



'''simple docstring'''
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets


@contextmanager
def 	lowercase_						( ):

			"""simple docstring"""





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

			__UpperCAmelCase :   Union[str, Any]    					=  datasets.utils.logging.get_logger("""transformers.modeling_utils"""			)
			logger.addFilter(lowerCAmelCase__			)
			try:
						yield
			finally:
						logger.removeFilter(lowerCAmelCase__			)


_UpperCamelCase									=      '''\
@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}
}
'''

_UpperCamelCase									=      '''\
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.
'''

_UpperCamelCase									=      '''
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 				__A							(  self					)							->  str:

					'''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 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase=None			,							__UpperCAmelCase=None			,							__UpperCAmelCase=None			,							__UpperCAmelCase=False			,							__UpperCAmelCase=False			,							__UpperCAmelCase=None			,							__UpperCAmelCase=64			,							__UpperCAmelCase=4			,							__UpperCAmelCase=False			,							__UpperCAmelCase=False			,							__UpperCAmelCase=None			,							__UpperCAmelCase=False			,							)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Tuple    					=  bert_score.utils.get_hash
					__UpperCAmelCase :   Any    					=  bert_score.BERTScorer

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

					if num_layers is None:
								__UpperCAmelCase :   Dict    					=  bert_score.utils.modelalayers[model_type]

					__UpperCAmelCase :   Dict    					=  get_hash(
					    model=__UpperCAmelCase			,							num_layers=__UpperCAmelCase			,							idf=__UpperCAmelCase			,							rescale_with_baseline=__UpperCAmelCase			,							use_custom_baseline=baseline_path is not None			,							)

					with filter_logging_context():
								if not hasattr(self			,							"""cached_bertscorer"""					) or self.cached_bertscorer.hash != hashcode:
											__UpperCAmelCase :   Union[str, Any]    					=  scorer(
											    model_type=__UpperCAmelCase			,							num_layers=__UpperCAmelCase			,							batch_size=__UpperCAmelCase			,							nthreads=__UpperCAmelCase			,							all_layers=__UpperCAmelCase			,							idf=__UpperCAmelCase			,							device=__UpperCAmelCase			,							lang=__UpperCAmelCase			,							rescale_with_baseline=__UpperCAmelCase			,							baseline_path=__UpperCAmelCase			,							)

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





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

					'''simple docstring'''
					# References can be strings or lists of strings
					# Let's change strings to lists of strings with one element
					if references is not None:
								__UpperCAmelCase :   Optional[Any]    					=  [[ref] if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else ref for ref in references]
					super().add_batch(predictions=__UpperCAmelCase			,							references=__UpperCAmelCase			,							**__UpperCAmelCase					)





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

					'''simple docstring'''
					# References can be strings or lists of strings
					# Let's change strings to lists of strings with one element
					if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   List[str]    					=  [reference]
					super().add(prediction=__UpperCAmelCase			,							reference=__UpperCAmelCase			,							**__UpperCAmelCase					)









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase									=      {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5Tokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      ['''T5TokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''T5EncoderModel''',
					    '''T5ForConditionalGeneration''',
					    '''T5Model''',
					    '''T5PreTrainedModel''',
					    '''load_tf_weights_in_t5''',
					    '''T5ForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFT5EncoderModel''',
					    '''TFT5ForConditionalGeneration''',
					    '''TFT5Model''',
					    '''TFT5PreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCamelCase									=      [
					    '''FlaxT5EncoderModel''',
					    '''FlaxT5ForConditionalGeneration''',
					    '''FlaxT5Model''',
					    '''FlaxT5PreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta import TaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_ta_fast import TaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_ta import (
										    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TaEncoderModel,
										    TaForConditionalGeneration,
										    TaForQuestionAnswering,
										    TaModel,
										    TaPreTrainedModel,
										    load_tf_weights_in_ta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_ta import (
										    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFTaEncoderModel,
										    TFTaForConditionalGeneration,
										    TFTaModel,
										    TFTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_ta import (
										    FlaxTaEncoderModel,
										    FlaxTaForConditionalGeneration,
										    FlaxTaModel,
										    FlaxTaPreTrainedModel,
										)


else:
					import sys

					_UpperCamelCase									=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




