






'''simple docstring'''







import os

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

import datasets


_A			:    int					  =datasets.logging.get_logger(__name__)


_A			:    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[int]					  ='''\
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			:    Tuple					  ='''
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			:    Optional[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 _lowercase			(       datasets.Metric	):




       def 	lowerCamelCase_       (							self:       Union[str, 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 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       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')."""  )
                     lowerCamelCase__      :			str 						= """bleurt-base-128"""

              if self.config_name.lower() in CHECKPOINT_URLS:
                     lowerCamelCase__      :			int 						= self.config_name.lower()

              elif self.config_name.upper() in CHECKPOINT_URLS:
                     lowerCamelCase__      :			List[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
              lowerCamelCase__      :			Union[str, Any] 						= dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]  )
              lowerCamelCase__      :			Optional[int] 						= score.BleurtScorer(os.path.join(UpperCamelCase__    ,							UpperCamelCase__  )  )




       def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Any  ):
              lowerCamelCase__      :			int 						= self.scorer.score(references=UpperCamelCase__    ,							candidates=UpperCamelCase__  )
              return {"scores": scores}









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

