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import copy import os import cva import numpy as np from matplotlib import pyplot as plt class SCREAMING_SNAKE_CASE_ : def __init__( self : Tuple ): """simple docstring""" UpperCamelCase = '''''' UpperCamelCase = '''''' UpperCamelCase = [] UpperCamelCase = 0 UpperCamelCase = 256 UpperCamelCase = 0 UpperCamelCase = 0 UpperCamelCase = 0 UpperCamelCase = 0 def lowerCamelCase_ ( self : Any , lowerCamelCase_ : Any ): """simple docstring""" UpperCamelCase = cva.imread(SCREAMING_SNAKE_CASE_ , 0 ) UpperCamelCase = copy.deepcopy(self.img ) UpperCamelCase = plt.hist(self.img.ravel() , 256 , [0, 256] , label="""x""" ) UpperCamelCase = np.sum(SCREAMING_SNAKE_CASE_ ) for i in range(len(SCREAMING_SNAKE_CASE_ ) ): UpperCamelCase = x[i] / self.k self.sk += prk UpperCamelCase = (self.L - 1) * self.sk if self.rem != 0: UpperCamelCase = int(last % last ) UpperCamelCase = int(last + 1 if self.rem >= 0.5 else last ) self.last_list.append(SCREAMING_SNAKE_CASE_ ) UpperCamelCase = int(np.ma.count(self.img ) / self.img[1].size ) UpperCamelCase = self.img[1].size for i in range(self.number_of_cols ): for j in range(self.number_of_rows ): UpperCamelCase = self.img[j][i] if num != self.last_list[num]: UpperCamelCase = self.last_list[num] cva.imwrite("""output_data/output.jpg""" , self.img ) def lowerCamelCase_ ( self : Any ): """simple docstring""" plt.hist(self.img.ravel() , 256 , [0, 256] ) def lowerCamelCase_ ( self : Tuple ): """simple docstring""" cva.imshow("""Output-Image""" , self.img ) cva.imshow("""Input-Image""" , self.original_image ) cva.waitKey(5000 ) cva.destroyAllWindows() if __name__ == "__main__": _SCREAMING_SNAKE_CASE = os.path.join(os.path.basename(__file__), """image_data/input.jpg""") _SCREAMING_SNAKE_CASE = ConstantStretch() stretcher.stretch(file_path) stretcher.plot_histogram() stretcher.show_image()
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'''simple docstring''' from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging UpperCamelCase = logging.get_logger(__name__) UpperCamelCase = { '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''', '''YituTech/conv-bert-medium-small''': ( '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json''' ), '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''', # See all ConvBERT models at https://huggingface.co/models?filter=convbert } class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' UpperCamelCase_ : Optional[Any] = """convbert""" def __init__( self : Dict , SCREAMING_SNAKE_CASE_ : Dict=3_05_22 , SCREAMING_SNAKE_CASE_ : int=7_68 , SCREAMING_SNAKE_CASE_ : List[str]=12 , SCREAMING_SNAKE_CASE_ : List[str]=12 , SCREAMING_SNAKE_CASE_ : Dict=30_72 , SCREAMING_SNAKE_CASE_ : Optional[int]="gelu" , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : int=0.1 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=5_12 , SCREAMING_SNAKE_CASE_ : List[Any]=2 , SCREAMING_SNAKE_CASE_ : List[str]=0.02 , SCREAMING_SNAKE_CASE_ : int=1E-12 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=1 , SCREAMING_SNAKE_CASE_ : int=0 , SCREAMING_SNAKE_CASE_ : str=2 , SCREAMING_SNAKE_CASE_ : List[Any]=7_68 , SCREAMING_SNAKE_CASE_ : Optional[Any]=2 , SCREAMING_SNAKE_CASE_ : Any=9 , SCREAMING_SNAKE_CASE_ : Tuple=1 , SCREAMING_SNAKE_CASE_ : List[Any]=None , **SCREAMING_SNAKE_CASE_ : List[str] , ) -> List[Any]: '''simple docstring''' super().__init__( pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , ) A: Dict = vocab_size A: Tuple = hidden_size A: Optional[int] = num_hidden_layers A: List[str] = num_attention_heads A: int = intermediate_size A: int = hidden_act A: List[str] = hidden_dropout_prob A: int = attention_probs_dropout_prob A: Tuple = max_position_embeddings A: Any = type_vocab_size A: str = initializer_range A: Union[str, Any] = layer_norm_eps A: str = embedding_size A: Optional[int] = head_ratio A: List[Any] = conv_kernel_size A: List[Any] = num_groups A: Optional[int] = classifier_dropout class lowerCAmelCase_ ( UpperCAmelCase_ ): '''simple docstring''' @property def _snake_case ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]: '''simple docstring''' if self.task == "multiple-choice": A: Tuple = {0: '''batch''', 1: '''choice''', 2: '''sequence'''} else: A: List[str] = {0: '''batch''', 1: '''sequence'''} return OrderedDict( [ ('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis), ] )
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import json from typing import TYPE_CHECKING, List, Optional, Tuple from tokenizers import pre_tokenizers, processors from ...tokenization_utils_base import AddedToken, BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_blenderbot import BlenderbotTokenizer if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation UpperCAmelCase : Tuple = logging.get_logger(__name__) UpperCAmelCase : str = { """vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_config_file""": """tokenizer_config.json""", } UpperCAmelCase : Union[str, Any] = { """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""}, """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""}, """tokenizer_config_file""": { """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json""" }, } UpperCAmelCase : Optional[int] = {"""facebook/blenderbot-3B""": 128} class __lowerCAmelCase ( UpperCamelCase__): _lowercase : Dict = VOCAB_FILES_NAMES _lowercase : str = PRETRAINED_VOCAB_FILES_MAP _lowercase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _lowercase : Dict = ["""input_ids""", """attention_mask"""] _lowercase : Tuple = BlenderbotTokenizer def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__="replace" , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__=False , lowerCAmelCase__=True , **lowerCAmelCase__ , ) -> int: '''simple docstring''' super().__init__( lowerCAmelCase__ , lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , errors=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , add_prefix_space=lowerCAmelCase__ , trim_offsets=lowerCAmelCase__ , **lowerCAmelCase__ , ) a__ : List[str] =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() ) if pre_tok_state.get("add_prefix_space" , lowerCAmelCase__ ) != add_prefix_space: a__ : List[Any] =getattr(lowerCAmelCase__ , pre_tok_state.pop("type" ) ) a__ : Union[str, Any] =add_prefix_space a__ : Dict =pre_tok_class(**lowerCAmelCase__ ) a__ : Dict =add_prefix_space a__ : Optional[int] ="post_processor" a__ : List[str] =getattr(self.backend_tokenizer , lowerCAmelCase__ , lowerCAmelCase__ ) if tokenizer_component_instance: a__ : Optional[int] =json.loads(tokenizer_component_instance.__getstate__() ) # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class` if "sep" in state: a__ : Dict =tuple(state["sep"] ) if "cls" in state: a__ : List[Any] =tuple(state["cls"] ) a__ : List[str] =False if state.get("add_prefix_space" , lowerCAmelCase__ ) != add_prefix_space: a__ : Union[str, Any] =add_prefix_space a__ : Any =True if state.get("trim_offsets" , lowerCAmelCase__ ) != trim_offsets: a__ : int =trim_offsets a__ : Optional[int] =True if changes_to_apply: a__ : str =getattr(lowerCAmelCase__ , state.pop("type" ) ) a__ : Tuple =component_class(**lowerCAmelCase__ ) setattr(self.backend_tokenizer , lowerCAmelCase__ , lowerCAmelCase__ ) @property # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot def _lowercase ( self ) -> str: '''simple docstring''' if self._mask_token is None: if self.verbose: logger.error("Using mask_token, but it is not set yet." ) return None return str(self._mask_token ) @mask_token.setter def _lowercase ( self , lowerCAmelCase__ ) -> Optional[int]: '''simple docstring''' a__ : int =AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else value a__ : Union[str, Any] =value def _lowercase ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> BatchEncoding: '''simple docstring''' a__ : int =kwargs.get("is_split_into_words" , lowerCAmelCase__ ) assert self.add_prefix_space or not is_split_into_words, ( F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True ''' "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*lowerCAmelCase__ , **lowerCAmelCase__ ) def _lowercase ( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> BatchEncoding: '''simple docstring''' a__ : Dict =kwargs.get("is_split_into_words" , lowerCAmelCase__ ) assert self.add_prefix_space or not is_split_into_words, ( F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True ''' "to use it with pretokenized inputs." ) return super()._encode_plus(*lowerCAmelCase__ , **lowerCAmelCase__ ) def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]: '''simple docstring''' a__ : Union[str, Any] =self._tokenizer.model.save(lowerCAmelCase__ , name=lowerCAmelCase__ ) return tuple(lowerCAmelCase__ ) def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]: '''simple docstring''' a__ : Any =[self.sep_token_id] a__ : 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 + sep + token_ids_a + sep ) * [0] def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Optional[Any]: '''simple docstring''' return token_ids_a + [self.eos_token_id] def _lowercase ( self , lowerCAmelCase__ ) -> List[int]: '''simple docstring''' a__ : int =[] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text ) else: # Generated responses should contain them already. inputs.append(lowerCAmelCase__ ) a__ : List[str] =" ".join(lowerCAmelCase__ ) a__ : Union[str, Any] =self.encode(lowerCAmelCase__ ) if len(lowerCAmelCase__ ) > self.model_max_length: a__ : int =input_ids[-self.model_max_length :] logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' ) return input_ids
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import warnings from transformers import AutoTokenizer from transformers.utils import is_torch_available from transformers.utils.generic import ExplicitEnum from ...processing_utils import ProcessorMixin if is_torch_available(): import torch class __lowerCAmelCase ( UpperCamelCase__): _lowercase : Any = """char""" _lowercase : str = """bpe""" _lowercase : List[Any] = """wp""" UpperCAmelCase : List[Any] = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE) class __lowerCAmelCase ( UpperCamelCase__): _lowercase : Tuple = ["""image_processor""", """char_tokenizer"""] _lowercase : Any = """ViTImageProcessor""" _lowercase : Optional[Any] = """MgpstrTokenizer""" def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Optional[Any]: '''simple docstring''' a__ : Optional[int] =None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." , lowerCAmelCase__ , ) a__ : List[str] =kwargs.pop("feature_extractor" ) a__ : List[str] =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`." ) a__ : str =tokenizer a__ : List[str] =AutoTokenizer.from_pretrained("gpt2" ) a__ : Optional[int] =AutoTokenizer.from_pretrained("bert-base-uncased" ) super().__init__(lowerCAmelCase__ , lowerCAmelCase__ ) def __call__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ ) -> Union[str, Any]: '''simple docstring''' if images is None and text is None: raise ValueError("You need to specify either an `images` or `text` input to process." ) if images is not None: a__ : Union[str, Any] =self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ ) if text is not None: a__ : int =self.char_tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ ) if text is None: return inputs elif images is None: return encodings else: a__ : Tuple =encodings["input_ids"] return inputs def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]: '''simple docstring''' a__ , a__ , a__ : Any =sequences a__ : Union[str, Any] =char_preds.size(0 ) a__ , a__ : Dict =self._decode_helper(lowerCAmelCase__ , "char" ) a__ , a__ : List[Any] =self._decode_helper(lowerCAmelCase__ , "bpe" ) a__ , a__ : Optional[int] =self._decode_helper(lowerCAmelCase__ , "wp" ) a__ : List[Any] =[] a__ : Dict =[] for i in range(lowerCAmelCase__ ): a__ : int =[char_scores[i], bpe_scores[i], wp_scores[i]] a__ : Tuple =[char_strs[i], bpe_strs[i], wp_strs[i]] a__ : Any =scores.index(max(lowerCAmelCase__ ) ) final_strs.append(strs[max_score_index] ) final_scores.append(scores[max_score_index] ) a__ : Dict ={} a__ : str =final_strs a__ : Optional[int] =final_scores a__ : Union[str, Any] =char_strs a__ : List[str] =bpe_strs a__ : Union[str, Any] =wp_strs return out def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple: '''simple docstring''' if format == DecodeType.CHARACTER: a__ : Optional[Any] =self.char_decode a__ : Dict =1 a__ : Tuple ="[s]" elif format == DecodeType.BPE: a__ : str =self.bpe_decode a__ : Dict =2 a__ : Optional[int] ="#" elif format == DecodeType.WORDPIECE: a__ : Union[str, Any] =self.wp_decode a__ : List[Any] =1_0_2 a__ : Dict ="[SEP]" else: raise ValueError(F'''Format {format} is not supported.''' ) a__ , a__ : Any =[], [] a__ : str =pred_logits.size(0 ) a__ : Optional[Any] =pred_logits.size(1 ) a__ , a__ : Optional[int] =pred_logits.topk(1 , dim=-1 , largest=lowerCAmelCase__ , sorted=lowerCAmelCase__ ) a__ : Optional[Any] =preds_index.view(-1 , lowerCAmelCase__ )[:, 1:] a__ : Dict =decoder(lowerCAmelCase__ ) a__ , a__ : Any =torch.nn.functional.softmax(lowerCAmelCase__ , dim=2 ).max(dim=2 ) a__ : int =preds_max_prob[:, 1:] for index in range(lowerCAmelCase__ ): a__ : Optional[Any] =preds_str[index].find(lowerCAmelCase__ ) a__ : Optional[int] =preds_str[index][:pred_eos] a__ : List[Any] =preds_index[index].cpu().tolist() a__ : List[Any] =pred_index.index(lowerCAmelCase__ ) if eos_token in pred_index else -1 a__ : Union[str, Any] =preds_max_prob[index][: pred_eos_index + 1] a__ : List[Any] =pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0 dec_strs.append(lowerCAmelCase__ ) conf_scores.append(lowerCAmelCase__ ) return dec_strs, conf_scores def _lowercase ( self , lowerCAmelCase__ ) -> str: '''simple docstring''' a__ : int =[seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(lowerCAmelCase__ )] return decode_strs def _lowercase ( self , lowerCAmelCase__ ) -> Tuple: '''simple docstring''' return self.bpe_tokenizer.batch_decode(lowerCAmelCase__ ) def _lowercase ( self , lowerCAmelCase__ ) -> Optional[Any]: '''simple docstring''' a__ : List[Any] =[seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(lowerCAmelCase__ )] return decode_strs
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'''simple docstring''' import math def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Tuple ) -> Optional[Any]: if not isinstance(lowercase__ ,lowercase__ ): _a : Any =F"Input value of [number={number}] must be an integer" raise TypeError(lowercase__ ) if number < 1: _a : Tuple =F"Input value of [number={number}] must be > 0" raise ValueError(lowercase__ ) elif number == 1: return 3 elif number == 2: return 5 else: _a : Optional[Any] =int(math.log(number // 3 ,2 ) ) + 2 _a : int =[3, 5] _a : Dict =2 _a : Optional[int] =3 for block in range(1 ,lowercase__ ): for _ in range(lowercase__ ): proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] ) proth_index += 1 increment *= 2 return proth_list[number - 1] if __name__ == "__main__": import doctest doctest.testmod() for number in range(11): A__: List[str] = 0 try: A__: Any = proth(number) except ValueError: print(F"ValueError: there is no {number}th Proth number") continue print(F"The {number}th Proth number: {value}")
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"""simple docstring""" from sklearn.metrics import fa_score, matthews_corrcoef import datasets from .record_evaluation import evaluate as evaluate_record lowercase__ = """\ @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} } """ lowercase__ = """\ 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. """ lowercase__ = """ 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 ( lowercase__ , lowercase__ ): return float((preds == labels).mean() ) def _snake_case ( lowercase__ , lowercase__ , lowercase__="binary" ): _lowerCamelCase : str = simple_accuracy(lowercase__ , lowercase__ ) _lowerCamelCase : Any = float(fa_score(y_true=lowercase__ , y_pred=lowercase__ , average=lowercase__ ) ) return { "accuracy": acc, "f1": fa, } def _snake_case ( lowercase__ , lowercase__ ): _lowerCamelCase : Any = {} for id_pred, label in zip(lowercase__ , lowercase__ ): _lowerCamelCase : Tuple = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}''' _lowerCamelCase : Union[str, Any] = id_pred['prediction'] if question_id in question_map: question_map[question_id].append((pred, label) ) else: _lowerCamelCase : Optional[Any] = [(pred, label)] _lowerCamelCase, _lowerCamelCase : Optional[int] = [], [] for question, preds_labels in question_map.items(): _lowerCamelCase, _lowerCamelCase : Tuple = zip(*lowercase__ ) _lowerCamelCase : List[str] = fa_score(y_true=lowercase__ , y_pred=lowercase__ , average='macro' ) fas.append(lowercase__ ) _lowerCamelCase : int = int(sum(pred == label for pred, label in preds_labels ) == len(lowercase__ ) ) ems.append(lowercase__ ) _lowerCamelCase : Optional[Any] = float(sum(lowercase__ ) / len(lowercase__ ) ) _lowerCamelCase : Optional[int] = sum(lowercase__ ) / len(lowercase__ ) _lowerCamelCase : List[Any] = float(fa_score(y_true=lowercase__ , 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 lowerCAmelCase__ ( datasets.Metric ): '''simple docstring''' def A_ ( self ): 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 A_ ( self ): 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 A_ ( self , lowercase , lowercase ): if self.config_name == "axb": return {"matthews_correlation": matthews_corrcoef(lowercase , lowercase )} elif self.config_name == "cb": return acc_and_fa(lowercase , lowercase , fa_avg='macro' ) elif self.config_name == "record": _lowerCamelCase : List[str] = [ { 'qas': [ {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]} for ref in references ] } ] _lowerCamelCase : Union[str, Any] = {pred['idx']['query']: pred['prediction_text'] for pred in predictions} return evaluate_record(lowercase , lowercase )[0] elif self.config_name == "multirc": return evaluate_multirc(lowercase , lowercase ) elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]: return {"accuracy": simple_accuracy(lowercase , lowercase )} 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 logging import os import sys from dataclasses import dataclass, field from typing import Optional import evaluate import numpy as np import torch from datasets import load_dataset from PIL import Image from torchvision.transforms import ( CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, Resize, ToTensor, ) import transformers from transformers import ( MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, AutoConfig, AutoImageProcessor, AutoModelForImageClassification, HfArgumentParser, Trainer, TrainingArguments, set_seed, ) from transformers.trainer_utils import get_last_checkpoint from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version A : Dict = logging.getLogger(__name__) # Will error if the minimal version of Transformers is not installed. Remove at your own risks. check_min_version("4.31.0") require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt") A : str = list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys()) A : Optional[int] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) def _lowerCamelCase ( _UpperCamelCase ): '''simple docstring''' with open(_UpperCamelCase , "rb" ) as f: __lowerCAmelCase = Image.open(_UpperCamelCase ) return im.convert("RGB" ) @dataclass class _UpperCamelCase : '''simple docstring''' __UpperCAmelCase : Optional[str] =field( default=lowerCAmelCase__ ,metadata={ """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).""" } ,) __UpperCAmelCase : Optional[str] =field( default=lowerCAmelCase__ ,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} ) __UpperCAmelCase : Optional[str] =field(default=lowerCAmelCase__ ,metadata={"""help""": """A folder containing the training data."""} ) __UpperCAmelCase : Optional[str] =field(default=lowerCAmelCase__ ,metadata={"""help""": """A folder containing the validation data."""} ) __UpperCAmelCase : Optional[float] =field( default=0.15 ,metadata={"""help""": """Percent to split off of train for validation."""} ) __UpperCAmelCase : Optional[int] =field( default=lowerCAmelCase__ ,metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of training examples to this """ """value if set.""" ) } ,) __UpperCAmelCase : Optional[int] =field( default=lowerCAmelCase__ ,metadata={ """help""": ( """For debugging purposes or quicker training, truncate the number of evaluation examples to this """ """value if set.""" ) } ,) def snake_case ( self ): if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None): raise ValueError( "You must specify either a dataset name from the hub or a train and/or validation directory." ) @dataclass class _UpperCamelCase : '''simple docstring''' __UpperCAmelCase : str =field( default="""google/vit-base-patch16-224-in21k""" ,metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} ,) __UpperCAmelCase : Optional[str] =field( default=lowerCAmelCase__ ,metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(lowerCAmelCase__ )} ,) __UpperCAmelCase : Optional[str] =field( default=lowerCAmelCase__ ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} ) __UpperCAmelCase : Optional[str] =field( default=lowerCAmelCase__ ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} ) __UpperCAmelCase : str =field( default="""main""" ,metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} ,) __UpperCAmelCase : str =field(default=lowerCAmelCase__ ,metadata={"""help""": """Name or path of preprocessor config."""} ) __UpperCAmelCase : bool =field( default=lowerCAmelCase__ ,metadata={ """help""": ( """Will use the token generated when running `huggingface-cli login` (necessary to use this script """ """with private models).""" ) } ,) __UpperCAmelCase : bool =field( default=lowerCAmelCase__ ,metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} ,) def _lowerCamelCase ( _UpperCamelCase ): '''simple docstring''' __lowerCAmelCase = torch.stack([example["pixel_values"] for example in examples] ) __lowerCAmelCase = torch.tensor([example["labels"] for example in examples] ) return {"pixel_values": pixel_values, "labels": labels} def _lowerCamelCase ( ): '''simple docstring''' __lowerCAmelCase = 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 = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 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_image_classification" , _UpperCamelCase , _UpperCamelCase ) # 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() __lowerCAmelCase = training_args.get_process_log_level() logger.setLevel(_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. __lowerCAmelCase = None if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir: __lowerCAmelCase = 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 ) # Initialize our dataset and prepare it for the 'image-classification' task. if data_args.dataset_name is not None: __lowerCAmelCase = load_dataset( data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir , task="image-classification" , use_auth_token=True if model_args.use_auth_token else None , ) else: __lowerCAmelCase = {} if data_args.train_dir is not None: __lowerCAmelCase = os.path.join(data_args.train_dir , "**" ) if data_args.validation_dir is not None: __lowerCAmelCase = os.path.join(data_args.validation_dir , "**" ) __lowerCAmelCase = load_dataset( "imagefolder" , data_files=_UpperCamelCase , cache_dir=model_args.cache_dir , task="image-classification" , ) # If we don't have a validation split, split off a percentage of train as validation. __lowerCAmelCase = None if "validation" in dataset.keys() else data_args.train_val_split if isinstance(data_args.train_val_split , _UpperCamelCase ) and data_args.train_val_split > 0.0: __lowerCAmelCase = dataset["train"].train_test_split(data_args.train_val_split ) __lowerCAmelCase = split["train"] __lowerCAmelCase = split["test"] # Prepare label mappings. # We'll include these in the model's config to get human readable labels in the Inference API. __lowerCAmelCase = dataset["train"].features["labels"].names __lowerCAmelCase , __lowerCAmelCase = {}, {} for i, label in enumerate(_UpperCamelCase ): __lowerCAmelCase = str(_UpperCamelCase ) __lowerCAmelCase = label # Load the accuracy metric from the datasets package __lowerCAmelCase = evaluate.load("accuracy" ) # Define our 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 ): return metric.compute(predictions=np.argmax(p.predictions , axis=1 ) , references=p.label_ids ) __lowerCAmelCase = AutoConfig.from_pretrained( model_args.config_name or model_args.model_name_or_path , num_labels=len(_UpperCamelCase ) , labelaid=_UpperCamelCase , idalabel=_UpperCamelCase , finetuning_task="image-classification" , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ) __lowerCAmelCase = AutoModelForImageClassification.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 , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , ) __lowerCAmelCase = AutoImageProcessor.from_pretrained( model_args.image_processor_name or 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 , ) # Define torchvision transforms to be applied to each image. if "shortest_edge" in image_processor.size: __lowerCAmelCase = image_processor.size["shortest_edge"] else: __lowerCAmelCase = (image_processor.size["height"], image_processor.size["width"]) __lowerCAmelCase = Normalize(mean=image_processor.image_mean , std=image_processor.image_std ) __lowerCAmelCase = Compose( [ RandomResizedCrop(_UpperCamelCase ), RandomHorizontalFlip(), ToTensor(), normalize, ] ) __lowerCAmelCase = Compose( [ Resize(_UpperCamelCase ), CenterCrop(_UpperCamelCase ), ToTensor(), normalize, ] ) def train_transforms(_UpperCamelCase ): __lowerCAmelCase = [ _train_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"] ] return example_batch def val_transforms(_UpperCamelCase ): __lowerCAmelCase = [_val_transforms(pil_img.convert("RGB" ) ) for pil_img in example_batch["image"]] return example_batch if training_args.do_train: if "train" not in dataset: raise ValueError("--do_train requires a train dataset" ) if data_args.max_train_samples is not None: __lowerCAmelCase = ( dataset["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) ) ) # Set the training transforms dataset["train"].set_transform(_UpperCamelCase ) if training_args.do_eval: if "validation" not in dataset: raise ValueError("--do_eval requires a validation dataset" ) if data_args.max_eval_samples is not None: __lowerCAmelCase = ( dataset["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) ) ) # Set the validation transforms dataset["validation"].set_transform(_UpperCamelCase ) # Initalize our trainer __lowerCAmelCase = Trainer( model=_UpperCamelCase , args=_UpperCamelCase , train_dataset=dataset["train"] if training_args.do_train else None , eval_dataset=dataset["validation"] if training_args.do_eval else None , compute_metrics=_UpperCamelCase , tokenizer=_UpperCamelCase , data_collator=_UpperCamelCase , ) # Training if training_args.do_train: __lowerCAmelCase = None if training_args.resume_from_checkpoint is not None: __lowerCAmelCase = training_args.resume_from_checkpoint elif last_checkpoint is not None: __lowerCAmelCase = last_checkpoint __lowerCAmelCase = trainer.train(resume_from_checkpoint=_UpperCamelCase ) trainer.save_model() trainer.log_metrics("train" , train_result.metrics ) trainer.save_metrics("train" , train_result.metrics ) trainer.save_state() # Evaluation if training_args.do_eval: __lowerCAmelCase = trainer.evaluate() trainer.log_metrics("eval" , _UpperCamelCase ) trainer.save_metrics("eval" , _UpperCamelCase ) # Write model card and (optionally) push to hub __lowerCAmelCase = { "finetuned_from": model_args.model_name_or_path, "tasks": "image-classification", "dataset": data_args.dataset_name, "tags": ["image-classification", "vision"], } if training_args.push_to_hub: trainer.push_to_hub(**_UpperCamelCase ) else: trainer.create_model_card(**_UpperCamelCase ) if __name__ == "__main__": main()
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"""simple docstring""" def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ): '''simple docstring''' __lowerCAmelCase = [[] for _ in range(_UpperCamelCase )] __lowerCAmelCase = key - 1 if key <= 0: raise ValueError("Height of grid can't be 0 or negative" ) if key == 1 or len(_UpperCamelCase ) <= key: return input_string for position, character in enumerate(_UpperCamelCase ): __lowerCAmelCase = position % (lowest * 2) # puts it in bounds __lowerCAmelCase = min(_UpperCamelCase , lowest * 2 - num ) # creates zigzag pattern temp_grid[num].append(_UpperCamelCase ) __lowerCAmelCase = ["".join(_UpperCamelCase ) for row in temp_grid] __lowerCAmelCase = "".join(_UpperCamelCase ) return output_string def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ): '''simple docstring''' __lowerCAmelCase = [] __lowerCAmelCase = key - 1 if key <= 0: raise ValueError("Height of grid can't be 0 or negative" ) if key == 1: return input_string __lowerCAmelCase = [[] for _ in range(_UpperCamelCase )] # generates template for position in range(len(_UpperCamelCase ) ): __lowerCAmelCase = position % (lowest * 2) # puts it in bounds __lowerCAmelCase = min(_UpperCamelCase , lowest * 2 - num ) # creates zigzag pattern temp_grid[num].append("*" ) __lowerCAmelCase = 0 for row in temp_grid: # fills in the characters __lowerCAmelCase = input_string[counter : counter + len(_UpperCamelCase )] grid.append(list(_UpperCamelCase ) ) counter += len(_UpperCamelCase ) __lowerCAmelCase = "" # reads as zigzag for position in range(len(_UpperCamelCase ) ): __lowerCAmelCase = position % (lowest * 2) # puts it in bounds __lowerCAmelCase = min(_UpperCamelCase , lowest * 2 - num ) # creates zigzag pattern output_string += grid[num][0] grid[num].pop(0 ) return output_string def _lowerCamelCase ( _UpperCamelCase ): '''simple docstring''' __lowerCAmelCase = {} for key_guess in range(1 , len(_UpperCamelCase ) ): # tries every key __lowerCAmelCase = decrypt(_UpperCamelCase , _UpperCamelCase ) return results if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations def SCREAMING_SNAKE_CASE__ ( __a = 4 ): snake_case_ : str = abs(__a ) or 4 return [[1 + x + y * row_size for x in range(__a )] for y in range(__a )] def SCREAMING_SNAKE_CASE__ ( __a ): return reverse_row(transpose(__a ) ) # OR.. transpose(reverse_column(matrix)) def SCREAMING_SNAKE_CASE__ ( __a ): return reverse_row(reverse_column(__a ) ) # OR.. reverse_column(reverse_row(matrix)) def SCREAMING_SNAKE_CASE__ ( __a ): return reverse_column(transpose(__a ) ) # OR.. transpose(reverse_row(matrix)) def SCREAMING_SNAKE_CASE__ ( __a ): snake_case_ : Any = [list(__a ) for x in zip(*__a )] return matrix def SCREAMING_SNAKE_CASE__ ( __a ): snake_case_ : int = matrix[::-1] return matrix def SCREAMING_SNAKE_CASE__ ( __a ): snake_case_ : str = [x[::-1] for x in matrix] return matrix def SCREAMING_SNAKE_CASE__ ( __a ): for i in matrix: print(*__a ) if __name__ == "__main__": _SCREAMING_SNAKE_CASE = make_matrix() print("""\norigin:\n""") print_matrix(matrix) print("""\nrotate 90 counterclockwise:\n""") print_matrix(rotate_aa(matrix)) _SCREAMING_SNAKE_CASE = make_matrix() print("""\norigin:\n""") print_matrix(matrix) print("""\nrotate 180:\n""") print_matrix(rotate_aaa(matrix)) _SCREAMING_SNAKE_CASE = make_matrix() print("""\norigin:\n""") print_matrix(matrix) print("""\nrotate 270 counterclockwise:\n""") print_matrix(rotate_aaa(matrix))
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import copy from typing import Dict, List, Optional from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto import CONFIG_MAPPING SCREAMING_SNAKE_CASE__ = { """facebook/mask2former-swin-small-coco-instance""": ( """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json""" ) # See all Mask2Former models at https://huggingface.co/models?filter=mask2former } SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) class A__ ( lowerCAmelCase__ ): lowerCAmelCase__ : Tuple = "mask2former" lowerCAmelCase__ : List[Any] = ["swin"] lowerCAmelCase__ : str = {"hidden_size": "hidden_dim"} def __init__( self : Optional[int] , _UpperCAmelCase : Optional[Dict] = None , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 2_56 , _UpperCAmelCase : int = 10_24 , _UpperCAmelCase : str = "relu" , _UpperCAmelCase : int = 6 , _UpperCAmelCase : int = 10 , _UpperCAmelCase : int = 8 , _UpperCAmelCase : float = 0.0 , _UpperCAmelCase : int = 20_48 , _UpperCAmelCase : bool = False , _UpperCAmelCase : bool = False , _UpperCAmelCase : int = 4 , _UpperCAmelCase : int = 2_55 , _UpperCAmelCase : int = 1_00 , _UpperCAmelCase : float = 0.1 , _UpperCAmelCase : float = 2.0 , _UpperCAmelCase : float = 5.0 , _UpperCAmelCase : float = 5.0 , _UpperCAmelCase : int = 1_25_44 , _UpperCAmelCase : float = 3.0 , _UpperCAmelCase : float = 0.75 , _UpperCAmelCase : float = 0.02 , _UpperCAmelCase : float = 1.0 , _UpperCAmelCase : bool = True , _UpperCAmelCase : List[int] = [4, 8, 16, 32] , _UpperCAmelCase : bool = None , **_UpperCAmelCase : List[str] , ) -> int: """simple docstring""" if backbone_config is None: logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.' ) __lowercase = CONFIG_MAPPING['swin']( image_size=2_24 , in_channels=3 , patch_size=4 , embed_dim=96 , depths=[2, 2, 18, 2] , num_heads=[3, 6, 12, 24] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=_UpperCAmelCase , out_features=['stage1', 'stage2', 'stage3', 'stage4'] , ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ): __lowercase = backbone_config.pop('model_type' ) __lowercase = CONFIG_MAPPING[backbone_model_type] __lowercase = config_class.from_dict(_UpperCAmelCase ) # verify that the backbone is supported if backbone_config.model_type not in self.backbones_supported: logger.warning_once( f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """ f"""Supported model types: {",".join(self.backbones_supported )}""" ) __lowercase = backbone_config __lowercase = feature_size __lowercase = mask_feature_size __lowercase = hidden_dim __lowercase = encoder_feedforward_dim __lowercase = activation_function __lowercase = encoder_layers __lowercase = decoder_layers __lowercase = num_attention_heads __lowercase = dropout __lowercase = dim_feedforward __lowercase = pre_norm __lowercase = enforce_input_projection __lowercase = common_stride __lowercase = ignore_value __lowercase = num_queries __lowercase = no_object_weight __lowercase = class_weight __lowercase = mask_weight __lowercase = dice_weight __lowercase = train_num_points __lowercase = oversample_ratio __lowercase = importance_sample_ratio __lowercase = init_std __lowercase = init_xavier_std __lowercase = use_auxiliary_loss __lowercase = feature_strides __lowercase = output_auxiliary_logits __lowercase = decoder_layers super().__init__(**_UpperCAmelCase ) @classmethod def a__ ( cls : Union[str, Any] , _UpperCAmelCase : PretrainedConfig , **_UpperCAmelCase : Optional[int] ) -> Dict: """simple docstring""" return cls( backbone_config=_UpperCAmelCase , **_UpperCAmelCase , ) def a__ ( self : str ) -> Dict[str, any]: """simple docstring""" __lowercase = copy.deepcopy(self.__dict__ ) __lowercase = self.backbone_config.to_dict() __lowercase = self.__class__.model_type return output
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available _SCREAMING_SNAKE_CASE = { 'configuration_x_clip': [ 'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XCLIPConfig', 'XCLIPTextConfig', 'XCLIPVisionConfig', ], 'processing_x_clip': ['XCLIPProcessor'], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _SCREAMING_SNAKE_CASE = [ 'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST', 'XCLIPModel', 'XCLIPPreTrainedModel', 'XCLIPTextModel', 'XCLIPVisionModel', ] if TYPE_CHECKING: from .configuration_x_clip import ( XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, XCLIPConfig, XCLIPTextConfig, XCLIPVisionConfig, ) from .processing_x_clip import XCLIPProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_x_clip import ( XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST, XCLIPModel, XCLIPPreTrainedModel, XCLIPTextModel, XCLIPVisionModel, ) else: import sys _SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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import importlib import sys from argparse import REMAINDER, ArgumentParser from pathlib import Path import torch_xla.distributed.xla_multiprocessing as xmp def snake_case ( ) -> List[Any]: _A = ArgumentParser( description=( """PyTorch TPU distributed training launch """ """helper utility that will spawn up """ """multiple distributed processes""" )) # Optional arguments for the launch helper parser.add_argument("""--num_cores""" , type=snake_case__ , default=1 , help="""Number of TPU cores to use (1 or 8).""") # positional parser.add_argument( """training_script""" , type=snake_case__ , help=( """The full path to the single TPU training """ """program/script to be launched in parallel, """ """followed by all the arguments for the """ """training script""" ) , ) # rest from the training program parser.add_argument("""training_script_args""" , nargs=snake_case__) return parser.parse_args() def snake_case ( ) -> List[str]: _A = parse_args() # Import training_script as a module. _A = Path(args.training_script) sys.path.append(str(script_fpath.parent.resolve())) _A = script_fpath.stem _A = importlib.import_module(snake_case__) # Patch sys.argv _A = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores)] xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores) if __name__ == "__main__": main()
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1
import copy import random from transformers import CLIPTokenizer class UpperCamelCase_ ( a_ ): '''simple docstring''' def __init__( self , *a , **a ) -> Tuple: super().__init__(*snake_case__ , **snake_case__ ) snake_case_ = {} def _UpperCamelCase ( self , a , *a , **a ) -> Union[str, Any]: snake_case_ = super().add_tokens(snake_case__ , *snake_case__ , **snake_case__ ) if num_added_tokens == 0: raise ValueError( F'''The tokenizer already contains the token {placeholder_token}. Please pass a different''' ' `placeholder_token` that is not already in the tokenizer.' ) def _UpperCamelCase ( self , a , *a , a=1 , **a ) -> Any: snake_case_ = [] if num_vec_per_token == 1: self.try_adding_tokens(snake_case__ , *snake_case__ , **snake_case__ ) output.append(snake_case__ ) else: snake_case_ = [] for i in range(snake_case__ ): snake_case_ = placeholder_token + F'''_{i}''' self.try_adding_tokens(snake_case__ , *snake_case__ , **snake_case__ ) output.append(snake_case__ ) # handle cases where there is a new placeholder token that contains the current placeholder token but is larger for token in self.token_map: if token in placeholder_token: raise ValueError( F'''The tokenizer already has placeholder token {token} that can get confused with''' F''' {placeholder_token}keep placeholder tokens independent''' ) snake_case_ = output def _UpperCamelCase ( self , a , a=False , a=1.0 ) -> List[str]: if isinstance(snake_case__ , snake_case__ ): snake_case_ = [] for i in range(len(snake_case__ ) ): output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=snake_case__ ) ) return output for placeholder_token in self.token_map: if placeholder_token in text: snake_case_ = self.token_map[placeholder_token] snake_case_ = tokens[: 1 + int(len(snake_case__ ) * prop_tokens_to_load )] if vector_shuffle: snake_case_ = copy.copy(snake_case__ ) random.shuffle(snake_case__ ) snake_case_ = text.replace(snake_case__ , ' '.join(snake_case__ ) ) return text def __call__( self , a , *a , a=False , a=1.0 , **a ) -> Dict: return super().__call__( self.replace_placeholder_tokens_in_text( snake_case__ , vector_shuffle=snake_case__ , prop_tokens_to_load=snake_case__ ) , *snake_case__ , **snake_case__ , ) def _UpperCamelCase ( self , a , *a , a=False , a=1.0 , **a ) -> Union[str, Any]: return super().encode( self.replace_placeholder_tokens_in_text( snake_case__ , vector_shuffle=snake_case__ , prop_tokens_to_load=snake_case__ ) , *snake_case__ , **snake_case__ , )
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UpperCAmelCase : Optional[Any] ={ """Pillow""": """Pillow<10.0.0""", """accelerate""": """accelerate>=0.20.3""", """av""": """av==9.2.0""", """beautifulsoup4""": """beautifulsoup4""", """black""": """black~=23.1""", """codecarbon""": """codecarbon==1.2.0""", """cookiecutter""": """cookiecutter==1.7.3""", """dataclasses""": """dataclasses""", """datasets""": """datasets!=2.5.0""", """decord""": """decord==0.6.0""", """deepspeed""": """deepspeed>=0.9.3""", """diffusers""": """diffusers""", """dill""": """dill<0.3.5""", """evaluate""": """evaluate>=0.2.0""", """fairscale""": """fairscale>0.3""", """faiss-cpu""": """faiss-cpu""", """fastapi""": """fastapi""", """filelock""": """filelock""", """flax""": """flax>=0.4.1,<=0.7.0""", """ftfy""": """ftfy""", """fugashi""": """fugashi>=1.0""", """GitPython""": """GitPython<3.1.19""", """hf-doc-builder""": """hf-doc-builder>=0.3.0""", """huggingface-hub""": """huggingface-hub>=0.14.1,<1.0""", """importlib_metadata""": """importlib_metadata""", """ipadic""": """ipadic>=1.0.0,<2.0""", """isort""": """isort>=5.5.4""", """jax""": """jax>=0.2.8,!=0.3.2,<=0.4.13""", """jaxlib""": """jaxlib>=0.1.65,<=0.4.13""", """jieba""": """jieba""", """kenlm""": """kenlm""", """keras-nlp""": """keras-nlp>=0.3.1""", """librosa""": """librosa""", """nltk""": """nltk""", """natten""": """natten>=0.14.6""", """numpy""": """numpy>=1.17""", """onnxconverter-common""": """onnxconverter-common""", """onnxruntime-tools""": """onnxruntime-tools>=1.4.2""", """onnxruntime""": """onnxruntime>=1.4.0""", """opencv-python""": """opencv-python""", """optuna""": """optuna""", """optax""": """optax>=0.0.8,<=0.1.4""", """packaging""": """packaging>=20.0""", """parameterized""": """parameterized""", """phonemizer""": """phonemizer""", """protobuf""": """protobuf""", """psutil""": """psutil""", """pyyaml""": """pyyaml>=5.1""", """pydantic""": """pydantic<2""", """pytest""": """pytest>=7.2.0""", """pytest-timeout""": """pytest-timeout""", """pytest-xdist""": """pytest-xdist""", """python""": """python>=3.8.0""", """ray[tune]""": """ray[tune]""", """regex""": """regex!=2019.12.17""", """requests""": """requests""", """rhoknp""": """rhoknp>=1.1.0,<1.3.1""", """rjieba""": """rjieba""", """rouge-score""": """rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1""", """ruff""": """ruff>=0.0.241,<=0.0.259""", """sacrebleu""": """sacrebleu>=1.4.12,<2.0.0""", """sacremoses""": """sacremoses""", """safetensors""": """safetensors>=0.3.1""", """sagemaker""": """sagemaker>=2.31.0""", """scikit-learn""": """scikit-learn""", """sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""", """sigopt""": """sigopt""", """starlette""": """starlette""", """sudachipy""": """sudachipy>=0.6.6""", """sudachidict_core""": """sudachidict_core>=20220729""", """tensorflow-cpu""": """tensorflow-cpu>=2.6,<2.14""", """tensorflow""": """tensorflow>=2.6,<2.14""", """tensorflow-text""": """tensorflow-text<2.14""", """tf2onnx""": """tf2onnx""", """timeout-decorator""": """timeout-decorator""", """timm""": """timm""", """tokenizers""": """tokenizers>=0.11.1,!=0.11.3,<0.14""", """torch""": """torch>=1.9,!=1.12.0""", """torchaudio""": """torchaudio""", """torchvision""": """torchvision""", """pyctcdecode""": """pyctcdecode>=0.4.0""", """tqdm""": """tqdm>=4.27""", """unidic""": """unidic>=1.0.2""", """unidic_lite""": """unidic_lite>=1.0.7""", """urllib3""": """urllib3<2.0.0""", """uvicorn""": """uvicorn""", }
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import os def snake_case (__lowercase = "input.txt" ) -> int: '''simple docstring''' with open(os.path.join(os.path.dirname(__lowercase ) , __lowercase ) ) as input_file: _snake_case : List[Any] = [ [int(__lowercase ) for element in line.split("," )] for line in input_file.readlines() ] _snake_case : Optional[Any] = len(__lowercase ) _snake_case : Optional[Any] = len(matrix[0] ) _snake_case : Optional[int] = [[-1 for _ in range(__lowercase )] for _ in range(__lowercase )] for i in range(__lowercase ): _snake_case : Optional[int] = matrix[i][0] for j in range(1 , __lowercase ): for i in range(__lowercase ): _snake_case : Union[str, Any] = minimal_path_sums[i][j - 1] + matrix[i][j] for i in range(1 , __lowercase ): _snake_case : int = min( minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j] ) for i in range(rows - 2 , -1 , -1 ): _snake_case : int = 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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from dataclasses import dataclass, field from typing import Tuple from ..utils import cached_property, is_tf_available, logging, requires_backends from .benchmark_args_utils import BenchmarkArguments if is_tf_available(): import tensorflow as tf __SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__) @dataclass class lowercase_ ( __snake_case ): _lowerCamelCase = [ 'no_inference', 'no_cuda', 'no_tpu', 'no_speed', 'no_memory', 'no_env_print', 'no_multi_process', ] def __init__( self , **lowercase_ ): for deprecated_arg in self.deprecated_args: if deprecated_arg in kwargs: _snake_case : List[str] = deprecated_arg[3:] _snake_case : Optional[int] = not kwargs.pop(lowercase_ ) logger.warning( f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or""" f""" {positive_arg}={kwargs[positive_arg]}""" ) _snake_case : Tuple = kwargs.pop("tpu_name" , self.tpu_name ) _snake_case : Any = kwargs.pop("device_idx" , self.device_idx ) _snake_case : List[str] = kwargs.pop("eager_mode" , self.eager_mode ) _snake_case : List[str] = kwargs.pop("use_xla" , self.use_xla ) super().__init__(**lowercase_ ) _lowerCamelCase = field( default=__snake_case , metadata={'help': 'Name of TPU'} , ) _lowerCamelCase = field( default=0 , metadata={'help': 'CPU / GPU device index. Defaults to 0.'} , ) _lowerCamelCase = field(default=__snake_case , metadata={'help': 'Benchmark models in eager model.'} ) _lowerCamelCase = field( default=__snake_case , metadata={ 'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.' } , ) @cached_property def UpperCamelCase ( self ): requires_backends(self , ["tf"] ) _snake_case : str = None if self.tpu: try: if self.tpu_name: _snake_case : Optional[Any] = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name ) else: _snake_case : List[str] = tf.distribute.cluster_resolver.TPUClusterResolver() except ValueError: _snake_case : Union[str, Any] = None return tpu @cached_property def UpperCamelCase ( self ): requires_backends(self , ["tf"] ) if self.is_tpu: tf.config.experimental_connect_to_cluster(self._setup_tpu ) tf.tpu.experimental.initialize_tpu_system(self._setup_tpu ) _snake_case : List[str] = tf.distribute.TPUStrategy(self._setup_tpu ) else: # currently no multi gpu is allowed if self.is_gpu: # TODO: Currently only single GPU is supported tf.config.set_visible_devices(self.gpu_list[self.device_idx] , "GPU" ) _snake_case : Any = tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}""" ) else: tf.config.set_visible_devices([] , "GPU" ) # disable GPU _snake_case : Any = tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}""" ) return strategy @property def UpperCamelCase ( self ): requires_backends(self , ["tf"] ) return self._setup_tpu is not None @property def UpperCamelCase ( self ): requires_backends(self , ["tf"] ) return self._setup_strategy @property def UpperCamelCase ( self ): requires_backends(self , ["tf"] ) return tf.config.list_physical_devices("GPU" ) @property def UpperCamelCase ( self ): requires_backends(self , ["tf"] ) if self.cuda: return len(self.gpu_list ) return 0 @property def UpperCamelCase ( self ): return self.n_gpu > 0
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"""simple docstring""" import json import logging import os import sys from pathlib import Path import finetune_rag from transformers.file_utils import is_apex_available from transformers.testing_utils import ( TestCasePlus, execute_subprocess_async, require_ray, require_torch_gpu, require_torch_multi_gpu, ) logging.basicConfig(level=logging.DEBUG) _SCREAMING_SNAKE_CASE : int = logging.getLogger() _SCREAMING_SNAKE_CASE : str = logging.StreamHandler(sys.stdout) logger.addHandler(stream_handler) class a ( __snake_case ): def UpperCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] ) -> Dict: os.makedirs(__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ = {'source': 'What is love ?', 'target': 'life'} lowerCamelCase_ = {'train': 12, 'val': 2, 'test': 2} for split in ["train", "test", "val"]: for field in ["source", "target"]: lowerCamelCase_ = '\n'.join([contents[field]] * n_lines[split] ) with open(os.path.join(__SCREAMING_SNAKE_CASE , F'''{split}.{field}''' ) , 'w' ) as f: f.write(__SCREAMING_SNAKE_CASE ) def UpperCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : int = "pytorch" ) -> int: lowerCamelCase_ = self.get_auto_remove_tmp_dir() lowerCamelCase_ = os.path.join(__SCREAMING_SNAKE_CASE , 'output' ) lowerCamelCase_ = os.path.join(__SCREAMING_SNAKE_CASE , 'data' ) self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE ) lowerCamelCase_ = F''' --data_dir {data_dir} \ --output_dir {output_dir} \ --model_name_or_path facebook/rag-sequence-base \ --model_type rag_sequence \ --do_train \ --do_predict \ --n_val -1 \ --val_check_interval 1.0 \ --train_batch_size 2 \ --eval_batch_size 1 \ --max_source_length 25 \ --max_target_length 25 \ --val_max_target_length 25 \ --test_max_target_length 25 \ --label_smoothing 0.1 \ --dropout 0.1 \ --attention_dropout 0.1 \ --weight_decay 0.001 \ --adam_epsilon 1e-08 \ --max_grad_norm 0.1 \ --lr_scheduler polynomial \ --learning_rate 3e-04 \ --num_train_epochs 1 \ --warmup_steps 4 \ --gradient_accumulation_steps 1 \ --distributed-port 8787 \ --use_dummy_dataset 1 \ --distributed_retriever {distributed_retriever} \ '''.split() if gpus > 0: testargs.append(F'''--gpus={gpus}''' ) if is_apex_available(): testargs.append('--fp16' ) else: testargs.append('--gpus=0' ) testargs.append('--distributed_backend=ddp_cpu' ) testargs.append('--num_processes=2' ) lowerCamelCase_ = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs execute_subprocess_async(__SCREAMING_SNAKE_CASE , env=self.get_env() ) lowerCamelCase_ = os.path.join(__SCREAMING_SNAKE_CASE , 'metrics.json' ) with open(__SCREAMING_SNAKE_CASE ) as f: lowerCamelCase_ = json.load(__SCREAMING_SNAKE_CASE ) return result @require_torch_gpu def UpperCamelCase ( self : str ) -> List[str]: lowerCamelCase_ = self._run_finetune(gpus=1 ) self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 ) @require_torch_multi_gpu def UpperCamelCase ( self : List[str] ) -> Tuple: lowerCamelCase_ = self._run_finetune(gpus=2 ) self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 ) @require_torch_gpu @require_ray def UpperCamelCase ( self : str ) -> Tuple: lowerCamelCase_ = self._run_finetune(gpus=1 , distributed_retriever='ray' ) self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 ) @require_torch_multi_gpu @require_ray def UpperCamelCase ( self : Optional[int] ) -> List[str]: lowerCamelCase_ = self._run_finetune(gpus=1 , distributed_retriever='ray' ) self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
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'''simple docstring''' def lowerCAmelCase (__A): """simple docstring""" if not isinstance(__A , __A): raise ValueError('''multiplicative_persistence() only accepts integral values''') if num < 0: raise ValueError('''multiplicative_persistence() does not accept negative values''') _a = 0 _a = str(__A) while len(__A) != 1: _a = [int(__A) for i in num_string] _a = 1 for i in range(0 , len(__A)): total *= numbers[i] _a = str(__A) steps += 1 return steps def lowerCAmelCase (__A): """simple docstring""" if not isinstance(__A , __A): raise ValueError('''additive_persistence() only accepts integral values''') if num < 0: raise ValueError('''additive_persistence() does not accept negative values''') _a = 0 _a = str(__A) while len(__A) != 1: _a = [int(__A) for i in num_string] _a = 0 for i in range(0 , len(__A)): total += numbers[i] _a = str(__A) steps += 1 return steps if __name__ == "__main__": import doctest doctest.testmod()
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0
from typing import Optional import pyspark from .. import Features, NamedSplit from ..download import DownloadMode from ..packaged_modules.spark.spark import Spark from .abc import AbstractDatasetReader class a_ ( SCREAMING_SNAKE_CASE ): '''simple docstring''' def __init__( self : str , lowercase__ : pyspark.sql.DataFrame , lowercase__ : Optional[NamedSplit] = None , lowercase__ : Optional[Features] = None , lowercase__ : bool = True , lowercase__ : str = None , lowercase__ : bool = False , lowercase__ : str = None , lowercase__ : bool = True , lowercase__ : str = "arrow" , **lowercase__ : Any , ): '''simple docstring''' super().__init__( split=lowercase__ , features=lowercase__ , cache_dir=lowercase__ , keep_in_memory=lowercase__ , streaming=lowercase__ , **lowercase__ , ) lowerCAmelCase__ = load_from_cache_file lowerCAmelCase__ = file_format lowerCAmelCase__ = Spark( df=lowercase__ , features=lowercase__ , cache_dir=lowercase__ , working_dir=lowercase__ , **lowercase__ , ) def __snake_case ( self : Tuple): '''simple docstring''' if self.streaming: return self.builder.as_streaming_dataset(split=self.split) lowerCAmelCase__ = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD self.builder.download_and_prepare( download_mode=lowercase__ , file_format=self._file_format , ) return self.builder.as_dataset(split=self.split)
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available lowerCAmelCase__ = { 'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'], 'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCAmelCase__ = [ 'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST', 'AdaptiveEmbedding', 'TransfoXLForSequenceClassification', 'TransfoXLLMHeadModel', 'TransfoXLModel', 'TransfoXLPreTrainedModel', 'load_tf_weights_in_transfo_xl', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowerCAmelCase__ = [ 'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST', 'TFAdaptiveEmbedding', 'TFTransfoXLForSequenceClassification', 'TFTransfoXLLMHeadModel', 'TFTransfoXLMainLayer', 'TFTransfoXLModel', 'TFTransfoXLPreTrainedModel', ] if TYPE_CHECKING: from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_transfo_xl import ( TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, AdaptiveEmbedding, TransfoXLForSequenceClassification, TransfoXLLMHeadModel, TransfoXLModel, TransfoXLPreTrainedModel, load_tf_weights_in_transfo_xl, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_transfo_xl import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFAdaptiveEmbedding, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLMainLayer, TFTransfoXLModel, TFTransfoXLPreTrainedModel, ) else: import sys lowerCAmelCase__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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1
import collections import gzip import os import urllib import numpy from tensorflow.python.framework import dtypes, random_seed from tensorflow.python.platform import gfile from tensorflow.python.util.deprecation import deprecated __UpperCAmelCase : List[str] = collections.namedtuple("_Datasets", ["train", "validation", "test"]) # CVDF mirror of http://yann.lecun.com/exdb/mnist/ __UpperCAmelCase : str = "https://storage.googleapis.com/cvdf-datasets/mnist/" def A__ ( SCREAMING_SNAKE_CASE__) -> Dict: __snake_case: List[Any] = numpy.dtype(numpy.uintaa).newbyteorder(""">""") return numpy.frombuffer(bytestream.read(4) , dtype=lowerCAmelCase_)[0] @deprecated(lowerCAmelCase_ , """Please use tf.data to implement this functionality.""") def A__ ( SCREAMING_SNAKE_CASE__) -> List[str]: print("""Extracting""" , f.name) with gzip.GzipFile(fileobj=lowerCAmelCase_) as bytestream: __snake_case: List[Any] = _readaa(lowerCAmelCase_) if magic != 2051: raise ValueError( """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name)) __snake_case: Any = _readaa(lowerCAmelCase_) __snake_case: Dict = _readaa(lowerCAmelCase_) __snake_case: int = _readaa(lowerCAmelCase_) __snake_case: List[Any] = bytestream.read(rows * cols * num_images) __snake_case: List[Any] = numpy.frombuffer(lowerCAmelCase_ , dtype=numpy.uinta) __snake_case: List[str] = data.reshape(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , 1) return data @deprecated(lowerCAmelCase_ , """Please use tf.one_hot on tensors.""") def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]: __snake_case: Tuple = labels_dense.shape[0] __snake_case: int = numpy.arange(lowerCAmelCase_) * num_classes __snake_case: List[Any] = numpy.zeros((num_labels, num_classes)) __snake_case: int = 1 return labels_one_hot @deprecated(lowerCAmelCase_ , """Please use tf.data to implement this functionality.""") def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=10) -> List[str]: print("""Extracting""" , f.name) with gzip.GzipFile(fileobj=lowerCAmelCase_) as bytestream: __snake_case: Tuple = _readaa(lowerCAmelCase_) if magic != 2049: raise ValueError( """Invalid magic number %d in MNIST label file: %s""" % (magic, f.name)) __snake_case: Tuple = _readaa(lowerCAmelCase_) __snake_case: Union[str, Any] = bytestream.read(lowerCAmelCase_) __snake_case: Any = numpy.frombuffer(lowerCAmelCase_ , dtype=numpy.uinta) if one_hot: return _dense_to_one_hot(lowerCAmelCase_ , lowerCAmelCase_) return labels class __snake_case : '''simple docstring''' @deprecated( A , """Please use alternatives such as official/mnist/_DataSet.py""" """ from tensorflow/models.""" , ) def __init__( self : Union[str, Any] , A : int , A : Union[str, Any] , A : Any=False , A : Optional[int]=False , A : Tuple=dtypes.floataa , A : List[Any]=True , A : Optional[int]=None , ): __snake_case , __snake_case: Union[str, Any] = random_seed.get_seed(A ) # If op level seed is not set, use whatever graph level seed is returned numpy.random.seed(seeda if seed is None else seeda ) __snake_case: Tuple = dtypes.as_dtype(A ).base_dtype if dtype not in (dtypes.uinta, dtypes.floataa): raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype ) if fake_data: __snake_case: List[Any] = 10_000 __snake_case: Tuple = one_hot else: assert ( images.shape[0] == labels.shape[0] ), f'''images.shape: {images.shape} labels.shape: {labels.shape}''' __snake_case: List[str] = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) if reshape: assert images.shape[3] == 1 __snake_case: Any = images.reshape( images.shape[0] , images.shape[1] * images.shape[2] ) if dtype == dtypes.floataa: # Convert from [0, 255] -> [0.0, 1.0]. __snake_case: str = images.astype(numpy.floataa ) __snake_case: Dict = numpy.multiply(A , 1.0 / 255.0 ) __snake_case: Tuple = images __snake_case: Optional[Any] = labels __snake_case: Dict = 0 __snake_case: str = 0 @property def UpperCAmelCase__ ( self : str ): return self._images @property def UpperCAmelCase__ ( self : str ): return self._labels @property def UpperCAmelCase__ ( self : Tuple ): return self._num_examples @property def UpperCAmelCase__ ( self : Dict ): return self._epochs_completed def UpperCAmelCase__ ( self : Union[str, Any] , A : int , A : Tuple=False , A : Tuple=True ): if fake_data: __snake_case: Tuple = [1] * 784 __snake_case: str = [1] + [0] * 9 if self.one_hot else 0 return ( [fake_image for _ in range(A )], [fake_label for _ in range(A )], ) __snake_case: Tuple = self._index_in_epoch # Shuffle for the first epoch if self._epochs_completed == 0 and start == 0 and shuffle: __snake_case: str = numpy.arange(self._num_examples ) numpy.random.shuffle(A ) __snake_case: Dict = self.images[perma] __snake_case: Dict = self.labels[perma] # Go to the next epoch if start + batch_size > self._num_examples: # Finished epoch self._epochs_completed += 1 # Get the rest examples in this epoch __snake_case: Tuple = self._num_examples - start __snake_case: Union[str, Any] = self._images[start : self._num_examples] __snake_case: int = self._labels[start : self._num_examples] # Shuffle the data if shuffle: __snake_case: Any = numpy.arange(self._num_examples ) numpy.random.shuffle(A ) __snake_case: Optional[int] = self.images[perm] __snake_case: List[str] = self.labels[perm] # Start next epoch __snake_case: Union[str, Any] = 0 __snake_case: List[str] = batch_size - rest_num_examples __snake_case: int = self._index_in_epoch __snake_case: Optional[int] = self._images[start:end] __snake_case: Any = self._labels[start:end] return ( numpy.concatenate((images_rest_part, images_new_part) , axis=0 ), numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ), ) else: self._index_in_epoch += batch_size __snake_case: List[Any] = self._index_in_epoch return self._images[start:end], self._labels[start:end] @deprecated(lowerCAmelCase_ , """Please write your own downloading logic.""") def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Tuple: if not gfile.Exists(lowerCAmelCase_): gfile.MakeDirs(lowerCAmelCase_) __snake_case: Any = os.path.join(lowerCAmelCase_ , lowerCAmelCase_) if not gfile.Exists(lowerCAmelCase_): urllib.request.urlretrieve(lowerCAmelCase_ , lowerCAmelCase_) # noqa: S310 with gfile.GFile(lowerCAmelCase_) as f: __snake_case: Union[str, Any] = f.size() print("""Successfully downloaded""" , lowerCAmelCase_ , lowerCAmelCase_ , """bytes.""") return filepath @deprecated( lowerCAmelCase_ , """Please use alternatives such as:""" """ tensorflow_datasets.load(\'mnist\')""") def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=dtypes.floataa , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=5000 , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=DEFAULT_SOURCE_URL , ) -> str: if fake_data: def fake(): return _DataSet( [] , [] , fake_data=lowerCAmelCase_ , one_hot=lowerCAmelCase_ , dtype=lowerCAmelCase_ , seed=lowerCAmelCase_) __snake_case: str = fake() __snake_case: Any = fake() __snake_case: Tuple = fake() return _Datasets(train=lowerCAmelCase_ , validation=lowerCAmelCase_ , test=lowerCAmelCase_) if not source_url: # empty string check __snake_case: Any = DEFAULT_SOURCE_URL __snake_case: List[str] = """train-images-idx3-ubyte.gz""" __snake_case: int = """train-labels-idx1-ubyte.gz""" __snake_case: Any = """t10k-images-idx3-ubyte.gz""" __snake_case: Optional[Any] = """t10k-labels-idx1-ubyte.gz""" __snake_case: Any = _maybe_download( lowerCAmelCase_ , lowerCAmelCase_ , source_url + train_images_file) with gfile.Open(lowerCAmelCase_ , """rb""") as f: __snake_case: Optional[int] = _extract_images(lowerCAmelCase_) __snake_case: List[str] = _maybe_download( lowerCAmelCase_ , lowerCAmelCase_ , source_url + train_labels_file) with gfile.Open(lowerCAmelCase_ , """rb""") as f: __snake_case: int = _extract_labels(lowerCAmelCase_ , one_hot=lowerCAmelCase_) __snake_case: List[Any] = _maybe_download( lowerCAmelCase_ , lowerCAmelCase_ , source_url + test_images_file) with gfile.Open(lowerCAmelCase_ , """rb""") as f: __snake_case: Optional[Any] = _extract_images(lowerCAmelCase_) __snake_case: str = _maybe_download( lowerCAmelCase_ , lowerCAmelCase_ , source_url + test_labels_file) with gfile.Open(lowerCAmelCase_ , """rb""") as f: __snake_case: Tuple = _extract_labels(lowerCAmelCase_ , one_hot=lowerCAmelCase_) if not 0 <= validation_size <= len(lowerCAmelCase_): __snake_case: Tuple = ( """Validation size should be between 0 and """ F'''{len(lowerCAmelCase_)}. Received: {validation_size}.''' ) raise ValueError(lowerCAmelCase_) __snake_case: Union[str, Any] = train_images[:validation_size] __snake_case: List[str] = train_labels[:validation_size] __snake_case: str = train_images[validation_size:] __snake_case: str = train_labels[validation_size:] __snake_case: Union[str, Any] = {"""dtype""": dtype, """reshape""": reshape, """seed""": seed} __snake_case: Tuple = _DataSet(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_) __snake_case: Union[str, Any] = _DataSet(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_) __snake_case: Optional[Any] = _DataSet(lowerCAmelCase_ , lowerCAmelCase_ , **lowerCAmelCase_) return _Datasets(train=lowerCAmelCase_ , validation=lowerCAmelCase_ , test=lowerCAmelCase_)
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from ...configuration_utils import PretrainedConfig from ...utils import logging _lowerCamelCase =logging.get_logger(__name__) _lowerCamelCase ={ "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json", # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae } class a_ ( lowerCamelCase_ ): """simple docstring""" __UpperCAmelCase = 'vit_mae' def __init__( self : Union[str, Any] ,snake_case : Any=768 ,snake_case : List[str]=12 ,snake_case : Optional[int]=12 ,snake_case : int=3072 ,snake_case : List[Any]="gelu" ,snake_case : str=0.0 ,snake_case : str=0.0 ,snake_case : Optional[Any]=0.02 ,snake_case : Dict=1e-12 ,snake_case : List[str]=224 ,snake_case : Any=16 ,snake_case : Any=3 ,snake_case : Tuple=True ,snake_case : List[Any]=16 ,snake_case : List[str]=512 ,snake_case : List[Any]=8 ,snake_case : Dict=2048 ,snake_case : Union[str, Any]=0.75 ,snake_case : Union[str, Any]=False ,**snake_case : Optional[int] ,): super().__init__(**snake_case ) SCREAMING_SNAKE_CASE =hidden_size SCREAMING_SNAKE_CASE =num_hidden_layers SCREAMING_SNAKE_CASE =num_attention_heads SCREAMING_SNAKE_CASE =intermediate_size SCREAMING_SNAKE_CASE =hidden_act SCREAMING_SNAKE_CASE =hidden_dropout_prob SCREAMING_SNAKE_CASE =attention_probs_dropout_prob SCREAMING_SNAKE_CASE =initializer_range SCREAMING_SNAKE_CASE =layer_norm_eps SCREAMING_SNAKE_CASE =image_size SCREAMING_SNAKE_CASE =patch_size SCREAMING_SNAKE_CASE =num_channels SCREAMING_SNAKE_CASE =qkv_bias SCREAMING_SNAKE_CASE =decoder_num_attention_heads SCREAMING_SNAKE_CASE =decoder_hidden_size SCREAMING_SNAKE_CASE =decoder_num_hidden_layers SCREAMING_SNAKE_CASE =decoder_intermediate_size SCREAMING_SNAKE_CASE =mask_ratio SCREAMING_SNAKE_CASE =norm_pix_loss
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"""simple docstring""" from ...utils import is_note_seq_available, is_transformers_available, is_torch_available from ...utils import OptionalDependencyNotAvailable try: if not (is_transformers_available() and is_torch_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 else: from .notes_encoder import SpectrogramNotesEncoder from .continous_encoder import SpectrogramContEncoder from .pipeline_spectrogram_diffusion import ( SpectrogramContEncoder, SpectrogramDiffusionPipeline, TaFilmDecoder, ) try: if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .midi_utils import MidiProcessor
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"""simple docstring""" from math import factorial def snake_case ( A__ = 1_00 ): return sum(int(A__ ) for x in str(factorial(A__ ) ) ) if __name__ == "__main__": print(solution(int(input('''Enter the Number: ''').strip())))
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"""simple docstring""" import unittest from transformers import RoFormerTokenizer, RoFormerTokenizerFast from transformers.testing_utils import require_rjieba, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin @require_rjieba @require_tokenizers class A_ ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ): """simple docstring""" __UpperCamelCase = RoFormerTokenizer __UpperCamelCase = RoFormerTokenizerFast __UpperCamelCase = True __UpperCamelCase = True def UpperCAmelCase__ ( self :Optional[Any] ) -> str: super().setUp() def UpperCAmelCase__ ( self :int , **lowercase_ :List[str] ) -> List[Any]: return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **lowercase_ ) def UpperCAmelCase__ ( self :List[Any] , **lowercase_ :Any ) -> Any: return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base' , **lowercase_ ) def UpperCAmelCase__ ( self :Tuple ) -> Optional[Any]: UpperCAmelCase = '永和服装饰品有限公司,今天天气非常好' UpperCAmelCase = '永和 服装 饰品 有限公司 , 今 天 天 气 非常 好' return input_text, output_text def UpperCAmelCase__ ( self :int ) -> Dict: UpperCAmelCase = self.get_tokenizer() UpperCAmelCase , UpperCAmelCase = self.get_chinese_input_output_texts() UpperCAmelCase = tokenizer.tokenize(lowercase_ ) self.assertListEqual(lowercase_ , output_text.split() ) UpperCAmelCase = tokens + [tokenizer.unk_token] UpperCAmelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ ) def UpperCAmelCase__ ( self :List[str] ) -> int: UpperCAmelCase = self.get_rust_tokenizer() UpperCAmelCase , UpperCAmelCase = self.get_chinese_input_output_texts() UpperCAmelCase = tokenizer.tokenize(lowercase_ ) self.assertListEqual(lowercase_ , output_text.split() ) UpperCAmelCase = tokens + [tokenizer.unk_token] UpperCAmelCase = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase_ ) , lowercase_ ) def UpperCAmelCase__ ( self :Optional[Any] ) -> str: pass def UpperCAmelCase__ ( self :Any ) -> Optional[int]: pass def UpperCAmelCase__ ( self :Tuple ) -> Dict: pass
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from math import sqrt def lowerCamelCase_ ( UpperCamelCase__ : int ) -> bool: """simple docstring""" if 1 < number < 4: # 2 and 3 are primes return True elif number < 2 or number % 2 == 0 or number % 3 == 0: # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes return False # All primes number are in format of 6k +/- 1 for i in range(5 , int(sqrt(UpperCamelCase__ ) + 1 ) , 6 ): if number % i == 0 or number % (i + 2) == 0: return False return True def lowerCamelCase_ ( UpperCamelCase__ : int = 1_0001 ) -> int: """simple docstring""" __lowerCamelCase = 0 __lowerCamelCase = 1 while count != nth and number < 3: number += 1 if is_prime(UpperCamelCase__ ): count += 1 while count != nth: number += 2 if is_prime(UpperCamelCase__ ): count += 1 return number if __name__ == "__main__": print(f'''{solution() = }''')
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"""simple docstring""" from argparse import ArgumentParser from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline from ..utils import logging from . import BaseTransformersCLICommand __A : str = logging.get_logger(__name__) # pylint: disable=invalid-name def __UpperCamelCase ( _A : str ) ->Dict: """simple docstring""" if not path: return "pipe" for ext in PipelineDataFormat.SUPPORTED_FORMATS: if path.endswith(_A ): return ext raise Exception( f'Unable to determine file format from file extension {path}. ' f'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}' ) def __UpperCamelCase ( _A : str ) ->Any: """simple docstring""" lowerCamelCase_ =pipeline( task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , ) lowerCamelCase_ =try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format lowerCamelCase_ =PipelineDataFormat.from_str( format=_A , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , ) return RunCommand(_A , _A ) class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__): def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )-> Optional[int]: lowerCamelCase_ =nlp lowerCamelCase_ =reader @staticmethod def _snake_case ( _SCREAMING_SNAKE_CASE )-> List[str]: lowerCamelCase_ =parser.add_parser("""run""" , help="""Run a pipeline through the CLI""" ) run_parser.add_argument("""--task""" , choices=get_supported_tasks() , help="""Task to run""" ) run_parser.add_argument("""--input""" , type=_SCREAMING_SNAKE_CASE , help="""Path to the file to use for inference""" ) run_parser.add_argument("""--output""" , type=_SCREAMING_SNAKE_CASE , help="""Path to the file that will be used post to write results.""" ) run_parser.add_argument("""--model""" , type=_SCREAMING_SNAKE_CASE , help="""Name or path to the model to instantiate.""" ) run_parser.add_argument("""--config""" , type=_SCREAMING_SNAKE_CASE , help="""Name or path to the model's config to instantiate.""" ) run_parser.add_argument( """--tokenizer""" , type=_SCREAMING_SNAKE_CASE , help="""Name of the tokenizer to use. (default: same as the model name)""" ) run_parser.add_argument( """--column""" , type=_SCREAMING_SNAKE_CASE , help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)""" , ) run_parser.add_argument( """--format""" , type=_SCREAMING_SNAKE_CASE , default="""infer""" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="""Input format to read from""" , ) run_parser.add_argument( """--device""" , type=_SCREAMING_SNAKE_CASE , default=-1 , help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" , ) run_parser.add_argument("""--overwrite""" , action="""store_true""" , help="""Allow overwriting the output file.""" ) run_parser.set_defaults(func=_SCREAMING_SNAKE_CASE ) def _snake_case ( self )-> Optional[int]: lowerCamelCase_ , lowerCamelCase_ =self._nlp, [] for entry in self._reader: lowerCamelCase_ =nlp(**_SCREAMING_SNAKE_CASE ) if self._reader.is_multi_columns else nlp(_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): outputs.append(_SCREAMING_SNAKE_CASE ) else: outputs += output # Saving data if self._nlp.binary_output: lowerCamelCase_ =self._reader.save_binary(_SCREAMING_SNAKE_CASE ) logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' ) else: self._reader.save(_SCREAMING_SNAKE_CASE )
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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 __A : int = '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n' __A : Any = '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n' __A : Union[str, Any] = '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n 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\'].\n 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\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: "c" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric(\'mauve\')\n >>> predictions = ["hello there", "general kenobi"]\n >>> references = ["hello there", "general kenobi"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n' @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION) class _SCREAMING_SNAKE_CASE ( datasets.Metric): def _snake_case ( self )-> Any: 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 _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="auto" , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=0.9 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=500 , _SCREAMING_SNAKE_CASE="gpt2-large" , _SCREAMING_SNAKE_CASE=-1 , _SCREAMING_SNAKE_CASE=1024 , _SCREAMING_SNAKE_CASE=25 , _SCREAMING_SNAKE_CASE=5 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=25 , )-> List[str]: lowerCamelCase_ =compute_mauve( p_text=_SCREAMING_SNAKE_CASE , q_text=_SCREAMING_SNAKE_CASE , p_features=_SCREAMING_SNAKE_CASE , q_features=_SCREAMING_SNAKE_CASE , p_tokens=_SCREAMING_SNAKE_CASE , q_tokens=_SCREAMING_SNAKE_CASE , num_buckets=_SCREAMING_SNAKE_CASE , pca_max_data=_SCREAMING_SNAKE_CASE , kmeans_explained_var=_SCREAMING_SNAKE_CASE , kmeans_num_redo=_SCREAMING_SNAKE_CASE , kmeans_max_iter=_SCREAMING_SNAKE_CASE , featurize_model_name=_SCREAMING_SNAKE_CASE , device_id=_SCREAMING_SNAKE_CASE , max_text_length=_SCREAMING_SNAKE_CASE , divergence_curve_discretization_size=_SCREAMING_SNAKE_CASE , mauve_scaling_factor=_SCREAMING_SNAKE_CASE , verbose=_SCREAMING_SNAKE_CASE , seed=_SCREAMING_SNAKE_CASE , ) return out
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from datetime import datetime as dt import os from github import Github A : Optional[int] = [ "good first issue", "good second issue", "good difficult issue", "feature request", "new model", "wip", ] def __lowerCAmelCase ( ) -> Any: __a = Github(os.environ['''GITHUB_TOKEN'''] ) __a = g.get_repo('''huggingface/transformers''' ) __a = repo.get_issues(state='''open''' ) for issue in open_issues: __a = sorted([comment for comment in issue.get_comments()] , key=lambda a__ : i.created_at , reverse=__lowerCAmelCase ) __a = comments[0] if len(__lowerCAmelCase ) > 0 else None if ( last_comment is not None and last_comment.user.login == "github-actions[bot]" and (dt.utcnow() - issue.updated_at).days > 7 and (dt.utcnow() - issue.created_at).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.") issue.edit(state='''closed''' ) elif ( (dt.utcnow() - issue.updated_at).days > 23 and (dt.utcnow() - issue.created_at).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # print(f"Would add stale comment to {issue.number}") issue.create_comment( '''This issue has been automatically marked as stale because it has not had ''' '''recent activity. If you think this still needs to be addressed ''' '''please comment on this thread.\n\nPlease note that issues that do not follow the ''' '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) ''' '''are likely to be ignored.''' ) if __name__ == "__main__": main()
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from dataclasses import dataclass from typing import Optional, Tuple import torch from torch import nn from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel from transformers.utils import ModelOutput @dataclass class __lowerCAmelCase ( lowerCAmelCase_ ): """simple docstring""" A__ : Optional[torch.FloatTensor] = None A__ : torch.FloatTensor = None A__ : Optional[Tuple[torch.FloatTensor]] = None A__ : Optional[Tuple[torch.FloatTensor]] = None class __lowerCAmelCase ( lowerCAmelCase_ ): """simple docstring""" def __init__( self : Union[str, Any] , _snake_case : int=1 , _snake_case : int=0 , _snake_case : List[str]=2 , _snake_case : List[str]=512 , _snake_case : Tuple="cls" , _snake_case : Union[str, Any]=False , _snake_case : str=True , **_snake_case : Union[str, Any] , ): super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case ) __lowercase : Union[str, Any] = project_dim __lowercase : str = pooler_fn __lowercase : List[str] = learn_encoder __lowercase : int = use_attention_mask class __lowerCAmelCase ( lowerCAmelCase_ ): """simple docstring""" A__ : Any = [r'''pooler''', r'''logit_scale'''] A__ : Dict = [r'''position_ids''', r'''predictions.decoder.bias'''] A__ : Union[str, Any] = '''roberta''' A__ : str = RobertaSeriesConfig def __init__( self : List[str] , _snake_case : Any ): super().__init__(_snake_case ) __lowercase : Union[str, Any] = XLMRobertaModel(_snake_case ) __lowercase : Optional[int] = nn.Linear(config.hidden_size , config.project_dim ) __lowercase : Optional[int] = getattr(_snake_case , '''has_pre_transformation''' , _snake_case ) if self.has_pre_transformation: __lowercase : Union[str, Any] = nn.Linear(config.hidden_size , config.project_dim ) __lowercase : Any = nn.LayerNorm(config.hidden_size , eps=config.layer_norm_eps ) self.post_init() def snake_case_ ( self : Dict , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[torch.Tensor] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , _snake_case : Optional[bool] = None , ): __lowercase : List[str] = return_dict if return_dict is not None else self.config.use_return_dict __lowercase : Any = self.base_model( input_ids=_snake_case , attention_mask=_snake_case , token_type_ids=_snake_case , position_ids=_snake_case , head_mask=_snake_case , inputs_embeds=_snake_case , encoder_hidden_states=_snake_case , encoder_attention_mask=_snake_case , output_attentions=_snake_case , output_hidden_states=True if self.has_pre_transformation else output_hidden_states , return_dict=_snake_case , ) if self.has_pre_transformation: __lowercase : Optional[int] = outputs['''hidden_states'''][-2] __lowercase : Union[str, Any] = self.pre_LN(_snake_case ) __lowercase : Optional[int] = self.transformation_pre(_snake_case ) return TransformationModelOutput( projection_state=_snake_case , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , ) else: __lowercase : str = self.transformation(outputs.last_hidden_state ) return TransformationModelOutput( projection_state=_snake_case , last_hidden_state=outputs.last_hidden_state , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
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"""simple docstring""" from typing import TYPE_CHECKING from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available snake_case_ = {"""configuration_mmbt""": ["""MMBTConfig"""]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: snake_case_ = ["""MMBTForClassification""", """MMBTModel""", """ModalEmbeddings"""] if TYPE_CHECKING: from .configuration_mmbt import MMBTConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings else: import sys snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tf_available, is_tokenizers_available, is_torch_available, ) snake_case_ = { """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""] } try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: snake_case_ = ["""RemBertTokenizer"""] try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: snake_case_ = ["""RemBertTokenizerFast"""] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: snake_case_ = [ """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""", """RemBertForCausalLM""", """RemBertForMaskedLM""", """RemBertForMultipleChoice""", """RemBertForQuestionAnswering""", """RemBertForSequenceClassification""", """RemBertForTokenClassification""", """RemBertLayer""", """RemBertModel""", """RemBertPreTrainedModel""", """load_tf_weights_in_rembert""", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: snake_case_ = [ """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""", """TFRemBertForCausalLM""", """TFRemBertForMaskedLM""", """TFRemBertForMultipleChoice""", """TFRemBertForQuestionAnswering""", """TFRemBertForSequenceClassification""", """TFRemBertForTokenClassification""", """TFRemBertLayer""", """TFRemBertModel""", """TFRemBertPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_rembert import RemBertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_rembert_fast import RemBertTokenizerFast try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_rembert import ( REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, RemBertForCausalLM, RemBertForMaskedLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, RemBertLayer, RemBertModel, RemBertPreTrainedModel, load_tf_weights_in_rembert, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_rembert import ( TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST, TFRemBertForCausalLM, TFRemBertForMaskedLM, TFRemBertForMultipleChoice, TFRemBertForQuestionAnswering, TFRemBertForSequenceClassification, TFRemBertForTokenClassification, TFRemBertLayer, TFRemBertModel, TFRemBertPreTrainedModel, ) else: import sys snake_case_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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'''simple docstring''' # HF Trainer benchmarking tool # # This tool can be used to run and compare multiple dimensions of the HF Trainers args. # # It then prints a report once in github format with all the information that needs to be shared # with others and second time in a console-friendly format, so it's easier to use for tuning things up. # # The main idea is: # # ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \ # --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \ # --target-metric-key train_samples_per_second # # The variations can be any command line argument that you want to compare and not just dtype as in # the example. # # --variations allows you to compare variations in multiple dimensions. # # as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6 # times adding one of: # # 1. --tf32 0 --fp16 0 # 2. --tf32 0 --fp16 1 # 3. --tf32 0 --bf16 1 # 4. --tf32 1 --fp16 0 # 5. --tf32 1 --fp16 1 # 6. --tf32 1 --bf16 1 # # and print the results. This is just a cartesian product - and more than 2 dimensions can be used. # # If you want to rely on defaults, this: # --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' # is identical to this: # --variations '--tf32 0|--tf32 1' '|--fp16|--bf16' # # the leading empty variation in the 2nd dimension is a valid variation. # # So here we get the following 6 variations: # # 1. --tf32 0 # 2. --tf32 0 --fp16 # 3. --tf32 0 --bf16 # 4. --tf32 1 # 5. --tf32 1 --fp16 # 6. --tf32 1 --bf16 # # In this particular case we don't know what the default tf32 setting is as it's normally # pytorch-version dependent). That's why it's best to do an explicit setting of each variation: # `--tf32 0|--tf32 1` # # Here is a full example of a train: # # CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \ # --base-cmd \ # ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \ # --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \ # --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \ # --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \ # --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \ # --source_prefix "translate English to Romanian: " --warmup_steps 50 \ # --max_train_samples 20000 --dataloader_num_workers 2 ' \ # --target-metric-key train_samples_per_second --repeat-times 1 --variations \ # '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \ # --repeat-times 1 --base-variation '--tf32 0' # # and here is a possible output: # # # | Variation | Train | Diff | Train | # | | samples | % | loss | # | | per | | | # | | second | | | # |:----------------|----------:|-------:|--------:| # | --tf32 0 | 285.11 | 0 | 2.51 | # | --tf32 1 | 342.09 | 20 | 2.51 | # | --fp16 --tf32 0 | 423.49 | 49 | 2.51 | # | --fp16 --tf32 1 | 423.13 | 48 | 2.51 | # | --bf16 --tf32 0 | 416.80 | 46 | 2.52 | # | --bf16 --tf32 1 | 415.87 | 46 | 2.52 | # # # So you can quickly compare the different outcomes. # # Typically running each experiment once is enough, but if the environment is unstable you can # re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results. # # By default it'll use the lowest result as the base line to use as 100% and then compare the rest to # it as can be seen from the table above, but you can also specify which combination is the one to use as # the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0' # # --target-metric-key is there to tell the program which metrics to compare - the different metric keys are # inside output_dir/all_results.json. e.g., to measure eval performance instead of train use: # --target-metric-key eval_samples_per_second # but of course you will need to adjust the --base-cmd value in the example to perform evaluation as # well (as currently it doesn't) # import argparse import datetime import io import itertools import json import math import os import platform import re import shlex import subprocess import sys from pathlib import Path from statistics import fmean import pandas as pd import torch from tqdm import tqdm import transformers __a = float("nan") class UpperCAmelCase_ : """simple docstring""" def __init__( self : str , snake_case_ : str ): snake_case__ : Union[str, Any] = sys.stdout snake_case__ : int = open(snake_case_ , """a""" ) def __getattr__( self : Tuple , snake_case_ : Optional[Any] ): return getattr(self.stdout , snake_case_ ) def lowerCamelCase ( self : Any , snake_case_ : List[str] ): self.stdout.write(snake_case_ ) # strip tqdm codes self.file.write(re.sub(r"""^.*\r""" , """""" , snake_case_ , 0 , re.M ) ) def __snake_case( _lowerCAmelCase=80 , _lowerCAmelCase=False ) -> List[str]: snake_case__ : Union[str, Any] = [] # deal with critical env vars snake_case__ : Optional[int] = ["""CUDA_VISIBLE_DEVICES"""] for key in env_keys: snake_case__ : List[str] = os.environ.get(_lowerCAmelCase , _lowerCAmelCase ) if val is not None: cmd.append(f"{key}={val}" ) # python executable (not always needed if the script is executable) snake_case__ : Optional[Any] = sys.executable if full_python_path else sys.executable.split("""/""" )[-1] cmd.append(_lowerCAmelCase ) # now the normal args cmd += list(map(shlex.quote , sys.argv ) ) # split up into up to MAX_WIDTH lines with shell multi-line escapes snake_case__ : List[Any] = [] snake_case__ : Any = """""" while len(_lowerCAmelCase ) > 0: current_line += f"{cmd.pop(0 )} " if len(_lowerCAmelCase ) == 0 or len(_lowerCAmelCase ) + len(cmd[0] ) + 1 > max_width - 1: lines.append(_lowerCAmelCase ) snake_case__ : int = """""" return "\\\n".join(_lowerCAmelCase ) def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int: # unwrap multi-line input snake_case__ : Dict = re.sub(r"""[\\\n]+""" , """ """ , args.base_cmd ) # remove --output_dir if any and set our own snake_case__ : Any = re.sub("""--output_dir\s+[^\s]+""" , """""" , args.base_cmd ) args.base_cmd += f" --output_dir {output_dir}" # ensure we have --overwrite_output_dir snake_case__ : str = re.sub("""--overwrite_output_dir\s+""" , """""" , args.base_cmd ) args.base_cmd += " --overwrite_output_dir" return [sys.executable] + shlex.split(args.base_cmd ) def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]: # Enable to debug everything but the run itself, to do it fast and see the progress. # This is useful for debugging the output formatting quickly - we can remove it later once # everybody is happy with the output if 0: import random from time import sleep sleep(0 ) return dict( {k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 100.2, 55.6666, 222.2222_2222] )} , ) snake_case__ : Union[str, Any] = subprocess.run(_lowerCAmelCase , capture_output=_lowerCAmelCase , text=_lowerCAmelCase ) if verbose: print("""STDOUT""" , result.stdout ) print("""STDERR""" , result.stderr ) # save the streams snake_case__ : Dict = variation.replace(""" """ , """-""" ) with open(Path(_lowerCAmelCase ) / f"log.{prefix}.stdout.txt" , """w""" ) as f: f.write(result.stdout ) with open(Path(_lowerCAmelCase ) / f"log.{prefix}.stderr.txt" , """w""" ) as f: f.write(result.stderr ) if result.returncode != 0: if verbose: print("""failed""" ) return {target_metric_key: nan} with io.open(f"{output_dir}/all_results.json" , """r""" , encoding="""utf-8""" ) as f: snake_case__ : Dict = json.load(_lowerCAmelCase ) # filter out just the keys we want return {k: v for k, v in metrics.items() if k in metric_keys} def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> Dict: snake_case__ : Any = [] snake_case__ : int = [] snake_case__ : Tuple = f"{id}: {variation:<{longest_variation_len}}" snake_case__ : Optional[Any] = f"{preamble}: " snake_case__ : Optional[Any] = set(report_metric_keys + [target_metric_key] ) for i in tqdm(range(_lowerCAmelCase ) , desc=_lowerCAmelCase , leave=_lowerCAmelCase ): snake_case__ : Dict = process_run_single( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) snake_case__ : Tuple = single_run_metrics[target_metric_key] if not math.isnan(_lowerCAmelCase ): metrics.append(_lowerCAmelCase ) results.append(_lowerCAmelCase ) outcome += "✓" else: outcome += "✘" snake_case__ : str = f"\33[2K\r{outcome}" if len(_lowerCAmelCase ) > 0: snake_case__ : List[Any] = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()} snake_case__ : Any = round(mean_metrics[target_metric_key] , 2 ) snake_case__ : Optional[Any] = f"{outcome} {mean_target}" if len(_lowerCAmelCase ) > 1: results_str += f" {tuple(round(_lowerCAmelCase , 2 ) for x in results )}" print(_lowerCAmelCase ) snake_case__ : Optional[Any] = variation return mean_metrics else: print(_lowerCAmelCase ) return {variation_key: variation, target_metric_key: nan} def __snake_case( ) -> Any: snake_case__ : int = torch.cuda.get_device_properties(torch.device("""cuda""" ) ) return f"\nDatetime : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n" def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]: snake_case__ : List[Any] = pd.DataFrame(_lowerCAmelCase ) snake_case__ : Union[str, Any] = """variation""" snake_case__ : int = """diff_%""" snake_case__ : List[Any] = nan if base_variation is not None and len(df[df[variation_key] == base_variation] ): # this may still return nan snake_case__ : Tuple = df.loc[df[variation_key] == base_variation][target_metric_key].item() if math.isnan(_lowerCAmelCase ): # as a fallback, use the minimal value as the sentinel snake_case__ : Optional[int] = df.loc[df[target_metric_key] != nan][target_metric_key].min() # create diff column if possible if not math.isnan(_lowerCAmelCase ): snake_case__ : Optional[Any] = df.apply( lambda _lowerCAmelCase : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value ) if not math.isnan(r[target_metric_key] ) else 0 , axis="""columns""" , ) # re-order columns snake_case__ : str = [variation_key, target_metric_key, diff_key, *report_metric_keys] snake_case__ : int = df.reindex(_lowerCAmelCase , axis="""columns""" ) # reorder cols # capitalize snake_case__ : Any = df.rename(str.capitalize , axis="""columns""" ) # make the cols as narrow as possible snake_case__ : Optional[Any] = df.rename(lambda _lowerCAmelCase : c.replace("""_""" , """<br>""" ) , axis="""columns""" ) snake_case__ : Optional[Any] = df.rename(lambda _lowerCAmelCase : c.replace("""_""" , """\n""" ) , axis="""columns""" ) snake_case__ : Optional[int] = ["""""", """Copy between the cut-here-lines and paste as is to github or a forum"""] report += ["----------8<-----------------8<--------"] report += ["*** Results:", df_github.to_markdown(index=_lowerCAmelCase , floatfmt=""".2f""" )] report += ["```"] report += ["*** Setup:", get_versions()] report += ["*** The benchmark command line was:", get_original_command()] report += ["```"] report += ["----------8<-----------------8<--------"] report += ["*** Results (console):", df_console.to_markdown(index=_lowerCAmelCase , floatfmt=""".2f""" )] print("""\n\n""".join(_lowerCAmelCase ) ) def __snake_case( ) -> Any: snake_case__ : int = argparse.ArgumentParser() parser.add_argument( """--base-cmd""" , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help="""Base cmd""" , ) parser.add_argument( """--variations""" , default=_lowerCAmelCase , type=_lowerCAmelCase , nargs="""+""" , required=_lowerCAmelCase , help="""Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'""" , ) parser.add_argument( """--base-variation""" , default=_lowerCAmelCase , type=_lowerCAmelCase , help="""Baseline variation to compare to. if None the minimal target value will be used to compare against""" , ) parser.add_argument( """--target-metric-key""" , default=_lowerCAmelCase , type=_lowerCAmelCase , required=_lowerCAmelCase , help="""Target metric key in output_dir/all_results.json, e.g., train_samples_per_second""" , ) parser.add_argument( """--report-metric-keys""" , default="""""" , type=_lowerCAmelCase , help="""Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples""" , ) parser.add_argument( """--repeat-times""" , default=1 , type=_lowerCAmelCase , help="""How many times to re-run each variation - an average will be reported""" , ) parser.add_argument( """--output_dir""" , default="""output_benchmark""" , type=_lowerCAmelCase , help="""The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked""" , ) parser.add_argument( """--verbose""" , default=_lowerCAmelCase , action="""store_true""" , help="""Whether to show the outputs of each run or just the benchmark progress""" , ) snake_case__ : int = parser.parse_args() snake_case__ : Dict = args.output_dir Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase ) snake_case__ : Dict = get_base_command(_lowerCAmelCase , _lowerCAmelCase ) # split each dimension into its --foo variations snake_case__ : Dict = [list(map(str.strip , re.split(r"""\|""" , _lowerCAmelCase ) ) ) for x in args.variations] # build a cartesian product of dimensions and convert those back into cmd-line arg strings, # while stripping white space for inputs that were empty snake_case__ : List[str] = list(map(str.strip , map(""" """.join , itertools.product(*_lowerCAmelCase ) ) ) ) snake_case__ : List[str] = max(len(_lowerCAmelCase ) for x in variations ) # split wanted keys snake_case__ : int = args.report_metric_keys.split() # capture prints into a log file for convenience snake_case__ : str = f"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S' )}.txt" print(f"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt" ) print(f"and this script's output is also piped into {report_fn}" ) snake_case__ : Optional[int] = Tee(_lowerCAmelCase ) print(f"\n*** Running {len(_lowerCAmelCase )} benchmarks:" ) print(f"Base command: {' '.join(_lowerCAmelCase )}" ) snake_case__ : Any = """variation""" snake_case__ : str = [] for id, variation in enumerate(tqdm(_lowerCAmelCase , desc="""Total completion: """ , leave=_lowerCAmelCase ) ): snake_case__ : str = base_cmd + variation.split() results.append( process_run( id + 1 , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , args.target_metric_key , _lowerCAmelCase , args.repeat_times , _lowerCAmelCase , args.verbose , ) ) process_results(_lowerCAmelCase , args.target_metric_key , _lowerCAmelCase , args.base_variation , _lowerCAmelCase ) if __name__ == "__main__": main()
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'''simple docstring''' import colorsys from PIL import Image # type: ignore def _A ( A__ , A__ , A__ ): """simple docstring""" __lowercase = x __lowercase = y for step in range(A__ ): # noqa: B007 __lowercase = a * a - b * b + x __lowercase = 2 * a * b + y __lowercase = a_new # divergence happens for all complex number with an absolute value # greater than 4 if a * a + b * b > 4: break return step / (max_step - 1) def _A ( A__ ): """simple docstring""" if distance == 1: return (0, 0, 0) else: return (255, 255, 255) def _A ( A__ ): """simple docstring""" if distance == 1: return (0, 0, 0) else: return tuple(round(i * 255 ) for i in colorsys.hsv_to_rgb(A__ , 1 , 1 ) ) def _A ( A__ = 800 , A__ = 600 , A__ = -0.6 , A__ = 0 , A__ = 3.2 , A__ = 50 , A__ = True , ): """simple docstring""" __lowercase = Image.new('''RGB''' , (image_width, image_height) ) __lowercase = img.load() # loop through the image-coordinates for image_x in range(A__ ): for image_y in range(A__ ): # determine the figure-coordinates based on the image-coordinates __lowercase = figure_width / image_width * image_height __lowercase = figure_center_x + (image_x / image_width - 0.5) * figure_width __lowercase = figure_center_y + (image_y / image_height - 0.5) * figure_height __lowercase = get_distance(A__ , A__ , A__ ) # color the corresponding pixel based on the selected coloring-function if use_distance_color_coding: __lowercase = get_color_coded_rgb(A__ ) else: __lowercase = get_black_and_white_rgb(A__ ) return img if __name__ == "__main__": import doctest doctest.testmod() # colored version, full figure lowerCAmelCase__ = get_image() # uncomment for colored version, different section, zoomed in # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4, # figure_width = 0.8) # uncomment for black and white version, full figure # img = get_image(use_distance_color_coding = False) # uncomment to save the image # img.save("mandelbrot.png") img.show()
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0
import os import tempfile import unittest from transformers import NezhaConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, NezhaModel, ) from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST class UpperCAmelCase_ : '''simple docstring''' def __init__( self , _A , _A=13 , _A=7 , _A=True , _A=True , _A=True , _A=True , _A=99 , _A=32 , _A=5 , _A=4 , _A=37 , _A="gelu" , _A=0.1 , _A=0.1 , _A=128 , _A=32 , _A=16 , _A=2 , _A=0.0_2 , _A=3 , _A=4 , _A=None , ): '''simple docstring''' __SCREAMING_SNAKE_CASE = parent __SCREAMING_SNAKE_CASE = batch_size __SCREAMING_SNAKE_CASE = seq_length __SCREAMING_SNAKE_CASE = is_training __SCREAMING_SNAKE_CASE = use_input_mask __SCREAMING_SNAKE_CASE = use_token_type_ids __SCREAMING_SNAKE_CASE = use_labels __SCREAMING_SNAKE_CASE = vocab_size __SCREAMING_SNAKE_CASE = hidden_size __SCREAMING_SNAKE_CASE = num_hidden_layers __SCREAMING_SNAKE_CASE = num_attention_heads __SCREAMING_SNAKE_CASE = intermediate_size __SCREAMING_SNAKE_CASE = hidden_act __SCREAMING_SNAKE_CASE = hidden_dropout_prob __SCREAMING_SNAKE_CASE = attention_probs_dropout_prob __SCREAMING_SNAKE_CASE = max_position_embeddings __SCREAMING_SNAKE_CASE = type_vocab_size __SCREAMING_SNAKE_CASE = type_sequence_label_size __SCREAMING_SNAKE_CASE = initializer_range __SCREAMING_SNAKE_CASE = num_labels __SCREAMING_SNAKE_CASE = num_choices __SCREAMING_SNAKE_CASE = scope def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __SCREAMING_SNAKE_CASE = None if self.use_input_mask: __SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] ) __SCREAMING_SNAKE_CASE = None if self.use_token_type_ids: __SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) __SCREAMING_SNAKE_CASE = None __SCREAMING_SNAKE_CASE = None __SCREAMING_SNAKE_CASE = None if self.use_labels: __SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size ) __SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) __SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices ) __SCREAMING_SNAKE_CASE = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def _A ( self ): '''simple docstring''' return NezhaConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_A , initializer_range=self.initializer_range , ) def _A ( self ): '''simple docstring''' ( ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ) = self.prepare_config_and_inputs() __SCREAMING_SNAKE_CASE = True __SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] ) __SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 ) return ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, encoder_hidden_states, encoder_attention_mask, ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaModel(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A , token_type_ids=_A ) __SCREAMING_SNAKE_CASE = model(_A , token_type_ids=_A ) __SCREAMING_SNAKE_CASE = model(_A ) 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 _A ( self , _A , _A , _A , _A , _A , _A , _A , _A , _A , ): '''simple docstring''' __SCREAMING_SNAKE_CASE = True __SCREAMING_SNAKE_CASE = NezhaModel(_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model( _A , attention_mask=_A , token_type_ids=_A , encoder_hidden_states=_A , encoder_attention_mask=_A , ) __SCREAMING_SNAKE_CASE = model( _A , attention_mask=_A , token_type_ids=_A , encoder_hidden_states=_A , ) __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A , token_type_ids=_A ) 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 _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaForMaskedLM(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaForNextSentencePrediction(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model( _A , attention_mask=_A , token_type_ids=_A , labels=_A , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaForPreTraining(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model( _A , attention_mask=_A , token_type_ids=_A , labels=_A , next_sentence_label=_A , ) self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaForQuestionAnswering(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model( _A , attention_mask=_A , token_type_ids=_A , start_positions=_A , end_positions=_A , ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.num_labels __SCREAMING_SNAKE_CASE = NezhaForSequenceClassification(_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.num_labels __SCREAMING_SNAKE_CASE = NezhaForTokenClassification(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A , token_type_ids=_A , labels=_A ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def _A ( self , _A , _A , _A , _A , _A , _A , _A ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.num_choices __SCREAMING_SNAKE_CASE = NezhaForMultipleChoice(config=_A ) model.to(_A ) model.eval() __SCREAMING_SNAKE_CASE = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() __SCREAMING_SNAKE_CASE = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() __SCREAMING_SNAKE_CASE = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() __SCREAMING_SNAKE_CASE = model( _A , attention_mask=_A , token_type_ids=_A , labels=_A , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs() ( ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ) = config_and_inputs __SCREAMING_SNAKE_CASE = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask} return config, inputs_dict @require_torch class UpperCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase__ : int = ( ( NezhaModel, NezhaForMaskedLM, NezhaForMultipleChoice, NezhaForNextSentencePrediction, NezhaForPreTraining, NezhaForQuestionAnswering, NezhaForSequenceClassification, NezhaForTokenClassification, ) if is_torch_available() else () ) UpperCamelCase__ : Optional[int] = ( { '''feature-extraction''': NezhaModel, '''fill-mask''': NezhaForMaskedLM, '''question-answering''': NezhaForQuestionAnswering, '''text-classification''': NezhaForSequenceClassification, '''token-classification''': NezhaForTokenClassification, '''zero-shot''': NezhaForSequenceClassification, } if is_torch_available() else {} ) UpperCamelCase__ : List[Any] = True def _A ( self , _A , _A , _A=False ): '''simple docstring''' __SCREAMING_SNAKE_CASE = super()._prepare_for_class(_A , _A , return_labels=_A ) if return_labels: if model_class in get_values(_A ): __SCREAMING_SNAKE_CASE = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=_A ) __SCREAMING_SNAKE_CASE = torch.zeros( self.model_tester.batch_size , dtype=torch.long , device=_A ) return inputs_dict def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaModelTester(self ) __SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=_A , hidden_size=37 ) def _A ( self ): '''simple docstring''' self.config_tester.run_common_tests() def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(*_A ) def _A ( self ): '''simple docstring''' ( ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ( __SCREAMING_SNAKE_CASE ) , ) = self.model_tester.prepare_config_and_inputs_for_decoder() __SCREAMING_SNAKE_CASE = None self.model_tester.create_and_check_model_as_decoder( _A , _A , _A , _A , _A , _A , _A , _A , _A , ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_next_sequence_prediction(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*_A ) def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*_A ) @slow def _A ( self ): '''simple docstring''' for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: __SCREAMING_SNAKE_CASE = NezhaModel.from_pretrained(_A ) self.assertIsNotNone(_A ) @slow @require_torch_gpu def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # NezhaForMultipleChoice behaves incorrectly in JIT environments. if model_class == NezhaForMultipleChoice: return __SCREAMING_SNAKE_CASE = True __SCREAMING_SNAKE_CASE = model_class(config=_A ) __SCREAMING_SNAKE_CASE = self._prepare_for_class(_A , _A ) __SCREAMING_SNAKE_CASE = torch.jit.trace( _A , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(_A , os.path.join(_A , 'bert.pt' ) ) __SCREAMING_SNAKE_CASE = torch.jit.load(os.path.join(_A , 'bert.pt' ) , map_location=_A ) loaded(inputs_dict['input_ids'].to(_A ) , inputs_dict['attention_mask'].to(_A ) ) @require_torch class UpperCAmelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaModel.from_pretrained('sijunhe/nezha-cn-base' ) __SCREAMING_SNAKE_CASE = torch.tensor([[0, 1, 2, 3, 4, 5]] ) __SCREAMING_SNAKE_CASE = torch.tensor([[0, 1, 1, 1, 1, 1]] ) with torch.no_grad(): __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A )[0] __SCREAMING_SNAKE_CASE = torch.Size((1, 6, 768) ) self.assertEqual(output.shape , _A ) __SCREAMING_SNAKE_CASE = torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _A , atol=1e-4 ) ) @slow def _A ( self ): '''simple docstring''' __SCREAMING_SNAKE_CASE = NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base' ) __SCREAMING_SNAKE_CASE = torch.tensor([[0, 1, 2, 3, 4, 5]] ) __SCREAMING_SNAKE_CASE = torch.tensor([[1, 1, 1, 1, 1, 1]] ) with torch.no_grad(): __SCREAMING_SNAKE_CASE = model(_A , attention_mask=_A )[0] __SCREAMING_SNAKE_CASE = torch.Size((1, 6, 21_128) ) self.assertEqual(output.shape , _A ) __SCREAMING_SNAKE_CASE = torch.tensor( [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _A , atol=1e-4 ) )
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import os def __lowercase ( a__ = "input.txt" ) -> int: with open(os.path.join(os.path.dirname(a__ ) , a__ ) ) as input_file: __SCREAMING_SNAKE_CASE = [ [int(a__ ) for element in line.split(',' )] for line in input_file.readlines() ] __SCREAMING_SNAKE_CASE = len(a__ ) __SCREAMING_SNAKE_CASE = len(matrix[0] ) __SCREAMING_SNAKE_CASE = [[-1 for _ in range(a__ )] for _ in range(a__ )] for i in range(a__ ): __SCREAMING_SNAKE_CASE = matrix[i][0] for j in range(1 , a__ ): for i in range(a__ ): __SCREAMING_SNAKE_CASE = minimal_path_sums[i][j - 1] + matrix[i][j] for i in range(1 , a__ ): __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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1
"""simple docstring""" from __future__ import annotations import os import tempfile import unittest from transformers import ConvBertConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TFConvBertForMaskedLM, TFConvBertForMultipleChoice, TFConvBertForQuestionAnswering, TFConvBertForSequenceClassification, TFConvBertForTokenClassification, TFConvBertModel, ) class A_ : '''simple docstring''' def __init__( self , lowercase_ , lowercase_=13 , lowercase_=7 , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=True , lowercase_=99 , lowercase_=32 , lowercase_=2 , lowercase_=4 , lowercase_=37 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=512 , lowercase_=16 , lowercase_=2 , lowercase_=0.02 , lowercase_=3 , lowercase_=4 , lowercase_=None , ): """simple docstring""" UpperCAmelCase_ : List[Any] = parent UpperCAmelCase_ : Dict = 13 UpperCAmelCase_ : List[str] = 7 UpperCAmelCase_ : Dict = True UpperCAmelCase_ : Optional[Any] = True UpperCAmelCase_ : Union[str, Any] = True UpperCAmelCase_ : Dict = True UpperCAmelCase_ : Union[str, Any] = 99 UpperCAmelCase_ : Tuple = 384 UpperCAmelCase_ : Optional[Any] = 2 UpperCAmelCase_ : Any = 4 UpperCAmelCase_ : int = 37 UpperCAmelCase_ : Dict = "gelu" UpperCAmelCase_ : Optional[int] = 0.1 UpperCAmelCase_ : Tuple = 0.1 UpperCAmelCase_ : Optional[Any] = 512 UpperCAmelCase_ : Dict = 16 UpperCAmelCase_ : int = 2 UpperCAmelCase_ : Union[str, Any] = 0.02 UpperCAmelCase_ : int = 3 UpperCAmelCase_ : Optional[Any] = 4 UpperCAmelCase_ : Any = 128 UpperCAmelCase_ : List[str] = 2 UpperCAmelCase_ : List[Any] = 9 UpperCAmelCase_ : List[Any] = 1 UpperCAmelCase_ : List[Any] = None def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) UpperCAmelCase_ : List[str] = None if self.use_input_mask: UpperCAmelCase_ : Tuple = random_attention_mask([self.batch_size, self.seq_length] ) UpperCAmelCase_ : List[Any] = None if self.use_token_type_ids: UpperCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size ) UpperCAmelCase_ : Optional[Any] = None UpperCAmelCase_ : Dict = None UpperCAmelCase_ : Tuple = None if self.use_labels: UpperCAmelCase_ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size ) UpperCAmelCase_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) UpperCAmelCase_ : Optional[Any] = ids_tensor([self.batch_size] , self.num_choices ) UpperCAmelCase_ : str = ConvBertConfig( 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 , return_dict=lowercase_ , ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : Optional[int] = TFConvBertModel(config=lowercase_ ) UpperCAmelCase_ : int = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids} UpperCAmelCase_ : List[str] = [input_ids, input_mask] UpperCAmelCase_ : Union[str, Any] = model(lowercase_ ) UpperCAmelCase_ : str = model(lowercase_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : Optional[Any] = TFConvBertForMaskedLM(config=lowercase_ ) UpperCAmelCase_ : Tuple = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase_ : Tuple = model(lowercase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : List[Any] = self.num_labels UpperCAmelCase_ : List[str] = TFConvBertForSequenceClassification(config=lowercase_ ) UpperCAmelCase_ : List[str] = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase_ : List[Any] = model(lowercase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : str = self.num_choices UpperCAmelCase_ : Tuple = TFConvBertForMultipleChoice(config=lowercase_ ) UpperCAmelCase_ : str = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase_ : Tuple = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase_ : Optional[Any] = tf.tile(tf.expand_dims(lowercase_ , 1 ) , (1, self.num_choices, 1) ) UpperCAmelCase_ : Any = { "input_ids": multiple_choice_inputs_ids, "attention_mask": multiple_choice_input_mask, "token_type_ids": multiple_choice_token_type_ids, } UpperCAmelCase_ : Union[str, Any] = model(lowercase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : Dict = self.num_labels UpperCAmelCase_ : int = TFConvBertForTokenClassification(config=lowercase_ ) UpperCAmelCase_ : Optional[Any] = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase_ : Any = model(lowercase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def UpperCamelCase__ ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ): """simple docstring""" UpperCAmelCase_ : Tuple = TFConvBertForQuestionAnswering(config=lowercase_ ) UpperCAmelCase_ : str = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } UpperCAmelCase_ : Optional[int] = model(lowercase_ ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Dict = self.prepare_config_and_inputs() ( ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ( UpperCAmelCase_ ) , ) : Optional[int] = config_and_inputs UpperCAmelCase_ : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict @require_tf class A_ (lowercase__ ,lowercase__ ,unittest.TestCase ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : Union[str, Any] = ( ( TFConvBertModel, TFConvBertForMaskedLM, TFConvBertForQuestionAnswering, TFConvBertForSequenceClassification, TFConvBertForTokenClassification, TFConvBertForMultipleChoice, ) if is_tf_available() else () ) SCREAMING_SNAKE_CASE__ : Optional[int] = ( { """feature-extraction""": TFConvBertModel, """fill-mask""": TFConvBertForMaskedLM, """question-answering""": TFConvBertForQuestionAnswering, """text-classification""": TFConvBertForSequenceClassification, """token-classification""": TFConvBertForTokenClassification, """zero-shot""": TFConvBertForSequenceClassification, } if is_tf_available() else {} ) SCREAMING_SNAKE_CASE__ : Optional[int] = False SCREAMING_SNAKE_CASE__ : List[str] = False SCREAMING_SNAKE_CASE__ : Dict = False def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : str = TFConvBertModelTester(self ) UpperCAmelCase_ : int = ConfigTester(self , config_class=lowercase_ , hidden_size=37 ) def UpperCamelCase__ ( self ): """simple docstring""" self.config_tester.run_common_tests() def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_multiple_choice(*lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*lowercase_ ) @slow def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common() UpperCAmelCase_ : Optional[int] = True UpperCAmelCase_ : Tuple = True if hasattr(lowercase_ , "use_cache" ): UpperCAmelCase_ : Optional[int] = True UpperCAmelCase_ : Any = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length ) UpperCAmelCase_ : List[Any] = getattr(self.model_tester , "key_length" , lowercase_ ) for model_class in self.all_model_classes: UpperCAmelCase_ : Dict = self._prepare_for_class(lowercase_ , lowercase_ ) UpperCAmelCase_ : Dict = model_class(lowercase_ ) UpperCAmelCase_ : Dict = len(model(lowercase_ ) ) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(lowercase_ , saved_model=lowercase_ ) UpperCAmelCase_ : Union[str, Any] = os.path.join(lowercase_ , "saved_model" , "1" ) UpperCAmelCase_ : str = tf.keras.models.load_model(lowercase_ ) UpperCAmelCase_ : Optional[int] = model(lowercase_ ) if self.is_encoder_decoder: UpperCAmelCase_ : List[Any] = outputs["encoder_hidden_states"] UpperCAmelCase_ : Union[str, Any] = outputs["encoder_attentions"] else: UpperCAmelCase_ : List[Any] = outputs["hidden_states"] UpperCAmelCase_ : int = outputs["attentions"] self.assertEqual(len(lowercase_ ) , lowercase_ ) UpperCAmelCase_ : str = getattr( self.model_tester , "expected_num_hidden_layers" , self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(lowercase_ ) , lowercase_ ) self.assertListEqual( list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , ) self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , ) @slow def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Tuple = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" ) self.assertIsNotNone(lowercase_ ) def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ , UpperCAmelCase_ : str = self.model_tester.prepare_config_and_inputs_for_common() UpperCAmelCase_ : Any = True UpperCAmelCase_ : List[Any] = getattr(self.model_tester , "decoder_seq_length" , self.model_tester.seq_length ) UpperCAmelCase_ : Tuple = getattr(self.model_tester , "encoder_seq_length" , self.model_tester.seq_length ) UpperCAmelCase_ : Optional[Any] = getattr(self.model_tester , "key_length" , lowercase_ ) UpperCAmelCase_ : List[Any] = getattr(self.model_tester , "key_length" , lowercase_ ) def check_decoder_attentions_output(lowercase_ ): UpperCAmelCase_ : Tuple = len(lowercase_ ) self.assertEqual(out_len % 2 , 0 ) UpperCAmelCase_ : Any = outputs.decoder_attentions self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , ) def check_encoder_attentions_output(lowercase_ ): UpperCAmelCase_ : Union[str, Any] = [ t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions) ] self.assertEqual(len(lowercase_ ) , self.model_tester.num_hidden_layers ) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , ) for model_class in self.all_model_classes: UpperCAmelCase_ : str = True UpperCAmelCase_ : Any = False UpperCAmelCase_ : List[str] = model_class(lowercase_ ) UpperCAmelCase_ : int = model(self._prepare_for_class(lowercase_ , lowercase_ ) ) UpperCAmelCase_ : Any = len(lowercase_ ) self.assertEqual(config.output_hidden_states , lowercase_ ) check_encoder_attentions_output(lowercase_ ) if self.is_encoder_decoder: UpperCAmelCase_ : List[Any] = model_class(lowercase_ ) UpperCAmelCase_ : Optional[Any] = model(self._prepare_for_class(lowercase_ , lowercase_ ) ) self.assertEqual(config.output_hidden_states , lowercase_ ) check_decoder_attentions_output(lowercase_ ) # Check that output attentions can also be changed via the config del inputs_dict["output_attentions"] UpperCAmelCase_ : Tuple = True UpperCAmelCase_ : List[Any] = model_class(lowercase_ ) UpperCAmelCase_ : Optional[Any] = model(self._prepare_for_class(lowercase_ , lowercase_ ) ) self.assertEqual(config.output_hidden_states , lowercase_ ) check_encoder_attentions_output(lowercase_ ) # Check attention is always last and order is fine UpperCAmelCase_ : Union[str, Any] = True UpperCAmelCase_ : List[Any] = True UpperCAmelCase_ : Optional[Any] = model_class(lowercase_ ) UpperCAmelCase_ : List[str] = model(self._prepare_for_class(lowercase_ , lowercase_ ) ) self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(lowercase_ ) ) self.assertEqual(model.config.output_hidden_states , lowercase_ ) check_encoder_attentions_output(lowercase_ ) @require_tf class A_ (unittest.TestCase ): '''simple docstring''' @slow def UpperCamelCase__ ( self ): """simple docstring""" UpperCAmelCase_ : Union[str, Any] = TFConvBertModel.from_pretrained("YituTech/conv-bert-base" ) UpperCAmelCase_ : Tuple = tf.constant([[0, 1, 2, 3, 4, 5]] ) UpperCAmelCase_ : str = model(lowercase_ )[0] UpperCAmelCase_ : Any = [1, 6, 768] self.assertEqual(output.shape , lowercase_ ) UpperCAmelCase_ : str = tf.constant( [ [ [-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32], [0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24], [0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84], ] ] ) tf.debugging.assert_near(output[:, :3, :3] , lowercase_ , atol=1E-4 )
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'''simple docstring''' import argparse from torch import nn # transformers_old should correspond to branch `save_old_prophetnet_model_structure` here # original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively from transformers_old.modeling_prophetnet import ( ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld, ) from transformers_old.modeling_xlm_prophetnet import ( XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld, ) from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging _UpperCamelCase = logging.get_logger(__name__) logging.set_verbosity_info() def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> Optional[int]: if "xprophetnet" in prophetnet_checkpoint_path: __lowerCamelCase : Union[str, Any] = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase ) __lowerCamelCase ,__lowerCamelCase : List[str] = XLMProphetNetForConditionalGeneration.from_pretrained( _lowerCAmelCase ,output_loading_info=_lowerCAmelCase ) else: __lowerCamelCase : Optional[int] = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase ) __lowerCamelCase ,__lowerCamelCase : List[str] = ProphetNetForConditionalGeneration.from_pretrained( _lowerCAmelCase ,output_loading_info=_lowerCAmelCase ) __lowerCamelCase : Union[str, Any] = ['key_proj', 'value_proj', 'query_proj'] __lowerCamelCase : Optional[Any] = { 'self_attn': 'ngram_self_attn', 'cross_attn': 'encoder_attn', 'cross_attn_layer_norm': 'encoder_attn_layer_norm', 'feed_forward_layer_norm': 'final_layer_norm', 'feed_forward': '', 'intermediate': 'fc1', 'output': 'fc2', 'key_proj': 'k_proj', 'query_proj': 'q_proj', 'value_proj': 'v_proj', 'word_embeddings': 'embed_tokens', 'embeddings_layer_norm': 'emb_layer_norm', 'relative_pos_embeddings': 'relative_linear', 'ngram_embeddings': 'ngram_input_embed', 'position_embeddings': 'embed_positions', } for key in loading_info["missing_keys"]: __lowerCamelCase : Optional[int] = key.split('.' ) if attributes[0] == "lm_head": __lowerCamelCase : Dict = prophet __lowerCamelCase : List[Any] = prophet_old else: __lowerCamelCase : Any = prophet.prophetnet __lowerCamelCase : Any = prophet_old.model __lowerCamelCase : Optional[Any] = False for attribute in attributes: if attribute in mapping: __lowerCamelCase : Any = mapping[attribute] if not hasattr(_lowerCAmelCase ,_lowerCAmelCase ) and len(_lowerCAmelCase ) > 0: __lowerCamelCase : int = attribute elif hasattr(_lowerCAmelCase ,_lowerCAmelCase ): __lowerCamelCase : Optional[int] = attribute if attribute == "weight": assert old_model.weight.shape == model.weight.shape, "Shapes have to match!" __lowerCamelCase : List[Any] = old_model.weight logger.info(F'{attribute} is initialized.' ) __lowerCamelCase : List[Any] = True break elif attribute == "bias": assert old_model.bias.shape == model.bias.shape, "Shapes have to match!" __lowerCamelCase : List[Any] = old_model.bias logger.info(F'{attribute} is initialized' ) __lowerCamelCase : Dict = True break elif attribute in special_keys and hasattr(_lowerCAmelCase ,'in_proj_weight' ): __lowerCamelCase : Optional[Any] = old_model.in_proj_weight.shape[0] // 3 __lowerCamelCase : Optional[Any] = getattr(_lowerCAmelCase ,_lowerCAmelCase ) param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match" param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match" if attribute == "query_proj": __lowerCamelCase : Optional[int] = nn.Parameter(old_model.in_proj_weight[:embed_dim, :] ) __lowerCamelCase : Dict = nn.Parameter(old_model.in_proj_bias[:embed_dim] ) elif attribute == "key_proj": __lowerCamelCase : List[str] = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] ) __lowerCamelCase : Dict = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] ) elif attribute == "value_proj": __lowerCamelCase : str = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] ) __lowerCamelCase : Optional[int] = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] ) __lowerCamelCase : Optional[int] = True break elif attribute == "position_embeddings": assert ( model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1] ), "Hidden size has to match" assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings." __lowerCamelCase : Optional[int] = nn.Parameter(old_model.embed_positions.weight[:512, :] ) __lowerCamelCase : Dict = True break if attribute.isdigit(): __lowerCamelCase : List[str] = model[int(_lowerCAmelCase )] __lowerCamelCase : Union[str, Any] = old_model[int(_lowerCAmelCase )] else: __lowerCamelCase : Union[str, Any] = getattr(_lowerCAmelCase ,_lowerCAmelCase ) if old_attribute == "": __lowerCamelCase : str = old_model else: if not hasattr(_lowerCAmelCase ,_lowerCAmelCase ): raise ValueError(F'{old_model} does not have {old_attribute}' ) __lowerCamelCase : str = getattr(_lowerCAmelCase ,_lowerCAmelCase ) if not is_key_init: raise ValueError(F'{key} was not correctly initialized!' ) print(F'Saving model to {pytorch_dump_folder_path}' ) prophet.save_pretrained(_lowerCAmelCase ) if __name__ == "__main__": _UpperCamelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.' ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.' ) _UpperCamelCase = parser.parse_args() convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)
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"""simple docstring""" import unittest from transformers import is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow if is_torch_available(): import torch from transformers import XLMRobertaModel @require_sentencepiece @require_tokenizers @require_torch class lowercase__ ( unittest.TestCase ): '''simple docstring''' @slow def lowercase__ ( self : Tuple ) -> str: '''simple docstring''' UpperCAmelCase_ = XLMRobertaModel.from_pretrained("xlm-roberta-base" ) UpperCAmelCase_ = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] ) # The dog is cute and lives in the garden house UpperCAmelCase_ = torch.Size((1, 12, 768) ) # batch_size, sequence_length, embedding_vector_dim UpperCAmelCase_ = torch.tensor( [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]] ) # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.eval() # expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1] with torch.no_grad(): UpperCAmelCase_ = model(_UpperCAmelCase )["last_hidden_state"].detach() self.assertEqual(output.shape , _UpperCAmelCase ) # compare the actual values for a slice of last dim self.assertTrue(torch.allclose(output[:, :, -1] , _UpperCAmelCase , atol=1e-3 ) ) @slow def lowercase__ ( self : List[Any] ) -> Optional[int]: '''simple docstring''' UpperCAmelCase_ = XLMRobertaModel.from_pretrained("xlm-roberta-large" ) UpperCAmelCase_ = torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]] ) # The dog is cute and lives in the garden house UpperCAmelCase_ = torch.Size((1, 12, 1024) ) # batch_size, sequence_length, embedding_vector_dim UpperCAmelCase_ = torch.tensor( [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]] ) # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large') # xlmr.eval() # expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1] with torch.no_grad(): UpperCAmelCase_ = model(_UpperCAmelCase )["last_hidden_state"].detach() self.assertEqual(output.shape , _UpperCAmelCase ) # compare the actual values for a slice of last dim self.assertTrue(torch.allclose(output[:, :, -1] , _UpperCAmelCase , atol=1e-3 ) )
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"""simple docstring""" def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ): return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__ ) - ngram_size + 1 )] if __name__ == "__main__": from doctest import testmod testmod()
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"""simple docstring""" from __future__ import annotations import random import unittest from transformers import TransfoXLConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST, TFTransfoXLForSequenceClassification, TFTransfoXLLMHeadModel, TFTransfoXLModel, ) class UpperCAmelCase_ : def __init__( self , UpperCamelCase_ , ) -> str: __lowercase : Optional[int] = parent __lowercase : Dict = 13 __lowercase : str = 7 __lowercase : List[str] = 30 __lowercase : Any = self.seq_length + self.mem_len __lowercase : Optional[Any] = 15 __lowercase : Dict = True __lowercase : str = True __lowercase : List[str] = 99 __lowercase : Optional[int] = [10, 50, 80] __lowercase : List[str] = 32 __lowercase : Optional[int] = 32 __lowercase : Any = 4 __lowercase : Tuple = 8 __lowercase : Any = 1_28 __lowercase : Dict = 2 __lowercase : Optional[Any] = 2 __lowercase : List[str] = None __lowercase : List[str] = 1 __lowercase : List[str] = 0 __lowercase : List[Any] = 3 __lowercase : Dict = self.vocab_size - 1 __lowercase : List[Any] = 0.0_1 def _lowerCamelCase ( self ) -> List[str]: __lowercase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __lowercase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __lowercase : Any = None if self.use_labels: __lowercase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) __lowercase : Any = TransfoXLConfig( vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , ) return (config, input_ids_a, input_ids_a, lm_labels) def _lowerCamelCase ( self ) -> List[Any]: random.seed(self.seed ) tf.random.set_seed(self.seed ) def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]: __lowercase : List[str] = TFTransfoXLModel(UpperCamelCase_ ) __lowercase ,__lowercase : Optional[int] = model(UpperCamelCase_ ).to_tuple() __lowercase : List[Any] = {'''input_ids''': input_ids_a, '''mems''': mems_a} __lowercase ,__lowercase : Union[str, Any] = model(UpperCamelCase_ ).to_tuple() self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) ) self.parent.assertListEqual( [mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , ) self.parent.assertListEqual( [mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , ) def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> str: __lowercase : List[Any] = TFTransfoXLLMHeadModel(UpperCamelCase_ ) __lowercase ,__lowercase : Union[str, Any] = model(UpperCamelCase_ ).to_tuple() __lowercase : List[str] = {'''input_ids''': input_ids_a, '''labels''': lm_labels} __lowercase ,__lowercase : Tuple = model(UpperCamelCase_ ).to_tuple() __lowercase ,__lowercase : str = model([input_ids_a, mems_a] ).to_tuple() __lowercase : Any = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels} __lowercase ,__lowercase : Any = model(UpperCamelCase_ ).to_tuple() self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) ) self.parent.assertListEqual( [mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , ) self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) ) self.parent.assertListEqual( [mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , ) def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> str: __lowercase : List[Any] = TFTransfoXLForSequenceClassification(UpperCamelCase_ ) __lowercase : str = model(UpperCamelCase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def _lowerCamelCase ( self ) -> int: __lowercase : Tuple = self.prepare_config_and_inputs() ((__lowercase) ,(__lowercase) ,(__lowercase) ,(__lowercase)) : Union[str, Any] = config_and_inputs __lowercase : Dict = {'''input_ids''': input_ids_a} return config, inputs_dict @require_tf class UpperCAmelCase_ ( snake_case , snake_case , unittest.TestCase ): UpperCamelCase =( (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else () ) UpperCamelCase =() if is_tf_available() else () UpperCamelCase =( { "feature-extraction": TFTransfoXLModel, "text-classification": TFTransfoXLForSequenceClassification, "text-generation": TFTransfoXLLMHeadModel, "zero-shot": TFTransfoXLForSequenceClassification, } if is_tf_available() else {} ) # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented UpperCamelCase =False UpperCamelCase =False UpperCamelCase =False UpperCamelCase =False def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Union[str, Any]: if pipeline_test_casse_name == "TextGenerationPipelineTests": # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`. # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple # tokenizer. return True return False def _lowerCamelCase ( self ) -> Union[str, Any]: __lowercase : List[str] = TFTransfoXLModelTester(self ) __lowercase : Optional[int] = ConfigTester(self , config_class=UpperCamelCase_ , d_embed=37 ) def _lowerCamelCase ( self ) -> Optional[Any]: self.config_tester.run_common_tests() def _lowerCamelCase ( self ) -> Optional[int]: self.model_tester.set_seed() __lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase_ ) def _lowerCamelCase ( self ) -> List[Any]: self.model_tester.set_seed() __lowercase : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase_ ) def _lowerCamelCase ( self ) -> List[Any]: __lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase_ ) def _lowerCamelCase ( self ) -> str: __lowercase ,__lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common() __lowercase : Optional[Any] = [TFTransfoXLForSequenceClassification] for model_class in self.all_model_classes: __lowercase : List[Any] = model_class(UpperCamelCase_ ) assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer ) if model_class in list_other_models_with_output_ebd: __lowercase : str = model.get_output_embeddings() assert isinstance(UpperCamelCase_ , tf.keras.layers.Layer ) __lowercase : int = model.get_bias() assert name is None else: __lowercase : Optional[Any] = model.get_output_embeddings() assert x is None __lowercase : int = model.get_bias() assert name is None def _lowerCamelCase ( self ) -> List[str]: # TODO JP: Make TransfoXL XLA compliant pass @slow def _lowerCamelCase ( self ) -> Tuple: for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: __lowercase : List[Any] = TFTransfoXLModel.from_pretrained(UpperCamelCase_ ) self.assertIsNotNone(UpperCamelCase_ ) @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' ) def _lowerCamelCase ( self ) -> List[Any]: pass @require_tf class UpperCAmelCase_ ( unittest.TestCase ): @unittest.skip('''Skip test until #12651 is resolved.''' ) @slow def _lowerCamelCase ( self ) -> List[str]: __lowercase : Any = TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' ) # fmt: off __lowercase : Optional[Any] = tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]] , dtype=tf.intaa ) # noqa: E231 # fmt: on # In 1991 , the remains of Russian Tsar Nicholas II and his family # ( except for Alexei and Maria ) are discovered . # The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the # remainder of the story . 1883 Western Siberia , # a young Grigori Rasputin is asked by his father and a group of men to perform magic . # Rasputin has a vision and denounces one of the men as a horse thief . Although his # father initially slaps him for making such an accusation , Rasputin watches as the # man is chased outside and beaten . Twenty years later , Rasputin sees a vision of # the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous , # with people , even a bishop , begging for his blessing . <eod> </s> <eos> # fmt: off __lowercase : Union[str, Any] = [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0] # noqa: E231 # fmt: on # In 1991, the remains of Russian Tsar Nicholas II and his family ( # except for Alexei and Maria ) are discovered. The voice of young son, # Tsarevich Alexei Nikolaevich, narrates the remainder of the story. # 1883 Western Siberia, a young Grigori Rasputin is asked by his father # and a group of men to perform magic. Rasputin has a vision and # denounces one of the men as a horse thief. Although his father initially # slaps him for making such an accusation, Rasputin watches as the man # is chased outside and beaten. Twenty years later, Rasputin sees a vision # of the Virgin Mary, prompting him to become a priest. # Rasputin quickly becomes famous, with people, even a bishop, begging for # his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar # Nicholas II and his family were discovered. The voice of <unk> young son, # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos> __lowercase : List[str] = model.generate(UpperCamelCase_ , max_length=2_00 , do_sample=UpperCamelCase_ ) self.assertListEqual(output_ids[0].numpy().tolist() , UpperCamelCase_ )
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"""simple docstring""" from __future__ import annotations a_ = [ [-1, 0], # left [0, -1], # down [1, 0], # right [0, 1], # up ] def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ): __lowercase : Union[str, Any] = [ [0 for col in range(len(grid[0] ) )] for row in range(len(__UpperCamelCase ) ) ] # the reference grid __lowercase : Optional[int] = 1 __lowercase : str = [ [0 for col in range(len(grid[0] ) )] for row in range(len(__UpperCamelCase ) ) ] # the action grid __lowercase : List[str] = init[0] __lowercase : Optional[Any] = init[1] __lowercase : int = 0 __lowercase : List[Any] = g + heuristic[x][y] # cost from starting cell to destination cell __lowercase : Optional[Any] = [[f, g, x, y]] __lowercase : Union[str, Any] = False # flag that is set when search is complete __lowercase : List[Any] = False # flag set if we can't find expand while not found and not resign: if len(__UpperCamelCase ) == 0: raise ValueError('''Algorithm is unable to find solution''' ) else: # to choose the least costliest action so as to move closer to the goal cell.sort() cell.reverse() __lowercase : str = cell.pop() __lowercase : List[Any] = next_cell[2] __lowercase : Optional[int] = next_cell[3] __lowercase : Dict = next_cell[1] if x == goal[0] and y == goal[1]: __lowercase : List[Any] = True else: for i in range(len(__UpperCamelCase ) ): # to try out different valid actions __lowercase : Union[str, Any] = x + DIRECTIONS[i][0] __lowercase : Optional[int] = y + DIRECTIONS[i][1] if xa >= 0 and xa < len(__UpperCamelCase ) and ya >= 0 and ya < len(grid[0] ): if closed[xa][ya] == 0 and grid[xa][ya] == 0: __lowercase : str = g + cost __lowercase : Optional[int] = ga + heuristic[xa][ya] cell.append([fa, ga, xa, ya] ) __lowercase : Dict = 1 __lowercase : List[Any] = i __lowercase : Dict = [] __lowercase : List[Any] = goal[0] __lowercase : Tuple = goal[1] invpath.append([x, y] ) # we get the reverse path from here while x != init[0] or y != init[1]: __lowercase : Any = x - DIRECTIONS[action[x][y]][0] __lowercase : Dict = y - DIRECTIONS[action[x][y]][1] __lowercase : List[Any] = xa __lowercase : Optional[Any] = ya invpath.append([x, y] ) __lowercase : Optional[int] = [] for i in range(len(__UpperCamelCase ) ): path.append(invpath[len(__UpperCamelCase ) - 1 - i] ) return path, action if __name__ == "__main__": a_ = [ [0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles [0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 1, 0], [0, 0, 0, 0, 1, 0], ] a_ = [0, 0] # all coordinates are given in format [y,x] a_ = [len(grid) - 1, len(grid[0]) - 1] a_ = 1 # the cost map which pushes the path closer to the goal a_ = [[0 for row in range(len(grid[0]))] for col in range(len(grid))] for i in range(len(grid)): for j in range(len(grid[0])): a_ = abs(i - goal[0]) + abs(j - goal[1]) if grid[i][j] == 1: # added extra penalty in the heuristic map a_ = 9_9 a_ , a_ = search(grid, init, goal, cost, heuristic) print('ACTION MAP') for i in range(len(action)): print(action[i]) for i in range(len(path)): print(path[i])
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"""simple docstring""" import argparse import os import numpy as np import tensorflow as tf import torch from transformers import BertModel def UpperCamelCase_ ( lowerCAmelCase__ : BertModel , lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> str: """simple docstring""" lowerCAmelCase_ : Tuple = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value') lowerCAmelCase_ : Union[str, Any] = ( ('layer.', 'layer_'), ('word_embeddings.weight', 'word_embeddings'), ('position_embeddings.weight', 'position_embeddings'), ('token_type_embeddings.weight', 'token_type_embeddings'), ('.', '/'), ('LayerNorm/weight', 'LayerNorm/gamma'), ('LayerNorm/bias', 'LayerNorm/beta'), ('weight', 'kernel'), ) if not os.path.isdir(lowerCAmelCase__ ): os.makedirs(lowerCAmelCase__ ) lowerCAmelCase_ : str = model.state_dict() def to_tf_var_name(lowerCAmelCase__ : str ): for patt, repl in iter(lowerCAmelCase__ ): lowerCAmelCase_ : int = name.replace(lowerCAmelCase__ , lowerCAmelCase__ ) return f"bert/{name}" def create_tf_var(lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : str , lowerCAmelCase__ : tf.Session ): lowerCAmelCase_ : Optional[Any] = tf.dtypes.as_dtype(tensor.dtype ) lowerCAmelCase_ : str = tf.get_variable(dtype=lowerCAmelCase__ , shape=tensor.shape , name=lowerCAmelCase__ , initializer=tf.zeros_initializer() ) session.run(tf.variables_initializer([tf_var] ) ) session.run(lowerCAmelCase__ ) return tf_var tf.reset_default_graph() with tf.Session() as session: for var_name in state_dict: lowerCAmelCase_ : List[str] = to_tf_var_name(lowerCAmelCase__ ) lowerCAmelCase_ : Any = state_dict[var_name].numpy() if any(x in var_name for x in tensors_to_transpose ): lowerCAmelCase_ : str = torch_tensor.T lowerCAmelCase_ : Tuple = create_tf_var(tensor=lowerCAmelCase__ , name=lowerCAmelCase__ , session=lowerCAmelCase__ ) tf.keras.backend.set_value(lowerCAmelCase__ , lowerCAmelCase__ ) lowerCAmelCase_ : int = session.run(lowerCAmelCase__ ) print(f"Successfully created {tf_name}: {np.allclose(lowerCAmelCase__ , lowerCAmelCase__ )}" ) lowerCAmelCase_ : List[Any] = tf.train.Saver(tf.trainable_variables() ) saver.save(lowerCAmelCase__ , os.path.join(lowerCAmelCase__ , model_name.replace('-' , '_' ) + '.ckpt' ) ) def UpperCamelCase_ ( lowerCAmelCase__ : Union[str, Any]=None ) -> str: """simple docstring""" lowerCAmelCase_ : List[str] = argparse.ArgumentParser() parser.add_argument('--model_name' , type=lowerCAmelCase__ , required=lowerCAmelCase__ , help='model name e.g. bert-base-uncased' ) parser.add_argument( '--cache_dir' , type=lowerCAmelCase__ , default=lowerCAmelCase__ , required=lowerCAmelCase__ , help='Directory containing pytorch model' ) parser.add_argument('--pytorch_model_path' , type=lowerCAmelCase__ , required=lowerCAmelCase__ , help='/path/to/<pytorch-model-name>.bin' ) parser.add_argument('--tf_cache_dir' , type=lowerCAmelCase__ , required=lowerCAmelCase__ , help='Directory in which to save tensorflow model' ) lowerCAmelCase_ : Optional[Any] = parser.parse_args(lowerCAmelCase__ ) lowerCAmelCase_ : Union[str, Any] = BertModel.from_pretrained( pretrained_model_name_or_path=args.model_name , state_dict=torch.load(args.pytorch_model_path ) , cache_dir=args.cache_dir , ) convert_pytorch_checkpoint_to_tf(model=lowerCAmelCase__ , ckpt_dir=args.tf_cache_dir , model_name=args.model_name ) if __name__ == "__main__": main()
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_speech_available, is_torch_available, ) lowercase__ : Optional[int] = { """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""], """processing_trocr""": ["""TrOCRProcessor"""], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase__ : Dict = [ """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""", """TrOCRForCausalLM""", """TrOCRPreTrainedModel""", ] if TYPE_CHECKING: from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig from .processing_trocr import TrOCRProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel else: import sys lowercase__ : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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"""simple docstring""" from math import pi def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> float: return 2 * pi * radius * (angle / 3_6_0) if __name__ == "__main__": print(arc_length(9_0, 1_0))
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'''simple docstring''' from . import __version__ # Backward compatibility imports, to make sure all those objects can be found in file_utils from .utils import ( CLOUDFRONT_DISTRIB_PREFIX, CONFIG_NAME, DISABLE_TELEMETRY, DUMMY_INPUTS, DUMMY_MASK, ENV_VARS_TRUE_AND_AUTO_VALUES, ENV_VARS_TRUE_VALUES, FEATURE_EXTRACTOR_NAME, FLAX_WEIGHTS_NAME, HF_MODULES_CACHE, HUGGINGFACE_CO_PREFIX, HUGGINGFACE_CO_RESOLVE_ENDPOINT, MODEL_CARD_NAME, MULTIPLE_CHOICE_DUMMY_INPUTS, PYTORCH_PRETRAINED_BERT_CACHE, PYTORCH_TRANSFORMERS_CACHE, S3_BUCKET_PREFIX, SENTENCEPIECE_UNDERLINE, SPIECE_UNDERLINE, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME, TORCH_FX_REQUIRED_VERSION, TRANSFORMERS_CACHE, TRANSFORMERS_DYNAMIC_MODULE_NAME, USE_JAX, USE_TF, USE_TORCH, WEIGHTS_INDEX_NAME, WEIGHTS_NAME, ContextManagers, DummyObject, EntryNotFoundError, ExplicitEnum, ModelOutput, PaddingStrategy, PushToHubMixin, RepositoryNotFoundError, RevisionNotFoundError, TensorType, _LazyModule, add_code_sample_docstrings, add_end_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, cached_property, copy_func, default_cache_path, define_sagemaker_information, get_cached_models, get_file_from_repo, get_full_repo_name, get_torch_version, has_file, http_user_agent, is_apex_available, is_bsa_available, is_coloredlogs_available, is_datasets_available, is_detectrona_available, is_faiss_available, is_flax_available, is_ftfy_available, is_in_notebook, is_ipex_available, is_librosa_available, is_offline_mode, is_onnx_available, is_pandas_available, is_phonemizer_available, is_protobuf_available, is_psutil_available, is_pyanvml_available, is_pyctcdecode_available, is_pytesseract_available, is_pytorch_quantization_available, is_rjieba_available, is_sagemaker_dp_enabled, is_sagemaker_mp_enabled, is_scipy_available, is_sentencepiece_available, is_seqio_available, is_sklearn_available, is_soundfile_availble, is_spacy_available, is_speech_available, is_tensor, is_tensorflow_probability_available, is_tfaonnx_available, is_tf_available, is_timm_available, is_tokenizers_available, is_torch_available, is_torch_bfaa_available, is_torch_cuda_available, is_torch_fx_available, is_torch_fx_proxy, is_torch_mps_available, is_torch_tfaa_available, is_torch_tpu_available, is_torchaudio_available, is_training_run_on_sagemaker, is_vision_available, replace_return_docstrings, requires_backends, to_numpy, to_py_obj, torch_only_method, )
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0
from __future__ import annotations import random # Maximum size of the population. Bigger could be faster but is more memory expensive. _snake_case = 200 # Number of elements selected in every generation of evolution. The selection takes # place from best to worst of that generation and must be smaller than N_POPULATION. _snake_case = 50 # Probability that an element of a generation can mutate, changing one of its genes. # This will guarantee that all genes will be used during evolution. _snake_case = 0.4 # Just a seed to improve randomness required by the algorithm. random.seed(random.randint(0, 1000)) def A ( _lowerCamelCase , _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : Tuple = len([g for position, g in enumerate(_lowerCamelCase ) if g == main_target[position]] ) return (item, float(_lowerCamelCase )) def A ( _lowerCamelCase , _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : Optional[Any] = random.randint(0 , len(_lowerCamelCase ) - 1 ) _lowerCAmelCase : Dict = parent_a[:random_slice] + parent_a[random_slice:] _lowerCAmelCase : Dict = parent_a[:random_slice] + parent_a[random_slice:] return (child_a, child_a) def A ( _lowerCamelCase , _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : int = list(_lowerCamelCase ) if random.uniform(0 , 1 ) < MUTATION_PROBABILITY: _lowerCAmelCase : List[str] = random.choice(_lowerCamelCase ) return "".join(_lowerCamelCase ) def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ): '''simple docstring''' _lowerCAmelCase : str = [] # Generate more children proportionally to the fitness score. _lowerCAmelCase : Any = int(parent_a[1] * 100 ) + 1 _lowerCAmelCase : Tuple = 10 if child_n >= 10 else child_n for _ in range(_lowerCamelCase ): _lowerCAmelCase : List[str] = population_score[random.randint(0 , _lowerCamelCase )][0] _lowerCAmelCase , _lowerCAmelCase : Optional[int] = crossover(parent_a[0] , _lowerCamelCase ) # Append new string to the population list. pop.append(mutate(_lowerCamelCase , _lowerCamelCase ) ) pop.append(mutate(_lowerCamelCase , _lowerCamelCase ) ) return pop def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = True ): '''simple docstring''' if N_POPULATION < N_SELECTED: _lowerCAmelCase : Optional[int] = F"{N_POPULATION} must be bigger than {N_SELECTED}" raise ValueError(_lowerCamelCase ) # Verify that the target contains no genes besides the ones inside genes variable. _lowerCAmelCase : List[Any] = sorted({c for c in target if c not in genes} ) if not_in_genes_list: _lowerCAmelCase : Optional[Any] = F"{not_in_genes_list} is not in genes list, evolution cannot converge" raise ValueError(_lowerCamelCase ) # Generate random starting population. _lowerCAmelCase : Tuple = [] for _ in range(_lowerCamelCase ): population.append("".join([random.choice(_lowerCamelCase ) for i in range(len(_lowerCamelCase ) )] ) ) # Just some logs to know what the algorithms is doing. _lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = 0, 0 # This loop will end when we find a perfect match for our target. while True: generation += 1 total_population += len(_lowerCamelCase ) # Random population created. Now it's time to evaluate. # Adding a bit of concurrency can make everything faster, # # import concurrent.futures # population_score: list[tuple[str, float]] = [] # with concurrent.futures.ThreadPoolExecutor( # max_workers=NUM_WORKERS) as executor: # futures = {executor.submit(evaluate, item) for item in population} # concurrent.futures.wait(futures) # population_score = [item.result() for item in futures] # # but with a simple algorithm like this, it will probably be slower. # We just need to call evaluate for every item inside the population. _lowerCAmelCase : List[str] = [evaluate(_lowerCamelCase , _lowerCamelCase ) for item in population] # Check if there is a matching evolution. _lowerCAmelCase : int = sorted(_lowerCamelCase , key=lambda _lowerCamelCase : x[1] , reverse=_lowerCamelCase ) if population_score[0][0] == target: return (generation, total_population, population_score[0][0]) # Print the best result every 10 generation. # Just to know that the algorithm is working. if debug and generation % 10 == 0: print( F"\nGeneration: {generation}" F"\nTotal Population:{total_population}" F"\nBest score: {population_score[0][1]}" F"\nBest string: {population_score[0][0]}" ) # Flush the old population, keeping some of the best evolutions. # Keeping this avoid regression of evolution. _lowerCAmelCase : Optional[int] = population[: int(N_POPULATION / 3 )] population.clear() population.extend(_lowerCamelCase ) # Normalize population score to be between 0 and 1. _lowerCAmelCase : Union[str, Any] = [ (item, score / len(_lowerCamelCase )) for item, score in population_score ] # This is selection for i in range(_lowerCamelCase ): population.extend(select(population_score[int(_lowerCamelCase )] , _lowerCamelCase , _lowerCamelCase ) ) # Check if the population has already reached the maximum value and if so, # break the cycle. If this check is disabled, the algorithm will take # forever to compute large strings, but will also calculate small strings in # a far fewer generations. if len(_lowerCamelCase ) > N_POPULATION: break if __name__ == "__main__": _snake_case = ( "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!" ) _snake_case = list( " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm" "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\" ) _snake_case, _snake_case, _snake_case = basic(target_str, genes_list) print( f'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}''' )
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import argparse import json from pathlib import Path import requests import timm import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel from transformers.utils import logging logging.set_verbosity_info() _snake_case = logging.get_logger(__name__) def A ( _lowerCamelCase , _lowerCamelCase=False ): '''simple docstring''' _lowerCAmelCase : Optional[int] = [] 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"), ("pre_logits.fc.weight", "pooler.dense.weight"), ("pre_logits.fc.bias", "pooler.dense.bias"), ] ) # if just the base model, we should remove "vit" from all keys that start with "vit" _lowerCAmelCase : str = [(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 , _lowerCamelCase , _lowerCamelCase=False ): '''simple docstring''' for i in range(config.num_hidden_layers ): if base_model: _lowerCAmelCase : int = "" else: _lowerCAmelCase : Union[str, Any] = "vit." # read in weights + bias of input projection layer (in timm, this is a single matrix + bias) _lowerCAmelCase : Dict = state_dict.pop(F"blocks.{i}.attn.qkv.weight" ) _lowerCAmelCase : Any = state_dict.pop(F"blocks.{i}.attn.qkv.bias" ) # next, add query, keys and values (in that order) to the state dict _lowerCAmelCase : Dict = in_proj_weight[ : config.hidden_size, : ] _lowerCAmelCase : List[str] = in_proj_bias[: config.hidden_size] _lowerCAmelCase : Union[str, Any] = in_proj_weight[ config.hidden_size : config.hidden_size * 2, : ] _lowerCAmelCase : int = in_proj_bias[ config.hidden_size : config.hidden_size * 2 ] _lowerCAmelCase : int = in_proj_weight[ -config.hidden_size :, : ] _lowerCAmelCase : Optional[int] = in_proj_bias[-config.hidden_size :] def A ( _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : int = ["head.weight", "head.bias"] for k in ignore_keys: state_dict.pop(_lowerCamelCase , _lowerCamelCase ) def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : Union[str, Any] = dct.pop(_lowerCamelCase ) _lowerCAmelCase : Tuple = val def A ( ): '''simple docstring''' _lowerCAmelCase : int = "http://images.cocodataset.org/val2017/000000039769.jpg" _lowerCAmelCase : List[str] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw ) return im @torch.no_grad() def A ( _lowerCamelCase , _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : List[Any] = ViTConfig() _lowerCAmelCase : str = False # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size if vit_name[-5:] == "in21k": _lowerCAmelCase : str = True _lowerCAmelCase : List[str] = int(vit_name[-12:-10] ) _lowerCAmelCase : str = int(vit_name[-9:-6] ) else: _lowerCAmelCase : List[str] = 1_000 _lowerCAmelCase : int = "huggingface/label-files" _lowerCAmelCase : Dict = "imagenet-1k-id2label.json" _lowerCAmelCase : Dict = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) ) _lowerCAmelCase : List[str] = {int(_lowerCamelCase ): v for k, v in idalabel.items()} _lowerCAmelCase : Optional[int] = idalabel _lowerCAmelCase : Dict = {v: k for k, v in idalabel.items()} _lowerCAmelCase : str = int(vit_name[-6:-4] ) _lowerCAmelCase : List[str] = int(vit_name[-3:] ) # size of the architecture if "deit" in vit_name: if vit_name[9:].startswith("tiny" ): _lowerCAmelCase : str = 192 _lowerCAmelCase : Union[str, Any] = 768 _lowerCAmelCase : str = 12 _lowerCAmelCase : Any = 3 elif vit_name[9:].startswith("small" ): _lowerCAmelCase : Any = 384 _lowerCAmelCase : Any = 1_536 _lowerCAmelCase : List[str] = 12 _lowerCAmelCase : Tuple = 6 else: pass else: if vit_name[4:].startswith("small" ): _lowerCAmelCase : Optional[Any] = 768 _lowerCAmelCase : str = 2_304 _lowerCAmelCase : Optional[int] = 8 _lowerCAmelCase : List[str] = 8 elif vit_name[4:].startswith("base" ): pass elif vit_name[4:].startswith("large" ): _lowerCAmelCase : Optional[Any] = 1_024 _lowerCAmelCase : List[str] = 4_096 _lowerCAmelCase : Dict = 24 _lowerCAmelCase : int = 16 elif vit_name[4:].startswith("huge" ): _lowerCAmelCase : Union[str, Any] = 1_280 _lowerCAmelCase : Optional[int] = 5_120 _lowerCAmelCase : Optional[Any] = 32 _lowerCAmelCase : str = 16 # load original model from timm _lowerCAmelCase : List[Any] = timm.create_model(_lowerCamelCase , pretrained=_lowerCamelCase ) timm_model.eval() # load state_dict of original model, remove and rename some keys _lowerCAmelCase : List[str] = timm_model.state_dict() if base_model: remove_classification_head_(_lowerCamelCase ) _lowerCAmelCase : Union[str, Any] = create_rename_keys(_lowerCamelCase , _lowerCamelCase ) for src, dest in rename_keys: rename_key(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) read_in_q_k_v(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) # load HuggingFace model if vit_name[-5:] == "in21k": _lowerCAmelCase : Optional[int] = ViTModel(_lowerCamelCase ).eval() else: _lowerCAmelCase : Optional[int] = ViTForImageClassification(_lowerCamelCase ).eval() model.load_state_dict(_lowerCamelCase ) # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor if "deit" in vit_name: _lowerCAmelCase : Tuple = DeiTImageProcessor(size=config.image_size ) else: _lowerCAmelCase : Dict = ViTImageProcessor(size=config.image_size ) _lowerCAmelCase : Optional[int] = image_processor(images=prepare_img() , return_tensors="pt" ) _lowerCAmelCase : Union[str, Any] = encoding["pixel_values"] _lowerCAmelCase : List[str] = model(_lowerCamelCase ) if base_model: _lowerCAmelCase : List[str] = timm_model.forward_features(_lowerCamelCase ) assert timm_pooled_output.shape == outputs.pooler_output.shape assert torch.allclose(_lowerCamelCase , outputs.pooler_output , atol=1e-3 ) else: _lowerCAmelCase : Any = timm_model(_lowerCamelCase ) assert timm_logits.shape == outputs.logits.shape assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 ) Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase ) print(F"Saving model {vit_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( "--vit_name", default="vit_base_patch16_224", type=str, help="Name of the ViT timm model you'd like to convert.", ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory." ) _snake_case = parser.parse_args() convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)
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'''simple docstring''' import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_rembert import RemBertTokenizer else: __lowercase : List[Any] = None __lowercase : Any = logging.get_logger(__name__) __lowercase : Any = {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''} __lowercase : Optional[Any] = { '''vocab_file''': { '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''', }, '''tokenizer_file''': { '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''', }, } __lowercase : List[str] = { '''google/rembert''': 256, } __lowercase : List[Any] = '''▁''' class __lowercase ( _lowercase ): lowerCamelCase : int = VOCAB_FILES_NAMES lowerCamelCase : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase : Optional[Any] = RemBertTokenizer def __init__(self , A=None , A=None , A=True , A=True , A=False , A="[CLS]" , A="[SEP]" , A="<unk>" , A="[SEP]" , A="<pad>" , A="[CLS]" , A="[MASK]" , **A , ): # Mask token behave like a normal word, i.e. include the space before it lowerCamelCase_ : Tuple = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else mask_token super().__init__( A , tokenizer_file=A , do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , **A , ) lowerCamelCase_ : Any = do_lower_case lowerCamelCase_ : Union[str, Any] = remove_space lowerCamelCase_ : Optional[Any] = keep_accents lowerCamelCase_ : str = vocab_file lowerCamelCase_ : str = False if not self.vocab_file else True def UpperCAmelCase__ (self , A , A = None ): lowerCamelCase_ : Union[str, Any] = [self.sep_token_id] lowerCamelCase_ : Dict = [self.cls_token_id] if token_ids_a is None: return cls + token_ids_a + sep return cls + token_ids_a + sep + token_ids_a + sep def UpperCAmelCase__ (self , A , A = None , A = False ): if already_has_special_tokens: if token_ids_a is not None: raise ValueError( '''You should not supply a second sequence if the provided sequence of ''' '''ids is already formatted with special tokens for the model.''' ) return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a] if token_ids_a is not None: return [1] + ([0] * len(A )) + [1] + ([0] * len(A )) + [1] return [1] + ([0] * len(A )) + [1] def UpperCAmelCase__ (self , A , A = None ): lowerCamelCase_ : int = [self.sep_token_id] lowerCamelCase_ : 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 UpperCAmelCase__ (self , A , A = None ): if not os.path.isdir(A ): logger.error('''Vocabulary path ({}) should be a directory'''.format(A ) ) return lowerCamelCase_ : Dict = os.path.join( A , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(A ): copyfile(self.vocab_file , A ) return (out_vocab_file,)
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'''simple docstring''' import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import BertTokenizer, BertTokenizerFast from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES from transformers.testing_utils import require_vision from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available if is_vision_available(): from PIL import Image from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor @require_vision class __lowercase ( unittest.TestCase ): def UpperCAmelCase__ (self ): lowerCamelCase_ : List[str] = tempfile.mkdtemp() lowerCamelCase_ : Optional[int] = [ '''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''的''', '''价''', '''格''', '''是''', '''15''', '''便''', '''alex''', '''##andra''', ''',''', '''。''', '''-''', '''t''', '''shirt''', ] lowerCamelCase_ : str = 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] ) ) lowerCamelCase_ : Tuple = { '''do_resize''': True, '''size''': {'''height''': 2_2_4, '''width''': 2_2_4}, '''do_center_crop''': True, '''crop_size''': {'''height''': 1_8, '''width''': 1_8}, '''do_normalize''': True, '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73], '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11], '''do_convert_rgb''': True, } lowerCamelCase_ : Tuple = os.path.join(self.tmpdirname , A ) with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp: json.dump(A , A ) def UpperCAmelCase__ (self , **A ): return BertTokenizer.from_pretrained(self.tmpdirname , **A ) def UpperCAmelCase__ (self , **A ): return BertTokenizerFast.from_pretrained(self.tmpdirname , **A ) def UpperCAmelCase__ (self , **A ): return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **A ) def UpperCAmelCase__ (self ): shutil.rmtree(self.tmpdirname ) def UpperCAmelCase__ (self ): lowerCamelCase_ : List[Any] = [np.random.randint(2_5_5 , size=(3, 3_0, 4_0_0) , dtype=np.uinta )] lowerCamelCase_ : Optional[Any] = [Image.fromarray(np.moveaxis(A , 0 , -1 ) ) for x in image_inputs] return image_inputs def UpperCAmelCase__ (self ): lowerCamelCase_ : str = self.get_tokenizer() lowerCamelCase_ : List[Any] = self.get_rust_tokenizer() lowerCamelCase_ : List[Any] = self.get_image_processor() lowerCamelCase_ : Optional[Any] = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) processor_slow.save_pretrained(self.tmpdirname ) lowerCamelCase_ : Any = ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=A ) lowerCamelCase_ : List[Any] = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) processor_fast.save_pretrained(self.tmpdirname ) lowerCamelCase_ : Union[str, Any] = ChineseCLIPProcessor.from_pretrained(self.tmpdirname ) self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() ) self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() ) self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() ) self.assertIsInstance(processor_slow.tokenizer , A ) self.assertIsInstance(processor_fast.tokenizer , A ) self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor_slow.image_processor , A ) self.assertIsInstance(processor_fast.image_processor , A ) def UpperCAmelCase__ (self ): lowerCamelCase_ : Tuple = ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() ) processor.save_pretrained(self.tmpdirname ) lowerCamelCase_ : List[str] = self.get_tokenizer(cls_token='''(CLS)''' , sep_token='''(SEP)''' ) lowerCamelCase_ : Dict = self.get_image_processor(do_normalize=A ) lowerCamelCase_ : Tuple = ChineseCLIPProcessor.from_pretrained( self.tmpdirname , cls_token='''(CLS)''' , sep_token='''(SEP)''' , do_normalize=A ) self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.tokenizer , A ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , A ) def UpperCAmelCase__ (self ): lowerCamelCase_ : Any = self.get_image_processor() lowerCamelCase_ : Optional[int] = self.get_tokenizer() lowerCamelCase_ : List[str] = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) lowerCamelCase_ : Any = self.prepare_image_inputs() lowerCamelCase_ : List[Any] = image_processor(A , return_tensors='''np''' ) lowerCamelCase_ : Optional[int] = processor(images=A , return_tensors='''np''' ) for key in input_feat_extract.keys(): self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 ) def UpperCAmelCase__ (self ): lowerCamelCase_ : Dict = self.get_image_processor() lowerCamelCase_ : Union[str, Any] = self.get_tokenizer() lowerCamelCase_ : str = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) lowerCamelCase_ : int = '''Alexandra,T-shirt的价格是15便士。''' lowerCamelCase_ : int = processor(text=A ) lowerCamelCase_ : Dict = tokenizer(A ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def UpperCAmelCase__ (self ): lowerCamelCase_ : Any = self.get_image_processor() lowerCamelCase_ : int = self.get_tokenizer() lowerCamelCase_ : Union[str, Any] = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) lowerCamelCase_ : Any = '''Alexandra,T-shirt的价格是15便士。''' lowerCamelCase_ : List[Any] = self.prepare_image_inputs() lowerCamelCase_ : Optional[int] = processor(text=A , images=A ) self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] ) # test if it raises when no input is passed with pytest.raises(A ): processor() def UpperCAmelCase__ (self ): lowerCamelCase_ : Optional[int] = self.get_image_processor() lowerCamelCase_ : int = self.get_tokenizer() lowerCamelCase_ : Any = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) lowerCamelCase_ : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]] lowerCamelCase_ : Union[str, Any] = processor.batch_decode(A ) lowerCamelCase_ : Any = tokenizer.batch_decode(A ) self.assertListEqual(A , A ) def UpperCAmelCase__ (self ): lowerCamelCase_ : Union[str, Any] = self.get_image_processor() lowerCamelCase_ : Optional[int] = self.get_tokenizer() lowerCamelCase_ : Optional[Any] = ChineseCLIPProcessor(tokenizer=A , image_processor=A ) lowerCamelCase_ : int = '''Alexandra,T-shirt的价格是15便士。''' lowerCamelCase_ : str = self.prepare_image_inputs() lowerCamelCase_ : int = processor(text=A , images=A ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
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from sklearn.metrics import fa_score, matthews_corrcoef import datasets from .record_evaluation import evaluate as evaluate_record _snake_case = """\ @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} } """ _snake_case = """\ 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. """ _snake_case = """ 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 _A ( __magic_name__ , __magic_name__ ): return float((preds == labels).mean() ) def _A ( __magic_name__ , __magic_name__ , __magic_name__="binary" ): lowercase__ = simple_accuracy(__magic_name__ , __magic_name__ ) lowercase__ = float(fa_score(y_true=__magic_name__ , y_pred=__magic_name__ , average=__magic_name__ ) ) return { "accuracy": acc, "f1": fa, } def _A ( __magic_name__ , __magic_name__ ): lowercase__ = {} for id_pred, label in zip(__magic_name__ , __magic_name__ ): lowercase__ = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}''' lowercase__ = id_pred["prediction"] if question_id in question_map: question_map[question_id].append((pred, label) ) else: lowercase__ = [(pred, label)] lowercase__ , lowercase__ = [], [] for question, preds_labels in question_map.items(): lowercase__ , lowercase__ = zip(*__magic_name__ ) lowercase__ = fa_score(y_true=__magic_name__ , y_pred=__magic_name__ , average="macro" ) fas.append(__magic_name__ ) lowercase__ = int(sum(pred == label for pred, label in preds_labels ) == len(__magic_name__ ) ) ems.append(__magic_name__ ) lowercase__ = float(sum(__magic_name__ ) / len(__magic_name__ ) ) lowercase__ = sum(__magic_name__ ) / len(__magic_name__ ) lowercase__ = float(fa_score(y_true=__magic_name__ , 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 lowerCAmelCase ( datasets.Metric ): def UpperCAmelCase ( self :Dict ): '''simple docstring''' 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 UpperCAmelCase ( self :str ): '''simple docstring''' 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 UpperCAmelCase ( self :List[str] , _lowercase :List[Any] , _lowercase :Optional[int] ): '''simple docstring''' if self.config_name == "axb": return {"matthews_correlation": matthews_corrcoef(_lowercase , _lowercase )} elif self.config_name == "cb": return acc_and_fa(_lowercase , _lowercase , fa_avg="macro" ) elif self.config_name == "record": lowercase__ = [ { "qas": [ {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]} for ref in references ] } ] lowercase__ = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions} return evaluate_record(_lowercase , _lowercase )[0] elif self.config_name == "multirc": return evaluate_multirc(_lowercase , _lowercase ) elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]: return {"accuracy": simple_accuracy(_lowercase , _lowercase )} 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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import argparse import random import joblib import numpy as np import torch from igf.igf import ( SecondaryLearner, collect_objective_set, compute_perplexity, generate_datasets, load_gpta, recopy_gpta, set_seed, train_secondary_learner, ) from torch.utils.data import DataLoader, RandomSampler from transformers import GPTaLMHeadModel def _A ( __magic_name__=32 , __magic_name__=10 , __magic_name__=100 , __magic_name__=1026 , __magic_name__=True , __magic_name__="data/tokenized_stories_train_wikitext103.jbl" , __magic_name__="igf_context_pairs.jbl" , ): set_seed(3 ) # generate train_data and objective_set lowercase__ , lowercase__ = generate_datasets( __magic_name__ , __magic_name__ , number=__magic_name__ , min_len=1026 , trim=__magic_name__ ) # keeps model same across runs set_seed(4 ) # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights # can we train on GPU? lowercase__ = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" ) # load pretrained model lowercase__ = load_gpta("gpt2" ).to(__magic_name__ ) print("computing perplexity on objective set" ) lowercase__ = compute_perplexity(__magic_name__ , __magic_name__ , __magic_name__ ).item() print("perplexity on objective set:" , __magic_name__ ) # collect igf pairs and save to file demo.jbl collect_objective_set(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ) # clean up, delete model and data we don't need anymore del model, train_data, objective_set torch.cuda.empty_cache() def _A ( __magic_name__ , __magic_name__=15 , __magic_name__=128 , __magic_name__=100 , __magic_name__="igf_model.pt" , ): set_seed(42 ) # Load pre-trained model lowercase__ = GPTaLMHeadModel.from_pretrained("gpt2" ) # Initialize secondary learner to use embedding weights of model lowercase__ = SecondaryLearner(__magic_name__ ) # Train secondary learner lowercase__ = train_secondary_learner( __magic_name__ , __magic_name__ , max_epochs=__magic_name__ , batch_size=__magic_name__ , eval_freq=100 , igf_model_path=__magic_name__ , ) del model, secondary_learner_train_data torch.cuda.empty_cache() return secondary_learner def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=32 , __magic_name__=1000 , __magic_name__=16 , __magic_name__=1.0 , __magic_name__=recopy_gpta , __magic_name__=None , __magic_name__=10 , __magic_name__="gpt2_finetuned.pt" , ): lowercase__ = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" ) lowercase__ = RandomSampler(__magic_name__ ) lowercase__ = DataLoader(__magic_name__ , sampler=__magic_name__ ) lowercase__ = max_steps // (len(__magic_name__ )) + 1 lowercase__ = 0 lowercase__ = torch.zeros((1, context_len) , dtype=torch.long , device=__magic_name__ ) lowercase__ , lowercase__ , lowercase__ = recopy_model(__magic_name__ , __magic_name__ , __magic_name__ ) model.train() if secondary_learner is not None: secondary_learner.to(__magic_name__ ) secondary_learner.eval() lowercase__ = [] lowercase__ = 0 lowercase__ = [] lowercase__ = [] # Compute the performance of the transformer model at the beginning lowercase__ = compute_perplexity(__magic_name__ , __magic_name__ , __magic_name__ ) test_perps.append(__magic_name__ ) print("Test perplexity, step" , __magic_name__ , ":" , __magic_name__ ) for epoch in range(int(__magic_name__ ) ): for step, example in enumerate(__magic_name__ ): torch.cuda.empty_cache() lowercase__ = random.randint(0 , example.size(2 ) - context_len - 1 ) lowercase__ = example[0, 0, start : start + context_len] lm_optimizer.zero_grad() lowercase__ = model(__magic_name__ , labels=__magic_name__ ) lowercase__ = True if secondary_learner is not None: lowercase__ = secondary_learner.forward( torch.tensor(__magic_name__ , dtype=torch.long , device=__magic_name__ ).unsqueeze(0 ) )[0].item() observed_qs.append(float(__magic_name__ ) ) # Here we implement the simple non-constant threshold for the predicted IG(X) value # We will decay the selectivity of our secondary learner filter from # 1 standard deviation above average to 1 below average after 10 batches. if global_step == 10: lowercase__ = -1 if predicted_q < threshold: lowercase__ = False # If we passed the filter, add the context to the batch! if do_backprop: contexts.append(np.array(context.cpu() ) ) lowercase__ = outputs[0] lm_loss.backward() examples += 1 del outputs # Once the batch is filled with enough contexts, backprop on the batch. if examples == batch_size: torch.cuda.empty_cache() lowercase__ = 0 # Do LM backprop torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 ) lm_optimizer.step() lm_scheduler.step() # Update learning rate schedule global_step += 1 # Compute the performance of the transformer model at this batch if global_step % eval_interval == 0: lowercase__ = compute_perplexity(__magic_name__ , __magic_name__ , __magic_name__ ) test_perps.append(__magic_name__ ) print("Test perplexity, step" , __magic_name__ , ":" , __magic_name__ ) # Break out of the loop after 60 batches if max_steps > 0 and global_step > 60: break if max_steps > 0 and global_step > 60: break # save finetuned transformer model torch.save(model.state_dict() , __magic_name__ ) torch.cuda.empty_cache() # Do some cleaning up so we can reinitialize for the next run of this function del lm_optimizer del lm_scheduler return model def _A ( ): lowercase__ = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" ) # Required parameters parser.add_argument( "--data_dir" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="The input data dir. Should contain data files for WikiText." , ) parser.add_argument( "--model_name_or_path" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="Path to pretrained model or model identifier from huggingface.co/models" , ) parser.add_argument( "--data_file" , type=__magic_name__ , default=__magic_name__ , help=( "A jbl file containing tokenized data which can be split as objective dataset, " "train_dataset and test_dataset." ) , ) parser.add_argument( "--igf_data_file" , type=__magic_name__ , default=__magic_name__ , help="A jbl file containing the context and information gain pairs to train secondary learner." , ) parser.add_argument( "--output_dir" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="The output directory where the final fine-tuned model is stored." , ) parser.add_argument( "--tokenizer_name" , default=__magic_name__ , type=__magic_name__ , help="Pretrained tokenizer name or path if not the same as model_name" , ) parser.add_argument("--seed" , type=__magic_name__ , default=__magic_name__ , help="A seed for reproducible training." ) parser.add_argument( "--context_len" , default=32 , type=__magic_name__ , help=( "The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded." ) , ) parser.add_argument( "--size_objective_set" , default=100 , type=__magic_name__ , help="number of articles that are long enough to be used as our objective set" , ) parser.add_argument( "--eval_freq" , default=100 , type=__magic_name__ , help="secondary model evaluation is triggered at eval_freq" ) parser.add_argument("--max_steps" , default=1000 , type=__magic_name__ , help="To calculate training epochs" ) parser.add_argument( "--secondary_learner_batch_size" , default=128 , type=__magic_name__ , help="batch size of training data for secondary learner" , ) parser.add_argument( "--batch_size" , default=16 , type=__magic_name__ , help="batch size of training data of language model(gpt2) " ) parser.add_argument( "--eval_interval" , default=10 , type=__magic_name__ , help=( "decay the selectivity of our secondary learner filter from" "1 standard deviation above average to 1 below average after 10 batches" ) , ) parser.add_argument( "--number" , default=100 , type=__magic_name__ , help="The number of examples split to be used as objective_set/test_data" ) parser.add_argument( "--min_len" , default=1026 , type=__magic_name__ , help="The minimum length of the article to be used as objective set" ) parser.add_argument( "--secondary_learner_max_epochs" , default=15 , type=__magic_name__ , help="number of epochs to train secondary learner" ) parser.add_argument("--trim" , default=__magic_name__ , type=__magic_name__ , help="truncate the example if it exceeds context length" ) parser.add_argument( "--threshold" , default=1.0 , type=__magic_name__ , help=( "The threshold value used by secondary learner to filter the train_data and allow only" " informative data as input to the model" ) , ) parser.add_argument("--finetuned_model_name" , default="gpt2_finetuned.pt" , type=__magic_name__ , help="finetuned_model_name" ) parser.add_argument( "--recopy_model" , default=__magic_name__ , type=__magic_name__ , help="Reset the model to the original pretrained GPT-2 weights after each iteration" , ) # function calls # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner generate_n_pairs( context_len=32 , max_steps=10 , size_objective_set=100 , min_len=1026 , trim=__magic_name__ , data_file="data/tokenized_stories_train_wikitext103.jbl" , igf_data_file="igf_context_pairs.jbl" , ) # Load train data for secondary learner lowercase__ = joblib.load("data/IGF_values.jbl" ) # Train secondary learner lowercase__ = training_secondary_learner( __magic_name__ , secondary_learner_max_epochs=15 , secondary_learner_batch_size=128 , eval_freq=100 , igf_model_path="igf_model.pt" , ) # load pretrained gpt2 model lowercase__ = GPTaLMHeadModel.from_pretrained("gpt2" ) set_seed(42 ) # Generate train and test data to train and evaluate gpt2 model lowercase__ , lowercase__ = generate_datasets( context_len=32 , file="data/tokenized_stories_train_wikitext103.jbl" , number=100 , min_len=1026 , trim=__magic_name__ ) # fine-tuning of the gpt2 model using igf (Information Gain Filtration) finetune( __magic_name__ , __magic_name__ , __magic_name__ , context_len=32 , max_steps=1000 , batch_size=16 , threshold=1.0 , recopy_model=__magic_name__ , secondary_learner=__magic_name__ , eval_interval=10 , finetuned_model_name="gpt2_finetuned.pt" , ) if __name__ == "__main__": main()
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1
import tempfile import unittest from make_student import create_student_by_copying_alternating_layers from transformers import AutoConfig from transformers.file_utils import cached_property from transformers.testing_utils import require_torch __a = 'sshleifer/bart-tiny-random' __a = 'patrickvonplaten/t5-tiny-random' @require_torch class lowercase__( unittest.TestCase ): """simple docstring""" @cached_property def _lowercase ( self : Any ) -> Tuple: return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ ) def _lowercase ( self : int ) -> List[Any]: lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 ) self.assertEqual(student.config.num_hidden_layers , 1 ) def _lowercase ( self : Union[str, Any] ) -> Optional[int]: lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ ) def _lowercase ( self : List[str] ) -> List[Any]: lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=SCREAMING_SNAKE_CASE_ ) self.assertEqual(student.config.encoder_layers , 1 ) self.assertEqual(student.config.decoder_layers , self.teacher_config.encoder_layers ) def _lowercase ( self : List[Any] ) -> List[Any]: lowercase_ , *lowercase_ = create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=1 , d=1 ) self.assertEqual(student.config.encoder_layers , 1 ) self.assertEqual(student.config.decoder_layers , 1 ) def _lowercase ( self : Union[str, Any] ) -> Optional[int]: with self.assertRaises(SCREAMING_SNAKE_CASE_ ): create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_ , tempfile.mkdtemp() , e=SCREAMING_SNAKE_CASE_ , d=SCREAMING_SNAKE_CASE_ )
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"""simple docstring""" import json import os import tempfile import datasets from utils import generate_example_dataset, get_duration a_ = 50000 a_ = 5000 a_ , a_ = os.path.split(__file__) a_ = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json""")) @get_duration def __lowercase ( snake_case_ : datasets.Dataset ,snake_case_ : Tuple ) ->Tuple: '''simple docstring''' for i in range(snake_case_ ): __A : int = dataset[i] @get_duration def __lowercase ( snake_case_ : datasets.Dataset ,snake_case_ : Optional[Any] ,snake_case_ : int ) ->Tuple: '''simple docstring''' for i in range(0 ,len(snake_case_ ) ,snake_case_ ): __A : List[str] = dataset[i : i + batch_size] @get_duration def __lowercase ( snake_case_ : datasets.Dataset ,snake_case_ : List[Any] ,snake_case_ : Any ) ->int: '''simple docstring''' with dataset.formatted_as(type=snake_case_ ): for i in range(snake_case_ ): __A : Union[str, Any] = dataset[i] @get_duration def __lowercase ( snake_case_ : datasets.Dataset ,snake_case_ : Any ,snake_case_ : Union[str, Any] ,snake_case_ : Optional[int] ) ->Union[str, Any]: '''simple docstring''' with dataset.formatted_as(type=snake_case_ ): for i in range(0 ,snake_case_ ,snake_case_ ): __A : Dict = dataset[i : i + batch_size] def __lowercase ( ) ->Optional[int]: '''simple docstring''' __A : int = {'''num examples''': SPEED_TEST_N_EXAMPLES} __A : Optional[int] = [ (read, {'''length''': SMALL_TEST}), (read, {'''length''': SPEED_TEST_N_EXAMPLES}), (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}), (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}), (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}), (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}), (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}), (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}), (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}), (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}), (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}), ] __A : int = [ (read, {'''length''': SMALL_TEST}), (read, {'''length''': SPEED_TEST_N_EXAMPLES}), (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}), (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}), (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}), (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}), (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}), (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}), ] with tempfile.TemporaryDirectory() as tmp_dir: print('''generating dataset''' ) __A : Any = datasets.Features( {'''list''': datasets.Sequence(datasets.Value('''float32''' ) ), '''numbers''': datasets.Value('''float32''' )} ) __A : List[Any] = generate_example_dataset( os.path.join(snake_case_ ,'''dataset.arrow''' ) ,snake_case_ ,num_examples=snake_case_ ,seq_shapes={'''list''': (100,)} ,) print('''first set of iterations''' ) for func, kwargs in functions: print(func.__name__ ,str(snake_case_ ) ) __A : Dict = func(snake_case_ ,**snake_case_ ) print('''shuffling dataset''' ) __A : int = dataset.shuffle() print('''Second set of iterations (after shuffling''' ) for func, kwargs in functions_shuffled: print('''shuffled ''' ,func.__name__ ,str(snake_case_ ) ) __A : Optional[Any] = func( snake_case_ ,**snake_case_ ) with open(snake_case_ ,'''wb''' ) as f: f.write(json.dumps(snake_case_ ).encode('''utf-8''' ) ) if __name__ == "__main__": # useful to run the profiler benchmark_iterating()
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"""simple docstring""" def UpperCamelCase ( _A : int = 1000 )-> int: """simple docstring""" A__ = 2**power A__ = str(__lowerCAmelCase ) A__ = list(__lowerCAmelCase ) A__ = 0 for i in list_num: sum_of_num += int(__lowerCAmelCase ) return sum_of_num if __name__ == "__main__": UpperCAmelCase_ : Optional[Any] = int(input("Enter the power of 2: ").strip()) print("2 ^ ", power, " = ", 2**power) UpperCAmelCase_ : Tuple = solution(power) print("Sum of the digits is: ", result)
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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, StableDiffusionAttendAndExcitePipeline, UNetaDConditionModel, ) from diffusers.utils import load_numpy, skip_mps, slow from diffusers.utils.testing_utils import require_torch_gpu from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin UpperCAmelCase_ : Dict = False @skip_mps class UpperCamelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ): lowerCAmelCase : Optional[Any] = StableDiffusionAttendAndExcitePipeline lowerCAmelCase : Optional[Any] = False lowerCAmelCase : Any = TEXT_TO_IMAGE_PARAMS lowerCAmelCase : Dict = TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""} ) lowerCAmelCase : List[str] = TEXT_TO_IMAGE_IMAGE_PARAMS lowerCAmelCase : List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS @classmethod def __A ( cls ): super().setUpClass() torch.use_deterministic_algorithms(UpperCAmelCase__ ) @classmethod def __A ( cls ): super().tearDownClass() torch.use_deterministic_algorithms(UpperCAmelCase__ ) def __A ( self ): torch.manual_seed(0 ) A__ = 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 , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , ) A__ = DDIMScheduler( beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=UpperCAmelCase__ , set_alpha_to_one=UpperCAmelCase__ , ) torch.manual_seed(0 ) A__ = AutoencoderKL( block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=128 , ) torch.manual_seed(0 ) A__ = CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="gelu" , projection_dim=512 , ) A__ = CLIPTextModel(UpperCAmelCase__ ) A__ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) A__ = { "unet": unet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def __A ( self , UpperCAmelCase__ , UpperCAmelCase__=0 ): if str(UpperCAmelCase__ ).startswith("mps" ): A__ = torch.manual_seed(UpperCAmelCase__ ) else: A__ = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ ) A__ = A__ = { "prompt": "a cat and a frog", "token_indices": [2, 5], "generator": generator, "num_inference_steps": 1, "guidance_scale": 6.0, "output_type": "numpy", "max_iter_to_alter": 2, "thresholds": {0: 0.7}, } return inputs def __A ( self ): A__ = "cpu" A__ = self.get_dummy_components() A__ = self.pipeline_class(**UpperCAmelCase__ ) pipe.to(UpperCAmelCase__ ) pipe.set_progress_bar_config(disable=UpperCAmelCase__ ) A__ = self.get_dummy_inputs(UpperCAmelCase__ ) A__ = pipe(**UpperCAmelCase__ ).images A__ = image[0, -3:, -3:, -1] self.assertEqual(image.shape , (1, 64, 64, 3) ) A__ = np.array( [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496] ) A__ = np.abs(image_slice.flatten() - expected_slice ).max() self.assertLessEqual(UpperCAmelCase__ , 1e-3 ) def __A ( self ): super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 ) def __A ( self ): # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches self._test_inference_batch_consistent(batch_sizes=[1, 2] ) def __A ( self ): self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7e-4 ) def __A ( self ): super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 ) def __A ( self ): super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 ) def __A ( self ): super().test_save_load_local(expected_max_difference=5e-4 ) def __A ( self ): super().test_save_load_optional_components(expected_max_difference=4e-4 ) @require_torch_gpu @slow class UpperCamelCase ( unittest.TestCase ): @classmethod def __A ( cls ): super().setUpClass() torch.use_deterministic_algorithms(UpperCAmelCase__ ) @classmethod def __A ( cls ): super().tearDownClass() torch.use_deterministic_algorithms(UpperCAmelCase__ ) def __A ( self ): super().tearDown() gc.collect() torch.cuda.empty_cache() def __A ( self ): A__ = torch.manual_seed(51 ) A__ = StableDiffusionAttendAndExcitePipeline.from_pretrained( "CompVis/stable-diffusion-v1-4" , safety_checker=UpperCAmelCase__ , torch_dtype=torch.floataa ) pipe.to("cuda" ) A__ = "a painting of an elephant with glasses" A__ = [5, 7] A__ = pipe( prompt=UpperCAmelCase__ , token_indices=UpperCAmelCase__ , guidance_scale=7.5 , generator=UpperCAmelCase__ , num_inference_steps=5 , max_iter_to_alter=5 , output_type="numpy" , ).images[0] A__ = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy" ) assert np.abs((expected_image - image).max() ) < 5e-1
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0
from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) snake_case_ = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: snake_case_ = [ 'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST', 'UniSpeechForCTC', 'UniSpeechForPreTraining', 'UniSpeechForSequenceClassification', 'UniSpeechModel', 'UniSpeechPreTrainedModel', ] if TYPE_CHECKING: from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_unispeech import ( UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST, UniSpeechForCTC, UniSpeechForPreTraining, UniSpeechForSequenceClassification, UniSpeechModel, UniSpeechPreTrainedModel, ) else: import sys snake_case_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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from __future__ import annotations def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]: __snake_case = [] __snake_case = [] __snake_case = 0 __snake_case = sum(snake_case_ ) create_state_space_tree(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) return result def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int , snake_case_ : int , snake_case_ : list[int] , snake_case_ : list[list[int]] , snake_case_ : int , ) -> None: if sum(snake_case_ ) > max_sum or (remaining_nums_sum + sum(snake_case_ )) < max_sum: return if sum(snake_case_ ) == max_sum: result.append(snake_case_ ) return for index in range(snake_case_ , len(snake_case_ ) ): create_state_space_tree( snake_case_ , snake_case_ , index + 1 , [*path, nums[index]] , snake_case_ , remaining_nums_sum - nums[index] , ) snake_case_ = [3, 34, 4, 12, 5, 2] snake_case_ = 9 snake_case_ = generate_sum_of_subsets_soln(nums, max_sum) print(*result)
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"""simple docstring""" 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 from lavis.models import load_model_and_preprocess from PIL import Image from transformers import ( AutoTokenizer, BlipaConfig, BlipaForConditionalGeneration, BlipaProcessor, BlipaVisionConfig, BlipImageProcessor, OPTConfig, TaConfig, ) from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD def _SCREAMING_SNAKE_CASE (): lowerCAmelCase = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png' lowerCAmelCase = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw ).convert('RGB' ) return image def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Any ): 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.layernorm.weight') ) rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias') ) # fmt: on return rename_keys def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : Any , _UpperCAmelCase : List[Any] ): lowerCAmelCase = dct.pop(_UpperCAmelCase ) lowerCAmelCase = val def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ): 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(_UpperCAmelCase , requires_grad=_UpperCAmelCase ), v_bias) ) lowerCAmelCase = qkv_bias def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] , _UpperCAmelCase : str ): lowerCAmelCase = 364 if 'coco' in model_name else 224 lowerCAmelCase = BlipaVisionConfig(image_size=_UpperCAmelCase ).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 "opt-2.7b" in model_name: lowerCAmelCase = OPTConfig.from_pretrained('facebook/opt-2.7b' , eos_token_id=_UpperCAmelCase ).to_dict() elif "opt-6.7b" in model_name: lowerCAmelCase = OPTConfig.from_pretrained('facebook/opt-6.7b' , eos_token_id=_UpperCAmelCase ).to_dict() elif "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() lowerCAmelCase = BlipaConfig(vision_config=_UpperCAmelCase , text_config=_UpperCAmelCase ) return config, image_size @torch.no_grad() def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : int=None , _UpperCAmelCase : Any=False ): lowerCAmelCase = ( AutoTokenizer.from_pretrained('facebook/opt-2.7b' ) if 'opt' in model_name else AutoTokenizer.from_pretrained('google/flan-t5-xl' ) ) lowerCAmelCase = tokenizer('\n' , add_special_tokens=_UpperCAmelCase ).input_ids[0] lowerCAmelCase ,lowerCAmelCase = get_blipa_config(_UpperCAmelCase , eos_token_id=_UpperCAmelCase ) lowerCAmelCase = BlipaForConditionalGeneration(_UpperCAmelCase ).eval() lowerCAmelCase = { 'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'), 'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'), 'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'), 'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'), 'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'), 'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'), 'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'), } lowerCAmelCase ,lowerCAmelCase = model_name_to_original[model_name] # load original model print('Loading original model...' ) lowerCAmelCase = 'cuda' if torch.cuda.is_available() else 'cpu' lowerCAmelCase ,lowerCAmelCase ,lowerCAmelCase = load_model_and_preprocess( name=_UpperCAmelCase , model_type=_UpperCAmelCase , is_eval=_UpperCAmelCase , device=_UpperCAmelCase ) original_model.eval() print('Done!' ) # update state dict keys lowerCAmelCase = original_model.state_dict() lowerCAmelCase = create_rename_keys(_UpperCAmelCase ) for src, dest in rename_keys: rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) # some keys can be renamed efficiently for key, val in state_dict.copy().items(): lowerCAmelCase = state_dict.pop(_UpperCAmelCase ) if key.startswith('Qformer.bert' ): lowerCAmelCase = key.replace('Qformer.bert' , 'qformer' ) if "attention.self" in key: lowerCAmelCase = key.replace('self' , 'attention' ) if "opt_proj" in key: lowerCAmelCase = key.replace('opt_proj' , 'language_projection' ) if "t5_proj" in key: lowerCAmelCase = key.replace('t5_proj' , 'language_projection' ) if key.startswith('opt' ): lowerCAmelCase = key.replace('opt' , 'language' ) if key.startswith('t5' ): lowerCAmelCase = key.replace('t5' , 'language' ) lowerCAmelCase = val # read in qv biases read_in_q_v_bias(_UpperCAmelCase , _UpperCAmelCase ) lowerCAmelCase ,lowerCAmelCase = hf_model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase ) assert len(_UpperCAmelCase ) == 0 assert unexpected_keys == ["qformer.embeddings.position_ids"] lowerCAmelCase = load_demo_image() lowerCAmelCase = vis_processors['eval'](_UpperCAmelCase ).unsqueeze(0 ).to(_UpperCAmelCase ) lowerCAmelCase = tokenizer(['\n'] , return_tensors='pt' ).input_ids.to(_UpperCAmelCase ) # create processor lowerCAmelCase = BlipImageProcessor( size={'height': image_size, 'width': image_size} , image_mean=_UpperCAmelCase , image_std=_UpperCAmelCase ) lowerCAmelCase = BlipaProcessor(image_processor=_UpperCAmelCase , tokenizer=_UpperCAmelCase ) lowerCAmelCase = processor(images=_UpperCAmelCase , return_tensors='pt' ).pixel_values.to(_UpperCAmelCase ) # make sure processor creates exact same pixel values assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase ) original_model.to(_UpperCAmelCase ) hf_model.to(_UpperCAmelCase ) with torch.no_grad(): if "opt" in model_name: lowerCAmelCase = original_model({'image': original_pixel_values, 'text_input': ['']} ).logits lowerCAmelCase = hf_model(_UpperCAmelCase , _UpperCAmelCase ).logits else: lowerCAmelCase = original_model( {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits lowerCAmelCase = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 ) lowerCAmelCase = hf_model(_UpperCAmelCase , _UpperCAmelCase , labels=_UpperCAmelCase ).logits assert original_logits.shape == logits.shape print('First values of original logits:' , original_logits[0, :3, :3] ) print('First values of HF logits:' , logits[0, :3, :3] ) # assert values if model_name == "blip2-flan-t5-xl": lowerCAmelCase = torch.tensor( [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] , device=_UpperCAmelCase ) assert torch.allclose(logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 ) elif model_name == "blip2-flan-t5-xl-coco": lowerCAmelCase = torch.tensor( [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] , device=_UpperCAmelCase ) else: # cast to same type lowerCAmelCase = logits.dtype assert torch.allclose(original_logits.to(_UpperCAmelCase ) , _UpperCAmelCase , atol=1e-2 ) print('Looks ok!' ) print('Generating a caption...' ) lowerCAmelCase = '' lowerCAmelCase = tokenizer(_UpperCAmelCase , return_tensors='pt' ).input_ids.to(_UpperCAmelCase ) lowerCAmelCase = original_model.generate({'image': original_pixel_values} ) lowerCAmelCase = hf_model.generate( _UpperCAmelCase , _UpperCAmelCase , do_sample=_UpperCAmelCase , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , ) print('Original generation:' , _UpperCAmelCase ) lowerCAmelCase = input_ids.shape[1] lowerCAmelCase = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=_UpperCAmelCase ) lowerCAmelCase = [text.strip() for text in output_text] print('HF generation:' , _UpperCAmelCase ) if pytorch_dump_folder_path is not None: processor.save_pretrained(_UpperCAmelCase ) hf_model.save_pretrained(_UpperCAmelCase ) if push_to_hub: processor.push_to_hub(F'nielsr/{model_name}' ) hf_model.push_to_hub(F'nielsr/{model_name}' ) if __name__ == "__main__": __UpperCamelCase : Tuple = argparse.ArgumentParser() __UpperCamelCase : Dict = [ '''blip2-opt-2.7b''', '''blip2-opt-6.7b''', '''blip2-opt-2.7b-coco''', '''blip2-opt-6.7b-coco''', '''blip2-flan-t5-xl''', '''blip2-flan-t5-xl-coco''', '''blip2-flan-t5-xxl''', ] parser.add_argument( '''--model_name''', default='''blip2-opt-2.7b''', 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''', ) __UpperCamelCase : List[Any] = parser.parse_args() convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
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"""simple docstring""" # tests directory-specific settings - this file is run automatically # by pytest before any tests are run import sys import warnings from os.path import abspath, dirname, join # allow having multiple repository checkouts and not needing to remember to rerun # 'pip install -e .[dev]' when switching between checkouts and running tests. __UpperCamelCase : Any = abspath(join(dirname(dirname(__file__)), '''src''')) sys.path.insert(1, git_repo_path) # silence FutureWarning warnings in tests since often we can't act on them until # they become normal warnings - i.e. the tests still need to test the current functionality warnings.simplefilter(action='''ignore''', category=FutureWarning) def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] ): from diffusers.utils.testing_utils import pytest_addoption_shared pytest_addoption_shared(_UpperCAmelCase ) def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ): from diffusers.utils.testing_utils import pytest_terminal_summary_main lowerCAmelCase = terminalreporter.config.getoption('--make-reports' ) if make_reports: pytest_terminal_summary_main(_UpperCAmelCase , id=_UpperCAmelCase )
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"""simple docstring""" import functools import operator from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase__ : str = logging.get_logger(__name__) lowerCAmelCase__ : Union[str, Any] = { 'microsoft/unispeech-large-1500h-cv': ( 'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json' ), # See all UniSpeech models at https://huggingface.co/models?filter=unispeech } class snake_case ( __UpperCAmelCase ): """simple docstring""" snake_case__ = "unispeech" def __init__( self : Union[str, Any] ,lowerCamelCase__ : Dict=32 ,lowerCamelCase__ : Union[str, Any]=768 ,lowerCamelCase__ : Optional[Any]=12 ,lowerCamelCase__ : List[str]=12 ,lowerCamelCase__ : Tuple=3_072 ,lowerCamelCase__ : Union[str, Any]="gelu" ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[Any]=0.1 ,lowerCamelCase__ : Union[str, Any]=0.1 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : Optional[Any]=0.0_2 ,lowerCamelCase__ : Union[str, Any]=1e-5 ,lowerCamelCase__ : str="group" ,lowerCamelCase__ : List[str]="gelu" ,lowerCamelCase__ : List[str]=(512, 512, 512, 512, 512, 512, 512) ,lowerCamelCase__ : Union[str, Any]=(5, 2, 2, 2, 2, 2, 2) ,lowerCamelCase__ : Union[str, Any]=(10, 3, 3, 3, 3, 2, 2) ,lowerCamelCase__ : int=False ,lowerCamelCase__ : int=128 ,lowerCamelCase__ : Tuple=16 ,lowerCamelCase__ : Any=False ,lowerCamelCase__ : Union[str, Any]=True ,lowerCamelCase__ : List[Any]=0.0_5 ,lowerCamelCase__ : Tuple=10 ,lowerCamelCase__ : Union[str, Any]=2 ,lowerCamelCase__ : Any=0.0 ,lowerCamelCase__ : Dict=10 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Optional[Any]=320 ,lowerCamelCase__ : Any=2 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[int]=100 ,lowerCamelCase__ : Optional[Any]=256 ,lowerCamelCase__ : int=256 ,lowerCamelCase__ : Union[str, Any]=0.1 ,lowerCamelCase__ : Tuple="mean" ,lowerCamelCase__ : Optional[Any]=False ,lowerCamelCase__ : List[Any]=False ,lowerCamelCase__ : Optional[int]=256 ,lowerCamelCase__ : Optional[Any]=80 ,lowerCamelCase__ : Optional[Any]=0 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : int=2 ,lowerCamelCase__ : Union[str, Any]=0.5 ,**lowerCamelCase__ : Dict ,): super().__init__(**_lowerCAmelCase ,pad_token_id=_lowerCAmelCase ,bos_token_id=_lowerCAmelCase ,eos_token_id=_lowerCAmelCase ) UpperCAmelCase__ = hidden_size UpperCAmelCase__ = feat_extract_norm UpperCAmelCase__ = feat_extract_activation UpperCAmelCase__ = list(_lowerCAmelCase ) UpperCAmelCase__ = list(_lowerCAmelCase ) UpperCAmelCase__ = list(_lowerCAmelCase ) UpperCAmelCase__ = conv_bias UpperCAmelCase__ = num_conv_pos_embeddings UpperCAmelCase__ = num_conv_pos_embedding_groups UpperCAmelCase__ = len(self.conv_dim ) UpperCAmelCase__ = num_hidden_layers UpperCAmelCase__ = intermediate_size UpperCAmelCase__ = hidden_act UpperCAmelCase__ = num_attention_heads UpperCAmelCase__ = hidden_dropout UpperCAmelCase__ = attention_dropout UpperCAmelCase__ = activation_dropout UpperCAmelCase__ = feat_proj_dropout UpperCAmelCase__ = final_dropout UpperCAmelCase__ = layerdrop UpperCAmelCase__ = layer_norm_eps UpperCAmelCase__ = initializer_range UpperCAmelCase__ = num_ctc_classes UpperCAmelCase__ = vocab_size UpperCAmelCase__ = do_stable_layer_norm UpperCAmelCase__ = use_weighted_layer_sum UpperCAmelCase__ = classifier_proj_size if ( (len(self.conv_stride ) != self.num_feat_extract_layers) or (len(self.conv_kernel ) != self.num_feat_extract_layers) or (len(self.conv_dim ) != self.num_feat_extract_layers) ): raise ValueError( 'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` ==' ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) =' f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,''' f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' ) # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779 UpperCAmelCase__ = apply_spec_augment UpperCAmelCase__ = mask_time_prob UpperCAmelCase__ = mask_time_length UpperCAmelCase__ = mask_time_min_masks UpperCAmelCase__ = mask_feature_prob UpperCAmelCase__ = mask_feature_length UpperCAmelCase__ = mask_feature_min_masks # parameters for pretraining with codevector quantized representations UpperCAmelCase__ = num_codevectors_per_group UpperCAmelCase__ = num_codevector_groups UpperCAmelCase__ = contrastive_logits_temperature UpperCAmelCase__ = feat_quantizer_dropout UpperCAmelCase__ = num_negatives UpperCAmelCase__ = codevector_dim UpperCAmelCase__ = proj_codevector_dim UpperCAmelCase__ = diversity_loss_weight # ctc loss UpperCAmelCase__ = ctc_loss_reduction UpperCAmelCase__ = ctc_zero_infinity # pretraining loss UpperCAmelCase__ = replace_prob @property def __lowerCAmelCase ( self : List[str] ): return functools.reduce(operator.mul ,self.conv_stride ,1 )
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'''simple docstring''' import math from numpy import inf from scipy.integrate import quad def __a(SCREAMING_SNAKE_CASE_ : float ): '''simple docstring''' if num <= 0: raise ValueError("math domain error" ) return quad(SCREAMING_SNAKE_CASE_ , 0 , SCREAMING_SNAKE_CASE_ , args=(SCREAMING_SNAKE_CASE_) )[0] def __a(SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ): '''simple docstring''' return math.pow(SCREAMING_SNAKE_CASE_ , z - 1 ) * math.exp(-x ) if __name__ == "__main__": from doctest import testmod testmod()
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'''simple docstring''' import unittest from transformers import is_tf_available from transformers.testing_utils import require_tf if is_tf_available(): import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from transformers import GradientAccumulator, create_optimizer @require_tf class SCREAMING_SNAKE_CASE__ ( unittest.TestCase): def UpperCAmelCase_ ( self , A_ , A_ , A_ )-> Any: '''simple docstring''' self.assertEqual(len(A_ ) , len(A_ ) ) for a, b in zip(A_ , A_ ): self.assertAlmostEqual(A_ , A_ , delta=A_ ) def UpperCAmelCase_ ( self )-> Optional[int]: '''simple docstring''' UpperCamelCase = GradientAccumulator() accumulator([tf.constant([1.0, 2.0] )] ) accumulator([tf.constant([-2.0, 1.0] )] ) accumulator([tf.constant([-1.0, 2.0] )] ) with self.assertRaises(A_ ): accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] ) self.assertEqual(accumulator.step , 3 ) self.assertEqual(len(accumulator.gradients ) , 1 ) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 ) accumulator.reset() self.assertEqual(accumulator.step , 0 ) self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 ) def UpperCAmelCase_ ( self )-> List[str]: '''simple docstring''' UpperCamelCase = None ops.enable_eager_execution_internal() UpperCamelCase = tf.config.list_physical_devices('CPU' ) if len(A_ ) == 1: tf.config.set_logical_device_configuration( physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] ) UpperCamelCase = tf.config.list_logical_devices(device_type='CPU' ) UpperCamelCase = tf.distribute.MirroredStrategy(devices=devices[:2] ) with strategy.scope(): UpperCamelCase = GradientAccumulator() UpperCamelCase = tf.Variable([4.0, 3.0] ) UpperCamelCase , UpperCamelCase = create_optimizer(5e-5 , 10 , 5 ) UpperCamelCase = tf.Variable([0.0, 0.0] , trainable=A_ ) def accumulate_on_replica(A_ ): accumulator([gradient] ) def apply_on_replica(): optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) ) @tf.function def accumulate(A_ , A_ ): with strategy.scope(): UpperCamelCase = strategy.experimental_local_results(A_ ) local_variables[0].assign(A_ ) local_variables[1].assign(A_ ) strategy.run(A_ , args=(gradient_placeholder,) ) @tf.function def apply_grad(): with strategy.scope(): strategy.run(A_ ) def _check_local_values(A_ , A_ ): UpperCamelCase = strategy.experimental_local_results(accumulator._gradients[0] ) self.assertListAlmostEqual(values[0].value() , A_ , tol=1e-2 ) self.assertListAlmostEqual(values[1].value() , A_ , tol=1e-2 ) accumulate([1.0, 2.0] , [-1.0, 1.0] ) accumulate([3.0, -1.0] , [-1.0, -1.0] ) accumulate([-2.0, 2.0] , [3.0, -2.0] ) self.assertEqual(accumulator.step , 3 ) _check_local_values([2.0, 3.0] , [1.0, -2.0] ) apply_grad() self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 ) accumulator.reset() self.assertEqual(accumulator.step , 0 ) _check_local_values([0.0, 0.0] , [0.0, 0.0] )
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'''simple docstring''' import json import os import unittest from typing import Tuple from transformers import WavaVecaPhonemeCTCTokenizer from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput from transformers.testing_utils import require_phonemizer from ...test_tokenization_common import TokenizerTesterMixin @require_phonemizer class SCREAMING_SNAKE_CASE__ ( snake_case_ , unittest.TestCase): lowerCAmelCase_ = WavaVecaPhonemeCTCTokenizer lowerCAmelCase_ = False def UpperCAmelCase_ ( self )-> Any: '''simple docstring''' super().setUp() UpperCamelCase = ( '<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː ' 'ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː ' 'ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 ' 'oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ ' 'pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ ' 'yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ ' 'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ ' 'ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ ' 'ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ ' 'uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ ' 'ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ ' 'ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ ' 'ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4' ).split(' ' ) UpperCamelCase = dict(zip(A_ , range(len(A_ ) ) ) ) UpperCamelCase = {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'} UpperCamelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] ) with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp: fp.write(json.dumps(A_ ) + '\n' ) def UpperCAmelCase_ ( self , A_ , A_=False , A_=20 , A_=5 )-> Tuple[str, list]: '''simple docstring''' UpperCamelCase = [(i, tokenizer.decode([i] , clean_up_tokenization_spaces=A_ )) for i in range(len(A_ ) )] UpperCamelCase = list(filter(lambda A_ : [t[0]] == tokenizer.encode(t[1] , do_phonemize=A_ ) , A_ ) ) if max_length is not None and len(A_ ) > max_length: UpperCamelCase = toks[:max_length] if min_length is not None and len(A_ ) < min_length and len(A_ ) > 0: while len(A_ ) < min_length: UpperCamelCase = toks + toks # toks_str = [t[1] for t in toks] UpperCamelCase = [t[0] for t in toks] # Ensure consistency UpperCamelCase = tokenizer.decode(A_ , clean_up_tokenization_spaces=A_ ) if " " not in output_txt and len(A_ ) > 1: UpperCamelCase = ( tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=A_ ) + ' ' + tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=A_ ) ) if with_prefix_space: UpperCamelCase = ' ' + output_txt UpperCamelCase = tokenizer.encode(A_ , add_special_tokens=A_ ) return output_txt, output_ids def UpperCAmelCase_ ( self , **A_ )-> str: '''simple docstring''' kwargs.update(self.special_tokens_map ) return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname , **A_ ) def UpperCAmelCase_ ( self )-> Any: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) # check adding a single token tokenizer.add_tokens('xxx' ) UpperCamelCase = tokenizer('m xxx ɪ' , do_phonemize=A_ ).input_ids self.assertEqual(A_ , [13, 392, 17] ) # xxx should be last token tokenizer.add_tokens(['aaa', 'bbb', 'ccc'] ) UpperCamelCase = tokenizer('m aaa ɪ ccc' , do_phonemize=A_ ).input_ids self.assertEqual(A_ , [13, 393, 17, 395] ) # aaa and ccc should be after xxx and 2 after aaa UpperCamelCase = tokenizer('maɪ c' , do_phonemize=A_ ).input_ids self.assertEqual(A_ , [3, 200] ) # mai should be <unk> (=3) def UpperCAmelCase_ ( self )-> Tuple: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) self.assertEqual(A_ , 'h ə l oʊ h aʊ ɑːɹ j uː' ) def UpperCAmelCase_ ( self )-> Optional[Any]: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) self.assertEqual(tokenizer(A_ ).input_ids , tokenizer(A_ , do_phonemize=A_ ).input_ids ) def UpperCAmelCase_ ( self )-> Any: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) UpperCamelCase = tokenizer.decode(tokenizer(A_ ).input_ids ) self.assertEqual(A_ , A_ ) def UpperCAmelCase_ ( self )-> Tuple: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) UpperCamelCase = [ [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98], [24, 22, 5, 24, 22, 5, 77], ] UpperCamelCase = tokenizer.decode(sample_ids[0] ) UpperCamelCase = tokenizer.batch_decode(A_ ) self.assertEqual(A_ , batch_tokens[0] ) self.assertEqual(A_ , ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ'] ) def UpperCAmelCase_ ( self )-> Optional[Any]: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained( 'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' ) tokenizer.add_tokens('|' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) self.assertEqual(A_ , 'h ə l oʊ | h aʊ | ɑːɹ | j uː |' ) def UpperCAmelCase_ ( self )-> Any: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained( 'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' ) tokenizer.add_tokens('|' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) self.assertEqual(tokenizer(A_ ).input_ids , tokenizer(A_ , do_phonemize=A_ ).input_ids ) def UpperCAmelCase_ ( self )-> Any: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained( 'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' ) tokenizer.add_tokens('|' ) # fmt: off UpperCamelCase = [ [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98], [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77], ] # fmt: on # decode with word_del_token filter UpperCamelCase = tokenizer.decode(sample_ids[0] ) UpperCamelCase = tokenizer.batch_decode(A_ ) self.assertEqual(A_ , batch_tokens[0] ) self.assertEqual(A_ , ['k s ɾ ɾ l ɭʲ', 'j ð s j ð s oːɹ'] ) # decode with no word_del_token filter UpperCamelCase = tokenizer.decode(sample_ids[0] , filter_word_delimiter_token=A_ ) UpperCamelCase = tokenizer.batch_decode(A_ , filter_word_delimiter_token=A_ ) self.assertEqual(A_ , batch_tokens[0] ) self.assertEqual(A_ , ['k s ɾ | ɾ l | ɭʲ', '| j ð | s j ð s oːɹ'] ) def UpperCAmelCase_ ( self )-> List[str]: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained( 'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' ) tokenizer.add_tokens('|' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) UpperCamelCase = tokenizer.decode(tokenizer(A_ ).input_ids , filter_word_delimiter_token=A_ ) self.assertEqual(A_ , A_ ) def UpperCAmelCase_ ( self )-> int: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained( 'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token='|' ) tokenizer.add_tokens('|' ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer.phonemize(A_ , phonemizer_lang='en-us' ) UpperCamelCase = tokenizer.decode(tokenizer(A_ ).input_ids , filter_word_delimiter_token=A_ ) self.assertEqual(' '.join([p.strip() for p in phonemes.split(' |' )] ).strip() , A_ ) def UpperCAmelCase_ ( self )-> str: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained( 'facebook/wav2vec2-lv-60-espeak-cv-ft' , word_delimiter_token=A_ ) UpperCamelCase = 'Hello how are you' UpperCamelCase = tokenizer(A_ , phonemizer_lang='en-us' ).input_ids UpperCamelCase = tokenizer(A_ , phonemizer_lang='fr-fr' ).input_ids self.assertNotEqual(A_ , A_ ) UpperCamelCase = tokenizer.decode(A_ ) UpperCamelCase = tokenizer.decode(A_ ) self.assertEqual(A_ , 'h ə l oʊ h aʊ ɑːɹ j uː' ) self.assertEqual(A_ , 'ɛ l o h aʊ a ʁ j u' ) def UpperCAmelCase_ ( self )-> Tuple: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) UpperCamelCase = 'Hello how Are you' UpperCamelCase = 'hello how are you' UpperCamelCase = tokenizer(A_ ).input_ids UpperCamelCase = tokenizer(A_ ).input_ids self.assertEqual(A_ , A_ ) def UpperCAmelCase_ ( self )-> Optional[int]: '''simple docstring''' UpperCamelCase = self.tokenizer_class.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft' ) tokenizer.add_tokens(['!', '?'] ) tokenizer.add_special_tokens({'cls_token': '$$$'} ) # fmt: off UpperCamelCase = [ [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394], [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394], ] # fmt: on UpperCamelCase = tokenizer.batch_decode(A_ ) self.assertEqual(A_ , ['k s ɾ ɾ l ɭʲ!?!? $$$', 'j ð s j ð s oːɹ $$$'] ) @staticmethod def UpperCAmelCase_ ( A_ , A_ )-> Dict: '''simple docstring''' UpperCamelCase = [d[key] for d in offsets] return retrieved_list def UpperCAmelCase_ ( self )-> Union[str, Any]: '''simple docstring''' UpperCamelCase = self.get_tokenizer(word_delimiter_token='|' ) tokenizer.add_tokens('|' ) # fmt: off # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ" UpperCamelCase = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98] # fmt: on UpperCamelCase = tokenizer.decode(A_ , output_char_offsets=A_ , filter_word_delimiter_token=A_ ) # check Wav2Vec2CTCTokenizerOutput keys for char self.assertEqual(len(outputs.keys() ) , 2 ) self.assertTrue('text' in outputs ) self.assertTrue('char_offsets' in outputs ) self.assertTrue(isinstance(A_ , A_ ) ) # check that order of chars is correct and identical for both outputs self.assertEqual(' '.join(self.get_from_offsets(outputs['char_offsets'] , 'char' ) ) , outputs.text ) self.assertListEqual( self.get_from_offsets(outputs['char_offsets'] , 'char' ) , ['k', 's', 'ɾ', 'ɾ', '|', 'ɾ', 'l', '|', 'ɭʲ'] ) # check that offsets are actually correct for char # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token, # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98 self.assertListEqual( self.get_from_offsets(outputs['char_offsets'] , 'start_offset' ) , [0, 1, 4, 7, 9, 11, 12, 15, 16] ) self.assertListEqual( self.get_from_offsets(outputs['char_offsets'] , 'end_offset' ) , [1, 4, 6, 9, 10, 12, 15, 16, 17] ) def UpperCAmelCase_ ( self )-> str: '''simple docstring''' UpperCamelCase = self.get_tokenizer(word_delimiter_token='|' ) def check_list_tuples_equal(A_ , A_ ): self.assertTrue(isinstance(A_ , A_ ) ) self.assertTrue(isinstance(outputs_list[0] , A_ ) ) # transform list to ModelOutput UpperCamelCase = WavaVecaPhonemeCTCTokenizerOutput( {k: [d[k] for d in outputs_list] for k in outputs_list[0]} ) self.assertListEqual(outputs_batch['text'] , outputs_batch_a['text'] ) def recursive_check(A_ , A_ ): if isinstance(A_ , A_ ): [recursive_check(A_ , A_ ) for la, la in zip(A_ , A_ )] self.assertEqual(A_ , A_ ) if "char_offsets" in outputs_batch: recursive_check(outputs_batch['char_offsets'] , outputs_batch_a['char_offsets'] ) # fmt: off UpperCamelCase = [ [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34], [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34], ] # fmt: on # We assume that `decode` works as expected. All we will check now is # the output type is correct and the output is identical to `decode` # char UpperCamelCase = tokenizer.batch_decode(A_ , output_char_offsets=A_ ) UpperCamelCase = [tokenizer.decode(A_ , output_char_offsets=A_ ) for ids in sample_ids] check_list_tuples_equal(A_ , A_ ) @unittest.skip('Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes' ) def UpperCAmelCase_ ( self )-> Tuple: '''simple docstring''' pass @unittest.skip('Wav2Vec2PhonemeTokenizer always puts spaces between phonemes' ) def UpperCAmelCase_ ( self )-> Dict: '''simple docstring''' pass @unittest.skip('encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency' ) def UpperCAmelCase_ ( self )-> Tuple: '''simple docstring''' pass @unittest.skip('Wav2Vec2PhonemeModel has no max model length => no testing' ) def UpperCAmelCase_ ( self )-> Tuple: '''simple docstring''' pass def UpperCAmelCase_ ( self )-> int: '''simple docstring''' UpperCamelCase = self.get_tokenizers(do_lower_case=A_ ) for tokenizer in tokenizers: with self.subTest(F'''{tokenizer.__class__.__name__}''' ): UpperCamelCase = tokenizer.vocab_size UpperCamelCase = len(A_ ) self.assertNotEqual(A_ , 0 ) # We usually have added tokens from the start in tests because our vocab fixtures are # smaller than the original vocabs - let's not assert this # self.assertEqual(vocab_size, all_size) UpperCamelCase = ['aaaaa bbbbbb', 'cccccccccdddddddd'] UpperCamelCase = tokenizer.add_tokens(A_ ) UpperCamelCase = tokenizer.vocab_size UpperCamelCase = len(A_ ) self.assertNotEqual(A_ , 0 ) self.assertEqual(A_ , A_ ) self.assertEqual(A_ , len(A_ ) ) self.assertEqual(A_ , all_size + len(A_ ) ) UpperCamelCase = tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l' , add_special_tokens=A_ ) self.assertGreaterEqual(len(A_ ) , 4 ) self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 ) UpperCamelCase = {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'} UpperCamelCase = tokenizer.add_special_tokens(A_ ) UpperCamelCase = tokenizer.vocab_size UpperCamelCase = len(A_ ) self.assertNotEqual(A_ , 0 ) self.assertEqual(A_ , A_ ) self.assertEqual(A_ , len(A_ ) ) self.assertEqual(A_ , all_size_a + len(A_ ) ) UpperCamelCase = tokenizer.encode( '>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l' , add_special_tokens=A_ ) self.assertGreaterEqual(len(A_ ) , 6 ) self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[0] , tokens[1] ) self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 ) self.assertGreater(tokens[-3] , tokens[-4] ) self.assertEqual(tokens[0] , tokenizer.eos_token_id ) self.assertEqual(tokens[-3] , tokenizer.pad_token_id ) @unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.' ) def UpperCAmelCase_ ( self )-> int: '''simple docstring''' pass @unittest.skip('The tokenizer shouldn\'t be used to encode input IDs (except for labels), only to decode.' ) def UpperCAmelCase_ ( self )-> Union[str, Any]: '''simple docstring''' pass def UpperCAmelCase_ ( self )-> Optional[Any]: '''simple docstring''' UpperCamelCase = self.get_tokenizers(fast=A_ , do_lower_case=A_ ) for tokenizer in tokenizers: with self.subTest(F'''{tokenizer.__class__.__name__}''' ): UpperCamelCase = ['ð', 'ɪ', 's', 'ɪ', 'z', 'ɐ', 't', 'ɛ', 'k', 's', 't'] UpperCamelCase = tokenizer.convert_tokens_to_string(A_ ) self.assertIsInstance(output['text'] , A_ )
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0
import gc import math import unittest import torch from diffusers import UNetaDModel from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device from diffusers.utils.testing_utils import enable_full_determinism from .test_modeling_common import ModelTesterMixin, UNetTesterMixin lowercase__ : Dict = logging.get_logger(__name__) enable_full_determinism() class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UNetaDModel lowerCAmelCase_ = '''sample''' @property def snake_case__ ( self : Optional[int] ): """simple docstring""" snake_case_ = 4 snake_case_ = 3 snake_case_ = (32, 32) snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(__lowercase ) snake_case_ = torch.tensor([10] ).to(__lowercase ) return {"sample": noise, "timestep": time_step} @property def snake_case__ ( self : int ): """simple docstring""" return (3, 32, 32) @property def snake_case__ ( self : Optional[int] ): """simple docstring""" return (3, 32, 32) def snake_case__ ( self : str ): """simple docstring""" snake_case_ = { "block_out_channels": (32, 64), "down_block_types": ("DownBlock2D", "AttnDownBlock2D"), "up_block_types": ("AttnUpBlock2D", "UpBlock2D"), "attention_head_dim": 3, "out_channels": 3, "in_channels": 3, "layers_per_block": 2, "sample_size": 32, } snake_case_ = self.dummy_input return init_dict, inputs_dict class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UNetaDModel lowerCAmelCase_ = '''sample''' @property def snake_case__ ( self : Tuple ): """simple docstring""" snake_case_ = 4 snake_case_ = 4 snake_case_ = (32, 32) snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(__lowercase ) snake_case_ = torch.tensor([10] ).to(__lowercase ) return {"sample": noise, "timestep": time_step} @property def snake_case__ ( self : Tuple ): """simple docstring""" return (4, 32, 32) @property def snake_case__ ( self : str ): """simple docstring""" return (4, 32, 32) def snake_case__ ( self : str ): """simple docstring""" snake_case_ = { "sample_size": 32, "in_channels": 4, "out_channels": 4, "layers_per_block": 2, "block_out_channels": (32, 64), "attention_head_dim": 32, "down_block_types": ("DownBlock2D", "DownBlock2D"), "up_block_types": ("UpBlock2D", "UpBlock2D"), } snake_case_ = self.dummy_input return init_dict, inputs_dict def snake_case__ ( self : Union[str, Any] ): """simple docstring""" snake_case_ , snake_case_ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=__lowercase ) self.assertIsNotNone(__lowercase ) self.assertEqual(len(loading_info["missing_keys"] ) , 0 ) model.to(__lowercase ) snake_case_ = model(**self.dummy_input ).sample assert image is not None, "Make sure output is not None" @unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" ) def snake_case__ ( self : Tuple ): """simple docstring""" snake_case_ , snake_case_ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=__lowercase ) model.to(__lowercase ) snake_case_ = model(**self.dummy_input ).sample assert image is not None, "Make sure output is not None" @unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" ) def snake_case__ ( self : List[Any] ): """simple docstring""" snake_case_ , snake_case_ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=__lowercase ) model_accelerate.to(__lowercase ) model_accelerate.eval() snake_case_ = torch.randn( 1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , ) snake_case_ = noise.to(__lowercase ) snake_case_ = torch.tensor([10] * noise.shape[0] ).to(__lowercase ) snake_case_ = model_accelerate(__lowercase , __lowercase )["sample"] # two models don't need to stay in the device at the same time del model_accelerate torch.cuda.empty_cache() gc.collect() snake_case_ , snake_case_ = UNetaDModel.from_pretrained( "fusing/unet-ldm-dummy-update" , output_loading_info=__lowercase , low_cpu_mem_usage=__lowercase ) model_normal_load.to(__lowercase ) model_normal_load.eval() snake_case_ = model_normal_load(__lowercase , __lowercase )["sample"] assert torch_all_close(__lowercase , __lowercase , rtol=1E-3 ) def snake_case__ ( self : Union[str, Any] ): """simple docstring""" snake_case_ = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" ) model.eval() model.to(__lowercase ) snake_case_ = torch.randn( 1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , ) snake_case_ = noise.to(__lowercase ) snake_case_ = torch.tensor([10] * noise.shape[0] ).to(__lowercase ) with torch.no_grad(): snake_case_ = model(__lowercase , __lowercase ).sample snake_case_ = output[0, -1, -3:, -3:].flatten().cpu() # fmt: off snake_case_ = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800] ) # fmt: on self.assertTrue(torch_all_close(__lowercase , __lowercase , rtol=1E-3 ) ) class UpperCAmelCase ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase_ = UNetaDModel lowerCAmelCase_ = '''sample''' @property def snake_case__ ( self : Any , __lowercase : Any=(32, 32) ): """simple docstring""" snake_case_ = 4 snake_case_ = 3 snake_case_ = floats_tensor((batch_size, num_channels) + sizes ).to(__lowercase ) snake_case_ = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=__lowercase ) return {"sample": noise, "timestep": time_step} @property def snake_case__ ( self : Tuple ): """simple docstring""" return (3, 32, 32) @property def snake_case__ ( self : Tuple ): """simple docstring""" return (3, 32, 32) def snake_case__ ( self : int ): """simple docstring""" snake_case_ = { "block_out_channels": [32, 64, 64, 64], "in_channels": 3, "layers_per_block": 1, "out_channels": 3, "time_embedding_type": "fourier", "norm_eps": 1E-6, "mid_block_scale_factor": math.sqrt(2.0 ), "norm_num_groups": None, "down_block_types": [ "SkipDownBlock2D", "AttnSkipDownBlock2D", "SkipDownBlock2D", "SkipDownBlock2D", ], "up_block_types": [ "SkipUpBlock2D", "SkipUpBlock2D", "AttnSkipUpBlock2D", "SkipUpBlock2D", ], } snake_case_ = self.dummy_input return init_dict, inputs_dict @slow def snake_case__ ( self : Dict ): """simple docstring""" snake_case_ , snake_case_ = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" , output_loading_info=__lowercase ) self.assertIsNotNone(__lowercase ) self.assertEqual(len(loading_info["missing_keys"] ) , 0 ) model.to(__lowercase ) snake_case_ = self.dummy_input snake_case_ = floats_tensor((4, 3) + (2_56, 2_56) ).to(__lowercase ) snake_case_ = noise snake_case_ = model(**__lowercase ) assert image is not None, "Make sure output is not None" @slow def snake_case__ ( self : Optional[int] ): """simple docstring""" snake_case_ = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" ) model.to(__lowercase ) snake_case_ = 4 snake_case_ = 3 snake_case_ = (2_56, 2_56) snake_case_ = torch.ones((batch_size, num_channels) + sizes ).to(__lowercase ) snake_case_ = torch.tensor(batch_size * [1E-4] ).to(__lowercase ) with torch.no_grad(): snake_case_ = model(__lowercase , __lowercase ).sample snake_case_ = output[0, -3:, -3:, -1].flatten().cpu() # fmt: off snake_case_ = torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608] ) # fmt: on self.assertTrue(torch_all_close(__lowercase , __lowercase , rtol=1E-2 ) ) def snake_case__ ( self : List[Any] ): """simple docstring""" snake_case_ = UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" ) model.to(__lowercase ) snake_case_ = 4 snake_case_ = 3 snake_case_ = (32, 32) snake_case_ = torch.ones((batch_size, num_channels) + sizes ).to(__lowercase ) snake_case_ = torch.tensor(batch_size * [1E-4] ).to(__lowercase ) with torch.no_grad(): snake_case_ = model(__lowercase , __lowercase ).sample snake_case_ = output[0, -3:, -3:, -1].flatten().cpu() # fmt: off snake_case_ = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256] ) # fmt: on self.assertTrue(torch_all_close(__lowercase , __lowercase , rtol=1E-2 ) ) def snake_case__ ( self : Dict ): """simple docstring""" pass
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import argparse import os.path as osp import re import torch from safetensors.torch import load_file, save_file # =================# # UNet Conversion # # =================# lowercase__ : Optional[int] = [ # (stable-diffusion, HF Diffusers) ("time_embed.0.weight", "time_embedding.linear_1.weight"), ("time_embed.0.bias", "time_embedding.linear_1.bias"), ("time_embed.2.weight", "time_embedding.linear_2.weight"), ("time_embed.2.bias", "time_embedding.linear_2.bias"), ("input_blocks.0.0.weight", "conv_in.weight"), ("input_blocks.0.0.bias", "conv_in.bias"), ("out.0.weight", "conv_norm_out.weight"), ("out.0.bias", "conv_norm_out.bias"), ("out.2.weight", "conv_out.weight"), ("out.2.bias", "conv_out.bias"), ] lowercase__ : List[Any] = [ # (stable-diffusion, HF Diffusers) ("in_layers.0", "norm1"), ("in_layers.2", "conv1"), ("out_layers.0", "norm2"), ("out_layers.3", "conv2"), ("emb_layers.1", "time_emb_proj"), ("skip_connection", "conv_shortcut"), ] lowercase__ : Optional[Any] = [] # hardcoded number of downblocks and resnets/attentions... # would need smarter logic for other networks. for i in range(4): # loop over downblocks/upblocks for j in range(2): # loop over resnets/attentions for downblocks lowercase__ : str = f'''down_blocks.{i}.resnets.{j}.''' lowercase__ : Union[str, Any] = f'''input_blocks.{3*i + j + 1}.0.''' unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) if i < 3: # no attention layers in down_blocks.3 lowercase__ : Tuple = f'''down_blocks.{i}.attentions.{j}.''' lowercase__ : Dict = f'''input_blocks.{3*i + j + 1}.1.''' unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) for j in range(3): # loop over resnets/attentions for upblocks lowercase__ : List[Any] = f'''up_blocks.{i}.resnets.{j}.''' lowercase__ : int = f'''output_blocks.{3*i + j}.0.''' unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) if i > 0: # no attention layers in up_blocks.0 lowercase__ : List[str] = f'''up_blocks.{i}.attentions.{j}.''' lowercase__ : Tuple = f'''output_blocks.{3*i + j}.1.''' unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) if i < 3: # no downsample in down_blocks.3 lowercase__ : List[str] = f'''down_blocks.{i}.downsamplers.0.conv.''' lowercase__ : Any = f'''input_blocks.{3*(i+1)}.0.op.''' unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) # no upsample in up_blocks.3 lowercase__ : Optional[int] = f'''up_blocks.{i}.upsamplers.0.''' lowercase__ : int = f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.''' unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) lowercase__ : Union[str, Any] = "mid_block.attentions.0." lowercase__ : List[Any] = "middle_block.1." unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) for j in range(2): lowercase__ : Tuple = f'''mid_block.resnets.{j}.''' lowercase__ : List[str] = f'''middle_block.{2*j}.''' unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) def lowerCamelCase__ ( _A ): '''simple docstring''' snake_case_ = {k: k for k in unet_state_dict.keys()} for sd_name, hf_name in unet_conversion_map: snake_case_ = sd_name for k, v in mapping.items(): if "resnets" in k: for sd_part, hf_part in unet_conversion_map_resnet: snake_case_ = v.replace(_A , _A ) snake_case_ = v for k, v in mapping.items(): for sd_part, hf_part in unet_conversion_map_layer: snake_case_ = v.replace(_A , _A ) snake_case_ = v snake_case_ = {v: unet_state_dict[k] for k, v in mapping.items()} return new_state_dict # ================# # VAE Conversion # # ================# lowercase__ : Dict = [ # (stable-diffusion, HF Diffusers) ("nin_shortcut", "conv_shortcut"), ("norm_out", "conv_norm_out"), ("mid.attn_1.", "mid_block.attentions.0."), ] for i in range(4): # down_blocks have two resnets for j in range(2): lowercase__ : Any = f'''encoder.down_blocks.{i}.resnets.{j}.''' lowercase__ : List[Any] = f'''encoder.down.{i}.block.{j}.''' vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) if i < 3: lowercase__ : Optional[int] = f'''down_blocks.{i}.downsamplers.0.''' lowercase__ : Tuple = f'''down.{i}.downsample.''' vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) lowercase__ : List[str] = f'''up_blocks.{i}.upsamplers.0.''' lowercase__ : Optional[int] = f'''up.{3-i}.upsample.''' vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) # up_blocks have three resnets # also, up blocks in hf are numbered in reverse from sd for j in range(3): lowercase__ : int = f'''decoder.up_blocks.{i}.resnets.{j}.''' lowercase__ : Union[str, Any] = f'''decoder.up.{3-i}.block.{j}.''' vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) # this part accounts for mid blocks in both the encoder and the decoder for i in range(2): lowercase__ : Dict = f'''mid_block.resnets.{i}.''' lowercase__ : int = f'''mid.block_{i+1}.''' vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) lowercase__ : Optional[Any] = [ # (stable-diffusion, HF Diffusers) ("norm.", "group_norm."), ("q.", "query."), ("k.", "key."), ("v.", "value."), ("proj_out.", "proj_attn."), ] def lowerCamelCase__ ( _A ): '''simple docstring''' return w.reshape(*w.shape , 1 , 1 ) def lowerCamelCase__ ( _A ): '''simple docstring''' snake_case_ = {k: k for k in vae_state_dict.keys()} for k, v in mapping.items(): for sd_part, hf_part in vae_conversion_map: snake_case_ = v.replace(_A , _A ) snake_case_ = v for k, v in mapping.items(): if "attentions" in k: for sd_part, hf_part in vae_conversion_map_attn: snake_case_ = v.replace(_A , _A ) snake_case_ = v snake_case_ = {v: vae_state_dict[k] for k, v in mapping.items()} snake_case_ = ["q", "k", "v", "proj_out"] for k, v in new_state_dict.items(): for weight_name in weights_to_convert: if f"mid.attn_1.{weight_name}.weight" in k: print(f"Reshaping {k} for SD format" ) snake_case_ = reshape_weight_for_sd(_A ) return new_state_dict # =========================# # Text Encoder Conversion # # =========================# lowercase__ : int = [ # (stable-diffusion, HF Diffusers) ("resblocks.", "text_model.encoder.layers."), ("ln_1", "layer_norm1"), ("ln_2", "layer_norm2"), (".c_fc.", ".fc1."), (".c_proj.", ".fc2."), (".attn", ".self_attn"), ("ln_final.", "transformer.text_model.final_layer_norm."), ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), ] lowercase__ : Dict = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} lowercase__ : Tuple = re.compile("|".join(protected.keys())) # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp lowercase__ : Dict = {"q": 0, "k": 1, "v": 2} def lowerCamelCase__ ( _A ): '''simple docstring''' snake_case_ = {} snake_case_ = {} snake_case_ = {} for k, v in text_enc_dict.items(): if ( k.endswith(".self_attn.q_proj.weight" ) or k.endswith(".self_attn.k_proj.weight" ) or k.endswith(".self_attn.v_proj.weight" ) ): snake_case_ = k[: -len(".q_proj.weight" )] snake_case_ = k[-len("q_proj.weight" )] if k_pre not in capture_qkv_weight: snake_case_ = [None, None, None] snake_case_ = v continue if ( k.endswith(".self_attn.q_proj.bias" ) or k.endswith(".self_attn.k_proj.bias" ) or k.endswith(".self_attn.v_proj.bias" ) ): snake_case_ = k[: -len(".q_proj.bias" )] snake_case_ = k[-len("q_proj.bias" )] if k_pre not in capture_qkv_bias: snake_case_ = [None, None, None] snake_case_ = v continue snake_case_ = textenc_pattern.sub(lambda _A : protected[re.escape(m.group(0 ) )] , _A ) snake_case_ = v for k_pre, tensors in capture_qkv_weight.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" ) snake_case_ = textenc_pattern.sub(lambda _A : protected[re.escape(m.group(0 ) )] , _A ) snake_case_ = torch.cat(_A ) for k_pre, tensors in capture_qkv_bias.items(): if None in tensors: raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" ) snake_case_ = textenc_pattern.sub(lambda _A : protected[re.escape(m.group(0 ) )] , _A ) snake_case_ = torch.cat(_A ) return new_state_dict def lowerCamelCase__ ( _A ): '''simple docstring''' return text_enc_dict if __name__ == "__main__": lowercase__ : int = argparse.ArgumentParser() parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") parser.add_argument("--half", action="store_true", help="Save weights in half precision.") parser.add_argument( "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." ) lowercase__ : Dict = parser.parse_args() assert args.model_path is not None, "Must provide a model path!" assert args.checkpoint_path is not None, "Must provide a checkpoint path!" # Path for safetensors lowercase__ : Tuple = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") lowercase__ : int = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") lowercase__ : Any = osp.join(args.model_path, "text_encoder", "model.safetensors") # Load models from safetensors if it exists, if it doesn't pytorch if osp.exists(unet_path): lowercase__ : str = load_file(unet_path, device="cpu") else: lowercase__ : Optional[Any] = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") lowercase__ : Any = torch.load(unet_path, map_location="cpu") if osp.exists(vae_path): lowercase__ : Tuple = load_file(vae_path, device="cpu") else: lowercase__ : Any = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") lowercase__ : Dict = torch.load(vae_path, map_location="cpu") if osp.exists(text_enc_path): lowercase__ : Union[str, Any] = load_file(text_enc_path, device="cpu") else: lowercase__ : Union[str, Any] = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") lowercase__ : Optional[int] = torch.load(text_enc_path, map_location="cpu") # Convert the UNet model lowercase__ : Dict = convert_unet_state_dict(unet_state_dict) lowercase__ : Any = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} # Convert the VAE model lowercase__ : Dict = convert_vae_state_dict(vae_state_dict) lowercase__ : Union[str, Any] = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper lowercase__ : Optional[Any] = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict if is_vaa_model: # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm lowercase__ : Any = {"transformer." + k: v for k, v in text_enc_dict.items()} lowercase__ : List[Any] = convert_text_enc_state_dict_vaa(text_enc_dict) lowercase__ : List[str] = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} else: lowercase__ : Tuple = convert_text_enc_state_dict(text_enc_dict) lowercase__ : Any = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} # Put together new checkpoint lowercase__ : Tuple = {**unet_state_dict, **vae_state_dict, **text_enc_dict} if args.half: lowercase__ : Any = {k: v.half() for k, v in state_dict.items()} if args.use_safetensors: save_file(state_dict, args.checkpoint_path) else: lowercase__ : Union[str, Any] = {"state_dict": state_dict} torch.save(state_dict, args.checkpoint_path)
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import argparse import json from pathlib import Path import requests import timm import torch from huggingface_hub import hf_hub_download from PIL import Image from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification def A_ ( _lowerCAmelCase ) -> Tuple: UpperCamelCase : Any = SwinvaConfig() UpperCamelCase : List[Any] = swinva_name.split("_" ) UpperCamelCase : Tuple = name_split[1] if "to" in name_split[3]: UpperCamelCase : Tuple = int(name_split[3][-3:] ) else: UpperCamelCase : Union[str, Any] = int(name_split[3] ) if "to" in name_split[2]: UpperCamelCase : List[Any] = int(name_split[2][-2:] ) else: UpperCamelCase : Optional[Any] = int(name_split[2][6:] ) if model_size == "tiny": UpperCamelCase : Tuple = 96 UpperCamelCase : List[Any] = (2, 2, 6, 2) UpperCamelCase : str = (3, 6, 12, 24) elif model_size == "small": UpperCamelCase : List[str] = 96 UpperCamelCase : List[Any] = (2, 2, 18, 2) UpperCamelCase : Any = (3, 6, 12, 24) elif model_size == "base": UpperCamelCase : Optional[Any] = 128 UpperCamelCase : str = (2, 2, 18, 2) UpperCamelCase : str = (4, 8, 16, 32) else: UpperCamelCase : Any = 192 UpperCamelCase : Union[str, Any] = (2, 2, 18, 2) UpperCamelCase : int = (6, 12, 24, 48) if "to" in swinva_name: UpperCamelCase : List[str] = (12, 12, 12, 6) if ("22k" in swinva_name) and ("to" not in swinva_name): UpperCamelCase : Optional[Any] = 2_1841 UpperCamelCase : List[str] = "huggingface/label-files" UpperCamelCase : List[Any] = "imagenet-22k-id2label.json" UpperCamelCase : List[str] = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) ) UpperCamelCase : Optional[int] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCamelCase : Union[str, Any] = idalabel UpperCamelCase : Optional[Any] = {v: k for k, v in idalabel.items()} else: UpperCamelCase : List[Any] = 1000 UpperCamelCase : Optional[int] = "huggingface/label-files" UpperCamelCase : Dict = "imagenet-1k-id2label.json" UpperCamelCase : Any = json.load(open(hf_hub_download(_lowerCAmelCase , _lowerCAmelCase , repo_type="dataset" ) , "r" ) ) UpperCamelCase : Optional[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()} UpperCamelCase : Dict = idalabel UpperCamelCase : Optional[Any] = {v: k for k, v in idalabel.items()} UpperCamelCase : Tuple = img_size UpperCamelCase : str = num_classes UpperCamelCase : int = embed_dim UpperCamelCase : Any = depths UpperCamelCase : Union[str, Any] = num_heads UpperCamelCase : Dict = window_size return config def A_ ( _lowerCAmelCase ) -> Optional[int]: if "patch_embed.proj" in name: UpperCamelCase : Optional[Any] = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" ) if "patch_embed.norm" in name: UpperCamelCase : Optional[int] = name.replace("patch_embed.norm" , "embeddings.norm" ) if "layers" in name: UpperCamelCase : Union[str, Any] = "encoder." + name if "attn.proj" in name: UpperCamelCase : Dict = name.replace("attn.proj" , "attention.output.dense" ) if "attn" in name: UpperCamelCase : int = name.replace("attn" , "attention.self" ) if "norm1" in name: UpperCamelCase : List[Any] = name.replace("norm1" , "layernorm_before" ) if "norm2" in name: UpperCamelCase : Tuple = name.replace("norm2" , "layernorm_after" ) if "mlp.fc1" in name: UpperCamelCase : List[Any] = name.replace("mlp.fc1" , "intermediate.dense" ) if "mlp.fc2" in name: UpperCamelCase : Optional[int] = name.replace("mlp.fc2" , "output.dense" ) if "q_bias" in name: UpperCamelCase : Union[str, Any] = name.replace("q_bias" , "query.bias" ) if "k_bias" in name: UpperCamelCase : List[str] = name.replace("k_bias" , "key.bias" ) if "v_bias" in name: UpperCamelCase : Tuple = name.replace("v_bias" , "value.bias" ) if "cpb_mlp" in name: UpperCamelCase : Union[str, Any] = name.replace("cpb_mlp" , "continuous_position_bias_mlp" ) if name == "norm.weight": UpperCamelCase : str = "layernorm.weight" if name == "norm.bias": UpperCamelCase : Any = "layernorm.bias" if "head" in name: UpperCamelCase : int = name.replace("head" , "classifier" ) else: UpperCamelCase : Any = "swinv2." + name return name def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]: for key in orig_state_dict.copy().keys(): UpperCamelCase : List[str] = orig_state_dict.pop(_lowerCAmelCase ) if "mask" in key: continue elif "qkv" in key: UpperCamelCase : Tuple = key.split("." ) UpperCamelCase : Union[str, Any] = int(key_split[1] ) UpperCamelCase : Union[str, Any] = int(key_split[3] ) UpperCamelCase : List[Any] = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size if "weight" in key: UpperCamelCase : List[str] = val[:dim, :] UpperCamelCase : int = val[dim : dim * 2, :] UpperCamelCase : Union[str, Any] = val[-dim:, :] else: UpperCamelCase : List[Any] = val[:dim] UpperCamelCase : List[str] = val[ dim : dim * 2 ] UpperCamelCase : Union[str, Any] = val[-dim:] else: UpperCamelCase : List[Any] = val return orig_state_dict def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]: UpperCamelCase : Optional[Any] = timm.create_model(_lowerCAmelCase , pretrained=_lowerCAmelCase ) timm_model.eval() UpperCamelCase : Optional[int] = get_swinva_config(_lowerCAmelCase ) UpperCamelCase : Union[str, Any] = SwinvaForImageClassification(_lowerCAmelCase ) model.eval() UpperCamelCase : Optional[int] = convert_state_dict(timm_model.state_dict() , _lowerCAmelCase ) model.load_state_dict(_lowerCAmelCase ) UpperCamelCase : Optional[Any] = "http://images.cocodataset.org/val2017/000000039769.jpg" UpperCamelCase : str = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) ) UpperCamelCase : Union[str, Any] = Image.open(requests.get(_lowerCAmelCase , stream=_lowerCAmelCase ).raw ) UpperCamelCase : str = image_processor(images=_lowerCAmelCase , return_tensors="pt" ) UpperCamelCase : str = timm_model(inputs["pixel_values"] ) UpperCamelCase : List[str] = model(**_lowerCAmelCase ).logits assert torch.allclose(_lowerCAmelCase , _lowerCAmelCase , atol=1e-3 ) print(F"""Saving model {swinva_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 ) model.push_to_hub( repo_path_or_name=Path(_lowerCAmelCase , _lowerCAmelCase ) , organization="nandwalritik" , commit_message="Add model" , ) if __name__ == "__main__": __lowerCamelCase : List[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( """--swinv2_name""", default="""swinv2_tiny_patch4_window8_256""", type=str, help="""Name of the Swinv2 timm model you'd like to convert.""", ) parser.add_argument( """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory.""" ) __lowerCamelCase : List[Any] = parser.parse_args() convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) __lowerCamelCase : str = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]} try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __lowerCamelCase : Optional[Any] = ["""EncoderDecoderModel"""] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __lowerCamelCase : Optional[Any] = ["""TFEncoderDecoderModel"""] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __lowerCamelCase : Union[str, Any] = ["""FlaxEncoderDecoderModel"""] if TYPE_CHECKING: from .configuration_encoder_decoder import EncoderDecoderConfig try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_encoder_decoder import EncoderDecoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_encoder_decoder import TFEncoderDecoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel else: import sys __lowerCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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import json import re from typing import TYPE_CHECKING, List, Optional, Tuple, Union import numpy as np from ...utils import is_tf_available, is_torch_available, logging if TYPE_CHECKING: if is_torch_available(): import torch if is_tf_available(): import tensorflow as tf from tokenizers import pre_tokenizers from ...tokenization_utils_base import BatchEncoding from ...tokenization_utils_fast import PreTrainedTokenizerFast from .tokenization_codegen import CodeGenTokenizer UpperCAmelCase_ = logging.get_logger(__name__) UpperCAmelCase_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'} UpperCAmelCase_ = { 'vocab_file': { 'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json', }, 'merges_file': { 'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt', }, 'tokenizer_file': { 'Salesforce/codegen-350M-mono': ( 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json' ), }, } UpperCAmelCase_ = { 'Salesforce/codegen-350M-mono': 2048, } class lowercase__ ( __lowerCamelCase ): '''simple docstring''' a : List[str] = VOCAB_FILES_NAMES a : Dict = PRETRAINED_VOCAB_FILES_MAP a : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES a : str = ["input_ids", "attention_mask"] a : Optional[Any] = CodeGenTokenizer def __init__( self, __magic_name__=None, __magic_name__=None, __magic_name__=None, __magic_name__="<|endoftext|>", __magic_name__="<|endoftext|>", __magic_name__="<|endoftext|>", __magic_name__=False, **__magic_name__, ) -> List[str]: """simple docstring""" super().__init__( __magic_name__, __magic_name__, tokenizer_file=__magic_name__, unk_token=__magic_name__, bos_token=__magic_name__, eos_token=__magic_name__, add_prefix_space=__magic_name__, **__magic_name__, ) if kwargs.pop('''add_bos_token''', __magic_name__ ): UpperCamelCase__ : Dict = kwargs.pop('''name_or_path''', '''''' ) raise ValueError( '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.''' '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n''' f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n" f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n" '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.''' ''' so that the fast tokenizer works correctly.''' ) UpperCamelCase__ : Optional[int] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() ) if pre_tok_state.get('''add_prefix_space''', __magic_name__ ) != add_prefix_space: UpperCamelCase__ : List[str] = getattr(__magic_name__, pre_tok_state.pop('''type''' ) ) UpperCamelCase__ : Union[str, Any] = add_prefix_space UpperCamelCase__ : int = pre_tok_class(**__magic_name__ ) UpperCamelCase__ : Dict = add_prefix_space def UpperCamelCase__ ( self, *__magic_name__, **__magic_name__ ) -> BatchEncoding: """simple docstring""" UpperCamelCase__ : Union[str, Any] = kwargs.get('''is_split_into_words''', __magic_name__ ) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._batch_encode_plus(*__magic_name__, **__magic_name__ ) def UpperCamelCase__ ( self, *__magic_name__, **__magic_name__ ) -> BatchEncoding: """simple docstring""" UpperCamelCase__ : Union[str, Any] = kwargs.get('''is_split_into_words''', __magic_name__ ) assert self.add_prefix_space or not is_split_into_words, ( f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True " "to use it with pretokenized inputs." ) return super()._encode_plus(*__magic_name__, **__magic_name__ ) def UpperCamelCase__ ( self, __magic_name__, __magic_name__ = None ) -> Tuple[str]: """simple docstring""" UpperCamelCase__ : Optional[int] = self._tokenizer.model.save(__magic_name__, name=__magic_name__ ) return tuple(__magic_name__ ) def UpperCamelCase__ ( self, __magic_name__, __magic_name__ = False, __magic_name__ = None, __magic_name__ = None, **__magic_name__, ) -> str: """simple docstring""" UpperCamelCase__ : Optional[Any] = super().decode( token_ids=__magic_name__, skip_special_tokens=__magic_name__, clean_up_tokenization_spaces=__magic_name__, **__magic_name__, ) if truncate_before_pattern is not None and len(__magic_name__ ) > 0: UpperCamelCase__ : Any = self.truncate(__magic_name__, __magic_name__ ) return decoded_text def UpperCamelCase__ ( self, __magic_name__, __magic_name__ ) -> Any: """simple docstring""" def find_re(__magic_name__, __magic_name__, __magic_name__ ): UpperCamelCase__ : Optional[Any] = pattern.search(__magic_name__, __magic_name__ ) return m.start() if m else -1 UpperCamelCase__ : Any = [re.compile(__magic_name__, re.MULTILINE ) for pattern in truncate_before_pattern] UpperCamelCase__ : Dict = list(re.finditer('''^print''', __magic_name__, re.MULTILINE ) ) if len(__magic_name__ ) > 1: UpperCamelCase__ : List[str] = completion[: prints[1].start()] UpperCamelCase__ : Optional[Any] = list(re.finditer('''^def''', __magic_name__, re.MULTILINE ) ) if len(__magic_name__ ) > 1: UpperCamelCase__ : Tuple = completion[: defs[1].start()] UpperCamelCase__ : List[Any] = 0 UpperCamelCase__ : int = [ pos for pos in [find_re(__magic_name__, __magic_name__, __magic_name__ ) for terminal in terminals] if pos != -1 ] if len(__magic_name__ ) > 0: return completion[: min(__magic_name__ )] else: return completion
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def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> int: if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or number < 0: raise ValueError('''Input must be a non-negative integer''' ) UpperCamelCase__ : Optional[Any] = 0 while number: # This way we arrive at next set bit (next 1) instead of looping # through each bit and checking for 1s hence the # loop won't run 32 times it will only run the number of `1` times number &= number - 1 count += 1 return count if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' import os import string import sys a : List[str] = 1 << 8 a : Tuple = { 'tab': ord('\t'), 'newline': ord('\r'), 'esc': 27, 'up': 65 + ARROW_KEY_FLAG, 'down': 66 + ARROW_KEY_FLAG, 'right': 67 + ARROW_KEY_FLAG, 'left': 68 + ARROW_KEY_FLAG, 'mod_int': 91, 'undefined': sys.maxsize, 'interrupt': 3, 'insert': 50, 'delete': 51, 'pg_up': 53, 'pg_down': 54, } a : Any = KEYMAP['up'] a : Tuple = KEYMAP['left'] if sys.platform == "win32": a : Union[str, Any] = [] a : Any = { b'\xe0H': KEYMAP['up'] - ARROW_KEY_FLAG, b'\x00H': KEYMAP['up'] - ARROW_KEY_FLAG, b'\xe0P': KEYMAP['down'] - ARROW_KEY_FLAG, b'\x00P': KEYMAP['down'] - ARROW_KEY_FLAG, b'\xe0M': KEYMAP['right'] - ARROW_KEY_FLAG, b'\x00M': KEYMAP['right'] - ARROW_KEY_FLAG, b'\xe0K': KEYMAP['left'] - ARROW_KEY_FLAG, b'\x00K': KEYMAP['left'] - ARROW_KEY_FLAG, } for i in range(10): a : Any = ord(str(i)) def __magic_name__ ( ) -> List[str]: '''simple docstring''' if os.name == "nt": import msvcrt snake_case_ = '''mbcs''' # Flush the keyboard buffer while msvcrt.kbhit(): msvcrt.getch() if len(__UpperCAmelCase ) == 0: # Read the keystroke snake_case_ = msvcrt.getch() # If it is a prefix char, get second part if ch in (b"\x00", b"\xe0"): snake_case_ = ch + msvcrt.getch() # Translate actual Win chars to bullet char types try: snake_case_ = chr(WIN_KEYMAP[cha] ) WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) ) WIN_CH_BUFFER.append(__UpperCAmelCase ) if ord(__UpperCAmelCase ) in ( KEYMAP["insert"] - 1 << 9, KEYMAP["delete"] - 1 << 9, KEYMAP["pg_up"] - 1 << 9, KEYMAP["pg_down"] - 1 << 9, ): WIN_CH_BUFFER.append(chr(126 ) ) snake_case_ = chr(KEYMAP['''esc'''] ) except KeyError: snake_case_ = cha[1] else: snake_case_ = ch.decode(__UpperCAmelCase ) else: snake_case_ = WIN_CH_BUFFER.pop(0 ) elif os.name == "posix": import termios import tty snake_case_ = sys.stdin.fileno() snake_case_ = termios.tcgetattr(__UpperCAmelCase ) try: tty.setraw(__UpperCAmelCase ) snake_case_ = sys.stdin.read(1 ) finally: termios.tcsetattr(__UpperCAmelCase, termios.TCSADRAIN, __UpperCAmelCase ) return ch def __magic_name__ ( ) -> List[Any]: '''simple docstring''' snake_case_ = get_raw_chars() if ord(__UpperCAmelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]: return char elif ord(__UpperCAmelCase ) == KEYMAP["esc"]: snake_case_ = get_raw_chars() if ord(__UpperCAmelCase ) == KEYMAP["mod_int"]: snake_case_ = get_raw_chars() if ord(__UpperCAmelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(__UpperCAmelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG: return chr(ord(__UpperCAmelCase ) + ARROW_KEY_FLAG ) else: return KEYMAP["undefined"] else: return get_raw_chars() else: if char in string.printable: return char else: return KEYMAP["undefined"]
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'''simple docstring''' import os import torch from ..logging import get_logger from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME from .versions import is_torch_version if is_torch_version('>=', FSDP_PYTORCH_VERSION): import torch.distributed.checkpoint as dist_cp from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType a : Union[str, Any] = get_logger(__name__) def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> Tuple: '''simple docstring''' os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase ) with FSDP.state_dict_type( __UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): snake_case_ = model.state_dict() if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: snake_case_ = F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin" snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase ) if accelerator.process_index == 0: logger.info(F"Saving model to {output_model_file}" ) torch.save(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Model saved to {output_model_file}" ) elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT: snake_case_ = ( F"{MODEL_NAME}_rank{accelerator.process_index}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin" ) snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Saving model to {output_model_file}" ) torch.save(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Model saved to {output_model_file}" ) elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT: snake_case_ = os.path.join(__UpperCAmelCase, F"{MODEL_NAME}_{model_index}" ) os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase ) logger.info(F"Saving model to {ckpt_dir}" ) snake_case_ = {'''model''': state_dict} dist_cp.save_state_dict( state_dict=__UpperCAmelCase, storage_writer=dist_cp.FileSystemWriter(__UpperCAmelCase ), planner=DefaultSavePlanner(), ) logger.info(F"Model saved to {ckpt_dir}" ) def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> str: '''simple docstring''' accelerator.wait_for_everyone() with FSDP.state_dict_type( __UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: if type(__UpperCAmelCase ) != FSDP and accelerator.process_index != 0: if not fsdp_plugin.sync_module_states: raise ValueError( '''Set the `sync_module_states` flag to `True` so that model states are synced across processes when ''' '''initializing FSDP object''' ) return snake_case_ = F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin" snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Loading model from {input_model_file}" ) snake_case_ = torch.load(__UpperCAmelCase ) logger.info(F"Model loaded from {input_model_file}" ) elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT: snake_case_ = ( F"{MODEL_NAME}_rank{accelerator.process_index}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin" ) snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Loading model from {input_model_file}" ) snake_case_ = torch.load(__UpperCAmelCase ) logger.info(F"Model loaded from {input_model_file}" ) elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT: snake_case_ = ( os.path.join(__UpperCAmelCase, F"{MODEL_NAME}_{model_index}" ) if F"{MODEL_NAME}" not in input_dir else input_dir ) logger.info(F"Loading model from {ckpt_dir}" ) snake_case_ = {'''model''': model.state_dict()} dist_cp.load_state_dict( state_dict=__UpperCAmelCase, storage_reader=dist_cp.FileSystemReader(__UpperCAmelCase ), planner=DefaultLoadPlanner(), ) snake_case_ = state_dict['''model'''] logger.info(F"Model loaded from {ckpt_dir}" ) model.load_state_dict(__UpperCAmelCase ) def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> Dict: '''simple docstring''' os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase ) with FSDP.state_dict_type( __UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): snake_case_ = FSDP.optim_state_dict(__UpperCAmelCase, __UpperCAmelCase ) if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: if accelerator.process_index == 0: snake_case_ = ( F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin" ) snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Saving Optimizer state to {output_optimizer_file}" ) torch.save(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Optimizer state saved in {output_optimizer_file}" ) else: snake_case_ = os.path.join(__UpperCAmelCase, F"{OPTIMIZER_NAME}_{optimizer_index}" ) os.makedirs(__UpperCAmelCase, exist_ok=__UpperCAmelCase ) logger.info(F"Saving Optimizer state to {ckpt_dir}" ) dist_cp.save_state_dict( state_dict={'''optimizer''': optim_state}, storage_writer=dist_cp.FileSystemWriter(__UpperCAmelCase ), planner=DefaultSavePlanner(), ) logger.info(F"Optimizer state saved in {ckpt_dir}" ) def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=0 ) -> Union[str, Any]: '''simple docstring''' accelerator.wait_for_everyone() with FSDP.state_dict_type( __UpperCAmelCase, fsdp_plugin.state_dict_type, fsdp_plugin.state_dict_config, fsdp_plugin.optim_state_dict_config ): if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT: snake_case_ = None # below check should work but currently it isn't working (mostly opytorch issue), # in the meantime disabling it at the cost of excess memory usage # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only: snake_case_ = ( F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin" ) snake_case_ = os.path.join(__UpperCAmelCase, __UpperCAmelCase ) logger.info(F"Loading Optimizer state from {input_optimizer_file}" ) snake_case_ = torch.load(__UpperCAmelCase ) logger.info(F"Optimizer state loaded from {input_optimizer_file}" ) else: snake_case_ = ( os.path.join(__UpperCAmelCase, F"{OPTIMIZER_NAME}_{optimizer_index}" ) if F"{OPTIMIZER_NAME}" not in input_dir else input_dir ) logger.info(F"Loading Optimizer from {ckpt_dir}" ) snake_case_ = load_sharded_optimizer_state_dict( model_state_dict=model.state_dict(), optimizer_key='''optimizer''', storage_reader=dist_cp.FileSystemReader(__UpperCAmelCase ), ) snake_case_ = optim_state['''optimizer'''] logger.info(F"Optimizer loaded from {ckpt_dir}" ) snake_case_ = FSDP.optim_state_dict_to_load(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) optimizer.load_state_dict(__UpperCAmelCase )
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0
"""simple docstring""" def a__ ( SCREAMING_SNAKE_CASE : int ): '''simple docstring''' assert ( isinstance(__UpperCamelCase , __UpperCamelCase ) and number_of_steps > 0 ), f"""number_of_steps needs to be positive integer, your input {number_of_steps}""" if number_of_steps == 1: return 1 lowerCAmelCase , lowerCAmelCase : Optional[Any] = 1, 1 for _ in range(number_of_steps - 1 ): lowerCAmelCase , lowerCAmelCase : Union[str, Any] = current + previous, current return current if __name__ == "__main__": import doctest doctest.testmod()
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from typing import List, Optional, Union import numpy as np import PIL.Image from ...image_processing_utils import BaseImageProcessor, BatchFeature from ...image_transforms import rescale, resize, to_channel_dimension_format from ...image_utils import ( ChannelDimension, PILImageResampling, get_image_size, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, logging __lowerCamelCase : Union[str, Any] = logging.get_logger(__name__) class __snake_case ( lowerCamelCase_ ): lowerCAmelCase_ = ["pixel_values"] def __init__( self : Any , _lowercase : bool = True , _lowercase : int = 32 , _lowercase : Any=PILImageResampling.BILINEAR , _lowercase : bool = True , **_lowercase : Union[str, Any] , ): """simple docstring""" SCREAMING_SNAKE_CASE__ = do_resize SCREAMING_SNAKE_CASE__ = do_rescale SCREAMING_SNAKE_CASE__ = size_divisor SCREAMING_SNAKE_CASE__ = resample super().__init__(**_lowercase ) def __a ( self : int , _lowercase : np.ndarray , _lowercase : int , _lowercase : int , _lowercase : Optional[ChannelDimension] = None , **_lowercase : int ): """simple docstring""" SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_image_size(_lowercase ) # Rounds the height and width down to the closest multiple of size_divisor SCREAMING_SNAKE_CASE__ = height // size_divisor * size_divisor SCREAMING_SNAKE_CASE__ = width // size_divisor * size_divisor SCREAMING_SNAKE_CASE__ = resize(_lowercase , (new_h, new_w) , resample=_lowercase , data_format=_lowercase , **_lowercase ) return image def __a ( self : int , _lowercase : np.ndarray , _lowercase : float , _lowercase : Optional[ChannelDimension] = None , **_lowercase : str ): """simple docstring""" return rescale(image=_lowercase , scale=_lowercase , data_format=_lowercase , **_lowercase ) def __a ( self : Tuple , _lowercase : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]] , _lowercase : Optional[bool] = None , _lowercase : Optional[int] = None , _lowercase : int=None , _lowercase : Optional[bool] = None , _lowercase : Optional[Union[TensorType, str]] = None , _lowercase : ChannelDimension = ChannelDimension.FIRST , **_lowercase : Optional[int] , ): """simple docstring""" SCREAMING_SNAKE_CASE__ = do_resize if do_resize is not None else self.do_resize SCREAMING_SNAKE_CASE__ = do_rescale if do_rescale is not None else self.do_rescale SCREAMING_SNAKE_CASE__ = size_divisor if size_divisor is not None else self.size_divisor SCREAMING_SNAKE_CASE__ = resample if resample is not None else self.resample if do_resize and size_divisor is None: raise ValueError("""size_divisor is required for resizing""" ) SCREAMING_SNAKE_CASE__ = make_list_of_images(_lowercase ) if not valid_images(_lowercase ): raise ValueError("""Invalid image(s)""" ) # All transformations expect numpy arrays. SCREAMING_SNAKE_CASE__ = [to_numpy_array(_lowercase ) for img in images] if do_resize: SCREAMING_SNAKE_CASE__ = [self.resize(_lowercase , size_divisor=_lowercase , resample=_lowercase ) for image in images] if do_rescale: SCREAMING_SNAKE_CASE__ = [self.rescale(_lowercase , scale=1 / 2_55 ) for image in images] SCREAMING_SNAKE_CASE__ = [to_channel_dimension_format(_lowercase , _lowercase ) for image in images] SCREAMING_SNAKE_CASE__ = {"""pixel_values""": images} return BatchFeature(data=_lowercase , tensor_type=_lowercase )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available UpperCAmelCase__ = { "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: UpperCAmelCase__ = ["BridgeTowerImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCAmelCase__ = [ "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 UpperCAmelCase__ = _LazyModule(__name__, globals()["__file__"], _import_structure)
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def A ( _UpperCAmelCase : int ) -> "list[int]": '''simple docstring''' if upper_limit < 0: raise ValueError('Limit for the Catalan sequence must be ≥ 0' ) _UpperCAmelCase = [0] * (upper_limit + 1) # Base case: C(0) = C(1) = 1 _UpperCAmelCase = 1 if upper_limit > 0: _UpperCAmelCase = 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(_UpperCAmelCase ): 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: UpperCAmelCase__ = 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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from scipy.stats import pearsonr, spearmanr from sklearn.metrics import fa_score, matthews_corrcoef import datasets a__ = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n" a__ = "\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n" a__ = "\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n predictions: list of predictions to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n \"accuracy\": Accuracy\n \"f1\": F1 score\n \"pearson\": Pearson Correlation\n \"spearmanr\": Spearman Correlation\n \"matthews_correlation\": Matthew Correlation\nExamples:\n\n >>> glue_metric = datasets.load_metric('glue', 'sst2') # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'mrpc') # 'mrpc' or 'qqp'\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'stsb')\n >>> references = [0., 1., 2., 3., 4., 5.]\n >>> predictions = [0., 1., 2., 3., 4., 5.]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n {'pearson': 1.0, 'spearmanr': 1.0}\n\n >>> glue_metric = datasets.load_metric('glue', 'cola')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n" def lowercase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[Any]: return float((preds == labels).mean() ) def lowercase ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[Any] ) -> Optional[int]: _snake_case : Any = simple_accuracy(__lowerCamelCase , __lowerCamelCase ) _snake_case : Union[str, Any] = float(fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase ) ) return { "accuracy": acc, "f1": fa, } def lowercase ( SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> List[Any]: _snake_case : Dict = float(pearsonr(__lowerCamelCase , __lowerCamelCase )[0] ) _snake_case : int = float(spearmanr(__lowerCamelCase , __lowerCamelCase )[0] ) return { "pearson": pearson_corr, "spearmanr": spearman_corr, } @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION ) class snake_case ( datasets.Metric ): '''simple docstring''' def UpperCamelCase_ ( self : Dict) -> Any: """simple docstring""" if self.config_name not in [ "sst2", "mnli", "mnli_mismatched", "mnli_matched", "cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans", ]: raise KeyError( """You should supply a configuration name selected in """ """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """ """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""") return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""), """references""": datasets.Value("""int64""" if self.config_name != """stsb""" else """float32"""), }) , codebase_urls=[] , reference_urls=[] , format="""numpy""" , ) def UpperCamelCase_ ( self : List[str] , lowerCAmelCase : int , lowerCAmelCase : Union[str, Any]) -> int: """simple docstring""" if self.config_name == "cola": return {"matthews_correlation": matthews_corrcoef(__lowercase , __lowercase)} elif self.config_name == "stsb": return pearson_and_spearman(__lowercase , __lowercase) elif self.config_name in ["mrpc", "qqp"]: return acc_and_fa(__lowercase , __lowercase) elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]: return {"accuracy": simple_accuracy(__lowercase , __lowercase)} else: raise KeyError( """You should supply a configuration name selected in """ """[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", """ """\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]""")
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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 lowerCamelCase__ ( __lowerCamelCase : Dict , __lowerCamelCase : int ): __UpperCAmelCase : List[Any] = checkpoint __UpperCAmelCase : Tuple = {} __UpperCAmelCase : Dict = vae_state_dict["""encoder.conv_in.weight"""] __UpperCAmelCase : Optional[Any] = vae_state_dict["""encoder.conv_in.bias"""] __UpperCAmelCase : Dict = vae_state_dict["""encoder.conv_out.weight"""] __UpperCAmelCase : Union[str, Any] = vae_state_dict["""encoder.conv_out.bias"""] __UpperCAmelCase : List[Any] = vae_state_dict["""encoder.norm_out.weight"""] __UpperCAmelCase : Tuple = vae_state_dict["""encoder.norm_out.bias"""] __UpperCAmelCase : Dict = vae_state_dict["""decoder.conv_in.weight"""] __UpperCAmelCase : Tuple = vae_state_dict["""decoder.conv_in.bias"""] __UpperCAmelCase : Optional[int] = vae_state_dict["""decoder.conv_out.weight"""] __UpperCAmelCase : Optional[int] = vae_state_dict["""decoder.conv_out.bias"""] __UpperCAmelCase : Optional[Any] = vae_state_dict["""decoder.norm_out.weight"""] __UpperCAmelCase : Union[str, Any] = vae_state_dict["""decoder.norm_out.bias"""] __UpperCAmelCase : Optional[int] = vae_state_dict["""quant_conv.weight"""] __UpperCAmelCase : int = vae_state_dict["""quant_conv.bias"""] __UpperCAmelCase : Union[str, Any] = vae_state_dict["""post_quant_conv.weight"""] __UpperCAmelCase : Any = vae_state_dict["""post_quant_conv.bias"""] # Retrieves the keys for the encoder down blocks only __UpperCAmelCase : int = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """encoder.down""" in layer} ) __UpperCAmelCase : Optional[Any] = { layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__lowerCamelCase ) } # Retrieves the keys for the decoder up blocks only __UpperCAmelCase : Dict = len({""".""".join(layer.split(""".""" )[:3] ) for layer in vae_state_dict if """decoder.up""" in layer} ) __UpperCAmelCase : Optional[int] = { layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__lowerCamelCase ) } for i in range(__lowerCamelCase ): __UpperCAmelCase : List[Any] = [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: __UpperCAmelCase : Optional[Any] = vae_state_dict.pop( f"""encoder.down.{i}.downsample.conv.weight""" ) __UpperCAmelCase : int = vae_state_dict.pop( f"""encoder.down.{i}.downsample.conv.bias""" ) __UpperCAmelCase : Optional[int] = renew_vae_resnet_paths(__lowerCamelCase ) __UpperCAmelCase : Optional[Any] = {"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""} assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase ) __UpperCAmelCase : Tuple = [key for key in vae_state_dict if """encoder.mid.block""" in key] __UpperCAmelCase : Optional[Any] = 2 for i in range(1 , num_mid_res_blocks + 1 ): __UpperCAmelCase : Dict = [key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key] __UpperCAmelCase : Tuple = renew_vae_resnet_paths(__lowerCamelCase ) __UpperCAmelCase : Tuple = {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""} assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase ) __UpperCAmelCase : List[Any] = [key for key in vae_state_dict if """encoder.mid.attn""" in key] __UpperCAmelCase : str = renew_vae_attention_paths(__lowerCamelCase ) __UpperCAmelCase : List[str] = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""} assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase ) conv_attn_to_linear(__lowerCamelCase ) for i in range(__lowerCamelCase ): __UpperCAmelCase : Optional[Any] = num_up_blocks - 1 - i __UpperCAmelCase : Union[str, 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: __UpperCAmelCase : int = vae_state_dict[ f"""decoder.up.{block_id}.upsample.conv.weight""" ] __UpperCAmelCase : Dict = vae_state_dict[ f"""decoder.up.{block_id}.upsample.conv.bias""" ] __UpperCAmelCase : Dict = renew_vae_resnet_paths(__lowerCamelCase ) __UpperCAmelCase : Optional[Any] = {"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""} assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase ) __UpperCAmelCase : Tuple = [key for key in vae_state_dict if """decoder.mid.block""" in key] __UpperCAmelCase : Union[str, Any] = 2 for i in range(1 , num_mid_res_blocks + 1 ): __UpperCAmelCase : Dict = [key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key] __UpperCAmelCase : List[Any] = renew_vae_resnet_paths(__lowerCamelCase ) __UpperCAmelCase : int = {"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""} assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase ) __UpperCAmelCase : Dict = [key for key in vae_state_dict if """decoder.mid.attn""" in key] __UpperCAmelCase : List[Any] = renew_vae_attention_paths(__lowerCamelCase ) __UpperCAmelCase : List[str] = {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""} assign_to_checkpoint(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , additional_replacements=[meta_path] , config=__lowerCamelCase ) conv_attn_to_linear(__lowerCamelCase ) return new_checkpoint def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : str , ): # Only support V1 __UpperCAmelCase : Optional[int] = requests.get( """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml""" ) __UpperCAmelCase : Optional[int] = io.BytesIO(r.content ) __UpperCAmelCase : Dict = OmegaConf.load(__lowerCamelCase ) __UpperCAmelCase : str = 512 __UpperCAmelCase : Any = """cuda""" if torch.cuda.is_available() else """cpu""" if checkpoint_path.endswith("""safetensors""" ): from safetensors import safe_open __UpperCAmelCase : List[Any] = {} with safe_open(__lowerCamelCase , framework="""pt""" , device="""cpu""" ) as f: for key in f.keys(): __UpperCAmelCase : str = f.get_tensor(__lowerCamelCase ) else: __UpperCAmelCase : Optional[int] = torch.load(__lowerCamelCase , map_location=__lowerCamelCase )["""state_dict"""] # Convert the VAE model. __UpperCAmelCase : Optional[int] = create_vae_diffusers_config(__lowerCamelCase , image_size=__lowerCamelCase ) __UpperCAmelCase : Union[str, Any] = custom_convert_ldm_vae_checkpoint(__lowerCamelCase , __lowerCamelCase ) __UpperCAmelCase : Optional[int] = AutoencoderKL(**__lowerCamelCase ) vae.load_state_dict(__lowerCamelCase ) vae.save_pretrained(__lowerCamelCase ) if __name__ == "__main__": a : List[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 : Optional[int] = parser.parse_args() vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
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lowerCamelCase : Optional[int] = tuple[float, float, float] lowerCamelCase : List[Any] = tuple[float, float, float] def _SCREAMING_SNAKE_CASE ( lowercase : Pointad , lowercase : Pointad ): '''simple docstring''' lowerCamelCase_ = end_pointa[0] - end_pointa[0] lowerCamelCase_ = end_pointa[1] - end_pointa[1] lowerCamelCase_ = end_pointa[2] - end_pointa[2] return (x, y, z) def _SCREAMING_SNAKE_CASE ( lowercase : Vectorad , lowercase : Vectorad ): '''simple docstring''' lowerCamelCase_ = ab[1] * ac[2] - ab[2] * ac[1] # *i lowerCamelCase_ = (ab[0] * ac[2] - ab[2] * ac[0]) * -1 # *j lowerCamelCase_ = ab[0] * ac[1] - ab[1] * ac[0] # *k return (x, y, z) def _SCREAMING_SNAKE_CASE ( lowercase : Vectorad , lowercase : int ): '''simple docstring''' return tuple(round(lowercase , lowercase ) for x in vector ) == (0, 0, 0) def _SCREAMING_SNAKE_CASE ( lowercase : Pointad , lowercase : Pointad , lowercase : Pointad , lowercase : int = 10 ): '''simple docstring''' lowerCamelCase_ = create_vector(lowercase , lowercase ) lowerCamelCase_ = create_vector(lowercase , lowercase ) return is_zero_vector(get_ad_vectors_cross(lowercase , lowercase ) , lowercase )
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import argparse import pathlib import fairseq import torch from fairseq.models.roberta import RobertaModel as FairseqRobertaModel from fairseq.modules import TransformerSentenceEncoderLayer from packaging import version from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput, ) from transformers.models.roberta.modeling_roberta import RobertaAttention from transformers.utils import logging if version.parse(fairseq.__version__) < version.parse("1.0.0a"): raise Exception("requires fairseq >= 1.0.0a") logging.set_verbosity_info() lowerCamelCase : Tuple = logging.get_logger(__name__) lowerCamelCase : Union[str, Any] = "Hello world! cécé herlolip" def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : str , lowercase : bool ): '''simple docstring''' lowerCamelCase_ = FairseqRobertaModel.from_pretrained(lowercase ) roberta.eval() # disable dropout lowerCamelCase_ = roberta.model.encoder.sentence_encoder lowerCamelCase_ = XLMRobertaConfig( vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1e-5 , ) if classification_head: lowerCamelCase_ = roberta.model.classification_heads['mnli'].out_proj.weight.shape[0] print('Our RoBERTa config:' , lowercase ) lowerCamelCase_ = XLMRobertaXLForSequenceClassification(lowercase ) if classification_head else XLMRobertaXLForMaskedLM(lowercase ) model.eval() # Now let's copy all the weights. # Embeddings lowerCamelCase_ = roberta_sent_encoder.embed_tokens.weight lowerCamelCase_ = roberta_sent_encoder.embed_positions.weight lowerCamelCase_ = torch.zeros_like( model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them. lowerCamelCase_ = roberta_sent_encoder.layer_norm.weight lowerCamelCase_ = roberta_sent_encoder.layer_norm.bias for i in range(config.num_hidden_layers ): # Encoder: start of layer lowerCamelCase_ = model.roberta.encoder.layer[i] lowerCamelCase_ = roberta_sent_encoder.layers[i] lowerCamelCase_ = layer.attention lowerCamelCase_ = roberta_layer.self_attn_layer_norm.weight lowerCamelCase_ = roberta_layer.self_attn_layer_norm.bias # self attention lowerCamelCase_ = layer.attention.self assert ( roberta_layer.self_attn.k_proj.weight.data.shape == roberta_layer.self_attn.q_proj.weight.data.shape == roberta_layer.self_attn.v_proj.weight.data.shape == torch.Size((config.hidden_size, config.hidden_size) ) ) lowerCamelCase_ = roberta_layer.self_attn.q_proj.weight lowerCamelCase_ = roberta_layer.self_attn.q_proj.bias lowerCamelCase_ = roberta_layer.self_attn.k_proj.weight lowerCamelCase_ = roberta_layer.self_attn.k_proj.bias lowerCamelCase_ = roberta_layer.self_attn.v_proj.weight lowerCamelCase_ = roberta_layer.self_attn.v_proj.bias # self-attention output lowerCamelCase_ = layer.attention.output assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape lowerCamelCase_ = roberta_layer.self_attn.out_proj.weight lowerCamelCase_ = roberta_layer.self_attn.out_proj.bias # this one is final layer norm lowerCamelCase_ = roberta_layer.final_layer_norm.weight lowerCamelCase_ = roberta_layer.final_layer_norm.bias # intermediate lowerCamelCase_ = layer.intermediate assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape lowerCamelCase_ = roberta_layer.fca.weight lowerCamelCase_ = roberta_layer.fca.bias # output lowerCamelCase_ = layer.output assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape lowerCamelCase_ = roberta_layer.fca.weight lowerCamelCase_ = roberta_layer.fca.bias # end of layer if classification_head: lowerCamelCase_ = roberta.model.classification_heads['mnli'].dense.weight lowerCamelCase_ = roberta.model.classification_heads['mnli'].dense.bias lowerCamelCase_ = roberta.model.classification_heads['mnli'].out_proj.weight lowerCamelCase_ = roberta.model.classification_heads['mnli'].out_proj.bias else: # LM Head lowerCamelCase_ = roberta.model.encoder.lm_head.dense.weight lowerCamelCase_ = roberta.model.encoder.lm_head.dense.bias lowerCamelCase_ = roberta.model.encoder.lm_head.layer_norm.weight lowerCamelCase_ = roberta.model.encoder.lm_head.layer_norm.bias lowerCamelCase_ = roberta.model.encoder.lm_head.weight lowerCamelCase_ = roberta.model.encoder.lm_head.bias # Let's check that we get the same results. lowerCamelCase_ = roberta.encode(lowercase ).unsqueeze(0 ) # batch of size 1 lowerCamelCase_ = model(lowercase )[0] if classification_head: lowerCamelCase_ = roberta.model.classification_heads['mnli'](roberta.extract_features(lowercase ) ) else: lowerCamelCase_ = roberta.model(lowercase )[0] print(our_output.shape , their_output.shape ) lowerCamelCase_ = torch.max(torch.abs(our_output - their_output ) ).item() print(f"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7 lowerCamelCase_ = torch.allclose(lowercase , lowercase , atol=1e-3 ) print('Do both models output the same tensors?' , '🔥' if success else '💩' ) if not success: raise Exception('Something went wRoNg' ) pathlib.Path(lowercase ).mkdir(parents=lowercase , exist_ok=lowercase ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(lowercase ) if __name__ == "__main__": lowerCamelCase : Optional[Any] = argparse.ArgumentParser() # Required parameters parser.add_argument( "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump." ) parser.add_argument( "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model." ) parser.add_argument( "--classification_head", action="store_true", help="Whether to convert a final classification head." ) lowerCamelCase : List[Any] = parser.parse_args() convert_xlm_roberta_xl_checkpoint_to_pytorch( args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head )
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def UpperCAmelCase_( a__ ): """simple docstring""" assert isinstance(__lowercase , __lowercase ), F"""The input value of [n={number}] is not an integer""" if number == 1: return 2 elif number < 1: SCREAMING_SNAKE_CASE : Any = F"""The input value of [n={number}] has to be > 0""" raise ValueError(__lowercase ) else: SCREAMING_SNAKE_CASE : Any = sylvester(number - 1 ) SCREAMING_SNAKE_CASE : Any = num - 1 SCREAMING_SNAKE_CASE : List[str] = num return lower * upper + 1 if __name__ == "__main__": print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")
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'''simple docstring''' from PIL import Image def __lowercase ( __lowercase , __lowercase ) -> Image: '''simple docstring''' _A = (259 * (level + 255)) / (255 * (259 - level)) def contrast(__lowercase ) -> int: return int(128 + factor * (c - 128) ) return img.point(__lowercase ) if __name__ == "__main__": # Load image with Image.open('''image_data/lena.jpg''') as img: # Change contrast to 170 lowerCamelCase_ = change_contrast(img, 1_70) cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')
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"""simple docstring""" from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging lowercase_ = logging.get_logger(__name__) lowercase_ = { "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json", "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json", "junnyu/roformer_chinese_char_small": ( "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json" ), "junnyu/roformer_chinese_char_base": ( "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json" ), "junnyu/roformer_small_discriminator": ( "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json" ), "junnyu/roformer_small_generator": ( "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json" ), # See all RoFormer models at https://huggingface.co/models?filter=roformer } class __lowerCAmelCase ( _SCREAMING_SNAKE_CASE ): '''simple docstring''' __UpperCAmelCase : List[Any] = 'roformer' def __init__( self , _a=50_000 , _a=None , _a=768 , _a=12 , _a=12 , _a=3_072 , _a="gelu" , _a=0.1 , _a=0.1 , _a=1_536 , _a=2 , _a=0.02 , _a=1E-12 , _a=0 , _a=False , _a=True , **_a , ): super().__init__(pad_token_id=_a , **_a ) __a = vocab_size __a = hidden_size if embedding_size is None else embedding_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 = rotary_value __a = use_cache class __lowerCAmelCase ( _SCREAMING_SNAKE_CASE ): '''simple docstring''' @property def __UpperCAmelCase ( self ): if self.task == "multiple-choice": __a = {0: """batch""", 1: """choice""", 2: """sequence"""} else: __a = {0: """batch""", 1: """sequence"""} __a = {0: """batch""", 1: """sequence"""} return OrderedDict( [ ('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis), ] )
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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_efficientformer": [ "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "EfficientFormerConfig", ] } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase_ = ["EfficientFormerImageProcessor"] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase_ = [ "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "EfficientFormerForImageClassification", "EfficientFormerForImageClassificationWithTeacher", "EfficientFormerModel", "EfficientFormerPreTrainedModel", ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: lowercase_ = [ "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", "TFEfficientFormerForImageClassification", "TFEfficientFormerForImageClassificationWithTeacher", "TFEfficientFormerModel", "TFEfficientFormerPreTrainedModel", ] if TYPE_CHECKING: from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .image_processing_efficientformer import EfficientFormerImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_efficientformer import ( EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, EfficientFormerForImageClassification, EfficientFormerForImageClassificationWithTeacher, EfficientFormerModel, EfficientFormerPreTrainedModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_efficientformer import ( TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, TFEfficientFormerForImageClassification, TFEfficientFormerForImageClassificationWithTeacher, TFEfficientFormerModel, TFEfficientFormerPreTrainedModel, ) else: import sys lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
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"""simple docstring""" import torch from diffusers import DDPMScheduler from .test_schedulers import SchedulerCommonTest class _lowerCAmelCase ( lowercase ): """simple docstring""" __UpperCAmelCase : Tuple = (DDPMScheduler,) def _lowercase ( self : Optional[int], **UpperCAmelCase__ : List[Any] ): __lowercase = { "num_train_timesteps": 1_0_0_0, "beta_start": 0.0_001, "beta_end": 0.02, "beta_schedule": "linear", "variance_type": "fixed_small", "clip_sample": True, } config.update(**UpperCAmelCase__ ) return config def _lowercase ( self : Tuple ): for timesteps in [1, 5, 1_0_0, 1_0_0_0]: self.check_over_configs(num_train_timesteps=UpperCAmelCase__ ) def _lowercase ( self : Any ): for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1], [0.002, 0.02, 0.2, 2] ): self.check_over_configs(beta_start=UpperCAmelCase__, beta_end=UpperCAmelCase__ ) def _lowercase ( self : Tuple ): for schedule in ["linear", "squaredcos_cap_v2"]: self.check_over_configs(beta_schedule=UpperCAmelCase__ ) def _lowercase ( self : int ): for variance in ["fixed_small", "fixed_large", "other"]: self.check_over_configs(variance_type=UpperCAmelCase__ ) def _lowercase ( self : str ): for clip_sample in [True, False]: self.check_over_configs(clip_sample=UpperCAmelCase__ ) def _lowercase ( self : Tuple ): self.check_over_configs(thresholding=UpperCAmelCase__ ) for threshold in [0.5, 1.0, 2.0]: for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs( thresholding=UpperCAmelCase__, prediction_type=UpperCAmelCase__, sample_max_value=UpperCAmelCase__, ) def _lowercase ( self : Tuple ): for prediction_type in ["epsilon", "sample", "v_prediction"]: self.check_over_configs(prediction_type=UpperCAmelCase__ ) def _lowercase ( self : Dict ): for t in [0, 5_0_0, 9_9_9]: self.check_over_forward(time_step=UpperCAmelCase__ ) def _lowercase ( self : str ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config() __lowercase = scheduler_class(**UpperCAmelCase__ ) assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.00_979 ) ) < 1E-5 assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1E-5 def _lowercase ( self : Union[str, Any] ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config() __lowercase = scheduler_class(**UpperCAmelCase__ ) __lowercase = len(UpperCAmelCase__ ) __lowercase = self.dummy_model() __lowercase = self.dummy_sample_deter __lowercase = torch.manual_seed(0 ) for t in reversed(range(UpperCAmelCase__ ) ): # 1. predict noise residual __lowercase = model(UpperCAmelCase__, UpperCAmelCase__ ) # 2. predict previous mean of sample x_t-1 __lowercase = scheduler.step(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, generator=UpperCAmelCase__ ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance __lowercase = pred_prev_sample __lowercase = torch.sum(torch.abs(UpperCAmelCase__ ) ) __lowercase = torch.mean(torch.abs(UpperCAmelCase__ ) ) assert abs(result_sum.item() - 258.9_606 ) < 1E-2 assert abs(result_mean.item() - 0.3_372 ) < 1E-3 def _lowercase ( self : List[str] ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config(prediction_type="v_prediction" ) __lowercase = scheduler_class(**UpperCAmelCase__ ) __lowercase = len(UpperCAmelCase__ ) __lowercase = self.dummy_model() __lowercase = self.dummy_sample_deter __lowercase = torch.manual_seed(0 ) for t in reversed(range(UpperCAmelCase__ ) ): # 1. predict noise residual __lowercase = model(UpperCAmelCase__, UpperCAmelCase__ ) # 2. predict previous mean of sample x_t-1 __lowercase = scheduler.step(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, generator=UpperCAmelCase__ ).prev_sample # if t > 0: # noise = self.dummy_sample_deter # variance = scheduler.get_variance(t) ** (0.5) * noise # # sample = pred_prev_sample + variance __lowercase = pred_prev_sample __lowercase = torch.sum(torch.abs(UpperCAmelCase__ ) ) __lowercase = torch.mean(torch.abs(UpperCAmelCase__ ) ) assert abs(result_sum.item() - 202.0_296 ) < 1E-2 assert abs(result_mean.item() - 0.2_631 ) < 1E-3 def _lowercase ( self : Optional[Any] ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config() __lowercase = scheduler_class(**UpperCAmelCase__ ) __lowercase = [1_0_0, 8_7, 5_0, 1, 0] scheduler.set_timesteps(timesteps=UpperCAmelCase__ ) __lowercase = scheduler.timesteps for i, timestep in enumerate(UpperCAmelCase__ ): if i == len(UpperCAmelCase__ ) - 1: __lowercase = -1 else: __lowercase = timesteps[i + 1] __lowercase = scheduler.previous_timestep(UpperCAmelCase__ ) __lowercase = prev_t.item() self.assertEqual(UpperCAmelCase__, UpperCAmelCase__ ) def _lowercase ( self : Union[str, Any] ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config() __lowercase = scheduler_class(**UpperCAmelCase__ ) __lowercase = [1_0_0, 8_7, 5_0, 5_1, 0] with self.assertRaises(UpperCAmelCase__, msg="`custom_timesteps` must be in descending order." ): scheduler.set_timesteps(timesteps=UpperCAmelCase__ ) def _lowercase ( self : List[Any] ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config() __lowercase = scheduler_class(**UpperCAmelCase__ ) __lowercase = [1_0_0, 8_7, 5_0, 1, 0] __lowercase = len(UpperCAmelCase__ ) with self.assertRaises(UpperCAmelCase__, msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ): scheduler.set_timesteps(num_inference_steps=UpperCAmelCase__, timesteps=UpperCAmelCase__ ) def _lowercase ( self : Optional[int] ): __lowercase = self.scheduler_classes[0] __lowercase = self.get_scheduler_config() __lowercase = scheduler_class(**UpperCAmelCase__ ) __lowercase = [scheduler.config.num_train_timesteps] with self.assertRaises( UpperCAmelCase__, msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}", ): scheduler.set_timesteps(timesteps=UpperCAmelCase__ )
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import inspect import unittest from transformers import RegNetConfig, is_flax_available from transformers.testing_utils import require_flax, slow from transformers.utils import cached_property, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor if is_flax_available(): import jax import jax.numpy as jnp from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ): def __init__( self,__lowerCamelCase,__lowerCamelCase=3,__lowerCamelCase=32,__lowerCamelCase=3,__lowerCamelCase=10,__lowerCamelCase=[10, 20, 30, 40],__lowerCamelCase=[1, 1, 2, 1],__lowerCamelCase=True,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=3,__lowerCamelCase=None,): A__ = parent A__ = batch_size A__ = image_size A__ = num_channels A__ = embeddings_size A__ = hidden_sizes A__ = depths A__ = is_training A__ = use_labels A__ = hidden_act A__ = num_labels A__ = scope A__ = len(__lowerCamelCase ) def UpperCamelCase ( self ): A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) A__ = self.get_config() return config, pixel_values def UpperCamelCase ( self ): return RegNetConfig( num_channels=self.num_channels,embeddings_size=self.embeddings_size,hidden_sizes=self.hidden_sizes,depths=self.depths,hidden_act=self.hidden_act,num_labels=self.num_labels,image_size=self.image_size,) def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ): A__ = FlaxRegNetModel(config=__lowerCamelCase ) A__ = model(__lowerCamelCase ) # Output shape (b, c, h, w) self.parent.assertEqual( result.last_hidden_state.shape,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),) def UpperCamelCase ( self,__lowerCamelCase,__lowerCamelCase ): A__ = self.num_labels A__ = FlaxRegNetForImageClassification(config=__lowerCamelCase ) A__ = model(__lowerCamelCase ) self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels) ) def UpperCamelCase ( self ): A__ = self.prepare_config_and_inputs() A__ , A__ = config_and_inputs A__ = {'''pixel_values''': pixel_values} return config, inputs_dict @require_flax class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ , unittest.TestCase ): __SCREAMING_SNAKE_CASE = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else () __SCREAMING_SNAKE_CASE = False __SCREAMING_SNAKE_CASE = False __SCREAMING_SNAKE_CASE = False def UpperCamelCase ( self ): A__ = FlaxRegNetModelTester(self ) A__ = ConfigTester(self,config_class=__lowerCamelCase,has_text_modality=__lowerCamelCase ) def UpperCamelCase ( self ): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def UpperCamelCase ( self ): return def UpperCamelCase ( self ): A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*__lowerCamelCase ) def UpperCamelCase ( self ): A__ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase ) @unittest.skip(reason='''RegNet does not use inputs_embeds''' ) def UpperCamelCase ( self ): pass @unittest.skip(reason='''RegNet does not support input and output embeddings''' ) def UpperCamelCase ( self ): pass def UpperCamelCase ( self ): A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: A__ = model_class(__lowerCamelCase ) A__ = inspect.signature(model.__call__ ) # signature.parameters is an OrderedDict => so arg_names order is deterministic A__ = [*signature.parameters.keys()] A__ = ['''pixel_values'''] self.assertListEqual(arg_names[:1],__lowerCamelCase ) def UpperCamelCase ( self ): def check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ): A__ = model_class(__lowerCamelCase ) A__ = model(**self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) ) A__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states A__ = self.model_tester.num_stages self.assertEqual(len(__lowerCamelCase ),expected_num_stages + 1 ) 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(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] A__ = True check_hidden_states_output(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ) def UpperCamelCase ( self ): A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: with self.subTest(model_class.__name__ ): A__ = self._prepare_for_class(__lowerCamelCase,__lowerCamelCase ) A__ = model_class(__lowerCamelCase ) @jax.jit def model_jitted(__lowerCamelCase,**__lowerCamelCase ): return model(pixel_values=__lowerCamelCase,**__lowerCamelCase ) with self.subTest('''JIT Enabled''' ): A__ = model_jitted(**__lowerCamelCase ).to_tuple() with self.subTest('''JIT Disabled''' ): with jax.disable_jit(): A__ = model_jitted(**__lowerCamelCase ).to_tuple() self.assertEqual(len(__lowerCamelCase ),len(__lowerCamelCase ) ) for jitted_output, output in zip(__lowerCamelCase,__lowerCamelCase ): self.assertEqual(jitted_output.shape,output.shape ) def UpperCamelCase__( )->Optional[int]: A__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_flax class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ): @cached_property def UpperCamelCase ( self ): return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None @slow def UpperCamelCase ( self ): A__ = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' ) A__ = self.default_image_processor A__ = prepare_img() A__ = image_processor(images=__lowerCamelCase,return_tensors='''np''' ) A__ = model(**__lowerCamelCase ) # verify the logits A__ = (1, 1000) self.assertEqual(outputs.logits.shape,__lowerCamelCase ) A__ = jnp.array([-0.4180, -1.5051, -3.4836] ) self.assertTrue(jnp.allclose(outputs.logits[0, :3],__lowerCamelCase,atol=1E-4 ) )
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available, ) SCREAMING_SNAKE_CASE_ = { 'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'], 'tokenization_perceiver': ['PerceiverTokenizer'], } try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE_ = ['PerceiverFeatureExtractor'] SCREAMING_SNAKE_CASE_ = ['PerceiverImageProcessor'] try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: SCREAMING_SNAKE_CASE_ = [ 'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST', 'PerceiverForImageClassificationConvProcessing', 'PerceiverForImageClassificationFourier', 'PerceiverForImageClassificationLearned', 'PerceiverForMaskedLM', 'PerceiverForMultimodalAutoencoding', 'PerceiverForOpticalFlow', 'PerceiverForSequenceClassification', 'PerceiverLayer', 'PerceiverModel', 'PerceiverPreTrainedModel', ] if TYPE_CHECKING: from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig from .tokenization_perceiver import PerceiverTokenizer try: if not is_vision_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .feature_extraction_perceiver import PerceiverFeatureExtractor from .image_processing_perceiver import PerceiverImageProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_perceiver import ( PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST, PerceiverForImageClassificationConvProcessing, PerceiverForImageClassificationFourier, PerceiverForImageClassificationLearned, PerceiverForMaskedLM, PerceiverForMultimodalAutoencoding, PerceiverForOpticalFlow, PerceiverForSequenceClassification, PerceiverLayer, PerceiverModel, PerceiverPreTrainedModel, ) else: import sys SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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import warnings from typing import List import numpy as np from ...processing_utils import ProcessorMixin from ...tokenization_utils_base import BatchEncoding from ...utils import is_flax_available, is_tf_available, is_torch_available class a ( UpperCAmelCase ): _lowercase = ["image_processor", "tokenizer"] _lowercase = "OwlViTImageProcessor" _lowercase = ("CLIPTokenizer", "CLIPTokenizerFast") def __init__( self , A_=None , A_=None , **A_ ): '''simple docstring''' _UpperCAmelCase : List[str] = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." , A_ , ) _UpperCAmelCase : Union[str, Any] = kwargs.pop("feature_extractor" ) _UpperCAmelCase : Any = image_processor if image_processor is not None else feature_extractor if image_processor is None: raise ValueError("You need to specify an `image_processor`." ) if tokenizer is None: raise ValueError("You need to specify a `tokenizer`." ) super().__init__(A_ , A_ ) def __call__( self , A_=None , A_=None , A_=None , A_="max_length" , A_="np" , **A_ ): '''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(A_ , A_ ) or (isinstance(A_ , A_ ) and not isinstance(text[0] , A_ )): _UpperCAmelCase : Optional[int] = [self.tokenizer(A_ , padding=A_ , return_tensors=A_ , **A_ )] elif isinstance(A_ , A_ ) and isinstance(text[0] , A_ ): _UpperCAmelCase : Optional[int] = [] # Maximum number of queries across batch _UpperCAmelCase : Optional[Any] = max([len(A_ ) for t in text] ) # Pad all batch samples to max number of text queries for t in text: if len(A_ ) != max_num_queries: _UpperCAmelCase : Optional[int] = t + [" "] * (max_num_queries - len(A_ )) _UpperCAmelCase : str = self.tokenizer(A_ , padding=A_ , return_tensors=A_ , **A_ ) encodings.append(A_ ) else: raise TypeError("Input text should be a string, a list of strings or a nested list of strings" ) if return_tensors == "np": _UpperCAmelCase : List[str] = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 ) _UpperCAmelCase : Tuple = 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 : Optional[Any] = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 ) _UpperCAmelCase : str = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 ) elif return_tensors == "pt" and is_torch_available(): import torch _UpperCAmelCase : str = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 ) _UpperCAmelCase : Dict = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 ) elif return_tensors == "tf" and is_tf_available(): import tensorflow as tf _UpperCAmelCase : Union[str, Any] = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 ) _UpperCAmelCase : Optional[int] = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 ) else: raise ValueError("Target return tensor type could not be returned" ) _UpperCAmelCase : Optional[int] = BatchEncoding() _UpperCAmelCase : str = input_ids _UpperCAmelCase : Optional[Any] = attention_mask if query_images is not None: _UpperCAmelCase : int = BatchEncoding() _UpperCAmelCase : str = self.image_processor( A_ , return_tensors=A_ , **A_ ).pixel_values _UpperCAmelCase : Optional[Any] = query_pixel_values if images is not None: _UpperCAmelCase : int = self.image_processor(A_ , return_tensors=A_ , **A_ ) if text is not None and images is not None: _UpperCAmelCase : Optional[int] = image_features.pixel_values return encoding elif query_images is not None and images is not None: _UpperCAmelCase : Any = image_features.pixel_values return encoding elif text is not None or query_images is not None: return encoding else: return BatchEncoding(data=dict(**A_ ) , tensor_type=A_ ) def _UpperCAmelCase ( self , *A_ , **A_ ): '''simple docstring''' return self.image_processor.post_process(*A_ , **A_ ) def _UpperCAmelCase ( self , *A_ , **A_ ): '''simple docstring''' return self.image_processor.post_process_object_detection(*A_ , **A_ ) def _UpperCAmelCase ( self , *A_ , **A_ ): '''simple docstring''' return self.image_processor.post_process_image_guided_detection(*A_ , **A_ ) def _UpperCAmelCase ( self , *A_ , **A_ ): '''simple docstring''' return self.tokenizer.batch_decode(*A_ , **A_ ) def _UpperCAmelCase ( self , *A_ , **A_ ): '''simple docstring''' return self.tokenizer.decode(*A_ , **A_ ) @property def _UpperCAmelCase ( self ): '''simple docstring''' warnings.warn( "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , A_ , ) return self.image_processor_class @property def _UpperCAmelCase ( self ): '''simple docstring''' warnings.warn( "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , A_ , ) return self.image_processor
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0
from typing import Dict, List from nltk.translate import gleu_score import datasets from datasets import MetricInfo _UpperCAmelCase : List[Any] = """\ @misc{wu2016googles, title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation}, author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes and Jeffrey Dean}, year={2016}, eprint={1609.08144}, archivePrefix={arXiv}, primaryClass={cs.CL} } """ _UpperCAmelCase : Union[str, Any] = """\ The BLEU score has some undesirable properties when used for single sentences, as it was designed to be a corpus measure. We therefore use a slightly different score for our RL experiments which we call the 'GLEU score'. For the GLEU score, we record all sub-sequences of 1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then compute a recall, which is the ratio of the number of matching n-grams to the number of total n-grams in the target (ground truth) sequence, and a precision, which is the ratio of the number of matching n-grams to the number of total n-grams in the generated output sequence. Then GLEU score is simply the minimum of recall and precision. This GLEU score's range is always between 0 (no matches) and 1 (all match) and it is symmetrical when switching output and target. According to our experiments, GLEU score correlates quite well with the BLEU metric on a corpus level but does not have its drawbacks for our per sentence reward objective. """ _UpperCAmelCase : Optional[int] = """\ Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references. Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values. Args: predictions (list of str): list of translations to score. Each translation should be tokenized into a list of tokens. references (list of list of str): list of lists of references for each translation. Each reference should be tokenized into a list of tokens. min_len (int): The minimum order of n-gram this function should extract. Defaults to 1. max_len (int): The maximum order of n-gram this function should extract. Defaults to 4. Returns: 'google_bleu': google_bleu score Examples: Example 1: >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always', ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat'] >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never', ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat'] >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', ... 'interested', 'in', 'world', 'history'] >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', ... 'because', 'he', 'read', 'the', 'book'] >>> list_of_references = [[ref1a], [ref2a]] >>> hypotheses = [hyp1, hyp2] >>> google_bleu = datasets.load_metric(\"google_bleu\") >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references) >>> print(round(results[\"google_bleu\"], 2)) 0.44 Example 2: >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always', ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat'] >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never', ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat'] >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never', ... 'heed', 'the', 'cat', 'commands'] >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions', ... 'of', 'the', 'cat'] >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', ... 'interested', 'in', 'world', 'history'] >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', ... 'because', 'he', 'read', 'the', 'book'] >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] >>> hypotheses = [hyp1, hyp2] >>> google_bleu = datasets.load_metric(\"google_bleu\") >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references) >>> print(round(results[\"google_bleu\"], 2)) 0.61 Example 3: >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always', ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat'] >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never', ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat'] >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never', ... 'heed', 'the', 'cat', 'commands'] >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions', ... 'of', 'the', 'cat'] >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', ... 'interested', 'in', 'world', 'history'] >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', ... 'because', 'he', 'read', 'the', 'book'] >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] >>> hypotheses = [hyp1, hyp2] >>> google_bleu = datasets.load_metric(\"google_bleu\") >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2) >>> print(round(results[\"google_bleu\"], 2)) 0.53 Example 4: >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'always', ... 'disobeys', 'the', 'commands', 'of', 'the', 'cat'] >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which', ... 'guarantees', 'the', 'rubber', 'duck', 'forces', 'never', ... 'being', 'under', 'the', 'command', 'of', 'the', 'cat'] >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', ... 'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never', ... 'heed', 'the', 'cat', 'commands'] >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the', ... 'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions', ... 'of', 'the', 'cat'] >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was', ... 'interested', 'in', 'world', 'history'] >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history', ... 'because', 'he', 'read', 'the', 'book'] >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]] >>> hypotheses = [hyp1, hyp2] >>> google_bleu = datasets.load_metric(\"google_bleu\") >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6) >>> print(round(results[\"google_bleu\"], 2)) 0.4 """ @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION ) class lowerCAmelCase ( datasets.Metric ): def A_ ( self : str ) -> MetricInfo: return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { 'predictions': datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ), 'references': datasets.Sequence( datasets.Sequence(datasets.Value('string' , id='token' ) , id='sequence' ) , id='references' ), } ) , ) def A_ ( self : Any , UpperCAmelCase : List[List[List[str]]] , UpperCAmelCase : List[List[str]] , UpperCAmelCase : int = 1 , UpperCAmelCase : int = 4 , ) -> Dict[str, float]: return { "google_bleu": gleu_score.corpus_gleu( list_of_references=UpperCAmelCase , hypotheses=UpperCAmelCase , min_len=UpperCAmelCase , max_len=UpperCAmelCase ) }
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def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 100_0000 ) -> int: lowerCamelCase__ : int = limit + 1 lowerCamelCase__ : Optional[Any] = [0] * limit for first_term in range(1 , _UpperCAmelCase ): for n in range(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ): lowerCamelCase__ : Optional[Any] = first_term + n / first_term if common_difference % 4: # d must be divisble by 4 continue else: common_difference /= 4 if ( first_term > common_difference and first_term < 4 * common_difference ): # since x,y,z are positive integers frequency[n] += 1 # so z>0 and a>d ,also 4d<a lowerCamelCase__ : List[str] = sum(1 for x in frequency[1:limit] if x == 10 ) return count if __name__ == "__main__": print(F"""{solution() = }""")
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1
'''simple docstring''' import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_torch, require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import MgpstrProcessor, ViTImageProcessor @require_torch @require_vision class _A ( unittest.TestCase ): _SCREAMING_SNAKE_CASE : List[str] = ViTImageProcessor if is_vision_available() else None @property def __A ( self ) -> Optional[Any]: '''simple docstring''' return self.image_processor_tester.prepare_image_processor_dict() def __A ( self ) -> Dict: '''simple docstring''' __UpperCAmelCase : str = (3, 32, 128) __UpperCAmelCase : Tuple = tempfile.mkdtemp() # fmt: off __UpperCAmelCase : Any = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""] # fmt: on __UpperCAmelCase : Optional[int] = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) ) __UpperCAmelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] ) with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp: fp.write(json.dumps(__UpperCAmelCase ) + """\n""" ) __UpperCAmelCase : List[Any] = { """do_normalize""": False, """do_resize""": True, """image_processor_type""": """ViTImageProcessor""", """resample""": 3, """size""": {"""height""": 32, """width""": 128}, } __UpperCAmelCase : Tuple = os.path.join(self.tmpdirname , __UpperCAmelCase ) with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp: json.dump(__UpperCAmelCase , __UpperCAmelCase ) def __A ( self , **__UpperCAmelCase ) -> Tuple: '''simple docstring''' return MgpstrTokenizer.from_pretrained(self.tmpdirname , **__UpperCAmelCase ) def __A ( self , **__UpperCAmelCase ) -> List[str]: '''simple docstring''' return ViTImageProcessor.from_pretrained(self.tmpdirname , **__UpperCAmelCase ) def __A ( self ) -> str: '''simple docstring''' shutil.rmtree(self.tmpdirname ) def __A ( self ) -> Tuple: '''simple docstring''' __UpperCAmelCase : Tuple = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta ) __UpperCAmelCase : Dict = Image.fromarray(np.moveaxis(__UpperCAmelCase , 0 , -1 ) ) return image_input def __A ( self ) -> str: '''simple docstring''' __UpperCAmelCase : str = self.get_tokenizer() __UpperCAmelCase : Optional[Any] = self.get_image_processor() __UpperCAmelCase : Optional[Any] = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) processor.save_pretrained(self.tmpdirname ) __UpperCAmelCase : Tuple = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=__UpperCAmelCase ) self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() ) self.assertIsInstance(processor.char_tokenizer , __UpperCAmelCase ) self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() ) self.assertIsInstance(processor.image_processor , __UpperCAmelCase ) def __A ( self ) -> Optional[int]: '''simple docstring''' __UpperCAmelCase : List[str] = self.get_tokenizer() __UpperCAmelCase : List[Any] = self.get_image_processor() __UpperCAmelCase : Dict = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) processor.save_pretrained(self.tmpdirname ) __UpperCAmelCase : Union[str, Any] = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" ) __UpperCAmelCase : Union[str, Any] = self.get_image_processor(do_normalize=__UpperCAmelCase , padding_value=1.0 ) __UpperCAmelCase : List[Any] = MgpstrProcessor.from_pretrained( self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__UpperCAmelCase , padding_value=1.0 ) self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.char_tokenizer , __UpperCAmelCase ) self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor , __UpperCAmelCase ) def __A ( self ) -> List[Any]: '''simple docstring''' __UpperCAmelCase : Dict = self.get_image_processor() __UpperCAmelCase : Tuple = self.get_tokenizer() __UpperCAmelCase : Tuple = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) __UpperCAmelCase : List[str] = self.prepare_image_inputs() __UpperCAmelCase : str = image_processor(__UpperCAmelCase , return_tensors="""np""" ) __UpperCAmelCase : int = processor(images=__UpperCAmelCase , return_tensors="""np""" ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 ) def __A ( self ) -> Tuple: '''simple docstring''' __UpperCAmelCase : Tuple = self.get_image_processor() __UpperCAmelCase : List[Any] = self.get_tokenizer() __UpperCAmelCase : int = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) __UpperCAmelCase : Dict = """test""" __UpperCAmelCase : Union[str, Any] = processor(text=__UpperCAmelCase ) __UpperCAmelCase : Optional[Any] = tokenizer(__UpperCAmelCase ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] , encoded_processor[key] ) def __A ( self ) -> Union[str, Any]: '''simple docstring''' __UpperCAmelCase : List[Any] = self.get_image_processor() __UpperCAmelCase : Tuple = self.get_tokenizer() __UpperCAmelCase : Optional[int] = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) __UpperCAmelCase : List[Any] = """test""" __UpperCAmelCase : int = self.prepare_image_inputs() __UpperCAmelCase : Tuple = processor(text=__UpperCAmelCase , images=__UpperCAmelCase ) self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] ) # test if it raises when no input is passed with pytest.raises(__UpperCAmelCase ): processor() def __A ( self ) -> Union[str, Any]: '''simple docstring''' __UpperCAmelCase : Optional[Any] = self.get_image_processor() __UpperCAmelCase : List[Any] = self.get_tokenizer() __UpperCAmelCase : List[str] = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) __UpperCAmelCase : Tuple = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]] __UpperCAmelCase : Optional[Any] = processor.char_decode(__UpperCAmelCase ) __UpperCAmelCase : Union[str, Any] = tokenizer.batch_decode(__UpperCAmelCase ) __UpperCAmelCase : int = [seq.replace(""" """ , """""" ) for seq in decoded_tok] self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase ) def __A ( self ) -> Optional[int]: '''simple docstring''' __UpperCAmelCase : Dict = self.get_image_processor() __UpperCAmelCase : Optional[Any] = self.get_tokenizer() __UpperCAmelCase : Any = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) __UpperCAmelCase : str = None __UpperCAmelCase : Dict = self.prepare_image_inputs() __UpperCAmelCase : Union[str, Any] = processor(text=__UpperCAmelCase , images=__UpperCAmelCase ) self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) def __A ( self ) -> int: '''simple docstring''' __UpperCAmelCase : Any = self.get_image_processor() __UpperCAmelCase : List[str] = self.get_tokenizer() __UpperCAmelCase : str = MgpstrProcessor(tokenizer=__UpperCAmelCase , image_processor=__UpperCAmelCase ) __UpperCAmelCase : Tuple = torch.randn(1 , 27 , 38 ) __UpperCAmelCase : Union[str, Any] = torch.randn(1 , 27 , 50_257 ) __UpperCAmelCase : Any = torch.randn(1 , 27 , 30_522 ) __UpperCAmelCase : Tuple = processor.batch_decode([char_input, bpe_input, wp_input] ) self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
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'''simple docstring''' from collections.abc import Sequence def lowercase_ ( lowerCAmelCase__ : Sequence[int] | None = None ): """simple docstring""" if nums is None or not nums: raise ValueError("""Input sequence should not be empty""" ) __UpperCAmelCase : Any = nums[0] for i in range(1 , len(lowerCAmelCase__ ) ): __UpperCAmelCase : Union[str, Any] = nums[i] __UpperCAmelCase : List[Any] = max(lowerCAmelCase__ , ans + num , lowerCAmelCase__ ) return ans if __name__ == "__main__": import doctest doctest.testmod() # Try on a sample input from the user _UpperCamelCase = int(input('''Enter number of elements : ''').strip()) _UpperCamelCase = list(map(int, input('''\nEnter the numbers : ''').strip().split()))[:n] print(max_subsequence_sum(array))
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import logging import os from dataclasses import dataclass, field from typing import Dict, Optional import datasets import numpy as np import tensorflow as tf from transformers import ( AutoConfig, AutoTokenizer, EvalPrediction, HfArgumentParser, PreTrainedTokenizer, TFAutoModelForSequenceClassification, TFTrainer, TFTrainingArguments, ) from transformers.utils import logging as hf_logging hf_logging.set_verbosity_info() hf_logging.enable_default_handler() hf_logging.enable_explicit_format() def lowercase__ ( __snake_case : str , __snake_case : str , __snake_case : str , __snake_case : PreTrainedTokenizer , __snake_case : int , __snake_case : Optional[int] = None , ): '''simple docstring''' UpperCAmelCase_ : Any = {} if train_file is not None: UpperCAmelCase_ : Any = [train_file] if eval_file is not None: UpperCAmelCase_ : int = [eval_file] if test_file is not None: UpperCAmelCase_ : str = [test_file] UpperCAmelCase_ : int = datasets.load_dataset('csv' , data_files=__snake_case ) UpperCAmelCase_ : List[Any] = list(ds[list(files.keys() )[0]].features.keys() ) UpperCAmelCase_ : List[str] = features_name.pop(__snake_case ) UpperCAmelCase_ : Any = list(set(ds[list(files.keys() )[0]][label_name] ) ) UpperCAmelCase_ : List[str] = {label: i for i, label in enumerate(__snake_case )} UpperCAmelCase_ : Dict = tokenizer.model_input_names UpperCAmelCase_ : Optional[int] = {} if len(__snake_case ) == 1: for k in files.keys(): UpperCAmelCase_ : List[Any] = ds[k].map( lambda __snake_case : tokenizer.batch_encode_plus( example[features_name[0]] , truncation=__snake_case , max_length=__snake_case , padding='max_length' ) , batched=__snake_case , ) elif len(__snake_case ) == 2: for k in files.keys(): UpperCAmelCase_ : Union[str, Any] = ds[k].map( lambda __snake_case : tokenizer.batch_encode_plus( (example[features_name[0]], example[features_name[1]]) , truncation=__snake_case , max_length=__snake_case , padding='max_length' , ) , batched=__snake_case , ) def gen_train(): for ex in transformed_ds[datasets.Split.TRAIN]: UpperCAmelCase_ : Tuple = {k: v for k, v in ex.items() if k in input_names} UpperCAmelCase_ : str = labelaid[ex[label_name]] yield (d, label) def gen_val(): for ex in transformed_ds[datasets.Split.VALIDATION]: UpperCAmelCase_ : Any = {k: v for k, v in ex.items() if k in input_names} UpperCAmelCase_ : List[str] = labelaid[ex[label_name]] yield (d, label) def gen_test(): for ex in transformed_ds[datasets.Split.TEST]: UpperCAmelCase_ : Any = {k: v for k, v in ex.items() if k in input_names} UpperCAmelCase_ : Dict = labelaid[ex[label_name]] yield (d, label) UpperCAmelCase_ : str = ( tf.data.Dataset.from_generator( __snake_case , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , ) if datasets.Split.TRAIN in transformed_ds else None ) if train_ds is not None: UpperCAmelCase_ : Tuple = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) ) UpperCAmelCase_ : str = ( tf.data.Dataset.from_generator( __snake_case , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , ) if datasets.Split.VALIDATION in transformed_ds else None ) if val_ds is not None: UpperCAmelCase_ : int = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) ) UpperCAmelCase_ : List[str] = ( tf.data.Dataset.from_generator( __snake_case , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , ) if datasets.Split.TEST in transformed_ds else None ) if test_ds is not None: UpperCAmelCase_ : List[Any] = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) ) return train_ds, val_ds, test_ds, labelaid __UpperCAmelCase = logging.getLogger(__name__) @dataclass class lowerCamelCase : '''simple docstring''' _snake_case : int = field(metadata={'''help''': '''Which column contains the label'''} ) _snake_case : str = field(default=_snake_case , metadata={'''help''': '''The path of the training file'''} ) _snake_case : Optional[str] = field(default=_snake_case , metadata={'''help''': '''The path of the development file'''} ) _snake_case : Optional[str] = field(default=_snake_case , metadata={'''help''': '''The path of the test file'''} ) _snake_case : 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.''' ) } , ) _snake_case : bool = field( default=_snake_case , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} ) @dataclass class lowerCamelCase : '''simple docstring''' _snake_case : str = field( metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ) _snake_case : Optional[str] = field( default=_snake_case , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} ) _snake_case : Optional[str] = field( default=_snake_case , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} ) _snake_case : bool = field(default=_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. _snake_case : Optional[str] = field( default=_snake_case , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , ) def lowercase__ ( ): '''simple docstring''' UpperCAmelCase_ : Dict = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) ) UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : 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.' ) # Setup logging logging.basicConfig( format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , level=logging.INFO , ) logger.info( F"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, " F"16-bits training: {training_args.fpaa}" ) logger.info(F"Training/evaluation parameters {training_args}" ) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. UpperCAmelCase_ : List[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 , ) UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Dict = get_tfds( train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=__snake_case , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , ) UpperCAmelCase_ : Any = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(__snake_case ) , labelaid=__snake_case , idalabel={id: label for label, id in labelaid.items()} , finetuning_task='text-classification' , cache_dir=model_args.cache_dir , ) with training_args.strategy.scope(): UpperCAmelCase_ : Dict = TFAutoModelForSequenceClassification.from_pretrained( model_args.model_name_or_path , from_pt=bool('.bin' in model_args.model_name_or_path ) , config=__snake_case , cache_dir=model_args.cache_dir , ) def compute_metrics(__snake_case : EvalPrediction ) -> Dict: UpperCAmelCase_ : Tuple = np.argmax(p.predictions , axis=1 ) return {"acc": (preds == p.label_ids).mean()} # Initialize our Trainer UpperCAmelCase_ : List[str] = TFTrainer( model=__snake_case , args=__snake_case , train_dataset=__snake_case , eval_dataset=__snake_case , compute_metrics=__snake_case , ) # Training if training_args.do_train: trainer.train() trainer.save_model() tokenizer.save_pretrained(training_args.output_dir ) # Evaluation UpperCAmelCase_ : List[Any] = {} if training_args.do_eval: logger.info('*** Evaluate ***' ) UpperCAmelCase_ : Dict = trainer.evaluate() UpperCAmelCase_ : Dict = os.path.join(training_args.output_dir , 'eval_results.txt' ) with open(__snake_case , 'w' ) as writer: logger.info('***** Eval results *****' ) for key, value in result.items(): logger.info(F" {key} = {value}" ) writer.write(F"{key} = {value}\n" ) results.update(__snake_case ) return results if __name__ == "__main__": main()
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'''simple docstring''' from __future__ import annotations import time import numpy as np __lowerCAmelCase : Optional[Any] =[8, 5, 9, 7] __lowerCAmelCase : Optional[int] =[ [2, 0, 1, 1], [0, 1, 2, 1], [4, 0, 0, 3], [0, 2, 1, 0], [1, 0, 3, 0], ] __lowerCAmelCase : List[Any] =[ [3, 2, 1, 4], [0, 2, 5, 2], [5, 1, 0, 5], [1, 5, 3, 0], [3, 0, 3, 3], ] class UpperCAmelCase : def __init__( self :Optional[int] , lowercase_ :list[int] , lowercase_ :list[list[int]] , lowercase_ :list[list[int]] , )-> None: A__ = claim_vector A__ = allocated_resources_table A__ = maximum_claim_table def UpperCAmelCase_ ( self :Union[str, Any] )-> list[int]: return [ sum(p_item[i] for p_item in self.__allocated_resources_table ) for i in range(len(self.__allocated_resources_table[0] ) ) ] def UpperCAmelCase_ ( self :Tuple )-> list[int]: return np.array(self.__claim_vector ) - np.array( self.__processes_resource_summation() ) def UpperCAmelCase_ ( self :Dict )-> list[list[int]]: return [ list(np.array(self.__maximum_claim_table[i] ) - np.array(lowercase_ ) ) for i, allocated_resource in enumerate(self.__allocated_resources_table ) ] def UpperCAmelCase_ ( self :Any )-> dict[int, list[int]]: return {self.__need().index(lowercase_ ): i for i in self.__need()} def UpperCAmelCase_ ( self :Tuple , **lowercase_ :List[str] )-> None: A__ = self.__need() A__ = self.__allocated_resources_table A__ = self.__available_resources() A__ = self.__need_index_manager() for kw, val in kwargs.items(): if kw and val is True: self.__pretty_data() print("_" * 50 + "\n" ) while need_list: A__ = False for each_need in need_list: A__ = True for index, need in enumerate(lowercase_ ): if need > available_resources[index]: A__ = False break if execution: A__ = True # get the original index of the process from ind_ctrl db for original_need_index, need_clone in need_index_manager.items(): if each_need == need_clone: A__ = original_need_index print(F"Process {process_number + 1} is executing." ) # remove the process run from stack need_list.remove(lowercase_ ) # update available/freed resources stack A__ = np.array(lowercase_ ) + np.array( alloc_resources_table[process_number] ) print( "Updated available resource stack for processes: " + " ".join([str(lowercase_ ) for x in available_resources] ) ) break if safe: print("The process is in a safe state.\n" ) else: print("System in unsafe state. Aborting...\n" ) break def UpperCAmelCase_ ( self :Optional[Any] )-> Tuple: print(" " * 9 + "Allocated Resource Table" ) for item in self.__allocated_resources_table: print( F"P{self.__allocated_resources_table.index(lowercase_ ) + 1}" + " ".join(F"{it:>8}" for it in item ) + "\n" ) print(" " * 9 + "System Resource Table" ) for item in self.__maximum_claim_table: print( F"P{self.__maximum_claim_table.index(lowercase_ ) + 1}" + " ".join(F"{it:>8}" for it in item ) + "\n" ) print( "Current Usage by Active Processes: " + " ".join(str(lowercase_ ) for x in self.__claim_vector ) ) print( "Initial Available Resources: " + " ".join(str(lowercase_ ) for x in self.__available_resources() ) ) time.sleep(1 ) if __name__ == "__main__": import doctest doctest.testmod()
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import argparse lowercase_ = """docs/source/_static/js/custom.js""" def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[str]: with open(__snake_case , encoding='utf-8' , newline='\n' ) as f: lowercase__ = f.readlines() lowercase__ = 0 # First let's put the right version while not lines[index].startswith('const stableVersion =' ): index += 1 lowercase__ = F"""const stableVersion = \"v{version}\"\n""" # Then update the dictionary while not lines[index].startswith('const versionMapping = {' ): index += 1 # We go until the end while not lines[index].startswith('}' ): index += 1 # We add the new version at the end lines[index - 1] += F""" \"v{version}\": \"v{version}\",\n""" with open(__snake_case , 'w' , encoding='utf-8' , newline='\n' ) as f: f.writelines(__snake_case ) if __name__ == "__main__": lowercase_ = argparse.ArgumentParser() parser.add_argument("""--version""", help="""Release version.""") lowercase_ = parser.parse_args() update_custom_js(args.version)
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from typing import TYPE_CHECKING from ..utils import _LazyModule lowercase_ = { """config""": [ """EXTERNAL_DATA_FORMAT_SIZE_LIMIT""", """OnnxConfig""", """OnnxConfigWithPast""", """OnnxSeq2SeqConfigWithPast""", """PatchingSpec""", ], """convert""": ["""export""", """validate_model_outputs"""], """features""": ["""FeaturesManager"""], """utils""": ["""ParameterFormat""", """compute_serialized_parameters_size"""], } if TYPE_CHECKING: from .config import ( EXTERNAL_DATA_FORMAT_SIZE_LIMIT, OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast, PatchingSpec, ) from .convert import export, validate_model_outputs from .features import FeaturesManager from .utils import ParameterFormat, compute_serialized_parameters_size else: import sys lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) _SCREAMING_SNAKE_CASE : Dict = { "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"], "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _SCREAMING_SNAKE_CASE : List[str] = ["VisionTextDualEncoderModel"] try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _SCREAMING_SNAKE_CASE : int = ["FlaxVisionTextDualEncoderModel"] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _SCREAMING_SNAKE_CASE : int = ["TFVisionTextDualEncoderModel"] if TYPE_CHECKING: from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel try: if not is_flax_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel else: import sys _SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()["__file__"], _import_structure)
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class A__ : """simple docstring""" def __init__( self , __snake_case , __snake_case , __snake_case ): snake_case = name snake_case = value snake_case = weight def __repr__( self ): return F'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})''' def a_ ( self ): return self.value def a_ ( self ): return self.name def a_ ( self ): return self.weight def a_ ( self ): return self.value / self.weight def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ): """simple docstring""" snake_case = [] for i in range(len(UpperCamelCase_ ) ): menu.append(Things(name[i] ,value[i] ,weight[i] ) ) return menu def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ): """simple docstring""" snake_case = sorted(UpperCamelCase_ ,key=UpperCamelCase_ ,reverse=UpperCamelCase_ ) snake_case = [] snake_case , snake_case = 0.0, 0.0 for i in range(len(UpperCamelCase_ ) ): if (total_cost + items_copy[i].get_weight()) <= max_cost: result.append(items_copy[i] ) total_cost += items_copy[i].get_weight() total_value += items_copy[i].get_value() return (result, total_value) def UpperCAmelCase__ (): """simple docstring""" if __name__ == "__main__": import doctest doctest.testmod()
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"""simple docstring""" from __future__ import annotations import os from typing import Any import requests a : List[str] = '''https://api.github.com''' # https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user a : Tuple = BASE_URL + '''/user''' # https://github.com/settings/tokens a : Tuple = os.environ.get('''USER_TOKEN''', '''''') def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->dict[Any, Any]: '''simple docstring''' a : Dict = { "Authorization": F"""token {auth_token}""", "Accept": "application/vnd.github.v3+json", } return requests.get(_lowerCAmelCase , headers=_lowerCAmelCase ).json() if __name__ == "__main__": # pragma: no cover if USER_TOKEN: for key, value in fetch_github_info(USER_TOKEN).items(): print(F'''{key}: {value}''') else: raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
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"""simple docstring""" import jax.numpy as jnp from ...utils import logging from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel from .configuration_mta import MTaConfig a : List[str] = logging.get_logger(__name__) a : Optional[int] = '''T5Config''' def _SCREAMING_SNAKE_CASE ( _lowercase : jnp.array , _lowercase : int , _lowercase : int ) ->jnp.ndarray: '''simple docstring''' a : Tuple = jnp.zeros_like(_lowercase ) a : Tuple = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] ) a : Dict = shifted_input_ids.at[:, 0].set(_lowercase ) a : Optional[Any] = jnp.where(shifted_input_ids == -100 , _lowercase , _lowercase ) return shifted_input_ids class __UpperCamelCase ( a__ ): lowerCamelCase : Any ="""mt5""" lowerCamelCase : Dict =MTaConfig class __UpperCamelCase ( a__ ): lowerCamelCase : str ="""mt5""" lowerCamelCase : Tuple =MTaConfig class __UpperCamelCase ( a__ ): lowerCamelCase : List[str] ="""mt5""" lowerCamelCase : Tuple =MTaConfig
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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 __lowerCAmelCase = logging.get_logger(__name__) __lowerCAmelCase = { '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''', } class __magic_name__ ( _UpperCamelCase ): lowerCAmelCase : Tuple = 'blip_2_vision_model' def __init__( self : List[Any] ,_UpperCAmelCase : List[Any]=1408 ,_UpperCAmelCase : Optional[int]=6144 ,_UpperCAmelCase : List[str]=39 ,_UpperCAmelCase : Dict=16 ,_UpperCAmelCase : Optional[Any]=224 ,_UpperCAmelCase : Any=14 ,_UpperCAmelCase : Optional[int]="gelu" ,_UpperCAmelCase : Optional[int]=0.0_00_01 ,_UpperCAmelCase : Tuple=0.0 ,_UpperCAmelCase : Union[str, Any]=1E-10 ,_UpperCAmelCase : Any=True ,**_UpperCAmelCase : Dict ,): super().__init__(**_UpperCAmelCase ) _a : int = hidden_size _a : List[str] = intermediate_size _a : List[Any] = num_hidden_layers _a : Optional[int] = num_attention_heads _a : Tuple = patch_size _a : List[str] = image_size _a : int = initializer_range _a : Union[str, Any] = attention_dropout _a : Union[str, Any] = layer_norm_eps _a : List[str] = hidden_act _a : Any = qkv_bias @classmethod def __lowercase ( cls : Tuple ,_UpperCAmelCase : Union[str, os.PathLike] ,**_UpperCAmelCase : Any ): cls._set_token_in_kwargs(_UpperCAmelCase ) _a , _a : List[Any] = cls.get_config_dict(_UpperCAmelCase ,**_UpperCAmelCase ) # get the vision config dict if we are loading from Blip2Config if config_dict.get('model_type' ) == "blip-2": _a : Union[str, Any] = 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 __magic_name__ ( _UpperCamelCase ): lowerCAmelCase : Tuple = 'blip_2_qformer' def __init__( self : int ,_UpperCAmelCase : List[str]=30522 ,_UpperCAmelCase : Dict=768 ,_UpperCAmelCase : Tuple=12 ,_UpperCAmelCase : Any=12 ,_UpperCAmelCase : int=3072 ,_UpperCAmelCase : str="gelu" ,_UpperCAmelCase : Dict=0.1 ,_UpperCAmelCase : str=0.1 ,_UpperCAmelCase : int=512 ,_UpperCAmelCase : Union[str, Any]=0.02 ,_UpperCAmelCase : List[str]=1E-12 ,_UpperCAmelCase : int=0 ,_UpperCAmelCase : Optional[int]="absolute" ,_UpperCAmelCase : List[str]=2 ,_UpperCAmelCase : Any=1408 ,**_UpperCAmelCase : Optional[int] ,): super().__init__(pad_token_id=_UpperCAmelCase ,**_UpperCAmelCase ) _a : Any = vocab_size _a : Optional[int] = hidden_size _a : Dict = num_hidden_layers _a : Dict = num_attention_heads _a : str = hidden_act _a : Any = intermediate_size _a : Dict = hidden_dropout_prob _a : str = attention_probs_dropout_prob _a : Union[str, Any] = max_position_embeddings _a : Tuple = initializer_range _a : Optional[int] = layer_norm_eps _a : int = position_embedding_type _a : Optional[Any] = cross_attention_frequency _a : List[str] = encoder_hidden_size @classmethod def __lowercase ( cls : Dict ,_UpperCAmelCase : Union[str, os.PathLike] ,**_UpperCAmelCase : Union[str, Any] ): cls._set_token_in_kwargs(_UpperCAmelCase ) _a , _a : str = cls.get_config_dict(_UpperCAmelCase ,**_UpperCAmelCase ) # get the qformer config dict if we are loading from Blip2Config if config_dict.get('model_type' ) == "blip-2": _a : Any = 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(_UpperCAmelCase ,**_UpperCAmelCase ) class __magic_name__ ( _UpperCamelCase ): lowerCAmelCase : Tuple = 'blip-2' lowerCAmelCase : Any = True def __init__( self : Any ,_UpperCAmelCase : Union[str, Any]=None ,_UpperCAmelCase : Tuple=None ,_UpperCAmelCase : Dict=None ,_UpperCAmelCase : int=32 ,**_UpperCAmelCase : str ): super().__init__(**_UpperCAmelCase ) if vision_config is None: _a : str = {} logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' ) if qformer_config is None: _a : List[str] = {} logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' ) if text_config is None: _a : List[Any] = {} logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' ) _a : Optional[Any] = BlipaVisionConfig(**_UpperCAmelCase ) _a : Tuple = BlipaQFormerConfig(**_UpperCAmelCase ) _a : Optional[int] = text_config['model_type'] if 'model_type' in text_config else 'opt' _a : Any = CONFIG_MAPPING[text_model_type](**_UpperCAmelCase ) _a : Dict = self.text_config.tie_word_embeddings _a : Union[str, Any] = self.text_config.is_encoder_decoder _a : int = num_query_tokens _a : str = self.vision_config.hidden_size _a : Optional[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES _a : Optional[int] = 1.0 _a : str = 0.02 @classmethod def __lowercase ( cls : str ,_UpperCAmelCase : BlipaVisionConfig ,_UpperCAmelCase : BlipaQFormerConfig ,_UpperCAmelCase : PretrainedConfig ,**_UpperCAmelCase : List[Any] ,): return cls( vision_config=vision_config.to_dict() ,qformer_config=qformer_config.to_dict() ,text_config=text_config.to_dict() ,**_UpperCAmelCase ,) def __lowercase ( self : int ): _a : Dict = copy.deepcopy(self.__dict__ ) _a : Any = self.vision_config.to_dict() _a : Optional[int] = self.qformer_config.to_dict() _a : Dict = self.text_config.to_dict() _a : Optional[Any] = self.__class__.model_type return output
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# HF Trainer benchmarking tool # # This tool can be used to run and compare multiple dimensions of the HF Trainers args. # # It then prints a report once in github format with all the information that needs to be shared # with others and second time in a console-friendly format, so it's easier to use for tuning things up. # # The main idea is: # # ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \ # --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \ # --target-metric-key train_samples_per_second # # The variations can be any command line argument that you want to compare and not just dtype as in # the example. # # --variations allows you to compare variations in multiple dimensions. # # as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6 # times adding one of: # # 1. --tf32 0 --fp16 0 # 2. --tf32 0 --fp16 1 # 3. --tf32 0 --bf16 1 # 4. --tf32 1 --fp16 0 # 5. --tf32 1 --fp16 1 # 6. --tf32 1 --bf16 1 # # and print the results. This is just a cartesian product - and more than 2 dimensions can be used. # # If you want to rely on defaults, this: # --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' # is identical to this: # --variations '--tf32 0|--tf32 1' '|--fp16|--bf16' # # the leading empty variation in the 2nd dimension is a valid variation. # # So here we get the following 6 variations: # # 1. --tf32 0 # 2. --tf32 0 --fp16 # 3. --tf32 0 --bf16 # 4. --tf32 1 # 5. --tf32 1 --fp16 # 6. --tf32 1 --bf16 # # In this particular case we don't know what the default tf32 setting is as it's normally # pytorch-version dependent). That's why it's best to do an explicit setting of each variation: # `--tf32 0|--tf32 1` # # Here is a full example of a train: # # CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \ # --base-cmd \ # ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \ # --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \ # --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \ # --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \ # --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \ # --source_prefix "translate English to Romanian: " --warmup_steps 50 \ # --max_train_samples 20000 --dataloader_num_workers 2 ' \ # --target-metric-key train_samples_per_second --repeat-times 1 --variations \ # '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \ # --repeat-times 1 --base-variation '--tf32 0' # # and here is a possible output: # # # | Variation | Train | Diff | Train | # | | samples | % | loss | # | | per | | | # | | second | | | # |:----------------|----------:|-------:|--------:| # | --tf32 0 | 285.11 | 0 | 2.51 | # | --tf32 1 | 342.09 | 20 | 2.51 | # | --fp16 --tf32 0 | 423.49 | 49 | 2.51 | # | --fp16 --tf32 1 | 423.13 | 48 | 2.51 | # | --bf16 --tf32 0 | 416.80 | 46 | 2.52 | # | --bf16 --tf32 1 | 415.87 | 46 | 2.52 | # # # So you can quickly compare the different outcomes. # # Typically running each experiment once is enough, but if the environment is unstable you can # re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results. # # By default it'll use the lowest result as the base line to use as 100% and then compare the rest to # it as can be seen from the table above, but you can also specify which combination is the one to use as # the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0' # # --target-metric-key is there to tell the program which metrics to compare - the different metric keys are # inside output_dir/all_results.json. e.g., to measure eval performance instead of train use: # --target-metric-key eval_samples_per_second # but of course you will need to adjust the --base-cmd value in the example to perform evaluation as # well (as currently it doesn't) # import argparse import datetime import io import itertools import json import math import os import platform import re import shlex import subprocess import sys from pathlib import Path from statistics import fmean import pandas as pd import torch from tqdm import tqdm import transformers lowerCAmelCase__ = float('nan') class lowerCAmelCase__ : '''simple docstring''' def __init__( self , __lowerCamelCase) -> Optional[Any]: _A : List[Any] = sys.stdout _A : str = open(__lowerCamelCase , "a") def __getattr__( self , __lowerCamelCase) -> List[str]: return getattr(self.stdout , __lowerCamelCase) def _lowerCamelCase ( self , __lowerCamelCase) -> str: self.stdout.write(__lowerCamelCase) # strip tqdm codes self.file.write(re.sub(r"^.*\r" , "" , __lowerCamelCase , 0 , re.M)) def _UpperCAmelCase (UpperCamelCase__ : str=80 , UpperCamelCase__ : Tuple=False ): _A : Tuple = [] # deal with critical env vars _A : Dict = ["CUDA_VISIBLE_DEVICES"] for key in env_keys: _A : Optional[int] = os.environ.get(UpperCamelCase__ , UpperCamelCase__ ) if val is not None: cmd.append(f"{key}={val}" ) # python executable (not always needed if the script is executable) _A : Optional[int] = sys.executable if full_python_path else sys.executable.split("/" )[-1] cmd.append(UpperCamelCase__ ) # now the normal args cmd += list(map(shlex.quote , sys.argv ) ) # split up into up to MAX_WIDTH lines with shell multi-line escapes _A : Tuple = [] _A : Dict = "" while len(UpperCamelCase__ ) > 0: current_line += f"{cmd.pop(0 )} " if len(UpperCamelCase__ ) == 0 or len(UpperCamelCase__ ) + len(cmd[0] ) + 1 > max_width - 1: lines.append(UpperCamelCase__ ) _A : Union[str, Any] = "" return "\\\n".join(UpperCamelCase__ ) def _UpperCAmelCase (UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple ): # unwrap multi-line input _A : Union[str, Any] = re.sub(r"[\\\n]+" , " " , args.base_cmd ) # remove --output_dir if any and set our own _A : int = re.sub("--output_dir\s+[^\s]+" , "" , args.base_cmd ) args.base_cmd += f" --output_dir {output_dir}" # ensure we have --overwrite_output_dir _A : int = re.sub("--overwrite_output_dir\s+" , "" , args.base_cmd ) args.base_cmd += " --overwrite_output_dir" return [sys.executable] + shlex.split(args.base_cmd ) def _UpperCAmelCase (UpperCamelCase__ : List[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[str] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[int] ): # Enable to debug everything but the run itself, to do it fast and see the progress. # This is useful for debugging the output formatting quickly - we can remove it later once # everybody is happy with the output if 0: import random from time import sleep sleep(0 ) return dict( {k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 10.31, 1_00.2, 55.66_66, 2_22.22_22_22_22] )} , ) _A : Dict = subprocess.run(UpperCamelCase__ , capture_output=UpperCamelCase__ , text=UpperCamelCase__ ) if verbose: print("STDOUT" , result.stdout ) print("STDERR" , result.stderr ) # save the streams _A : Tuple = variation.replace(" " , "-" ) with open(Path(UpperCamelCase__ ) / f"log.{prefix}.stdout.txt" , "w" ) as f: f.write(result.stdout ) with open(Path(UpperCamelCase__ ) / f"log.{prefix}.stderr.txt" , "w" ) as f: f.write(result.stderr ) if result.returncode != 0: if verbose: print("failed" ) return {target_metric_key: nan} with io.open(f"{output_dir}/all_results.json" , "r" , encoding="utf-8" ) as f: _A : List[str] = json.load(UpperCamelCase__ ) # filter out just the keys we want return {k: v for k, v in metrics.items() if k in metric_keys} def _UpperCAmelCase (UpperCamelCase__ : int , UpperCamelCase__ : Dict , UpperCamelCase__ : Tuple , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Tuple , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str , UpperCamelCase__ : Any , ): _A : Union[str, Any] = [] _A : Optional[int] = [] _A : Any = f"{id}: {variation:<{longest_variation_len}}" _A : Dict = f"{preamble}: " _A : Union[str, Any] = set(report_metric_keys + [target_metric_key] ) for i in tqdm(range(UpperCamelCase__ ) , desc=UpperCamelCase__ , leave=UpperCamelCase__ ): _A : Optional[Any] = process_run_single( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) _A : Optional[Any] = single_run_metrics[target_metric_key] if not math.isnan(UpperCamelCase__ ): metrics.append(UpperCamelCase__ ) results.append(UpperCamelCase__ ) outcome += "✓" else: outcome += "✘" _A : str = f"\33[2K\r{outcome}" if len(UpperCamelCase__ ) > 0: _A : List[str] = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()} _A : Any = round(mean_metrics[target_metric_key] , 2 ) _A : Tuple = f"{outcome} {mean_target}" if len(UpperCamelCase__ ) > 1: results_str += f" {tuple(round(UpperCamelCase__ , 2 ) for x in results )}" print(UpperCamelCase__ ) _A : Optional[int] = variation return mean_metrics else: print(UpperCamelCase__ ) return {variation_key: variation, target_metric_key: nan} def _UpperCAmelCase (): _A : int = torch.cuda.get_device_properties(torch.device("cuda" ) ) return f"\nDatetime : {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S' )}\n\nSoftware:\ntransformers: {transformers.__version__}\ntorch : {torch.__version__}\ncuda : {torch.version.cuda}\npython : {platform.python_version()}\n\nHardware:\n{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB\n" def _UpperCAmelCase (UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Dict ): _A : Any = pd.DataFrame(UpperCamelCase__ ) _A : List[str] = "variation" _A : List[Any] = "diff_%" _A : int = nan if base_variation is not None and len(df[df[variation_key] == base_variation] ): # this may still return nan _A : int = df.loc[df[variation_key] == base_variation][target_metric_key].item() if math.isnan(UpperCamelCase__ ): # as a fallback, use the minimal value as the sentinel _A : List[str] = df.loc[df[target_metric_key] != nan][target_metric_key].min() # create diff column if possible if not math.isnan(UpperCamelCase__ ): _A : Optional[Any] = df.apply( lambda UpperCamelCase__ : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value ) if not math.isnan(r[target_metric_key] ) else 0 , axis="columns" , ) # re-order columns _A : Union[str, Any] = [variation_key, target_metric_key, diff_key, *report_metric_keys] _A : Any = df.reindex(UpperCamelCase__ , axis="columns" ) # reorder cols # capitalize _A : Tuple = df.rename(str.capitalize , axis="columns" ) # make the cols as narrow as possible _A : List[str] = df.rename(lambda UpperCamelCase__ : c.replace("_" , "<br>" ) , axis="columns" ) _A : Union[str, Any] = df.rename(lambda UpperCamelCase__ : c.replace("_" , "\n" ) , axis="columns" ) _A : Optional[int] = ["", "Copy between the cut-here-lines and paste as is to github or a forum"] report += ["----------8<-----------------8<--------"] report += ["*** Results:", df_github.to_markdown(index=UpperCamelCase__ , floatfmt=".2f" )] report += ["```"] report += ["*** Setup:", get_versions()] report += ["*** The benchmark command line was:", get_original_command()] report += ["```"] report += ["----------8<-----------------8<--------"] report += ["*** Results (console):", df_console.to_markdown(index=UpperCamelCase__ , floatfmt=".2f" )] print("\n\n".join(UpperCamelCase__ ) ) def _UpperCAmelCase (): _A : int = argparse.ArgumentParser() parser.add_argument( "--base-cmd" , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help="Base cmd" , ) parser.add_argument( "--variations" , default=UpperCamelCase__ , type=UpperCamelCase__ , nargs="+" , required=UpperCamelCase__ , help="Multi-dimensional variations, example: '|--fp16|--bf16' '|--tf32'" , ) parser.add_argument( "--base-variation" , default=UpperCamelCase__ , type=UpperCamelCase__ , help="Baseline variation to compare to. if None the minimal target value will be used to compare against" , ) parser.add_argument( "--target-metric-key" , default=UpperCamelCase__ , type=UpperCamelCase__ , required=UpperCamelCase__ , help="Target metric key in output_dir/all_results.json, e.g., train_samples_per_second" , ) parser.add_argument( "--report-metric-keys" , default="" , type=UpperCamelCase__ , help="Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., 'train_loss train_samples" , ) parser.add_argument( "--repeat-times" , default=1 , type=UpperCamelCase__ , help="How many times to re-run each variation - an average will be reported" , ) parser.add_argument( "--output_dir" , default="output_benchmark" , type=UpperCamelCase__ , help="The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked" , ) parser.add_argument( "--verbose" , default=UpperCamelCase__ , action="store_true" , help="Whether to show the outputs of each run or just the benchmark progress" , ) _A : int = parser.parse_args() _A : Union[str, Any] = args.output_dir Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ ) _A : Tuple = get_base_command(UpperCamelCase__ , UpperCamelCase__ ) # split each dimension into its --foo variations _A : Dict = [list(map(str.strip , re.split(r"\|" , UpperCamelCase__ ) ) ) for x in args.variations] # build a cartesian product of dimensions and convert those back into cmd-line arg strings, # while stripping white space for inputs that were empty _A : Union[str, Any] = list(map(str.strip , map(" ".join , itertools.product(*UpperCamelCase__ ) ) ) ) _A : Union[str, Any] = max(len(UpperCamelCase__ ) for x in variations ) # split wanted keys _A : str = args.report_metric_keys.split() # capture prints into a log file for convenience _A : Optional[int] = f"benchmark-report-{datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S' )}.txt" print(f"\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt" ) print(f"and this script's output is also piped into {report_fn}" ) _A : Tuple = Tee(UpperCamelCase__ ) print(f"\n*** Running {len(UpperCamelCase__ )} benchmarks:" ) print(f"Base command: {' '.join(UpperCamelCase__ )}" ) _A : str = "variation" _A : Union[str, Any] = [] for id, variation in enumerate(tqdm(UpperCamelCase__ , desc="Total completion: " , leave=UpperCamelCase__ ) ): _A : Dict = base_cmd + variation.split() results.append( process_run( id + 1 , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , args.target_metric_key , UpperCamelCase__ , args.repeat_times , UpperCamelCase__ , args.verbose , ) ) process_results(UpperCamelCase__ , args.target_metric_key , UpperCamelCase__ , args.base_variation , UpperCamelCase__ ) if __name__ == "__main__": main()
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0
'''simple docstring''' from collections.abc import Iterator, MutableMapping from dataclasses import dataclass from typing import Generic, TypeVar SCREAMING_SNAKE_CASE__ = TypeVar('KEY') SCREAMING_SNAKE_CASE__ = TypeVar('VAL') @dataclass(frozen=lowerCamelCase , slots=lowerCamelCase ) class a_ ( Generic[KEY, VAL] ): lowercase = 42 lowercase = 42 class a_ ( _Item ): def __init__( self ) -> None: """simple docstring""" super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) def __bool__( self ) -> bool: """simple docstring""" return False SCREAMING_SNAKE_CASE__ = _DeletedItem() class a_ ( MutableMapping[KEY, VAL] ): def __init__( self , _SCREAMING_SNAKE_CASE = 8 , _SCREAMING_SNAKE_CASE = 0.7_5 ) -> None: """simple docstring""" UpperCamelCase = initial_block_size UpperCamelCase = [None] * initial_block_size assert 0.0 < capacity_factor < 1.0 UpperCamelCase = capacity_factor UpperCamelCase = 0 def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int: """simple docstring""" return hash(_SCREAMING_SNAKE_CASE ) % len(self._buckets ) def A__ ( self , _SCREAMING_SNAKE_CASE ) -> int: """simple docstring""" return (ind + 1) % len(self._buckets ) def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool: """simple docstring""" UpperCamelCase = self._buckets[ind] if not stored: UpperCamelCase = _Item(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) self._len += 1 return True elif stored.key == key: UpperCamelCase = _Item(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) return True else: return False def A__ ( self ) -> bool: """simple docstring""" UpperCamelCase = len(self._buckets ) * self._capacity_factor return len(self ) >= int(_SCREAMING_SNAKE_CASE ) def A__ ( self ) -> bool: """simple docstring""" if len(self._buckets ) <= self._initial_block_size: return False UpperCamelCase = len(self._buckets ) * self._capacity_factor / 2 return len(self ) < limit def A__ ( self , _SCREAMING_SNAKE_CASE ) -> None: """simple docstring""" UpperCamelCase = self._buckets UpperCamelCase = [None] * new_size UpperCamelCase = 0 for item in old_buckets: if item: self._add_item(item.key , item.val ) def A__ ( self ) -> None: """simple docstring""" self._resize(len(self._buckets ) * 2 ) def A__ ( self ) -> None: """simple docstring""" self._resize(len(self._buckets ) // 2 ) def A__ ( self , _SCREAMING_SNAKE_CASE ) -> Iterator[int]: """simple docstring""" UpperCamelCase = self._get_bucket_index(_SCREAMING_SNAKE_CASE ) for _ in range(len(self._buckets ) ): yield ind UpperCamelCase = self._get_next_ind(_SCREAMING_SNAKE_CASE ) def A__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None: """simple docstring""" for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ): if self._try_set(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ): break def __setitem__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> None: """simple docstring""" if self._is_full(): self._size_up() self._add_item(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) def __delitem__( self , _SCREAMING_SNAKE_CASE ) -> None: """simple docstring""" for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ): UpperCamelCase = self._buckets[ind] if item is None: raise KeyError(_SCREAMING_SNAKE_CASE ) if item is _deleted: continue if item.key == key: UpperCamelCase = _deleted self._len -= 1 break if self._is_sparse(): self._size_down() def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> VAL: """simple docstring""" for ind in self._iterate_buckets(_SCREAMING_SNAKE_CASE ): UpperCamelCase = self._buckets[ind] if item is None: break if item is _deleted: continue if item.key == key: return item.val raise KeyError(_SCREAMING_SNAKE_CASE ) def __len__( self ) -> int: """simple docstring""" return self._len def __iter__( self ) -> Iterator[KEY]: """simple docstring""" yield from (item.key for item in self._buckets if item) def __repr__( self ) -> str: """simple docstring""" UpperCamelCase = """ ,""".join( F"{item.key}: {item.val}" for item in self._buckets if item ) return F"HashMap({val_string})"
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'''simple docstring''' import argparse import numpy as np import torch from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging logging.set_verbosity_info() SCREAMING_SNAKE_CASE__ = logging.get_logger('transformers.models.speecht5') def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> List[str]: hf_model.apply_weight_norm() UpperCamelCase = checkpoint["""input_conv.weight_g"""] UpperCamelCase = checkpoint["""input_conv.weight_v"""] UpperCamelCase = checkpoint["""input_conv.bias"""] for i in range(len(config.upsample_rates ) ): UpperCamelCase = checkpoint[F"upsamples.{i}.1.weight_g"] UpperCamelCase = checkpoint[F"upsamples.{i}.1.weight_v"] UpperCamelCase = checkpoint[F"upsamples.{i}.1.bias"] for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ): for j in range(len(config.resblock_dilation_sizes ) ): UpperCamelCase = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_g"] UpperCamelCase = checkpoint[F"blocks.{i}.convs1.{j}.1.weight_v"] UpperCamelCase = checkpoint[F"blocks.{i}.convs1.{j}.1.bias"] UpperCamelCase = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_g"] UpperCamelCase = checkpoint[F"blocks.{i}.convs2.{j}.1.weight_v"] UpperCamelCase = checkpoint[F"blocks.{i}.convs2.{j}.1.bias"] UpperCamelCase = checkpoint["""output_conv.1.weight_g"""] UpperCamelCase = checkpoint["""output_conv.1.weight_v"""] UpperCamelCase = checkpoint["""output_conv.1.bias"""] hf_model.remove_weight_norm() @torch.no_grad() def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase=None , )-> List[Any]: if config_path is not None: UpperCamelCase = SpeechTaHifiGanConfig.from_pretrained(__UpperCamelCase ) else: UpperCamelCase = SpeechTaHifiGanConfig() UpperCamelCase = SpeechTaHifiGan(__UpperCamelCase ) UpperCamelCase = torch.load(__UpperCamelCase ) load_weights(orig_checkpoint["""model"""]["""generator"""] , __UpperCamelCase , __UpperCamelCase ) UpperCamelCase = np.load(__UpperCamelCase ) UpperCamelCase = stats[0].reshape(-1 ) UpperCamelCase = stats[1].reshape(-1 ) UpperCamelCase = torch.from_numpy(__UpperCamelCase ).float() UpperCamelCase = torch.from_numpy(__UpperCamelCase ).float() model.save_pretrained(__UpperCamelCase ) if repo_id: print("""Pushing to the hub...""" ) model.push_to_hub(__UpperCamelCase ) if __name__ == "__main__": SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser() parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint') parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file') parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert') parser.add_argument( '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.' ) parser.add_argument( '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.' ) SCREAMING_SNAKE_CASE__ = parser.parse_args() convert_hifigan_checkpoint( args.checkpoint_path, args.stats_path, args.pytorch_dump_folder_path, args.config_path, args.push_to_hub, )
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import argparse from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt if __name__ == "__main__": a__ = argparse.ArgumentParser() parser.add_argument( '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.''' ) parser.add_argument( '''--original_config_file''', type=str, required=True, help='''The YAML config file corresponding to the original architecture.''', ) parser.add_argument( '''--num_in_channels''', default=None, type=int, help='''The number of input channels. If `None` number of input channels will be automatically inferred.''', ) parser.add_argument( '''--image_size''', default=512, type=int, help=( '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2''' ''' Base. Use 768 for Stable Diffusion v2.''' ), ) parser.add_argument( '''--extract_ema''', action='''store_true''', help=( '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights''' ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield''' ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.''' ), ) parser.add_argument( '''--upcast_attention''', action='''store_true''', help=( '''Whether the attention computation should always be upcasted. This is necessary when running stable''' ''' diffusion 2.1.''' ), ) parser.add_argument( '''--from_safetensors''', action='''store_true''', help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''', ) parser.add_argument( '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.''', ) parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''') parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''') def __UpperCAmelCase ( __a : Any ) -> Optional[int]: """simple docstring""" if string == "True": return True elif string == "False": return False else: raise ValueError(F"""could not parse string as bool {string}""" ) parser.add_argument( '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool ) parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int) a__ = parser.parse_args() a__ = download_controlnet_from_original_ckpt( checkpoint_path=args.checkpoint_path, original_config_file=args.original_config_file, image_size=args.image_size, extract_ema=args.extract_ema, num_in_channels=args.num_in_channels, upcast_attention=args.upcast_attention, from_safetensors=args.from_safetensors, device=args.device, use_linear_projection=args.use_linear_projection, cross_attention_dim=args.cross_attention_dim, ) controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
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'''simple docstring''' from math import factorial, radians def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 18 , _SCREAMING_SNAKE_CASE = 10 ): _snake_case = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0) # Converting from degrees to radians _snake_case = radians(_SCREAMING_SNAKE_CASE ) _snake_case = angle_in_radians _snake_case = 3 _snake_case = -1 for _ in range(_SCREAMING_SNAKE_CASE ): result += (b * (angle_in_radians**a)) / factorial(_SCREAMING_SNAKE_CASE ) _snake_case = -b # One positive term and the next will be negative and so on... a += 2 # Increased by 2 for every term. return round(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if __name__ == "__main__": __import__('doctest').testmod()
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available _lowerCamelCase = {'tokenization_herbert': ['HerbertTokenizer']} try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: _lowerCamelCase = ['HerbertTokenizerFast'] if TYPE_CHECKING: from .tokenization_herbert import HerbertTokenizer try: if not is_tokenizers_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .tokenization_herbert_fast import HerbertTokenizerFast else: import sys _lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging _lowerCamelCase = logging.get_logger(__name__) _lowerCamelCase = { 'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json', } class a ( _A ): '''simple docstring''' lowerCAmelCase : Optional[Any] = 'data2vec-text' def __init__( self : Optional[Any] , __snake_case : Optional[int]=3_05_22 , __snake_case : List[str]=7_68 , __snake_case : Tuple=12 , __snake_case : int=12 , __snake_case : Union[str, Any]=30_72 , __snake_case : List[Any]="gelu" , __snake_case : Any=0.1 , __snake_case : Union[str, Any]=0.1 , __snake_case : Tuple=5_12 , __snake_case : str=2 , __snake_case : str=0.02 , __snake_case : List[Any]=1E-12 , __snake_case : Any=1 , __snake_case : List[Any]=0 , __snake_case : Dict=2 , __snake_case : Any="absolute" , __snake_case : Union[str, Any]=True , __snake_case : Any=None , **__snake_case : List[Any] , ): super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case ) UpperCAmelCase_ = vocab_size UpperCAmelCase_ = hidden_size UpperCAmelCase_ = num_hidden_layers UpperCAmelCase_ = num_attention_heads UpperCAmelCase_ = hidden_act UpperCAmelCase_ = intermediate_size UpperCAmelCase_ = hidden_dropout_prob UpperCAmelCase_ = attention_probs_dropout_prob UpperCAmelCase_ = max_position_embeddings UpperCAmelCase_ = type_vocab_size UpperCAmelCase_ = initializer_range UpperCAmelCase_ = layer_norm_eps UpperCAmelCase_ = position_embedding_type UpperCAmelCase_ = use_cache UpperCAmelCase_ = classifier_dropout class a ( _A ): '''simple docstring''' @property def lowerCamelCase_ ( self : str ): if self.task == "multiple-choice": UpperCAmelCase_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''} else: UpperCAmelCase_ = {0: '''batch''', 1: '''sequence'''} return OrderedDict( [ ('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ] )
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"""simple docstring""" import json import os import shutil import tempfile import unittest import numpy as np import pytest from transformers import MgpstrTokenizer from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES from transformers.testing_utils import require_torch, require_vision from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import MgpstrProcessor, ViTImageProcessor @require_torch @require_vision class __A ( unittest.TestCase ): '''simple docstring''' lowerCAmelCase : List[str] = ViTImageProcessor if is_vision_available() else None @property def UpperCAmelCase ( self : str ) -> Union[str, Any]: """simple docstring""" return self.image_processor_tester.prepare_image_processor_dict() def UpperCAmelCase ( self : List[str] ) -> List[str]: """simple docstring""" lowercase__ : int = (3, 32, 128) lowercase__ : List[str] = tempfile.mkdtemp() # fmt: off lowercase__ : Any = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z'''] # fmt: on lowercase__ : Tuple = dict(zip(_snake_case ,range(len(_snake_case ) ) ) ) lowercase__ : Optional[Any] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['''vocab_file'''] ) with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as fp: fp.write(json.dumps(_snake_case ) + '''\n''' ) lowercase__ : Optional[Any] = { '''do_normalize''': False, '''do_resize''': True, '''image_processor_type''': '''ViTImageProcessor''', '''resample''': 3, '''size''': {'''height''': 32, '''width''': 128}, } lowercase__ : Optional[int] = os.path.join(self.tmpdirname ,_snake_case ) with open(self.image_processor_file ,'''w''' ,encoding='''utf-8''' ) as fp: json.dump(_snake_case ,_snake_case ) def UpperCAmelCase ( self : Any ,**_snake_case : Dict ) -> List[str]: """simple docstring""" return MgpstrTokenizer.from_pretrained(self.tmpdirname ,**_snake_case ) def UpperCAmelCase ( self : Any ,**_snake_case : List[Any] ) -> Dict: """simple docstring""" return ViTImageProcessor.from_pretrained(self.tmpdirname ,**_snake_case ) def UpperCAmelCase ( self : Tuple ) -> Any: """simple docstring""" shutil.rmtree(self.tmpdirname ) def UpperCAmelCase ( self : int ) -> List[str]: """simple docstring""" lowercase__ : Optional[Any] = np.random.randint(255 ,size=(3, 30, 400) ,dtype=np.uinta ) lowercase__ : Any = Image.fromarray(np.moveaxis(_snake_case ,0 ,-1 ) ) return image_input def UpperCAmelCase ( self : Optional[int] ) -> int: """simple docstring""" lowercase__ : List[str] = self.get_tokenizer() lowercase__ : Union[str, Any] = self.get_image_processor() lowercase__ : int = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) processor.save_pretrained(self.tmpdirname ) lowercase__ : Dict = MgpstrProcessor.from_pretrained(self.tmpdirname ,use_fast=_snake_case ) self.assertEqual(processor.char_tokenizer.get_vocab() ,tokenizer.get_vocab() ) self.assertIsInstance(processor.char_tokenizer ,_snake_case ) self.assertEqual(processor.image_processor.to_json_string() ,image_processor.to_json_string() ) self.assertIsInstance(processor.image_processor ,_snake_case ) def UpperCAmelCase ( self : int ) -> Dict: """simple docstring""" lowercase__ : Dict = self.get_tokenizer() lowercase__ : Dict = self.get_image_processor() lowercase__ : List[str] = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) processor.save_pretrained(self.tmpdirname ) lowercase__ : List[Any] = self.get_tokenizer(bos_token='''(BOS)''' ,eos_token='''(EOS)''' ) lowercase__ : List[str] = self.get_image_processor(do_normalize=_snake_case ,padding_value=1.0 ) lowercase__ : Dict = MgpstrProcessor.from_pretrained( self.tmpdirname ,bos_token='''(BOS)''' ,eos_token='''(EOS)''' ,do_normalize=_snake_case ,padding_value=1.0 ) self.assertEqual(processor.char_tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() ) self.assertIsInstance(processor.char_tokenizer ,_snake_case ) self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() ) self.assertIsInstance(processor.image_processor ,_snake_case ) def UpperCAmelCase ( self : Dict ) -> Dict: """simple docstring""" lowercase__ : List[Any] = self.get_image_processor() lowercase__ : Dict = self.get_tokenizer() lowercase__ : List[Any] = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) lowercase__ : Tuple = self.prepare_image_inputs() lowercase__ : Any = image_processor(_snake_case ,return_tensors='''np''' ) lowercase__ : str = processor(images=_snake_case ,return_tensors='''np''' ) for key in input_image_proc.keys(): self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1e-2 ) def UpperCAmelCase ( self : List[str] ) -> Any: """simple docstring""" lowercase__ : List[str] = self.get_image_processor() lowercase__ : Any = self.get_tokenizer() lowercase__ : str = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) lowercase__ : Tuple = '''test''' lowercase__ : str = processor(text=_snake_case ) lowercase__ : List[Any] = tokenizer(_snake_case ) for key in encoded_tok.keys(): self.assertListEqual(encoded_tok[key] ,encoded_processor[key] ) def UpperCAmelCase ( self : int ) -> Any: """simple docstring""" lowercase__ : Any = self.get_image_processor() lowercase__ : Optional[int] = self.get_tokenizer() lowercase__ : Union[str, Any] = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) lowercase__ : Union[str, Any] = '''test''' lowercase__ : List[Any] = self.prepare_image_inputs() lowercase__ : Tuple = processor(text=_snake_case ,images=_snake_case ) self.assertListEqual(list(inputs.keys() ) ,['''pixel_values''', '''labels'''] ) # test if it raises when no input is passed with pytest.raises(_snake_case ): processor() def UpperCAmelCase ( self : Tuple ) -> str: """simple docstring""" lowercase__ : Optional[int] = self.get_image_processor() lowercase__ : Dict = self.get_tokenizer() lowercase__ : Union[str, Any] = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) lowercase__ : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]] lowercase__ : List[Any] = processor.char_decode(_snake_case ) lowercase__ : Dict = tokenizer.batch_decode(_snake_case ) lowercase__ : Optional[int] = [seq.replace(''' ''' ,'''''' ) for seq in decoded_tok] self.assertListEqual(_snake_case ,_snake_case ) def UpperCAmelCase ( self : Dict ) -> List[str]: """simple docstring""" lowercase__ : int = self.get_image_processor() lowercase__ : Union[str, Any] = self.get_tokenizer() lowercase__ : int = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) lowercase__ : List[Any] = None lowercase__ : List[Any] = self.prepare_image_inputs() lowercase__ : Tuple = processor(text=_snake_case ,images=_snake_case ) self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names ) def UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]: """simple docstring""" lowercase__ : Optional[int] = self.get_image_processor() lowercase__ : Tuple = self.get_tokenizer() lowercase__ : Any = MgpstrProcessor(tokenizer=_snake_case ,image_processor=_snake_case ) lowercase__ : Tuple = torch.randn(1 ,27 ,38 ) lowercase__ : int = torch.randn(1 ,27 ,50_257 ) lowercase__ : List[Any] = torch.randn(1 ,27 ,30_522 ) lowercase__ : Any = processor.batch_decode([char_input, bpe_input, wp_input] ) self.assertListEqual(list(results.keys() ) ,['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds'''] )
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"""simple docstring""" import os from argparse import ArgumentParser from typing import List import torch.utils.data from datasets import Dataset, IterableDataset from datasets.distributed import split_dataset_by_node lowerCAmelCase_ = 4 lowerCAmelCase_ = 3 class __A ( A_ ): '''simple docstring''' pass def __UpperCAmelCase ( __lowerCamelCase ) -> Dict: for shard in shards: for i in range(__lowerCamelCase ): yield {"i": i, "shard": shard} def __UpperCAmelCase ( ) -> Tuple: lowercase__ : int = int(os.environ['''RANK'''] ) lowercase__ : str = int(os.environ['''WORLD_SIZE'''] ) lowercase__ : List[Any] = ArgumentParser() parser.add_argument('''--streaming''' , type=__lowerCamelCase ) parser.add_argument('''--local_rank''' , type=__lowerCamelCase ) parser.add_argument('''--num_workers''' , type=__lowerCamelCase , default=0 ) lowercase__ : int = parser.parse_args() lowercase__ : Optional[Any] = args.streaming lowercase__ : List[Any] = args.num_workers lowercase__ : Optional[Any] = {'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase )]} lowercase__ : Dict = IterableDataset.from_generator(__lowerCamelCase , gen_kwargs=__lowerCamelCase ) if not streaming: lowercase__ : int = Dataset.from_list(list(__lowerCamelCase ) ) lowercase__ : int = split_dataset_by_node(__lowerCamelCase , rank=__lowerCamelCase , world_size=__lowerCamelCase ) lowercase__ : Optional[Any] = torch.utils.data.DataLoader(__lowerCamelCase , num_workers=__lowerCamelCase ) lowercase__ : Optional[Any] = NUM_SHARDS * NUM_ITEMS_PER_SHARD lowercase__ : str = full_size // world_size expected_local_size += int(rank < (full_size % world_size) ) lowercase__ : str = sum(1 for _ in dataloader ) if local_size != expected_local_size: raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}""" ) if __name__ == "__main__": main()
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"""simple docstring""" import timeit import numpy as np import datasets from datasets.arrow_writer import ArrowWriter from datasets.features.features import _ArrayXD def A_ ( _lowerCAmelCase : Tuple ): """simple docstring""" def wrapper(*_lowerCAmelCase : List[str], **_lowerCAmelCase : Any ): _a = timeit.default_timer() _a = func(*_lowerCAmelCase, **_lowerCAmelCase ) _a = timeit.default_timer() - starttime return delta _a = func.__name__ return wrapper def A_ ( _lowerCAmelCase : dict, _lowerCAmelCase : Union[str, Any]=1_00, _lowerCAmelCase : Union[str, Any]=None ): """simple docstring""" _a = [] _a = seq_shapes or {} for i in range(_lowerCAmelCase ): _a = {} for col_id, (k, v) in enumerate(features.items() ): if isinstance(_lowerCAmelCase, _ArrayXD ): _a = np.random.rand(*v.shape ).astype(v.dtype ) elif isinstance(_lowerCAmelCase, datasets.Value ): if v.dtype == "string": _a = '''The small grey turtle was surprisingly fast when challenged.''' else: _a = np.random.randint(10, size=1 ).astype(v.dtype ).item() elif isinstance(_lowerCAmelCase, datasets.Sequence ): while isinstance(_lowerCAmelCase, datasets.Sequence ): _a = v.feature _a = seq_shapes[k] _a = np.random.rand(*_lowerCAmelCase ).astype(v.dtype ) _a = data dummy_data.append((i, example) ) return dummy_data def A_ ( _lowerCAmelCase : Optional[int], _lowerCAmelCase : Any, _lowerCAmelCase : int=1_00, _lowerCAmelCase : Tuple=None ): """simple docstring""" _a = generate_examples(_lowerCAmelCase, num_examples=_lowerCAmelCase, seq_shapes=_lowerCAmelCase ) with ArrowWriter(features=_lowerCAmelCase, path=_lowerCAmelCase ) as writer: for key, record in dummy_data: _a = features.encode_example(_lowerCAmelCase ) writer.write(_lowerCAmelCase ) _a , _a = writer.finalize() if not num_final_examples == num_examples: raise ValueError( f'Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.' ) _a = datasets.Dataset.from_file(filename=_lowerCAmelCase, info=datasets.DatasetInfo(features=_lowerCAmelCase ) ) return dataset
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"""simple docstring""" import argparse import torch from safetensors.torch import load_file from diffusers import StableDiffusionPipeline def A_ ( _lowerCAmelCase : Union[str, Any], _lowerCAmelCase : Tuple, _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[Any], _lowerCAmelCase : Optional[int] ): """simple docstring""" _a = StableDiffusionPipeline.from_pretrained(_lowerCAmelCase, torch_dtype=torch.floataa ) # load LoRA weight from .safetensors _a = load_file(_lowerCAmelCase ) _a = [] # directly update weight in diffusers model for key in state_dict: # it is suggested to print out the key, it usually will be something like below # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight" # as we have set the alpha beforehand, so just skip if ".alpha" in key or key in visited: continue if "text" in key: _a = key.split('''.''' )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_''' )[-1].split('''_''' ) _a = pipeline.text_encoder else: _a = key.split('''.''' )[0].split(LORA_PREFIX_UNET + '''_''' )[-1].split('''_''' ) _a = pipeline.unet # find the target layer _a = layer_infos.pop(0 ) while len(_lowerCAmelCase ) > -1: try: _a = curr_layer.__getattr__(_lowerCAmelCase ) if len(_lowerCAmelCase ) > 0: _a = layer_infos.pop(0 ) elif len(_lowerCAmelCase ) == 0: break except Exception: if len(_lowerCAmelCase ) > 0: temp_name += "_" + layer_infos.pop(0 ) else: _a = layer_infos.pop(0 ) _a = [] if "lora_down" in key: pair_keys.append(key.replace('''lora_down''', '''lora_up''' ) ) pair_keys.append(_lowerCAmelCase ) else: pair_keys.append(_lowerCAmelCase ) pair_keys.append(key.replace('''lora_up''', '''lora_down''' ) ) # update weight if len(state_dict[pair_keys[0]].shape ) == 4: _a = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa ) _a = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa ) curr_layer.weight.data += alpha * torch.mm(_lowerCAmelCase, _lowerCAmelCase ).unsqueeze(2 ).unsqueeze(3 ) else: _a = state_dict[pair_keys[0]].to(torch.floataa ) _a = state_dict[pair_keys[1]].to(torch.floataa ) curr_layer.weight.data += alpha * torch.mm(_lowerCAmelCase, _lowerCAmelCase ) # update visited list for item in pair_keys: visited.append(_lowerCAmelCase ) return pipeline if __name__ == "__main__": __snake_case = argparse.ArgumentParser() parser.add_argument( '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.''' ) parser.add_argument( '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.''' ) parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''') parser.add_argument( '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors''' ) parser.add_argument( '''--lora_prefix_text_encoder''', default='''lora_te''', type=str, help='''The prefix of text encoder weight in safetensors''', ) parser.add_argument('''--alpha''', default=0.75, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''') parser.add_argument( '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.''' ) parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''') __snake_case = parser.parse_args() __snake_case = args.base_model_path __snake_case = args.checkpoint_path __snake_case = args.dump_path __snake_case = args.lora_prefix_unet __snake_case = args.lora_prefix_text_encoder __snake_case = args.alpha __snake_case = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha) __snake_case = pipe.to(args.device) pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
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"""simple docstring""" from ..utils import DummyObject, requires_backends class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : str ,*_snake_case : List[Any] ,**_snake_case : Dict ) -> int: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Dict = ["sentencepiece"] def __init__( self : str ,*_snake_case : Any ,**_snake_case : str ) -> Tuple: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : List[str] = ["sentencepiece"] def __init__( self : Any ,*_snake_case : Any ,**_snake_case : Optional[Any] ) -> Tuple: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Union[str, Any] = ["sentencepiece"] def __init__( self : List[Any] ,*_snake_case : Union[str, Any] ,**_snake_case : Union[str, Any] ) -> Optional[int]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : List[Any] = ["sentencepiece"] def __init__( self : Optional[Any] ,*_snake_case : List[str] ,**_snake_case : str ) -> Union[str, Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : int ,*_snake_case : Optional[int] ,**_snake_case : Any ) -> Dict: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Optional[int] = ["sentencepiece"] def __init__( self : Optional[int] ,*_snake_case : Tuple ,**_snake_case : Any ) -> Optional[Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Dict = ["sentencepiece"] def __init__( self : List[str] ,*_snake_case : Optional[int] ,**_snake_case : List[Any] ) -> Dict: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : List[str] ,*_snake_case : str ,**_snake_case : int ) -> List[Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Dict = ["sentencepiece"] def __init__( self : int ,*_snake_case : Tuple ,**_snake_case : List[Any] ) -> int: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Union[str, Any] = ["sentencepiece"] def __init__( self : Optional[Any] ,*_snake_case : Optional[int] ,**_snake_case : int ) -> Optional[int]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : str = ["sentencepiece"] def __init__( self : Dict ,*_snake_case : Optional[Any] ,**_snake_case : Optional[Any] ) -> List[str]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : List[str] = ["sentencepiece"] def __init__( self : Optional[Any] ,*_snake_case : Dict ,**_snake_case : Dict ) -> Any: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Union[str, Any] = ["sentencepiece"] def __init__( self : List[str] ,*_snake_case : List[str] ,**_snake_case : List[Any] ) -> Dict: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : Optional[Any] ,*_snake_case : List[Any] ,**_snake_case : Any ) -> Dict: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : int = ["sentencepiece"] def __init__( self : List[Any] ,*_snake_case : Tuple ,**_snake_case : Optional[Any] ) -> Any: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : str = ["sentencepiece"] def __init__( self : str ,*_snake_case : List[Any] ,**_snake_case : int ) -> Union[str, Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : str = ["sentencepiece"] def __init__( self : Dict ,*_snake_case : Dict ,**_snake_case : Union[str, Any] ) -> List[Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : Union[str, Any] ,*_snake_case : str ,**_snake_case : List[Any] ) -> Any: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Optional[int] = ["sentencepiece"] def __init__( self : Dict ,*_snake_case : Tuple ,**_snake_case : Union[str, Any] ) -> str: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Any = ["sentencepiece"] def __init__( self : Dict ,*_snake_case : List[str] ,**_snake_case : Optional[int] ) -> Union[str, Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : List[str] = ["sentencepiece"] def __init__( self : Union[str, Any] ,*_snake_case : int ,**_snake_case : List[Any] ) -> Any: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : int = ["sentencepiece"] def __init__( self : Union[str, Any] ,*_snake_case : Any ,**_snake_case : Union[str, Any] ) -> str: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Any = ["sentencepiece"] def __init__( self : Dict ,*_snake_case : Dict ,**_snake_case : List[Any] ) -> Tuple: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : str = ["sentencepiece"] def __init__( self : Any ,*_snake_case : str ,**_snake_case : List[str] ) -> Union[str, Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Dict = ["sentencepiece"] def __init__( self : Optional[Any] ,*_snake_case : Optional[int] ,**_snake_case : Optional[int] ) -> Union[str, Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : str = ["sentencepiece"] def __init__( self : Optional[Any] ,*_snake_case : Tuple ,**_snake_case : List[str] ) -> str: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : Any ,*_snake_case : Optional[Any] ,**_snake_case : Optional[Any] ) -> List[Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Optional[int] = ["sentencepiece"] def __init__( self : List[Any] ,*_snake_case : Optional[int] ,**_snake_case : str ) -> List[Any]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Tuple = ["sentencepiece"] def __init__( self : str ,*_snake_case : Union[str, Any] ,**_snake_case : Optional[Any] ) -> List[str]: """simple docstring""" requires_backends(self ,['''sentencepiece'''] ) class __A ( metaclass=A_ ): '''simple docstring''' lowerCAmelCase : Optional[Any] = ["sentencepiece"] def __init__( self : List[Any] ,*_snake_case : Tuple ,**_snake_case : Union[str, Any] ) -> str: """simple docstring""" requires_backends(self ,['''sentencepiece'''] )
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"""simple docstring""" from ..utils import DummyObject, requires_backends class A__ ( metaclass=__SCREAMING_SNAKE_CASE ): '''simple docstring''' SCREAMING_SNAKE_CASE = ['torch', 'torchsde'] def __init__( self: int , *_SCREAMING_SNAKE_CASE: Optional[Any] , **_SCREAMING_SNAKE_CASE: str) -> Dict: """simple docstring""" requires_backends(self , ["torch", "torchsde"]) @classmethod def _SCREAMING_SNAKE_CASE ( cls: Optional[Any] , *_SCREAMING_SNAKE_CASE: Optional[int] , **_SCREAMING_SNAKE_CASE: Optional[int]) -> Optional[Any]: """simple docstring""" requires_backends(cls , ["torch", "torchsde"]) @classmethod def _SCREAMING_SNAKE_CASE ( cls: Dict , *_SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: Any) -> Optional[int]: """simple docstring""" requires_backends(cls , ["torch", "torchsde"])
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def lowercase__ ( __snake_case : int ): '''simple docstring''' if n == 1 or not isinstance(_a , _a ): return 0 elif n == 2: return 1 else: UpperCAmelCase_ : Any = [0, 1] for i in range(2 , n + 1 ): sequence.append(sequence[i - 1] + sequence[i - 2] ) return sequence[n] def lowercase__ ( __snake_case : int ): '''simple docstring''' UpperCAmelCase_ : List[Any] = 0 UpperCAmelCase_ : str = 2 while digits < n: index += 1 UpperCAmelCase_ : Any = len(str(fibonacci(_a ) ) ) return index def lowercase__ ( __snake_case : int = 1_000 ): '''simple docstring''' return fibonacci_digits_index(_a ) if __name__ == "__main__": print(solution(int(str(input()).strip())))
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def lowercase__ ( __snake_case : str , __snake_case : int , __snake_case : List[str] ): '''simple docstring''' if n == 0: return 1 elif n % 2 == 1: return (binary_exponentiation(__snake_case , n - 1 , __snake_case ) * a) % mod else: UpperCAmelCase_ : Optional[int] = binary_exponentiation(__snake_case , n / 2 , __snake_case ) return (b * b) % mod # a prime number __UpperCAmelCase = 701 __UpperCAmelCase = 1000000000 __UpperCAmelCase = 10 # using binary exponentiation function, O(log(p)): print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p) print((a / b) % p == (a * b ** (p - 2)) % p)
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'''simple docstring''' import collections import inspect import unittest from transformers import SwinvaConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class a_ : '''simple docstring''' def __init__( self , A , A=13 , A=32 , A=2 , A=3 , A=16 , A=[1, 2, 1] , A=[2, 2, 4] , A=2 , A=2.0 , A=True , A=0.0 , A=0.0 , A=0.1 , A="gelu" , A=False , A=True , A=0.02 , A=1e-5 , A=True , A=None , A=True , A=10 , A=8 , ) -> Optional[Any]: _SCREAMING_SNAKE_CASE = parent _SCREAMING_SNAKE_CASE = batch_size _SCREAMING_SNAKE_CASE = image_size _SCREAMING_SNAKE_CASE = patch_size _SCREAMING_SNAKE_CASE = num_channels _SCREAMING_SNAKE_CASE = embed_dim _SCREAMING_SNAKE_CASE = depths _SCREAMING_SNAKE_CASE = num_heads _SCREAMING_SNAKE_CASE = window_size _SCREAMING_SNAKE_CASE = mlp_ratio _SCREAMING_SNAKE_CASE = qkv_bias _SCREAMING_SNAKE_CASE = hidden_dropout_prob _SCREAMING_SNAKE_CASE = attention_probs_dropout_prob _SCREAMING_SNAKE_CASE = drop_path_rate _SCREAMING_SNAKE_CASE = hidden_act _SCREAMING_SNAKE_CASE = use_absolute_embeddings _SCREAMING_SNAKE_CASE = patch_norm _SCREAMING_SNAKE_CASE = layer_norm_eps _SCREAMING_SNAKE_CASE = initializer_range _SCREAMING_SNAKE_CASE = is_training _SCREAMING_SNAKE_CASE = scope _SCREAMING_SNAKE_CASE = use_labels _SCREAMING_SNAKE_CASE = type_sequence_label_size _SCREAMING_SNAKE_CASE = encoder_stride def snake_case_( self ) -> Union[str, Any]: _SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) _SCREAMING_SNAKE_CASE = None if self.use_labels: _SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size ) _SCREAMING_SNAKE_CASE = self.get_config() return config, pixel_values, labels def snake_case_( self ) -> str: return SwinvaConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , ) def snake_case_( self , A , A , A ) -> Dict: _SCREAMING_SNAKE_CASE = SwinvaModel(config=A ) model.to(A ) model.eval() _SCREAMING_SNAKE_CASE = model(A ) _SCREAMING_SNAKE_CASE = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1)) _SCREAMING_SNAKE_CASE = int(config.embed_dim * 2 ** (len(config.depths ) - 1) ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) ) def snake_case_( self , A , A , A ) -> Tuple: _SCREAMING_SNAKE_CASE = SwinvaForMaskedImageModeling(config=A ) model.to(A ) model.eval() _SCREAMING_SNAKE_CASE = model(A ) self.parent.assertEqual( result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images _SCREAMING_SNAKE_CASE = 1 _SCREAMING_SNAKE_CASE = SwinvaForMaskedImageModeling(A ) model.to(A ) model.eval() _SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) _SCREAMING_SNAKE_CASE = model(A ) self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) ) def snake_case_( self , A , A , A ) -> Dict: _SCREAMING_SNAKE_CASE = self.type_sequence_label_size _SCREAMING_SNAKE_CASE = SwinvaForImageClassification(A ) model.to(A ) model.eval() _SCREAMING_SNAKE_CASE = model(A , labels=A ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def snake_case_( self ) -> List[str]: _SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs() _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs _SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values} return config, inputs_dict @require_torch class a_ ( snake_case_ , snake_case_ , unittest.TestCase ): '''simple docstring''' UpperCamelCase = ( (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else () ) UpperCamelCase = ( {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification} if is_torch_available() else {} ) UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False UpperCamelCase = False def snake_case_( self ) -> Dict: _SCREAMING_SNAKE_CASE = SwinvaModelTester(self ) _SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A , embed_dim=37 ) def snake_case_( self ) -> Optional[int]: self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def snake_case_( self ) -> List[str]: _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*A ) @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" ) def snake_case_( self ) -> Optional[Any]: pass @unittest.skip(reason="""Swinv2 does not use inputs_embeds""" ) def snake_case_( self ) -> int: pass def snake_case_( self ) -> List[Any]: _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _SCREAMING_SNAKE_CASE = model_class(A ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) _SCREAMING_SNAKE_CASE = model.get_output_embeddings() self.assertTrue(x is None or isinstance(A , nn.Linear ) ) def snake_case_( self ) -> List[str]: _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: _SCREAMING_SNAKE_CASE = model_class(A ) _SCREAMING_SNAKE_CASE = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic _SCREAMING_SNAKE_CASE = [*signature.parameters.keys()] _SCREAMING_SNAKE_CASE = ["""pixel_values"""] self.assertListEqual(arg_names[:1] , A ) def snake_case_( self ) -> int: _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() _SCREAMING_SNAKE_CASE = True for model_class in self.all_model_classes: _SCREAMING_SNAKE_CASE = True _SCREAMING_SNAKE_CASE = False _SCREAMING_SNAKE_CASE = True _SCREAMING_SNAKE_CASE = model_class(A ) model.to(A ) model.eval() with torch.no_grad(): _SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A , A ) ) _SCREAMING_SNAKE_CASE = outputs.attentions _SCREAMING_SNAKE_CASE = len(self.model_tester.depths ) self.assertEqual(len(A ) , A ) # check that output_attentions also work using config del inputs_dict["output_attentions"] _SCREAMING_SNAKE_CASE = True _SCREAMING_SNAKE_CASE = config.window_size**2 _SCREAMING_SNAKE_CASE = model_class(A ) model.to(A ) model.eval() with torch.no_grad(): _SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A , A ) ) _SCREAMING_SNAKE_CASE = outputs.attentions self.assertEqual(len(A ) , A ) self.assertListEqual( list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , ) _SCREAMING_SNAKE_CASE = len(A ) # Check attention is always last and order is fine _SCREAMING_SNAKE_CASE = True _SCREAMING_SNAKE_CASE = True _SCREAMING_SNAKE_CASE = model_class(A ) model.to(A ) model.eval() with torch.no_grad(): _SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A , A ) ) if hasattr(self.model_tester , """num_hidden_states_types""" ): _SCREAMING_SNAKE_CASE = self.model_tester.num_hidden_states_types else: # also another +1 for reshaped_hidden_states _SCREAMING_SNAKE_CASE = 2 self.assertEqual(out_len + added_hidden_states , len(A ) ) _SCREAMING_SNAKE_CASE = outputs.attentions self.assertEqual(len(A ) , A ) self.assertListEqual( list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , ) def snake_case_( self , A , A , A , A ) -> Dict: _SCREAMING_SNAKE_CASE = model_class(A ) model.to(A ) model.eval() with torch.no_grad(): _SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A , A ) ) _SCREAMING_SNAKE_CASE = outputs.hidden_states _SCREAMING_SNAKE_CASE = getattr( self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 ) self.assertEqual(len(A ) , A ) # Swinv2 has a different seq_length _SCREAMING_SNAKE_CASE = ( config.patch_size if isinstance(config.patch_size , collections.abc.Iterable ) else (config.patch_size, config.patch_size) ) _SCREAMING_SNAKE_CASE = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.assertListEqual( list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , ) _SCREAMING_SNAKE_CASE = outputs.reshaped_hidden_states self.assertEqual(len(A ) , A ) _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = reshaped_hidden_states[0].shape _SCREAMING_SNAKE_CASE = ( reshaped_hidden_states[0].view(A , A , height * width ).permute(0 , 2 , 1 ) ) self.assertListEqual( list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , ) def snake_case_( self ) -> Optional[int]: _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() _SCREAMING_SNAKE_CASE = ( self.model_tester.image_size if isinstance(self.model_tester.image_size , collections.abc.Iterable ) else (self.model_tester.image_size, self.model_tester.image_size) ) for model_class in self.all_model_classes: _SCREAMING_SNAKE_CASE = True self.check_hidden_states_output(A , A , A , A ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _SCREAMING_SNAKE_CASE = True self.check_hidden_states_output(A , A , A , A ) def snake_case_( self ) -> Union[str, Any]: _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() _SCREAMING_SNAKE_CASE = 3 _SCREAMING_SNAKE_CASE = ( self.model_tester.image_size if isinstance(self.model_tester.image_size , collections.abc.Iterable ) else (self.model_tester.image_size, self.model_tester.image_size) ) _SCREAMING_SNAKE_CASE = ( config.patch_size if isinstance(config.patch_size , collections.abc.Iterable ) else (config.patch_size, config.patch_size) ) _SCREAMING_SNAKE_CASE = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0]) _SCREAMING_SNAKE_CASE = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1]) for model_class in self.all_model_classes: _SCREAMING_SNAKE_CASE = True self.check_hidden_states_output(A , A , A , (padded_height, padded_width) ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] _SCREAMING_SNAKE_CASE = True self.check_hidden_states_output(A , A , A , (padded_height, padded_width) ) def snake_case_( self ) -> Union[str, Any]: _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*A ) def snake_case_( self ) -> Any: _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*A ) @slow def snake_case_( self ) -> List[str]: for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: _SCREAMING_SNAKE_CASE = SwinvaModel.from_pretrained(A ) self.assertIsNotNone(A ) def snake_case_( self ) -> List[Any]: _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common() _SCREAMING_SNAKE_CASE = _config_zero_init(A ) for model_class in self.all_model_classes: _SCREAMING_SNAKE_CASE = model_class(config=A ) for name, param in model.named_parameters(): if "embeddings" not in name and "logit_scale" not in name and param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , ) @require_vision @require_torch class a_ ( unittest.TestCase ): '''simple docstring''' @cached_property def snake_case_( self ) -> Optional[Any]: return ( AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ) if is_vision_available() else None ) @slow def snake_case_( self ) -> Tuple: _SCREAMING_SNAKE_CASE = SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256""" ).to( A ) _SCREAMING_SNAKE_CASE = self.default_image_processor _SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) _SCREAMING_SNAKE_CASE = image_processor(images=A , return_tensors="""pt""" ).to(A ) # forward pass with torch.no_grad(): _SCREAMING_SNAKE_CASE = model(**A ) # verify the logits _SCREAMING_SNAKE_CASE = torch.Size((1, 1000) ) self.assertEqual(outputs.logits.shape , A ) _SCREAMING_SNAKE_CASE = torch.tensor([-0.3947, -0.4306, 0.0026] ).to(A ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1e-4 ) )
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"""simple docstring""" from ...configuration_utils import PretrainedConfig from ...utils import logging A__ : Dict = logging.get_logger(__name__) A__ : Union[str, Any] = { 'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json', # See all CANINE models at https://huggingface.co/models?filter=canine } class lowercase__ ( snake_case__ ): _UpperCAmelCase :List[str] = "canine" def __init__( self : Optional[Any] , snake_case__ : Union[str, Any]=768 , snake_case__ : Tuple=12 , snake_case__ : Optional[Any]=12 , snake_case__ : Union[str, Any]=3072 , snake_case__ : Optional[Any]="gelu" , snake_case__ : Tuple=0.1 , snake_case__ : Tuple=0.1 , snake_case__ : int=1_6384 , snake_case__ : str=16 , snake_case__ : Tuple=0.02 , snake_case__ : Dict=1E-12 , snake_case__ : Any=0 , snake_case__ : Optional[int]=0xe_000 , snake_case__ : List[str]=0xe_001 , snake_case__ : List[str]=4 , snake_case__ : List[str]=4 , snake_case__ : List[Any]=8 , snake_case__ : List[str]=1_6384 , snake_case__ : Union[str, Any]=128 , **snake_case__ : Tuple , ): super().__init__(pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , **snake_case__ ) lowerCamelCase_ : Tuple =max_position_embeddings lowerCamelCase_ : Optional[int] =hidden_size lowerCamelCase_ : Tuple =num_hidden_layers lowerCamelCase_ : Dict =num_attention_heads lowerCamelCase_ : str =intermediate_size lowerCamelCase_ : Dict =hidden_act lowerCamelCase_ : List[Any] =hidden_dropout_prob lowerCamelCase_ : Union[str, Any] =attention_probs_dropout_prob lowerCamelCase_ : Dict =initializer_range lowerCamelCase_ : Tuple =type_vocab_size lowerCamelCase_ : Optional[Any] =layer_norm_eps # Character config: lowerCamelCase_ : List[str] =downsampling_rate lowerCamelCase_ : List[Any] =upsampling_kernel_size lowerCamelCase_ : Any =num_hash_functions lowerCamelCase_ : Optional[int] =num_hash_buckets lowerCamelCase_ : Union[str, Any] =local_transformer_stride
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0
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 UpperCAmelCase_ : Any = logging.get_logger(__name__) UpperCAmelCase_ : Tuple = { 'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json', } class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ ): snake_case__ : Optional[int] = '''resnet''' snake_case__ : Dict = ['''basic''', '''bottleneck'''] def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ : Any=3 , SCREAMING_SNAKE_CASE__ : Optional[int]=6_4 , SCREAMING_SNAKE_CASE__ : int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=[3, 4, 6, 3] , SCREAMING_SNAKE_CASE__ : Union[str, Any]="bottleneck" , SCREAMING_SNAKE_CASE__ : Tuple="relu" , SCREAMING_SNAKE_CASE__ : Dict=False , SCREAMING_SNAKE_CASE__ : Dict=None , SCREAMING_SNAKE_CASE__ : Optional[int]=None , **SCREAMING_SNAKE_CASE__ : List[str] , ) -> Dict: super().__init__(**SCREAMING_SNAKE_CASE__ ) if layer_type not in self.layer_types: raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" ) a_ : int = num_channels a_ : Optional[int] = embedding_size a_ : List[str] = hidden_sizes a_ : Tuple = depths a_ : int = layer_type a_ : Dict = hidden_act a_ : Any = downsample_in_first_stage a_ : int = ['stem'] + [F"""stage{idx}""" for idx in range(1 , len(SCREAMING_SNAKE_CASE__ ) + 1 )] a_ , a_ : str = get_aligned_output_features_output_indices( out_features=SCREAMING_SNAKE_CASE__ , out_indices=SCREAMING_SNAKE_CASE__ , stage_names=self.stage_names ) class SCREAMING_SNAKE_CASE__ ( lowercase__ ): snake_case__ : Dict = version.parse('''1.11''' ) @property def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}), ] ) @property def SCREAMING_SNAKE_CASE ( self : Any ) -> float: return 1E-3
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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch import math from typing import Union import torch from ..configuration_utils import ConfigMixin, register_to_config from ..utils import randn_tensor from .scheduling_utils import SchedulerMixin class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ ): snake_case__ : Dict = 1 @register_to_config def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple=2_0_0_0 , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Optional[int]=2_0 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1E-3 ) -> Optional[int]: a_ : Tuple = None a_ : int = None a_ : Tuple = None def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, torch.device] = None ) -> List[str]: a_ : Tuple = torch.linspace(1 , self.config.sampling_eps , SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ ) def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple=None ) -> Tuple: if self.timesteps is None: raise ValueError( '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' ) # TODO(Patrick) better comments + non-PyTorch # postprocess model score a_ : Tuple = ( -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min ) a_ : int = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) ) a_ : Dict = std.flatten() while len(std.shape ) < len(score.shape ): a_ : str = std.unsqueeze(-1 ) a_ : List[str] = -score / std # compute a_ : List[str] = -1.0 / len(self.timesteps ) a_ : Optional[int] = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min) a_ : Optional[Any] = beta_t.flatten() while len(beta_t.shape ) < len(x.shape ): a_ : List[str] = beta_t.unsqueeze(-1 ) a_ : Optional[Any] = -0.5 * beta_t * x a_ : Tuple = torch.sqrt(SCREAMING_SNAKE_CASE__ ) a_ : List[Any] = drift - diffusion**2 * score a_ : List[str] = x + drift * dt # add noise a_ : Optional[Any] = randn_tensor(x.shape , layout=x.layout , generator=SCREAMING_SNAKE_CASE__ , device=x.device , dtype=x.dtype ) a_ : Optional[Any] = x_mean + diffusion * math.sqrt(-dt ) * noise return x, x_mean def __len__( self : int ) -> Tuple: return self.config.num_train_timesteps
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1
import copy import re class _SCREAMING_SNAKE_CASE : snake_case__ : str = """hp""" snake_case__ : Dict = {} snake_case__ : Tuple = None @classmethod def _A ( cls : Union[str, Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : int ): UpperCamelCase :List[str] = prefix UpperCamelCase :List[str] = defaults cls.build_naming_info() @staticmethod def _A ( __lowerCamelCase : Tuple , __lowerCamelCase : Tuple ): if len(__lowerCamelCase ) == 0: return "" UpperCamelCase :Union[str, Any] = None if any(char.isdigit() for char in word ): raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number""" ) if word in info["short_word"]: return info["short_word"][word] for prefix_len in range(1 , len(__lowerCamelCase ) + 1 ): UpperCamelCase :Any = word[:prefix_len] if prefix in info["reverse_short_word"]: continue else: UpperCamelCase :Optional[Any] = prefix break if short_word is None: # Paranoid fallback def int_to_alphabetic(__lowerCamelCase : Optional[Any] ): UpperCamelCase :Optional[int] = """""" while integer != 0: UpperCamelCase :List[str] = chr(ord("""A""" ) + integer % 10 ) + s integer //= 10 return s UpperCamelCase :Tuple = 0 while True: UpperCamelCase :List[Any] = word + """#""" + int_to_alphabetic(__lowerCamelCase ) if sword in info["reverse_short_word"]: continue else: UpperCamelCase :List[str] = sword break UpperCamelCase :Dict = short_word UpperCamelCase :Optional[Any] = word return short_word @staticmethod def _A ( __lowerCamelCase : Union[str, Any] , __lowerCamelCase : List[str] ): UpperCamelCase :Any = param_name.split("""_""" ) UpperCamelCase :int = [TrialShortNamer.shortname_for_word(__lowerCamelCase , __lowerCamelCase ) for word in words] # We try to create a separatorless short name, but if there is a collision we have to fallback # to a separated short name UpperCamelCase :Tuple = ["""""", """_"""] for separator in separators: UpperCamelCase :List[str] = separator.join(__lowerCamelCase ) if shortname not in info["reverse_short_param"]: UpperCamelCase :List[Any] = shortname UpperCamelCase :List[Any] = param_name return shortname return param_name @staticmethod def _A ( __lowerCamelCase : str , __lowerCamelCase : Dict ): UpperCamelCase :Dict = TrialShortNamer.shortname_for_key(__lowerCamelCase , __lowerCamelCase ) UpperCamelCase :Dict = short_name UpperCamelCase :int = param_name @classmethod def _A ( cls : Tuple ): if cls.NAMING_INFO is not None: return UpperCamelCase :Optional[Any] = { """short_word""": {}, """reverse_short_word""": {}, """short_param""": {}, """reverse_short_param""": {}, } UpperCamelCase :Any = list(cls.DEFAULTS.keys() ) for k in field_keys: cls.add_new_param_name(__lowerCamelCase , __lowerCamelCase ) UpperCamelCase :int = info @classmethod def _A ( cls : Dict , __lowerCamelCase : Optional[Any] ): cls.build_naming_info() assert cls.PREFIX is not None UpperCamelCase :Any = [copy.copy(cls.PREFIX )] for k, v in params.items(): if k not in cls.DEFAULTS: raise Exception(F"""You should provide a default value for the param name {k} with value {v}""" ) if v == cls.DEFAULTS[k]: # The default value is not added to the name continue UpperCamelCase :int = cls.NAMING_INFO["""short_param"""][k] if isinstance(__lowerCamelCase , __lowerCamelCase ): UpperCamelCase :List[Any] = 1 if v else 0 UpperCamelCase :int = """""" if isinstance(__lowerCamelCase , (int, float) ) else """-""" UpperCamelCase :Dict = F"""{key}{sep}{v}""" name.append(__lowerCamelCase ) return "_".join(__lowerCamelCase ) @classmethod def _A ( cls : Tuple , __lowerCamelCase : Any ): UpperCamelCase :Optional[int] = repr[len(cls.PREFIX ) + 1 :] if repr == "": UpperCamelCase :Union[str, Any] = [] else: UpperCamelCase :Any = repr.split("""_""" ) UpperCamelCase :List[str] = {} for value in values: if "-" in value: UpperCamelCase , UpperCamelCase :Dict = value.split("""-""" ) else: UpperCamelCase :Union[str, Any] = re.sub("""[0-9.]""" , """""" , __lowerCamelCase ) UpperCamelCase :Union[str, Any] = float(re.sub("""[^0-9.]""" , """""" , __lowerCamelCase ) ) UpperCamelCase :Any = cls.NAMING_INFO["""reverse_short_param"""][p_k] UpperCamelCase :str = p_v for k in cls.DEFAULTS: if k not in parameters: UpperCamelCase :Tuple = cls.DEFAULTS[k] return parameters
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import os import unittest from huggingface_hub.utils import are_progress_bars_disabled import transformers.models.bart.tokenization_bart from transformers import logging from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context from transformers.utils.logging import disable_progress_bar, enable_progress_bar class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ): def a (self : Dict ): """simple docstring""" __snake_case = logging.get_logger() # the current default level is logging.WARNING __snake_case = logging.get_verbosity() logging.set_verbosity_error() self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() ) logging.set_verbosity_warning() self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() ) logging.set_verbosity_info() self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() ) logging.set_verbosity_debug() self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() ) # restore to the original level logging.set_verbosity(a__ ) def a (self : Dict ): """simple docstring""" __snake_case = logging.get_verbosity() __snake_case = logging.get_logger('''transformers.models.bart.tokenization_bart''' ) __snake_case = '''Testing 1, 2, 3''' # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`) if level_origin <= logging.WARNING: with CaptureLogger(a__ ) as cl: logger.warning(a__ ) self.assertEqual(cl.out , msg + '''\n''' ) # this is setting the level for all of `transformers.*` loggers logging.set_verbosity_error() # should not be able to log warnings with CaptureLogger(a__ ) as cl: logger.warning(a__ ) self.assertEqual(cl.out , '''''' ) # should be able to log warnings again logging.set_verbosity_warning() with CaptureLogger(a__ ) as cl: logger.warning(a__ ) self.assertEqual(cl.out , msg + '''\n''' ) # restore to the original level logging.set_verbosity(a__ ) @mockenv(TRANSFORMERS_VERBOSITY='''error''' ) def a (self : Dict ): """simple docstring""" transformers.utils.logging._reset_library_root_logger() # this action activates the env var __snake_case = logging.get_logger('''transformers.models.bart.tokenization_bart''' ) __snake_case = os.getenv('''TRANSFORMERS_VERBOSITY''' , a__ ) __snake_case = logging.log_levels[env_level_str] __snake_case = logging.get_verbosity() self.assertEqual( a__ , a__ , f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}""" , ) # restore to the original level __snake_case = '''''' transformers.utils.logging._reset_library_root_logger() @mockenv(TRANSFORMERS_VERBOSITY='''super-error''' ) def a (self : List[Any] ): """simple docstring""" transformers.utils.logging._reset_library_root_logger() __snake_case = logging.logging.getLogger() with CaptureLogger(a__ ) as cl: # this action activates the env var logging.get_logger('''transformers.models.bart.tokenization_bart''' ) self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error''' , cl.out ) # no need to restore as nothing was changed def a (self : Any ): """simple docstring""" transformers.utils.logging._reset_library_root_logger() __snake_case = logging.get_logger('''transformers.models.bart.tokenization_bart''' ) __snake_case = '''Testing 1, 2, 3''' with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1''' ): # nothing should be logged as env var disables this method with CaptureLogger(a__ ) as cl: logger.warning_advice(a__ ) self.assertEqual(cl.out , '''''' ) with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''''' ): # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset with CaptureLogger(a__ ) as cl: logger.warning_advice(a__ ) self.assertEqual(cl.out , msg + '''\n''' ) def lowerCamelCase__ ( ) -> str: disable_progress_bar() assert are_progress_bars_disabled() enable_progress_bar() assert not are_progress_bars_disabled()
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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 _UpperCAmelCase = logging.getLogger(__name__) @dataclass class _UpperCamelCase : _UpperCamelCase : str = field( metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} ) _UpperCamelCase : Optional[str] = field( default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} ) _UpperCamelCase : Optional[str] = field( default='''NER''' , metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''} ) _UpperCamelCase : Optional[str] = field( default=lowerCAmelCase_ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} ) _UpperCamelCase : bool = field(default=lowerCAmelCase_ , metadata={'''help''': '''Set this flag to use fast tokenization.'''} ) # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script, # or just modify its tokenizer_config.json. _UpperCamelCase : Optional[str] = field( default=lowerCAmelCase_ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , ) @dataclass class _UpperCamelCase : _UpperCamelCase : str = field( metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''} ) _UpperCamelCase : Optional[str] = field( default=lowerCAmelCase_ , metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''} , ) _UpperCamelCase : 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.''' ) } , ) _UpperCamelCase : bool = field( default=lowerCAmelCase_ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} ) def lowerCAmelCase_ ( ) -> 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. UpperCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) ) if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ): # If we pass only one argument to the script and it's the path to a json file, # let's parse it to get our arguments. UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) ) else: UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = parser.parse_args_into_dataclasses() if ( os.path.exists(training_args.output_dir ) and os.listdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir ): raise ValueError( F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use''' " --overwrite_output_dir to overcome." ) UpperCamelCase_ = import_module("tasks" ) try: UpperCamelCase_ = getattr(UpperCamelCase_ , model_args.task_type ) UpperCamelCase_ = token_classification_task_clazz() except AttributeError: raise ValueError( F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. ''' F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}''' ) # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , ) # Set the verbosity to info of the Transformers logger (on main process only): if is_main_process(training_args.local_rank ): transformers.utils.logging.set_verbosity_info() transformers.utils.logging.enable_default_handler() transformers.utils.logging.enable_explicit_format() logger.info("Training/evaluation parameters %s" , UpperCamelCase_ ) # Set seed set_seed(training_args.seed ) # Prepare CONLL-2003 task UpperCamelCase_ = token_classification_task.get_labels(data_args.labels ) UpperCamelCase_ = dict(enumerate(UpperCamelCase_ ) ) UpperCamelCase_ = len(UpperCamelCase_ ) # Load pretrained model and tokenizer # # Distributed training: # The .from_pretrained methods guarantee that only one local process can concurrently # download model & vocab. UpperCamelCase_ = AutoConfig.from_pretrained( model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=UpperCamelCase_ , idalabel=UpperCamelCase_ , labelaid={label: i for i, label in enumerate(UpperCamelCase_ )} , cache_dir=model_args.cache_dir , ) UpperCamelCase_ = AutoTokenizer.from_pretrained( model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast , ) UpperCamelCase_ = AutoModelForTokenClassification.from_pretrained( model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCamelCase_ , cache_dir=model_args.cache_dir , ) # Get datasets UpperCamelCase_ = ( TokenClassificationDataset( token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , ) if training_args.do_train else None ) UpperCamelCase_ = ( TokenClassificationDataset( token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , 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(UpperCamelCase_ , UpperCamelCase_ ) -> Tuple[List[int], List[int]]: UpperCamelCase_ = np.argmax(UpperCamelCase_ , axis=2 ) UpperCamelCase_ , UpperCamelCase_ = preds.shape UpperCamelCase_ = [[] for _ in range(UpperCamelCase_ )] UpperCamelCase_ = [[] for _ in range(UpperCamelCase_ )] for i in range(UpperCamelCase_ ): for j in range(UpperCamelCase_ ): 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(UpperCamelCase_ ) -> Dict: UpperCamelCase_ , UpperCamelCase_ = align_predictions(p.predictions , p.label_ids ) return { "accuracy_score": accuracy_score(UpperCamelCase_ , UpperCamelCase_ ), "precision": precision_score(UpperCamelCase_ , UpperCamelCase_ ), "recall": recall_score(UpperCamelCase_ , UpperCamelCase_ ), "f1": fa_score(UpperCamelCase_ , UpperCamelCase_ ), } # Data collator UpperCamelCase_ = DataCollatorWithPadding(UpperCamelCase_ , pad_to_multiple_of=8 ) if training_args.fpaa else None # Initialize our Trainer UpperCamelCase_ = Trainer( model=UpperCamelCase_ , args=UpperCamelCase_ , train_dataset=UpperCamelCase_ , eval_dataset=UpperCamelCase_ , compute_metrics=UpperCamelCase_ , data_collator=UpperCamelCase_ , ) # Training if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None ) trainer.save_model() # For convenience, we also re-save the tokenizer to the same directory, # so that you can share your model easily on huggingface.co/models =) if trainer.is_world_process_zero(): tokenizer.save_pretrained(training_args.output_dir ) # Evaluation UpperCamelCase_ = {} if training_args.do_eval: logger.info("*** Evaluate ***" ) UpperCamelCase_ = trainer.evaluate() UpperCamelCase_ = os.path.join(training_args.output_dir , "eval_results.txt" ) if trainer.is_world_process_zero(): with open(UpperCamelCase_ , "w" ) as writer: logger.info("***** Eval results *****" ) for key, value in result.items(): logger.info(" %s = %s" , UpperCamelCase_ , UpperCamelCase_ ) writer.write("%s = %s\n" % (key, value) ) results.update(UpperCamelCase_ ) # Predict if training_args.do_predict: UpperCamelCase_ = TokenClassificationDataset( token_classification_task=UpperCamelCase_ , data_dir=data_args.data_dir , tokenizer=UpperCamelCase_ , labels=UpperCamelCase_ , model_type=config.model_type , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.test , ) UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = trainer.predict(UpperCamelCase_ ) UpperCamelCase_ , UpperCamelCase_ = align_predictions(UpperCamelCase_ , UpperCamelCase_ ) UpperCamelCase_ = os.path.join(training_args.output_dir , "test_results.txt" ) if trainer.is_world_process_zero(): with open(UpperCamelCase_ , "w" ) as writer: for key, value in metrics.items(): logger.info(" %s = %s" , UpperCamelCase_ , UpperCamelCase_ ) writer.write("%s = %s\n" % (key, value) ) # Save predictions UpperCamelCase_ = os.path.join(training_args.output_dir , "test_predictions.txt" ) if trainer.is_world_process_zero(): with open(UpperCamelCase_ , "w" ) as writer: with open(os.path.join(data_args.data_dir , "test.txt" ) , "r" ) as f: token_classification_task.write_predictions_to_file(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) return results def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]: # For xla_spawn (TPUs) main() if __name__ == "__main__": main()
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def lowerCAmelCase_ ( UpperCamelCase_ ) -> int: UpperCamelCase_ = len(UpperCamelCase_ ) UpperCamelCase_ = len(matrix[0] ) UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ ) for row in range(UpperCamelCase_ ): # Check if diagonal element is not zero if matrix[row][row] != 0: # Eliminate all the elements below the diagonal for col in range(row + 1 , UpperCamelCase_ ): UpperCamelCase_ = matrix[col][row] / matrix[row][row] for i in range(UpperCamelCase_ , UpperCamelCase_ ): matrix[col][i] -= multiplier * matrix[row][i] else: # Find a non-zero diagonal element to swap rows UpperCamelCase_ = True for i in range(row + 1 , UpperCamelCase_ ): if matrix[i][row] != 0: UpperCamelCase_ , UpperCamelCase_ = matrix[i], matrix[row] UpperCamelCase_ = False break if reduce: rank -= 1 for i in range(UpperCamelCase_ ): UpperCamelCase_ = matrix[i][rank] # Reduce the row pointer by one to stay on the same row row -= 1 return rank if __name__ == "__main__": import doctest doctest.testmod()
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0
import argparse import os from . import ( ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BART_PRETRAINED_MODEL_ARCHIVE_LIST, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, BartConfig, BertConfig, CamembertConfig, CTRLConfig, DistilBertConfig, DPRConfig, ElectraConfig, FlaubertConfig, GPTaConfig, LayoutLMConfig, LxmertConfig, OpenAIGPTConfig, RobertaConfig, TaConfig, TFAlbertForPreTraining, TFBartForConditionalGeneration, TFBartForSequenceClassification, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFCamembertForMaskedLM, TFCTRLLMHeadModel, TFDistilBertForMaskedLM, TFDistilBertForQuestionAnswering, TFDPRContextEncoder, TFDPRQuestionEncoder, TFDPRReader, TFElectraForPreTraining, TFFlaubertWithLMHeadModel, TFGPTaLMHeadModel, TFLayoutLMForMaskedLM, TFLxmertForPreTraining, TFLxmertVisualFeatureEncoder, TFOpenAIGPTLMHeadModel, TFRobertaForCausalLM, TFRobertaForMaskedLM, TFRobertaForSequenceClassification, TFTaForConditionalGeneration, TFTransfoXLLMHeadModel, TFWavaVecaModel, TFXLMRobertaForMaskedLM, TFXLMWithLMHeadModel, TFXLNetLMHeadModel, TransfoXLConfig, WavaVecaConfig, WavaVecaModel, XLMConfig, XLMRobertaConfig, XLNetConfig, is_torch_available, load_pytorch_checkpoint_in_tfa_model, ) from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging if is_torch_available(): import numpy as np import torch from . import ( AlbertForPreTraining, BartForConditionalGeneration, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, CamembertForMaskedLM, CTRLLMHeadModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DPRContextEncoder, DPRQuestionEncoder, DPRReader, ElectraForPreTraining, FlaubertWithLMHeadModel, GPTaLMHeadModel, LayoutLMForMaskedLM, LxmertForPreTraining, LxmertVisualFeatureEncoder, OpenAIGPTLMHeadModel, RobertaForMaskedLM, RobertaForSequenceClassification, TaForConditionalGeneration, TransfoXLLMHeadModel, XLMRobertaForMaskedLM, XLMWithLMHeadModel, XLNetLMHeadModel, ) logging.set_verbosity_info() _a = { '''bart''': ( BartConfig, TFBartForConditionalGeneration, TFBartForSequenceClassification, BartForConditionalGeneration, BART_PRETRAINED_MODEL_ARCHIVE_LIST, ), '''bert''': ( BertConfig, TFBertForPreTraining, BertForPreTraining, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''bert-large-uncased-whole-word-masking-finetuned-squad''': ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''bert-large-cased-whole-word-masking-finetuned-squad''': ( BertConfig, TFBertForQuestionAnswering, BertForQuestionAnswering, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''bert-base-cased-finetuned-mrpc''': ( BertConfig, TFBertForSequenceClassification, BertForSequenceClassification, BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''dpr''': ( DPRConfig, TFDPRQuestionEncoder, TFDPRContextEncoder, TFDPRReader, DPRQuestionEncoder, DPRContextEncoder, DPRReader, DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST, DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST, ), '''gpt2''': ( GPTaConfig, TFGPTaLMHeadModel, GPTaLMHeadModel, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''xlnet''': ( XLNetConfig, TFXLNetLMHeadModel, XLNetLMHeadModel, XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''xlm''': ( XLMConfig, TFXLMWithLMHeadModel, XLMWithLMHeadModel, XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''xlm-roberta''': ( XLMRobertaConfig, TFXLMRobertaForMaskedLM, XLMRobertaForMaskedLM, XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''transfo-xl''': ( TransfoXLConfig, TFTransfoXLLMHeadModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''openai-gpt''': ( OpenAIGPTConfig, TFOpenAIGPTLMHeadModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''roberta''': ( RobertaConfig, TFRobertaForCausalLM, TFRobertaForMaskedLM, RobertaForMaskedLM, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''layoutlm''': ( LayoutLMConfig, TFLayoutLMForMaskedLM, LayoutLMForMaskedLM, LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST, ), '''roberta-large-mnli''': ( RobertaConfig, TFRobertaForSequenceClassification, RobertaForSequenceClassification, ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''camembert''': ( CamembertConfig, TFCamembertForMaskedLM, CamembertForMaskedLM, CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''flaubert''': ( FlaubertConfig, TFFlaubertWithLMHeadModel, FlaubertWithLMHeadModel, FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''distilbert''': ( DistilBertConfig, TFDistilBertForMaskedLM, DistilBertForMaskedLM, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''distilbert-base-distilled-squad''': ( DistilBertConfig, TFDistilBertForQuestionAnswering, DistilBertForQuestionAnswering, DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''lxmert''': ( LxmertConfig, TFLxmertForPreTraining, LxmertForPreTraining, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''lxmert-visual-feature-encoder''': ( LxmertConfig, TFLxmertVisualFeatureEncoder, LxmertVisualFeatureEncoder, LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''ctrl''': ( CTRLConfig, TFCTRLLMHeadModel, CTRLLMHeadModel, CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''albert''': ( AlbertConfig, TFAlbertForPreTraining, AlbertForPreTraining, ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''t5''': ( TaConfig, TFTaForConditionalGeneration, TaForConditionalGeneration, T5_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''electra''': ( ElectraConfig, TFElectraForPreTraining, ElectraForPreTraining, ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ), '''wav2vec2''': ( WavaVecaConfig, TFWavaVecaModel, WavaVecaModel, WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, ), } def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=False , __lowerCAmelCase=True )-> Optional[Any]: """simple docstring""" if model_type not in MODEL_CLASSES: raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys() )}.""" ) _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = MODEL_CLASSES[model_type] # Initialise TF model if config_file in aws_config_map: _UpperCAmelCase = cached_file(__lowerCAmelCase , __lowerCAmelCase , force_download=not use_cached_models ) _UpperCAmelCase = config_class.from_json_file(__lowerCAmelCase ) _UpperCAmelCase = True _UpperCAmelCase = True print(F"""Building TensorFlow model from configuration: {config}""" ) _UpperCAmelCase = model_class(__lowerCAmelCase ) # Load weights from tf checkpoint if pytorch_checkpoint_path in aws_config_map.keys(): _UpperCAmelCase = cached_file( __lowerCAmelCase , __lowerCAmelCase , force_download=not use_cached_models ) # Load PyTorch checkpoint in tf2 model: _UpperCAmelCase = load_pytorch_checkpoint_in_tfa_model(__lowerCAmelCase , __lowerCAmelCase ) if compare_with_pt_model: _UpperCAmelCase = tf_model(tf_model.dummy_inputs , training=__lowerCAmelCase ) # build the network _UpperCAmelCase = torch.load(__lowerCAmelCase , map_location='cpu' ) _UpperCAmelCase = pt_model_class.from_pretrained( pretrained_model_name_or_path=__lowerCAmelCase , config=__lowerCAmelCase , state_dict=__lowerCAmelCase ) with torch.no_grad(): _UpperCAmelCase = pt_model(**pt_model.dummy_inputs ) _UpperCAmelCase = pto[0].numpy() _UpperCAmelCase = tfo[0].numpy() _UpperCAmelCase = np.amax(np.abs(np_pt - np_tf ) ) print(F"""Max absolute difference between models outputs {diff}""" ) assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}""" # Save pytorch-model print(F"""Save TensorFlow model to {tf_dump_path}""" ) tf_model.save_weights(__lowerCAmelCase , save_format='h5' ) def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=False , __lowerCAmelCase=False , )-> Tuple: """simple docstring""" if args_model_type is None: _UpperCAmelCase = list(MODEL_CLASSES.keys() ) else: _UpperCAmelCase = [args_model_type] for j, model_type in enumerate(__lowerCAmelCase , start=1 ): print('=' * 100 ) print(F""" Converting model type {j}/{len(__lowerCAmelCase )}: {model_type}""" ) print('=' * 100 ) if model_type not in MODEL_CLASSES: raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys() )}.""" ) _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = MODEL_CLASSES[model_type] if model_shortcut_names_or_path is None: _UpperCAmelCase = list(aws_model_maps.keys() ) if config_shortcut_names_or_path is None: _UpperCAmelCase = model_shortcut_names_or_path for i, (model_shortcut_name, config_shortcut_name) in enumerate( zip(__lowerCAmelCase , __lowerCAmelCase ) , start=1 ): print('-' * 100 ) if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name: if not only_convert_finetuned_models: print(F""" Skipping finetuned checkpoint {model_shortcut_name}""" ) continue _UpperCAmelCase = model_shortcut_name elif only_convert_finetuned_models: print(F""" Skipping not finetuned checkpoint {model_shortcut_name}""" ) continue print( F""" Converting checkpoint {i}/{len(__lowerCAmelCase )}: {model_shortcut_name} - model_type {model_type}""" ) print('-' * 100 ) if config_shortcut_name in aws_config_map: _UpperCAmelCase = cached_file(__lowerCAmelCase , __lowerCAmelCase , force_download=not use_cached_models ) else: _UpperCAmelCase = config_shortcut_name if model_shortcut_name in aws_model_maps: _UpperCAmelCase = cached_file(__lowerCAmelCase , __lowerCAmelCase , force_download=not use_cached_models ) else: _UpperCAmelCase = model_shortcut_name if os.path.isfile(__lowerCAmelCase ): _UpperCAmelCase = 'converted_model' convert_pt_checkpoint_to_tf( model_type=__lowerCAmelCase , pytorch_checkpoint_path=__lowerCAmelCase , config_file=__lowerCAmelCase , tf_dump_path=os.path.join(__lowerCAmelCase , model_shortcut_name + '-tf_model.h5' ) , compare_with_pt_model=__lowerCAmelCase , ) if remove_cached_files: os.remove(__lowerCAmelCase ) os.remove(__lowerCAmelCase ) if __name__ == "__main__": _a = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.''' ) parser.add_argument( '''--model_type''', default=None, type=str, help=( F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and ''' '''convert all the models from AWS.''' ), ) parser.add_argument( '''--pytorch_checkpoint_path''', default=None, type=str, help=( '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. ''' '''If not given, will download and convert all the checkpoints from AWS.''' ), ) parser.add_argument( '''--config_file''', default=None, type=str, help=( '''The config json file corresponding to the pre-trained model. \n''' '''This specifies the model architecture. If not given and ''' '''--pytorch_checkpoint_path is not given or is a shortcut name ''' '''use the configuration associated to the shortcut name on the AWS''' ), ) parser.add_argument( '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.''' ) parser.add_argument( '''--use_cached_models''', action='''store_true''', help='''Use cached models if possible instead of updating to latest checkpoint versions.''', ) parser.add_argument( '''--remove_cached_files''', action='''store_true''', help='''Remove pytorch models after conversion (save memory when converting in batches).''', ) parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''') _a = parser.parse_args() # if args.pytorch_checkpoint_path is not None: # convert_pt_checkpoint_to_tf(args.model_type.lower(), # args.pytorch_checkpoint_path, # args.config_file if args.config_file is not None else args.pytorch_checkpoint_path, # args.tf_dump_path, # compare_with_pt_model=args.compare_with_pt_model, # use_cached_models=args.use_cached_models) # else: convert_all_pt_checkpoints_to_tf( args.model_type.lower() if args.model_type is not None else None, args.tf_dump_path, model_shortcut_names_or_path=[args.pytorch_checkpoint_path] if args.pytorch_checkpoint_path is not None else None, config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None, compare_with_pt_model=args.compare_with_pt_model, use_cached_models=args.use_cached_models, remove_cached_files=args.remove_cached_files, only_convert_finetuned_models=args.only_convert_finetuned_models, )
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import os import unittest from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer from ...test_tokenization_common import TokenizerTesterMixin class __lowerCamelCase ( snake_case__ , unittest.TestCase): """simple docstring""" UpperCamelCase__ = TransfoXLTokenizer UpperCamelCase__ = False UpperCamelCase__ = False def UpperCamelCase ( self ): """simple docstring""" super().setUp() _UpperCAmelCase = [ '<unk>', '[CLS]', '[SEP]', 'want', 'unwanted', 'wa', 'un', 'running', ',', 'low', 'l', ] _UpperCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] ) with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer: vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) ) def UpperCamelCase ( self , **UpperCAmelCase ): """simple docstring""" _UpperCAmelCase = True return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase ) def UpperCamelCase ( self , UpperCAmelCase ): """simple docstring""" _UpperCAmelCase = '<unk> UNwanted , running' _UpperCAmelCase = '<unk> unwanted, running' return input_text, output_text def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=UpperCAmelCase ) _UpperCAmelCase = tokenizer.tokenize('<unk> UNwanted , running' ) self.assertListEqual(UpperCAmelCase , ['<unk>', 'unwanted', ',', 'running'] ) self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase ) , [0, 4, 8, 7] ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = TransfoXLTokenizer(lower_case=UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = TransfoXLTokenizer(lower_case=UpperCAmelCase ) self.assertListEqual( tokenizer.tokenize(' \tHeLLo ! how \n Are yoU ? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = TransfoXLTokenizer(lower_case=UpperCAmelCase ) _UpperCAmelCase = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?' _UpperCAmelCase = [ 'Hello', '(', 'bracket', ')', 'and', 'side', '@-@', 'scrolled', '[', 'and', ']', 'Henry', '\'s', '$', '5', '@,@', '000', 'with', '3', '@.@', '34', 'm', '.', 'What', '\'s', 'up', '!', '?', ] self.assertListEqual(tokenizer.tokenize(UpperCAmelCase ) , UpperCAmelCase ) self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase ) , UpperCAmelCase ) def UpperCamelCase ( self ): """simple docstring""" _UpperCAmelCase = self.get_tokenizer() _UpperCAmelCase = len(UpperCAmelCase ) tokenizer.add_tokens(['new1', 'new2'] ) tokenizer.move_added_token('new1' , 1 ) # Check that moved token is not copied (duplicate) self.assertEqual(len(UpperCAmelCase ) , original_len + 2 ) # Check that token is moved to specified id self.assertEqual(tokenizer.encode('new1' ) , [1] ) self.assertEqual(tokenizer.decode([1] ) , 'new1' )
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1
from typing import List, Optional, Tuple, Union import torch from ...models import UNetaDModel from ...schedulers import ScoreSdeVeScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class _snake_case ( _lowercase ): lowerCamelCase__: UNetaDModel lowerCamelCase__: ScoreSdeVeScheduler def __init__( self: List[Any] , __lowerCamelCase: UNetaDModel , __lowerCamelCase: ScoreSdeVeScheduler ) -> Optional[int]: super().__init__() self.register_modules(unet=__lowerCamelCase , scheduler=__lowerCamelCase ) @torch.no_grad() def __call__( self: Union[str, Any] , __lowerCamelCase: int = 1 , __lowerCamelCase: int = 20_00 , __lowerCamelCase: Optional[Union[torch.Generator, List[torch.Generator]]] = None , __lowerCamelCase: Optional[str] = "pil" , __lowerCamelCase: bool = True , **__lowerCamelCase: Union[str, Any] , ) -> Union[ImagePipelineOutput, Tuple]: __UpperCAmelCase : List[Any] = self.unet.config.sample_size __UpperCAmelCase : Dict = (batch_size, 3, img_size, img_size) __UpperCAmelCase : Optional[int] = self.unet __UpperCAmelCase : Optional[Any] = randn_tensor(__lowerCamelCase , generator=__lowerCamelCase ) * self.scheduler.init_noise_sigma __UpperCAmelCase : List[Any] = sample.to(self.device ) self.scheduler.set_timesteps(__lowerCamelCase ) self.scheduler.set_sigmas(__lowerCamelCase ) for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ): __UpperCAmelCase : Dict = self.scheduler.sigmas[i] * torch.ones(shape[0] , device=self.device ) # correction step for _ in range(self.scheduler.config.correct_steps ): __UpperCAmelCase : Dict = self.unet(__lowerCamelCase , __lowerCamelCase ).sample __UpperCAmelCase : Optional[int] = self.scheduler.step_correct(__lowerCamelCase , __lowerCamelCase , generator=__lowerCamelCase ).prev_sample # prediction step __UpperCAmelCase : Optional[int] = model(__lowerCamelCase , __lowerCamelCase ).sample __UpperCAmelCase : Tuple = self.scheduler.step_pred(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , generator=__lowerCamelCase ) __UpperCAmelCase , __UpperCAmelCase : Optional[Any] = output.prev_sample, output.prev_sample_mean __UpperCAmelCase : Optional[int] = sample_mean.clamp(0 , 1 ) __UpperCAmelCase : Dict = sample.cpu().permute(0 , 2 , 3 , 1 ).numpy() if output_type == "pil": __UpperCAmelCase : Dict = self.numpy_to_pil(__lowerCamelCase ) if not return_dict: return (sample,) return ImagePipelineOutput(images=__lowerCamelCase )
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import json import os from functools import lru_cache from typing import TYPE_CHECKING, List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging if TYPE_CHECKING: from transformers.pipelines.conversational import Conversation _snake_case = logging.get_logger(__name__) _snake_case = { '''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_config_file''': '''tokenizer_config.json''', } _snake_case = { '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''}, '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''}, '''tokenizer_config_file''': { '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json''' }, } _snake_case = {'''facebook/blenderbot-3B''': 128} @lru_cache() # Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode def _UpperCamelCase ( ) -> Dict: __UpperCAmelCase : Tuple = ( list(range(ord("!" ), ord("~" ) + 1 ) ) + list(range(ord("¡" ), ord("¬" ) + 1 ) ) + list(range(ord("®" ), ord("ÿ" ) + 1 ) ) ) __UpperCAmelCase : str = bs[:] __UpperCAmelCase : Any = 0 for b in range(2**8 ): if b not in bs: bs.append(snake_case__ ) cs.append(2**8 + n ) n += 1 __UpperCAmelCase : Optional[Any] = [chr(snake_case__ ) for n in cs] return dict(zip(snake_case__, snake_case__ ) ) def _UpperCamelCase ( snake_case__ ) -> Any: __UpperCAmelCase : List[Any] = set() __UpperCAmelCase : Any = word[0] for char in word[1:]: pairs.add((prev_char, char) ) __UpperCAmelCase : Union[str, Any] = char return pairs class _snake_case ( _lowercase ): lowerCamelCase__: str = VOCAB_FILES_NAMES lowerCamelCase__: List[Any] = PRETRAINED_VOCAB_FILES_MAP lowerCamelCase__: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowerCamelCase__: Dict = ["input_ids", "attention_mask"] def __init__( self: Tuple , __lowerCamelCase: Union[str, Any] , __lowerCamelCase: Optional[int] , __lowerCamelCase: List[str]="replace" , __lowerCamelCase: List[str]="<s>" , __lowerCamelCase: List[str]="</s>" , __lowerCamelCase: str="</s>" , __lowerCamelCase: Tuple="<s>" , __lowerCamelCase: Optional[int]="<unk>" , __lowerCamelCase: Any="<pad>" , __lowerCamelCase: List[str]="<mask>" , __lowerCamelCase: List[str]=False , **__lowerCamelCase: int , ) -> List[str]: __UpperCAmelCase : int = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else bos_token __UpperCAmelCase : List[Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else eos_token __UpperCAmelCase : Any = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else sep_token __UpperCAmelCase : Tuple = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else cls_token __UpperCAmelCase : Optional[Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else unk_token __UpperCAmelCase : List[Any] = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else pad_token # Mask token behave like a normal word, i.e. include the space before it __UpperCAmelCase : Dict = AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase , __lowerCamelCase ) else mask_token super().__init__( errors=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , cls_token=__lowerCamelCase , pad_token=__lowerCamelCase , mask_token=__lowerCamelCase , add_prefix_space=__lowerCamelCase , **__lowerCamelCase , ) with open(__lowerCamelCase , encoding="utf-8" ) as vocab_handle: __UpperCAmelCase : List[Any] = json.load(__lowerCamelCase ) __UpperCAmelCase : Optional[Any] = {v: k for k, v in self.encoder.items()} __UpperCAmelCase : Dict = errors # how to handle errors in decoding __UpperCAmelCase : Optional[int] = bytes_to_unicode() __UpperCAmelCase : Dict = {v: k for k, v in self.byte_encoder.items()} with open(__lowerCamelCase , encoding="utf-8" ) as merges_handle: __UpperCAmelCase : List[Any] = merges_handle.read().split("\n" )[1:-1] __UpperCAmelCase : Union[str, Any] = [tuple(merge.split() ) for merge in bpe_merges] __UpperCAmelCase : int = dict(zip(__lowerCamelCase , range(len(__lowerCamelCase ) ) ) ) __UpperCAmelCase : List[Any] = {} __UpperCAmelCase : Tuple = add_prefix_space # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions __UpperCAmelCase : int = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" ) @property # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot def _lowerCamelCase ( self: Dict ) -> Any: return len(self.encoder ) def _lowerCamelCase ( self: Optional[Any] ) -> List[str]: return dict(self.encoder , **self.added_tokens_encoder ) def _lowerCamelCase ( self: int , __lowerCamelCase: List[Any] ) -> Union[str, Any]: if token in self.cache: return self.cache[token] __UpperCAmelCase : List[Any] = tuple(__lowerCamelCase ) __UpperCAmelCase : Dict = get_pairs(__lowerCamelCase ) if not pairs: return token while True: __UpperCAmelCase : Optional[int] = min(__lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(__lowerCamelCase , float("inf" ) ) ) if bigram not in self.bpe_ranks: break __UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = bigram __UpperCAmelCase : Optional[int] = [] __UpperCAmelCase : str = 0 while i < len(__lowerCamelCase ): try: __UpperCAmelCase : Union[str, Any] = word.index(__lowerCamelCase , __lowerCamelCase ) except ValueError: new_word.extend(word[i:] ) break else: new_word.extend(word[i:j] ) __UpperCAmelCase : Union[str, Any] = j if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second: new_word.append(first + second ) i += 2 else: new_word.append(word[i] ) i += 1 __UpperCAmelCase : List[Any] = tuple(__lowerCamelCase ) __UpperCAmelCase : str = new_word if len(__lowerCamelCase ) == 1: break else: __UpperCAmelCase : Optional[Any] = get_pairs(__lowerCamelCase ) __UpperCAmelCase : Optional[Any] = " ".join(__lowerCamelCase ) __UpperCAmelCase : Union[str, Any] = word return word def _lowerCamelCase ( self: Dict , __lowerCamelCase: Optional[Any] ) -> Dict: __UpperCAmelCase : Any = [] for token in re.findall(self.pat , __lowerCamelCase ): __UpperCAmelCase : int = "".join( self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase ).split(" " ) ) return bpe_tokens def _lowerCamelCase ( self: int , __lowerCamelCase: str ) -> Dict: return self.encoder.get(__lowerCamelCase , self.encoder.get(self.unk_token ) ) def _lowerCamelCase ( self: Tuple , __lowerCamelCase: List[Any] ) -> List[str]: return self.decoder.get(__lowerCamelCase ) def _lowerCamelCase ( self: Any , __lowerCamelCase: Any ) -> int: __UpperCAmelCase : Dict = "".join(__lowerCamelCase ) __UpperCAmelCase : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors ) return text def _lowerCamelCase ( self: List[Any] , __lowerCamelCase: str , __lowerCamelCase: Optional[str] = None ) -> Tuple[str]: if not os.path.isdir(__lowerCamelCase ): logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' ) return __UpperCAmelCase : Any = os.path.join( __lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) __UpperCAmelCase : Dict = os.path.join( __lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(__lowerCamelCase , "w" , encoding="utf-8" ) as f: f.write(json.dumps(self.encoder , indent=2 , sort_keys=__lowerCamelCase , ensure_ascii=__lowerCamelCase ) + "\n" ) __UpperCAmelCase : Optional[Any] = 0 with open(__lowerCamelCase , "w" , encoding="utf-8" ) as writer: writer.write("#version: 0.2\n" ) for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda __lowerCamelCase : kv[1] ): if index != token_index: logger.warning( f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.''' " Please check that the tokenizer is not corrupted!" ) __UpperCAmelCase : Optional[Any] = token_index writer.write(" ".join(__lowerCamelCase ) + "\n" ) index += 1 return vocab_file, merge_file def _lowerCamelCase ( self: Dict , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None , __lowerCamelCase: bool = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__lowerCamelCase , token_ids_a=__lowerCamelCase , already_has_special_tokens=__lowerCamelCase ) if token_ids_a is None: return [1] + ([0] * len(__lowerCamelCase )) + [1] return [1] + ([0] * len(__lowerCamelCase )) + [1, 1] + ([0] * len(__lowerCamelCase )) + [1] def _lowerCamelCase ( self: Tuple , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None ) -> List[int]: __UpperCAmelCase : int = [self.sep_token_id] __UpperCAmelCase : Union[str, Any] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0] def _lowerCamelCase ( self: str , __lowerCamelCase: Optional[int] , __lowerCamelCase: List[str]=False , **__lowerCamelCase: int ) -> List[Any]: __UpperCAmelCase : Optional[Any] = kwargs.pop("add_prefix_space" , self.add_prefix_space ) if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase ) > 0 and not text[0].isspace()): __UpperCAmelCase : Optional[Any] = " " + text return (text, kwargs) def _lowerCamelCase ( self: List[str] , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None ) -> List[str]: return token_ids_a + [self.eos_token_id] def _lowerCamelCase ( self: List[str] , __lowerCamelCase: "Conversation" ) -> List[int]: __UpperCAmelCase : Tuple = [] for is_user, text in conversation.iter_texts(): if is_user: # We need to space prefix as it's being done within blenderbot inputs.append(" " + text ) else: # Generated responses should contain them already. inputs.append(__lowerCamelCase ) __UpperCAmelCase : Optional[int] = " ".join(__lowerCamelCase ) __UpperCAmelCase : Optional[Any] = self.encode(__lowerCamelCase ) if len(__lowerCamelCase ) > self.model_max_length: __UpperCAmelCase : List[Any] = input_ids[-self.model_max_length :] logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''' ) return input_ids
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_convbert import ConvBertTokenizer lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__) lowerCAmelCase__ : Union[str, Any] = {'''vocab_file''': '''vocab.txt'''} lowerCAmelCase__ : Optional[int] = { '''vocab_file''': { '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''', '''YituTech/conv-bert-medium-small''': ( '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt''' ), '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''', } } lowerCAmelCase__ : Optional[Any] = { '''YituTech/conv-bert-base''': 5_12, '''YituTech/conv-bert-medium-small''': 5_12, '''YituTech/conv-bert-small''': 5_12, } lowerCAmelCase__ : int = { '''YituTech/conv-bert-base''': {'''do_lower_case''': True}, '''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True}, '''YituTech/conv-bert-small''': {'''do_lower_case''': True}, } class __snake_case ( _lowerCamelCase ): __lowerCamelCase = VOCAB_FILES_NAMES __lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP __lowerCamelCase = PRETRAINED_INIT_CONFIGURATION __lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES __lowerCamelCase = ConvBertTokenizer def __init__( self , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=True , __UpperCamelCase="[UNK]" , __UpperCamelCase="[SEP]" , __UpperCamelCase="[PAD]" , __UpperCamelCase="[CLS]" , __UpperCamelCase="[MASK]" , __UpperCamelCase=True , __UpperCamelCase=None , **__UpperCamelCase , ) -> Optional[int]: '''simple docstring''' super().__init__( __UpperCamelCase , tokenizer_file=__UpperCamelCase , do_lower_case=__UpperCamelCase , unk_token=__UpperCamelCase , sep_token=__UpperCamelCase , pad_token=__UpperCamelCase , cls_token=__UpperCamelCase , mask_token=__UpperCamelCase , tokenize_chinese_chars=__UpperCamelCase , strip_accents=__UpperCamelCase , **__UpperCamelCase , ) snake_case__ : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() ) if ( normalizer_state.get('lowercase' , __UpperCamelCase ) != do_lower_case or normalizer_state.get('strip_accents' , __UpperCamelCase ) != strip_accents or normalizer_state.get('handle_chinese_chars' , __UpperCamelCase ) != tokenize_chinese_chars ): snake_case__ : List[str] = getattr(__UpperCamelCase , normalizer_state.pop('type' ) ) snake_case__ : List[Any] = do_lower_case snake_case__ : List[Any] = strip_accents snake_case__ : Any = tokenize_chinese_chars snake_case__ : Dict = normalizer_class(**__UpperCamelCase ) snake_case__ : Dict = do_lower_case def __a ( self , __UpperCamelCase , __UpperCamelCase=None ) -> List[Any]: '''simple docstring''' snake_case__ : str = [self.cls_token_id] + token_ids_a + [self.sep_token_id] if token_ids_a: output += token_ids_a + [self.sep_token_id] return output def __a ( self , __UpperCamelCase , __UpperCamelCase = None ) -> List[int]: '''simple docstring''' snake_case__ : Union[str, Any] = [self.sep_token_id] snake_case__ : 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 __a ( self , __UpperCamelCase , __UpperCamelCase = None ) -> Tuple[str]: '''simple docstring''' snake_case__ : Optional[int] = self._tokenizer.model.save(__UpperCamelCase , name=__UpperCamelCase ) return tuple(__UpperCamelCase )
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import gc import random import unittest import numpy as np import torch from diffusers import ( DDIMScheduler, KandinskyVaaControlnetPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel, ) from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference enable_full_determinism() class __snake_case ( _lowerCamelCase ,unittest.TestCase ): __lowerCamelCase = KandinskyVaaControlnetPipeline __lowerCamelCase = ["""image_embeds""", """negative_image_embeds""", """hint"""] __lowerCamelCase = ["""image_embeds""", """negative_image_embeds""", """hint"""] __lowerCamelCase = [ """generator""", """height""", """width""", """latents""", """guidance_scale""", """num_inference_steps""", """return_dict""", """guidance_scale""", """num_images_per_prompt""", """output_type""", """return_dict""", ] __lowerCamelCase = False @property def __a ( self ) -> List[Any]: '''simple docstring''' return 32 @property def __a ( self ) -> int: '''simple docstring''' return 32 @property def __a ( self ) -> List[str]: '''simple docstring''' return self.time_input_dim @property def __a ( self ) -> Any: '''simple docstring''' return self.time_input_dim * 4 @property def __a ( self ) -> List[Any]: '''simple docstring''' return 100 @property def __a ( self ) -> Optional[Any]: '''simple docstring''' torch.manual_seed(0 ) snake_case__ : Tuple = { 'in_channels': 8, # Out channels is double in channels because predicts mean and variance 'out_channels': 8, 'addition_embed_type': 'image_hint', 'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'), 'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'), 'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn', 'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2), 'layers_per_block': 1, 'encoder_hid_dim': self.text_embedder_hidden_size, 'encoder_hid_dim_type': 'image_proj', 'cross_attention_dim': self.cross_attention_dim, 'attention_head_dim': 4, 'resnet_time_scale_shift': 'scale_shift', 'class_embed_type': None, } snake_case__ : Tuple = UNetaDConditionModel(**__UpperCamelCase ) return model @property def __a ( self ) -> Tuple: '''simple docstring''' return { "block_out_channels": [32, 32, 64, 64], "down_block_types": [ "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "AttnDownEncoderBlock2D", ], "in_channels": 3, "latent_channels": 4, "layers_per_block": 1, "norm_num_groups": 8, "norm_type": "spatial", "num_vq_embeddings": 12, "out_channels": 3, "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"], "vq_embed_dim": 4, } @property def __a ( self ) -> int: '''simple docstring''' torch.manual_seed(0 ) snake_case__ : Tuple = VQModel(**self.dummy_movq_kwargs ) return model def __a ( self ) -> Dict: '''simple docstring''' snake_case__ : int = self.dummy_unet snake_case__ : Tuple = self.dummy_movq snake_case__ : Union[str, Any] = DDIMScheduler( num_train_timesteps=1000 , beta_schedule='linear' , beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , clip_sample=__UpperCamelCase , set_alpha_to_one=__UpperCamelCase , steps_offset=1 , prediction_type='epsilon' , thresholding=__UpperCamelCase , ) snake_case__ : str = { 'unet': unet, 'scheduler': scheduler, 'movq': movq, } return components def __a ( self , __UpperCamelCase , __UpperCamelCase=0 ) -> int: '''simple docstring''' snake_case__ : Any = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase ) snake_case__ : List[str] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to( __UpperCamelCase ) # create hint snake_case__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase ) if str(__UpperCamelCase ).startswith('mps' ): snake_case__ : Any = torch.manual_seed(__UpperCamelCase ) else: snake_case__ : str = torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase ) snake_case__ : int = { 'image_embeds': image_embeds, 'negative_image_embeds': negative_image_embeds, 'hint': hint, 'generator': generator, 'height': 64, 'width': 64, 'guidance_scale': 4.0, 'num_inference_steps': 2, 'output_type': 'np', } return inputs def __a ( self ) -> List[Any]: '''simple docstring''' snake_case__ : List[Any] = 'cpu' snake_case__ : Any = self.get_dummy_components() snake_case__ : Optional[Any] = self.pipeline_class(**__UpperCamelCase ) snake_case__ : Dict = pipe.to(__UpperCamelCase ) pipe.set_progress_bar_config(disable=__UpperCamelCase ) snake_case__ : Optional[Any] = pipe(**self.get_dummy_inputs(__UpperCamelCase ) ) snake_case__ : Dict = output.images snake_case__ : Any = pipe( **self.get_dummy_inputs(__UpperCamelCase ) , return_dict=__UpperCamelCase , )[0] snake_case__ : Optional[int] = image[0, -3:, -3:, -1] snake_case__ : Optional[int] = image_from_tuple[0, -3:, -3:, -1] assert image.shape == (1, 64, 64, 3) snake_case__ : str = np.array( [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] ) assert ( np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}""" assert ( np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2 ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}""" @slow @require_torch_gpu class __snake_case ( unittest.TestCase ): def __a ( self ) -> str: '''simple docstring''' super().tearDown() gc.collect() torch.cuda.empty_cache() def __a ( self ) -> Optional[int]: '''simple docstring''' snake_case__ : List[Any] = load_numpy( 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy' ) snake_case__ : Union[str, Any] = load_image( 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinskyv22/hint_image_cat.png' ) snake_case__ : List[str] = torch.from_numpy(np.array(__UpperCamelCase ) ).float() / 2_5_5.0 snake_case__ : Dict = hint.permute(2 , 0 , 1 ).unsqueeze(0 ) snake_case__ : int = KandinskyVaaPriorPipeline.from_pretrained( 'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa ) pipe_prior.to(__UpperCamelCase ) snake_case__ : int = KandinskyVaaControlnetPipeline.from_pretrained( 'kandinsky-community/kandinsky-2-2-controlnet-depth' , torch_dtype=torch.floataa ) snake_case__ : List[Any] = pipeline.to(__UpperCamelCase ) pipeline.set_progress_bar_config(disable=__UpperCamelCase ) snake_case__ : Optional[int] = 'A robot, 4k photo' snake_case__ : List[Any] = torch.Generator(device='cuda' ).manual_seed(0 ) snake_case__ , snake_case__ : Tuple = pipe_prior( __UpperCamelCase , generator=__UpperCamelCase , num_inference_steps=5 , negative_prompt='' , ).to_tuple() snake_case__ : List[Any] = torch.Generator(device='cuda' ).manual_seed(0 ) snake_case__ : Dict = pipeline( image_embeds=__UpperCamelCase , negative_image_embeds=__UpperCamelCase , hint=__UpperCamelCase , generator=__UpperCamelCase , num_inference_steps=100 , output_type='np' , ) snake_case__ : Union[str, Any] = output.images[0] assert image.shape == (512, 512, 3) assert_mean_pixel_difference(__UpperCamelCase , __UpperCamelCase )
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'''simple docstring''' import unittest import numpy as np from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING from transformers.pipelines import AudioClassificationPipeline, pipeline from transformers.testing_utils import ( is_pipeline_test, nested_simplify, require_tf, require_torch, require_torchaudio, slow, ) from .test_pipelines_common import ANY @is_pipeline_test class A_ ( unittest.TestCase ): '''simple docstring''' UpperCAmelCase_ : Any = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING UpperCAmelCase_ : Union[str, Any] = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING def UpperCAmelCase_ ( self : Dict , lowercase_ : Any , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] ) -> Optional[Any]: UpperCAmelCase : Optional[Any] = AudioClassificationPipeline(model=lowercase_ , feature_extractor=lowercase_ ) # test with a raw waveform UpperCAmelCase : str = np.zeros((34_000,) ) UpperCAmelCase : List[Any] = np.zeros((14_000,) ) return audio_classifier, [audioa, audio] def UpperCAmelCase_ ( self : Any , lowercase_ : List[Any] , lowercase_ : List[str] ) -> Tuple: UpperCAmelCase : Union[str, Any] = examples UpperCAmelCase : Union[str, Any] = audio_classifier(lowercase_ ) # by default a model is initialized with num_labels=2 self.assertEqual( lowercase_ , [ {'score': ANY(lowercase_ ), 'label': ANY(lowercase_ )}, {'score': ANY(lowercase_ ), 'label': ANY(lowercase_ )}, ] , ) UpperCAmelCase : Union[str, Any] = audio_classifier(lowercase_ , top_k=1 ) self.assertEqual( lowercase_ , [ {'score': ANY(lowercase_ ), 'label': ANY(lowercase_ )}, ] , ) self.run_torchaudio(lowercase_ ) @require_torchaudio def UpperCAmelCase_ ( self : int , lowercase_ : Any ) -> Dict: import datasets # test with a local file UpperCAmelCase : str = datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy' , 'clean' , split='validation' ) UpperCAmelCase : Union[str, Any] = dataset[0]['audio']['array'] UpperCAmelCase : List[str] = audio_classifier(lowercase_ ) self.assertEqual( lowercase_ , [ {'score': ANY(lowercase_ ), 'label': ANY(lowercase_ )}, {'score': ANY(lowercase_ ), 'label': ANY(lowercase_ )}, ] , ) @require_torch def UpperCAmelCase_ ( self : List[Any] ) -> List[str]: UpperCAmelCase : Any = 'anton-l/wav2vec2-random-tiny-classifier' UpperCAmelCase : Any = pipeline('audio-classification' , model=lowercase_ ) UpperCAmelCase : Dict = np.ones((8_000,) ) UpperCAmelCase : List[Any] = audio_classifier(lowercase_ , top_k=4 ) UpperCAmelCase : Union[str, Any] = [ {'score': 0.0842, 'label': 'no'}, {'score': 0.0838, 'label': 'up'}, {'score': 0.0837, 'label': 'go'}, {'score': 0.0834, 'label': 'right'}, ] UpperCAmelCase : Any = [ {'score': 0.0845, 'label': 'stop'}, {'score': 0.0844, 'label': 'on'}, {'score': 0.0841, 'label': 'right'}, {'score': 0.0834, 'label': 'left'}, ] self.assertIn(nested_simplify(lowercase_ , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] ) UpperCAmelCase : int = {'array': np.ones((8_000,) ), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate} UpperCAmelCase : List[str] = audio_classifier(lowercase_ , top_k=4 ) self.assertIn(nested_simplify(lowercase_ , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] ) @require_torch @slow def UpperCAmelCase_ ( self : str ) -> List[str]: import datasets UpperCAmelCase : Union[str, Any] = 'superb/wav2vec2-base-superb-ks' UpperCAmelCase : Optional[int] = pipeline('audio-classification' , model=lowercase_ ) UpperCAmelCase : Optional[int] = datasets.load_dataset('anton-l/superb_dummy' , 'ks' , split='test' ) UpperCAmelCase : Union[str, Any] = np.array(dataset[3]['speech'] , dtype=np.floataa ) UpperCAmelCase : Optional[int] = audio_classifier(lowercase_ , top_k=4 ) self.assertEqual( nested_simplify(lowercase_ , decimals=3 ) , [ {'score': 0.981, 'label': 'go'}, {'score': 0.007, 'label': 'up'}, {'score': 0.006, 'label': '_unknown_'}, {'score': 0.001, 'label': 'down'}, ] , ) @require_tf @unittest.skip('Audio classification is not implemented for TF' ) def UpperCAmelCase_ ( self : List[str] ) -> Any: pass
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'''simple docstring''' import argparse import os from pathlib import Path import torch from bark.generation import _load_model as _bark_load_model from huggingface_hub import hf_hub_download from transformers import EncodecConfig, EncodecModel, set_seed from transformers.models.bark.configuration_bark import ( BarkCoarseConfig, BarkConfig, BarkFineConfig, BarkSemanticConfig, ) from transformers.models.bark.generation_configuration_bark import ( BarkCoarseGenerationConfig, BarkFineGenerationConfig, BarkGenerationConfig, BarkSemanticGenerationConfig, ) from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel from transformers.utils import logging logging.set_verbosity_info() lowercase__ = logging.get_logger(__name__) set_seed(770) lowercase__ = { "c_attn": "att_proj", "c_proj": "out_proj", "c_fc": "in_proj", "transformer.": "", "h.": "layers.", "ln_1": "layernorm_1", "ln_2": "layernorm_2", "ln_f": "layernorm_final", "wpe": "position_embeds_layer", "wte": "input_embeds_layer", } lowercase__ = { "text_small": { "repo_id": "suno/bark", "file_name": "text.pt", }, "coarse_small": { "repo_id": "suno/bark", "file_name": "coarse.pt", }, "fine_small": { "repo_id": "suno/bark", "file_name": "fine.pt", }, "text": { "repo_id": "suno/bark", "file_name": "text_2.pt", }, "coarse": { "repo_id": "suno/bark", "file_name": "coarse_2.pt", }, "fine": { "repo_id": "suno/bark", "file_name": "fine_2.pt", }, } lowercase__ = os.path.dirname(os.path.abspath(__file__)) lowercase__ = os.path.join(os.path.expanduser("~"), ".cache") lowercase__ = os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0") def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_=False ): UpperCAmelCase : List[str] = model_type if use_small: key += "_small" return os.path.join(UpperCAmelCase_ , REMOTE_MODEL_PATHS[key]['file_name'] ) def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ): os.makedirs(UpperCAmelCase_ , exist_ok=UpperCAmelCase_ ) hf_hub_download(repo_id=UpperCAmelCase_ , filename=UpperCAmelCase_ , local_dir=UpperCAmelCase_ ) def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=False , UpperCAmelCase_="text" ): if model_type == "text": UpperCAmelCase : Dict = BarkSemanticModel UpperCAmelCase : List[Any] = BarkSemanticConfig UpperCAmelCase : Optional[int] = BarkSemanticGenerationConfig elif model_type == "coarse": UpperCAmelCase : List[str] = BarkCoarseModel UpperCAmelCase : Dict = BarkCoarseConfig UpperCAmelCase : int = BarkCoarseGenerationConfig elif model_type == "fine": UpperCAmelCase : List[Any] = BarkFineModel UpperCAmelCase : Optional[Any] = BarkFineConfig UpperCAmelCase : Dict = BarkFineGenerationConfig else: raise NotImplementedError() UpperCAmelCase : Optional[int] = F"""{model_type}_small""" if use_small else model_type UpperCAmelCase : Tuple = REMOTE_MODEL_PATHS[model_key] if not os.path.exists(UpperCAmelCase_ ): logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" ) _download(model_info['repo_id'] , model_info['file_name'] ) UpperCAmelCase : Optional[Any] = torch.load(UpperCAmelCase_ , map_location=UpperCAmelCase_ ) # this is a hack UpperCAmelCase : str = checkpoint['model_args'] if "input_vocab_size" not in model_args: UpperCAmelCase : Union[str, Any] = model_args['vocab_size'] UpperCAmelCase : Union[str, Any] = model_args['vocab_size'] del model_args["vocab_size"] # convert Bark model arguments to HF Bark model arguments UpperCAmelCase : Any = model_args.pop('n_head' ) UpperCAmelCase : Optional[Any] = model_args.pop('n_embd' ) UpperCAmelCase : Union[str, Any] = model_args.pop('n_layer' ) UpperCAmelCase : List[Any] = ConfigClass(**checkpoint['model_args'] ) UpperCAmelCase : List[str] = ModelClass(config=UpperCAmelCase_ ) UpperCAmelCase : List[str] = GenerationConfigClass() UpperCAmelCase : Dict = model_generation_config UpperCAmelCase : int = checkpoint['model'] # fixup checkpoint UpperCAmelCase : Tuple = '_orig_mod.' for k, v in list(state_dict.items() ): if k.startswith(UpperCAmelCase_ ): # replace part of the key with corresponding layer name in HF implementation UpperCAmelCase : str = k[len(UpperCAmelCase_ ) :] for old_layer_name in new_layer_name_dict: UpperCAmelCase : List[Any] = new_k.replace(UpperCAmelCase_ , new_layer_name_dict[old_layer_name] ) UpperCAmelCase : List[Any] = state_dict.pop(UpperCAmelCase_ ) UpperCAmelCase : Tuple = set(state_dict.keys() ) - set(model.state_dict().keys() ) UpperCAmelCase : Optional[int] = {k for k in extra_keys if not k.endswith('.attn.bias' )} UpperCAmelCase : str = set(model.state_dict().keys() ) - set(state_dict.keys() ) UpperCAmelCase : str = {k for k in missing_keys if not k.endswith('.attn.bias' )} if len(UpperCAmelCase_ ) != 0: raise ValueError(F"""extra keys found: {extra_keys}""" ) if len(UpperCAmelCase_ ) != 0: raise ValueError(F"""missing keys: {missing_keys}""" ) model.load_state_dict(UpperCAmelCase_ , strict=UpperCAmelCase_ ) UpperCAmelCase : List[str] = model.num_parameters(exclude_embeddings=UpperCAmelCase_ ) UpperCAmelCase : Optional[Any] = checkpoint['best_val_loss'].item() logger.info(F"""model loaded: {round(n_params/1E6 , 1 )}M params, {round(UpperCAmelCase_ , 3 )} loss""" ) model.eval() model.to(UpperCAmelCase_ ) del checkpoint, state_dict return model def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_=False , UpperCAmelCase_="text" ): if model_type not in ("text", "coarse", "fine"): raise NotImplementedError() UpperCAmelCase : List[str] = 'cpu' # do conversion on cpu UpperCAmelCase : List[str] = _get_ckpt_path(UpperCAmelCase_ , use_small=UpperCAmelCase_ ) UpperCAmelCase : Optional[int] = _load_model(UpperCAmelCase_ , UpperCAmelCase_ , model_type=UpperCAmelCase_ , use_small=UpperCAmelCase_ ) # load bark initial model UpperCAmelCase : List[str] = _bark_load_model(UpperCAmelCase_ , 'cpu' , model_type=UpperCAmelCase_ , use_small=UpperCAmelCase_ ) if model_type == "text": UpperCAmelCase : Tuple = bark_model['model'] if model.num_parameters(exclude_embeddings=UpperCAmelCase_ ) != bark_model.get_num_params(): raise ValueError('initial and new models don\'t have the same number of parameters' ) # check if same output as the bark model UpperCAmelCase : Optional[int] = 5 UpperCAmelCase : Optional[int] = 10 if model_type in ["text", "coarse"]: UpperCAmelCase : List[Any] = torch.randint(2_56 , (batch_size, sequence_length) , dtype=torch.int ) UpperCAmelCase : Optional[Any] = bark_model(UpperCAmelCase_ )[0] UpperCAmelCase : List[str] = model(UpperCAmelCase_ ) # take last logits UpperCAmelCase : str = output_new_model_total.logits[:, [-1], :] else: UpperCAmelCase : Optional[int] = 3 UpperCAmelCase : List[Any] = 8 UpperCAmelCase : Dict = torch.randint(2_56 , (batch_size, sequence_length, n_codes_total) , dtype=torch.int ) UpperCAmelCase : str = model(UpperCAmelCase_ , UpperCAmelCase_ ) UpperCAmelCase : Dict = bark_model(UpperCAmelCase_ , UpperCAmelCase_ ) UpperCAmelCase : Any = output_new_model_total.logits # output difference should come from the difference of self-attention implementation design if output_new_model.shape != output_old_model.shape: raise ValueError('initial and new outputs don\'t have the same shape' ) if (output_new_model - output_old_model).abs().max().item() > 1E-3: raise ValueError('initial and new outputs are not equal' ) Path(UpperCAmelCase_ ).mkdir(exist_ok=UpperCAmelCase_ ) model.save_pretrained(UpperCAmelCase_ ) def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ): UpperCAmelCase : int = os.path.join(UpperCAmelCase_ , UpperCAmelCase_ ) UpperCAmelCase : Dict = BarkSemanticConfig.from_pretrained(os.path.join(UpperCAmelCase_ , 'config.json' ) ) UpperCAmelCase : Any = BarkCoarseConfig.from_pretrained(os.path.join(UpperCAmelCase_ , 'config.json' ) ) UpperCAmelCase : Union[str, Any] = BarkFineConfig.from_pretrained(os.path.join(UpperCAmelCase_ , 'config.json' ) ) UpperCAmelCase : Any = EncodecConfig.from_pretrained('facebook/encodec_24khz' ) UpperCAmelCase : Dict = BarkSemanticModel.from_pretrained(UpperCAmelCase_ ) UpperCAmelCase : Tuple = BarkCoarseModel.from_pretrained(UpperCAmelCase_ ) UpperCAmelCase : Optional[int] = BarkFineModel.from_pretrained(UpperCAmelCase_ ) UpperCAmelCase : str = EncodecModel.from_pretrained('facebook/encodec_24khz' ) UpperCAmelCase : Optional[Any] = BarkConfig.from_sub_model_configs( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) UpperCAmelCase : Optional[int] = BarkGenerationConfig.from_sub_model_configs( semantic.generation_config , coarseAcoustic.generation_config , fineAcoustic.generation_config ) UpperCAmelCase : str = BarkModel(UpperCAmelCase_ ) UpperCAmelCase : int = semantic UpperCAmelCase : Tuple = coarseAcoustic UpperCAmelCase : Union[str, Any] = fineAcoustic UpperCAmelCase : Union[str, Any] = codec UpperCAmelCase : Optional[int] = bark_generation_config Path(UpperCAmelCase_ ).mkdir(exist_ok=UpperCAmelCase_ ) bark.save_pretrained(UpperCAmelCase_ , repo_id=UpperCAmelCase_ , push_to_hub=UpperCAmelCase_ ) if __name__ == "__main__": lowercase__ = argparse.ArgumentParser() # Required parameters parser.add_argument("model_type", type=str, help="text, coarse or fine.") parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.") parser.add_argument("--is_small", action="store_true", help="convert the small version instead of the large.") lowercase__ = parser.parse_args() load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
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import math_equivalence # From: git+https://github.com/hendrycks/math.git import datasets _SCREAMING_SNAKE_CASE = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n" _SCREAMING_SNAKE_CASE = "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n" _SCREAMING_SNAKE_CASE = r"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n >>> metric = datasets.load_metric(\"competition_math\")\n >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n >>> print(results)\n {'accuracy': 1.0}\n" @datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION ) class SCREAMING_SNAKE_CASE_ ( datasets.Metric ): def lowerCamelCase_ ( self : Union[str, Any] ): """simple docstring""" return datasets.MetricInfo( description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features( { """predictions""": datasets.Value("""string""" ), """references""": datasets.Value("""string""" ), } ) , homepage="""https://github.com/hendrycks/math""" , codebase_urls=["""https://github.com/hendrycks/math"""] , ) def lowerCamelCase_ ( self : int , lowerCamelCase_ : int , lowerCamelCase_ : List[Any] ): """simple docstring""" UpperCamelCase = 0.0 for i, j in zip(__snake_case , __snake_case ): n_correct += 1.0 if math_equivalence.is_equiv(__snake_case , __snake_case ) else 0.0 UpperCamelCase = n_correct / len(__snake_case ) return { "accuracy": accuracy, }
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'''simple docstring''' # coding=utf-8 # Copyright 2020 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # this script dumps information about the environment import os import sys import transformers UpperCamelCase__: Optional[int] = "3" print("Python version:", sys.version) print("transformers version:", transformers.__version__) try: import torch print("Torch version:", torch.__version__) print("Cuda available:", torch.cuda.is_available()) print("Cuda version:", torch.version.cuda) print("CuDNN version:", torch.backends.cudnn.version()) print("Number of GPUs available:", torch.cuda.device_count()) print("NCCL version:", torch.cuda.nccl.version()) except ImportError: print("Torch version:", None) try: import deepspeed print("DeepSpeed version:", deepspeed.__version__) except ImportError: print("DeepSpeed version:", None) try: import tensorflow as tf print("TensorFlow version:", tf.__version__) print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU"))) print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU"))) except ImportError: print("TensorFlow version:", None)
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"""simple docstring""" import random import unittest import numpy as np import torch from diffusers import ( DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionUpscalePipeline, PNDMScheduler, ) from diffusers.utils import floats_tensor from diffusers.utils.testing_utils import ( is_onnx_available, load_image, nightly, require_onnxruntime, require_torch_gpu, ) from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin if is_onnx_available(): import onnxruntime as ort class __A ( _SCREAMING_SNAKE_CASE, unittest.TestCase ): """simple docstring""" __lowerCAmelCase = "ssube/stable-diffusion-x4-upscaler-onnx" def SCREAMING_SNAKE_CASE ( self , __A=0 ) -> Dict: a =floats_tensor((1, 3, 128, 128) , rng=random.Random(__A ) ) a =torch.manual_seed(__A ) a ={ '''prompt''': '''A painting of a squirrel eating a burger''', '''image''': image, '''generator''': generator, '''num_inference_steps''': 3, '''guidance_scale''': 7.5, '''output_type''': '''numpy''', } return inputs def SCREAMING_SNAKE_CASE ( self ) -> Tuple: a =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' ) pipe.set_progress_bar_config(disable=__A ) a =self.get_dummy_inputs() a =pipe(**__A ).images a =image[0, -3:, -3:, -1].flatten() # started as 128, should now be 512 assert image.shape == (1, 512, 512, 3) a =np.array( [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] ) assert np.abs(image_slice - expected_slice ).max() < 1E-1 def SCREAMING_SNAKE_CASE ( self ) -> str: a =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' ) a =PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A ) pipe.set_progress_bar_config(disable=__A ) a =self.get_dummy_inputs() a =pipe(**__A ).images a =image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) a =np.array( [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1 def SCREAMING_SNAKE_CASE ( self ) -> List[Any]: a =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' ) a =DPMSolverMultistepScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=__A ) a =self.get_dummy_inputs() a =pipe(**__A ).images a =image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) a =np.array( [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1 def SCREAMING_SNAKE_CASE ( self ) -> Any: a =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' ) a =EulerDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=__A ) a =self.get_dummy_inputs() a =pipe(**__A ).images a =image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) a =np.array( [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1 def SCREAMING_SNAKE_CASE ( self ) -> List[str]: a =OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider='''CPUExecutionProvider''' ) a =EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config ) pipe.set_progress_bar_config(disable=__A ) a =self.get_dummy_inputs() a =pipe(**__A ).images a =image[0, -3:, -3:, -1] assert image.shape == (1, 512, 512, 3) a =np.array( [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1 @nightly @require_onnxruntime @require_torch_gpu class __A ( unittest.TestCase ): """simple docstring""" @property def SCREAMING_SNAKE_CASE ( self ) -> List[str]: return ( "CUDAExecutionProvider", { "gpu_mem_limit": "15000000000", # 15GB "arena_extend_strategy": "kSameAsRequested", }, ) @property def SCREAMING_SNAKE_CASE ( self ) -> List[str]: a =ort.SessionOptions() a =False return options def SCREAMING_SNAKE_CASE ( self ) -> List[Any]: a =load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/img2img/sketch-mountains-input.jpg''' ) a =init_image.resize((128, 128) ) # using the PNDM scheduler by default a =OnnxStableDiffusionUpscalePipeline.from_pretrained( '''ssube/stable-diffusion-x4-upscaler-onnx''' , provider=self.gpu_provider , sess_options=self.gpu_options , ) pipe.set_progress_bar_config(disable=__A ) a ='''A fantasy landscape, trending on artstation''' a =torch.manual_seed(0 ) a =pipe( prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type='''np''' , ) a =output.images a =images[0, 255:258, 383:386, -1] assert images.shape == (1, 512, 512, 3) a =np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972] ) # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2 def SCREAMING_SNAKE_CASE ( self ) -> List[Any]: a =load_image( '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/img2img/sketch-mountains-input.jpg''' ) a =init_image.resize((128, 128) ) a =LMSDiscreteScheduler.from_pretrained( '''ssube/stable-diffusion-x4-upscaler-onnx''' , subfolder='''scheduler''' ) a =OnnxStableDiffusionUpscalePipeline.from_pretrained( '''ssube/stable-diffusion-x4-upscaler-onnx''' , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , ) pipe.set_progress_bar_config(disable=__A ) a ='''A fantasy landscape, trending on artstation''' a =torch.manual_seed(0 ) a =pipe( prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type='''np''' , ) a =output.images a =images[0, 255:258, 383:386, -1] assert images.shape == (1, 512, 512, 3) a =np.array( [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566] ) # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
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"""simple docstring""" import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ..auto.configuration_auto import CONFIG_MAPPING lowerCamelCase_ : int = logging.get_logger(__name__) class __A ( _SCREAMING_SNAKE_CASE ): """simple docstring""" __lowerCAmelCase = "upernet" def __init__( self , __A=None , __A=512 , __A=0.02 , __A=[1, 2, 3, 6] , __A=True , __A=0.4 , __A=384 , __A=256 , __A=1 , __A=False , __A=255 , **__A , ) -> Tuple: super().__init__(**__A ) if backbone_config is None: logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' ) a =CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] ) elif isinstance(__A , __A ): a =backbone_config.get('''model_type''' ) a =CONFIG_MAPPING[backbone_model_type] a =config_class.from_dict(__A ) a =backbone_config a =hidden_size a =initializer_range a =pool_scales a =use_auxiliary_head a =auxiliary_loss_weight a =auxiliary_in_channels a =auxiliary_channels a =auxiliary_num_convs a =auxiliary_concat_input a =loss_ignore_index def SCREAMING_SNAKE_CASE ( self ) -> Dict: a =copy.deepcopy(self.__dict__ ) a =self.backbone_config.to_dict() a =self.__class__.model_type return output
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"""simple docstring""" import warnings from ...utils import logging from .image_processing_yolos import YolosImageProcessor _lowercase = logging.get_logger(__name__) class lowerCAmelCase_ ( _lowercase ): '''simple docstring''' def __init__( self : Union[str, Any] ,*A_ : List[str] ,**A_ : int ) -> None: warnings.warn( 'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please' ' use YolosImageProcessor instead.' ,A_ ,) super().__init__(*A_ ,**A_ )
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"""simple docstring""" import argparse import torch from torch import nn from transformers import MBartConfig, MBartForConditionalGeneration def _snake_case ( snake_case__ : Dict ): A = [ 'encoder.version', 'decoder.version', 'model.encoder.version', 'model.decoder.version', '_float_tensor', 'decoder.output_projection.weight', ] for k in ignore_keys: state_dict.pop(snake_case__ , snake_case__ ) def _snake_case ( snake_case__ : int ): A , A = emb.weight.shape A = nn.Linear(snake_case__ , snake_case__ , bias=snake_case__ ) A = emb.weight.data return lin_layer def _snake_case ( snake_case__ : List[str] , snake_case__ : Any="facebook/mbart-large-en-ro" , snake_case__ : Optional[int]=False , snake_case__ : List[str]=False ): A = torch.load(snake_case__ , map_location='cpu' )['model'] remove_ignore_keys_(snake_case__ ) A = state_dict['encoder.embed_tokens.weight'].shape[0] A = MBartConfig.from_pretrained(snake_case__ , vocab_size=snake_case__ ) if mbart_aa and finetuned: A = 'relu' A = state_dict['decoder.embed_tokens.weight'] A = MBartForConditionalGeneration(snake_case__ ) model.model.load_state_dict(snake_case__ ) if finetuned: A = make_linear_from_emb(model.model.shared ) return model if __name__ == "__main__": _lowercase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.''' ) parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''') parser.add_argument( '''--hf_config''', default='''facebook/mbart-large-cc25''', type=str, help='''Which huggingface architecture to use: mbart-large''', ) parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''') parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''') _lowercase = parser.parse_args() _lowercase = convert_fairseq_mbart_checkpoint_from_disk( args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa ) model.save_pretrained(args.pytorch_dump_folder_path)
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'''simple docstring''' import PIL.Image import PIL.ImageOps from packaging import version from PIL import Image if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'): lowerCAmelCase_ : str = { 'linear': PIL.Image.Resampling.BILINEAR, 'bilinear': PIL.Image.Resampling.BILINEAR, 'bicubic': PIL.Image.Resampling.BICUBIC, 'lanczos': PIL.Image.Resampling.LANCZOS, 'nearest': PIL.Image.Resampling.NEAREST, } else: lowerCAmelCase_ : Union[str, Any] = { 'linear': PIL.Image.LINEAR, 'bilinear': PIL.Image.BILINEAR, 'bicubic': PIL.Image.BICUBIC, 'lanczos': PIL.Image.LANCZOS, 'nearest': PIL.Image.NEAREST, } def _lowerCamelCase ( lowercase : List[str] ) -> List[Any]: _a = (images / 2 + 0.5).clamp(0 , 1 ) _a = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy() _a = numpy_to_pil(lowercase ) return images def _lowerCamelCase ( lowercase : int ) -> List[Any]: if images.ndim == 3: _a = images[None, ...] _a = (images * 255).round().astype("uint8" ) if images.shape[-1] == 1: # special case for grayscale (single channel) images _a = [Image.fromarray(image.squeeze() , mode="L" ) for image in images] else: _a = [Image.fromarray(lowercase ) for image in images] return pil_images
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'''simple docstring''' from typing import Union from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging from .base import PIPELINE_INIT_ARGS, Pipeline if is_vision_available(): from PIL import Image from ..image_utils import load_image if is_torch_available(): from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__) @add_end_docstrings(lowerCamelCase_ ) class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ): """simple docstring""" def __init__( self : List[Any] , *__a : Optional[int] , **__a : List[str] ): super().__init__(*__a , **__a ) self.check_model_type(__a ) def UpperCamelCase__ ( self : Optional[Any] , __a : Dict=None , __a : int=None , __a : Optional[Any]=None , **__a : List[Any] ): _a , _a = {}, {} if padding is not None: _a = padding if truncation is not None: _a = truncation if top_k is not None: _a = top_k return preprocess_params, {}, postprocess_params def __call__( self : Union[str, Any] , __a : Union["Image.Image", str] , __a : str = None , **__a : Any ): if isinstance(__a , (Image.Image, str) ) and isinstance(__a , __a ): _a = {"image": image, "question": question} else: _a = image _a = super().__call__(__a , **__a ) return results def UpperCamelCase__ ( self : Tuple , __a : Tuple , __a : Optional[Any]=False , __a : List[Any]=False ): _a = load_image(inputs["image"] ) _a = self.tokenizer( inputs["question"] , return_tensors=self.framework , padding=__a , truncation=__a ) _a = self.image_processor(images=__a , return_tensors=self.framework ) model_inputs.update(__a ) return model_inputs def UpperCamelCase__ ( self : List[Any] , __a : List[str] ): _a = self.model(**__a ) return model_outputs def UpperCamelCase__ ( self : int , __a : Optional[int] , __a : Dict=5 ): if top_k > self.model.config.num_labels: _a = self.model.config.num_labels if self.framework == "pt": _a = model_outputs.logits.sigmoid()[0] _a , _a = probs.topk(__a ) else: raise ValueError(f'Unsupported framework: {self.framework}' ) _a = scores.tolist() _a = ids.tolist() return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__a , __a )]
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"""simple docstring""" def _snake_case ( lowerCamelCase__ : str ) -> None: lowerCamelCase_ : Optional[int] =generate_pascal_triangle(__a ) for row_idx in range(__a ): # Print left spaces for _ in range(num_rows - row_idx - 1 ): print(end=" " ) # Print row values for col_idx in range(row_idx + 1 ): if col_idx != row_idx: print(triangle[row_idx][col_idx] , end=" " ) else: print(triangle[row_idx][col_idx] , end="" ) print() def _snake_case ( lowerCamelCase__ : int ) -> list[list[int]]: if not isinstance(__a , __a ): raise TypeError("The input value of \'num_rows\' should be \'int\'" ) if num_rows == 0: return [] elif num_rows < 0: raise ValueError( "The input value of \'num_rows\' should be greater than or equal to 0" ) lowerCamelCase_ : list[list[int]] =[] for current_row_idx in range(__a ): lowerCamelCase_ : List[Any] =populate_current_row(__a , __a ) triangle.append(__a ) return triangle def _snake_case ( lowerCamelCase__ : int , lowerCamelCase__ : Dict ) -> list[int]: lowerCamelCase_ : int =[-1] * (current_row_idx + 1) # first and last elements of current row are equal to 1 lowerCamelCase_ : Dict =1, 1 for current_col_idx in range(1 , __a ): calculate_current_element( __a , __a , __a , __a ) return current_row def _snake_case ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : Tuple , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : int , ) -> None: lowerCamelCase_ : Any =triangle[current_row_idx - 1][current_col_idx - 1] lowerCamelCase_ : List[Any] =triangle[current_row_idx - 1][current_col_idx] lowerCamelCase_ : List[Any] =above_to_left_elt + above_to_right_elt def _snake_case ( lowerCamelCase__ : List[str] ) -> list[list[int]]: if not isinstance(__a , __a ): raise TypeError("The input value of \'num_rows\' should be \'int\'" ) if num_rows == 0: return [] elif num_rows < 0: raise ValueError( "The input value of \'num_rows\' should be greater than or equal to 0" ) lowerCamelCase_ : list[list[int]] =[[1]] for row_index in range(1 , __a ): lowerCamelCase_ : Any =[0] + result[-1] + [0] lowerCamelCase_ : Optional[int] =row_index + 1 # Calculate the number of distinct elements in a row lowerCamelCase_ : Any =sum(divmod(__a , 2 ) ) lowerCamelCase_ : Optional[int] =[ temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 ) ] lowerCamelCase_ : Optional[int] =row_first_half[: (row_index + 1) // 2] row_second_half.reverse() lowerCamelCase_ : Dict =row_first_half + row_second_half result.append(__a ) return result def _snake_case ( ) -> None: from collections.abc import Callable from timeit import timeit def benchmark_a_function(lowerCamelCase__ : List[Any] , lowerCamelCase__ : str ) -> None: lowerCamelCase_ : List[str] =F"""{func.__name__}({value})""" lowerCamelCase_ : List[str] =timeit(F"""__main__.{call}""" , setup="import __main__" ) # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds") print(F"""{call:38} -- {timing:.4f} seconds""" ) for value in range(15 ): # (1, 7, 14): for func in (generate_pascal_triangle, generate_pascal_triangle_optimized): benchmark_a_function(__a , __a ) print() if __name__ == "__main__": import doctest doctest.testmod() benchmark()
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'''simple docstring''' from datetime import datetime as dt import os from github import Github __snake_case = [ '''good first issue''', '''good second issue''', '''good difficult issue''', '''feature request''', '''new model''', '''wip''', ] def a ( ) -> List[str]: '''simple docstring''' UpperCamelCase__ :List[str] = Github(os.environ['''GITHUB_TOKEN'''] ) UpperCamelCase__ :Tuple = g.get_repo('''huggingface/transformers''' ) UpperCamelCase__ :Union[str, Any] = repo.get_issues(state='''open''' ) for issue in open_issues: UpperCamelCase__ :List[Any] = sorted([comment for comment in issue.get_comments()] , key=lambda __a : i.created_at , reverse=__a ) UpperCamelCase__ :List[Any] = comments[0] if len(__a ) > 0 else None if ( last_comment is not None and last_comment.user.login == "github-actions[bot]" and (dt.utcnow() - issue.updated_at).days > 7 and (dt.utcnow() - issue.created_at).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.") issue.edit(state='''closed''' ) elif ( (dt.utcnow() - issue.updated_at).days > 23 and (dt.utcnow() - issue.created_at).days >= 30 and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() ) ): # print(f"Would add stale comment to {issue.number}") issue.create_comment( '''This issue has been automatically marked as stale because it has not had ''' '''recent activity. If you think this still needs to be addressed ''' '''please comment on this thread.\n\nPlease note that issues that do not follow the ''' '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) ''' '''are likely to be ignored.''' ) if __name__ == "__main__": main()
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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 ( MobileViTConfig, MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTImageProcessor, ) from transformers.utils import logging logging.set_verbosity_info() __lowerCAmelCase = logging.get_logger(__name__) def __lowerCamelCase ( lowerCAmelCase_ ) -> int: _a : Dict = MobileViTConfig() # size of the architecture if "mobilevit_s" in mobilevit_name: _a : int = [144, 192, 240] _a : int = [16, 32, 64, 96, 128, 160, 640] elif "mobilevit_xs" in mobilevit_name: _a : List[Any] = [96, 120, 144] _a : Dict = [16, 32, 48, 64, 80, 96, 384] elif "mobilevit_xxs" in mobilevit_name: _a : Tuple = [64, 80, 96] _a : List[Any] = [16, 16, 24, 48, 64, 80, 320] _a : Optional[int] = 0.05 _a : List[str] = 2.0 if mobilevit_name.startswith('deeplabv3_' ): _a : List[str] = 512 _a : Union[str, Any] = 16 _a : List[str] = 21 _a : Optional[int] = 'pascal-voc-id2label.json' else: _a : Tuple = 1000 _a : str = 'imagenet-1k-id2label.json' _a : str = 'huggingface/label-files' _a : Optional[int] = json.load(open(hf_hub_download(lowerCAmelCase_ , lowerCAmelCase_ , repo_type='dataset' ) , 'r' ) ) _a : Union[str, Any] = {int(lowerCAmelCase_ ): v for k, v in idalabel.items()} _a : List[Any] = idalabel _a : Optional[int] = {v: k for k, v in idalabel.items()} return config def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_=False ) -> List[Any]: for i in range(1 , 6 ): if f"""layer_{i}.""" in name: _a : str = name.replace(f"""layer_{i}.""" , f"""encoder.layer.{i - 1}.""" ) if "conv_1." in name: _a : Tuple = name.replace('conv_1.' , 'conv_stem.' ) if ".block." in name: _a : Optional[int] = name.replace('.block.' , '.' ) if "exp_1x1" in name: _a : int = name.replace('exp_1x1' , 'expand_1x1' ) if "red_1x1" in name: _a : List[Any] = name.replace('red_1x1' , 'reduce_1x1' ) if ".local_rep.conv_3x3." in name: _a : Optional[Any] = name.replace('.local_rep.conv_3x3.' , '.conv_kxk.' ) if ".local_rep.conv_1x1." in name: _a : Dict = name.replace('.local_rep.conv_1x1.' , '.conv_1x1.' ) if ".norm." in name: _a : Any = name.replace('.norm.' , '.normalization.' ) if ".conv." in name: _a : Optional[Any] = name.replace('.conv.' , '.convolution.' ) if ".conv_proj." in name: _a : Dict = name.replace('.conv_proj.' , '.conv_projection.' ) for i in range(0 , 2 ): for j in range(0 , 4 ): if f""".{i}.{j}.""" in name: _a : Tuple = name.replace(f""".{i}.{j}.""" , f""".{i}.layer.{j}.""" ) for i in range(2 , 6 ): for j in range(0 , 4 ): if f""".{i}.{j}.""" in name: _a : List[str] = name.replace(f""".{i}.{j}.""" , f""".{i}.""" ) if "expand_1x1" in name: _a : Dict = name.replace('expand_1x1' , 'downsampling_layer.expand_1x1' ) if "conv_3x3" in name: _a : Union[str, Any] = name.replace('conv_3x3' , 'downsampling_layer.conv_3x3' ) if "reduce_1x1" in name: _a : Optional[Any] = name.replace('reduce_1x1' , 'downsampling_layer.reduce_1x1' ) for i in range(2 , 5 ): if f""".global_rep.{i}.weight""" in name: _a : List[Any] = name.replace(f""".global_rep.{i}.weight""" , '.layernorm.weight' ) if f""".global_rep.{i}.bias""" in name: _a : Dict = name.replace(f""".global_rep.{i}.bias""" , '.layernorm.bias' ) if ".global_rep." in name: _a : Tuple = name.replace('.global_rep.' , '.transformer.' ) if ".pre_norm_mha.0." in name: _a : Optional[Any] = name.replace('.pre_norm_mha.0.' , '.layernorm_before.' ) if ".pre_norm_mha.1.out_proj." in name: _a : int = name.replace('.pre_norm_mha.1.out_proj.' , '.attention.output.dense.' ) if ".pre_norm_ffn.0." in name: _a : Optional[Any] = name.replace('.pre_norm_ffn.0.' , '.layernorm_after.' ) if ".pre_norm_ffn.1." in name: _a : Tuple = name.replace('.pre_norm_ffn.1.' , '.intermediate.dense.' ) if ".pre_norm_ffn.4." in name: _a : int = name.replace('.pre_norm_ffn.4.' , '.output.dense.' ) if ".transformer." in name: _a : Optional[Any] = name.replace('.transformer.' , '.transformer.layer.' ) if ".aspp_layer." in name: _a : Tuple = name.replace('.aspp_layer.' , '.' ) if ".aspp_pool." in name: _a : Any = name.replace('.aspp_pool.' , '.' ) if "seg_head." in name: _a : Union[str, Any] = name.replace('seg_head.' , 'segmentation_head.' ) if "segmentation_head.classifier.classifier." in name: _a : str = name.replace('segmentation_head.classifier.classifier.' , 'segmentation_head.classifier.' ) if "classifier.fc." in name: _a : Optional[int] = name.replace('classifier.fc.' , 'classifier.' ) elif (not base_model) and ("segmentation_head." not in name): _a : List[str] = 'mobilevit.' + name return name def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False ) -> Dict: if base_model: _a : str = '' else: _a : List[Any] = 'mobilevit.' for key in orig_state_dict.copy().keys(): _a : int = orig_state_dict.pop(lowerCAmelCase_ ) if key[:8] == "encoder.": _a : Dict = key[8:] if "qkv" in key: _a : Tuple = key.split('.' ) _a : List[str] = int(key_split[0][6:] ) - 1 _a : Any = int(key_split[3] ) _a : Optional[int] = model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}""" ) _a : int = layer.transformer.layer[transformer_num].attention.attention.all_head_size _a : Dict = ( f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.""" ) if "weight" in key: _a : Union[str, Any] = val[:dim, :] _a : List[Any] = val[dim : dim * 2, :] _a : str = val[-dim:, :] else: _a : Dict = val[:dim] _a : List[Any] = val[dim : dim * 2] _a : List[Any] = val[-dim:] else: _a : int = val return orig_state_dict def __lowerCamelCase ( ) -> str: _a : List[str] = 'http://images.cocodataset.org/val2017/000000039769.jpg' _a : int = Image.open(requests.get(lowerCAmelCase_ , stream=lowerCAmelCase_ ).raw ) return im @torch.no_grad() def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False ) -> Any: _a : Optional[int] = get_mobilevit_config(lowerCAmelCase_ ) # load original state_dict _a : Union[str, Any] = torch.load(lowerCAmelCase_ , map_location='cpu' ) # load 🤗 model if mobilevit_name.startswith('deeplabv3_' ): _a : Union[str, Any] = MobileViTForSemanticSegmentation(lowerCAmelCase_ ).eval() else: _a : Tuple = MobileViTForImageClassification(lowerCAmelCase_ ).eval() _a : int = convert_state_dict(lowerCAmelCase_ , lowerCAmelCase_ ) model.load_state_dict(lowerCAmelCase_ ) # Check outputs on an image, prepared by MobileViTImageProcessor _a : Optional[int] = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 ) _a : int = image_processor(images=prepare_img() , return_tensors='pt' ) _a : List[str] = model(**lowerCAmelCase_ ) _a : Any = outputs.logits if mobilevit_name.startswith('deeplabv3_' ): assert logits.shape == (1, 21, 32, 32) if mobilevit_name == "deeplabv3_mobilevit_s": _a : Tuple = torch.tensor( [ [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]], [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]], [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]], ] ) elif mobilevit_name == "deeplabv3_mobilevit_xs": _a : Optional[Any] = torch.tensor( [ [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]], [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]], [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]], ] ) elif mobilevit_name == "deeplabv3_mobilevit_xxs": _a : Union[str, Any] = torch.tensor( [ [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]], [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8_624, -9.5_964], [-10.8840, -10.8158, -10.6659]], [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]], ] ) else: raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" ) assert torch.allclose(logits[0, :3, :3, :3] , lowerCAmelCase_ , atol=1E-4 ) else: assert logits.shape == (1, 1000) if mobilevit_name == "mobilevit_s": _a : Any = torch.tensor([-0.9_866, 0.2_392, -1.1_241] ) elif mobilevit_name == "mobilevit_xs": _a : Optional[Any] = torch.tensor([-2.4_761, -0.9_399, -1.9_587] ) elif mobilevit_name == "mobilevit_xxs": _a : List[str] = torch.tensor([-1.9_364, -1.2_327, -0.4_653] ) else: raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}""" ) assert torch.allclose(logits[0, :3] , lowerCAmelCase_ , atol=1E-4 ) Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ ) print(f"""Saving model {mobilevit_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 push_to_hub: _a : List[str] = { 'mobilevit_s': 'mobilevit-small', 'mobilevit_xs': 'mobilevit-x-small', 'mobilevit_xxs': 'mobilevit-xx-small', 'deeplabv3_mobilevit_s': 'deeplabv3-mobilevit-small', 'deeplabv3_mobilevit_xs': 'deeplabv3-mobilevit-x-small', 'deeplabv3_mobilevit_xxs': 'deeplabv3-mobilevit-xx-small', } print('Pushing to the hub...' ) _a : Optional[int] = model_mapping[mobilevit_name] image_processor.push_to_hub(lowerCAmelCase_ , organization='apple' ) model.push_to_hub(lowerCAmelCase_ , organization='apple' ) if __name__ == "__main__": __lowerCAmelCase = argparse.ArgumentParser() # Required parameters parser.add_argument( '''--mobilevit_name''', default='''mobilevit_s''', type=str, help=( '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\',''' ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.''' ), ) parser.add_argument( '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).''' ) parser.add_argument( '''--pytorch_dump_folder_path''', required=True, 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 = parser.parse_args() convert_movilevit_checkpoint( args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub )
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'''simple docstring''' def __lowerCamelCase ( lowerCAmelCase_ ) -> list: if any(not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or x < 0 for x in sequence ): raise TypeError('Sequence must be list of non-negative integers' ) for _ in range(len(lowerCAmelCase_ ) ): for i, (rod_upper, rod_lower) in enumerate(zip(lowerCAmelCase_ , sequence[1:] ) ): if rod_upper > rod_lower: sequence[i] -= rod_upper - rod_lower sequence[i + 1] += rod_upper - rod_lower return sequence if __name__ == "__main__": assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5] assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
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"""simple docstring""" from typing import Dict, List, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import ( center_crop, get_resize_output_image_size, normalize, rescale, resize, to_channel_dimension_format, ) from ...image_utils import ( IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ChannelDimension, ImageInput, PILImageResampling, make_list_of_images, to_numpy_array, valid_images, ) from ...utils import TensorType, logging _UpperCamelCase : Optional[Any] = logging.get_logger(__name__) class UpperCAmelCase_ ( _a): lowerCamelCase__ : Union[str, Any] = ["pixel_values"] def __init__( self , a = True , a = None , a = PILImageResampling.BILINEAR , a = True , a = None , a = True , a = 1 / 2_5_5 , a = True , a = None , a = None , **a , ) -> None: super().__init__(**a ) lowercase__ : Dict = size if size is not None else {'shortest_edge': 2_5_6} lowercase__ : Union[str, Any] = get_size_dict(a , default_to_square=a ) lowercase__ : str = crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4} lowercase__ : Tuple = get_size_dict(a ) lowercase__ : Union[str, Any] = do_resize lowercase__ : List[Any] = size lowercase__ : List[Any] = resample lowercase__ : Any = do_center_crop lowercase__ : Optional[Any] = crop_size lowercase__ : int = do_rescale lowercase__ : int = rescale_factor lowercase__ : Dict = do_normalize lowercase__ : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN lowercase__ : Tuple = image_std if image_std is not None else IMAGENET_STANDARD_STD def _UpperCAmelCase ( self , a , a , a = PILImageResampling.BICUBIC , a = None , **a , ) -> np.ndarray: lowercase__ : int = get_size_dict(a , default_to_square=a ) if "shortest_edge" not in size: raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" ) lowercase__ : Optional[int] = 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 _UpperCAmelCase ( self , a , a , a = None , **a , ) -> np.ndarray: lowercase__ : List[str] = get_size_dict(a ) return center_crop(a , size=(size['height'], size['width']) , data_format=a , **a ) def _UpperCAmelCase ( self , a , a , a = None , **a ) -> np.ndarray: return rescale(a , scale=a , data_format=a , **a ) def _UpperCAmelCase ( self , a , a , a , a = None , **a , ) -> np.ndarray: return normalize(a , mean=a , std=a , data_format=a , **a ) def _UpperCAmelCase ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = ChannelDimension.FIRST , **a , ) -> Tuple: lowercase__ : Optional[Any] = do_resize if do_resize is not None else self.do_resize lowercase__ : Tuple = size if size is not None else self.size lowercase__ : Tuple = get_size_dict(a , default_to_square=a ) lowercase__ : Optional[int] = resample if resample is not None else self.resample lowercase__ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop lowercase__ : Union[str, Any] = crop_size if crop_size is not None else self.crop_size lowercase__ : Union[str, Any] = get_size_dict(a ) lowercase__ : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale lowercase__ : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor lowercase__ : Dict = do_normalize if do_normalize is not None else self.do_normalize lowercase__ : List[Any] = image_mean if image_mean is not None else self.image_mean lowercase__ : Optional[Any] = image_std if image_std is not None else self.image_std lowercase__ : Tuple = make_list_of_images(a ) if not valid_images(a ): raise ValueError( 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, ' 'torch.Tensor, tf.Tensor or jax.ndarray.' ) if do_resize and size is None: raise ValueError('Size must be specified if do_resize is True.' ) if do_center_crop and crop_size is None: raise ValueError('Crop size must be specified if do_center_crop is True.' ) if do_rescale and rescale_factor is None: raise ValueError('Rescale factor must be specified if do_rescale is True.' ) if do_normalize and (image_mean is None or image_std is None): raise ValueError('Image mean and std must be specified if do_normalize is True.' ) # All transformations expect numpy arrays. lowercase__ : int = [to_numpy_array(a ) for image in images] if do_resize: lowercase__ : List[Any] = [self.resize(image=a , size=a , resample=a ) for image in images] if do_center_crop: lowercase__ : Any = [self.center_crop(image=a , size=a ) for image in images] if do_rescale: lowercase__ : Any = [self.rescale(image=a , scale=a ) for image in images] if do_normalize: lowercase__ : Any = [self.normalize(image=a , mean=a , std=a ) for image in images] lowercase__ : str = [to_channel_dimension_format(a , a ) for image in images] lowercase__ : int = {'pixel_values': images} return BatchFeature(data=a , tensor_type=a )
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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 A ( SCREAMING_SNAKE_CASE__ ): '''simple docstring''' A = ["image_processor", "tokenizer"] A = "OwlViTImageProcessor" A = ("CLIPTokenizer", "CLIPTokenizerFast") def __init__(self , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase ) -> str: __UpperCamelCase : Tuple = None if "feature_extractor" in kwargs: warnings.warn( "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`" " instead." , _UpperCAmelCase , ) __UpperCamelCase : str = kwargs.pop("feature_extractor" ) __UpperCamelCase : Tuple = 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 ) -> str: 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 : Tuple = [self.tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase )] elif isinstance(_UpperCAmelCase , _UpperCAmelCase ) and isinstance(text[0] , _UpperCAmelCase ): __UpperCamelCase : List[str] = [] # Maximum number of queries across batch __UpperCamelCase : List[str] = 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 : Any = t + [" "] * (max_num_queries - len(_UpperCAmelCase )) __UpperCamelCase : int = 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 : List[str] = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 ) __UpperCamelCase : int = 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 : Tuple = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 ) __UpperCamelCase : Optional[Any] = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 ) elif return_tensors == "pt" and is_torch_available(): import torch __UpperCamelCase : Any = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 ) __UpperCamelCase : List[Any] = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 ) elif return_tensors == "tf" and is_tf_available(): import tensorflow as tf __UpperCamelCase : Any = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 ) __UpperCamelCase : Optional[Any] = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 ) else: raise ValueError("Target return tensor type could not be returned" ) __UpperCamelCase : Optional[Any] = BatchEncoding() __UpperCamelCase : Union[str, Any] = input_ids __UpperCamelCase : List[str] = attention_mask if query_images is not None: __UpperCamelCase : str = BatchEncoding() __UpperCamelCase : Any = self.image_processor( _UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase ).pixel_values __UpperCamelCase : List[Any] = query_pixel_values if images is not None: __UpperCamelCase : Dict = self.image_processor(_UpperCAmelCase , return_tensors=_UpperCAmelCase , **_UpperCAmelCase ) if text is not None and images is not None: __UpperCamelCase : Optional[Any] = image_features.pixel_values return encoding elif query_images is not None and images is not None: __UpperCamelCase : Union[str, Any] = image_features.pixel_values return encoding elif text is not None or query_images is not None: return encoding else: return BatchEncoding(data=dict(**_UpperCAmelCase ) , tensor_type=_UpperCAmelCase ) def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Optional[int]: return self.image_processor.post_process(*_UpperCAmelCase , **_UpperCAmelCase ) def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> List[str]: return self.image_processor.post_process_object_detection(*_UpperCAmelCase , **_UpperCAmelCase ) def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Optional[int]: return self.image_processor.post_process_image_guided_detection(*_UpperCAmelCase , **_UpperCAmelCase ) def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Union[str, Any]: return self.tokenizer.batch_decode(*_UpperCAmelCase , **_UpperCAmelCase ) def a_ (self , *_UpperCAmelCase , **_UpperCAmelCase ) -> int: return self.tokenizer.decode(*_UpperCAmelCase , **_UpperCAmelCase ) @property def a_ (self ) -> Tuple: 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 a_ (self ) -> Union[str, Any]: 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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from __future__ import annotations from collections import namedtuple def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ): snake_case__ : Optional[Any] = namedtuple("result" , "name value" ) if (voltage, current, power).count(0 ) != 1: raise ValueError("Only one argument must be 0" ) elif power < 0: raise ValueError( "Power cannot be negative in any electrical/electronics system" ) elif voltage == 0: return result("voltage" , power / current ) elif current == 0: return result("current" , power / voltage ) elif power == 0: return result("power" , float(round(abs(voltage * current ) , 2 ) ) ) else: raise ValueError("Exactly one argument must be 0" ) if __name__ == "__main__": import doctest doctest.testmod()
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from __future__ import annotations def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ): if days_between_payments <= 0: raise ValueError("days_between_payments must be > 0" ) if daily_interest_rate < 0: raise ValueError("daily_interest_rate must be >= 0" ) if principal <= 0: raise ValueError("principal must be > 0" ) return principal * daily_interest_rate * days_between_payments def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ): if number_of_compounding_periods <= 0: raise ValueError("number_of_compounding_periods must be > 0" ) if nominal_annual_interest_rate_percentage < 0: raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" ) if principal <= 0: raise ValueError("principal must be > 0" ) return principal * ( (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods - 1 ) def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float , ): if number_of_years <= 0: raise ValueError("number_of_years must be > 0" ) if nominal_annual_percentage_rate < 0: raise ValueError("nominal_annual_percentage_rate must be >= 0" ) if principal <= 0: raise ValueError("principal must be > 0" ) return compound_interest( snake_case_ , nominal_annual_percentage_rate / 365 , number_of_years * 365 ) if __name__ == "__main__": import doctest doctest.testmod()
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0
import copy import inspect import unittest from transformers import PretrainedConfig, SwiftFormerConfig from transformers.testing_utils import ( require_torch, require_vision, slow, torch_device, ) from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import SwiftFormerForImageClassification, SwiftFormerModel from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class UpperCamelCase__ : """simple docstring""" def __init__( self , _A , _A=13 , _A=3 , _A=True , _A=True , _A=0.1 , _A=0.1 , _A=224 , _A=1000 , _A=[3, 3, 6, 4] , _A=[48, 56, 112, 220] , ) -> Union[str, Any]: SCREAMING_SNAKE_CASE_ = parent SCREAMING_SNAKE_CASE_ = batch_size SCREAMING_SNAKE_CASE_ = num_channels SCREAMING_SNAKE_CASE_ = is_training SCREAMING_SNAKE_CASE_ = use_labels SCREAMING_SNAKE_CASE_ = hidden_dropout_prob SCREAMING_SNAKE_CASE_ = attention_probs_dropout_prob SCREAMING_SNAKE_CASE_ = num_labels SCREAMING_SNAKE_CASE_ = image_size SCREAMING_SNAKE_CASE_ = layer_depths SCREAMING_SNAKE_CASE_ = embed_dims def _UpperCamelCase ( self ) -> Any: SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE_ = None if self.use_labels: SCREAMING_SNAKE_CASE_ = ids_tensor([self.batch_size] , self.num_labels ) SCREAMING_SNAKE_CASE_ = self.get_config() return config, pixel_values, labels def _UpperCamelCase ( self ) -> List[Any]: return SwiftFormerConfig( depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=a_ , layer_scale_init_value=1E-5 , ) def _UpperCamelCase ( self , _A , _A , _A ) -> List[Any]: SCREAMING_SNAKE_CASE_ = SwiftFormerModel(config=a_ ) model.to(a_ ) model.eval() SCREAMING_SNAKE_CASE_ = model(a_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) ) def _UpperCamelCase ( self , _A , _A , _A ) -> Any: SCREAMING_SNAKE_CASE_ = self.num_labels SCREAMING_SNAKE_CASE_ = SwiftFormerForImageClassification(a_ ) model.to(a_ ) model.eval() SCREAMING_SNAKE_CASE_ = model(a_ , labels=a_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) SCREAMING_SNAKE_CASE_ = SwiftFormerForImageClassification(a_ ) model.to(a_ ) model.eval() SCREAMING_SNAKE_CASE_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE_ = model(a_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def _UpperCamelCase ( self ) -> Union[str, Any]: (SCREAMING_SNAKE_CASE_) = self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE_ = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class UpperCamelCase__ ( A__ , A__ , unittest.TestCase ): """simple docstring""" UpperCAmelCase_ =(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else () UpperCAmelCase_ =( {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification} if is_torch_available() else {} ) UpperCAmelCase_ =False UpperCAmelCase_ =False UpperCAmelCase_ =False UpperCAmelCase_ =False UpperCAmelCase_ =False def _UpperCamelCase ( self ) -> Union[str, Any]: SCREAMING_SNAKE_CASE_ = SwiftFormerModelTester(self ) SCREAMING_SNAKE_CASE_ = ConfigTester( self , config_class=a_ , has_text_modality=a_ , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , ) def _UpperCamelCase ( self ) -> str: self.config_tester.run_common_tests() @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' ) def _UpperCamelCase ( self ) -> List[Any]: pass def _UpperCamelCase ( self ) -> List[Any]: SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE_ = model_class(a_ ) SCREAMING_SNAKE_CASE_ = model.get_output_embeddings() self.assertTrue(x is None or isinstance(a_ , nn.Linear ) ) def _UpperCamelCase ( self ) -> Optional[int]: SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE_ = model_class(a_ ) SCREAMING_SNAKE_CASE_ = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE_ = [*signature.parameters.keys()] SCREAMING_SNAKE_CASE_ = ["pixel_values"] self.assertListEqual(arg_names[:1] , a_ ) def _UpperCamelCase ( self ) -> int: SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*a_ ) def _UpperCamelCase ( self ) -> Dict: SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*a_ ) @slow def _UpperCamelCase ( self ) -> str: for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE_ = SwiftFormerModel.from_pretrained(a_ ) self.assertIsNotNone(a_ ) @unittest.skip(reason='''SwiftFormer does not output attentions''' ) def _UpperCamelCase ( self ) -> List[str]: pass def _UpperCamelCase ( self ) -> Optional[int]: def check_hidden_states_output(_A , _A , _A ): SCREAMING_SNAKE_CASE_ = model_class(a_ ) model.to(a_ ) model.eval() with torch.no_grad(): SCREAMING_SNAKE_CASE_ = model(**self._prepare_for_class(a_ , a_ ) ) SCREAMING_SNAKE_CASE_ = outputs.hidden_states SCREAMING_SNAKE_CASE_ = 8 self.assertEqual(len(a_ ) , a_ ) # TODO # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width) # with the width and height being successively divided by 2, after every 2 blocks for i in range(len(a_ ) ): self.assertEqual( hidden_states[i].shape , torch.Size( [ self.model_tester.batch_size, self.model_tester.embed_dims[i // 2], (self.model_tester.image_size // 4) // 2 ** (i // 2), (self.model_tester.image_size // 4) // 2 ** (i // 2), ] ) , ) SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE_ = True check_hidden_states_output(a_ , a_ , a_ ) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] SCREAMING_SNAKE_CASE_ = True check_hidden_states_output(a_ , a_ , a_ ) def _UpperCamelCase ( self ) -> Dict: def _config_zero_init(_A ): SCREAMING_SNAKE_CASE_ = copy.deepcopy(a_ ) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: setattr(a_ , a_ , 1E-10 ) if isinstance(getattr(a_ , a_ , a_ ) , a_ ): SCREAMING_SNAKE_CASE_ = _config_zero_init(getattr(a_ , a_ ) ) setattr(a_ , a_ , a_ ) return configs_no_init SCREAMING_SNAKE_CASE_ = self.model_tester.prepare_config_and_inputs_for_common() SCREAMING_SNAKE_CASE_ = _config_zero_init(a_ ) for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE_ = model_class(config=a_ ) for name, param in model.named_parameters(): if param.requires_grad: self.assertIn( ((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , ) @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' ) def _UpperCamelCase ( self ) -> int: pass def A__ ( ): SCREAMING_SNAKE_CASE_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class UpperCamelCase__ ( unittest.TestCase ): """simple docstring""" @cached_property def _UpperCamelCase ( self ) -> int: return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None @slow def _UpperCamelCase ( self ) -> List[Any]: SCREAMING_SNAKE_CASE_ = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(a_ ) SCREAMING_SNAKE_CASE_ = self.default_image_processor SCREAMING_SNAKE_CASE_ = prepare_img() SCREAMING_SNAKE_CASE_ = image_processor(images=a_ , return_tensors='''pt''' ).to(a_ ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE_ = model(**a_ ) # verify the logits SCREAMING_SNAKE_CASE_ = torch.Size((1, 1000) ) self.assertEqual(outputs.logits.shape , a_ ) SCREAMING_SNAKE_CASE_ = torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]] ).to(a_ ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , a_ , atol=1E-4 ) )
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"""simple docstring""" from random import randint from tempfile import TemporaryFile import numpy as np def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Dict: """simple docstring""" lowerCAmelCase_ : Union[str, Any] = 0 if start < end: lowerCAmelCase_ : Dict = randint(__UpperCamelCase , __UpperCamelCase ) lowerCAmelCase_ : List[str] = a[end] lowerCAmelCase_ : List[str] = a[pivot] lowerCAmelCase_ : Any = temp lowerCAmelCase_ , lowerCAmelCase_ : Any = _in_place_partition(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) count += _in_place_quick_sort(__UpperCamelCase , __UpperCamelCase , p - 1 ) count += _in_place_quick_sort(__UpperCamelCase , p + 1 , __UpperCamelCase ) return count def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> str: """simple docstring""" lowerCAmelCase_ : Optional[int] = 0 lowerCAmelCase_ : Tuple = randint(__UpperCamelCase , __UpperCamelCase ) lowerCAmelCase_ : str = a[end] lowerCAmelCase_ : List[Any] = a[pivot] lowerCAmelCase_ : Optional[Any] = temp lowerCAmelCase_ : Dict = start - 1 for index in range(__UpperCamelCase , __UpperCamelCase ): count += 1 if a[index] < a[end]: # check if current val is less than pivot value lowerCAmelCase_ : Dict = new_pivot_index + 1 lowerCAmelCase_ : Tuple = a[new_pivot_index] lowerCAmelCase_ : List[Any] = a[index] lowerCAmelCase_ : Optional[Any] = temp lowerCAmelCase_ : Any = a[new_pivot_index + 1] lowerCAmelCase_ : int = a[end] lowerCAmelCase_ : str = temp return new_pivot_index + 1, count lowercase__ = TemporaryFile() lowercase__ = 100 # 1000 elements are to be sorted lowercase__ , lowercase__ = 0, 1 # mean and standard deviation lowercase__ = np.random.normal(mu, sigma, p) np.save(outfile, X) print("""The array is""") print(X) outfile.seek(0) # using the same array lowercase__ = np.load(outfile) lowercase__ = len(M) - 1 lowercase__ = _in_place_quick_sort(M, 0, r) print( """No of Comparisons for 100 elements selected from a standard normal distribution""" """is :""" ) print(z)
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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, LDMTextToImagePipeline, UNetaDConditionModel from diffusers.utils.testing_utils import ( enable_full_determinism, load_numpy, nightly, require_torch_gpu, slow, torch_device, ) from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS from ..test_pipelines_common import PipelineTesterMixin enable_full_determinism() class lowerCamelCase__ ( _a , unittest.TestCase ): _lowerCAmelCase = LDMTextToImagePipeline _lowerCAmelCase = TEXT_TO_IMAGE_PARAMS - { '''negative_prompt''', '''negative_prompt_embeds''', '''cross_attention_kwargs''', '''prompt_embeds''', } _lowerCAmelCase = PipelineTesterMixin.required_optional_params - { '''num_images_per_prompt''', '''callback''', '''callback_steps''', } _lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS _lowerCAmelCase = False def _lowerCamelCase ( self : int ): torch.manual_seed(0 ) a__: Optional[int] =UNetaDConditionModel( block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , ) a__: int =DDIMScheduler( beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , clip_sample=_a , set_alpha_to_one=_a , ) torch.manual_seed(0 ) a__: List[str] =AutoencoderKL( block_out_channels=(3_2, 6_4) , in_channels=3 , out_channels=3 , down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D") , up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D") , latent_channels=4 , ) torch.manual_seed(0 ) a__: Optional[Any] =CLIPTextConfig( bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , ) a__: Optional[int] =CLIPTextModel(_a ) a__: List[str] =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" ) a__: Tuple ={ "unet": unet, "scheduler": scheduler, "vqvae": vae, "bert": text_encoder, "tokenizer": tokenizer, } return components def _lowerCamelCase ( self : List[str] , _a : Any , _a : Optional[Any]=0 ): if str(_a ).startswith("mps" ): a__: List[Any] =torch.manual_seed(_a ) else: a__: Optional[Any] =torch.Generator(device=_a ).manual_seed(_a ) a__: Optional[Any] ={ "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def _lowerCamelCase ( self : Dict ): a__: str ="cpu" # ensure determinism for the device-dependent torch.Generator a__: Dict =self.get_dummy_components() a__: Dict =LDMTextToImagePipeline(**_a ) pipe.to(_a ) pipe.set_progress_bar_config(disable=_a ) a__: Optional[int] =self.get_dummy_inputs(_a ) a__: List[Any] =pipe(**_a ).images a__: Any =image[0, -3:, -3:, -1] assert image.shape == (1, 1_6, 1_6, 3) a__: Any =np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4] ) assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3 @slow @require_torch_gpu class lowerCamelCase__ ( unittest.TestCase ): def _lowerCamelCase ( self : List[str] ): super().tearDown() gc.collect() torch.cuda.empty_cache() def _lowerCamelCase ( self : str , _a : Any , _a : Any=torch.floataa , _a : List[str]=0 ): a__: Union[str, Any] =torch.manual_seed(_a ) a__: List[str] =np.random.RandomState(_a ).standard_normal((1, 4, 3_2, 3_2) ) a__: List[Any] =torch.from_numpy(_a ).to(device=_a , dtype=_a ) a__: Any ={ "prompt": "A painting of a squirrel eating a burger", "latents": latents, "generator": generator, "num_inference_steps": 3, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def _lowerCamelCase ( self : Any ): a__: List[Any] =LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256" ).to(_a ) pipe.set_progress_bar_config(disable=_a ) a__: Dict =self.get_inputs(_a ) a__: Optional[Any] =pipe(**_a ).images a__: str =image[0, -3:, -3:, -1].flatten() assert image.shape == (1, 2_5_6, 2_5_6, 3) a__: Tuple =np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8] ) a__: Optional[int] =np.abs(expected_slice - image_slice ).max() assert max_diff < 1e-3 @nightly @require_torch_gpu class lowerCamelCase__ ( unittest.TestCase ): def _lowerCamelCase ( self : Optional[int] ): super().tearDown() gc.collect() torch.cuda.empty_cache() def _lowerCamelCase ( self : Dict , _a : str , _a : List[Any]=torch.floataa , _a : Tuple=0 ): a__: Optional[int] =torch.manual_seed(_a ) a__: List[Any] =np.random.RandomState(_a ).standard_normal((1, 4, 3_2, 3_2) ) a__: Dict =torch.from_numpy(_a ).to(device=_a , dtype=_a ) a__: Tuple ={ "prompt": "A painting of a squirrel eating a burger", "latents": latents, "generator": generator, "num_inference_steps": 5_0, "guidance_scale": 6.0, "output_type": "numpy", } return inputs def _lowerCamelCase ( self : int ): a__: Dict =LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256" ).to(_a ) pipe.set_progress_bar_config(disable=_a ) a__: int =self.get_inputs(_a ) a__: Dict =pipe(**_a ).images[0] a__: Tuple =load_numpy( "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy" ) a__: int =np.abs(expected_image - image ).max() assert max_diff < 1e-3
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import os import unicodedata from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import SPIECE_UNDERLINE, logging __UpperCAmelCase = logging.get_logger(__name__) __UpperCAmelCase = {'''vocab_file''': '''spiece.model'''} __UpperCAmelCase = { '''vocab_file''': { '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''', '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''', } } __UpperCAmelCase = { '''xlnet-base-cased''': None, '''xlnet-large-cased''': None, } # Segments (not really needed) __UpperCAmelCase = 0 __UpperCAmelCase = 1 __UpperCAmelCase = 2 __UpperCAmelCase = 3 __UpperCAmelCase = 4 class lowerCamelCase__ ( _a ): _lowerCAmelCase = VOCAB_FILES_NAMES _lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP _lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES _lowerCAmelCase = '''left''' def __init__( self : Dict , _a : List[Any] , _a : Any=False , _a : int=True , _a : Union[str, Any]=False , _a : Dict="<s>" , _a : str="</s>" , _a : Optional[int]="<unk>" , _a : Union[str, Any]="<sep>" , _a : List[Any]="<pad>" , _a : Optional[Any]="<cls>" , _a : str="<mask>" , _a : Any=["<eop>", "<eod>"] , _a : Optional[Dict[str, Any]] = None , **_a : Optional[int] , ): # Mask token behave like a normal word, i.e. include the space before it a__: Dict =AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token a__: Optional[int] ={} if sp_model_kwargs is None else sp_model_kwargs super().__init__( do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , additional_special_tokens=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , ) a__: Dict =3 a__: Tuple =do_lower_case a__: int =remove_space a__: List[Any] =keep_accents a__: List[str] =vocab_file a__: Union[str, Any] =spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(_a ) @property def _lowerCamelCase ( self : Any ): return len(self.sp_model ) def _lowerCamelCase ( self : List[Any] ): a__: Dict ={self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __getstate__( self : Tuple ): a__: Dict =self.__dict__.copy() a__: List[Any] =None return state def __setstate__( self : Optional[Any] , _a : Tuple ): a__: List[Any] =d # for backward compatibility if not hasattr(self , "sp_model_kwargs" ): a__: List[str] ={} a__: int =spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(self.vocab_file ) def _lowerCamelCase ( self : Dict , _a : str ): if self.remove_space: a__: Optional[int] =" ".join(inputs.strip().split() ) else: a__: Optional[int] =inputs a__: Dict =outputs.replace("``" , "\"" ).replace("''" , "\"" ) if not self.keep_accents: a__: Optional[int] =unicodedata.normalize("NFKD" , _a ) a__: int ="".join([c for c in outputs if not unicodedata.combining(_a )] ) if self.do_lower_case: a__: Dict =outputs.lower() return outputs def _lowerCamelCase ( self : List[Any] , _a : str ): a__: Dict =self.preprocess_text(_a ) a__: Dict =self.sp_model.encode(_a , out_type=_a ) a__: str =[] for piece in pieces: if len(_a ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit(): a__: Optional[Any] =self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , "" ) ) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0] ) == 1: a__: Optional[int] =cur_pieces[1:] else: a__: Tuple =cur_pieces[0][1:] cur_pieces.append(piece[-1] ) new_pieces.extend(_a ) else: new_pieces.append(_a ) return new_pieces def _lowerCamelCase ( self : Dict , _a : Dict ): return self.sp_model.PieceToId(_a ) def _lowerCamelCase ( self : Dict , _a : Optional[Any] ): return self.sp_model.IdToPiece(_a ) def _lowerCamelCase ( self : Optional[Any] , _a : Tuple ): a__: Tuple ="".join(_a ).replace(_a , " " ).strip() return out_string def _lowerCamelCase ( self : Optional[int] , _a : List[int] , _a : bool = False , _a : bool = None , _a : bool = True , **_a : Union[str, Any] , ): a__: Optional[int] =kwargs.pop("use_source_tokenizer" , _a ) a__: Any =self.convert_ids_to_tokens(_a , skip_special_tokens=_a ) # To avoid mixing byte-level and unicode for byte-level BPT # we need to build string separately for added tokens and byte-level tokens # cf. https://github.com/huggingface/transformers/issues/1133 a__: List[str] =[] a__: Any =[] for token in filtered_tokens: if skip_special_tokens and token in self.all_special_ids: continue if token in self.added_tokens_encoder: if current_sub_text: sub_texts.append(self.convert_tokens_to_string(_a ) ) a__: List[str] =[] sub_texts.append(_a ) else: current_sub_text.append(_a ) if current_sub_text: sub_texts.append(self.convert_tokens_to_string(_a ) ) # Mimic the behavior of the Rust tokenizer: # By default, there are no spaces between special tokens a__: Union[str, Any] ="".join(_a ) a__: List[Any] =( clean_up_tokenization_spaces if clean_up_tokenization_spaces is not None else self.clean_up_tokenization_spaces ) if clean_up_tokenization_spaces: a__: Optional[int] =self.clean_up_tokenization(_a ) return clean_text else: return text def _lowerCamelCase ( self : Tuple , _a : List[int] , _a : Optional[List[int]] = None ): a__: Dict =[self.sep_token_id] a__: Optional[Any] =[self.cls_token_id] if token_ids_a is None: return token_ids_a + sep + cls return token_ids_a + sep + token_ids_a + sep + cls def _lowerCamelCase ( self : Dict , _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 not None: return ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1, 1] return ([0] * len(_a )) + [1, 1] def _lowerCamelCase ( self : Dict , _a : List[int] , _a : Optional[List[int]] = None ): a__: Any =[self.sep_token_id] a__: List[Any] =[2] if token_ids_a is None: return len(token_ids_a + sep ) * [0] + cls_segment_id return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id def _lowerCamelCase ( self : List[str] , _a : str , _a : Optional[str] = None ): if not os.path.isdir(_a ): logger.error(F"Vocabulary path ({save_directory}) should be a directory" ) return a__: List[Any] =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: a__: Optional[Any] =self.sp_model.serialized_model_proto() fi.write(_a ) return (out_vocab_file,)
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"""simple docstring""" import logging from transformers import PretrainedConfig _a = logging.getLogger(__name__) _a = { 'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json', } class A_ (lowercase__ ): '''simple docstring''' SCREAMING_SNAKE_CASE__ : Union[str, Any] = """bertabs""" def __init__( self , lowercase_=3_0522 , lowercase_=512 , lowercase_=6 , lowercase_=512 , lowercase_=8 , lowercase_=512 , lowercase_=0.2 , lowercase_=6 , lowercase_=768 , lowercase_=8 , lowercase_=2048 , lowercase_=0.2 , **lowercase_ , ): """simple docstring""" super().__init__(**lowercase_ ) UpperCAmelCase_ : List[Any] = vocab_size UpperCAmelCase_ : List[Any] = max_pos UpperCAmelCase_ : int = enc_layers UpperCAmelCase_ : str = enc_hidden_size UpperCAmelCase_ : str = enc_heads UpperCAmelCase_ : Dict = enc_ff_size UpperCAmelCase_ : Tuple = enc_dropout UpperCAmelCase_ : List[str] = dec_layers UpperCAmelCase_ : Dict = dec_hidden_size UpperCAmelCase_ : str = dec_heads UpperCAmelCase_ : Union[str, Any] = dec_ff_size UpperCAmelCase_ : Optional[int] = dec_dropout
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"""simple docstring""" import math def lowercase ( lowerCAmelCase__ : list , lowerCAmelCase__ : int ) -> int: __a = len(lowerCAmelCase__ ) __a = int(math.floor(math.sqrt(lowerCAmelCase__ ) ) ) __a = 0 while arr[min(lowerCAmelCase__ , lowerCAmelCase__ ) - 1] < x: __a = step step += int(math.floor(math.sqrt(lowerCAmelCase__ ) ) ) if prev >= n: return -1 while arr[prev] < x: __a = prev + 1 if prev == min(lowerCAmelCase__ , lowerCAmelCase__ ): return -1 if arr[prev] == x: return prev return -1 if __name__ == "__main__": lowercase_ = input("Enter numbers separated by a comma:\n").strip() lowercase_ = [int(item) for item in user_input.split(",")] lowercase_ = int(input("Enter the number to be searched:\n")) lowercase_ = jump_search(arr, x) if res == -1: print("Number not found!") else: print(F'''Number {x} is at index {res}''')
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def snake_case_ ( lowerCAmelCase_ : int ): __lowercase : int = (1 + 24 * n) ** 0.5 return ((1 + root) / 6) % 1 == 0 def snake_case_ ( lowerCAmelCase_ : int = 5000 ): __lowercase : Optional[int] = [(i * (3 * i - 1)) // 2 for i in range(1 , lowerCAmelCase_ )] for i, pentagonal_i in enumerate(lowerCAmelCase_ ): for j in range(lowerCAmelCase_ , len(lowerCAmelCase_ ) ): __lowercase : int = pentagonal_nums[j] __lowercase : Optional[int] = pentagonal_i + pentagonal_j __lowercase : Union[str, Any] = pentagonal_j - pentagonal_i if is_pentagonal(lowerCAmelCase_ ) and is_pentagonal(lowerCAmelCase_ ): return b return -1 if __name__ == "__main__": print(f'''{solution() = }''')
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import collections import tempfile import unittest import numpy as np from transformers.testing_utils import ( is_pt_flax_cross_test, require_flax, require_torch, require_vision, slow, torch_device, ) from transformers.utils import is_flax_available, is_torch_available, is_vision_available from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask from ..bert.test_modeling_flax_bert import FlaxBertModelTester from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester from ..vit.test_modeling_flax_vit import FlaxViTModelTester if is_flax_available(): from transformers import ( FlaxBertModel, FlaxCLIPVisionModel, FlaxVisionTextDualEncoderModel, FlaxViTModel, VisionTextDualEncoderConfig, VisionTextDualEncoderProcessor, ) from transformers.modeling_flax_pytorch_utils import ( convert_pytorch_state_dict_to_flax, load_flax_weights_in_pytorch_model, ) if is_torch_available(): import torch from transformers import VisionTextDualEncoderModel if is_vision_available(): from PIL import Image def snake_case_ ( lowerCAmelCase_ : Tuple ): if isinstance(lowerCAmelCase_ , collections.abc.Iterable ): return x return (x, x) @require_flax class lowerCAmelCase : '''simple docstring''' def lowerCAmelCase ( self : Any , __a : Any , __a : List[Any] ) -> Optional[Any]: """simple docstring""" pass def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]: """simple docstring""" pass def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple: """simple docstring""" pass def lowerCAmelCase ( self : Tuple , __a : np.ndarray , __a : np.ndarray , __a : float ) -> List[Any]: """simple docstring""" __lowercase : List[str] = np.abs((a - b) ).max() self.assertLessEqual(__a , __a , F"Difference between torch and flax is {diff} (>= {tol})." ) def lowerCAmelCase ( self : Tuple , __a : int , __a : str , __a : Union[str, Any] , __a : Optional[Any] , __a : Optional[Any]=None , **__a : Tuple ) -> Optional[Any]: """simple docstring""" __lowercase : str = VisionTextDualEncoderConfig.from_vision_text_configs(__a , __a ) __lowercase : str = FlaxVisionTextDualEncoderModel(__a ) __lowercase : Optional[Any] = model(input_ids=__a , pixel_values=__a , attention_mask=__a ) self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], config.projection_dim) ) self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], config.projection_dim) ) def lowerCAmelCase ( self : Optional[int] , __a : Optional[int] , __a : Dict , __a : Dict , __a : List[str] , __a : Optional[Any]=None , **__a : str ) -> str: """simple docstring""" __lowercase , __lowercase : List[str] = self.get_vision_text_model(__a , __a ) __lowercase : Union[str, Any] = {"""vision_model""": vision_model, """text_model""": text_model} __lowercase : str = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__a ) __lowercase : Any = model(input_ids=__a , pixel_values=__a , attention_mask=__a ) self.assertEqual(output["""text_embeds"""].shape , (input_ids.shape[0], model.config.projection_dim) ) self.assertEqual(output["""image_embeds"""].shape , (pixel_values.shape[0], model.config.projection_dim) ) def lowerCAmelCase ( self : Tuple , __a : Union[str, Any] , __a : Union[str, Any] , __a : Union[str, Any] , __a : Dict , __a : int=None , **__a : int ) -> List[Any]: """simple docstring""" __lowercase , __lowercase : Tuple = self.get_vision_text_model(__a , __a ) __lowercase : Union[str, Any] = {"""vision_model""": vision_model, """text_model""": text_model} __lowercase : List[str] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__a ) __lowercase : List[Any] = model(input_ids=__a , pixel_values=__a , attention_mask=__a ) __lowercase : int = output[0] with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(__a ) __lowercase : int = FlaxVisionTextDualEncoderModel.from_pretrained(__a ) __lowercase : Tuple = model(input_ids=__a , pixel_values=__a , attention_mask=__a ) __lowercase : int = after_output[0] __lowercase : int = np.amax(np.abs(out_a - out_a ) ) self.assertLessEqual(__a , 1E-3 ) def lowerCAmelCase ( self : List[Any] , __a : Any , __a : Tuple , __a : Optional[int] , __a : str , __a : Optional[Any]=None , **__a : Optional[Any] ) -> List[Any]: """simple docstring""" __lowercase , __lowercase : str = self.get_vision_text_model(__a , __a ) __lowercase : Optional[Any] = {"""vision_model""": vision_model, """text_model""": text_model} __lowercase : Dict = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__a ) __lowercase : Union[str, Any] = model( input_ids=__a , pixel_values=__a , attention_mask=__a , output_attentions=__a ) __lowercase : Optional[int] = output.vision_model_output.attentions self.assertEqual(len(__a ) , vision_config.num_hidden_layers ) # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) __lowercase : Optional[int] = to_atuple(vision_model.config.image_size ) __lowercase : List[str] = to_atuple(vision_model.config.patch_size ) __lowercase : Optional[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) __lowercase : int = num_patches + 1 self.assertEqual(vision_attentions[0].shape[-3:] , (vision_config.num_attention_heads, seq_len, seq_len) ) __lowercase : Dict = output.text_model_output.attentions self.assertEqual(len(__a ) , text_config.num_hidden_layers ) self.assertEqual( text_attentions[0].shape[-3:] , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) , ) def lowerCAmelCase ( self : Optional[int] , __a : List[str] , __a : List[Any] , __a : Optional[Any] ) -> Optional[int]: """simple docstring""" pt_model.to(__a ) pt_model.eval() # prepare inputs __lowercase : Union[str, Any] = inputs_dict __lowercase : List[Any] = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()} with torch.no_grad(): __lowercase : Union[str, Any] = pt_model(**__a ).to_tuple() __lowercase : Tuple = fx_model(**__a ).to_tuple() self.assertEqual(len(__a ) , len(__a ) , """Output lengths differ between Flax and PyTorch""" ) for fx_output, pt_output in zip(fx_outputs[:4] , pt_outputs[:4] ): self.assert_almost_equals(__a , pt_output.numpy() , 4E-2 ) # PT -> Flax with tempfile.TemporaryDirectory() as tmpdirname: pt_model.save_pretrained(__a ) __lowercase : Any = FlaxVisionTextDualEncoderModel.from_pretrained(__a , from_pt=__a ) __lowercase : Dict = fx_model_loaded(**__a ).to_tuple() self.assertEqual(len(__a ) , len(__a ) , """Output lengths differ between Flax and PyTorch""" ) for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] , pt_outputs[:4] ): self.assert_almost_equals(__a , pt_output.numpy() , 4E-2 ) # Flax -> PT with tempfile.TemporaryDirectory() as tmpdirname: fx_model.save_pretrained(__a ) __lowercase : str = VisionTextDualEncoderModel.from_pretrained(__a , from_flax=__a ) pt_model_loaded.to(__a ) pt_model_loaded.eval() with torch.no_grad(): __lowercase : List[Any] = pt_model_loaded(**__a ).to_tuple() self.assertEqual(len(__a ) , len(__a ) , """Output lengths differ between Flax and PyTorch""" ) for fx_output, pt_output_loaded in zip(fx_outputs[:4] , pt_outputs_loaded[:4] ): self.assert_almost_equals(__a , pt_output_loaded.numpy() , 4E-2 ) def lowerCAmelCase ( self : Optional[int] , __a : List[Any] , __a : int , __a : Optional[int] ) -> Optional[int]: """simple docstring""" __lowercase : Union[str, Any] = VisionTextDualEncoderConfig.from_vision_text_configs(__a , __a ) __lowercase : str = VisionTextDualEncoderModel(__a ) __lowercase : Union[str, Any] = FlaxVisionTextDualEncoderModel(__a ) __lowercase : List[str] = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , __a ) __lowercase : Any = fx_state self.check_pt_flax_equivalence(__a , __a , __a ) def lowerCAmelCase ( self : Any , __a : Any , __a : Dict , __a : Tuple ) -> str: """simple docstring""" __lowercase : int = VisionTextDualEncoderConfig.from_vision_text_configs(__a , __a ) __lowercase : Union[str, Any] = VisionTextDualEncoderModel(__a ) __lowercase : Dict = FlaxVisionTextDualEncoderModel(__a ) __lowercase : Tuple = load_flax_weights_in_pytorch_model(__a , fx_model.params ) self.check_pt_flax_equivalence(__a , __a , __a ) def lowerCAmelCase ( self : str ) -> Optional[Any]: """simple docstring""" __lowercase : Optional[Any] = self.prepare_config_and_inputs() self.check_model_from_pretrained_configs(**__a ) def lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]: """simple docstring""" __lowercase : int = self.prepare_config_and_inputs() self.check_vision_text_dual_encoder_from_pretrained(**__a ) def lowerCAmelCase ( self : List[Any] ) -> Dict: """simple docstring""" __lowercase : List[str] = self.prepare_config_and_inputs() self.check_save_load(**__a ) def lowerCAmelCase ( self : Any ) -> Dict: """simple docstring""" __lowercase : str = self.prepare_config_and_inputs() self.check_vision_text_output_attention(**__a ) @is_pt_flax_cross_test def lowerCAmelCase ( self : List[str] ) -> Tuple: """simple docstring""" __lowercase : Optional[Any] = self.prepare_config_and_inputs() __lowercase : Optional[int] = config_inputs_dict.pop("""vision_config""" ) __lowercase : Optional[int] = config_inputs_dict.pop("""text_config""" ) __lowercase : Dict = config_inputs_dict self.check_equivalence_pt_to_flax(__a , __a , __a ) self.check_equivalence_flax_to_pt(__a , __a , __a ) @slow def lowerCAmelCase ( self : Union[str, Any] ) -> str: """simple docstring""" __lowercase , __lowercase : List[Any] = self.get_pretrained_model_and_inputs() __lowercase : Dict = model_a(**__a ) __lowercase : Any = outputs[0] with tempfile.TemporaryDirectory() as tmp_dirname: model_a.save_pretrained(__a ) __lowercase : Tuple = FlaxVisionTextDualEncoderModel.from_pretrained(__a ) __lowercase : Optional[int] = model_a(**__a ) __lowercase : Tuple = after_outputs[0] __lowercase : Union[str, Any] = np.amax(np.abs(out_a - out_a ) ) self.assertLessEqual(__a , 1E-5 ) @require_flax class lowerCAmelCase ( __a , unittest.TestCase ): '''simple docstring''' def lowerCAmelCase ( self : Dict ) -> Dict: """simple docstring""" __lowercase : int = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( """hf-internal-testing/tiny-random-vit""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=__a , text_from_pt=__a , ) __lowercase : int = 13 __lowercase : Union[str, Any] = floats_tensor( [ batch_size, model.config.vision_config.num_channels, model.config.vision_config.image_size, model.config.vision_config.image_size, ] ) __lowercase : Dict = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size ) __lowercase : Tuple = random_attention_mask([batch_size, 4] ) __lowercase : str = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask} return model, inputs def lowerCAmelCase ( self : Optional[Any] , __a : Union[str, Any] , __a : int ) -> Dict: """simple docstring""" __lowercase : int = FlaxViTModel(__a ) __lowercase : List[Any] = FlaxBertModel(__a ) return vision_model, text_model def lowerCAmelCase ( self : Tuple ) -> Optional[Any]: """simple docstring""" __lowercase : Tuple = FlaxViTModelTester(self ) __lowercase : str = FlaxBertModelTester(self ) __lowercase : List[str] = vit_model_tester.prepare_config_and_inputs() __lowercase : Union[str, Any] = bert_model_tester.prepare_config_and_inputs() __lowercase , __lowercase : Optional[int] = vision_config_and_inputs __lowercase , __lowercase , __lowercase , __lowercase : Any = text_config_and_inputs # make sure that cross attention layers are added return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": attention_mask, "input_ids": input_ids, "token_type_ids": token_type_ids, } @require_torch class lowerCAmelCase ( __a , unittest.TestCase ): '''simple docstring''' def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]: """simple docstring""" __lowercase : List[Any] = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained( """hf-internal-testing/tiny-random-clip""" , """hf-internal-testing/tiny-bert""" , vision_from_pt=__a , text_from_pt=__a , ) __lowercase : Tuple = 13 __lowercase : Optional[Any] = floats_tensor( [ batch_size, model.config.vision_config.num_channels, model.config.vision_config.image_size, model.config.vision_config.image_size, ] ) __lowercase : Tuple = ids_tensor([batch_size, 4] , model.config.text_config.vocab_size ) __lowercase : List[Any] = random_attention_mask([batch_size, 4] ) __lowercase : int = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask} return model, inputs def lowerCAmelCase ( self : str , __a : str , __a : Union[str, Any] ) -> Any: """simple docstring""" __lowercase : Dict = FlaxCLIPVisionModel(__a ) __lowercase : Optional[Any] = FlaxBertModel(__a ) return vision_model, text_model def lowerCAmelCase ( self : List[Any] ) -> List[str]: """simple docstring""" __lowercase : List[Any] = FlaxCLIPVisionModelTester(self ) __lowercase : Optional[Any] = FlaxBertModelTester(self ) __lowercase : Any = clip_model_tester.prepare_config_and_inputs() __lowercase : Optional[Any] = bert_model_tester.prepare_config_and_inputs() __lowercase , __lowercase : Dict = vision_config_and_inputs __lowercase , __lowercase , __lowercase , __lowercase : Optional[int] = text_config_and_inputs # make sure that cross attention layers are added return { "text_config": text_config, "vision_config": vision_config, "pixel_values": pixel_values, "attention_mask": attention_mask, "input_ids": input_ids, "token_type_ids": token_type_ids, } @require_flax @require_vision class lowerCAmelCase ( unittest.TestCase ): '''simple docstring''' @slow def lowerCAmelCase ( self : List[str] ) -> Optional[Any]: """simple docstring""" __lowercase : Any = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" , logit_scale_init_value=1.0 ) __lowercase : int = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" ) __lowercase : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ) __lowercase : Tuple = processor( text=["""una foto di un gatto""", """una foto di un cane"""] , images=__a , padding=__a , return_tensors="""np""" ) __lowercase : Optional[int] = model(**__a ) # verify the logits self.assertEqual(outputs.logits_per_image.shape , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) ) self.assertEqual( outputs.logits_per_text.shape , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) , ) __lowercase : Optional[Any] = np.array([[1.2284727, 0.3104122]] ) self.assertTrue(np.allclose(outputs.logits_per_image , __a , atol=1E-3 ) )
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from collections import namedtuple __snake_case :Tuple = namedtuple('''from_to''', '''from_ to''') __snake_case :Dict = { '''cubicmeter''': from_to(1, 1), '''litre''': from_to(0.0_0_1, 1000), '''kilolitre''': from_to(1, 1), '''gallon''': from_to(0.0_0_4_5_4, 2_6_4.1_7_2), '''cubicyard''': from_to(0.7_6_4_5_5, 1.3_0_7_9_5), '''cubicfoot''': from_to(0.0_2_8, 3_5.3_1_4_7), '''cup''': from_to(0.0_0_0_2_3_6_5_8_8, 4_2_2_6.7_5), } def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ): if from_type not in METRIC_CONVERSION: raise ValueError( f'Invalid \'from_type\' value: {from_type!r} Supported values are:\n' + ''', '''.join(_UpperCAmelCase ) ) if to_type not in METRIC_CONVERSION: raise ValueError( f'Invalid \'to_type\' value: {to_type!r}. Supported values are:\n' + ''', '''.join(_UpperCAmelCase ) ) return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' def snake_case_ ( SCREAMING_SNAKE_CASE__ ): """simple docstring""" _SCREAMING_SNAKE_CASE : int = 0 while len(SCREAMING_SNAKE_CASE__ ) > 1: _SCREAMING_SNAKE_CASE : Any = 0 # Consider two files with minimum cost to be merged for _ in range(2 ): _SCREAMING_SNAKE_CASE : Optional[int] = files.index(min(SCREAMING_SNAKE_CASE__ ) ) temp += files[min_index] files.pop(SCREAMING_SNAKE_CASE__ ) files.append(SCREAMING_SNAKE_CASE__ ) optimal_merge_cost += temp return optimal_merge_cost if __name__ == "__main__": import doctest doctest.testmod()
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from ...configuration_utils import PretrainedConfig from ...utils import logging _snake_case = logging.get_logger(__name__) _snake_case = { '''microsoft/trocr-base-handwritten''': ( '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json''' ), # See all TrOCR models at https://huggingface.co/models?filter=trocr } class _snake_case ( _lowercase ): lowerCamelCase__: Dict = "trocr" lowerCamelCase__: List[str] = ["past_key_values"] lowerCamelCase__: Any = { "num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model", "num_hidden_layers": "decoder_layers", } def __init__( self: Tuple , __lowerCamelCase: List[str]=5_02_65 , __lowerCamelCase: List[str]=10_24 , __lowerCamelCase: List[str]=12 , __lowerCamelCase: Optional[int]=16 , __lowerCamelCase: Optional[Any]=40_96 , __lowerCamelCase: Any="gelu" , __lowerCamelCase: Optional[int]=5_12 , __lowerCamelCase: Optional[int]=0.1 , __lowerCamelCase: List[str]=0.0 , __lowerCamelCase: List[str]=0.0 , __lowerCamelCase: Union[str, Any]=2 , __lowerCamelCase: List[Any]=0.02 , __lowerCamelCase: int=0.0 , __lowerCamelCase: List[str]=True , __lowerCamelCase: Union[str, Any]=False , __lowerCamelCase: List[Any]=True , __lowerCamelCase: List[Any]=True , __lowerCamelCase: Tuple=1 , __lowerCamelCase: Tuple=0 , __lowerCamelCase: Any=2 , **__lowerCamelCase: Dict , ) -> Any: __UpperCAmelCase : int = vocab_size __UpperCAmelCase : int = d_model __UpperCAmelCase : Tuple = decoder_layers __UpperCAmelCase : Union[str, Any] = decoder_attention_heads __UpperCAmelCase : Optional[Any] = decoder_ffn_dim __UpperCAmelCase : Optional[int] = activation_function __UpperCAmelCase : int = max_position_embeddings __UpperCAmelCase : str = dropout __UpperCAmelCase : List[Any] = attention_dropout __UpperCAmelCase : Union[str, Any] = activation_dropout __UpperCAmelCase : List[str] = init_std __UpperCAmelCase : Optional[Any] = decoder_layerdrop __UpperCAmelCase : List[Any] = use_cache __UpperCAmelCase : Union[str, Any] = scale_embedding __UpperCAmelCase : str = use_learned_position_embeddings __UpperCAmelCase : Tuple = layernorm_embedding super().__init__( pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , decoder_start_token_id=__lowerCamelCase , **__lowerCamelCase , )
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import argparse from typing import Dict import tensorflow as tf import torch from tqdm import tqdm from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration _snake_case = [ # tf -> hf ('''/''', '''.'''), ('''layer_''', '''layers.'''), ('''kernel''', '''weight'''), ('''beta''', '''bias'''), ('''gamma''', '''weight'''), ('''pegasus''', '''model'''), ] _snake_case = [ ('''.output.dense''', '''.fc2'''), ('''intermediate.LayerNorm''', '''final_layer_norm'''), ('''intermediate.dense''', '''fc1'''), ] _snake_case = ( INIT_COMMON + [ ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''), ('''attention.output.dense''', '''self_attn.out_proj'''), ('''attention.self''', '''self_attn'''), ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''), ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''), ('''attention.encdec''', '''encoder_attn'''), ('''key''', '''k_proj'''), ('''value''', '''v_proj'''), ('''query''', '''q_proj'''), ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''), ] + END_COMMON ) _snake_case = ( INIT_COMMON + [ ('''embeddings.word_embeddings''', '''shared.weight'''), ('''embeddings.position_embeddings''', '''embed_positions.weight'''), ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''), ('''attention.output.dense''', '''self_attn.output'''), ('''attention.self''', '''self_attn.self'''), ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''), ] + END_COMMON ) _snake_case = [ '''encdec/key/bias''', '''encdec/query/bias''', '''encdec/value/bias''', '''self/key/bias''', '''self/query/bias''', '''self/value/bias''', '''encdec_output/dense/bias''', '''attention/output/dense/bias''', ] def _UpperCamelCase ( snake_case__, snake_case__ ) -> Any: for tf_name, hf_name in patterns: __UpperCAmelCase : Optional[int] = k.replace(snake_case__, snake_case__ ) return k def _UpperCamelCase ( snake_case__, snake_case__ ) -> BigBirdPegasusForConditionalGeneration: __UpperCAmelCase : Dict = BigBirdPegasusConfig(**snake_case__ ) __UpperCAmelCase : Dict = BigBirdPegasusForConditionalGeneration(snake_case__ ) __UpperCAmelCase : Optional[Any] = torch_model.state_dict() __UpperCAmelCase : Optional[int] = {} # separating decoder weights __UpperCAmelCase : List[Any] = {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder" )} __UpperCAmelCase : str = {k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder" )} for k, v in tqdm(decoder_weights.items(), "tf -> hf conversion" ): __UpperCAmelCase : Optional[int] = [k.endswith(snake_case__ ) for ending in KEYS_TO_IGNORE] if any(snake_case__ ): continue __UpperCAmelCase : List[str] = DECODER_PATTERNS __UpperCAmelCase : str = rename_state_dict_key(snake_case__, snake_case__ ) if new_k not in state_dict: raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' ) if any(True if i in k else False for i in ["dense", "query", "key", "value"] ): __UpperCAmelCase : Optional[int] = v.T __UpperCAmelCase : str = torch.from_numpy(snake_case__ ) assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}''' for k, v in tqdm(remaining_weights.items(), "tf -> hf conversion" ): __UpperCAmelCase : int = [k.endswith(snake_case__ ) for ending in KEYS_TO_IGNORE] if any(snake_case__ ): continue __UpperCAmelCase : Optional[Any] = REMAINING_PATTERNS __UpperCAmelCase : Optional[int] = rename_state_dict_key(snake_case__, snake_case__ ) if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings": raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' ) if any(True if i in k else False for i in ["dense", "query", "key", "value"] ): __UpperCAmelCase : List[Any] = v.T __UpperCAmelCase : List[str] = torch.from_numpy(snake_case__ ) if k != "pegasus/embeddings/position_embeddings": assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}''' __UpperCAmelCase : List[Any] = mapping["model.embed_positions.weight"] __UpperCAmelCase : Optional[Any] = mapping.pop("model.embed_positions.weight" ) __UpperCAmelCase , __UpperCAmelCase : Any = torch_model.load_state_dict(snake_case__, strict=snake_case__ ) __UpperCAmelCase : str = [ k for k in missing if k not in [ "final_logits_bias", "model.encoder.embed_tokens.weight", "model.decoder.embed_tokens.weight", "lm_head.weight", ] ] assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}''' assert extra == [], f'''no matches found for the following tf keys {extra}''' return torch_model def _UpperCamelCase ( snake_case__ ) -> Dict: __UpperCAmelCase : Tuple = tf.train.list_variables(snake_case__ ) __UpperCAmelCase : List[str] = {} __UpperCAmelCase : str = ["global_step"] for name, shape in tqdm(snake_case__, desc="converting tf checkpoint to dict" ): __UpperCAmelCase : Tuple = any(pat in name for pat in ignore_name ) if skip_key: continue __UpperCAmelCase : Optional[Any] = tf.train.load_variable(snake_case__, snake_case__ ) __UpperCAmelCase : Tuple = array return tf_weights def _UpperCamelCase ( snake_case__, snake_case__, snake_case__ ) -> Dict: __UpperCAmelCase : str = get_tf_weights_as_numpy(snake_case__ ) __UpperCAmelCase : List[Any] = convert_bigbird_pegasus(snake_case__, snake_case__ ) torch_model.save_pretrained(snake_case__ ) if __name__ == "__main__": _snake_case = argparse.ArgumentParser() parser.add_argument('''--tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''') parser.add_argument('''--save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''') _snake_case = parser.parse_args() _snake_case = {} convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
342
0
"""simple docstring""" import tempfile import numpy as np import torch from transformers import AutoTokenizer, TaEncoderModel from diffusers import DDPMScheduler, UNetaDConditionModel from diffusers.models.attention_processor import AttnAddedKVProcessor from diffusers.pipelines.deepfloyd_if import IFWatermarker from diffusers.utils.testing_utils import torch_device from ..test_pipelines_common import to_np class __lowerCAmelCase : '''simple docstring''' def __UpperCAmelCase ( self ): torch.manual_seed(0 ) __a = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' ) torch.manual_seed(0 ) __a = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' ) torch.manual_seed(0 ) __a = UNetaDConditionModel( sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[ '''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D''', ] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , ) unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests torch.manual_seed(0 ) __a = DDPMScheduler( num_train_timesteps=1_000 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0001 , beta_end=0.02 , thresholding=_a , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , ) torch.manual_seed(0 ) __a = IFWatermarker() return { "text_encoder": text_encoder, "tokenizer": tokenizer, "unet": unet, "scheduler": scheduler, "watermarker": watermarker, "safety_checker": None, "feature_extractor": None, } def __UpperCAmelCase ( self ): torch.manual_seed(0 ) __a = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' ) torch.manual_seed(0 ) __a = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' ) torch.manual_seed(0 ) __a = UNetaDConditionModel( sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[ '''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D''', ] , mid_block_type='''UNetMidBlock2DSimpleCrossAttn''' , up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='''text''' , addition_embed_type_num_heads=2 , cross_attention_norm='''group_norm''' , resnet_time_scale_shift='''scale_shift''' , act_fn='''gelu''' , class_embed_type='''timestep''' , mid_block_scale_factor=1.414 , time_embedding_act_fn='''gelu''' , time_embedding_dim=32 , ) unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests torch.manual_seed(0 ) __a = DDPMScheduler( num_train_timesteps=1_000 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0001 , beta_end=0.02 , thresholding=_a , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='''epsilon''' , variance_type='''learned_range''' , ) torch.manual_seed(0 ) __a = DDPMScheduler( num_train_timesteps=1_000 , beta_schedule='''squaredcos_cap_v2''' , beta_start=0.0001 , beta_end=0.02 , ) torch.manual_seed(0 ) __a = IFWatermarker() return { "text_encoder": text_encoder, "tokenizer": tokenizer, "unet": unet, "scheduler": scheduler, "image_noising_scheduler": image_noising_scheduler, "watermarker": watermarker, "safety_checker": None, "feature_extractor": None, } def __UpperCAmelCase ( self ): __a = self.get_dummy_components() __a = self.pipeline_class(**_a ) pipe.to(_a ) pipe.set_progress_bar_config(disable=_a ) __a = self.get_dummy_inputs(_a ) __a = inputs['''prompt'''] __a = inputs['''generator'''] __a = inputs['''num_inference_steps'''] __a = inputs['''output_type'''] if "image" in inputs: __a = inputs['''image'''] else: __a = None if "mask_image" in inputs: __a = inputs['''mask_image'''] else: __a = None if "original_image" in inputs: __a = inputs['''original_image'''] else: __a = None __a , __a = pipe.encode_prompt(_a ) # inputs with prompt converted to embeddings __a = { '''prompt_embeds''': prompt_embeds, '''negative_prompt_embeds''': negative_prompt_embeds, '''generator''': generator, '''num_inference_steps''': num_inference_steps, '''output_type''': output_type, } if image is not None: __a = image if mask_image is not None: __a = mask_image if original_image is not None: __a = original_image # set all optional components to None for optional_component in pipe._optional_components: setattr(_a , _a , _a ) __a = pipe(**_a )[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(_a ) __a = self.pipeline_class.from_pretrained(_a ) pipe_loaded.to(_a ) pipe_loaded.set_progress_bar_config(disable=_a ) pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests for optional_component in pipe._optional_components: self.assertTrue( getattr(_a , _a ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , ) __a = self.get_dummy_inputs(_a ) __a = inputs['''generator'''] __a = inputs['''num_inference_steps'''] __a = inputs['''output_type'''] # inputs with prompt converted to embeddings __a = { '''prompt_embeds''': prompt_embeds, '''negative_prompt_embeds''': negative_prompt_embeds, '''generator''': generator, '''num_inference_steps''': num_inference_steps, '''output_type''': output_type, } if image is not None: __a = image if mask_image is not None: __a = mask_image if original_image is not None: __a = original_image __a = pipe_loaded(**_a )[0] __a = np.abs(to_np(_a ) - to_np(_a ) ).max() self.assertLess(_a , 1E-4 ) def __UpperCAmelCase ( self ): __a = self.get_dummy_components() __a = self.pipeline_class(**_a ) pipe.to(_a ) pipe.set_progress_bar_config(disable=_a ) __a = self.get_dummy_inputs(_a ) __a = pipe(**_a )[0] with tempfile.TemporaryDirectory() as tmpdir: pipe.save_pretrained(_a ) __a = self.pipeline_class.from_pretrained(_a ) pipe_loaded.to(_a ) pipe_loaded.set_progress_bar_config(disable=_a ) pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests __a = self.get_dummy_inputs(_a ) __a = pipe_loaded(**_a )[0] __a = np.abs(to_np(_a ) - to_np(_a ) ).max() self.assertLess(_a , 1E-4 )
45
"""simple docstring""" def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ) -> str: __a = '''''' for word_or_phrase in separated: if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ): raise Exception('''join() accepts only strings to be joined''' ) joined += word_or_phrase + separator return joined.strip(lowerCAmelCase__ ) if __name__ == "__main__": from doctest import testmod testmod()
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1
import argparse import json import os import evaluate import torch from datasets import load_dataset from torch.optim import AdamW from torch.utils.data import DataLoader from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed from accelerate import Accelerator, DistributedType from accelerate.utils.deepspeed import DummyOptim, DummyScheduler _SCREAMING_SNAKE_CASE : Any = 16 _SCREAMING_SNAKE_CASE : Tuple = 32 def UpperCAmelCase_ ( _A , _A = 16 , _A = "bert-base-cased" ): '''simple docstring''' SCREAMING_SNAKE_CASE__ = AutoTokenizer.from_pretrained(_A ) SCREAMING_SNAKE_CASE__ = load_dataset('''glue''' , '''mrpc''' ) def tokenize_function(_A ): # max_length=None => use the model max length (it's actually the default) SCREAMING_SNAKE_CASE__ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_A , max_length=_A ) return outputs # Apply the method we just defined to all the examples in all the splits of the dataset SCREAMING_SNAKE_CASE__ = datasets.map( _A , batched=_A , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , load_from_cache_file=_A ) # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the # transformers library SCREAMING_SNAKE_CASE__ = tokenized_datasets.rename_column('''label''' , '''labels''' ) def collate_fn(_A ): # On TPU it's best to pad everything to the same length or training will be very slow. if accelerator.distributed_type == DistributedType.TPU: return tokenizer.pad(_A , padding='''max_length''' , max_length=1_28 , return_tensors='''pt''' ) return tokenizer.pad(_A , padding='''longest''' , return_tensors='''pt''' ) # Instantiate dataloaders. SCREAMING_SNAKE_CASE__ = DataLoader( tokenized_datasets['''train'''] , shuffle=_A , collate_fn=_A , batch_size=_A ) SCREAMING_SNAKE_CASE__ = DataLoader( tokenized_datasets['''validation'''] , shuffle=_A , collate_fn=_A , batch_size=_A ) return train_dataloader, eval_dataloader def UpperCAmelCase_ ( _A , _A , _A , _A ): '''simple docstring''' model.eval() SCREAMING_SNAKE_CASE__ = 0 for step, batch in enumerate(_A ): # We could avoid this line since we set the accelerator with `device_placement=True`. batch.to(accelerator.device ) with torch.no_grad(): SCREAMING_SNAKE_CASE__ = model(**_A ) SCREAMING_SNAKE_CASE__ = outputs.logits.argmax(dim=-1 ) # It is slightly faster to call this once, than multiple times SCREAMING_SNAKE_CASE__ = accelerator.gather( (predictions, batch['''labels''']) ) # If we are in a multiprocess environment, the last batch has duplicates if accelerator.use_distributed: if step == len(_A ) - 1: SCREAMING_SNAKE_CASE__ = predictions[: len(eval_dataloader.dataset ) - samples_seen] SCREAMING_SNAKE_CASE__ = references[: len(eval_dataloader.dataset ) - samples_seen] else: samples_seen += references.shape[0] metric.add_batch( predictions=_A , references=_A , ) SCREAMING_SNAKE_CASE__ = metric.compute() return eval_metric["accuracy"] def UpperCAmelCase_ ( _A , _A ): '''simple docstring''' SCREAMING_SNAKE_CASE__ = Accelerator() # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs SCREAMING_SNAKE_CASE__ = config["""lr"""] SCREAMING_SNAKE_CASE__ = int(config['''num_epochs'''] ) SCREAMING_SNAKE_CASE__ = int(config['''seed'''] ) SCREAMING_SNAKE_CASE__ = int(config['''batch_size'''] ) SCREAMING_SNAKE_CASE__ = args.model_name_or_path set_seed(_A ) SCREAMING_SNAKE_CASE__ = get_dataloaders(_A , _A , _A ) # Instantiate the model (we build the model here so that the seed also control new weights initialization) SCREAMING_SNAKE_CASE__ = AutoModelForSequenceClassification.from_pretrained(_A , return_dict=_A ) # Instantiate optimizer SCREAMING_SNAKE_CASE__ = ( AdamW if accelerator.state.deepspeed_plugin is None or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config else DummyOptim ) SCREAMING_SNAKE_CASE__ = optimizer_cls(params=model.parameters() , lr=_A ) if accelerator.state.deepspeed_plugin is not None: SCREAMING_SNAKE_CASE__ = accelerator.state.deepspeed_plugin.deepspeed_config[ """gradient_accumulation_steps""" ] else: SCREAMING_SNAKE_CASE__ = 1 SCREAMING_SNAKE_CASE__ = (len(_A ) * num_epochs) // gradient_accumulation_steps # Instantiate scheduler if ( accelerator.state.deepspeed_plugin is None or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config ): SCREAMING_SNAKE_CASE__ = get_linear_schedule_with_warmup( optimizer=_A , num_warmup_steps=0 , num_training_steps=_A , ) else: SCREAMING_SNAKE_CASE__ = DummyScheduler(_A , total_num_steps=_A , warmup_num_steps=0 ) # 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. SCREAMING_SNAKE_CASE__ = accelerator.prepare( _A , _A , _A , _A , _A ) # We need to keep track of how many total steps we have iterated over SCREAMING_SNAKE_CASE__ = 0 # We also need to keep track of the stating epoch so files are named properly SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = evaluate.load('''glue''' , '''mrpc''' ) SCREAMING_SNAKE_CASE__ = num_epochs if args.partial_train_epoch is not None: SCREAMING_SNAKE_CASE__ = args.partial_train_epoch if args.resume_from_checkpoint: accelerator.load_state(args.resume_from_checkpoint ) SCREAMING_SNAKE_CASE__ = args.resume_from_checkpoint.split('''epoch_''' )[1] SCREAMING_SNAKE_CASE__ = """""" for char in epoch_string: if char.isdigit(): state_epoch_num += char else: break SCREAMING_SNAKE_CASE__ = int(_A ) + 1 SCREAMING_SNAKE_CASE__ = evaluation_loop(_A , _A , _A , _A ) accelerator.print('''resumed checkpoint performance:''' , _A ) accelerator.print('''resumed checkpoint\'s scheduler\'s lr:''' , lr_scheduler.get_lr()[0] ) accelerator.print('''resumed optimizers\'s lr:''' , optimizer.param_groups[0]['''lr'''] ) with open(os.path.join(args.output_dir , F'''state_{starting_epoch-1}.json''' ) , '''r''' ) as f: SCREAMING_SNAKE_CASE__ = json.load(_A ) assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed" assert ( resumed_state["lr"] == lr_scheduler.get_lr()[0] ), "Scheduler learning rate mismatch, loading from checkpoint failed" assert ( resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"] ), "Optimizer learning rate mismatch, loading from checkpoint failed" assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed" return # Now we train the model SCREAMING_SNAKE_CASE__ = {} for epoch in range(_A , _A ): model.train() for step, batch in enumerate(_A ): SCREAMING_SNAKE_CASE__ = model(**_A ) SCREAMING_SNAKE_CASE__ = outputs.loss SCREAMING_SNAKE_CASE__ = loss / gradient_accumulation_steps accelerator.backward(_A ) if step % gradient_accumulation_steps == 0: optimizer.step() lr_scheduler.step() optimizer.zero_grad() overall_step += 1 SCREAMING_SNAKE_CASE__ = F'''epoch_{epoch}''' SCREAMING_SNAKE_CASE__ = os.path.join(args.output_dir , _A ) accelerator.save_state(_A ) SCREAMING_SNAKE_CASE__ = evaluation_loop(_A , _A , _A , _A ) SCREAMING_SNAKE_CASE__ = accuracy SCREAMING_SNAKE_CASE__ = lr_scheduler.get_lr()[0] SCREAMING_SNAKE_CASE__ = optimizer.param_groups[0]["""lr"""] SCREAMING_SNAKE_CASE__ = epoch SCREAMING_SNAKE_CASE__ = overall_step accelerator.print(F'''epoch {epoch}:''' , _A ) accelerator.wait_for_everyone() if accelerator.is_main_process: with open(os.path.join(args.output_dir , F'''state_{epoch}.json''' ) , '''w''' ) as f: json.dump(_A , _A ) def UpperCAmelCase_ ( ): '''simple docstring''' SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' ) parser.add_argument( '''--model_name_or_path''' , type=_A , default='''bert-base-cased''' , help='''Path to pretrained model or model identifier from huggingface.co/models.''' , required=_A , ) parser.add_argument( '''--output_dir''' , type=_A , default='''.''' , help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' , ) parser.add_argument( '''--resume_from_checkpoint''' , type=_A , default=_A , help='''If the training should continue from a checkpoint folder.''' , ) parser.add_argument( '''--partial_train_epoch''' , type=_A , default=_A , help='''If passed, the training will stop after this number of epochs.''' , ) parser.add_argument( '''--num_epochs''' , type=_A , default=2 , help='''Number of train epochs.''' , ) SCREAMING_SNAKE_CASE__ = parser.parse_args() SCREAMING_SNAKE_CASE__ = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16} training_function(_A , _A ) if __name__ == "__main__": main()
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import random import unittest from torch.utils.data import BatchSampler, DataLoader, IterableDataset from accelerate import Accelerator from accelerate.data_loader import ( BatchSamplerShard, DataLoaderDispatcher, DataLoaderShard, IterableDatasetShard, SkipBatchSampler, SkipDataLoader, skip_first_batches, ) class UpperCAmelCase__ ( A__ ): """simple docstring""" def __init__( self : List[str] , __lowerCamelCase : Tuple=0.01 , __lowerCamelCase : Optional[Any]=1000 ) -> Union[str, Any]: SCREAMING_SNAKE_CASE__ = p_stop SCREAMING_SNAKE_CASE__ = max_length def __iter__( self : List[str] ) -> Optional[Any]: SCREAMING_SNAKE_CASE__ = 0 SCREAMING_SNAKE_CASE__ = False while not stop and count < self.max_length: yield count count += 1 SCREAMING_SNAKE_CASE__ = random.random() < self.p_stop class UpperCAmelCase__ ( unittest.TestCase ): """simple docstring""" def lowercase_ ( self : List[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str , __lowerCamelCase : Any=False , __lowerCamelCase : Optional[int]=True ) -> Dict: SCREAMING_SNAKE_CASE__ = [ BatchSamplerShard(__lowerCamelCase , 2 , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) for i in range(2 ) ] SCREAMING_SNAKE_CASE__ = [list(__lowerCamelCase ) for batch_sampler_shard in batch_sampler_shards] if not split_batches: self.assertListEqual([len(__lowerCamelCase ) for shard in batch_sampler_shards] , [len(__lowerCamelCase ) for e in expected] ) self.assertListEqual(__lowerCamelCase , __lowerCamelCase ) def lowercase_ ( self : Any ) -> Optional[Any]: # Check the shards when the dataset is a round multiple of total batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]], [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=__lowerCamelCase ) # Expected shouldn't change self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) # Check the shards when the dataset is a round multiple of batch size but not total batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]], [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size but has a multiple of # num_processes batch. SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]], [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of # num_processes batch. SCREAMING_SNAKE_CASE__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]], [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) # Check the shards when the dataset is very small. SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[[0, 1, 0]], [[1, 0, 1]]] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[], []] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase ) def lowercase_ ( self : Optional[int] ) -> int: # Check the shards when the dataset is a round multiple of batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=__lowerCamelCase ) # Expected shouldn't change self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size or num_processes. SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) # Check the shards when the dataset is very small. SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[[0, 1]], [[0, 1]]] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[], []] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase ) def lowercase_ ( self : str ) -> Dict: # Check the shards when the dataset is a round multiple of total batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]], [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=3 , drop_last=__lowerCamelCase ) # Expected shouldn't change self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is a round multiple of batch size but not total batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size but has a multiple of # num_processes batch. SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]], [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of # num_processes batch. SCREAMING_SNAKE_CASE__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(20 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1, 2], [6, 7, 8], [12, 13, 14]], [[3, 4, 5], [9, 10, 11], [15, 16, 17]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is very small. SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[[0, 1]], []] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=3 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[], []] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , even_batches=__lowerCamelCase ) def lowercase_ ( self : List[str] ) -> Tuple: # Check the shards when the dataset is a round multiple of batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(24 ) , batch_size=4 , drop_last=__lowerCamelCase ) # Expected shouldn't change self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size. SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(22 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is not a round multiple of batch size or num_processes. SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(21 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]], [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]], ] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) # Check the shards when the dataset is very small. SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[[0, 1]], []] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = BatchSampler(range(2 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [[], []] self.check_batch_sampler_shards(__lowerCamelCase , __lowerCamelCase , split_batches=__lowerCamelCase , even_batches=__lowerCamelCase ) def lowercase_ ( self : Optional[int] ) -> List[str]: SCREAMING_SNAKE_CASE__ = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]] SCREAMING_SNAKE_CASE__ = [BatchSamplerShard(__lowerCamelCase , 2 , __lowerCamelCase , even_batches=__lowerCamelCase ) for i in range(2 )] self.assertEqual(len(batch_sampler_shards[0] ) , 3 ) self.assertEqual(len(batch_sampler_shards[1] ) , 2 ) self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] ) self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] ) def lowercase_ ( self : Optional[Any] , __lowerCamelCase : Any , __lowerCamelCase : str , __lowerCamelCase : str , __lowerCamelCase : Tuple=False , __lowerCamelCase : Optional[int]=2 , __lowerCamelCase : Dict=False ) -> str: random.seed(__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = list(__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = [ IterableDatasetShard( __lowerCamelCase , batch_size=__lowerCamelCase , drop_last=__lowerCamelCase , num_processes=__lowerCamelCase , process_index=__lowerCamelCase , split_batches=__lowerCamelCase , ) for i in range(__lowerCamelCase ) ] SCREAMING_SNAKE_CASE__ = [] for iterable_dataset_shard in iterable_dataset_shards: # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results. random.seed(__lowerCamelCase ) iterable_dataset_lists.append(list(__lowerCamelCase ) ) SCREAMING_SNAKE_CASE__ = batch_size // num_processes if split_batches else batch_size # All iterable dataset shard should have the same length, a round multiple of shard_batch_size SCREAMING_SNAKE_CASE__ = iterable_dataset_lists[0] for l in iterable_dataset_lists[1:]: self.assertEqual(len(__lowerCamelCase ) , len(__lowerCamelCase ) ) self.assertTrue(len(__lowerCamelCase ) % shard_batch_size == 0 ) SCREAMING_SNAKE_CASE__ = [] for idx in range(0 , len(__lowerCamelCase ) , __lowerCamelCase ): for l in iterable_dataset_lists: observed += l[idx : idx + shard_batch_size] if not drop_last: while len(__lowerCamelCase ) < len(__lowerCamelCase ): reference += reference self.assertListEqual(__lowerCamelCase , reference[: len(__lowerCamelCase )] ) def lowercase_ ( self : str ) -> Dict: SCREAMING_SNAKE_CASE__ = 42 SCREAMING_SNAKE_CASE__ = RandomIterableDataset() self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) # Edge case with a very small dataset SCREAMING_SNAKE_CASE__ = RandomIterableDataset(max_length=2 ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) self.check_iterable_dataset_shards(__lowerCamelCase , __lowerCamelCase , batch_size=4 , drop_last=__lowerCamelCase , split_batches=__lowerCamelCase ) def lowercase_ ( self : Optional[Any] ) -> Any: SCREAMING_SNAKE_CASE__ = BatchSampler(range(16 ) , batch_size=4 , drop_last=__lowerCamelCase ) SCREAMING_SNAKE_CASE__ = SkipBatchSampler(__lowerCamelCase , 2 ) self.assertListEqual(list(__lowerCamelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] ) def lowercase_ ( self : Union[str, Any] ) -> str: SCREAMING_SNAKE_CASE__ = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 ) self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] ) def lowercase_ ( self : Dict ) -> List[Any]: SCREAMING_SNAKE_CASE__ = DataLoader(list(range(16 ) ) , batch_size=4 ) SCREAMING_SNAKE_CASE__ = skip_first_batches(__lowerCamelCase , num_batches=2 ) self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] ) def lowercase_ ( self : List[Any] ) -> List[str]: SCREAMING_SNAKE_CASE__ = DataLoaderShard(list(range(16 ) ) , batch_size=4 ) for idx, _ in enumerate(__lowerCamelCase ): self.assertEqual(dataloader.end_of_dataloader , idx == 3 ) # Test it also works on the second iteration for idx, _ in enumerate(__lowerCamelCase ): self.assertEqual(dataloader.end_of_dataloader , idx == 3 ) def lowercase_ ( self : Union[str, Any] ) -> str: Accelerator() SCREAMING_SNAKE_CASE__ = DataLoaderDispatcher(range(16 ) , batch_size=4 ) for idx, _ in enumerate(__lowerCamelCase ): self.assertEqual(dataloader.end_of_dataloader , idx == 3 ) # Test it also works on the second iteration for idx, _ in enumerate(__lowerCamelCase ): self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
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"""simple docstring""" from typing import TYPE_CHECKING from ...utils import _LazyModule lowerCAmelCase_ = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']} if TYPE_CHECKING: from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer else: import sys lowerCAmelCase_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
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"""simple docstring""" from arguments import InitializationArguments from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser # Configuration lowerCamelCase_ : Any = HfArgumentParser(InitializationArguments) lowerCamelCase_ : Union[str, Any] = parser.parse_args() # Load codeparrot tokenizer trained for Python code tokenization lowerCamelCase_ : List[Any] = AutoTokenizer.from_pretrained(args.tokenizer_name) # Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks lowerCamelCase_ : Tuple = { '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) lowerCamelCase_ : int = AutoConfig.from_pretrained(args.config_name, **config_kwargs) # Initialize new model with config lowerCamelCase_ : Any = 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''' from typing import Optional, Tuple, Union import torch from einops import rearrange, reduce from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput __lowerCAmelCase = 8 def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=BITS ): _snake_case = x.device _snake_case = (x * 255).int().clamp(0 , 255 ) _snake_case = 2 ** torch.arange(bits - 1 , -1 , -1 , device=_SCREAMING_SNAKE_CASE ) _snake_case = rearrange(_SCREAMING_SNAKE_CASE , """d -> d 1 1""" ) _snake_case = rearrange(_SCREAMING_SNAKE_CASE , """b c h w -> b c 1 h w""" ) _snake_case = ((x & mask) != 0).float() _snake_case = rearrange(_SCREAMING_SNAKE_CASE , """b c d h w -> b (c d) h w""" ) _snake_case = bits * 2 - 1 return bits def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=BITS ): _snake_case = x.device _snake_case = (x > 0).int() _snake_case = 2 ** torch.arange(bits - 1 , -1 , -1 , device=_SCREAMING_SNAKE_CASE , dtype=torch.intaa ) _snake_case = rearrange(_SCREAMING_SNAKE_CASE , """d -> d 1 1""" ) _snake_case = rearrange(_SCREAMING_SNAKE_CASE , """b (c d) h w -> b c d h w""" , d=8 ) _snake_case = reduce(x * mask , """b c d h w -> b c h w""" , """sum""" ) return (dec / 255).clamp(0.0 , 1.0 ) def __SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = True , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = True , ): if self.num_inference_steps is None: raise ValueError( """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" ) # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf # Ideally, read DDIM paper in-detail understanding # Notation (<variable name> -> <name in paper> # - pred_noise_t -> e_theta(x_t, t) # - pred_original_sample -> f_theta(x_t, t) or x_0 # - std_dev_t -> sigma_t # - eta -> η # - pred_sample_direction -> "direction pointing to x_t" # - pred_prev_sample -> "x_t-1" # 1. get previous step value (=t-1) _snake_case = timestep - self.config.num_train_timesteps // self.num_inference_steps # 2. compute alphas, betas _snake_case = self.alphas_cumprod[timestep] _snake_case = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod _snake_case = 1 - alpha_prod_t # 3. 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 = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 # 4. Clip "predicted x_0" _snake_case = self.bit_scale if self.config.clip_sample: _snake_case = torch.clamp(_SCREAMING_SNAKE_CASE , -scale , _SCREAMING_SNAKE_CASE ) # 5. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) _snake_case = self._get_variance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) _snake_case = eta * variance ** 0.5 if use_clipped_model_output: # the model_output is always re-derived from the clipped x_0 in Glide _snake_case = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5 # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf _snake_case = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf _snake_case = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction if eta > 0: # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072 _snake_case = model_output.device if torch.is_tensor(_SCREAMING_SNAKE_CASE ) else """cpu""" _snake_case = torch.randn(model_output.shape , dtype=model_output.dtype , generator=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ) _snake_case = self._get_variance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ** 0.5 * eta * noise _snake_case = prev_sample + variance if not return_dict: return (prev_sample,) return DDIMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE , pred_original_sample=_SCREAMING_SNAKE_CASE ) def __SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="epsilon" , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE = True , ): _snake_case = timestep if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]: _snake_case, _snake_case = torch.split(_SCREAMING_SNAKE_CASE , sample.shape[1] , dim=1 ) else: _snake_case = None # 1. compute alphas, betas _snake_case = self.alphas_cumprod[t] _snake_case = self.alphas_cumprod[t - 1] if t > 0 else self.one _snake_case = 1 - alpha_prod_t _snake_case = 1 - alpha_prod_t_prev # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if prediction_type == "epsilon": _snake_case = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 elif prediction_type == "sample": _snake_case = model_output else: raise ValueError(f"""Unsupported prediction_type {prediction_type}.""" ) # 3. Clip "predicted x_0" _snake_case = self.bit_scale if self.config.clip_sample: _snake_case = torch.clamp(_SCREAMING_SNAKE_CASE , -scale , _SCREAMING_SNAKE_CASE ) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf _snake_case = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t _snake_case = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf _snake_case = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise _snake_case = 0 if t > 0: _snake_case = torch.randn( model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=_SCREAMING_SNAKE_CASE ).to(model_output.device ) _snake_case = (self._get_variance(_SCREAMING_SNAKE_CASE , predicted_variance=_SCREAMING_SNAKE_CASE ) ** 0.5) * noise _snake_case = pred_prev_sample + variance if not return_dict: return (pred_prev_sample,) return DDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE , pred_original_sample=_SCREAMING_SNAKE_CASE ) class _lowerCAmelCase ( __snake_case ): '''simple docstring''' def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = 1.0 , ) -> Union[str, Any]: super().__init__() _snake_case = bit_scale _snake_case = ( ddim_bit_scheduler_step if isinstance(UpperCAmelCase , UpperCAmelCase ) else ddpm_bit_scheduler_step ) self.register_modules(unet=UpperCAmelCase , scheduler=UpperCAmelCase ) @torch.no_grad() def __call__(self , UpperCAmelCase = 256 , UpperCAmelCase = 256 , UpperCAmelCase = 50 , UpperCAmelCase = None , UpperCAmelCase = 1 , UpperCAmelCase = "pil" , UpperCAmelCase = True , **UpperCAmelCase , ) -> Union[Tuple, ImagePipelineOutput]: _snake_case = torch.randn( (batch_size, self.unet.config.in_channels, height, width) , generator=UpperCAmelCase , ) _snake_case = decimal_to_bits(UpperCAmelCase ) * self.bit_scale _snake_case = latents.to(self.device ) self.scheduler.set_timesteps(UpperCAmelCase ) for t in self.progress_bar(self.scheduler.timesteps ): # predict the noise residual _snake_case = self.unet(UpperCAmelCase , UpperCAmelCase ).sample # compute the previous noisy sample x_t -> x_t-1 _snake_case = self.scheduler.step(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ).prev_sample _snake_case = bits_to_decimal(UpperCAmelCase ) if output_type == "pil": _snake_case = self.numpy_to_pil(UpperCAmelCase ) if not return_dict: return (image,) return ImagePipelineOutput(images=UpperCAmelCase )
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'''simple docstring''' import warnings from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401 warnings.warn( 'The `inpainting.py` script is outdated. Please use directly `from diffusers import' ' StableDiffusionInpaintPipeline` instead.' )
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'''simple docstring''' from typing import Optional, Tuple, Union import torch from einops import rearrange, reduce from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput lowercase : Optional[Any] = 8 def SCREAMING_SNAKE_CASE__ ( __A , __A=BITS ) -> Tuple: _snake_case = x.device _snake_case = (x * 255).int().clamp(0 , 255 ) _snake_case = 2 ** torch.arange(bits - 1 , -1 , -1 , device=__A ) _snake_case = rearrange(__A , 'd -> d 1 1' ) _snake_case = rearrange(__A , 'b c h w -> b c 1 h w' ) _snake_case = ((x & mask) != 0).float() _snake_case = rearrange(__A , 'b c d h w -> b (c d) h w' ) _snake_case = bits * 2 - 1 return bits def SCREAMING_SNAKE_CASE__ ( __A , __A=BITS ) -> Optional[int]: _snake_case = x.device _snake_case = (x > 0).int() _snake_case = 2 ** torch.arange(bits - 1 , -1 , -1 , device=__A , dtype=torch.intaa ) _snake_case = rearrange(__A , 'd -> d 1 1' ) _snake_case = rearrange(__A , 'b (c d) h w -> b c d h w' , d=8 ) _snake_case = reduce(x * mask , 'b c d h w -> b c h w' , 'sum' ) return (dec / 255).clamp(0.0 , 1.0 ) def SCREAMING_SNAKE_CASE__ ( self , __A , __A , __A , __A = 0.0 , __A = True , __A=None , __A = True , ) -> Union[DDIMSchedulerOutput, Tuple]: if self.num_inference_steps is None: raise ValueError( 'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' ) # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf # Ideally, read DDIM paper in-detail understanding # Notation (<variable name> -> <name in paper> # - pred_noise_t -> e_theta(x_t, t) # - pred_original_sample -> f_theta(x_t, t) or x_0 # - std_dev_t -> sigma_t # - eta -> η # - pred_sample_direction -> "direction pointing to x_t" # - pred_prev_sample -> "x_t-1" # 1. get previous step value (=t-1) _snake_case = timestep - self.config.num_train_timesteps // self.num_inference_steps # 2. compute alphas, betas _snake_case = self.alphas_cumprod[timestep] _snake_case = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod _snake_case = 1 - alpha_prod_t # 3. 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 = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 # 4. Clip "predicted x_0" _snake_case = self.bit_scale if self.config.clip_sample: _snake_case = torch.clamp(__A , -scale , __A ) # 5. compute variance: "sigma_t(η)" -> see formula (16) # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) _snake_case = self._get_variance(__A , __A ) _snake_case = eta * variance ** 0.5 if use_clipped_model_output: # the model_output is always re-derived from the clipped x_0 in Glide _snake_case = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5 # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf _snake_case = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf _snake_case = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction if eta > 0: # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072 _snake_case = model_output.device if torch.is_tensor(__A ) else 'cpu' _snake_case = torch.randn(model_output.shape , dtype=model_output.dtype , generator=__A ).to(__A ) _snake_case = self._get_variance(__A , __A ) ** 0.5 * eta * noise _snake_case = prev_sample + variance if not return_dict: return (prev_sample,) return DDIMSchedulerOutput(prev_sample=__A , pred_original_sample=__A ) def SCREAMING_SNAKE_CASE__ ( self , __A , __A , __A , __A="epsilon" , __A=None , __A = True , ) -> Union[DDPMSchedulerOutput, Tuple]: _snake_case = timestep if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]: _snake_case , _snake_case = torch.split(__A , sample.shape[1] , dim=1 ) else: _snake_case = None # 1. compute alphas, betas _snake_case = self.alphas_cumprod[t] _snake_case = self.alphas_cumprod[t - 1] if t > 0 else self.one _snake_case = 1 - alpha_prod_t _snake_case = 1 - alpha_prod_t_prev # 2. compute predicted original sample from predicted noise also called # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf if prediction_type == "epsilon": _snake_case = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5 elif prediction_type == "sample": _snake_case = model_output else: raise ValueError(F'Unsupported prediction_type {prediction_type}.' ) # 3. Clip "predicted x_0" _snake_case = self.bit_scale if self.config.clip_sample: _snake_case = torch.clamp(__A , -scale , __A ) # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf _snake_case = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t _snake_case = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t # 5. Compute predicted previous sample µ_t # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf _snake_case = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample # 6. Add noise _snake_case = 0 if t > 0: _snake_case = torch.randn( model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=__A ).to(model_output.device ) _snake_case = (self._get_variance(__A , predicted_variance=__A ) ** 0.5) * noise _snake_case = pred_prev_sample + variance if not return_dict: return (pred_prev_sample,) return DDPMSchedulerOutput(prev_sample=__A , pred_original_sample=__A ) class __UpperCAmelCase ( _lowerCamelCase ): def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 1.0 , ): """simple docstring""" super().__init__() _snake_case = bit_scale _snake_case = ( ddim_bit_scheduler_step if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) else ddpm_bit_scheduler_step ) self.register_modules(unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ ) @torch.no_grad() def __call__( self , lowerCAmelCase_ = 2_56 , lowerCAmelCase_ = 2_56 , lowerCAmelCase_ = 50 , lowerCAmelCase_ = None , lowerCAmelCase_ = 1 , lowerCAmelCase_ = "pil" , lowerCAmelCase_ = True , **lowerCAmelCase_ , ): """simple docstring""" _snake_case = torch.randn( (batch_size, self.unet.config.in_channels, height, width) , generator=lowerCAmelCase_ , ) _snake_case = decimal_to_bits(lowerCAmelCase_ ) * self.bit_scale _snake_case = latents.to(self.device ) self.scheduler.set_timesteps(lowerCAmelCase_ ) for t in self.progress_bar(self.scheduler.timesteps ): # predict the noise residual _snake_case = self.unet(lowerCAmelCase_ , lowerCAmelCase_ ).sample # compute the previous noisy sample x_t -> x_t-1 _snake_case = self.scheduler.step(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ).prev_sample _snake_case = bits_to_decimal(lowerCAmelCase_ ) if output_type == "pil": _snake_case = self.numpy_to_pil(lowerCAmelCase_ ) if not return_dict: return (image,) return ImagePipelineOutput(images=lowerCAmelCase_ )
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'''simple docstring''' from __future__ import annotations import random # Maximum size of the population. Bigger could be faster but is more memory expensive. lowercase : Dict = 200 # Number of elements selected in every generation of evolution. The selection takes # place from best to worst of that generation and must be smaller than N_POPULATION. lowercase : Optional[int] = 50 # Probability that an element of a generation can mutate, changing one of its genes. # This will guarantee that all genes will be used during evolution. lowercase : Optional[Any] = 0.4 # Just a seed to improve randomness required by the algorithm. random.seed(random.randint(0, 1000)) def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> tuple[str, float]: _snake_case = len([g for position, g in enumerate(__A ) if g == main_target[position]] ) return (item, float(__A )) def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> tuple[str, str]: _snake_case = random.randint(0 , len(__A ) - 1 ) _snake_case = parent_a[:random_slice] + parent_a[random_slice:] _snake_case = parent_a[:random_slice] + parent_a[random_slice:] return (child_a, child_a) def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> str: _snake_case = list(__A ) if random.uniform(0 , 1 ) < MUTATION_PROBABILITY: _snake_case = random.choice(__A ) return "".join(__A ) def SCREAMING_SNAKE_CASE__ ( __A , __A , __A , ) -> list[str]: _snake_case = [] # Generate more children proportionally to the fitness score. _snake_case = int(parent_a[1] * 100 ) + 1 _snake_case = 10 if child_n >= 10 else child_n for _ in range(__A ): _snake_case = population_score[random.randint(0 , __A )][0] _snake_case , _snake_case = crossover(parent_a[0] , __A ) # Append new string to the population list. pop.append(mutate(__A , __A ) ) pop.append(mutate(__A , __A ) ) return pop def SCREAMING_SNAKE_CASE__ ( __A , __A , __A = True ) -> tuple[int, int, str]: # Verify if N_POPULATION is bigger than N_SELECTED if N_POPULATION < N_SELECTED: _snake_case = F'{N_POPULATION} must be bigger than {N_SELECTED}' raise ValueError(__A ) # Verify that the target contains no genes besides the ones inside genes variable. _snake_case = sorted({c for c in target if c not in genes} ) if not_in_genes_list: _snake_case = F'{not_in_genes_list} is not in genes list, evolution cannot converge' raise ValueError(__A ) # Generate random starting population. _snake_case = [] for _ in range(__A ): population.append(''.join([random.choice(__A ) for i in range(len(__A ) )] ) ) # Just some logs to know what the algorithms is doing. _snake_case , _snake_case = 0, 0 # This loop will end when we find a perfect match for our target. while True: generation += 1 total_population += len(__A ) # Random population created. Now it's time to evaluate. # Adding a bit of concurrency can make everything faster, # # import concurrent.futures # population_score: list[tuple[str, float]] = [] # with concurrent.futures.ThreadPoolExecutor( # max_workers=NUM_WORKERS) as executor: # futures = {executor.submit(evaluate, item) for item in population} # concurrent.futures.wait(futures) # population_score = [item.result() for item in futures] # # but with a simple algorithm like this, it will probably be slower. # We just need to call evaluate for every item inside the population. _snake_case = [evaluate(__A , __A ) for item in population] # Check if there is a matching evolution. _snake_case = sorted(__A , key=lambda __A : x[1] , reverse=__A ) if population_score[0][0] == target: return (generation, total_population, population_score[0][0]) # Print the best result every 10 generation. # Just to know that the algorithm is working. if debug and generation % 10 == 0: print( F'\nGeneration: {generation}' F'\nTotal Population:{total_population}' F'\nBest score: {population_score[0][1]}' F'\nBest string: {population_score[0][0]}' ) # Flush the old population, keeping some of the best evolutions. # Keeping this avoid regression of evolution. _snake_case = population[: int(N_POPULATION / 3 )] population.clear() population.extend(__A ) # Normalize population score to be between 0 and 1. _snake_case = [ (item, score / len(__A )) for item, score in population_score ] # This is selection for i in range(__A ): population.extend(select(population_score[int(__A )] , __A , __A ) ) # Check if the population has already reached the maximum value and if so, # break the cycle. If this check is disabled, the algorithm will take # forever to compute large strings, but will also calculate small strings in # a far fewer generations. if len(__A ) > N_POPULATION: break if __name__ == "__main__": lowercase : str = ( "This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!" ) lowercase : str = list( " ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm" "nopqrstuvwxyz.,;!?+-*#@^'èéòà€ù=)(&%$£/\\" ) lowercase , lowercase , lowercase : Tuple = basic(target_str, genes_list) print( F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}''' )
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from dataclasses import dataclass, field from typing import ClassVar, Dict from ..features import Features, Sequence, Value from .base import TaskTemplate @dataclass(frozen=lowercase_ ) class lowercase ( lowercase_ ): # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization __SCREAMING_SNAKE_CASE : str = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} ) __SCREAMING_SNAKE_CASE : ClassVar[Features] = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} ) __SCREAMING_SNAKE_CASE : ClassVar[Features] = Features( { '''answers''': Sequence( { '''text''': Value('''string''' ), '''answer_start''': Value('''int32''' ), } ) } ) __SCREAMING_SNAKE_CASE : str = "question" __SCREAMING_SNAKE_CASE : str = "context" __SCREAMING_SNAKE_CASE : str = "answers" @property def a ( self ): return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
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from __future__ import annotations import os from typing import Any import requests _UpperCAmelCase : int = """https://api.github.com""" # https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user _UpperCAmelCase : Dict = BASE_URL + """/user""" # https://github.com/settings/tokens _UpperCAmelCase : Optional[Any] = os.environ.get("""USER_TOKEN""", """""") def __lowerCamelCase ( UpperCamelCase__ ): '''simple docstring''' snake_case_ = { 'Authorization': F'''token {auth_token}''', 'Accept': 'application/vnd.github.v3+json', } return requests.get(UpperCamelCase__ , headers=UpperCamelCase__ ).json() if __name__ == "__main__": # pragma: no cover if USER_TOKEN: for key, value in fetch_github_info(USER_TOKEN).items(): print(F'''{key}: {value}''') else: raise ValueError("""'USER_TOKEN' field cannot be empty.""")
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import json import sys import tempfile import unittest from pathlib import Path import transformers from transformers import ( CONFIG_MAPPING, IMAGE_PROCESSOR_MAPPING, AutoConfig, AutoImageProcessor, CLIPConfig, CLIPImageProcessor, ) from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils')) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_image_processing import CustomImageProcessor # noqa E402 class lowercase__ ( unittest.TestCase): def __A ( self : List[Any] ): '''simple docstring''' SCREAMING_SNAKE_CASE : Dict = 0 def __A ( self : Dict ): '''simple docstring''' SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' ) self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ ) def __A ( self : Dict ): '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: SCREAMING_SNAKE_CASE : str = Path(UpperCamelCase__ ) / '''preprocessor_config.json''' SCREAMING_SNAKE_CASE : Dict = Path(UpperCamelCase__ ) / '''config.json''' json.dump( {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , ) json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) ) SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained(UpperCamelCase__ ) self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ ) def __A ( self : int ): '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: SCREAMING_SNAKE_CASE : Tuple = Path(UpperCamelCase__ ) / '''preprocessor_config.json''' SCREAMING_SNAKE_CASE : Dict = Path(UpperCamelCase__ ) / '''config.json''' json.dump( {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , ) json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) ) SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ ) self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ ) def __A ( self : List[str] ): '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: SCREAMING_SNAKE_CASE : Dict = CLIPConfig() # Create a dummy config file with image_proceesor_type SCREAMING_SNAKE_CASE : Dict = Path(UpperCamelCase__ ) / '''preprocessor_config.json''' SCREAMING_SNAKE_CASE : List[str] = Path(UpperCamelCase__ ) / '''config.json''' json.dump( {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , ) json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) ) # remove image_processor_type to make sure config.json alone is enough to load image processor locally SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ ).to_dict() config_dict.pop('''image_processor_type''' ) SCREAMING_SNAKE_CASE : str = CLIPImageProcessor(**UpperCamelCase__ ) # save in new folder model_config.save_pretrained(UpperCamelCase__ ) config.save_pretrained(UpperCamelCase__ ) SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained(UpperCamelCase__ ) # make sure private variable is not incorrectly saved SCREAMING_SNAKE_CASE : List[Any] = json.loads(config.to_json_string() ) self.assertTrue('''_processor_class''' not in dict_as_saved ) self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ ) def __A ( self : Tuple ): '''simple docstring''' with tempfile.TemporaryDirectory() as tmpdirname: SCREAMING_SNAKE_CASE : Optional[int] = Path(UpperCamelCase__ ) / '''preprocessor_config.json''' json.dump( {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , ) SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained(UpperCamelCase__ ) self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ ) def __A ( self : int ): '''simple docstring''' with self.assertRaisesRegex( UpperCamelCase__ , '''clip-base is not a local folder and is not a valid model identifier''' ): SCREAMING_SNAKE_CASE : List[str] = AutoImageProcessor.from_pretrained('''clip-base''' ) def __A ( self : List[str] ): '''simple docstring''' with self.assertRaisesRegex( UpperCamelCase__ , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ): SCREAMING_SNAKE_CASE : Tuple = AutoImageProcessor.from_pretrained(UpperCamelCase__ , revision='''aaaaaa''' ) def __A ( self : Dict ): '''simple docstring''' with self.assertRaisesRegex( UpperCamelCase__ , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ): SCREAMING_SNAKE_CASE : Dict = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' ) def __A ( self : List[Any] ): '''simple docstring''' with self.assertRaises(UpperCamelCase__ ): SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' ) # If remote code is disabled, we can't load this config. with self.assertRaises(UpperCamelCase__ ): SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained( '''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ ) SCREAMING_SNAKE_CASE : List[Any] = AutoImageProcessor.from_pretrained( '''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ ) self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' ) # Test image processor can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: image_processor.save_pretrained(UpperCamelCase__ ) SCREAMING_SNAKE_CASE : Optional[int] = AutoImageProcessor.from_pretrained(UpperCamelCase__ , trust_remote_code=UpperCamelCase__ ) self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' ) def __A ( self : Optional[Any] ): '''simple docstring''' try: AutoConfig.register('''custom''' , UpperCamelCase__ ) AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__ ) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(UpperCamelCase__ ): AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__ ) with tempfile.TemporaryDirectory() as tmpdirname: SCREAMING_SNAKE_CASE : Union[str, Any] = Path(UpperCamelCase__ ) / '''preprocessor_config.json''' SCREAMING_SNAKE_CASE : Any = Path(UpperCamelCase__ ) / '''config.json''' json.dump( {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(UpperCamelCase__ , '''w''' ) , ) json.dump({'''model_type''': '''clip'''} , open(UpperCamelCase__ , '''w''' ) ) SCREAMING_SNAKE_CASE : Tuple = CustomImageProcessor.from_pretrained(UpperCamelCase__ ) # Now that the config is registered, it can be used as any other config with the auto-API with tempfile.TemporaryDirectory() as tmp_dir: image_processor.save_pretrained(UpperCamelCase__ ) SCREAMING_SNAKE_CASE : Any = AutoImageProcessor.from_pretrained(UpperCamelCase__ ) self.assertIsInstance(UpperCamelCase__ , UpperCamelCase__ ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content: del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig] def __A ( self : Any ): '''simple docstring''' class lowercase__ ( UpperCamelCase_): UpperCamelCase_ = True try: AutoConfig.register('''custom''' , UpperCamelCase__ ) AutoImageProcessor.register(UpperCamelCase__ , UpperCamelCase__ ) # If remote code is not set, the default is to use local SCREAMING_SNAKE_CASE : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' ) self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' ) self.assertTrue(image_processor.is_local ) # If remote code is disabled, we load the local one. SCREAMING_SNAKE_CASE : Union[str, Any] = AutoImageProcessor.from_pretrained( '''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ ) self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' ) self.assertTrue(image_processor.is_local ) # If remote is enabled, we load from the Hub SCREAMING_SNAKE_CASE : Optional[Any] = AutoImageProcessor.from_pretrained( '''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=UpperCamelCase__ ) self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' ) self.assertTrue(not hasattr(UpperCamelCase__ , '''is_local''' ) ) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content: del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
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import math def A ( _lowercase ): return math.sqrt(_lowercase ) * math.sqrt(_lowercase ) == num def A ( _lowercase ): SCREAMING_SNAKE_CASE : Dict = 0 SCREAMING_SNAKE_CASE : Tuple = n while left <= right: SCREAMING_SNAKE_CASE : Union[str, Any] = (left + right) // 2 if mid**2 == n: return True elif mid**2 > n: SCREAMING_SNAKE_CASE : Optional[Any] = mid - 1 else: SCREAMING_SNAKE_CASE : Optional[int] = mid + 1 return False if __name__ == "__main__": import doctest doctest.testmod()
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from typing import Union import fire import torch from tqdm import tqdm def UpperCAmelCase ( a_ , a_ = "cpu" , a_ = None ) -> None: """simple docstring""" __A = torch.load(a_ , map_location=a_ ) for k, v in tqdm(state_dict.items() ): if not isinstance(a_ , torch.Tensor ): raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin" ) __A = v.half() if save_path is None: # overwrite src_path __A = src_path torch.save(a_ , a_ ) if __name__ == "__main__": fire.Fire(convert)
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from collections import Counter import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split SCREAMING_SNAKE_CASE :Tuple = datasets.load_iris() SCREAMING_SNAKE_CASE :Dict = np.array(data['data']) SCREAMING_SNAKE_CASE :Optional[int] = np.array(data['target']) SCREAMING_SNAKE_CASE :List[str] = data['target_names'] SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE :List[Any] = train_test_split(X, y) def UpperCAmelCase ( a_ , a_ ) -> Tuple: """simple docstring""" return np.linalg.norm(np.array(a_ ) - np.array(a_ ) ) def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_=5 ) -> Dict: """simple docstring""" __A = zip(a_ , a_ ) # List of distances of all points from the point to be classified __A = [] for data_point in data: __A = euclidean_distance(data_point[0] , a_ ) distances.append((distance, data_point[1]) ) # Choosing 'k' points with the least distances. __A = [i[1] for i in sorted(a_ )[:k]] # Most commonly occurring class among them # is the class into which the point is classified __A = Counter(a_ ).most_common(1 )[0][0] return classes[result] if __name__ == "__main__": print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
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def __UpperCAmelCase ( a_ , a_): return abs(a_) if a == 0 else greatest_common_divisor(b % a , a_) def __UpperCAmelCase ( a_ , a_): while y: # --> when y=0 then loop will terminate and return x as final GCD. snake_case_ , snake_case_ = y, x % y return abs(a_) def __UpperCAmelCase ( ): try: snake_case_ = input('Enter two integers separated by comma (,): ').split(',') snake_case_ = int(nums[0]) snake_case_ = int(nums[1]) print( f'''greatest_common_divisor({num_a}, {num_a}) = ''' f'''{greatest_common_divisor(a_ , a_)}''') print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(a_ , a_)}''') except (IndexError, UnboundLocalError, ValueError): print('Wrong input') if __name__ == "__main__": main()
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import unittest from parameterized import parameterized from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed 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, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( GPTNeoXForCausalLM, GPTNeoXForQuestionAnswering, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification, GPTNeoXModel, ) class UpperCamelCase_ : '''simple docstring''' def __init__( self , a , a=13 , a=7 , a=True , a=True , a=True , a=True , a=99 , a=64 , a=5 , a=4 , a=37 , a="gelu" , a=0.1 , a=0.1 , a=5_12 , a=16 , a=2 , a=0.02 , a=3 , a=4 , a=None , ) -> str: snake_case_ = parent snake_case_ = batch_size snake_case_ = seq_length snake_case_ = is_training snake_case_ = use_input_mask snake_case_ = use_token_type_ids snake_case_ = use_labels snake_case_ = vocab_size snake_case_ = hidden_size snake_case_ = num_hidden_layers snake_case_ = num_attention_heads snake_case_ = intermediate_size snake_case_ = hidden_act snake_case_ = hidden_dropout_prob snake_case_ = attention_probs_dropout_prob snake_case_ = max_position_embeddings snake_case_ = type_vocab_size snake_case_ = type_sequence_label_size snake_case_ = initializer_range snake_case_ = num_labels snake_case_ = num_choices snake_case_ = scope snake_case_ = vocab_size - 1 def _UpperCamelCase ( self ) -> Tuple: snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) snake_case_ = None if self.use_input_mask: snake_case_ = random_attention_mask([self.batch_size, self.seq_length] ) snake_case_ = None if self.use_labels: snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) snake_case_ = self.get_config() return config, input_ids, input_mask, token_labels def _UpperCamelCase ( self ) -> int: return GPTNeoXConfig( vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=a , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , ) def _UpperCamelCase ( self ) -> int: snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.prepare_config_and_inputs() snake_case_ = True return config, input_ids, input_mask, token_labels def _UpperCamelCase ( self , a , a , a ) -> Optional[int]: snake_case_ = GPTNeoXModel(config=a ) model.to(a ) model.eval() snake_case_ = model(a , attention_mask=a ) snake_case_ = model(a ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def _UpperCamelCase ( self , a , a , a ) -> List[str]: snake_case_ = True snake_case_ = GPTNeoXModel(a ) model.to(a ) model.eval() snake_case_ = model(a , attention_mask=a ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def _UpperCamelCase ( self , a , a , a , a ) -> int: snake_case_ = GPTNeoXForCausalLM(config=a ) model.to(a ) model.eval() snake_case_ = model(a , attention_mask=a , labels=a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def _UpperCamelCase ( self , a , a , a , a ) -> Optional[int]: snake_case_ = self.num_labels snake_case_ = GPTNeoXForQuestionAnswering(a ) model.to(a ) model.eval() snake_case_ = model(a , attention_mask=a ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def _UpperCamelCase ( self , a , a , a , a ) -> List[str]: snake_case_ = self.num_labels snake_case_ = GPTNeoXForSequenceClassification(a ) model.to(a ) model.eval() snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) snake_case_ = model(a , attention_mask=a , labels=a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def _UpperCamelCase ( self , a , a , a , a ) -> str: snake_case_ = self.num_labels snake_case_ = GPTNeoXForTokenClassification(a ) model.to(a ) model.eval() snake_case_ = model(a , attention_mask=a , labels=a ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def _UpperCamelCase ( self , a , a , a ) -> List[Any]: snake_case_ = True snake_case_ = GPTNeoXForCausalLM(config=a ) model.to(a ) model.eval() # first forward pass snake_case_ = model(a , attention_mask=a , use_cache=a ) snake_case_ = outputs.past_key_values # create hypothetical multiple next token and extent to next_input_ids snake_case_ = ids_tensor((self.batch_size, 3) , config.vocab_size ) snake_case_ = ids_tensor((self.batch_size, 3) , vocab_size=2 ) # append to next input_ids and snake_case_ = torch.cat([input_ids, next_tokens] , dim=-1 ) snake_case_ = torch.cat([input_mask, next_mask] , dim=-1 ) snake_case_ = model(a , attention_mask=a , output_hidden_states=a ) snake_case_ = output_from_no_past['hidden_states'][0] snake_case_ = model( a , attention_mask=a , past_key_values=a , output_hidden_states=a , )['hidden_states'][0] # select random slice snake_case_ = ids_tensor((1,) , output_from_past.shape[-1] ).item() snake_case_ = output_from_no_past[:, -3:, random_slice_idx].detach() snake_case_ = output_from_past[:, :, random_slice_idx].detach() self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] ) # test that outputs are equal for slice self.parent.assertTrue(torch.allclose(a , a , atol=1E-3 ) ) def _UpperCamelCase ( self ) -> str: snake_case_ = self.prepare_config_and_inputs() snake_case_ , snake_case_ , snake_case_ , snake_case_ = config_and_inputs snake_case_ = {'input_ids': input_ids, 'attention_mask': input_mask} return config, inputs_dict @require_torch class UpperCamelCase_ ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ): '''simple docstring''' lowerCAmelCase = ( ( GPTNeoXModel, GPTNeoXForCausalLM, GPTNeoXForQuestionAnswering, GPTNeoXForSequenceClassification, GPTNeoXForTokenClassification, ) if is_torch_available() else () ) lowerCAmelCase = (GPTNeoXForCausalLM,) if is_torch_available() else () lowerCAmelCase = ( { '''feature-extraction''': GPTNeoXModel, '''question-answering''': GPTNeoXForQuestionAnswering, '''text-classification''': GPTNeoXForSequenceClassification, '''text-generation''': GPTNeoXForCausalLM, '''token-classification''': GPTNeoXForTokenClassification, '''zero-shot''': GPTNeoXForSequenceClassification, } if is_torch_available() else {} ) lowerCAmelCase = False lowerCAmelCase = False lowerCAmelCase = False lowerCAmelCase = False def _UpperCamelCase ( self ) -> str: snake_case_ = GPTNeoXModelTester(self ) snake_case_ = ConfigTester(self , config_class=a , hidden_size=64 , num_attention_heads=8 ) def _UpperCamelCase ( self ) -> Any: self.config_tester.run_common_tests() def _UpperCamelCase ( self ) -> Optional[int]: snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(a , a , a ) def _UpperCamelCase ( self ) -> int: snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder() self.model_tester.create_and_check_model_as_decoder(a , a , a ) def _UpperCamelCase ( self ) -> Dict: # This regression test was failing with PyTorch < 1.3 snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_decoder() snake_case_ = None self.model_tester.create_and_check_model_as_decoder(a , a , a ) def _UpperCamelCase ( self ) -> str: snake_case_ , snake_case_ , snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_decoder_model_past_large_inputs(a , a , a ) def _UpperCamelCase ( self ) -> List[Any]: snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_causal_lm(*a ) def _UpperCamelCase ( self ) -> Any: snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*a ) def _UpperCamelCase ( self ) -> List[str]: snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*a ) def _UpperCamelCase ( self ) -> Any: snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*a ) @unittest.skip(reason='Feed forward chunking is not implemented' ) def _UpperCamelCase ( self ) -> List[str]: pass @parameterized.expand([('linear',), ('dynamic',)] ) def _UpperCamelCase ( self , a ) -> int: snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common() snake_case_ = ids_tensor([1, 10] , config.vocab_size ) snake_case_ = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size ) set_seed(42 ) # Fixed seed at init time so the two models get the same random weights snake_case_ = GPTNeoXModel(a ) original_model.to(a ) original_model.eval() snake_case_ = original_model(a ).last_hidden_state snake_case_ = original_model(a ).last_hidden_state set_seed(42 ) # Fixed seed at init time so the two models get the same random weights snake_case_ = {'type': scaling_type, 'factor': 10.0} snake_case_ = GPTNeoXModel(a ) scaled_model.to(a ) scaled_model.eval() snake_case_ = scaled_model(a ).last_hidden_state snake_case_ = scaled_model(a ).last_hidden_state # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original # maximum sequence length, so the outputs for the short input should match. if scaling_type == "dynamic": self.assertTrue(torch.allclose(a , a , atol=1E-5 ) ) else: self.assertFalse(torch.allclose(a , a , atol=1E-5 ) ) # The output should be different for long inputs self.assertFalse(torch.allclose(a , a , atol=1E-5 ) ) @require_torch class UpperCamelCase_ ( unittest.TestCase ): '''simple docstring''' @slow def _UpperCamelCase ( self ) -> List[str]: snake_case_ = AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped' ) for checkpointing in [True, False]: snake_case_ = GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped' ) if checkpointing: model.gradient_checkpointing_enable() else: model.gradient_checkpointing_disable() model.to(a ) snake_case_ = tokenizer('My favorite food is' , return_tensors='pt' ).to(a ) # The hub repo. is updated on 2023-04-04, resulting in poor outputs. # See: https://github.com/huggingface/transformers/pull/24193 snake_case_ = 'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure' snake_case_ = model.generate(**a , do_sample=a , max_new_tokens=20 ) snake_case_ = tokenizer.batch_decode(a )[0] self.assertEqual(a , a )
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1
'''simple docstring''' import functools def lowercase__ ( __UpperCamelCase , __UpperCamelCase )-> int: # Validation if not isinstance(__UpperCamelCase , __UpperCamelCase ) or not all(isinstance(__UpperCamelCase , __UpperCamelCase ) for day in days ): raise ValueError("""The parameter days should be a list of integers""" ) if len(__UpperCamelCase ) != 3 or not all(isinstance(__UpperCamelCase , __UpperCamelCase ) for cost in costs ): raise ValueError("""The parameter costs should be a list of three integers""" ) if len(__UpperCamelCase ) == 0: return 0 if min(__UpperCamelCase ) <= 0: raise ValueError("""All days elements should be greater than 0""" ) if max(__UpperCamelCase ) >= 366: raise ValueError("""All days elements should be less than 366""" ) UpperCamelCase = set(__UpperCamelCase ) @functools.cache def dynamic_programming(__UpperCamelCase ) -> int: if index > 365: return 0 if index not in days_set: return dynamic_programming(index + 1 ) return min( costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , ) return dynamic_programming(1 ) if __name__ == "__main__": import doctest doctest.testmod()
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'''simple docstring''' from timeit import timeit def lowercase__ ( __UpperCamelCase )-> int: if number < 0: raise ValueError("""the value of input must not be negative""" ) UpperCamelCase = 0 while number: number &= number - 1 result += 1 return result def lowercase__ ( __UpperCamelCase )-> int: if number < 0: raise ValueError("""the value of input must not be negative""" ) UpperCamelCase = 0 while number: if number % 2 == 1: result += 1 number >>= 1 return result def lowercase__ ( )-> None: def do_benchmark(__UpperCamelCase ) -> None: UpperCamelCase = """import __main__ as z""" print(F"Benchmark when {number = }:" ) print(F"{get_set_bits_count_using_modulo_operator(__UpperCamelCase ) = }" ) UpperCamelCase = timeit("""z.get_set_bits_count_using_modulo_operator(25)""" , setup=__UpperCamelCase ) print(F"timeit() runs in {timing} seconds" ) print(F"{get_set_bits_count_using_brian_kernighans_algorithm(__UpperCamelCase ) = }" ) UpperCamelCase = timeit( """z.get_set_bits_count_using_brian_kernighans_algorithm(25)""" , setup=__UpperCamelCase , ) print(F"timeit() runs in {timing} seconds" ) for number in (25, 37, 58, 0): do_benchmark(__UpperCamelCase ) print() if __name__ == "__main__": import doctest doctest.testmod() benchmark()
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def __lowercase ( a__ = 50 ) -> int: __SCREAMING_SNAKE_CASE = [[0] * 3 for _ in range(length + 1 )] for row_length in range(length + 1 ): for tile_length in range(2 , 5 ): for tile_start in range(row_length - tile_length + 1 ): different_colour_ways_number[row_length][tile_length - 2] += ( different_colour_ways_number[row_length - tile_start - tile_length][ tile_length - 2 ] + 1 ) return sum(different_colour_ways_number[length] ) if __name__ == "__main__": print(F'''{solution() = }''')
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from ..utils import is_flax_available, is_torch_available if is_torch_available(): from .autoencoder_kl import AutoencoderKL from .controlnet import ControlNetModel from .dual_transformer_ad import DualTransformeraDModel from .modeling_utils import ModelMixin from .prior_transformer import PriorTransformer from .ta_film_transformer import TaFilmDecoder from .transformer_ad import TransformeraDModel from .unet_ad import UNetaDModel from .unet_ad import UNetaDModel from .unet_ad_condition import UNetaDConditionModel from .unet_ad_condition import UNetaDConditionModel from .vq_model import VQModel if is_flax_available(): from .controlnet_flax import FlaxControlNetModel from .unet_ad_condition_flax import FlaxUNetaDConditionModel from .vae_flax import FlaxAutoencoderKL
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import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging a__ : Optional[int] = logging.get_logger(__name__) a__ : Optional[int] = '▁' a__ : Tuple = {'vocab_file': 'sentencepiece.bpe.model'} a__ : Dict = { 'vocab_file': { 'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model', } } a__ : List[str] = { 'facebook/xglm-564M': 2_0_4_8, } class UpperCAmelCase__ ( __SCREAMING_SNAKE_CASE): __SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES __SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP __SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES __SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"] def __init__( self , lowercase , lowercase="<s>" , lowercase="</s>" , lowercase="</s>" , lowercase="<s>" , lowercase="<unk>" , lowercase="<pad>" , lowercase = None , **lowercase , ) -> None: __UpperCamelCase = {} if sp_model_kwargs is None else sp_model_kwargs # Compatibility with the original tokenizer __UpperCamelCase = 7 __UpperCamelCase = [f"<madeupword{i}>" for i in range(self.num_madeup_words )] __UpperCamelCase = kwargs.get("""additional_special_tokens""" , [] ) kwargs["additional_special_tokens"] += [ word for word in madeup_words if word not in kwargs["additional_special_tokens"] ] super().__init__( bos_token=__UpperCAmelCase , eos_token=__UpperCAmelCase , unk_token=__UpperCAmelCase , sep_token=__UpperCAmelCase , cls_token=__UpperCAmelCase , pad_token=__UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__UpperCAmelCase , ) __UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.Load(str(__UpperCAmelCase ) ) __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' # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab __UpperCamelCase = 1 # Mimic fairseq token-to-id alignment for the first 4 token __UpperCamelCase = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3} __UpperCamelCase = len(self.sp_model ) __UpperCamelCase = {f"<madeupword{i}>": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )} self.fairseq_tokens_to_ids.update(__UpperCAmelCase ) __UpperCamelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()} def __getstate__( self ) -> List[Any]: __UpperCamelCase = self.__dict__.copy() __UpperCamelCase = None __UpperCamelCase = self.sp_model.serialized_model_proto() return state def __setstate__( self , lowercase ) -> Any: __UpperCamelCase = d # for backward compatibility if not hasattr(self , """sp_model_kwargs""" ): __UpperCamelCase = {} __UpperCamelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs ) self.sp_model.LoadFromSerializedProto(self.sp_model_proto ) def __lowerCamelCase ( self , lowercase , lowercase = None ) -> List[int]: if token_ids_a is None: return [self.sep_token_id] + token_ids_a __UpperCamelCase = [self.sep_token_id] return sep + token_ids_a + sep + sep + token_ids_a def __lowerCamelCase ( self , lowercase , lowercase = None , lowercase = False ) -> List[int]: if already_has_special_tokens: return super().get_special_tokens_mask( token_ids_a=__UpperCAmelCase , token_ids_a=__UpperCAmelCase , already_has_special_tokens=__UpperCAmelCase ) if token_ids_a is None: return [1] + ([0] * len(__UpperCAmelCase )) return [1] + ([0] * len(__UpperCAmelCase )) + [1, 1] + ([0] * len(__UpperCAmelCase )) def __lowerCamelCase ( self , lowercase , lowercase = None ) -> List[int]: __UpperCamelCase = [self.sep_token_id] if token_ids_a is None: return len(sep + token_ids_a ) * [0] return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0] @property def __lowerCamelCase ( self ) -> Any: return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words def __lowerCamelCase ( self ) -> Dict: __UpperCamelCase = {self.convert_ids_to_tokens(__UpperCAmelCase ): i for i in range(self.vocab_size )} vocab.update(self.added_tokens_encoder ) return vocab def __lowerCamelCase ( self , lowercase ) -> List[str]: return self.sp_model.encode(__UpperCAmelCase , out_type=__UpperCAmelCase ) def __lowerCamelCase ( self , lowercase ) -> List[Any]: if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] __UpperCamelCase = self.sp_model.PieceToId(__UpperCAmelCase ) # 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 , lowercase ) -> int: 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 , lowercase ) -> Optional[int]: __UpperCamelCase = """""".join(__UpperCAmelCase ).replace(__UpperCAmelCase , """ """ ).strip() return out_string def __lowerCamelCase ( self , lowercase , lowercase = None ) -> Tuple[str]: if not os.path.isdir(__UpperCAmelCase ): logger.error(f"Vocabulary path ({save_directory}) should be a directory" ) return __UpperCamelCase = os.path.join( __UpperCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCAmelCase ) and os.path.isfile(self.vocab_file ): copyfile(self.vocab_file , __UpperCAmelCase ) elif not os.path.isfile(self.vocab_file ): with open(__UpperCAmelCase , """wb""" ) as fi: __UpperCamelCase = self.sp_model.serialized_model_proto() fi.write(__UpperCAmelCase ) return (out_vocab_file,)
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'''simple docstring''' import csv import tweepy # Twitter API credentials a__ : Dict = '' a__ : List[str] = '' a__ : Optional[Any] = '' a__ : Any = '' def _lowercase ( __A ): '''simple docstring''' __UpperCamelCase = tweepy.OAuthHandler(__A ,__A ) auth.set_access_token(__A ,__A ) __UpperCamelCase = tweepy.API(__A ) # initialize a list to hold all the tweepy Tweets __UpperCamelCase = [] # make initial request for most recent tweets (200 is the maximum allowed count) __UpperCamelCase = api.user_timeline(screen_name=__A ,count=200 ) # save most recent tweets alltweets.extend(__A ) # save the id of the oldest tweet less one __UpperCamelCase = alltweets[-1].id - 1 # keep grabbing tweets until there are no tweets left to grab while len(__A ) > 0: print(f"getting tweets before {oldest}" ) # all subsequent requests use the max_id param to prevent duplicates __UpperCamelCase = api.user_timeline( screen_name=__A ,count=200 ,max_id=__A ) # save most recent tweets alltweets.extend(__A ) # update the id of the oldest tweet less one __UpperCamelCase = alltweets[-1].id - 1 print(f"...{len(__A )} tweets downloaded so far" ) # transform the tweepy tweets into a 2D array that will populate the csv __UpperCamelCase = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets] # write the csv with open(f"new_{screen_name}_tweets.csv" ,"""w""" ) as f: __UpperCamelCase = csv.writer(__A ) writer.writerow(["""id""", """created_at""", """text"""] ) writer.writerows(__A ) if __name__ == "__main__": # pass in the username of the account you want to download get_all_tweets('FirePing32')
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0
import pytest from datasets import inspect_metric, list_metrics, load_metric @pytest.fixture def UpperCamelCase ( __magic_name__ : int ) -> Dict: """simple docstring""" monkeypatch.setattr("""datasets.utils.deprecation_utils._emitted_deprecation_warnings""" , set() ) @pytest.fixture def UpperCamelCase ( __magic_name__ : Union[str, Any] ) -> str: """simple docstring""" class A : '''simple docstring''' def __init__(self : int , _UpperCAmelCase : Dict ) -> Tuple: """simple docstring""" lowercase__ = metric_id class A : '''simple docstring''' A__ = [MetricMock(__A ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]] def lowerCamelCase__ (self : Tuple ) -> Tuple: """simple docstring""" return self._metrics monkeypatch.setattr("""datasets.inspect.huggingface_hub""" , HfhMock() ) @pytest.mark.parametrize( """func, args""" , [(load_metric, ("""metrics/mse""",)), (list_metrics, ()), (inspect_metric, ("""metrics/mse""", """tmp_path"""))] ) def UpperCamelCase ( __magic_name__ : List[Any] , __magic_name__ : int , __magic_name__ : str , __magic_name__ : Tuple , __magic_name__ : Dict ) -> Optional[int]: """simple docstring""" if "tmp_path" in args: lowercase__ = tuple(arg if arg != """tmp_path""" else tmp_path for arg in args ) with pytest.warns(_lowerCamelCase , match="""https://huggingface.co/docs/evaluate""" ): func(*_lowerCamelCase )
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available UpperCamelCase = { '''configuration_groupvit''': [ '''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GroupViTConfig''', '''GroupViTOnnxConfig''', '''GroupViTTextConfig''', '''GroupViTVisionConfig''', ], } try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''GroupViTModel''', '''GroupViTPreTrainedModel''', '''GroupViTTextModel''', '''GroupViTVisionModel''', ] try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: UpperCamelCase = [ '''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''', '''TFGroupViTModel''', '''TFGroupViTPreTrainedModel''', '''TFGroupViTTextModel''', '''TFGroupViTVisionModel''', ] if TYPE_CHECKING: from .configuration_groupvit import ( GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GroupViTConfig, GroupViTOnnxConfig, GroupViTTextConfig, GroupViTVisionConfig, ) try: if not is_torch_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_groupvit import ( GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, GroupViTModel, GroupViTPreTrainedModel, GroupViTTextModel, GroupViTVisionModel, ) try: if not is_tf_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: from .modeling_tf_groupvit import ( TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST, TFGroupViTModel, TFGroupViTPreTrainedModel, TFGroupViTTextModel, TFGroupViTVisionModel, ) else: import sys UpperCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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'''simple docstring''' from PIL import Image def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Image: '''simple docstring''' def brightness(__UpperCAmelCase ) -> float: return 128 + level + (c - 128) if not -2_5_5.0 <= level <= 2_5_5.0: raise ValueError('''level must be between -255.0 (black) and 255.0 (white)''' ) return img.point(__UpperCAmelCase ) if __name__ == "__main__": # Load image with Image.open('image_data/lena.jpg') as img: # Change brightness to 100 a : Optional[int] = change_brightness(img, 100) brigt_img.save('image_data/lena_brightness.png', format='png')
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'''simple docstring''' import inspect import unittest from transformers import ViTHybridConfig from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image class a : def __init__( self : str , lowercase_ : Optional[Any] , lowercase_ : Optional[Any]=13 , lowercase_ : int=64 , lowercase_ : Tuple=2 , lowercase_ : List[str]=3 , lowercase_ : str=True , lowercase_ : Dict=True , lowercase_ : int=32 , lowercase_ : int=5 , lowercase_ : Optional[Any]=4 , lowercase_ : Optional[Any]=37 , lowercase_ : List[Any]="gelu" , lowercase_ : Tuple=0.1 , lowercase_ : str=0.1 , lowercase_ : Any=10 , lowercase_ : List[str]=0.02 , lowercase_ : Tuple=[1, 16, 4, 4] , lowercase_ : Tuple=None , ): snake_case_ = parent snake_case_ = batch_size snake_case_ = image_size snake_case_ = patch_size snake_case_ = num_channels snake_case_ = is_training snake_case_ = use_labels snake_case_ = hidden_size snake_case_ = num_hidden_layers snake_case_ = num_attention_heads snake_case_ = intermediate_size snake_case_ = hidden_act snake_case_ = hidden_dropout_prob snake_case_ = attention_probs_dropout_prob snake_case_ = type_sequence_label_size snake_case_ = initializer_range snake_case_ = scope snake_case_ = backbone_featmap_shape # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) # the number of patches is based on the feature map of the backbone, which by default uses an output stride # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size snake_case_ = (self.image_size // 32) ** 2 snake_case_ = num_patches + 1 def A_ ( self : List[Any] ): snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) snake_case_ = None if self.use_labels: snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size ) snake_case_ = self.get_config() return config, pixel_values, labels def A_ ( self : Any ): snake_case_ = { '''global_padding''': '''same''', '''layer_type''': '''bottleneck''', '''depths''': [3, 4, 9], '''out_features''': ['''stage1''', '''stage2''', '''stage3'''], '''embedding_dynamic_padding''': True, '''hidden_sizes''': [4, 8, 16, 32], '''num_groups''': 2, } return ViTHybridConfig( image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase_ , initializer_range=self.initializer_range , backbone_featmap_shape=self.backbone_featmap_shape , backbone_config=lowercase_ , ) def A_ ( self : Optional[int] , lowercase_ : List[Any] , lowercase_ : int , lowercase_ : int ): snake_case_ = ViTHybridModel(config=lowercase_ ) model.to(lowercase_ ) model.eval() snake_case_ = model(lowercase_ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def A_ ( self : List[Any] , lowercase_ : Any , lowercase_ : str , lowercase_ : Optional[int] ): snake_case_ = self.type_sequence_label_size snake_case_ = ViTHybridForImageClassification(lowercase_ ) model.to(lowercase_ ) model.eval() snake_case_ = model(lowercase_ , labels=lowercase_ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) ) def A_ ( self : List[Any] ): snake_case_ = self.prepare_config_and_inputs() snake_case_ ,snake_case_ ,snake_case_ = config_and_inputs snake_case_ = {'''pixel_values''': pixel_values} return config, inputs_dict @require_torch class a ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ): snake_case_ = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else () snake_case_ = ( {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification} if is_torch_available() else {} ) snake_case_ = False snake_case_ = False snake_case_ = False def A_ ( self : Optional[Any] ): snake_case_ = ViTHybridModelTester(self ) snake_case_ = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ , hidden_size=37 ) def A_ ( self : Optional[int] ): self.config_tester.run_common_tests() @unittest.skip(reason='''ViT does not use inputs_embeds''' ) def A_ ( self : Any ): pass def A_ ( self : Dict ): snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: snake_case_ = model_class(lowercase_ ) self.assertIsInstance(model.get_input_embeddings() , (nn.Module) ) snake_case_ = model.get_output_embeddings() self.assertTrue(x is None or isinstance(lowercase_ , nn.Linear ) ) def A_ ( self : Dict ): snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: snake_case_ = model_class(lowercase_ ) snake_case_ = inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic snake_case_ = [*signature.parameters.keys()] snake_case_ = ['''pixel_values'''] self.assertListEqual(arg_names[:1] , lowercase_ ) def A_ ( self : Tuple ): snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*lowercase_ ) def A_ ( self : List[Any] ): snake_case_ = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*lowercase_ ) def A_ ( self : Optional[Any] ): snake_case_ ,snake_case_ = self.model_tester.prepare_config_and_inputs_for_common() snake_case_ = _config_zero_init(lowercase_ ) for model_class in self.all_model_classes: snake_case_ = model_class(config=lowercase_ ) # Skip the check for the backbone for name, module in model.named_modules(): if module.__class__.__name__ == "ViTHybridPatchEmbeddings": snake_case_ = [F"{name}.{key}" for key in module.state_dict().keys()] break for name, param in model.named_parameters(): if param.requires_grad: if name in backbone_params: continue self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , ) @slow def A_ ( self : Tuple ): for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: snake_case_ = ViTHybridModel.from_pretrained(lowercase_ ) self.assertIsNotNone(lowercase_ ) def __magic_name__ ( ) -> List[Any]: '''simple docstring''' snake_case_ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ) return image @require_torch @require_vision class a ( unittest.TestCase ): @cached_property def A_ ( self : Any ): return ( ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None ) @slow def A_ ( self : List[str] ): snake_case_ = ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to( lowercase_ ) snake_case_ = self.default_image_processor snake_case_ = prepare_img() snake_case_ = image_processor(images=lowercase_ , return_tensors='''pt''' ).to(lowercase_ ) # forward pass with torch.no_grad(): snake_case_ = model(**lowercase_ ) # verify the logits snake_case_ = torch.Size((1, 1000) ) self.assertEqual(outputs.logits.shape , lowercase_ ) snake_case_ = torch.tensor([-1.9090, -0.4993, -0.2389] ).to(lowercase_ ) self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase_ , atol=1e-4 ) ) @slow @require_accelerate def A_ ( self : Dict ): snake_case_ = ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384''' ) snake_case_ = ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384''' , device_map='''auto''' ) snake_case_ = prepare_img() snake_case_ = image_processor(images=lowercase_ , return_tensors='''pt''' ) snake_case_ = model(**lowercase_ ) snake_case_ = outputs.logits # model predicts one of the 1000 ImageNet classes snake_case_ = logits.argmax(-1 ).item() self.assertTrue(model.config.idalabel[predicted_class_idx] , '''tabby, tabby cat''' )
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"""simple docstring""" import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_albert import AlbertTokenizer else: __UpperCamelCase : str = None __UpperCamelCase : Union[str, Any] = logging.get_logger(__name__) __UpperCamelCase : List[str] = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''} __UpperCamelCase : Optional[Any] = { '''vocab_file''': { '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''', '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''', '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''', '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''', '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''', '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''', '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''', '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''', }, '''tokenizer_file''': { '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''', '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''', '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''', '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''', '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''', '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''', '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''', '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''', }, } __UpperCamelCase : Dict = { '''albert-base-v1''': 5_1_2, '''albert-large-v1''': 5_1_2, '''albert-xlarge-v1''': 5_1_2, '''albert-xxlarge-v1''': 5_1_2, '''albert-base-v2''': 5_1_2, '''albert-large-v2''': 5_1_2, '''albert-xlarge-v2''': 5_1_2, '''albert-xxlarge-v2''': 5_1_2, } __UpperCamelCase : Optional[Any] = '''▁''' class SCREAMING_SNAKE_CASE ( A_ ): """simple docstring""" lowercase__ = VOCAB_FILES_NAMES lowercase__ = PRETRAINED_VOCAB_FILES_MAP lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES lowercase__ = AlbertTokenizer def __init__( self : Any ,lowercase_ : Any=None ,lowercase_ : Dict=None ,lowercase_ : Dict=True ,lowercase_ : int=True ,lowercase_ : Union[str, Any]=False ,lowercase_ : Optional[Any]="[CLS]" ,lowercase_ : Dict="[SEP]" ,lowercase_ : Dict="<unk>" ,lowercase_ : int="[SEP]" ,lowercase_ : str="<pad>" ,lowercase_ : int="[CLS]" ,lowercase_ : str="[MASK]" ,**lowercase_ : Union[str, Any] ,): # Mask token behave like a normal word, i.e. include the space before it and # is included in the raw text, there should be a match in a non-normalized sentence. lowerCAmelCase__ : Union[str, Any] = ( AddedToken(lowercase_ ,lstrip=lowercase_ ,rstrip=lowercase_ ,normalized=lowercase_ ) if isinstance(lowercase_ ,lowercase_ ) else mask_token ) super().__init__( lowercase_ ,tokenizer_file=lowercase_ ,do_lower_case=lowercase_ ,remove_space=lowercase_ ,keep_accents=lowercase_ ,bos_token=lowercase_ ,eos_token=lowercase_ ,unk_token=lowercase_ ,sep_token=lowercase_ ,pad_token=lowercase_ ,cls_token=lowercase_ ,mask_token=lowercase_ ,**lowercase_ ,) lowerCAmelCase__ : Tuple = do_lower_case lowerCAmelCase__ : Tuple = remove_space lowerCAmelCase__ : Optional[Any] = keep_accents lowerCAmelCase__ : Any = vocab_file lowerCAmelCase__ : Dict = False if not self.vocab_file else True def __lowerCAmelCase ( self : List[Any] ,lowercase_ : Tuple ,lowercase_ : str = None ): lowerCAmelCase__ : Tuple = [self.sep_token_id] lowerCAmelCase__ : List[str] = [self.cls_token_id] if token_ids_a is None: return cls + token_ids_a + sep return cls + token_ids_a + sep + token_ids_a + sep def __lowerCAmelCase ( self : Union[str, Any] ,lowercase_ : Tuple ,lowercase_ : List[str] = None ): lowerCAmelCase__ : Dict = [self.sep_token_id] lowerCAmelCase__ : List[str] = [self.cls_token_id] if token_ids_a is None: return len(cls + token_ids_a + sep ) * [0] return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] def __lowerCAmelCase ( self : List[str] ,lowercase_ : List[str] ,lowercase_ : List[Any] = None ): if not self.can_save_slow_tokenizer: raise ValueError( '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow ''' '''tokenizer.''' ) if not os.path.isdir(lowercase_ ): logger.error(F'Vocabulary path ({save_directory}) should be a directory' ) return lowerCAmelCase__ : str = os.path.join( lowercase_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] ) if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ): copyfile(self.vocab_file ,lowercase_ ) return (out_vocab_file,)
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import os import numpy import onnx def _lowerCamelCase( lowercase__ , lowercase__ ) -> Union[str, Any]: '''simple docstring''' __lowercase= a.name __lowercase= b.name __lowercase= '' __lowercase= '' __lowercase= a == b __lowercase= name_a __lowercase= name_b return res def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> Union[str, Any]: '''simple docstring''' for i, input_name in enumerate(node_proto.input ): if input_name == name: node_proto.input.insert(lowercase__ , lowercase__ ) node_proto.input.pop(i + 1 ) if node_proto.op_type == "If": _graph_replace_input_with(node_proto.attribute[0].g , lowercase__ , lowercase__ ) _graph_replace_input_with(node_proto.attribute[1].g , lowercase__ , lowercase__ ) if node_proto.op_type == "Loop": _graph_replace_input_with(node_proto.attribute[0].g , lowercase__ , lowercase__ ) def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> str: '''simple docstring''' for n in graph_proto.node: _node_replace_input_with(lowercase__ , lowercase__ , lowercase__ ) def _lowerCamelCase( lowercase__ , lowercase__ , lowercase__ ) -> Any: '''simple docstring''' __lowercase= list(model.graph.initializer ) __lowercase= 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= inits[i].name __lowercase= 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 , lowercase__ , lowercase__ ) def _lowerCamelCase( lowercase__ ) -> Dict: '''simple docstring''' __lowercase= os.path.dirname(lowercase__ ) __lowercase= os.path.basename(lowercase__ ) __lowercase= onnx.load(os.path.join(lowercase__ , lowercase__ ) ) __lowercase= list(model.graph.initializer ) __lowercase= set() __lowercase= {} __lowercase= [] __lowercase= 0 for i in range(len(lowercase__ ) ): if i in dup_set: continue for j in range(i + 1 , len(lowercase__ ) ): if j in dup_set: continue if _is_equal_tensor_proto(inits[i] , inits[j] ): dup_set.add(lowercase__ ) dup_set.add(lowercase__ ) __lowercase= inits[j].data_type __lowercase= numpy.prod(inits[j].dims ) if dtype == 1: mem_size *= 4 elif dtype == 6: mem_size *= 4 elif dtype == 7 or dtype == 1_1: mem_size *= 8 else: print('unexpected data type: ' , lowercase__ ) total_reduced_size += mem_size __lowercase= inits[i].name __lowercase= inits[j].name if name_i in dup_map: dup_map[name_i].append(lowercase__ ) else: __lowercase= [name_j] ind_to_replace.append((j, i) ) print('total reduced size: ' , total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 , 'GB' ) __lowercase= sorted(lowercase__ ) _remove_dup_initializers_from_model(lowercase__ , lowercase__ , lowercase__ ) __lowercase= 'optimized_' + model_file_name __lowercase= os.path.join(lowercase__ , lowercase__ ) onnx.save(lowercase__ , lowercase__ ) return new_model
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'''simple docstring''' from unittest import TestCase from datasets import Dataset from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters def lowerCAmelCase_ ( ) -> str: '''simple docstring''' UpperCAmelCase_ = { "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_ = Dataset.from_dict(snake_case_ ) return dataset class __A ( UpperCamelCase__ ): def _lowercase (self : str ): UpperCAmelCase_ = get_dataset() UpperCAmelCase_ = make_duplicate_clusters(__a , 0.85 ) self.assertEqual(len(duplicate_clusters[0] ) , 2 ) def _lowercase (self : Optional[Any] ): UpperCAmelCase_ = get_dataset() UpperCAmelCase_ , UpperCAmelCase_ = deduplicate_dataset(__a ) self.assertEqual(len(__a ) , 2 ) print(__a ) self.assertEqual(duplicate_clusters[0][0]["copies"] , 2 ) self.assertEqual(duplicate_clusters[0][0]["is_extreme"] , __a )
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'''simple docstring''' import argparse import pathlib import fairseq import torch from fairseq.models.roberta import RobertaModel as FairseqRobertaModel from fairseq.modules import TransformerSentenceEncoderLayer from packaging import version from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification from transformers.models.bert.modeling_bert import ( BertIntermediate, BertLayer, BertOutput, BertSelfAttention, BertSelfOutput, ) from transformers.models.roberta.modeling_roberta import RobertaAttention from transformers.utils import logging if version.parse(fairseq.__version__) < version.parse('1.0.0a'): raise Exception('requires fairseq >= 1.0.0a') logging.set_verbosity_info() SCREAMING_SNAKE_CASE_: List[Any] =logging.get_logger(__name__) SCREAMING_SNAKE_CASE_: Tuple ='Hello world! cécé herlolip' def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : str , snake_case_ : bool ) -> Any: '''simple docstring''' UpperCAmelCase_ = FairseqRobertaModel.from_pretrained(snake_case_ ) roberta.eval() # disable dropout UpperCAmelCase_ = roberta.model.encoder.sentence_encoder UpperCAmelCase_ = XLMRobertaConfig( vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1E-5 , ) if classification_head: UpperCAmelCase_ = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0] print("Our RoBERTa config:" , snake_case_ ) UpperCAmelCase_ = XLMRobertaXLForSequenceClassification(snake_case_ ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_ ) model.eval() # Now let's copy all the weights. # Embeddings UpperCAmelCase_ = roberta_sent_encoder.embed_tokens.weight UpperCAmelCase_ = roberta_sent_encoder.embed_positions.weight UpperCAmelCase_ = torch.zeros_like( model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them. UpperCAmelCase_ = roberta_sent_encoder.layer_norm.weight UpperCAmelCase_ = roberta_sent_encoder.layer_norm.bias for i in range(config.num_hidden_layers ): # Encoder: start of layer UpperCAmelCase_ = model.roberta.encoder.layer[i] UpperCAmelCase_ = roberta_sent_encoder.layers[i] UpperCAmelCase_ = layer.attention UpperCAmelCase_ = roberta_layer.self_attn_layer_norm.weight UpperCAmelCase_ = roberta_layer.self_attn_layer_norm.bias # self attention UpperCAmelCase_ = layer.attention.self assert ( roberta_layer.self_attn.k_proj.weight.data.shape == roberta_layer.self_attn.q_proj.weight.data.shape == roberta_layer.self_attn.v_proj.weight.data.shape == torch.Size((config.hidden_size, config.hidden_size) ) ) UpperCAmelCase_ = roberta_layer.self_attn.q_proj.weight UpperCAmelCase_ = roberta_layer.self_attn.q_proj.bias UpperCAmelCase_ = roberta_layer.self_attn.k_proj.weight UpperCAmelCase_ = roberta_layer.self_attn.k_proj.bias UpperCAmelCase_ = roberta_layer.self_attn.v_proj.weight UpperCAmelCase_ = roberta_layer.self_attn.v_proj.bias # self-attention output UpperCAmelCase_ = layer.attention.output assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape UpperCAmelCase_ = roberta_layer.self_attn.out_proj.weight UpperCAmelCase_ = roberta_layer.self_attn.out_proj.bias # this one is final layer norm UpperCAmelCase_ = roberta_layer.final_layer_norm.weight UpperCAmelCase_ = roberta_layer.final_layer_norm.bias # intermediate UpperCAmelCase_ = layer.intermediate assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape UpperCAmelCase_ = roberta_layer.fca.weight UpperCAmelCase_ = roberta_layer.fca.bias # output UpperCAmelCase_ = layer.output assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape UpperCAmelCase_ = roberta_layer.fca.weight UpperCAmelCase_ = roberta_layer.fca.bias # end of layer if classification_head: UpperCAmelCase_ = roberta.model.classification_heads["mnli"].dense.weight UpperCAmelCase_ = roberta.model.classification_heads["mnli"].dense.bias UpperCAmelCase_ = roberta.model.classification_heads["mnli"].out_proj.weight UpperCAmelCase_ = roberta.model.classification_heads["mnli"].out_proj.bias else: # LM Head UpperCAmelCase_ = roberta.model.encoder.lm_head.dense.weight UpperCAmelCase_ = roberta.model.encoder.lm_head.dense.bias UpperCAmelCase_ = roberta.model.encoder.lm_head.layer_norm.weight UpperCAmelCase_ = roberta.model.encoder.lm_head.layer_norm.bias UpperCAmelCase_ = roberta.model.encoder.lm_head.weight UpperCAmelCase_ = roberta.model.encoder.lm_head.bias # Let's check that we get the same results. UpperCAmelCase_ = roberta.encode(snake_case_ ).unsqueeze(0 ) # batch of size 1 UpperCAmelCase_ = model(snake_case_ )[0] if classification_head: UpperCAmelCase_ = roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_ ) ) else: UpperCAmelCase_ = roberta.model(snake_case_ )[0] print(our_output.shape , their_output.shape ) UpperCAmelCase_ = torch.max(torch.abs(our_output - their_output ) ).item() print(f"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7 UpperCAmelCase_ = torch.allclose(snake_case_ , snake_case_ , atol=1E-3 ) print("Do both models output the same tensors?" , "🔥" if success else "💩" ) if not success: raise Exception("Something went wRoNg" ) pathlib.Path(snake_case_ ).mkdir(parents=snake_case_ , exist_ok=snake_case_ ) print(f"""Saving model to {pytorch_dump_folder_path}""" ) model.save_pretrained(snake_case_ ) if __name__ == "__main__": SCREAMING_SNAKE_CASE_: Optional[int] =argparse.ArgumentParser() # Required parameters parser.add_argument( '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.' ) parser.add_argument( '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.' ) parser.add_argument( '--classification_head', action='store_true', help='Whether to convert a final classification head.' ) SCREAMING_SNAKE_CASE_: Union[str, Any] =parser.parse_args() convert_xlm_roberta_xl_checkpoint_to_pytorch( args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head )
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import fire from utils import calculate_rouge, save_json def _a ( a :Optional[int] , a :Tuple , a :Tuple=None , **a :Union[str, Any] ) -> Any: a = [x.strip() for x in open(a ).readlines()] a = [x.strip() for x in open(a ).readlines()][: len(a )] a = calculate_rouge(a , a , **a ) if save_path is not None: save_json(a , a , indent=a ) return metrics # these print nicely if __name__ == "__main__": fire.Fire(calculate_rouge_path)
0
import inspect import unittest from datasets import load_dataset from packaging import version from transformers import BeitConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( MODEL_MAPPING, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation, BeitModel, ) from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): import PIL from PIL import Image from transformers import BeitImageProcessor class a_ : """simple docstring""" def __init__( self : Optional[int] ,snake_case : Any ,snake_case : Dict=100 ,snake_case : List[Any]=13 ,snake_case : str=30 ,snake_case : List[str]=2 ,snake_case : List[Any]=3 ,snake_case : Tuple=True ,snake_case : Optional[Any]=True ,snake_case : int=32 ,snake_case : Tuple=4 ,snake_case : List[Any]=4 ,snake_case : Optional[Any]=37 ,snake_case : Optional[Any]="gelu" ,snake_case : Tuple=0.1 ,snake_case : Union[str, Any]=0.1 ,snake_case : List[Any]=10 ,snake_case : Tuple=0.02 ,snake_case : List[str]=3 ,snake_case : Any=None ,snake_case : int=[0, 1, 2, 3] ,): SCREAMING_SNAKE_CASE =parent SCREAMING_SNAKE_CASE =100 SCREAMING_SNAKE_CASE =batch_size SCREAMING_SNAKE_CASE =image_size SCREAMING_SNAKE_CASE =patch_size SCREAMING_SNAKE_CASE =num_channels SCREAMING_SNAKE_CASE =is_training SCREAMING_SNAKE_CASE =use_labels SCREAMING_SNAKE_CASE =hidden_size SCREAMING_SNAKE_CASE =num_hidden_layers SCREAMING_SNAKE_CASE =num_attention_heads SCREAMING_SNAKE_CASE =intermediate_size SCREAMING_SNAKE_CASE =hidden_act SCREAMING_SNAKE_CASE =hidden_dropout_prob SCREAMING_SNAKE_CASE =attention_probs_dropout_prob SCREAMING_SNAKE_CASE =type_sequence_label_size SCREAMING_SNAKE_CASE =initializer_range SCREAMING_SNAKE_CASE =scope SCREAMING_SNAKE_CASE =out_indices SCREAMING_SNAKE_CASE =num_labels # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) SCREAMING_SNAKE_CASE =(image_size // patch_size) ** 2 SCREAMING_SNAKE_CASE =num_patches + 1 def _lowerCAmelCase ( self : List[Any] ): SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE =None SCREAMING_SNAKE_CASE =None if self.use_labels: SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size] ,self.type_sequence_label_size ) SCREAMING_SNAKE_CASE =ids_tensor([self.batch_size, self.image_size, self.image_size] ,self.num_labels ) SCREAMING_SNAKE_CASE =self.get_config() return config, pixel_values, labels, pixel_labels def _lowerCAmelCase ( self : Dict ): return BeitConfig( vocab_size=self.vocab_size ,image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=snake_case ,initializer_range=self.initializer_range ,out_indices=self.out_indices ,) def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Tuple ,snake_case : Optional[Any] ,snake_case : Union[str, Any] ,snake_case : Optional[int] ): SCREAMING_SNAKE_CASE =BeitModel(config=snake_case ) model.to(snake_case ) model.eval() SCREAMING_SNAKE_CASE =model(snake_case ) self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) ) def _lowerCAmelCase ( self : Union[str, Any] ,snake_case : Optional[int] ,snake_case : Dict ,snake_case : Any ,snake_case : List[str] ): SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling(config=snake_case ) model.to(snake_case ) model.eval() SCREAMING_SNAKE_CASE =model(snake_case ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size) ) def _lowerCAmelCase ( self : Optional[Any] ,snake_case : Any ,snake_case : str ,snake_case : Any ,snake_case : str ): SCREAMING_SNAKE_CASE =self.type_sequence_label_size SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case ) model.to(snake_case ) model.eval() SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) ) # test greyscale images SCREAMING_SNAKE_CASE =1 SCREAMING_SNAKE_CASE =BeitForImageClassification(snake_case ) model.to(snake_case ) model.eval() SCREAMING_SNAKE_CASE =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] ) SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case ) self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) ) def _lowerCAmelCase ( self : List[str] ,snake_case : Tuple ,snake_case : str ,snake_case : Optional[int] ,snake_case : int ): SCREAMING_SNAKE_CASE =self.num_labels SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation(snake_case ) model.to(snake_case ) model.eval() SCREAMING_SNAKE_CASE =model(snake_case ) self.parent.assertEqual( result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) SCREAMING_SNAKE_CASE =model(snake_case ,labels=snake_case ) self.parent.assertEqual( result.logits.shape ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) def _lowerCAmelCase ( self : str ): SCREAMING_SNAKE_CASE =self.prepare_config_and_inputs() SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =config_and_inputs SCREAMING_SNAKE_CASE ={'pixel_values': pixel_values} return config, inputs_dict @require_torch class a_ ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ): """simple docstring""" __UpperCAmelCase = ( (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation) if is_torch_available() else () ) __UpperCAmelCase = ( { 'feature-extraction': BeitModel, 'image-classification': BeitForImageClassification, 'image-segmentation': BeitForSemanticSegmentation, } if is_torch_available() else {} ) __UpperCAmelCase = False __UpperCAmelCase = False __UpperCAmelCase = False def _lowerCAmelCase ( self : List[Any] ): SCREAMING_SNAKE_CASE =BeitModelTester(self ) SCREAMING_SNAKE_CASE =ConfigTester(self ,config_class=snake_case ,has_text_modality=snake_case ,hidden_size=37 ) def _lowerCAmelCase ( self : List[str] ): self.config_tester.run_common_tests() @unittest.skip(reason='BEiT does not use inputs_embeds' ) def _lowerCAmelCase ( self : List[Any] ): pass @require_torch_multi_gpu @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' ) def _lowerCAmelCase ( self : Union[str, Any] ): pass def _lowerCAmelCase ( self : Tuple ): SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE =model_class(snake_case ) self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) ) SCREAMING_SNAKE_CASE =model.get_output_embeddings() self.assertTrue(x is None or isinstance(snake_case ,nn.Linear ) ) def _lowerCAmelCase ( self : int ): SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE =model_class(snake_case ) SCREAMING_SNAKE_CASE =inspect.signature(model.forward ) # signature.parameters is an OrderedDict => so arg_names order is deterministic SCREAMING_SNAKE_CASE =[*signature.parameters.keys()] SCREAMING_SNAKE_CASE =['pixel_values'] self.assertListEqual(arg_names[:1] ,snake_case ) def _lowerCAmelCase ( self : List[str] ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*snake_case ) def _lowerCAmelCase ( self : Union[str, Any] ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*snake_case ) def _lowerCAmelCase ( self : Dict ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*snake_case ) def _lowerCAmelCase ( self : Union[str, Any] ): SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*snake_case ) def _lowerCAmelCase ( self : Any ): if not self.model_tester.is_training: return SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() SCREAMING_SNAKE_CASE =True for model_class in self.all_model_classes: # we don't test BeitForMaskedImageModeling if model_class in [*get_values(snake_case ), BeitForMaskedImageModeling]: continue SCREAMING_SNAKE_CASE =model_class(snake_case ) model.to(snake_case ) model.train() SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case ) SCREAMING_SNAKE_CASE =model(**snake_case ).loss loss.backward() def _lowerCAmelCase ( self : int ): SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() if not self.model_tester.is_training: return SCREAMING_SNAKE_CASE =False SCREAMING_SNAKE_CASE =True for model_class in self.all_model_classes: # we don't test BeitForMaskedImageModeling if ( model_class in [*get_values(snake_case ), BeitForMaskedImageModeling] or not model_class.supports_gradient_checkpointing ): continue SCREAMING_SNAKE_CASE =model_class(snake_case ) model.gradient_checkpointing_enable() model.to(snake_case ) model.train() SCREAMING_SNAKE_CASE =self._prepare_for_class(snake_case ,snake_case ,return_labels=snake_case ) SCREAMING_SNAKE_CASE =model(**snake_case ).loss loss.backward() def _lowerCAmelCase ( self : Union[str, Any] ): SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE =self.model_tester.prepare_config_and_inputs_for_common() SCREAMING_SNAKE_CASE =_config_zero_init(snake_case ) for model_class in self.all_model_classes: SCREAMING_SNAKE_CASE =model_class(config=snake_case ) for name, param in model.named_parameters(): # we skip lambda parameters as these require special initial values # determined by config.layer_scale_init_value if "lambda" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item() ,[0.0, 1.0] ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,) @slow def _lowerCAmelCase ( self : List[str] ): for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE =BeitModel.from_pretrained(snake_case ) self.assertIsNotNone(snake_case ) def snake_case__ ( ): """simple docstring""" SCREAMING_SNAKE_CASE =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ) return image @require_torch @require_vision class a_ ( unittest.TestCase ): """simple docstring""" @cached_property def _lowerCAmelCase ( self : Tuple ): return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224' ) if is_vision_available() else None @slow def _lowerCAmelCase ( self : int ): SCREAMING_SNAKE_CASE =BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k' ).to(snake_case ) SCREAMING_SNAKE_CASE =self.default_image_processor SCREAMING_SNAKE_CASE =prepare_img() SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).pixel_values.to(snake_case ) # prepare bool_masked_pos SCREAMING_SNAKE_CASE =torch.ones((1, 196) ,dtype=torch.bool ).to(snake_case ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE =model(pixel_values=snake_case ,bool_masked_pos=snake_case ) SCREAMING_SNAKE_CASE =outputs.logits # verify the logits SCREAMING_SNAKE_CASE =torch.Size((1, 196, 8192) ) self.assertEqual(logits.shape ,snake_case ) SCREAMING_SNAKE_CASE =torch.tensor( [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]] ).to(snake_case ) self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] ,snake_case ,atol=1e-2 ) ) @slow def _lowerCAmelCase ( self : List[str] ): SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224' ).to(snake_case ) SCREAMING_SNAKE_CASE =self.default_image_processor SCREAMING_SNAKE_CASE =prepare_img() SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE =model(**snake_case ) SCREAMING_SNAKE_CASE =outputs.logits # verify the logits SCREAMING_SNAKE_CASE =torch.Size((1, 1000) ) self.assertEqual(logits.shape ,snake_case ) SCREAMING_SNAKE_CASE =torch.tensor([-1.2_385, -1.0_987, -1.0_108] ).to(snake_case ) self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) ) SCREAMING_SNAKE_CASE =281 self.assertEqual(logits.argmax(-1 ).item() ,snake_case ) @slow def _lowerCAmelCase ( self : int ): SCREAMING_SNAKE_CASE =BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k' ).to( snake_case ) SCREAMING_SNAKE_CASE =self.default_image_processor SCREAMING_SNAKE_CASE =prepare_img() SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE =model(**snake_case ) SCREAMING_SNAKE_CASE =outputs.logits # verify the logits SCREAMING_SNAKE_CASE =torch.Size((1, 21841) ) self.assertEqual(logits.shape ,snake_case ) SCREAMING_SNAKE_CASE =torch.tensor([1.6_881, -0.2_787, 0.5_901] ).to(snake_case ) self.assertTrue(torch.allclose(logits[0, :3] ,snake_case ,atol=1e-4 ) ) SCREAMING_SNAKE_CASE =2396 self.assertEqual(logits.argmax(-1 ).item() ,snake_case ) @slow def _lowerCAmelCase ( self : Tuple ): SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' ) SCREAMING_SNAKE_CASE =model.to(snake_case ) SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case ) SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' ) SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] ) SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE =model(**snake_case ) SCREAMING_SNAKE_CASE =outputs.logits # verify the logits SCREAMING_SNAKE_CASE =torch.Size((1, 150, 160, 160) ) self.assertEqual(logits.shape ,snake_case ) SCREAMING_SNAKE_CASE =version.parse(PIL.__version__ ) < version.parse('9.0.0' ) if is_pillow_less_than_a: SCREAMING_SNAKE_CASE =torch.tensor( [ [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]], [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]], [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]], ] ,device=snake_case ,) else: SCREAMING_SNAKE_CASE =torch.tensor( [ [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]], [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]], [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]], ] ,device=snake_case ,) self.assertTrue(torch.allclose(logits[0, :3, :3, :3] ,snake_case ,atol=1e-4 ) ) @slow def _lowerCAmelCase ( self : int ): SCREAMING_SNAKE_CASE =BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640' ) SCREAMING_SNAKE_CASE =model.to(snake_case ) SCREAMING_SNAKE_CASE =BeitImageProcessor(do_resize=snake_case ,size=640 ,do_center_crop=snake_case ) SCREAMING_SNAKE_CASE =load_dataset('hf-internal-testing/fixtures_ade20k' ,split='test' ) SCREAMING_SNAKE_CASE =Image.open(ds[0]['file'] ) SCREAMING_SNAKE_CASE =image_processor(images=snake_case ,return_tensors='pt' ).to(snake_case ) # forward pass with torch.no_grad(): SCREAMING_SNAKE_CASE =model(**snake_case ) SCREAMING_SNAKE_CASE =outputs.logits.detach().cpu() SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case ,target_sizes=[(500, 300)] ) SCREAMING_SNAKE_CASE =torch.Size((500, 300) ) self.assertEqual(segmentation[0].shape ,snake_case ) SCREAMING_SNAKE_CASE =image_processor.post_process_semantic_segmentation(outputs=snake_case ) SCREAMING_SNAKE_CASE =torch.Size((160, 160) ) self.assertEqual(segmentation[0].shape ,snake_case )
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from typing import List, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCAmelCase__ : Tuple =logging.get_logger(__name__) lowerCAmelCase__ : str ={ 'huggingface/informer-tourism-monthly': ( 'https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json' ), # See all Informer models at https://huggingface.co/models?filter=informer } class __lowercase (A__ ): """simple docstring""" _UpperCAmelCase = """informer""" _UpperCAmelCase = { """hidden_size""": """d_model""", """num_attention_heads""": """encoder_attention_heads""", """num_hidden_layers""": """encoder_layers""", } def __init__( self , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = "student_t" , lowerCAmelCase__ = "nll" , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = "mean" , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = 0 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 6_4 , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = 3_2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = 2 , lowerCAmelCase__ = True , lowerCAmelCase__ = "gelu" , lowerCAmelCase__ = 0.05 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 0.1 , lowerCAmelCase__ = 1_0_0 , lowerCAmelCase__ = 0.02 , lowerCAmelCase__=True , lowerCAmelCase__ = "prob" , lowerCAmelCase__ = 5 , lowerCAmelCase__ = True , **lowerCAmelCase__ , ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = prediction_length SCREAMING_SNAKE_CASE_ : List[str] = context_length or prediction_length SCREAMING_SNAKE_CASE_ : str = distribution_output SCREAMING_SNAKE_CASE_ : Optional[Any] = loss SCREAMING_SNAKE_CASE_ : Tuple = input_size SCREAMING_SNAKE_CASE_ : Tuple = num_time_features SCREAMING_SNAKE_CASE_ : List[Any] = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7] SCREAMING_SNAKE_CASE_ : Tuple = scaling SCREAMING_SNAKE_CASE_ : List[Any] = num_dynamic_real_features SCREAMING_SNAKE_CASE_ : Any = num_static_real_features SCREAMING_SNAKE_CASE_ : Dict = num_static_categorical_features # set cardinality if cardinality and num_static_categorical_features > 0: if len(__snake_case ) != num_static_categorical_features: raise ValueError( 'The cardinality should be a list of the same length as `num_static_categorical_features`' ) SCREAMING_SNAKE_CASE_ : Optional[Any] = cardinality else: SCREAMING_SNAKE_CASE_ : Dict = [0] # set embedding_dimension if embedding_dimension and num_static_categorical_features > 0: if len(__snake_case ) != num_static_categorical_features: raise ValueError( 'The embedding dimension should be a list of the same length as `num_static_categorical_features`' ) SCREAMING_SNAKE_CASE_ : List[Any] = embedding_dimension else: SCREAMING_SNAKE_CASE_ : Tuple = [min(5_0 , (cat + 1) // 2 ) for cat in self.cardinality] SCREAMING_SNAKE_CASE_ : Dict = num_parallel_samples # Transformer architecture configuration SCREAMING_SNAKE_CASE_ : str = input_size * len(self.lags_sequence ) + self._number_of_features SCREAMING_SNAKE_CASE_ : Union[str, Any] = d_model SCREAMING_SNAKE_CASE_ : Optional[Any] = encoder_attention_heads SCREAMING_SNAKE_CASE_ : int = decoder_attention_heads SCREAMING_SNAKE_CASE_ : Union[str, Any] = encoder_ffn_dim SCREAMING_SNAKE_CASE_ : Union[str, Any] = decoder_ffn_dim SCREAMING_SNAKE_CASE_ : int = encoder_layers SCREAMING_SNAKE_CASE_ : Optional[Any] = decoder_layers SCREAMING_SNAKE_CASE_ : Any = dropout SCREAMING_SNAKE_CASE_ : Union[str, Any] = attention_dropout SCREAMING_SNAKE_CASE_ : int = activation_dropout SCREAMING_SNAKE_CASE_ : Dict = encoder_layerdrop SCREAMING_SNAKE_CASE_ : Any = decoder_layerdrop SCREAMING_SNAKE_CASE_ : List[str] = activation_function SCREAMING_SNAKE_CASE_ : List[Any] = init_std SCREAMING_SNAKE_CASE_ : Tuple = use_cache # Informer SCREAMING_SNAKE_CASE_ : int = attention_type SCREAMING_SNAKE_CASE_ : Union[str, Any] = sampling_factor SCREAMING_SNAKE_CASE_ : Optional[Any] = distil super().__init__(is_encoder_decoder=__snake_case , **__snake_case ) @property def UpperCamelCase__ ( self ): """simple docstring""" return ( sum(self.embedding_dimension ) + self.num_dynamic_real_features + self.num_time_features + self.num_static_real_features + self.input_size * 2 # the log1p(abs(loc)) and log(scale) features )
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import os import tempfile import unittest from transformers import DistilBertConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, ) class __lowercase (__SCREAMING_SNAKE_CASE ): """simple docstring""" def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=1_3 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=9_9 , lowerCAmelCase__=3_2 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=3_7 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=5_1_2 , lowerCAmelCase__=1_6 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=3 , lowerCAmelCase__=4 , lowerCAmelCase__=None , ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] = parent SCREAMING_SNAKE_CASE_ : Any = batch_size SCREAMING_SNAKE_CASE_ : Optional[int] = seq_length SCREAMING_SNAKE_CASE_ : Any = is_training SCREAMING_SNAKE_CASE_ : int = use_input_mask SCREAMING_SNAKE_CASE_ : Union[str, Any] = use_token_type_ids SCREAMING_SNAKE_CASE_ : Dict = use_labels SCREAMING_SNAKE_CASE_ : List[str] = vocab_size SCREAMING_SNAKE_CASE_ : Dict = hidden_size SCREAMING_SNAKE_CASE_ : Dict = num_hidden_layers SCREAMING_SNAKE_CASE_ : Tuple = num_attention_heads SCREAMING_SNAKE_CASE_ : Dict = intermediate_size SCREAMING_SNAKE_CASE_ : Any = hidden_act SCREAMING_SNAKE_CASE_ : Optional[Any] = hidden_dropout_prob SCREAMING_SNAKE_CASE_ : List[Any] = attention_probs_dropout_prob SCREAMING_SNAKE_CASE_ : List[str] = max_position_embeddings SCREAMING_SNAKE_CASE_ : Any = type_vocab_size SCREAMING_SNAKE_CASE_ : List[Any] = type_sequence_label_size SCREAMING_SNAKE_CASE_ : Tuple = initializer_range SCREAMING_SNAKE_CASE_ : int = num_labels SCREAMING_SNAKE_CASE_ : List[str] = num_choices SCREAMING_SNAKE_CASE_ : Tuple = scope def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) SCREAMING_SNAKE_CASE_ : Optional[int] = None if self.use_input_mask: SCREAMING_SNAKE_CASE_ : str = random_attention_mask([self.batch_size, self.seq_length] ) SCREAMING_SNAKE_CASE_ : str = None SCREAMING_SNAKE_CASE_ : Optional[int] = None SCREAMING_SNAKE_CASE_ : Any = None if self.use_labels: SCREAMING_SNAKE_CASE_ : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size ) SCREAMING_SNAKE_CASE_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels ) SCREAMING_SNAKE_CASE_ : Dict = ids_tensor([self.batch_size] , self.num_choices ) SCREAMING_SNAKE_CASE_ : Any = self.get_config() return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels def UpperCamelCase__ ( self ): """simple docstring""" return DistilBertConfig( vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , ) def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[Any] = DistilBertModel(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() SCREAMING_SNAKE_CASE_ : int = model(lowerCAmelCase__ , lowerCAmelCase__ ) SCREAMING_SNAKE_CASE_ : int = model(lowerCAmelCase__ ) self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) ) def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] = DistilBertForMaskedLM(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() SCREAMING_SNAKE_CASE_ : Any = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) ) def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = DistilBertForQuestionAnswering(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() SCREAMING_SNAKE_CASE_ : str = model( lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=lowerCAmelCase__ ) self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) ) self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) ) def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[Any] = self.num_labels SCREAMING_SNAKE_CASE_ : Optional[int] = DistilBertForSequenceClassification(lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() SCREAMING_SNAKE_CASE_ : str = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) ) def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[Any] = self.num_labels SCREAMING_SNAKE_CASE_ : Any = DistilBertForTokenClassification(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() SCREAMING_SNAKE_CASE_ : List[Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) ) def UpperCamelCase__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[Any] = self.num_choices SCREAMING_SNAKE_CASE_ : Union[str, Any] = DistilBertForMultipleChoice(config=lowerCAmelCase__ ) model.to(lowerCAmelCase__ ) model.eval() SCREAMING_SNAKE_CASE_ : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() SCREAMING_SNAKE_CASE_ : Any = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous() SCREAMING_SNAKE_CASE_ : Any = model( lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ , ) self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) ) def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[Any] = self.prepare_config_and_inputs() ((SCREAMING_SNAKE_CASE_) , (SCREAMING_SNAKE_CASE_) , (SCREAMING_SNAKE_CASE_) , (SCREAMING_SNAKE_CASE_) , (SCREAMING_SNAKE_CASE_) , (SCREAMING_SNAKE_CASE_)) : Tuple = config_and_inputs SCREAMING_SNAKE_CASE_ : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask} return config, inputs_dict @require_torch class __lowercase (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ): """simple docstring""" _UpperCAmelCase = ( ( DistilBertModel, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, ) if is_torch_available() else None ) _UpperCAmelCase = ( { """feature-extraction""": DistilBertModel, """fill-mask""": DistilBertForMaskedLM, """question-answering""": DistilBertForQuestionAnswering, """text-classification""": DistilBertForSequenceClassification, """token-classification""": DistilBertForTokenClassification, """zero-shot""": DistilBertForSequenceClassification, } if is_torch_available() else {} ) _UpperCAmelCase = True _UpperCAmelCase = True _UpperCAmelCase = True _UpperCAmelCase = True def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = DistilBertModelTester(self ) SCREAMING_SNAKE_CASE_ : str = ConfigTester(self , config_class=lowerCAmelCase__ , dim=3_7 ) def UpperCamelCase__ ( self ): """simple docstring""" self.config_tester.run_common_tests() def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_model(*lowerCAmelCase__ ) def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_masked_lm(*lowerCAmelCase__ ) def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_question_answering(*lowerCAmelCase__ ) def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowerCAmelCase__ ) def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : int = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_token_classification(*lowerCAmelCase__ ) def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowerCAmelCase__ ) @slow def UpperCamelCase__ ( self ): """simple docstring""" for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: SCREAMING_SNAKE_CASE_ : Any = DistilBertModel.from_pretrained(lowerCAmelCase__ ) self.assertIsNotNone(lowerCAmelCase__ ) @slow @require_torch_gpu def UpperCamelCase__ ( self ): """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: # BertForMultipleChoice behaves incorrectly in JIT environments. if model_class == DistilBertForMultipleChoice: return SCREAMING_SNAKE_CASE_ : Tuple = True SCREAMING_SNAKE_CASE_ : str = model_class(config=lowerCAmelCase__ ) SCREAMING_SNAKE_CASE_ : Union[str, Any] = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) SCREAMING_SNAKE_CASE_ : List[str] = torch.jit.trace( lowerCAmelCase__ , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(lowerCAmelCase__ , os.path.join(lowerCAmelCase__ , 'traced_model.pt' ) ) SCREAMING_SNAKE_CASE_ : Union[str, Any] = torch.jit.load(os.path.join(lowerCAmelCase__ , 'traced_model.pt' ) , map_location=lowerCAmelCase__ ) loaded(inputs_dict['input_ids'].to(lowerCAmelCase__ ) , inputs_dict['attention_mask'].to(lowerCAmelCase__ ) ) @require_torch class __lowercase (unittest.TestCase ): """simple docstring""" @slow def UpperCamelCase__ ( self ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = DistilBertModel.from_pretrained('distilbert-base-uncased' ) SCREAMING_SNAKE_CASE_ : Tuple = torch.tensor([[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]] ) SCREAMING_SNAKE_CASE_ : Dict = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] ) with torch.no_grad(): SCREAMING_SNAKE_CASE_ : Any = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )[0] SCREAMING_SNAKE_CASE_ : int = torch.Size((1, 1_1, 7_6_8) ) self.assertEqual(output.shape , lowerCAmelCase__ ) SCREAMING_SNAKE_CASE_ : List[Any] = torch.tensor( [[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , lowerCAmelCase__ , atol=1E-4 ) )
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import json import os import shutil import warnings from argparse import ArgumentParser, Namespace from pathlib import Path from typing import List from ..utils import logging from . import BaseTransformersCLICommand try: from cookiecutter.main import cookiecutter __snake_case :List[str] = True except ImportError: __snake_case :Any = False __snake_case :int = logging.get_logger(__name__) # pylint: disable=invalid-name def __snake_case ( _UpperCAmelCase ): return AddNewModelCommand(args.testing , args.testing_file , path=args.path ) class _A ( __lowerCAmelCase ): @staticmethod def _lowerCamelCase ( __SCREAMING_SNAKE_CASE : Tuple): '''simple docstring''' __a = parser.add_parser('''add-new-model''') add_new_model_parser.add_argument('''--testing''' , action='''store_true''' , help='''If in testing mode.''') add_new_model_parser.add_argument('''--testing_file''' , type=lowerCAmelCase_ , help='''Configuration file on which to run.''') add_new_model_parser.add_argument( '''--path''' , type=lowerCAmelCase_ , help='''Path to cookiecutter. Should only be used for testing purposes.''') add_new_model_parser.set_defaults(func=lowerCAmelCase_) def __init__( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Any=None , *__SCREAMING_SNAKE_CASE : Union[str, Any]): '''simple docstring''' __a = testing __a = testing_file __a = path def _lowerCamelCase ( self : List[str]): '''simple docstring''' warnings.warn( '''The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. ''' '''It is not actively maintained anymore, so might give a result that won\'t pass all tests and quality ''' '''checks, you should use `transformers-cli add-new-model-like` instead.''') if not _has_cookiecutter: raise ImportError( '''Model creation dependencies are required to use the `add_new_model` command. Install them by running ''' '''the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n''') # Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory __a = [directory for directory in os.listdir() if '''cookiecutter-template-''' == directory[:22]] if len(lowerCAmelCase_) > 0: raise ValueError( '''Several directories starting with `cookiecutter-template-` in current working directory. ''' '''Please clean your directory by removing all folders starting with `cookiecutter-template-` or ''' '''change your working directory.''') __a = ( Path(lowerCAmelCase_).parent.parent.parent.parent if self._path is None else Path(self._path).parent.parent ) __a = path_to_transformer_root / '''templates''' / '''adding_a_new_model''' # Execute cookiecutter if not self._testing: cookiecutter(str(lowerCAmelCase_)) else: with open(self._testing_file , '''r''') as configuration_file: __a = json.load(lowerCAmelCase_) cookiecutter( str(path_to_cookiecutter if self._path is None else self._path) , no_input=lowerCAmelCase_ , extra_context=lowerCAmelCase_ , ) __a = [directory for directory in os.listdir() if '''cookiecutter-template-''' in directory[:22]][0] # Retrieve configuration with open(directory + '''/configuration.json''' , '''r''') as configuration_file: __a = json.load(lowerCAmelCase_) __a = configuration['''lowercase_modelname'''] __a = configuration['''generate_tensorflow_pytorch_and_flax'''] os.remove(F'{directory}/configuration.json') __a = '''PyTorch''' in generate_tensorflow_pytorch_and_flax __a = '''TensorFlow''' in generate_tensorflow_pytorch_and_flax __a = '''Flax''' in generate_tensorflow_pytorch_and_flax __a = F'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}' os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_) os.makedirs(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}' , exist_ok=lowerCAmelCase_) # Tests require submodules as they have parent imports with open(F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' , '''w'''): pass shutil.move( F'{directory}/__init__.py' , F'{model_dir}/__init__.py' , ) shutil.move( F'{directory}/configuration_{lowercase_model_name}.py' , F'{model_dir}/configuration_{lowercase_model_name}.py' , ) def remove_copy_lines(__SCREAMING_SNAKE_CASE : Dict): with open(lowerCAmelCase_ , '''r''') as f: __a = f.readlines() with open(lowerCAmelCase_ , '''w''') as f: for line in lines: if "# Copied from transformers." not in line: f.write(lowerCAmelCase_) if output_pytorch: if not self._testing: remove_copy_lines(F'{directory}/modeling_{lowercase_model_name}.py') shutil.move( F'{directory}/modeling_{lowercase_model_name}.py' , F'{model_dir}/modeling_{lowercase_model_name}.py' , ) shutil.move( F'{directory}/test_modeling_{lowercase_model_name}.py' , F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' , ) else: os.remove(F'{directory}/modeling_{lowercase_model_name}.py') os.remove(F'{directory}/test_modeling_{lowercase_model_name}.py') if output_tensorflow: if not self._testing: remove_copy_lines(F'{directory}/modeling_tf_{lowercase_model_name}.py') shutil.move( F'{directory}/modeling_tf_{lowercase_model_name}.py' , F'{model_dir}/modeling_tf_{lowercase_model_name}.py' , ) shutil.move( F'{directory}/test_modeling_tf_{lowercase_model_name}.py' , F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' , ) else: os.remove(F'{directory}/modeling_tf_{lowercase_model_name}.py') os.remove(F'{directory}/test_modeling_tf_{lowercase_model_name}.py') if output_flax: if not self._testing: remove_copy_lines(F'{directory}/modeling_flax_{lowercase_model_name}.py') shutil.move( F'{directory}/modeling_flax_{lowercase_model_name}.py' , F'{model_dir}/modeling_flax_{lowercase_model_name}.py' , ) shutil.move( F'{directory}/test_modeling_flax_{lowercase_model_name}.py' , F'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' , ) else: os.remove(F'{directory}/modeling_flax_{lowercase_model_name}.py') os.remove(F'{directory}/test_modeling_flax_{lowercase_model_name}.py') shutil.move( F'{directory}/{lowercase_model_name}.md' , F'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' , ) shutil.move( F'{directory}/tokenization_{lowercase_model_name}.py' , F'{model_dir}/tokenization_{lowercase_model_name}.py' , ) shutil.move( F'{directory}/tokenization_fast_{lowercase_model_name}.py' , F'{model_dir}/tokenization_{lowercase_model_name}_fast.py' , ) from os import fdopen, remove from shutil import copymode, move from tempfile import mkstemp def replace(__SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Union[str, Any]): # Create temp file __a , __a = mkstemp() __a = False with fdopen(lowerCAmelCase_ , '''w''') as new_file: with open(lowerCAmelCase_) as old_file: for line in old_file: new_file.write(lowerCAmelCase_) if line_to_copy_below in line: __a = True for line_to_copy in lines_to_copy: new_file.write(lowerCAmelCase_) if not line_found: raise ValueError(F'Line {line_to_copy_below} was not found in file.') # Copy the file permissions from the old file to the new file copymode(lowerCAmelCase_ , lowerCAmelCase_) # Remove original file remove(lowerCAmelCase_) # Move new file move(lowerCAmelCase_ , lowerCAmelCase_) def skip_units(__SCREAMING_SNAKE_CASE : Tuple): return ( ("generating PyTorch" in line and not output_pytorch) or ("generating TensorFlow" in line and not output_tensorflow) or ("generating Flax" in line and not output_flax) ) def replace_in_files(__SCREAMING_SNAKE_CASE : Tuple): with open(lowerCAmelCase_) as datafile: __a = [] __a = False __a = False for line in datafile: if "# To replace in: " in line and "##" not in line: __a = line.split('''\"''')[1] __a = skip_units(lowerCAmelCase_) elif "# Below: " in line and "##" not in line: __a = line.split('''\"''')[1] __a = skip_units(lowerCAmelCase_) elif "# End." in line and "##" not in line: if not skip_file and not skip_snippet: replace(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_) __a = [] elif "# Replace with" in line and "##" not in line: __a = [] elif "##" not in line: lines_to_copy.append(lowerCAmelCase_) remove(lowerCAmelCase_) replace_in_files(F'{directory}/to_replace_{lowercase_model_name}.py') os.rmdir(lowerCAmelCase_)
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import math import sys def snake_case ( snake_case__ :int) -> int: if number != int(snake_case__): raise ValueError("""the value of input must be a natural number""") if number < 0: raise ValueError("""the value of input must not be a negative number""") if number == 0: return 1 _A = [-1] * (number + 1) _A = 0 for i in range(1 , number + 1): _A = sys.maxsize _A = int(math.sqrt(snake_case__)) for j in range(1 , root + 1): _A = 1 + answers[i - (j**2)] _A = min(snake_case__ , snake_case__) _A = answer return answers[number] if __name__ == "__main__": import doctest doctest.testmod()
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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 ( a , unittest.TestCase ): """simple docstring""" UpperCamelCase__ : Any =OpenAIGPTTokenizer UpperCamelCase__ : str =OpenAIGPTTokenizerFast UpperCamelCase__ : List[str] =True UpperCamelCase__ : int =False def __lowercase ( self ): """simple docstring""" super().setUp() # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt __UpperCamelCase : Optional[Any] =[ '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>', ] __UpperCamelCase : Optional[Any] =dict(zip(lowerCamelCase__ , range(len(lowerCamelCase__ ) ) ) ) __UpperCamelCase : int =['#version: 0.2', 'l o', 'lo w', 'e r</w>', ''] __UpperCamelCase : str =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] ) __UpperCamelCase : str =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] ) with open(self.vocab_file , 'w' ) as fp: fp.write(json.dumps(lowerCamelCase__ ) ) with open(self.merges_file , 'w' ) as fp: fp.write('\n'.join(lowerCamelCase__ ) ) def __lowercase ( self , lowerCamelCase__ ): """simple docstring""" return "lower newer", "lower newer" def __lowercase ( self ): """simple docstring""" __UpperCamelCase : Union[str, Any] =OpenAIGPTTokenizer(self.vocab_file , self.merges_file ) __UpperCamelCase : Dict ='lower' __UpperCamelCase : str =['low', 'er</w>'] __UpperCamelCase : List[Any] =tokenizer.tokenize(lowerCamelCase__ ) self.assertListEqual(lowerCamelCase__ , lowerCamelCase__ ) __UpperCamelCase : Dict =tokens + ['<unk>'] __UpperCamelCase : Optional[int] =[14, 15, 20] self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) , lowerCamelCase__ ) def __lowercase ( self , lowerCamelCase__=15 ): """simple docstring""" for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ): __UpperCamelCase : Tuple =self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ , **lowerCamelCase__ ) # Simple input __UpperCamelCase : Union[str, Any] ='This is a simple input' __UpperCamelCase : Tuple =['This is a simple input 1', 'This is a simple input 2'] __UpperCamelCase : Union[str, Any] =('This is a simple input', 'This is a pair') __UpperCamelCase : Union[str, Any] =[ ('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(lowerCamelCase__ , tokenizer_r.encode , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' ) # Simple input self.assertRaises(lowerCamelCase__ , tokenizer_r.encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' ) # Simple input self.assertRaises( lowerCamelCase__ , tokenizer_r.batch_encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' , ) # Pair input self.assertRaises(lowerCamelCase__ , tokenizer_r.encode , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' ) # Pair input self.assertRaises(lowerCamelCase__ , tokenizer_r.encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' ) # Pair input self.assertRaises( lowerCamelCase__ , tokenizer_r.batch_encode_plus , lowerCamelCase__ , max_length=lowerCamelCase__ , padding='max_length' , ) def __lowercase ( self ): """simple docstring""" pass @require_ftfy @require_spacy @require_tokenizers class __A ( a ): """simple docstring""" pass
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from __future__ import annotations def A ( a_ ) -> int: if not nums: return 0 __UpperCamelCase : Any =nums[0] __UpperCamelCase : Any =0 for num in nums[1:]: __UpperCamelCase , __UpperCamelCase : List[Any] =( max_excluding + num, max(a_ ,a_ ), ) return max(a_ ,a_ ) if __name__ == "__main__": import doctest doctest.testmod()
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1