





'''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}









import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       OrderedDict(
    [
        ("audio-spectrogram-transformer", "ASTFeatureExtractor"),
        ("beit", "BeitFeatureExtractor"),
        ("chinese_clip", "ChineseCLIPFeatureExtractor"),
        ("clap", "ClapFeatureExtractor"),
        ("clip", "CLIPFeatureExtractor"),
        ("clipseg", "ViTFeatureExtractor"),
        ("conditional_detr", "ConditionalDetrFeatureExtractor"),
        ("convnext", "ConvNextFeatureExtractor"),
        ("cvt", "ConvNextFeatureExtractor"),
        ("data2vec-audio", "Wav2Vec2FeatureExtractor"),
        ("data2vec-vision", "BeitFeatureExtractor"),
        ("deformable_detr", "DeformableDetrFeatureExtractor"),
        ("deit", "DeiTFeatureExtractor"),
        ("detr", "DetrFeatureExtractor"),
        ("dinat", "ViTFeatureExtractor"),
        ("donut-swin", "DonutFeatureExtractor"),
        ("dpt", "DPTFeatureExtractor"),
        ("encodec", "EncodecFeatureExtractor"),
        ("flava", "FlavaFeatureExtractor"),
        ("glpn", "GLPNFeatureExtractor"),
        ("groupvit", "CLIPFeatureExtractor"),
        ("hubert", "Wav2Vec2FeatureExtractor"),
        ("imagegpt", "ImageGPTFeatureExtractor"),
        ("layoutlmv2", "LayoutLMv2FeatureExtractor"),
        ("layoutlmv3", "LayoutLMv3FeatureExtractor"),
        ("levit", "LevitFeatureExtractor"),
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilenet_v1", "MobileNetV1FeatureExtractor"),
        ("mobilenet_v2", "MobileNetV2FeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("nat", "ViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
        ("resnet", "ConvNextFeatureExtractor"),
        ("segformer", "SegformerFeatureExtractor"),
        ("sew", "Wav2Vec2FeatureExtractor"),
        ("sew-d", "Wav2Vec2FeatureExtractor"),
        ("speech_to_text", "Speech2TextFeatureExtractor"),
        ("speecht5", "SpeechT5FeatureExtractor"),
        ("swiftformer", "ViTFeatureExtractor"),
        ("swin", "ViTFeatureExtractor"),
        ("swinv2", "ViTFeatureExtractor"),
        ("table-transformer", "DetrFeatureExtractor"),
        ("timesformer", "VideoMAEFeatureExtractor"),
        ("tvlt", "TvltFeatureExtractor"),
        ("unispeech", "Wav2Vec2FeatureExtractor"),
        ("unispeech-sat", "Wav2Vec2FeatureExtractor"),
        ("van", "ConvNextFeatureExtractor"),
        ("videomae", "VideoMAEFeatureExtractor"),
        ("vilt", "ViltFeatureExtractor"),
        ("vit", "ViTFeatureExtractor"),
        ("vit_mae", "ViTFeatureExtractor"),
        ("vit_msn", "ViTFeatureExtractor"),
        ("wav2vec2", "Wav2Vec2FeatureExtractor"),
        ("wav2vec2-conformer", "Wav2Vec2FeatureExtractor"),
        ("wavlm", "Wav2Vec2FeatureExtractor"),
        ("whisper", "WhisperFeatureExtractor"),
        ("xclip", "CLIPFeatureExtractor"),
        ("yolos", "YolosFeatureExtractor"),
    ]
)

_snake_case										=       _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def 				lowerCAmelCase_	(     snake_case_     ):
      for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
            if class_name in extractors:
                  _A							:     List[str]			=						model_type_to_module_name(snake_case_     )

                  _A							:     List[Any]			=						importlib.import_module(f'''.{module_name}''',"""transformers.models"""     )
                  try:
                        return getattr(snake_case_,snake_case_     )
                  except AttributeError:
                        continue

      for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
            if getattr(snake_case_,"""__name__""",snake_case_     ) == class_name:
                  return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
      _A							:     List[Any]			=						importlib.import_module("""transformers"""     )
      if hasattr(snake_case_,snake_case_     ):
            return getattr(snake_case_,snake_case_     )

      return None







def 				lowerCAmelCase_	(     snake_case_,snake_case_ = None,snake_case_ = False,snake_case_ = False,snake_case_ = None,snake_case_ = None,snake_case_ = None,snake_case_ = False,**snake_case_,):
      _A							:     Optional[int]			=						get_file_from_repo(
          snake_case_,snake_case_,cache_dir=snake_case_,force_download=snake_case_,resume_download=snake_case_,proxies=snake_case_,use_auth_token=snake_case_,revision=snake_case_,local_files_only=snake_case_,)
      if resolved_config_file is None:
            logger.info(
                """Could not locate the feature extractor configuration file, will try to use the model config instead."""     )
            return {}

      with open(snake_case_,encoding="""utf-8"""     ) as reader:
            return json.load(snake_case_     )







class 						lowercase							:

     def __init__(  self				)						->							List[Any]:
           raise EnvironmentError(
               """AutoFeatureExtractor is designed to be instantiated """
               """using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method."""				)

     @classmethod
     @replace_list_option_in_docstrings(_a				)
     def 					a__	(  cls		,     _a		,     **_a				)						->							Any:
           _A							:     Tuple			=						kwargs.pop("""config"""		,     _a				)
           _A							:     Tuple			=						kwargs.pop("""trust_remote_code"""		,     _a				)
           _A							:     List[Any]			=						True

           _A			,   _A							:     Tuple			=						FeatureExtractionMixin.get_feature_extractor_dict(_a		,     **_a				)
           _A							:     Tuple			=						config_dict.get("""feature_extractor_type"""		,     _a				)
           _A							:     int			=						None
           if "AutoFeatureExtractor" in config_dict.get("""auto_map"""		,     {}				):
                 _A							:     Optional[int]			=						config_dict["""auto_map"""]["""AutoFeatureExtractor"""]

           # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
           if feature_extractor_class is None and feature_extractor_auto_map is None:
                 if not isinstance(_a		,     _a				):
                       _A							:     int			=						AutoConfig.from_pretrained(_a		,     **_a				)
                 # It could be in `config.feature_extractor_type``
                 _A							:     Optional[int]			=						getattr(_a		,     """feature_extractor_type"""		,     _a				)
                 if hasattr(_a		,     """auto_map"""				) and "AutoFeatureExtractor" in config.auto_map:
                       _A							:     Tuple			=						config.auto_map["""AutoFeatureExtractor"""]

           if feature_extractor_class is not None:
                 _A							:     Optional[Any]			=						feature_extractor_class_from_name(_a				)

           _A							:     List[Any]			=						feature_extractor_auto_map is not None
           _A							:     Union[str, Any]			=						feature_extractor_class is not None or type(_a				) in FEATURE_EXTRACTOR_MAPPING
           _A							:     Optional[int]			=						resolve_trust_remote_code(
               _a		,     _a		,     _a		,     _a				)

           if has_remote_code and trust_remote_code:
                 _A							:     Dict			=						get_class_from_dynamic_module(
                     _a		,     _a		,     **_a				)
                 _A							:     str			=						kwargs.pop("""code_revision"""		,     _a				)
                 if os.path.isdir(_a				):
                       feature_extractor_class.register_for_auto_class()
                 return feature_extractor_class.from_dict(_a		,     **_a				)
           elif feature_extractor_class is not None:
                 return feature_extractor_class.from_dict(_a		,     **_a				)
           # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
           elif type(_a				) in FEATURE_EXTRACTOR_MAPPING:
                 _A							:     Dict			=						FEATURE_EXTRACTOR_MAPPING[type(_a				)]
                 return feature_extractor_class.from_dict(_a		,     **_a				)

           raise ValueError(
               F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
               F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
               F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()				)}'''				)



     @staticmethod
     def 					a__	(  _a		,     _a				)						->							Optional[int]:
           FEATURE_EXTRACTOR_MAPPING.register(_a		,     _a				)

