

import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets



@contextmanager
def    lowerCAmelCase__							(		)     -> Union[str, Any]:




				'''simple docstring'''





				def filter_log(SCREAMING_SNAKE_CASE_:							List[Any] ):
								return False if "This IS expected if you are initializing" in record.msg else True

				A__            =     datasets.utils.logging.get_logger("transformers.modeling_utils" )
				logger.addFilter(SCREAMING_SNAKE_CASE_ )
				try:
								yield
				finally:
								logger.removeFilter(SCREAMING_SNAKE_CASE_ )


lowerCAmelCase__						=	"""\
@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}
}
"""

lowerCAmelCase__						=	"""\
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.
"""

lowerCAmelCase__						=	"""
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     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  List[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        UpperCamelCase						(      self , lowercase , lowercase , lowercase=None , lowercase=None , lowercase=None , lowercase=False , lowercase=False , lowercase=None , lowercase=64 , lowercase=4 , lowercase=False , lowercase=False , lowercase=None , lowercase=False , )					->  Union[str, Any]:
							'''simple docstring'''






							A__            =     bert_score.utils.get_hash
							A__            =     bert_score.BERTScorer

							if version.parse(bert_score.__version__    ) >= version.parse("0.3.10"    ):
											A__            =     functools.partial(lowercase , use_fast_tokenizer=lowercase    )
											A__            =     functools.partial(lowercase , use_fast_tokenizer=lowercase    )
							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__            =     bert_score.utils.langamodel[lang.lower()]

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

							A__            =     get_hash(
							    model=lowercase , num_layers=lowercase , idf=lowercase , rescale_with_baseline=lowercase , 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__            =     scorer(
															    model_type=lowercase , num_layers=lowercase , batch_size=lowercase , nthreads=lowercase , all_layers=lowercase , idf=lowercase , device=lowercase , lang=lowercase , rescale_with_baseline=lowercase , baseline_path=lowercase , )

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


			def        UpperCamelCase						(      self , lowercase=None , lowercase=None , **lowercase    )					->  Union[str, Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase=None , lowercase=None , **lowercase    )					->  Dict:
							'''simple docstring'''






							if isinstance(lowercase , lowercase    ):
											A__            =     [reference]
							super().add(prediction=lowercase , reference=lowercase , **lowercase    )




from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


lowerCAmelCase__						=	[
    """AutomaticSpeechRecognition""",
    """AudioClassification""",
    """ImageClassification""",
    """LanguageModeling""",
    """QuestionAnsweringExtractive""",
    """Summarization""",
    """TaskTemplate""",
    """TextClassification""",
]

lowerCAmelCase__						=	get_logger(__name__)


lowerCAmelCase__						=	{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							dict )     -> Optional[TaskTemplate]:




				'''simple docstring'''





				A__            =     task_template_dict.get("task" )
				if task_name is None:
								logger.warning(F'Couldn\'t find template for task \'{task_name}\'. Available templates: {list(SCREAMING_SNAKE_CASE_ )}' )
								return None
				A__            =     NAMEaTEMPLATE.get(SCREAMING_SNAKE_CASE_ )
				return template.from_dict(SCREAMING_SNAKE_CASE_ )

