




import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets







@contextmanager
def  lowerCAmelCase  (	)  ->Optional[Any]:






					"""simple docstring"""






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

					__magic_name__					:							str		   =							datasets.utils.logging.get_logger('''transformers.modeling_utils'''       )
					logger.addFilter(UpperCAmelCase       )
					try:
										yield
					finally:
										logger.removeFilter(UpperCAmelCase       )


lowercase_		=							'''\
@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_		=							'''\
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_		=							'''
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 		A__				(   datasets.Metric ):






	def  lowercase      (	self						)      ->   Optional[int]:




						"""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  lowercase      (	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				,     )      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							str		   =							bert_score.utils.get_hash
						__magic_name__					:							Optional[Any]		   =							bert_score.BERTScorer

						if version.parse(bert_score.__version__						) >= version.parse('''0.3.10'''						):
											__magic_name__					:							Union[str, Any]		   =							functools.partial(lowerCamelCase				,     use_fast_tokenizer=lowerCamelCase						)
											__magic_name__					:							Optional[Any]		   =							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"
											__magic_name__					:							str		   =							bert_score.utils.langamodel[lang.lower()]

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

						__magic_name__					:							Optional[int]		   =							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:
																__magic_name__					:							Union[str, 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				,     )

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






	def  lowercase      (	self				,     lowerCamelCase=None				,     lowerCamelCase=None				,     **lowerCamelCase						)      ->   int:




						"""simple docstring"""






						if references is not None:
											__magic_name__					:							List[str]		   =							[[ref] if isinstance(lowerCamelCase				,     lowerCamelCase						) else ref for ref in references]
						super().add_batch(predictions=lowerCamelCase				,     references=lowerCamelCase				,     **lowerCamelCase						)






	def  lowercase      (	self				,     lowerCamelCase=None				,     lowerCamelCase=None				,     **lowerCamelCase						)      ->   str:




						"""simple docstring"""






						if isinstance(lowerCamelCase				,     lowerCamelCase						):
											__magic_name__					:							Any		   =							[reference]
						super().add(prediction=lowerCamelCase				,     reference=lowerCamelCase				,     **lowerCamelCase						)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}





class 		A__				(   __SCREAMING_SNAKE_CASE ):
	lowerCamelCase__		: Tuple				    ="yoso"






	def __init__(	self				,     lowerCamelCase=50265				,     lowerCamelCase=768				,     lowerCamelCase=12				,     lowerCamelCase=12				,     lowerCamelCase=3072				,     lowerCamelCase="gelu"				,     lowerCamelCase=0.1				,     lowerCamelCase=0.1				,     lowerCamelCase=4096				,     lowerCamelCase=1				,     lowerCamelCase=0.0_2				,     lowerCamelCase=1e-12				,     lowerCamelCase="absolute"				,     lowerCamelCase=True				,     lowerCamelCase=9				,     lowerCamelCase=64				,     lowerCamelCase=None				,     lowerCamelCase=True				,     lowerCamelCase=True				,     lowerCamelCase=1				,     lowerCamelCase=0				,     lowerCamelCase=2				,     **lowerCamelCase				,     )      ->   Tuple:




						"""simple docstring"""






						super().__init__(pad_token_id=lowerCamelCase				,     bos_token_id=lowerCamelCase				,     eos_token_id=lowerCamelCase				,     **lowerCamelCase						)

						__magic_name__					:							Union[str, Any]		   =							vocab_size
						__magic_name__					:							Union[str, Any]		   =							max_position_embeddings
						__magic_name__					:							Tuple		   =							hidden_size
						__magic_name__					:							Optional[Any]		   =							num_hidden_layers
						__magic_name__					:							Any		   =							num_attention_heads
						__magic_name__					:							List[str]		   =							intermediate_size
						__magic_name__					:							List[str]		   =							hidden_act
						__magic_name__					:							Tuple		   =							hidden_dropout_prob
						__magic_name__					:							str		   =							attention_probs_dropout_prob
						__magic_name__					:							str		   =							initializer_range
						__magic_name__					:							List[str]		   =							type_vocab_size
						__magic_name__					:							Dict		   =							layer_norm_eps
						__magic_name__					:							Any		   =							position_embedding_type
						__magic_name__					:							Optional[Any]		   =							use_expectation
						__magic_name__					:							Optional[Any]		   =							hash_code_len
						__magic_name__					:							Dict		   =							num_hash
						__magic_name__					:							str		   =							conv_window
						__magic_name__					:							Optional[Any]		   =							use_fast_hash
						__magic_name__					:							Union[str, Any]		   =							lsh_backward



