import os

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

import datasets


lowerCamelCase_    :		List[Any]	       =	datasets.logging.get_logger(__name__)


lowerCamelCase_    :		Optional[Any]	       =	'\\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'

lowerCamelCase_    :		List[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'

lowerCamelCase_    :		Tuple	       =	'\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'

lowerCamelCase_    :		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 __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Optional[int]:

								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(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE   )   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								__a		 =       self.scorer.score(references=_SCREAMING_SNAKE_CASE				,						candidates=_SCREAMING_SNAKE_CASE   )
								return {"scores": scores}





from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Dict							=	{"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Optional[Any]							=	["""SpeechEncoderDecoderModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	["""FlaxSpeechEncoderDecoderModel"""]

if TYPE_CHECKING:
	from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




