






import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets




@contextmanager
def    __A    (   ):

					'''simple docstring'''







					def filter_log(_lowercase       ):
										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(_lowercase       )
					try:
										yield
					finally:
										logger.removeFilter(_lowercase       )


__A          = '\\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'

__A          = '\\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'

__A          = '\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   SCREAMING_SNAKE_CASE (       datasets.Metric    ):





							"""simple docstring"""

							def 					__A       (     self:      Tuple      )			-> str:
												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:      int						,   __A:      str						,   __A:      str						,   __A:      Dict=None						,   __A:      Dict=None						,   __A:      Dict=None						,   __A:      List[str]=False						,   __A:      Optional[int]=False						,   __A:      Optional[Any]=None						,   __A:      Tuple=64						,   __A:      List[Any]=4						,   __A:      Optional[Any]=False						,   __A:      Any=False						,   __A:      List[Any]=None						,   __A:      Optional[int]=False						,   )			-> Optional[Any]:
												_A									=       bert_score.utils.get_hash
												_A									=       bert_score.BERTScorer

												if version.parse(bert_score.__version__      ) >= version.parse('''0.3.10'''      ):
																	_A									=       functools.partial(__A						,   use_fast_tokenizer=__A      )
																	_A									=       functools.partial(__A						,   use_fast_tokenizer=__A      )
												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=__A						,   num_layers=__A						,   idf=__A						,   rescale_with_baseline=__A						,   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=__A						,   num_layers=__A						,   batch_size=__A						,   nthreads=__A						,   all_layers=__A						,   idf=__A						,   device=__A						,   lang=__A						,   rescale_with_baseline=__A						,   baseline_path=__A						,   )

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

							def 					__A       (     self:      List[str]						,   __A:      str=None						,   __A:      Union[str, Any]=None						,   **__A:      Dict      )			-> Optional[int]:
												# References can be strings or lists of strings
												# Let's change strings to lists of strings with one element
												if references is not None:
																	_A									=       [[ref] if isinstance(__A						,   __A      ) else ref for ref in references]
												super().add_batch(predictions=__A						,   references=__A						,   **__A      )



							def 					__A       (     self:      int						,   __A:      int=None						,   __A:      List[str]=None						,   **__A:      Optional[int]      )			-> Optional[Any]:
												# References can be strings or lists of strings
												# Let's change strings to lists of strings with one element
												if isinstance(__A						,   __A      ):
																	_A									=       [reference]
												super().add(prediction=__A						,   reference=__A						,   **__A      )











"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 lowerCamelCase__				,     lowerCamelCase__     :			Optional[Any]     				=						img.shape[0], img.shape[1]

 def to_grayscale(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				):
  return 0.2_126 * red + 0.587 * green + 0.114 * blue

 def normalize(_lowerCamelCase				):
  return min(_lowerCamelCase			,   255				)

 for i in range(_lowerCamelCase				):
  for j in range(_lowerCamelCase				):
   lowerCamelCase__     :			List[Any]     				=						int(to_grayscale(*img[i][j]				)				)
   lowerCamelCase__     :			Optional[int]     				=						[
       normalize(_lowerCamelCase				),
       normalize(greyscale + factor				),
       normalize(greyscale + 2 * factor				),
   ]

 return img


if __name__ == "__main__":
      # read original image
      A_			: List[str]				=			{
          percentage: imread("image_data/lena.jpg", 1) for percentage in (10, 20, 30, 40)
      }

      for percentage, img in images.items():
            make_sepia(img, percentage)

      for percentage, img in images.items():
            imshow(f"Original image with sepia (factor: {percentage})", img)

      waitKey(0)
      destroyAllWindows()

