




"""simple docstring"""






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,
)


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		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"""),
    ]
)

lowercase__  		=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)







def   _snake_case    (       lowercase__	):
				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												_lowerCamelCase  :    int					=					model_type_to_module_name(_lowercase	)

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

				for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
								if getattr(_lowercase							,					'__name__'							,					_lowercase	) == 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.
				_lowerCamelCase  :    str					=					importlib.import_module('transformers'	)
				if hasattr(_lowercase							,					_lowercase	):
								return getattr(_lowercase							,					_lowercase	)

				return None







def   _snake_case    (       lowercase__							,					lowercase__ = None							,					lowercase__ = False							,					lowercase__ = False							,					lowercase__ = None							,					lowercase__ = None							,					lowercase__ = None							,					lowercase__ = False							,					**lowercase__							,					):
				_lowerCamelCase  :    List[Any]					=					get_file_from_repo(
				    _lowercase							,					_lowercase							,					cache_dir=_lowercase							,					force_download=_lowercase							,					resume_download=_lowercase							,					proxies=_lowercase							,					use_auth_token=_lowercase							,					revision=_lowercase							,					local_files_only=_lowercase							,					)
				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(_lowercase							,					encoding='utf-8'	) as reader:
								return json.load(_lowercase	)






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							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(UpperCamelCase_						)
			def       A_					(						cls						,   lowercase						,   **lowercase						):
							_lowerCamelCase  :    List[Any]					=					kwargs.pop('config'						,   UpperCamelCase_						)
							_lowerCamelCase  :    Optional[int]					=					kwargs.pop('trust_remote_code'						,   UpperCamelCase_						)
							_lowerCamelCase  :    Optional[Any]					=					True

							_lowerCamelCase  :    Any					=					FeatureExtractionMixin.get_feature_extractor_dict(UpperCamelCase_						,   **UpperCamelCase_						)
							_lowerCamelCase  :    List[str]					=					config_dict.get('feature_extractor_type'						,   UpperCamelCase_						)
							_lowerCamelCase  :    Dict					=					None
							if "AutoFeatureExtractor" in config_dict.get('auto_map'						,   {}						):
											_lowerCamelCase  :    Tuple					=					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(UpperCamelCase_						,   UpperCamelCase_						):
															_lowerCamelCase  :    Any					=					AutoConfig.from_pretrained(UpperCamelCase_						,   **UpperCamelCase_						)
											# It could be in `config.feature_extractor_type``
											_lowerCamelCase  :    Union[str, Any]					=					getattr(UpperCamelCase_						,   'feature_extractor_type'						,   UpperCamelCase_						)
											if hasattr(UpperCamelCase_						,   'auto_map'						) and "AutoFeatureExtractor" in config.auto_map:
															_lowerCamelCase  :    Optional[Any]					=					config.auto_map['AutoFeatureExtractor']

							if feature_extractor_class is not None:
											_lowerCamelCase  :    Any					=					feature_extractor_class_from_name(UpperCamelCase_						)

							_lowerCamelCase  :    str					=					feature_extractor_auto_map is not None
							_lowerCamelCase  :    str					=					feature_extractor_class is not None or type(UpperCamelCase_						) in FEATURE_EXTRACTOR_MAPPING
							_lowerCamelCase  :    List[Any]					=					resolve_trust_remote_code(
							    UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_						)

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

							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_					(						lowercase						,   lowercase						):
							FEATURE_EXTRACTOR_MAPPING.register(UpperCamelCase_						,   UpperCamelCase_						)





"""simple docstring"""






from typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


lowercase__  		=		"""\
@misc{wu2016googles,
      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey
              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin
              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto
              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and
              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes
              and Jeffrey Dean},
      year={2016},
      eprint={1609.08144},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

lowercase__  		=		"""\
The BLEU score has some undesirable properties when used for single
sentences, as it was designed to be a corpus measure. We therefore
use a slightly different score for our RL experiments which we call
the 'GLEU score'. For the GLEU score, we record all sub-sequences of
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
compute a recall, which is the ratio of the number of matching n-grams
to the number of total n-grams in the target (ground truth) sequence,
and a precision, which is the ratio of the number of matching n-grams
to the number of total n-grams in the generated output sequence. Then
GLEU score is simply the minimum of recall and precision. This GLEU
score's range is always between 0 (no matches) and 1 (all match) and
it is symmetrical when switching output and target. According to
our experiments, GLEU score correlates quite well with the BLEU
metric on a corpus level but does not have its drawbacks for our per
sentence reward objective.
"""

lowercase__  		=		"""\
Computes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.
Instead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching
tokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.

Args:
    predictions (list of str): list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references (list of list of str): list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.
    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.

Returns:
    'google_bleu': google_bleu score

Examples:
    Example 1:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.44

    Example 2:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)
        >>> print(round(results[\"google_bleu\"], 2))
        0.61

    Example 3:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)
        >>> print(round(results[\"google_bleu\"], 2))
        0.53

    Example 4:
        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',
        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']
        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',
        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',
        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']
        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',
        ...          'heed', 'the', 'cat', 'commands']
        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',
        ...          'of', 'the', 'cat']

        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
        ...         'interested', 'in', 'world', 'history']
        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
        ...          'because', 'he', 'read', 'the', 'book']

        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
        >>> hypotheses = [hyp1, hyp2]
        >>> google_bleu = datasets.load_metric(\"google_bleu\")
        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)
        >>> print(round(results[\"google_bleu\"], 2))
        0.4
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,			_KWARGS_DESCRIPTION		)
class lowerCAmelCase__			(  datasets.Metric		):






			'''simple docstring'''



			def       A_					(						self						):
							return datasets.MetricInfo(
							    description=_DESCRIPTION						,   citation=_CITATION						,   inputs_description=_KWARGS_DESCRIPTION						,   features=datasets.Features(
							        {
							            'predictions': datasets.Sequence(datasets.Value('string'						,   id='token'						)						,   id='sequence'						),
							            'references': datasets.Sequence(
							                datasets.Sequence(datasets.Value('string'						,   id='token'						)						,   id='sequence'						)						,   id='references'						),
							        }						)						,   )






			def       A_					(						self						,   lowercase						,   lowercase						,   lowercase = 1						,   lowercase = 4						,   ):
							return {
							    "google_bleu": gleu_score.corpus_gleu(
							        list_of_references=lowercase						,   hypotheses=lowercase						,   min_len=lowercase						,   max_len=lowercase						)
							}