





"""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)				, (__A)				, (__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 unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


a_				=    """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""






class 	__snake_case      (	unittest.TestCase							,							SCREAMING_SNAKE_CASE__  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[Any]						=  load_tool('''summarization'''						)
     self.tool.setup()
     __A					:		Tuple						=  load_tool('''summarization'''      ,		remote=__lowerCamelCase						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[str]						=  self.tool(__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		int						=  self.remote_tool(__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[Any]						=  self.tool(text=__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)







 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.remote_tool(text=__lowerCamelCase						)
     self.assertEqual(
         __lowerCamelCase      ,		'''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''      ,		)




