




"""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,
)


a      =							logging.get_logger(__name__)

a      =							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      =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)





def 							lowercase					(snake_case__   : str )     ->  int:


				'''simple docstring'''






				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												lowerCAmelCase  =				model_type_to_module_name(snake_case__ )

												lowerCAmelCase  =				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.
				lowerCAmelCase  =				importlib.import_module("""transformers""" )
				if hasattr(snake_case__      ,       snake_case__ ):
								return getattr(snake_case__      ,       snake_case__ )

				return None





def 							lowercase					(snake_case__   : Union[str, os.PathLike]      ,       snake_case__   : Optional[Union[str, os.PathLike]] = None      ,       snake_case__   : bool = False      ,       snake_case__   : bool = False      ,       snake_case__   : Optional[Dict[str, str]] = None      ,       snake_case__   : Optional[Union[bool, str]] = None      ,       snake_case__   : Optional[str] = None      ,       snake_case__   : bool = False      ,       **snake_case__   : List[Any]      ,       )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				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       SCREAMING_SNAKE_CASE__					:


						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(lowerCAmelCase					)
						def   __lowercase (							cls				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						**lowerCAmelCase				:			str					):
										lowerCAmelCase  =				kwargs.pop("""config"""						,						lowerCAmelCase					)
										lowerCAmelCase  =				kwargs.pop("""trust_remote_code"""						,						lowerCAmelCase					)
										lowerCAmelCase  =				True

										lowerCAmelCase ,						lowerCAmelCase  =				FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase						,						**lowerCAmelCase					)
										lowerCAmelCase  =				config_dict.get("""feature_extractor_type"""						,						lowerCAmelCase					)
										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(lowerCAmelCase						,						lowerCAmelCase					):
																		lowerCAmelCase  =				AutoConfig.from_pretrained(lowerCAmelCase						,						**lowerCAmelCase					)
														# It could be in `config.feature_extractor_type``
														lowerCAmelCase  =				getattr(lowerCAmelCase						,						"""feature_extractor_type"""						,						lowerCAmelCase					)
														if hasattr(lowerCAmelCase						,						"""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(lowerCAmelCase					)

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

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

										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   __lowercase (							lowerCAmelCase				:			int						,						lowerCAmelCase				:			Optional[Any]					):
										FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase						,						lowerCAmelCase					)











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a      =							{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							['OwlViTFeatureExtractor']
		a      =							['OwlViTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'OwlViTModel',
		    'OwlViTPreTrainedModel',
		    'OwlViTTextModel',
		    'OwlViTVisionModel',
		    'OwlViTForObjectDetection',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





