


'''simple docstring'''




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def 				a_							(    )      ->str:






				'''simple docstring'''

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

				__lowerCamelCase		:   Any           =       datasets.utils.logging.get_logger("transformers.modeling_utils"     )
				logger.addFilter(lowercase     )
				try:
								yield
				finally:
								logger.removeFilter(lowercase     )


__lowercase	:   Optional[Any]   		=					"""\
@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}
}
"""

__lowercase	:   Optional[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.
"""

__lowercase	:   Union[str, Any]   		=					"""
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 		__SCREAMING_SNAKE_CASE( 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 		__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		:   int           =       bert_score.utils.get_hash
											__lowerCamelCase		:   str           =       bert_score.BERTScorer

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

											if num_layers is None:
															__lowerCamelCase		:   Union[str, Any]           =       bert_score.utils.modelalayers[model_type]

											__lowerCamelCase		:   Union[str, Any]           =       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		:   Optional[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      ,					)

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







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A=None      ,					__A=None      ,					**__A						)  ->List[Any]:



											"""simple docstring"""







											if references is not None:
															__lowerCamelCase		:   Optional[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						)  ->Any:



											"""simple docstring"""







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









'''simple docstring'''




from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A      ,					__A						)  ->None:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       word
											__lowerCamelCase		:   Optional[Any]           =       count







							def __eq__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count == other.count







							def __lt__( self      ,					__A						)  ->bool:



											"""simple docstring"""







											if not isinstance(__A      ,					__A						):
															return NotImplemented
											return self.count < other.count





def 				a_							(    lowercase				,    lowercase     )      ->list[str]:






				'''simple docstring'''

				__lowerCamelCase		:   Heap[WordCount]           =       Heap()
				__lowerCamelCase		:   int           =       Counter(lowercase     )
				heap.build_max_heap(
				    [WordCount(lowercase				,    lowercase     ) for word, count in count_by_word.items()]     )
				return [heap.extract_max().word for _ in range(min(lowercase				,    len(lowercase     )     )     )]


if __name__ == "__main__":
				import doctest

				doctest.testmod()





