





import os

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

import datasets


_SCREAMING_SNAKE_CASE   =      datasets.logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE   =      '''\
@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}
}
'''

_SCREAMING_SNAKE_CASE   =      '''\
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.
'''

_SCREAMING_SNAKE_CASE   =      '''
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]
'''

_SCREAMING_SNAKE_CASE   =      {
    '''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  ):







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Any:







				'''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   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[Any]:







				'''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\').'''	)
							__lowercase			  =							'''bleurt-base-128'''

				if self.config_name.lower() in CHECKPOINT_URLS:
							__lowercase			  =							self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
							__lowercase			  =							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
				__lowercase			  =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]	)
				__lowercase			  =							score.BleurtScorer(os.path.join(_lowerCamelCase       ,_lowerCamelCase	)	)


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[Any]:







				'''simple docstring'''





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












'''simple docstring'''



# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)





