






"""simple docstring"""



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def 						lowercase   (    ):
		def filter_log(__snake_case    :			str     ):
				return False if "This IS expected if you are initializing" in record.msg else True

		lowercase_			:			Dict								=       datasets.utils.logging.get_logger('''transformers.modeling_utils'''     )
		logger.addFilter(__snake_case     )
		try:
				yield
		finally:
				logger.removeFilter(__snake_case     )


__A		:	int      =		'''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

__A		:	Any      =		'''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

__A		:	List[str]      =		'''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,						_KWARGS_DESCRIPTION							)
class  _UpperCAmelCase	(			datasets.Metric							):




					def   A  (    self					:						Any       )  ->  List[Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='''https://github.com/Tiiiger/bert_score'''   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''   ,						id='''sequence'''       ),
							            '''references''': datasets.Sequence(datasets.Value('''string'''   ,						id='''sequence'''       )   ,						id='''references'''       ),
							        }       )   ,						codebase_urls=['''https://github.com/Tiiiger/bert_score''']   ,						reference_urls=[
							        '''https://github.com/Tiiiger/bert_score''',
							        '''https://arxiv.org/abs/1904.09675''',
							    ]   ,						)




					def   A  (    self					:						Optional[int]   ,						A					:						Dict   ,						A					:						List[Any]   ,						A					:						int=None   ,						A					:						List[Any]=None   ,						A					:						Optional[Any]=None   ,						A					:						str=False   ,						A					:						Union[str, Any]=False   ,						A					:						Tuple=None   ,						A					:						Tuple=64   ,						A					:						int=4   ,						A					:						Dict=False   ,						A					:						Tuple=False   ,						A					:						Dict=None   ,						A					:						int=False   ,						)  ->  Any:
							lowercase_			:			Union[str, Any]								=       bert_score.utils.get_hash
							lowercase_			:			Union[str, Any]								=       bert_score.BERTScorer

							if version.parse(bert_score.__version__       ) >= version.parse('''0.3.10'''       ):
									lowercase_			:			Optional[Any]								=       functools.partial(A   ,						use_fast_tokenizer=A       )
									lowercase_			:			Tuple								=       functools.partial(A   ,						use_fast_tokenizer=A       )
							elif use_fast_tokenizer:
									raise ImportWarning(
									    '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
									    '''You can install it with `pip install "bert-score>=0.3.10"`.'''       )

							if model_type is None:
									assert lang is not None, "either lang or model_type should be specified"
									lowercase_			:			Dict								=       bert_score.utils.langamodel[lang.lower()]

							if num_layers is None:
									lowercase_			:			int								=       bert_score.utils.modelalayers[model_type]

							lowercase_			:			Dict								=       get_hash(
							    model=A   ,						num_layers=A   ,						idf=A   ,						rescale_with_baseline=A   ,						use_custom_baseline=baseline_path is not None   ,						)

							with filter_logging_context():
									if not hasattr(self   ,						'''cached_bertscorer'''       ) or self.cached_bertscorer.hash != hashcode:
											lowercase_			:			List[Any]								=       scorer(
											    model_type=A   ,						num_layers=A   ,						batch_size=A   ,						nthreads=A   ,						all_layers=A   ,						idf=A   ,						device=A   ,						lang=A   ,						rescale_with_baseline=A   ,						baseline_path=A   ,						)

							((lowercase_)	, (lowercase_)	, (lowercase_))			:			int								=       self.cached_bertscorer.score(
							    cands=A   ,						refs=A   ,						verbose=A   ,						batch_size=A   ,						)
							lowercase_			:			List[Any]								=       {
							    '''precision''': P.tolist(),
							    '''recall''': R.tolist(),
							    '''f1''': F.tolist(),
							    '''hashcode''': hashcode,
							}
							return output_dict




					def   A  (    self					:						List[str]   ,						A					:						List[Any]=None   ,						A					:						Any=None   ,						**A					:						Any       )  ->  Union[str, Any]:
							# References can be strings or lists of strings
							# Let's change strings to lists of strings with one element
							if references is not None:
									lowercase_			:			str								=       [[ref] if isinstance(A   ,						A       ) else ref for ref in references]
							super().add_batch(predictions=A   ,						references=A   ,						**A       )






					def   A  (    self					:						Optional[Any]   ,						A					:						Optional[int]=None   ,						A					:						int=None   ,						**A					:						List[str]       )  ->  List[Any]:
							# References can be strings or lists of strings
							# Let's change strings to lists of strings with one element
							if isinstance(A   ,						A       ):
									lowercase_			:			Any								=       [reference]
							super().add(prediction=A   ,						reference=A   ,						**A       )











"""simple docstring"""



import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 						lowercase   (    ):
		lowercase_			:			Tuple								=       set()
		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
				lowercase_			:			str								=       getattr(transformers.models.auto		, __snake_case		, __snake_case     )
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowercase_			:			Tuple								=       [
				    x
				    for x in dir(__snake_case     )
				    if x.endswith('''_MAPPING_NAMES'''     )
				    and (x.startswith('''MODEL_'''     ) or x.startswith('''TF_MODEL_'''     ) or x.startswith('''FLAX_MODEL_'''     ))
				]
				for name in mapping_names:
						lowercase_			:			Tuple								=       getattr(__snake_case		, __snake_case     )
						if mapping is not None:
								for v in mapping.values():
										if isinstance(__snake_case		, (list, tuple)     ):
												model_names.update(__snake_case     )
										elif isinstance(__snake_case		, __snake_case     ):
												model_names.add(__snake_case     )

		return sorted(__snake_case     )



def 						lowercase   (    ):
		# All model names defined in auto mappings
		lowercase_			:			Tuple								=       set(get_all_model_names()     )

		with open('''tests/utils/tiny_model_summary.json'''     ) as fp:
				lowercase_			:			Tuple								=       json.load(__snake_case     )
		lowercase_			:			Union[str, Any]								=       set()
		for model_base_name in tiny_model_info:
				tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']     )

		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
		lowercase_			:			Union[str, Any]								=       model_names.difference(__snake_case     )
		for model_name in copy.copy(__snake_case     ):
				if not model_name.startswith('''TF'''     ) and F'''TF{model_name}''' in not_on_hub:
						tiny_models_names.remove(__snake_case     )
				elif model_name.startswith('''TF'''     ) and model_name[2:] in not_on_hub:
						tiny_models_names.remove(__snake_case     )

		return sorted(__snake_case     )



def 						lowercase   (    __snake_case    :			List[Any]     ):
		lowercase_			:			List[str]								=       COMPOSITE_MODELS.values()

		# All tiny model base names on Hub
		lowercase_			:			List[Any]								=       get_all_model_names()
		lowercase_			:			Tuple								=       hf_api.list_models(
		    filter=ModelFilter(
		        author='''hf-internal-testing'''		, )     )
		lowercase_			:			Tuple								=       set()
		for x in models:
				lowercase_			:			Tuple								=       x.modelId
				lowercase_	, lowercase_			:			Tuple								=       model.split('''/'''     )
				if not model.startswith('''tiny-random-'''     ):
						continue
				lowercase_			:			List[str]								=       model.replace('''tiny-random-'''		, ''''''     )
				if not model[0].isupper():
						continue
				if model not in model_names and model not in special_models:
						continue
				_models.add(__snake_case     )

		lowercase_			:			Tuple								=       sorted(_models     )
		# All tiny model names on Hub
		lowercase_			:			int								=       {}
		for model in models:
				lowercase_			:			str								=       F'''hf-internal-testing/tiny-random-{model}'''
				lowercase_			:			str								=       model.split('''-'''     )[0]
				try:
						lowercase_			:			Any								=       hf_api.repo_info(__snake_case     )
						lowercase_			:			str								=       {
						    '''tokenizer_classes''': set(),
						    '''processor_classes''': set(),
						    '''model_classes''': set(),
						    '''sha''': repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1     )
						lowercase_			:			Any								=       AutoTokenizer.from_pretrained(__snake_case     )
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			List[str]								=       AutoTokenizer.from_pretrained(__snake_case		, use_fast=__snake_case     )
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Union[str, Any]								=       AutoImageProcessor.from_pretrained(__snake_case     )
						content["processor_classes"].add(img_p.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Optional[Any]								=       AutoFeatureExtractor.from_pretrained(__snake_case     )
						if not isinstance(__snake_case		, __snake_case     ):
								content["processor_classes"].add(feat_p.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			Union[str, Any]								=       getattr(__snake_case		, __snake_case     )
						lowercase_			:			Dict								=       model_class.from_pretrained(__snake_case     )
						content["model_classes"].add(m.__class__.__name__     )
				except Exception:
						pass
				try:
						time.sleep(1     )
						lowercase_			:			List[str]								=       getattr(__snake_case		, F'''TF{model}'''     )
						lowercase_			:			List[str]								=       model_class.from_pretrained(__snake_case     )
						content["model_classes"].add(m.__class__.__name__     )
				except Exception:
						pass

				lowercase_			:			Dict								=       sorted(content['''tokenizer_classes''']     )
				lowercase_			:			Any								=       sorted(content['''processor_classes''']     )
				lowercase_			:			Union[str, Any]								=       sorted(content['''model_classes''']     )

				lowercase_			:			str								=       content
				with open(os.path.join(__snake_case		, '''hub_tiny_model_summary.json'''     )		, '''w'''     ) as fp:
						json.dump(__snake_case		, __snake_case		, ensure_ascii=__snake_case		, indent=4     )


if __name__ == "__main__":
						__A		:	Optional[int]      =		argparse.ArgumentParser()
						parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
						__A		:	Optional[int]      =		parser.parse_args()

						# This has to be `spawn` to avoid hanging forever!
						multiprocessing.set_start_method('''spawn''')

						__A		:	List[str]      =		'''tiny_models'''
						__A		:	List[str]      =		True
						__A		:	int      =		None
						__A		:	Dict      =		get_tiny_model_names_from_repo()
						__A		:	Union[str, Any]      =		True
						__A		:	str      =		True
						__A		:	Optional[Any]      =		'''hf-internal-testing'''

						create_tiny_models(
						    output_path,
						    all,
						    model_types,
						    models_to_skip,
						    no_check,
						    upload,
						    organization,
						    token=os.environ.get('''TOKEN''', None),
						    num_workers=args.num_workers,
						)



