


'''simple docstring'''




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 				a_							(    )      ->List[Any]:






				'''simple docstring'''

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

				__lowerCamelCase		:   List[str]           =       datasets.utils.logging.get_logger("transformers.modeling_utils"     )
				logger.addFilter(SCREAMING_SNAKE_CASE__     )
				try:
								yield
				finally:
								logger.removeFilter(SCREAMING_SNAKE_CASE__     )


__lowercase	:   Optional[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"""

__lowercase	:   Tuple   		=					"""\\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"""

__lowercase	:   List[Any]   		=					"""\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 _snake_case (			datasets.Metric ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE( self						)  ->List[Any]:



											"""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 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A      ,					__A=None      ,					__A=None      ,					__A=None      ,					__A=False      ,					__A=False      ,					__A=None      ,					__A=64      ,					__A=4      ,					__A=False      ,					__A=False      ,					__A=None      ,					__A=False      ,					)  ->Tuple:



											"""simple docstring"""







											__lowerCamelCase		:   Any           =       bert_score.utils.get_hash
											__lowerCamelCase		:   str           =       bert_score.BERTScorer

											if version.parse(bert_score.__version__						) >= version.parse("0.3.10"						):
															__lowerCamelCase		:   Tuple           =       functools.partial(A_      ,					use_fast_tokenizer=A_						)
															__lowerCamelCase		:   List[Any]           =       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"
															__lowerCamelCase		:   Optional[int]           =       bert_score.utils.langamodel[lang.lower()]

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

											__lowerCamelCase		:   Optional[int]           =       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:
																			__lowerCamelCase		:   List[str]           =       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_      ,					)

											((__lowerCamelCase)    ,       (__lowerCamelCase)    ,       (__lowerCamelCase))		:   Optional[Any]           =       self.cached_bertscorer.score(
											    cands=A_      ,					refs=A_      ,					verbose=A_      ,					batch_size=A_      ,					)
											__lowerCamelCase		:   Any           =       {
											    "precision": P.tolist(),
											    "recall": R.tolist(),
											    "f1": F.tolist(),
											    "hashcode": hashcode,
											}
											return output_dict







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A=None      ,					__A=None      ,					**__A						)  ->str:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A=None      ,					__A=None      ,					**__A						)  ->int:



											"""simple docstring"""







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









import math

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int							):
     __UpperCamelCase       				=math.log(2     ,	10							) * number
     __UpperCamelCase       				=round((log_number - int(SCREAMING_SNAKE_CASE__							))     ,	15							)

     return difference

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int = 67_89_10							):
     __UpperCamelCase       				=90
     __UpperCamelCase       				=0

     __UpperCamelCase       				=math.log(1.23     ,	10							)
     __UpperCamelCase       				=math.log(1.24     ,	10							)
     __UpperCamelCase       				=0

     while position < number:
          __UpperCamelCase       				=log_difference(SCREAMING_SNAKE_CASE__							)

          if difference >= upper_limit:
               power_iterator += 93

          elif difference < lower_limit:
               power_iterator += 1_96

          else:
               __UpperCamelCase       				=power_iterator
               power_iterator += 1_96
               position += 1

     return previous_power


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   print(f"""{solution() = }""")





