'''simple docstring'''






import os

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

import datasets


a_					:    Optional[Any]          =    datasets.logging.get_logger(__name__)


a_					:    Optional[Any]          =    "\\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"

a_					:    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"

a_					:    str          =    "\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"

a_					:    Optional[int]          =    {
    "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 							__UpperCAmelCase		( self						)					->							int:
      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 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      # 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			      =							'bleurt-base-128'

      if self.config_name.lower() in CHECKPOINT_URLS:
           _a			      =							self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
           _a			      =							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			      =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
      _a			      =							score.BleurtScorer(os.path.join(__magic_name__      ,				__magic_name__						)						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Tuple:
      _a			      =							self.scorer.score(references=__magic_name__      ,				candidates=__magic_name__						)
      return {"scores": scores}





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

