import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets

@contextmanager
def 	__lowerCamelCase							(				)      ->       List[str]:
	def filter_log(__a    : str		):
		return False if "This IS expected if you are initializing" in record.msg else True

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


lowerCAmelCase__  	=      "\\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"

lowerCAmelCase__  	=      "\\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"

lowerCAmelCase__  	=      "\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 	_a  (							datasets.Metric					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		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 __lowerCAmelCase      (   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       ,  ):
		_lowercase   =bert_score.utils.get_hash
		_lowercase   =bert_score.BERTScorer

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

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

		_lowercase   =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:
				_lowercase   =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_       ,  )

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


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  **lowerCAmelCase_       ):
		# References can be strings or lists of strings
		# Let's change strings to lists of strings with one element
		if references is not None:
			_lowercase   =[[ref] if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ) else ref for ref in references]
		super().add_batch(predictions=lowerCAmelCase_       ,  references=lowerCAmelCase_       ,  **lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  **lowerCAmelCase_       ):
		# References can be strings or lists of strings
		# Let's change strings to lists of strings with one element
		if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =[reference]
		super().add(prediction=lowerCAmelCase_       ,  reference=lowerCAmelCase_       ,  **lowerCAmelCase_       )





from typing import TYPE_CHECKING

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


lowerCAmelCase__  	=      {
    "configuration_imagegpt": ["IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ImageGPTConfig", "ImageGPTOnnxConfig"]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      ["ImageGPTFeatureExtractor"]
						lowerCAmelCase__  	=      ["ImageGPTImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  	=      [
						    "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

						lowerCAmelCase__  	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




