





"""simple docstring"""



import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets







@contextmanager
def 	__lowercase  (     )     ->str:



				'''simple docstring'''







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

				__A					:		Any						=  datasets.utils.logging.get_logger('''transformers.modeling_utils'''		)
				logger.addFilter(snake_case_		)
				try:
								yield
				finally:
								logger.removeFilter(snake_case_		)


a_				=    """\
@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_				=    """\
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_				=    """
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 	__snake_case      (	datasets.Metric  ):





	"""simple docstring"""


	def       UpperCamelCase__(					self						):



					'''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       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase=None      ,		__lowerCamelCase=None      ,		__lowerCamelCase=None      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=None      ,		__lowerCamelCase=64      ,		__lowerCamelCase=4      ,		__lowerCamelCase=False      ,		__lowerCamelCase=False      ,		__lowerCamelCase=None      ,		__lowerCamelCase=False      ,		):



					'''simple docstring'''




					__A					:		Optional[int]						=  bert_score.utils.get_hash
					__A					:		str						=  bert_score.BERTScorer

					if version.parse(bert_score.__version__						) >= version.parse('''0.3.10'''						):
									__A					:		List[Any]						=  functools.partial(__lowerCamelCase      ,		use_fast_tokenizer=__lowerCamelCase						)
									__A					:		Dict						=  functools.partial(__lowerCamelCase      ,		use_fast_tokenizer=__lowerCamelCase						)
					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"
									__A					:		List[str]						=  bert_score.utils.langamodel[lang.lower()]

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

					__A					:		List[Any]						=  get_hash(
					    model=__lowerCamelCase      ,		num_layers=__lowerCamelCase      ,		idf=__lowerCamelCase      ,		rescale_with_baseline=__lowerCamelCase      ,		use_custom_baseline=baseline_path is not None      ,		)

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

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

	def       UpperCamelCase__(					self      ,		__lowerCamelCase=None      ,		__lowerCamelCase=None      ,		**__lowerCamelCase						):



					'''simple docstring'''




					if references is not None:
									__A					:		Union[str, Any]						=  [[ref] if isinstance(__lowerCamelCase      ,		__lowerCamelCase						) else ref for ref in references]
					super().add_batch(predictions=__lowerCamelCase      ,		references=__lowerCamelCase      ,		**__lowerCamelCase						)







	def       UpperCamelCase__(					self      ,		__lowerCamelCase=None      ,		__lowerCamelCase=None      ,		**__lowerCamelCase						):



					'''simple docstring'''




					if isinstance(__lowerCamelCase      ,		__lowerCamelCase						):
									__A					:		Optional[Any]						=  [reference]
					super().add(prediction=__lowerCamelCase      ,		reference=__lowerCamelCase      ,		**__lowerCamelCase						)











"""simple docstring"""



import decimal







def 	__lowercase  (     )     ->int:



    '''simple docstring'''







    __A					:		List[str]						=  0
    __A					:		Tuple						=  decimal.Context(prec=105		)
    for i in range(2       ,100		):
        __A					:		int						=  decimal.Decimal(snake_case_		)
        __A					:		str						=  number.sqrt(snake_case_		)
        if len(str(snake_case_		)		) > 1:
            answer += int(str(snake_case_		)[0]		)
            __A					:		Optional[Any]						=  str(snake_case_		)[2:101]
            answer += sum(int(snake_case_		) for x in sqrt_number_str		)
    return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f'''{solution() = }''')




