





'''simple docstring'''

import os

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

import datasets


lowercase_					= datasets.logging.get_logger(__name__)


lowercase_					= """\
@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}
}
"""

lowercase_					= """\
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.
"""

lowercase_					= """
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]
"""

lowercase_					= {
    """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       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   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 			snake_case_(		self ,		A       )		->   Optional[Any]:
        # 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')."""       )
            _SCREAMING_SNAKE_CASE									=			"""bleurt-base-128"""

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

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





    def 			snake_case_(		self ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.scorer.score(references=A ,		candidates=A       )
        return {"scores": scores}










'''simple docstring'''

import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowercase_					= HUGGINGFACE_HUB_CACHE


lowercase_					= """config.json"""
lowercase_					= """diffusion_pytorch_model.bin"""
lowercase_					= """diffusion_flax_model.msgpack"""
lowercase_					= """model.onnx"""
lowercase_					= """diffusion_pytorch_model.safetensors"""
lowercase_					= """weights.pb"""
lowercase_					= """https://huggingface.co"""
lowercase_					= default_cache_path
lowercase_					= """diffusers_modules"""
lowercase_					= os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowercase_					= ["""fp16""", """non-ema"""]
lowercase_					= """.self_attn"""



