optional[Any] = ( ( ASTModel, ASTForAudioClassification, ) if is_torch_available() else () ) _SCREAMING_SNAKE_CASE :Dict = ( {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel} if is_torch_available() else {} ) _SCREAMING_SNAKE_CASE :Union[str, Any] = False _SCREAMING_SNAKE_CASE :Any = False _SCREAMING_SNAKE_CASE :Union[str, Any] = False _SCREAMING_SNAKE_CASE :Tuple = False def _a ( self , _a , _a , _a , _a , _a ) -> Dict: """simple docstring""" if pipeline_test_casse_name == "AudioClassificationPipelineTests": return True return False def _a ( self ) -> Dict: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = ASTModelTester(self ) SCREAMING_SNAKE_CASE__ : str = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 ) def _a ( self ) -> List[str]: """simple docstring""" self.config_tester.run_common_tests() @unittest.skip(reason="""AST does not use inputs_embeds""" ) def _a ( self ) -> List[str]: """simple docstring""" pass def _a ( self ) -> Union[str, Any]: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE__ : str = model_class(_a ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) SCREAMING_SNAKE_CASE__ : Optional[int] = model.get_output_embeddings() self.assertTrue(x is None or isinstance(_a , nn.Linear ) ) def _a ( self ) -> str: """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE__ : Any = model_class(_a ) SCREAMING_SNAKE_CASE__ : List[str] = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE__ : Dict = [*signature.parameters.keys()] SCREAMING_SNAKE_CASE__ : Dict = ["""input_values"""] self.assertListEqual(arg_names[:1] , _a ) def _a ( self ) -> Optional[int]: """simple docstring""" SCREAMING_SNAKE_CASE__ : Dict = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_a ) @slow def _a ( self ) -> Union[str, Any]: """simple docstring""" for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE__ : Optional[Any] = ASTModel.from_pretrained(_a ) self.assertIsNotNone(_a ) def _lowercase ( ) -> int: SCREAMING_SNAKE_CASE__ : List[Any] = hf_hub_download( repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" ) SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Tuple = torchaudio.load(__lowerCAmelCase ) return audio, sampling_rate @require_torch @require_torchaudio class __a (unittest.TestCase): '''simple docstring''' @cached_property def _a ( self ) -> int: """simple docstring""" return ( ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ) if is_torchaudio_available() else None ) @slow def _a ( self ) -> Any: """simple docstring""" SCREAMING_SNAKE_CASE__ : Optional[int] = self.default_feature_extractor SCREAMING_SNAKE_CASE__ : Optional[Any] = ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ).to(_a ) SCREAMING_SNAKE_CASE__ : Dict = self.default_feature_extractor SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = prepare_audio() SCREAMING_SNAKE_CASE__ : List[str] = audio.squeeze().numpy() SCREAMING_SNAKE_CASE__ : List[str] = feature_extractor(_a , sampling_rate=_a , return_tensors="""pt""" ).to(_a ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE__ : List[Any] = model(**_a ) # verify the logits SCREAMING_SNAKE_CASE__ : List[Any] = torch.Size((1, 527) ) self.assertEqual(outputs.logits.shape , _a ) SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor([-0.8_760, -7.0_042, -8.6_602] ).to(_a ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1E-4 ) )
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a : Tuple = 'Input must be a string of 8 numbers plus letter' a : Dict = 'TRWAGMYFPDXBNJZSQVHLCKE' def lowerCAmelCase_ (lowerCAmelCase__: str ): """simple docstring""" if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): UpperCAmelCase_: List[Any] = F'Expected string as input, found {type(lowerCAmelCase__ ).__name__}' raise TypeError(lowerCAmelCase__ ) UpperCAmelCase_: List[str] = spanish_id.replace("""-""" , """""" ).upper() if len(lowerCAmelCase__ ) != 9: raise ValueError(lowerCAmelCase__ ) try: UpperCAmelCase_: Union[str, Any] = int(spanish_id_clean[0:8] ) UpperCAmelCase_: int = spanish_id_clean[8] except ValueError as ex: raise ValueError(lowerCAmelCase__ ) from ex if letter.isdigit(): raise ValueError(lowerCAmelCase__ ) return letter == LOOKUP_LETTERS[number % 2_3] if __name__ == "__main__": import doctest doctest.testmod()
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import math from typing import Optional import numpy as np from ...configuration_utils import PretrainedConfig from ...utils import logging a : Tuple = logging.get_logger(__name__) a : Optional[Any] = { 'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json', 'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json', } class _a ( _lowerCAmelCase ): A = '''encodec''' def __init__(self, SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 1_2.0, 2_4.0], SCREAMING_SNAKE_CASE_=24000, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=128, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=[8, 5, 4, 2], SCREAMING_SNAKE_CASE_="weight_norm", SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="reflect", SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1.0, SCREAMING_SNAKE_CASE_=1024, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]: UpperCAmelCase_: List[Any] = target_bandwidths UpperCAmelCase_: str = sampling_rate UpperCAmelCase_: Any = audio_channels UpperCAmelCase_: List[str] = normalize UpperCAmelCase_: List[Any] = chunk_length_s UpperCAmelCase_: List[Any] = overlap UpperCAmelCase_: Any = hidden_size UpperCAmelCase_: str = num_filters UpperCAmelCase_: Any = num_residual_layers UpperCAmelCase_: int = upsampling_ratios UpperCAmelCase_: Tuple = norm_type UpperCAmelCase_: Union[str, Any] = kernel_size UpperCAmelCase_: str = last_kernel_size UpperCAmelCase_: Union[str, Any] = residual_kernel_size UpperCAmelCase_: str = dilation_growth_rate UpperCAmelCase_: int = use_causal_conv UpperCAmelCase_: int = pad_mode UpperCAmelCase_: List[Any] = compress UpperCAmelCase_: Dict = num_lstm_layers UpperCAmelCase_: List[Any] = trim_right_ratio UpperCAmelCase_: List[Any] = codebook_size UpperCAmelCase_: List[Any] = codebook_dim if codebook_dim is not None else hidden_size UpperCAmelCase_: Optional[Any] = use_conv_shortcut if self.norm_type not in ["weight_norm", "time_group_norm"]: raise ValueError( f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}' ) super().__init__(**SCREAMING_SNAKE_CASE_ ) @property def __snake_case (self ) -> Optional[int]: if self.chunk_length_s is None: return None else: return int(self.chunk_length_s * self.sampling_rate ) @property def __snake_case (self ) -> Optional[int]: if self.chunk_length_s is None or self.overlap is None: return None else: return max(1, int((1.0 - self.overlap) * self.chunk_length ) ) @property def __snake_case (self ) -> int: UpperCAmelCase_: Optional[int] = np.prod(self.upsampling_ratios ) return math.ceil(self.sampling_rate / hop_length ) @property def __snake_case (self ) -> int: return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
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from math import ceil def A ( _lowerCamelCase = 1_001 ): '''simple docstring''' _lowerCAmelCase : int = 1 for i in range(1 , int(ceil(n / 2.0 ) ) ): _lowerCAmelCase : List[Any] = 2 * i + 1 _lowerCAmelCase : str = 2 * i _lowerCAmelCase : List[str] = total + 4 * odd**2 - 6 * even return total if __name__ == "__main__": import sys if len(sys.argv) == 1: print(solution()) else: try: _snake_case = int(sys.argv[1]) print(solution(n)) except ValueError: print("Invalid entry - please enter a number")
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def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int: '''simple docstring''' if exponent == 1: return base if exponent % 2 == 0: UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ ) % modulo_value return (x * x) % modulo_value else: return (base * _modexpt(UpperCamelCase__ , exponent - 1 , UpperCamelCase__ )) % modulo_value def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ = 1777 , UpperCamelCase__ = 1855 , UpperCamelCase__ = 8 ) -> int: '''simple docstring''' UpperCAmelCase = base for _ in range(1 , UpperCamelCase__ ): UpperCAmelCase = _modexpt(UpperCamelCase__ , UpperCamelCase__ , 10**digits ) return result if __name__ == "__main__": print(F'{solution() = }')
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"""simple docstring""" import torch from transformers import CamembertForMaskedLM, CamembertTokenizer def lowercase__ ( snake_case_ :Optional[Any] , snake_case_ :int , snake_case_ :Union[str, Any] , snake_case_ :List[str]=5 ): # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py assert masked_input.count('''<mask>''' ) == 1 __UpperCAmelCase = torch.tensor(tokenizer.encode(snake_case_ , add_special_tokens=snake_case_ ) ).unsqueeze(0 ) # Batch size 1 __UpperCAmelCase = model(snake_case_ )[0] # The last hidden-state is the first element of the output tuple __UpperCAmelCase = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item() __UpperCAmelCase = logits[0, masked_index, :] __UpperCAmelCase = logits.softmax(dim=0 ) __UpperCAmelCase , __UpperCAmelCase = prob.topk(k=snake_case_ , dim=0 ) __UpperCAmelCase = ''' '''.join( [tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(snake_case_ ) )] ) __UpperCAmelCase = tokenizer.mask_token __UpperCAmelCase = [] for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' ''' ) ): __UpperCAmelCase = predicted_token_bpe.replace('''\u2581''' , ''' ''' ) if " {0}".format(snake_case_ ) in masked_input: topk_filled_outputs.append( ( masked_input.replace(''' {0}'''.format(snake_case_ ) , snake_case_ ), values[index].item(), predicted_token, ) ) else: topk_filled_outputs.append( ( masked_input.replace(snake_case_ , snake_case_ ), values[index].item(), predicted_token, ) ) return topk_filled_outputs _lowercase : Any = CamembertTokenizer.from_pretrained('camembert-base') _lowercase : Optional[Any] = CamembertForMaskedLM.from_pretrained('camembert-base') model.eval() _lowercase : int = 'Le camembert est <mask> :)' print(fill_mask(masked_input, model, tokenizer, topk=3))
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"""simple docstring""" from collections import OrderedDict from typing import TYPE_CHECKING, Any, List, Mapping, Optional from packaging import version if TYPE_CHECKING: from ... import PreTrainedTokenizer, TensorType from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfigWithPast, PatchingSpec from ...utils import is_torch_available, logging _lowercase : List[Any] = logging.get_logger(__name__) _lowercase : int = { 'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json', 'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json', 'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json', 'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json', 'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json', 'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json', } class _UpperCAmelCase ( _lowerCAmelCase ): a__ : Optional[Any] = "bloom" a__ : List[Any] = ["past_key_values"] a__ : Optional[Any] = { "num_hidden_layers": "n_layer", "num_attention_heads": "n_head", } def __init__( self : Union[str, Any] , _lowercase : Dict=25_08_80 , _lowercase : str=64 , _lowercase : int=2 , _lowercase : Union[str, Any]=8 , _lowercase : Optional[Any]=1E-5 , _lowercase : Dict=0.02 , _lowercase : Optional[int]=True , _lowercase : Any=1 , _lowercase : Dict=2 , _lowercase : Optional[Any]=False , _lowercase : Union[str, Any]=0.0 , _lowercase : str=0.0 , _lowercase : str=1 , _lowercase : int=False , **_lowercase : List[str] , ): __UpperCAmelCase = vocab_size # Backward compatibility with n_embed kwarg __UpperCAmelCase = kwargs.pop('''n_embed''' , _lowercase ) __UpperCAmelCase = hidden_size if n_embed is None else n_embed __UpperCAmelCase = n_layer __UpperCAmelCase = n_head __UpperCAmelCase = layer_norm_epsilon __UpperCAmelCase = initializer_range __UpperCAmelCase = use_cache __UpperCAmelCase = pretraining_tp __UpperCAmelCase = apply_residual_connection_post_layernorm __UpperCAmelCase = hidden_dropout __UpperCAmelCase = attention_dropout __UpperCAmelCase = bos_token_id __UpperCAmelCase = eos_token_id __UpperCAmelCase = slow_but_exact super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase ) class _UpperCAmelCase ( _lowerCAmelCase ): a__ : List[str] = version.parse("1.12" ) def __init__( self : Optional[int] , _lowercase : PretrainedConfig , _lowercase : str = "default" , _lowercase : List[PatchingSpec] = None , _lowercase : bool = False , ): super().__init__(_lowercase , task=_lowercase , patching_specs=_lowercase , use_past=_lowercase ) if not getattr(self._config , '''pad_token_id''' , _lowercase ): # TODO: how to do that better? __UpperCAmelCase = 0 @property def a ( self : Optional[int] ): __UpperCAmelCase = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} ) if self.use_past: # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344 self.fill_with_past_key_values_(_lowercase , direction='''inputs''' , inverted_values_shape=_lowercase ) __UpperCAmelCase = {0: '''batch''', 1: '''past_sequence + sequence'''} else: __UpperCAmelCase = {0: '''batch''', 1: '''sequence'''} return common_inputs @property def a ( self : Any ): return self._config.n_layer @property def a ( self : Tuple ): return self._config.n_head @property def a ( self : Dict ): return 1E-3 def a ( self : List[str] , _lowercase : "PreTrainedTokenizer" , _lowercase : int = -1 , _lowercase : int = -1 , _lowercase : bool = False , _lowercase : Optional["TensorType"] = None , ): __UpperCAmelCase = super(_lowercase , self ).generate_dummy_inputs( _lowercase , batch_size=_lowercase , seq_length=_lowercase , is_pair=_lowercase , framework=_lowercase ) # We need to order the input in the way they appears in the forward() __UpperCAmelCase = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} ) # Need to add the past_keys if self.use_past: if not is_torch_available(): raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' ) else: import torch __UpperCAmelCase , __UpperCAmelCase = common_inputs['''input_ids'''].shape # Not using the same length for past_key_values __UpperCAmelCase = seqlen + 2 __UpperCAmelCase = self._config.hidden_size // self.num_attention_heads __UpperCAmelCase = ( batch * self.num_attention_heads, head_dim, past_key_values_length, ) __UpperCAmelCase = ( batch * self.num_attention_heads, past_key_values_length, head_dim, ) __UpperCAmelCase = [ (torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(self.num_layers ) ] __UpperCAmelCase = common_inputs['''attention_mask'''] if self.use_past: __UpperCAmelCase = ordered_inputs['''attention_mask'''].dtype __UpperCAmelCase = torch.cat( [ordered_inputs['''attention_mask'''], torch.ones(_lowercase , _lowercase , dtype=_lowercase )] , dim=1 ) return ordered_inputs @property def a ( self : Any ): return 13
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import inspect from typing import Optional, Union import numpy as np import PIL import torch from torch.nn import functional as F from torchvision import transforms from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput from diffusers.utils import ( PIL_INTERPOLATION, randn_tensor, ) def lowercase_ ( _lowerCamelCase : List[Any] , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[Any]): if isinstance(_lowerCamelCase , torch.Tensor): return image elif isinstance(_lowerCamelCase , PIL.Image.Image): lowercase__ : List[str] = [image] if isinstance(image[0] , PIL.Image.Image): lowercase__ : Any = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] lowercase__ : Tuple = np.concatenate(_lowerCamelCase , axis=0) lowercase__ : Any = np.array(_lowerCamelCase).astype(np.floataa) / 255.0 lowercase__ : List[Any] = image.transpose(0 , 3 , 1 , 2) lowercase__ : Tuple = 2.0 * image - 1.0 lowercase__ : Optional[int] = torch.from_numpy(_lowerCamelCase) elif isinstance(image[0] , torch.Tensor): lowercase__ : Any = torch.cat(_lowerCamelCase , dim=0) return image def lowercase_ ( _lowerCamelCase : Any , _lowerCamelCase : List[str] , _lowerCamelCase : Optional[int] , _lowerCamelCase : str=0.9995): if not isinstance(_lowerCamelCase , np.ndarray): lowercase__ : List[str] = True lowercase__ : Tuple = va.device lowercase__ : Union[str, Any] = va.cpu().numpy() lowercase__ : str = va.cpu().numpy() lowercase__ : Tuple = np.sum(va * va / (np.linalg.norm(_lowerCamelCase) * np.linalg.norm(_lowerCamelCase))) if np.abs(_lowerCamelCase) > DOT_THRESHOLD: lowercase__ : Any = (1 - t) * va + t * va else: lowercase__ : Any = np.arccos(_lowerCamelCase) lowercase__ : List[Any] = np.sin(_lowerCamelCase) lowercase__ : List[Any] = theta_a * t lowercase__ : Optional[Any] = np.sin(_lowerCamelCase) lowercase__ : List[Any] = np.sin(theta_a - theta_t) / sin_theta_a lowercase__ : Dict = sin_theta_t / sin_theta_a lowercase__ : List[Any] = sa * va + sa * va if inputs_are_torch: lowercase__ : Tuple = torch.from_numpy(_lowerCamelCase).to(_lowerCamelCase) return va def lowercase_ ( _lowerCamelCase : Tuple , _lowerCamelCase : Union[str, Any]): lowercase__ : Any = F.normalize(_lowerCamelCase , dim=-1) lowercase__ : Optional[Any] = F.normalize(_lowerCamelCase , dim=-1) return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2) def lowercase_ ( _lowerCamelCase : int , _lowerCamelCase : int): for param in model.parameters(): lowercase__ : str = value class snake_case_ ( __A ): def __init__( self : str , lowercase_ : AutoencoderKL , lowercase_ : CLIPTextModel , lowercase_ : CLIPModel , lowercase_ : CLIPTokenizer , lowercase_ : UNetaDConditionModel , lowercase_ : Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler] , lowercase_ : CLIPFeatureExtractor , lowercase_ : Dict=None , lowercase_ : Tuple=None , lowercase_ : Tuple=None , ) -> Union[str, Any]: super().__init__() self.register_modules( vae=lowercase_ , text_encoder=lowercase_ , clip_model=lowercase_ , tokenizer=lowercase_ , unet=lowercase_ , scheduler=lowercase_ , feature_extractor=lowercase_ , coca_model=lowercase_ , coca_tokenizer=lowercase_ , coca_transform=lowercase_ , ) lowercase__ : int = ( feature_extractor.size if isinstance(feature_extractor.size , lowercase_ ) else feature_extractor.size["shortest_edge"] ) lowercase__ : Any = transforms.Normalize(mean=feature_extractor.image_mean , std=feature_extractor.image_std ) set_requires_grad(self.text_encoder , lowercase_ ) set_requires_grad(self.clip_model , lowercase_ ) def __UpperCamelCase ( self : Any , lowercase_ : Optional[Union[str, int]] = "auto" ) -> int: if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory lowercase__ : int = self.unet.config.attention_head_dim // 2 self.unet.set_attention_slice(lowercase_ ) def __UpperCamelCase ( self : Optional[int] ) -> Tuple: self.enable_attention_slicing(lowercase_ ) def __UpperCamelCase ( self : Tuple ) -> int: set_requires_grad(self.vae , lowercase_ ) def __UpperCamelCase ( self : Tuple ) -> Optional[Any]: set_requires_grad(self.vae , lowercase_ ) def __UpperCamelCase ( self : Dict ) -> Optional[Any]: set_requires_grad(self.unet , lowercase_ ) def __UpperCamelCase ( self : List[Any] ) -> Optional[int]: set_requires_grad(self.unet , lowercase_ ) def __UpperCamelCase ( self : Dict , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : Dict ) -> List[Any]: # get the original timestep using init_timestep lowercase__ : Any = min(int(num_inference_steps * strength ) , lowercase_ ) lowercase__ : Optional[int] = max(num_inference_steps - init_timestep , 0 ) lowercase__ : List[Any] = self.scheduler.timesteps[t_start:] return timesteps, num_inference_steps - t_start def __UpperCamelCase ( self : Optional[int] , lowercase_ : Any , lowercase_ : Any , lowercase_ : int , lowercase_ : Optional[int] , lowercase_ : str , lowercase_ : Tuple=None ) -> Union[str, Any]: if not isinstance(lowercase_ , torch.Tensor ): raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowercase_ )}''' ) lowercase__ : int = image.to(device=lowercase_ , dtype=lowercase_ ) if isinstance(lowercase_ , lowercase_ ): lowercase__ : Tuple = [ self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowercase_ ) ] lowercase__ : Any = torch.cat(lowercase_ , dim=0 ) else: lowercase__ : List[str] = self.vae.encode(lowercase_ ).latent_dist.sample(lowercase_ ) # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor lowercase__ : Union[str, Any] = 0.1_82_15 * init_latents lowercase__ : Optional[Any] = init_latents.repeat_interleave(lowercase_ , dim=0 ) lowercase__ : int = randn_tensor(init_latents.shape , generator=lowercase_ , device=lowercase_ , dtype=lowercase_ ) # get latents lowercase__ : str = self.scheduler.add_noise(lowercase_ , lowercase_ , lowercase_ ) lowercase__ : Optional[Any] = init_latents return latents def __UpperCamelCase ( self : Dict , lowercase_ : Tuple ) -> List[str]: lowercase__ : int = self.coca_transform(lowercase_ ).unsqueeze(0 ) with torch.no_grad(), torch.cuda.amp.autocast(): lowercase__ : int = self.coca_model.generate(transformed_image.to(device=self.device , dtype=self.coca_model.dtype ) ) lowercase__ : List[Any] = self.coca_tokenizer.decode(generated[0].cpu().numpy() ) return generated.split("<end_of_text>" )[0].replace("<start_of_text>" , "" ).rstrip(" .," ) def __UpperCamelCase ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : Any ) -> int: lowercase__ : Optional[int] = self.feature_extractor.preprocess(lowercase_ ) lowercase__ : List[Any] = torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half() lowercase__ : int = self.clip_model.get_image_features(lowercase_ ) lowercase__ : List[str] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowercase_ ) lowercase__ : Tuple = image_embeddings_clip.repeat_interleave(lowercase_ , dim=0 ) return image_embeddings_clip @torch.enable_grad() def __UpperCamelCase ( self : List[Any] , lowercase_ : int , lowercase_ : List[Any] , lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : int , lowercase_ : int , lowercase_ : int , ) -> Optional[int]: lowercase__ : Dict = latents.detach().requires_grad_() lowercase__ : List[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ ) # predict the noise residual lowercase__ : Optional[Any] = self.unet(lowercase_ , lowercase_ , encoder_hidden_states=lowercase_ ).sample if isinstance(self.scheduler , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ): lowercase__ : Optional[int] = self.scheduler.alphas_cumprod[timestep] lowercase__ : Tuple = 1 - alpha_prod_t # compute predicted original sample from predicted noise also called # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf lowercase__ : Union[str, Any] = (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5 lowercase__ : Optional[Any] = torch.sqrt(lowercase_ ) lowercase__ : List[str] = pred_original_sample * (fac) + latents * (1 - fac) elif isinstance(self.scheduler , lowercase_ ): lowercase__ : Tuple = self.scheduler.sigmas[index] lowercase__ : Any = latents - sigma * noise_pred else: raise ValueError(F'''scheduler type {type(self.scheduler )} not supported''' ) # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor lowercase__ : Dict = 1 / 0.1_82_15 * sample lowercase__ : Optional[Any] = self.vae.decode(lowercase_ ).sample lowercase__ : Optional[Any] = (image / 2 + 0.5).clamp(0 , 1 ) lowercase__ : List[str] = transforms.Resize(self.feature_extractor_size )(lowercase_ ) lowercase__ : Optional[Any] = self.normalize(lowercase_ ).to(latents.dtype ) lowercase__ : List[Any] = self.clip_model.get_image_features(lowercase_ ) lowercase__ : List[str] = image_embeddings_clip / image_embeddings_clip.norm(p=2 , dim=-1 , keepdim=lowercase_ ) lowercase__ : int = spherical_dist_loss(lowercase_ , lowercase_ ).mean() * clip_guidance_scale lowercase__ : int = -torch.autograd.grad(lowercase_ , lowercase_ )[0] if isinstance(self.scheduler , lowercase_ ): lowercase__ : Optional[int] = latents.detach() + grads * (sigma**2) lowercase__ : Optional[int] = noise_pred_original else: lowercase__ : Union[str, Any] = noise_pred_original - torch.sqrt(lowercase_ ) * grads return noise_pred, latents @torch.no_grad() def __call__( self : Tuple , lowercase_ : Union[torch.FloatTensor, PIL.Image.Image] , lowercase_ : Union[torch.FloatTensor, PIL.Image.Image] , lowercase_ : Optional[str] = None , lowercase_ : Optional[str] = None , lowercase_ : Optional[int] = 5_12 , lowercase_ : Optional[int] = 5_12 , lowercase_ : float = 0.6 , lowercase_ : Optional[int] = 50 , lowercase_ : Optional[float] = 7.5 , lowercase_ : Optional[int] = 1 , lowercase_ : float = 0.0 , lowercase_ : Optional[float] = 1_00 , lowercase_ : Optional[torch.Generator] = None , lowercase_ : Optional[str] = "pil" , lowercase_ : bool = True , lowercase_ : float = 0.8 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , ) -> Dict: if isinstance(lowercase_ , lowercase_ ) and len(lowercase_ ) != batch_size: raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowercase_ )} generators.''' ) if height % 8 != 0 or width % 8 != 0: raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' ) if isinstance(lowercase_ , torch.Generator ) and batch_size > 1: lowercase__ : List[str] = [generator] + [None] * (batch_size - 1) lowercase__ : str = [ ("model", self.coca_model is None), ("tokenizer", self.coca_tokenizer is None), ("transform", self.coca_transform is None), ] lowercase__ : Dict = [x[0] for x in coca_is_none if x[1]] lowercase__ : int = ", ".join(lowercase_ ) # generate prompts with coca model if prompt is None if content_prompt is None: if len(lowercase_ ): raise ValueError( F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.''' F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' ) lowercase__ : Dict = self.get_image_description(lowercase_ ) if style_prompt is None: if len(lowercase_ ): raise ValueError( F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.''' F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' ) lowercase__ : Optional[int] = self.get_image_description(lowercase_ ) # get prompt text embeddings for content and style lowercase__ : Tuple = self.tokenizer( lowercase_ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowercase_ , return_tensors="pt" , ) lowercase__ : int = self.text_encoder(content_text_input.input_ids.to(self.device ) )[0] lowercase__ : List[str] = self.tokenizer( lowercase_ , padding="max_length" , max_length=self.tokenizer.model_max_length , truncation=lowercase_ , return_tensors="pt" , ) lowercase__ : Optional[Any] = self.text_encoder(style_text_input.input_ids.to(self.device ) )[0] lowercase__ : int = slerp(lowercase_ , lowercase_ , lowercase_ ) # duplicate text embeddings for each generation per prompt lowercase__ : Any = text_embeddings.repeat_interleave(lowercase_ , dim=0 ) # set timesteps lowercase__ : Optional[int] = "offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() ) lowercase__ : Optional[Any] = {} if accepts_offset: lowercase__ : Optional[Any] = 1 self.scheduler.set_timesteps(lowercase_ , **lowercase_ ) # Some schedulers like PNDM have timesteps as arrays # It's more optimized to move all timesteps to correct device beforehand self.scheduler.timesteps.to(self.device ) lowercase__ , lowercase__ : Optional[int] = self.get_timesteps(lowercase_ , lowercase_ , self.device ) lowercase__ : str = timesteps[:1].repeat(lowercase_ ) # Preprocess image lowercase__ : int = preprocess(lowercase_ , lowercase_ , lowercase_ ) lowercase__ : Tuple = self.prepare_latents( lowercase_ , lowercase_ , lowercase_ , text_embeddings.dtype , self.device , lowercase_ ) lowercase__ : List[Any] = preprocess(lowercase_ , lowercase_ , lowercase_ ) lowercase__ : Union[str, Any] = self.prepare_latents( lowercase_ , lowercase_ , lowercase_ , text_embeddings.dtype , self.device , lowercase_ ) lowercase__ : Any = slerp(lowercase_ , lowercase_ , lowercase_ ) if clip_guidance_scale > 0: lowercase__ : Tuple = self.get_clip_image_embeddings(lowercase_ , lowercase_ ) lowercase__ : Optional[Any] = self.get_clip_image_embeddings(lowercase_ , lowercase_ ) lowercase__ : Union[str, Any] = slerp( lowercase_ , lowercase_ , lowercase_ ) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. lowercase__ : Optional[int] = guidance_scale > 1.0 # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance: lowercase__ : int = content_text_input.input_ids.shape[-1] lowercase__ : Optional[Any] = self.tokenizer([""] , padding="max_length" , max_length=lowercase_ , return_tensors="pt" ) lowercase__ : List[str] = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0] # duplicate unconditional embeddings for each generation per prompt lowercase__ : Union[str, Any] = uncond_embeddings.repeat_interleave(lowercase_ , dim=0 ) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes lowercase__ : Tuple = torch.cat([uncond_embeddings, text_embeddings] ) # get the initial random noise unless the user supplied it # Unlike in other pipelines, latents need to be generated in the target device # for 1-to-1 results reproducibility with the CompVis implementation. # However this currently doesn't work in `mps`. lowercase__ : Union[str, Any] = (batch_size, self.unet.config.in_channels, height // 8, width // 8) lowercase__ : Union[str, Any] = text_embeddings.dtype if latents is None: if self.device.type == "mps": # randn does not work reproducibly on mps lowercase__ : str = torch.randn(lowercase_ , generator=lowercase_ , device="cpu" , dtype=lowercase_ ).to( self.device ) else: lowercase__ : Tuple = torch.randn(lowercase_ , generator=lowercase_ , device=self.device , dtype=lowercase_ ) else: if latents.shape != latents_shape: raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' ) lowercase__ : List[Any] = latents.to(self.device ) # scale the initial noise by the standard deviation required by the scheduler lowercase__ : Any = latents * self.scheduler.init_noise_sigma # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] lowercase__ : List[Any] = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() ) lowercase__ : List[str] = {} if accepts_eta: lowercase__ : List[str] = eta # check if the scheduler accepts generator lowercase__ : Optional[Any] = "generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() ) if accepts_generator: lowercase__ : Optional[int] = generator with self.progress_bar(total=lowercase_ ): for i, t in enumerate(lowercase_ ): # expand the latents if we are doing classifier free guidance lowercase__ : Tuple = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents lowercase__ : List[Any] = self.scheduler.scale_model_input(lowercase_ , lowercase_ ) # predict the noise residual lowercase__ : Tuple = self.unet(lowercase_ , lowercase_ , encoder_hidden_states=lowercase_ ).sample # perform classifier free guidance if do_classifier_free_guidance: lowercase__ , lowercase__ : int = noise_pred.chunk(2 ) lowercase__ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) # perform clip guidance if clip_guidance_scale > 0: lowercase__ : Optional[Any] = ( text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings ) lowercase__ , lowercase__ : Dict = self.cond_fn( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ) # compute the previous noisy sample x_t -> x_t-1 lowercase__ : Tuple = self.scheduler.step(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor lowercase__ : Union[str, Any] = 1 / 0.1_82_15 * latents lowercase__ : Optional[Any] = self.vae.decode(lowercase_ ).sample lowercase__ : List[Any] = (image / 2 + 0.5).clamp(0 , 1 ) lowercase__ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": lowercase__ : Optional[int] = self.numpy_to_pil(lowercase_ ) if not return_dict: return (image, None) return StableDiffusionPipelineOutput(images=lowercase_ , nsfw_content_detected=lowercase_ )
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"""simple docstring""" import os import sys import unittest lowerCAmelCase__ : Tuple = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, 'utils')) import get_test_info # noqa: E402 from get_test_info import ( # noqa: E402 get_model_to_test_mapping, get_model_to_tester_mapping, get_test_to_tester_mapping, ) lowerCAmelCase__ : Tuple = os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py') lowerCAmelCase__ : Optional[Any] = os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py') class snake_case ( unittest.TestCase ): """simple docstring""" def __lowerCAmelCase ( self : List[str] ): UpperCAmelCase__ = get_test_to_tester_mapping(lowerCamelCase__ ) UpperCAmelCase__ = get_test_to_tester_mapping(lowerCamelCase__ ) UpperCAmelCase__ = {'BertModelTest': 'BertModelTester'} UpperCAmelCase__ = { 'BlipModelTest': 'BlipModelTester', 'BlipTextImageModelTest': 'BlipTextImageModelsModelTester', 'BlipTextModelTest': 'BlipTextModelTester', 'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester', 'BlipVQAModelTest': 'BlipVQAModelTester', 'BlipVisionModelTest': 'BlipVisionModelTester', } self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ ) self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ ) def __lowerCAmelCase ( self : Optional[Any] ): UpperCAmelCase__ = get_model_to_test_mapping(lowerCamelCase__ ) UpperCAmelCase__ = get_model_to_test_mapping(lowerCamelCase__ ) UpperCAmelCase__ = { 'BertForMaskedLM': ['BertModelTest'], 'BertForMultipleChoice': ['BertModelTest'], 'BertForNextSentencePrediction': ['BertModelTest'], 'BertForPreTraining': ['BertModelTest'], 'BertForQuestionAnswering': ['BertModelTest'], 'BertForSequenceClassification': ['BertModelTest'], 'BertForTokenClassification': ['BertModelTest'], 'BertLMHeadModel': ['BertModelTest'], 'BertModel': ['BertModelTest'], } UpperCAmelCase__ = { 'BlipForConditionalGeneration': ['BlipTextImageModelTest'], 'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'], 'BlipForQuestionAnswering': ['BlipVQAModelTest'], 'BlipModel': ['BlipModelTest'], 'BlipTextModel': ['BlipTextModelTest'], 'BlipVisionModel': ['BlipVisionModelTest'], } self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ ) self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ ) def __lowerCAmelCase ( self : int ): UpperCAmelCase__ = get_model_to_tester_mapping(lowerCamelCase__ ) UpperCAmelCase__ = get_model_to_tester_mapping(lowerCamelCase__ ) UpperCAmelCase__ = { 'BertForMaskedLM': ['BertModelTester'], 'BertForMultipleChoice': ['BertModelTester'], 'BertForNextSentencePrediction': ['BertModelTester'], 'BertForPreTraining': ['BertModelTester'], 'BertForQuestionAnswering': ['BertModelTester'], 'BertForSequenceClassification': ['BertModelTester'], 'BertForTokenClassification': ['BertModelTester'], 'BertLMHeadModel': ['BertModelTester'], 'BertModel': ['BertModelTester'], } UpperCAmelCase__ = { 'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'], 'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'], 'BlipForQuestionAnswering': ['BlipVQAModelTester'], 'BlipModel': ['BlipModelTester'], 'BlipTextModel': ['BlipTextModelTester'], 'BlipVisionModel': ['BlipVisionModelTester'], } self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ ) self.assertEqual(get_test_info.to_json(lowerCamelCase__ ) ,lowerCamelCase__ )
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0
import unittest from transformers import LiltConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( LiltForQuestionAnswering, LiltForSequenceClassification, LiltForTokenClassification, LiltModel, ) from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST class UpperCAmelCase_ : '''simple docstring''' def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=24 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=6 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=512 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=1000 , ) -> int: snake_case_ : Union[str, Any] = parent snake_case_ : Dict = batch_size snake_case_ : Dict = seq_length snake_case_ : Optional[Any] = is_training snake_case_ : List[str] = use_input_mask snake_case_ : List[str] = use_token_type_ids snake_case_ : str = use_labels snake_case_ : List[str] = vocab_size snake_case_ : str = hidden_size snake_case_ : Tuple = num_hidden_layers snake_case_ : Dict = num_attention_heads snake_case_ : List[Any] = intermediate_size snake_case_ : List[str] = hidden_act snake_case_ : Optional[Any] = hidden_dropout_prob snake_case_ : str = attention_probs_dropout_prob snake_case_ : Tuple = max_position_embeddings snake_case_ : Any = type_vocab_size snake_case_ : int = type_sequence_label_size snake_case_ : Tuple = initializer_range snake_case_ : List[Any] = num_labels snake_case_ : Tuple = scope snake_case_ : str = range_bbox def _lowerCAmelCase ( self ) -> Dict: snake_case_ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ) # Ensure that bbox is legal for i in range(bbox.shape[0] ): for j in range(bbox.shape[1] ): if bbox[i, j, 3] < bbox[i, j, 1]: snake_case_ : List[Any] = bbox[i, j, 3] snake_case_ : Union[str, Any] = bbox[i, j, 1] snake_case_ : List[str] = t if bbox[i, j, 2] < bbox[i, j, 0]: snake_case_ : Optional[Any] = bbox[i, j, 2] snake_case_ : Dict = bbox[i, j, 0] snake_case_ : Dict = t snake_case_ : Tuple = None if self.use_input_mask: snake_case_ : Dict = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) snake_case_ : Optional[Any] = None if self.use_token_type_ids: snake_case_ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) snake_case_ : int = None snake_case_ : Union[str, Any] = None if self.use_labels: snake_case_ : int = ids_tensor([self.batch_size] , self.type_sequence_label_size ) snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) snake_case_ : Optional[Any] = self.get_config() return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels def _lowerCAmelCase ( self ) -> Union[str, Any]: return LiltConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , ) def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> Any: snake_case_ : Dict = LiltModel(config=_SCREAMING_SNAKE_CASE ) model.to(_SCREAMING_SNAKE_CASE ) model.eval() snake_case_ : int = model(_SCREAMING_SNAKE_CASE , bbox=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE ) snake_case_ : Optional[Any] = model(_SCREAMING_SNAKE_CASE , bbox=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE ) snake_case_ : List[str] = model(_SCREAMING_SNAKE_CASE , bbox=_SCREAMING_SNAKE_CASE ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) ) def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> List[str]: snake_case_ : int = self.num_labels snake_case_ : Optional[Any] = LiltForTokenClassification(config=_SCREAMING_SNAKE_CASE ) model.to(_SCREAMING_SNAKE_CASE ) model.eval() snake_case_ : Any = model( _SCREAMING_SNAKE_CASE , bbox=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> List[Any]: snake_case_ : str = LiltForQuestionAnswering(config=_SCREAMING_SNAKE_CASE ) model.to(_SCREAMING_SNAKE_CASE ) model.eval() snake_case_ : Optional[Any] = model( _SCREAMING_SNAKE_CASE , bbox=_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , token_type_ids=_SCREAMING_SNAKE_CASE , start_positions=_SCREAMING_SNAKE_CASE , end_positions=_SCREAMING_SNAKE_CASE , ) 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 _lowerCAmelCase ( self ) -> Tuple: snake_case_ : Dict = self.prepare_config_and_inputs() ( snake_case_ ) : Any = config_and_inputs snake_case_ : Optional[Any] = { "input_ids": input_ids, "bbox": bbox, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_torch class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ): '''simple docstring''' A : Optional[Any] = ( ( LiltModel, LiltForSequenceClassification, LiltForTokenClassification, LiltForQuestionAnswering, ) if is_torch_available() else () ) A : Tuple = ( { 'feature-extraction': LiltModel, 'question-answering': LiltForQuestionAnswering, 'text-classification': LiltForSequenceClassification, 'token-classification': LiltForTokenClassification, 'zero-shot': LiltForSequenceClassification, } if is_torch_available() else {} ) A : Optional[Any] = False A : Any = False def _lowerCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict: return True def _lowerCAmelCase ( self ) -> str: snake_case_ : Dict = LiltModelTester(self ) snake_case_ : Optional[Any] = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 ) def _lowerCAmelCase ( self ) -> str: self.config_tester.run_common_tests() def _lowerCAmelCase ( self ) -> int: snake_case_ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE ) def _lowerCAmelCase ( self ) -> Union[str, Any]: snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: snake_case_ : Dict = type self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE ) def _lowerCAmelCase ( self ) -> str: snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE ) def _lowerCAmelCase ( self ) -> List[str]: snake_case_ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE ) @slow def _lowerCAmelCase ( self ) -> Optional[Any]: for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: snake_case_ : Union[str, Any] = LiltModel.from_pretrained(_SCREAMING_SNAKE_CASE ) self.assertIsNotNone(_SCREAMING_SNAKE_CASE ) @require_torch @slow class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' def _lowerCAmelCase ( self ) -> str: snake_case_ : Any = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base" ).to(_SCREAMING_SNAKE_CASE ) snake_case_ : List[Any] = torch.tensor([[1, 2]] , device=_SCREAMING_SNAKE_CASE ) snake_case_ : Any = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]] , device=_SCREAMING_SNAKE_CASE ) # forward pass with torch.no_grad(): snake_case_ : Optional[int] = model(input_ids=_SCREAMING_SNAKE_CASE , bbox=_SCREAMING_SNAKE_CASE ) snake_case_ : str = torch.Size([1, 2, 768] ) snake_case_ : Optional[Any] = torch.tensor( [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]] , device=_SCREAMING_SNAKE_CASE , ) self.assertTrue(outputs.last_hidden_state.shape , _SCREAMING_SNAKE_CASE ) self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3] , _SCREAMING_SNAKE_CASE , atol=1e-3 ) )
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lowercase : Optional[int] = { '''Pillow''': '''Pillow''', '''accelerate''': '''accelerate>=0.11.0''', '''compel''': '''compel==0.1.8''', '''black''': '''black~=23.1''', '''datasets''': '''datasets''', '''filelock''': '''filelock''', '''flax''': '''flax>=0.4.1''', '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''', '''huggingface-hub''': '''huggingface-hub>=0.13.2''', '''requests-mock''': '''requests-mock==1.10.0''', '''importlib_metadata''': '''importlib_metadata''', '''invisible-watermark''': '''invisible-watermark''', '''isort''': '''isort>=5.5.4''', '''jax''': '''jax>=0.2.8,!=0.3.2''', '''jaxlib''': '''jaxlib>=0.1.65''', '''Jinja2''': '''Jinja2''', '''k-diffusion''': '''k-diffusion>=0.0.12''', '''torchsde''': '''torchsde''', '''note_seq''': '''note_seq''', '''librosa''': '''librosa''', '''numpy''': '''numpy''', '''omegaconf''': '''omegaconf''', '''parameterized''': '''parameterized''', '''protobuf''': '''protobuf>=3.20.3,<4''', '''pytest''': '''pytest''', '''pytest-timeout''': '''pytest-timeout''', '''pytest-xdist''': '''pytest-xdist''', '''ruff''': '''ruff>=0.0.241''', '''safetensors''': '''safetensors''', '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''', '''scipy''': '''scipy''', '''onnx''': '''onnx''', '''regex''': '''regex!=2019.12.17''', '''requests''': '''requests''', '''tensorboard''': '''tensorboard''', '''torch''': '''torch>=1.4''', '''torchvision''': '''torchvision''', '''transformers''': '''transformers>=4.25.1''', '''urllib3''': '''urllib3<=2.0.0''', }
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