





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def 			lowerCAmelCase_			(				)  ->		Optional[Any]:
       def filter_log(UpperCamelCase_	):
              return False if "This IS expected if you are initializing" in record.msg else True

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


_UpperCAmelCase										=					'\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n'

_UpperCAmelCase										=					'\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.\n'

_UpperCAmelCase										=					'\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. \'en\').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bertscore = datasets.load_metric("bertscore")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")\n    >>> print([round(v, 2) for v in results["f1"]])\n    [1.0, 1.0]\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,    _KWARGS_DESCRIPTION  )
class 				_UpperCamelCase       (						datasets.Metric  ):

       def lowercase			(		self:			Union[str, Any]		)							->			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 lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			List[Any]=None       , _SCREAMING_SNAKE_CASE:			str=False       , _SCREAMING_SNAKE_CASE:			int=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Tuple=4       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , _SCREAMING_SNAKE_CASE:			List[str]=False       , )							->			str:




              """simple docstring"""


              UpperCamelCase_ 							=	bert_score.utils.get_hash
              UpperCamelCase_ 							=	bert_score.BERTScorer

              if version.parse(bert_score.__version__		) >= version.parse("0.3.10"		):
                     UpperCamelCase_ 							=	functools.partial(_SCREAMING_SNAKE_CASE       , use_fast_tokenizer=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	functools.partial(_SCREAMING_SNAKE_CASE       , use_fast_tokenizer=_SCREAMING_SNAKE_CASE		)
              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_ 							=	bert_score.utils.langamodel[lang.lower()]

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

              UpperCamelCase_ 							=	get_hash(
                  model=_SCREAMING_SNAKE_CASE       , num_layers=_SCREAMING_SNAKE_CASE       , idf=_SCREAMING_SNAKE_CASE       , rescale_with_baseline=_SCREAMING_SNAKE_CASE       , 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_ 							=	scorer(
                                model_type=_SCREAMING_SNAKE_CASE       , num_layers=_SCREAMING_SNAKE_CASE       , batch_size=_SCREAMING_SNAKE_CASE       , nthreads=_SCREAMING_SNAKE_CASE       , all_layers=_SCREAMING_SNAKE_CASE       , idf=_SCREAMING_SNAKE_CASE       , device=_SCREAMING_SNAKE_CASE       , lang=_SCREAMING_SNAKE_CASE       , rescale_with_baseline=_SCREAMING_SNAKE_CASE       , baseline_path=_SCREAMING_SNAKE_CASE       , )

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

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			int=None       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			Any:




              """simple docstring"""


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



       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int=None       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=None       , **_SCREAMING_SNAKE_CASE:			int		)							->			Any:




              """simple docstring"""


              if isinstance(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_ 							=	[reference]
              super().add(prediction=_SCREAMING_SNAKE_CASE       , reference=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A      :     Tuple       	={
    '''configuration_imagegpt''': ['''IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ImageGPTConfig''', '''ImageGPTOnnxConfig''']
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Dict       	=['''ImageGPTFeatureExtractor''']
			_A      :     Union[str, Any]       	=['''ImageGPTImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     str       	=[
			    '''IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ImageGPTForCausalImageModeling''',
			    '''ImageGPTForImageClassification''',
			    '''ImageGPTModel''',
			    '''ImageGPTPreTrainedModel''',
			    '''load_tf_weights_in_imagegpt''',
			]


if TYPE_CHECKING:
			from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
						from .image_processing_imagegpt import ImageGPTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_imagegpt import (
						    IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ImageGPTForCausalImageModeling,
						    ImageGPTForImageClassification,
						    ImageGPTModel,
						    ImageGPTPreTrainedModel,
						    load_tf_weights_in_imagegpt,
						)

else:
			import sys

			_A      :     str       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

