

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def  a_	( )				->						Dict:






			def filter_log(__lowercase     :			Union[str, Any]				):
						return False if "This IS expected if you are initializing" in record.msg else True

			_snake_case						=    datasets.utils.logging.get_logger('transformers.modeling_utils'				)
			logger.addFilter(UpperCamelCase_				)
			try:
						yield
			finally:
						logger.removeFilter(UpperCamelCase_				)


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

_lowerCamelCase :       Optional[int]     					=    "\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

_lowerCamelCase :       int     					=    "\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"




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





	'''simple docstring'''



	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				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			:					int    ,						lowercase			:					Any    ,						lowercase			:					Tuple    ,						lowercase			:					List[Any]=None    ,						lowercase			:					Optional[Any]=None    ,						lowercase			:					Optional[Any]=None    ,						lowercase			:					Optional[int]=False    ,						lowercase			:					List[str]=False    ,						lowercase			:					Any=None    ,						lowercase			:					Tuple=64    ,						lowercase			:					str=4    ,						lowercase			:					Tuple=False    ,						lowercase			:					Optional[Any]=False    ,						lowercase			:					str=None    ,						lowercase			:					Tuple=False    ,						):


				'''simple docstring'''


				_snake_case						=    bert_score.utils.get_hash
				_snake_case						=    bert_score.BERTScorer

				if version.parse(bert_score.__version__			) >= version.parse('0.3.10'			):
							_snake_case						=    functools.partial(__snake_case    ,						use_fast_tokenizer=__snake_case			)
							_snake_case						=    functools.partial(__snake_case    ,						use_fast_tokenizer=__snake_case			)
				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"
							_snake_case						=    bert_score.utils.langamodel[lang.lower()]

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

				_snake_case						=    get_hash(
				    model=__snake_case    ,						num_layers=__snake_case    ,						idf=__snake_case    ,						rescale_with_baseline=__snake_case    ,						use_custom_baseline=baseline_path is not None    ,						)

				with filter_logging_context():
							if not hasattr(self    ,						'cached_bertscorer'			) or self.cached_bertscorer.hash != hashcode:
										_snake_case						=    scorer(
										    model_type=__snake_case    ,						num_layers=__snake_case    ,						batch_size=__snake_case    ,						nthreads=__snake_case    ,						all_layers=__snake_case    ,						idf=__snake_case    ,						device=__snake_case    ,						lang=__snake_case    ,						rescale_with_baseline=__snake_case    ,						baseline_path=__snake_case    ,						)

				((_snake_case)			,      (_snake_case)			,      (_snake_case))						=    self.cached_bertscorer.score(
				    cands=__snake_case    ,						refs=__snake_case    ,						verbose=__snake_case    ,						batch_size=__snake_case    ,						)
				_snake_case						=    {
				    'precision': P.tolist(),
				    'recall': R.tolist(),
				    'f1': F.tolist(),
				    'hashcode': hashcode,
				}
				return output_dict



	def 				A							(						self			:					Tuple    ,						lowercase			:					int=None    ,						lowercase			:					List[str]=None    ,						**lowercase			:					Any			):


				'''simple docstring'''


				if references is not None:
							_snake_case						=    [[ref] if isinstance(__snake_case    ,						__snake_case			) else ref for ref in references]
				super().add_batch(predictions=__snake_case    ,						references=__snake_case    ,						**__snake_case			)





	def 				A							(						self			:					Dict    ,						lowercase			:					int=None    ,						lowercase			:					Dict=None    ,						**lowercase			:					Any			):


				'''simple docstring'''


				if isinstance(__snake_case    ,						__snake_case			):
							_snake_case						=    [reference]
				super().add(prediction=__snake_case    ,						reference=__snake_case    ,						**__snake_case			)

def  a_	( __lowercase     :			int				)				->						bool:
			_snake_case						=    9

			while num > 0:
						if num % 10 != digit:
									return False
						num //= 100
						digit -= 1

			return True






def  a_	( )				->						int:
			_snake_case						=    138_902_663

			while not is_square_form(num * num				):
						if num % 10 == 3:
									num -= 6  # (3 - 6) % 10 = 7
						else:
									num -= 4  # (7 - 4) % 10 = 3

			return num * 10


if __name__ == "__main__":
			print(F'{solution() = }')