
"""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      :			List[Any]					=							"""\
@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}
}
"""

_a      :			Optional[Any]					=							"""\
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.
"""

_a      :			Union[str, Any]					=							"""
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]
"""

_a      :			str					=							{
    """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      _UpperCAmelCase				(					datasets.Metric):
    def 					lowerCamelCase__	(    self     ):
      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						,    snake_case_     ):



      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')."     )
        _snake_case     :						Dict						=   """bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
        _snake_case     :						Tuple						=   self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
        _snake_case     :						Dict						=   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
      _snake_case     :						str						=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]     )
      _snake_case     :						str						=   score.BleurtScorer(os.path.join(lowerCamelCase_						,    lowerCamelCase_     )     )


    def 					lowerCamelCase__	(    self						,    snake_case_						,    snake_case_     ):
      _snake_case     :						Any						=   self.scorer.score(references=lowerCamelCase_						,    candidates=lowerCamelCase_     )
      return {"scores": scores}



"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 				a__   (   a   :				Namespace		):

  """simple docstring"""







  return ConvertCommand(
      args.model_type       ,     args.tf_checkpoint       ,     args.pytorch_dump_output       ,     args.config       ,     args.finetuning_task_name		)


_a      :			int					=							"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""






class      _UpperCAmelCase				(					_snake_case):
    @staticmethod
    def 					lowerCamelCase__	(    snake_case_     ):
      _snake_case     :						Dict						=   parser.add_parser(
          "convert"						,    help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."						,    )
      train_parser.add_argument("--model_type"						,    type=snake_case_						,    required=snake_case_						,    help="Model's type."     )
      train_parser.add_argument(
          "--tf_checkpoint"						,    type=snake_case_						,    required=snake_case_						,    help="TensorFlow checkpoint path or folder."     )
      train_parser.add_argument(
          "--pytorch_dump_output"						,    type=snake_case_						,    required=snake_case_						,    help="Path to the PyTorch saved model output."     )
      train_parser.add_argument("--config"						,    type=snake_case_						,    default=""						,    help="Configuration file path or folder."     )
      train_parser.add_argument(
          "--finetuning_task_name"						,    type=snake_case_						,    default=snake_case_						,    help="Optional fine-tuning task name if the TF model was a finetuned model."						,    )
      train_parser.set_defaults(func=snake_case_     )
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    *snake_case_						,    ):
      _snake_case     :						str						=   logging.get_logger("transformers-cli/converting"     )

      self._logger.info(F'Loading model {model_type}'     )
      _snake_case     :						Optional[int]						=   model_type
      _snake_case     :						Any						=   tf_checkpoint
      _snake_case     :						Optional[int]						=   pytorch_dump_output
      _snake_case     :						Tuple						=   config
      _snake_case     :						Tuple						=   finetuning_task_name


    def 					lowerCamelCase__	(    self     ):
      if self._model_type == "albert":
        try:
          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "bert":
        try:
          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "funnel":
        try:
          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "t5":
        try:
          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "gpt":
        from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
            convert_openai_checkpoint_to_pytorch,
        )

        convert_openai_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "transfo_xl":
        try:
          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
              convert_transfo_xl_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        if "ckpt" in self._tf_checkpoint.lower():
          _snake_case     :						int						=   self._tf_checkpoint
          _snake_case     :						Optional[Any]						=   ""
        else:
          _snake_case     :						Optional[int]						=   self._tf_checkpoint
          _snake_case     :						List[str]						=   ""
        convert_transfo_xl_checkpoint_to_pytorch(
            snake_case_						,    self._config						,    self._pytorch_dump_output						,    snake_case_     )
      elif self._model_type == "gpt2":
        try:
          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
              convert_gpta_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "xlnet":
        try:
          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
              convert_xlnet_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_xlnet_checkpoint_to_pytorch(
            self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output						,    self._finetuning_task_name     )
      elif self._model_type == "xlm":
        from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
            convert_xlm_checkpoint_to_pytorch,
        )

        convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint						,    self._pytorch_dump_output     )
      elif self._model_type == "lxmert":
        from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
            convert_lxmert_checkpoint_to_pytorch,
        )

        convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint						,    self._pytorch_dump_output     )
      elif self._model_type == "rembert":
        from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
            convert_rembert_tf_checkpoint_to_pytorch,
        )

        convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      else:
        raise ValueError(
            "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"     )

