



'''simple docstring'''






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase: Tuple							=						datasets.logging.get_logger(__name__)


lowerCAmelCase: str							=						'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

lowerCAmelCase: Optional[Any]							=						'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

lowerCAmelCase: Union[str, Any]							=						'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

lowerCAmelCase: Union[str, Any]							=						{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}

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







    def       lowercase_	(				self			:  Any     ):
      return datasets.MetricInfo(
          description=_DESCRIPTION						,      citation=_CITATION						,      homepage='https://github.com/google-research/bleurt'						,      inputs_description=_KWARGS_DESCRIPTION						,      features=datasets.Features(
              {
                  'predictions': datasets.Value('string'						,      id='sequence'     ),
                  'references': datasets.Value('string'						,      id='sequence'     ),
              }     )						,      codebase_urls=['https://github.com/google-research/bleurt']						,      reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']						,      )







    def       lowercase_	(				self			:  List[Any]						,      __snake_case			:  Dict     ):
      # check that config name specifies a valid BLEURT model
      if self.config_name == "default":
        logger.warning(
            'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
            'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'     )
        a						: Any					       =					'bleurt-base-128'

      if self.config_name.lower() in CHECKPOINT_URLS:
        a						: Optional[Any]					       =					self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
        a						: Union[str, Any]					       =					self.config_name.upper()

      else:
        raise KeyError(
            F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""     )

      # download the model checkpoint specified by self.config_name and set up the scorer
      a						: Union[str, Any]					       =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
      a						: Tuple					       =					score.BleurtScorer(os.path.join(__snake_case						,      __snake_case     )     )







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Any						,      __snake_case			:  List[Any]     ):
      a						: Optional[int]					       =					self.scorer.score(references=__snake_case						,      candidates=__snake_case     )
      return {"scores": scores}




'''simple docstring'''






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class 							a__:







    def __init__(				self			:  Optional[int]     ):
      a						: int					       =					''
      a						: List[str]					       =					''
      a						: int					       =					[]
      a						: Optional[Any]					       =					0
      a						: Optional[Any]					       =					2_56
      a						: int					       =					0
      a						: Optional[int]					       =					0
      a						: str					       =					0
      a						: int					       =					0







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  int     ):
      a						: Optional[Any]					       =					cva.imread(__snake_case						,      0     )
      a						: int					       =					copy.deepcopy(self.img     )
      a       ,     a       ,     a						: Optional[int]					       =					plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]						,      label='x'     )
      a						: str					       =					np.sum(__snake_case     )
      for i in range(len(__snake_case     )     ):
        a						: List[str]					       =					x[i] / self.k
        self.sk += prk
        a						: List[Any]					       =					(self.L - 1) * self.sk
        if self.rem != 0:
          a						: Union[str, Any]					       =					int(last % last     )
        a						: int					       =					int(last + 1 if self.rem >= 0.5 else last     )
        self.last_list.append(__snake_case     )
        a						: int					       =					int(np.ma.count(self.img     ) / self.img[1].size     )
        a						: Dict					       =					self.img[1].size
      for i in range(self.number_of_cols     ):
        for j in range(self.number_of_rows     ):
          a						: Tuple					       =					self.img[j][i]
          if num != self.last_list[num]:
            a						: Union[str, Any]					       =					self.last_list[num]
      cva.imwrite('output_data/output.jpg'						,      self.img     )







    def       lowercase_	(				self			:  Union[str, Any]     ):
      plt.hist(self.img.ravel()						,      2_56						,      [0, 2_56]     )







    def       lowercase_	(				self			:  Any     ):
      cva.imshow('Output-Image'						,      self.img     )
      cva.imshow('Input-Image'						,      self.original_image     )
      cva.waitKey(50_00     )
      cva.destroyAllWindows()


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
     lowerCAmelCase: Optional[Any]							=						ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()