





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,
)


_UpperCAmelCase							:			Union[str, Any]        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Any        =							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"""),
    ]
)

_UpperCAmelCase							:			Dict        =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	Union[str, Any]:
    for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
        if class_name in extractors:
            lowerCamelCase__       : Tuple   					=						model_type_to_module_name(_UpperCAmelCase					)

            lowerCamelCase__       : Union[str, Any]   					=						importlib.import_module(F""".{module_name}"""		,      'transformers.models'					)
            try:
                return getattr(_UpperCAmelCase		,      _UpperCAmelCase					)
            except AttributeError:
                continue

    for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
        if getattr(_UpperCAmelCase		,      '__name__'		,      _UpperCAmelCase					) == 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__       : List[str]   					=						importlib.import_module('transformers'					)
    if hasattr(_UpperCAmelCase		,      _UpperCAmelCase					):
        return getattr(_UpperCAmelCase		,      _UpperCAmelCase					)

    return None


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False		,      _UpperCAmelCase = False		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = False		,      **_UpperCAmelCase		,      )     ->	List[Any]:
    lowerCamelCase__       : Dict   					=						get_file_from_repo(
        _UpperCAmelCase		,      _UpperCAmelCase		,      cache_dir=_UpperCAmelCase		,      force_download=_UpperCAmelCase		,      resume_download=_UpperCAmelCase		,      proxies=_UpperCAmelCase		,      use_auth_token=_UpperCAmelCase		,      revision=_UpperCAmelCase		,      local_files_only=_UpperCAmelCase		,      )
    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(_UpperCAmelCase		,      encoding='utf-8'					) as reader:
        return json.load(_UpperCAmelCase					)


class   lowerCAmelCase				:
 def __init__( self    : Union[str, Any]      )     ->      List[str]:
     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    : Optional[Any]     ,	UpperCAmelCase    : Tuple     ,	**UpperCAmelCase    : Optional[int]      )     ->      Dict:
     lowerCamelCase__       : Tuple   					=						kwargs.pop('config'     ,	UpperCAmelCase      )
     lowerCamelCase__       : Optional[int]   					=						kwargs.pop('trust_remote_code'     ,	UpperCAmelCase      )
     lowerCamelCase__       : Union[str, Any]   					=						True

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

     if feature_extractor_class is not None:
         lowerCamelCase__       : str   					=						feature_extractor_class_from_name(UpperCAmelCase      )

     lowerCamelCase__       : int   					=						feature_extractor_auto_map is not None
     lowerCamelCase__       : Dict   					=						feature_extractor_class is not None or type(UpperCAmelCase      ) in FEATURE_EXTRACTOR_MAPPING
     lowerCamelCase__       : int   					=						resolve_trust_remote_code(
         UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase     ,	UpperCAmelCase      )

     if has_remote_code and trust_remote_code:
         lowerCamelCase__       : Union[str, Any]   					=						get_class_from_dynamic_module(
             UpperCAmelCase     ,	UpperCAmelCase     ,	**UpperCAmelCase      )
         lowerCamelCase__       : List[Any]   					=						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__       : Any   					=						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_      ( UpperCAmelCase    : Optional[Any]     ,	UpperCAmelCase    : int      )     ->      Optional[int]:
     FEATURE_EXTRACTOR_MAPPING.register(UpperCAmelCase     ,	UpperCAmelCase      )












from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : Union[str, Any]      )     ->      List[Any]:
     lowerCamelCase__       : Tuple   					=						TFCamembertModel.from_pretrained('jplu/tf-camembert-base'      )

     lowerCamelCase__       : Any   					=						tf.convert_to_tensor(
         [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]     ,	dtype=tf.intaa     ,	)  # J'aime le camembert !"

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase      )['last_hidden_state']
     lowerCamelCase__       : Tuple   					=						tf.TensorShape((1, 10, 768)      )
     self.assertEqual(output.shape     ,	UpperCAmelCase      )
     # compare the actual values for a slice.
     lowerCamelCase__       : Optional[Any]   					=						tf.convert_to_tensor(
         [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]     ,	dtype=tf.floataa     ,	)
     # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
     # camembert.eval()
     # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

     self.assertTrue(np.allclose(output[:, :3, :3].numpy()     ,	expected_slice.numpy()     ,	atol=1e-4      )      )





