




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case   :   Optional[Any]     							=	{
    'configuration_wav2vec2': ['WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Wav2Vec2Config'],
    'feature_extraction_wav2vec2': ['Wav2Vec2FeatureExtractor'],
    'processing_wav2vec2': ['Wav2Vec2Processor'],
    'tokenization_wav2vec2': ['Wav2Vec2CTCTokenizer', 'Wav2Vec2Tokenizer'],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[Any]     							=	[
				    'WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Wav2Vec2ForAudioFrameClassification',
				    'Wav2Vec2ForCTC',
				    'Wav2Vec2ForMaskedLM',
				    'Wav2Vec2ForPreTraining',
				    'Wav2Vec2ForSequenceClassification',
				    'Wav2Vec2ForXVector',
				    'Wav2Vec2Model',
				    'Wav2Vec2PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Any     							=	[
				    'TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFWav2Vec2ForCTC',
				    'TFWav2Vec2Model',
				    'TFWav2Vec2PreTrainedModel',
				    'TFWav2Vec2ForSequenceClassification',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Dict     							=	[
				    'FlaxWav2Vec2ForCTC',
				    'FlaxWav2Vec2ForPreTraining',
				    'FlaxWav2Vec2Model',
				    'FlaxWav2Vec2PreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
				from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
				from .processing_wavaveca import WavaVecaProcessor
				from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_wavaveca import (
								    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    WavaVecaForAudioFrameClassification,
								    WavaVecaForCTC,
								    WavaVecaForMaskedLM,
								    WavaVecaForPreTraining,
								    WavaVecaForSequenceClassification,
								    WavaVecaForXVector,
								    WavaVecaModel,
								    WavaVecaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_wavaveca import (
								    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFWavaVecaForCTC,
								    TFWavaVecaForSequenceClassification,
								    TFWavaVecaModel,
								    TFWavaVecaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_wavaveca import (
								    FlaxWavaVecaForCTC,
								    FlaxWavaVecaForPreTraining,
								    FlaxWavaVecaModel,
								    FlaxWavaVecaPreTrainedModel,
								)


else:
				import sys

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







'''simple docstring'''


import os

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

import datasets


_snake_case   :   Any     							=	datasets.logging.get_logger(__name__)


_snake_case   :   Tuple     							=	'\\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'

_snake_case   :   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'

_snake_case   :   Optional[int]     							=	'\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'

_snake_case   :   Dict     							=	{
    '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      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""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 :						Dict							,    lowerCAmelCase_ :						str      )    ->							Tuple:


														"""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			  =       '''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(lowerCAmelCase_							,    lowerCAmelCase_      )      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.scorer.score(references=lowerCAmelCase_							,    candidates=lowerCAmelCase_      )
														return {"scores": scores}

