


import os

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

import datasets


lowerCAmelCase       		=    datasets.logging.get_logger(__name__)


lowerCAmelCase       		=    '''\
@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}
}
'''

lowerCAmelCase       		=    '''\
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.
'''

lowerCAmelCase       		=    '''
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]
'''

lowerCAmelCase       		=    {
    '''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 							lowerCamelCase   (  datasets.Metric ):


	def  A(    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  A(    self				,     lowercase__):
							# 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\').''')
													__UpperCAmelCase			:							Tuple	    =	'bleurt-base-128'

							if self.config_name.lower() in CHECKPOINT_URLS:
													__UpperCAmelCase			:							str	    =	self.config_name.lower()

							elif self.config_name.upper() in CHECKPOINT_URLS:
													__UpperCAmelCase			:							Tuple	    =	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
							__UpperCAmelCase			:							Union[str, Any]	    =	dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
							__UpperCAmelCase			:							List[str]	    =	score.BleurtScorer(os.path.join(__a				,     __a))


	def  A(    self				,     lowercase__				,     lowercase__):
							__UpperCAmelCase			:							Any	    =	self.scorer.score(references=__a				,     candidates=__a)
							return {"scores": scores}






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__    :   Optional[Any]           =					{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Tuple           =					['''SqueezeBertTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   str           =					[
        '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SqueezeBertForMaskedLM''',
        '''SqueezeBertForMultipleChoice''',
        '''SqueezeBertForQuestionAnswering''',
        '''SqueezeBertForSequenceClassification''',
        '''SqueezeBertForTokenClassification''',
        '''SqueezeBertModel''',
        '''SqueezeBertModule''',
        '''SqueezeBertPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_squeezebert import (
        SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SqueezeBertConfig,
        SqueezeBertOnnxConfig,
    )
    from .tokenization_squeezebert import SqueezeBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_squeezebert import (
            SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
            SqueezeBertModel,
            SqueezeBertModule,
            SqueezeBertPreTrainedModel,
        )

else:
    import sys

    A__    :   List[Any]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



