


'''simple docstring'''


import os

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

import datasets


_UpperCamelCase 							=  datasets.logging.get_logger(__name__)


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

_UpperCamelCase 							=  """\
BLEURT 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)
and 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
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

_UpperCamelCase 							=  """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

_UpperCamelCase 							=  {
    """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 							__magic_name__	(       datasets.Metric ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          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 lowerCAmelCase__  (   self			,     lowerCamelCase		):


          '''simple docstring'''





          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	:       Optional[Any]      =							"bleurt-base-128"

          if self.config_name.lower() in CHECKPOINT_URLS:
               __A	:       Union[str, Any]      =							self.config_name.lower()

          elif self.config_name.upper() in CHECKPOINT_URLS:
               __A	:       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	:       Any      =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]		)
          __A	:       Optional[int]      =							score.BleurtScorer(os.path.join(lowerCamelCase			,     lowerCamelCase		)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.scorer.score(references=lowerCamelCase			,     candidates=lowerCamelCase		)
          return {"scores": scores}





'''simple docstring'''


import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

_UpperCamelCase 							=  logging.get_logger(__name__)


_UpperCamelCase 							=  {name: getattr(transformers, name + """Fast""") for name in SLOW_TO_FAST_CONVERTERS}


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
          raise ValueError(f"Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()							)}."							)

     if tokenizer_name is None:
          __A	:       Tuple      =							TOKENIZER_CLASSES
     else:
          __A	:       Dict      =							{tokenizer_name: getattr(SCREAMING_SNAKE_CASE       ,					tokenizer_name + "Fast"							)}

     logger.info(f"Loading tokenizer classes: {tokenizer_names}"							)

     for tokenizer_name in tokenizer_names:
          __A	:       int      =							TOKENIZER_CLASSES[tokenizer_name]

          __A	:       Union[str, Any]      =							True
          if checkpoint_name is None:
               __A	:       Dict      =							list(tokenizer_class.max_model_input_sizes.keys()							)
          else:
               __A	:       str      =							[checkpoint_name]

          logger.info(f"For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"							)

          for checkpoint in checkpoint_names:
               logger.info(f"Loading {tokenizer_class.__class__.__name__} {checkpoint}"							)

               # Load tokenizer
               __A	:       List[str]      =							tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE       ,					force_download=SCREAMING_SNAKE_CASE							)

               # Save fast tokenizer
               logger.info(f"Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"							)

               # For organization names we create sub-directories
               if "/" in checkpoint:
                    __A  ,__A	:       Optional[Any]      =							checkpoint.split("/"							)
                    __A	:       str      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
               elif add_prefix:
                    __A	:       Optional[int]      =							checkpoint
                    __A	:       Any      =							dump_path
               else:
                    __A	:       int      =							None
                    __A	:       Optional[int]      =							dump_path

               logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"							)

               if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()							)[0]:
                    __A	:       Any      =							list(tokenizer.pretrained_vocab_files_map.values()							)[0][checkpoint]
                    __A	:       Optional[int]      =							file_path.split(SCREAMING_SNAKE_CASE							)[-1][0]
                    if next_char == "/":
                         __A	:       Union[str, Any]      =							os.path.join(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
                         __A	:       Union[str, Any]      =							None

                    logger.info(f"=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"							)

               __A	:       Tuple      =							tokenizer.save_pretrained(
                   SCREAMING_SNAKE_CASE       ,					legacy_format=SCREAMING_SNAKE_CASE       ,					filename_prefix=SCREAMING_SNAKE_CASE							)
               logger.info(f"=> File names {file_names}"							)

               for file_name in file_names:
                    if not file_name.endswith("tokenizer.json"							):
                         os.remove(SCREAMING_SNAKE_CASE							)
                         logger.info(f"=> removing {file_name}"							)


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--dump_path""", default=None, type=str, required=True, help="""Path to output generated fast tokenizer files."""
      )
      parser.add_argument(
          """--tokenizer_name""",
          default=None,
          type=str,
          help=(
              F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
              """download and convert all the checkpoints from AWS."""
          ),
      )
      parser.add_argument(
          """--checkpoint_name""",
          default=None,
          type=str,
          help="""Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.""",
      )
      parser.add_argument(
          """--force_download""",
          action="""store_true""",
          help="""Re-download checkpoints.""",
      )
      _UpperCamelCase 							=  parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)

