


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,
)


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

A  :     Tuple    					=    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"),
    ]
)

A  :     Optional[Any]    					=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
						if class_name in extractors:
									__lowerCAmelCase       					=    model_type_to_module_name(_UpperCamelCase   )

									__lowerCAmelCase       					=    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       					=    importlib.import_module("transformers"   )
			if hasattr(_UpperCamelCase					,				_UpperCamelCase   ):
						return getattr(_UpperCamelCase					,				_UpperCamelCase   )

			return None


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase = None					,				_UpperCamelCase = False					,				_UpperCamelCase = False					,				_UpperCamelCase = None					,				_UpperCamelCase = None					,				_UpperCamelCase = None					,				_UpperCamelCase = False					,				**_UpperCamelCase					,				):

			'''simple docstring'''




			__lowerCAmelCase       					=    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 						_UpperCamelCase						:
							'''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(__a			)
							def     snake_case       (  cls					,     __a					,     **__a			):
										__lowerCAmelCase       					=    kwargs.pop("config"					,     __a			)
										__lowerCAmelCase       					=    kwargs.pop("trust_remote_code"					,     __a			)
										__lowerCAmelCase       					=    True

										__lowerCAmelCase       					=    FeatureExtractionMixin.get_feature_extractor_dict(__a					,     **__a			)
										__lowerCAmelCase       					=    config_dict.get("feature_extractor_type"					,     __a			)
										__lowerCAmelCase       					=    None
										if "AutoFeatureExtractor" in config_dict.get("auto_map"					,     {}			):
													__lowerCAmelCase       					=    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			):
																__lowerCAmelCase       					=    AutoConfig.from_pretrained(__a					,     **__a			)
													# It could be in `config.feature_extractor_type``
													__lowerCAmelCase       					=    getattr(__a					,     "feature_extractor_type"					,     __a			)
													if hasattr(__a					,     "auto_map"			) and "AutoFeatureExtractor" in config.auto_map:
																__lowerCAmelCase       					=    config.auto_map['AutoFeatureExtractor']

										if feature_extractor_class is not None:
													__lowerCAmelCase       					=    feature_extractor_class_from_name(__a			)

										__lowerCAmelCase       					=    feature_extractor_auto_map is not None
										__lowerCAmelCase       					=    feature_extractor_class is not None or type(__a			) in FEATURE_EXTRACTOR_MAPPING
										__lowerCAmelCase       					=    resolve_trust_remote_code(
										    __a					,     __a					,     __a					,     __a			)

										if has_remote_code and trust_remote_code:
													__lowerCAmelCase       					=    get_class_from_dynamic_module(
													    __a					,     __a					,     **__a			)
													__lowerCAmelCase       					=    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:
													__lowerCAmelCase       					=    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     snake_case       (  __a					,     __a			):
										FEATURE_EXTRACTOR_MAPPING.register(__a					,     __a			)








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




