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

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_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__			:				Optional[int]		=	logging.get_logger(__name__)

lowercase__			:				Any		=	OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

lowercase__			:				Optional[int]		=	_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def    SCREAMING_SNAKE_CASE_     (						snake_case__							)     ->	Optional[Any]:
							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					lowerCAmelCase			=		model_type_to_module_name(lowerCAmelCase_							)

																					lowerCAmelCase			=		importlib.import_module(f".{module_name}"   ,  '''transformers.models'''							)
																					try:
																												return getattr(lowerCAmelCase_   ,  lowerCAmelCase_							)
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(lowerCAmelCase_   ,  '''__name__'''   ,  lowerCAmelCase_							) == 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(lowerCAmelCase_   ,  lowerCAmelCase_							):
														return getattr(lowerCAmelCase_   ,  lowerCAmelCase_							)

							return None

def    SCREAMING_SNAKE_CASE_     (						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__   ,  )     ->	Tuple:
							lowerCAmelCase			=		get_file_from_repo(
							    lowerCAmelCase_   ,  lowerCAmelCase_   ,  cache_dir=lowerCAmelCase_   ,  force_download=lowerCAmelCase_   ,  resume_download=lowerCAmelCase_   ,  proxies=lowerCAmelCase_   ,  use_auth_token=lowerCAmelCase_   ,  revision=lowerCAmelCase_   ,  local_files_only=lowerCAmelCase_   ,  )
							if resolved_config_file is None:
														logger.info(
														    '''Could not locate the image processor configuration file, will try to use the model config instead.'''							)
														return {}

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






class    lowercase_  :



	"""simple docstring"""





	def __init__(							self				)						->Optional[int]:
								raise EnvironmentError(
								    '''AutoImageProcessor is designed to be instantiated '''
								    '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''				)


	@classmethod
	@replace_list_option_in_docstrings(_UpperCAmelCase				)
	def 			SCREAMING_SNAKE_CASE_				(							cls		,			__SCREAMING_SNAKE_CASE		,			**__SCREAMING_SNAKE_CASE				)						->List[Any]:
								lowerCAmelCase			=		kwargs.pop('''config'''		,			_UpperCAmelCase				)
								lowerCAmelCase			=		kwargs.pop('''trust_remote_code'''		,			_UpperCAmelCase				)
								lowerCAmelCase			=		True

								lowerCAmelCase			=		ImageProcessingMixin.get_image_processor_dict(_UpperCAmelCase		,			**_UpperCAmelCase				)
								lowerCAmelCase			=		config_dict.get('''image_processor_type'''		,			_UpperCAmelCase				)
								lowerCAmelCase			=		None
								if "AutoImageProcessor" in config_dict.get('''auto_map'''		,			{}				):
															lowerCAmelCase			=		config_dict['auto_map']['AutoImageProcessor']

								# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
								# and if so, infer the image processor class from there.
								if image_processor_class is None and image_processor_auto_map is None:
															lowerCAmelCase			=		config_dict.pop('''feature_extractor_type'''		,			_UpperCAmelCase				)
															if feature_extractor_class is not None:
																						logger.warning(
																						    '''Could not find image processor class in the image processor config or the model config. Loading'''
																						    ''' based on pattern matching with the model\'s feature extractor configuration.'''				)
																						lowerCAmelCase			=		feature_extractor_class.replace('''FeatureExtractor'''		,			'''ImageProcessor'''				)
															if "AutoFeatureExtractor" in config_dict.get('''auto_map'''		,			{}				):
																						lowerCAmelCase			=		config_dict['auto_map']['AutoFeatureExtractor']
																						lowerCAmelCase			=		feature_extractor_auto_map.replace('''FeatureExtractor'''		,			'''ImageProcessor'''				)
																						logger.warning(
																						    '''Could not find image processor auto map in the image processor config or the model config.'''
																						    ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''				)

        # If we don't find the image processor class in the image processor config, let's try the model config.
								if image_processor_class is None and image_processor_auto_map is None:
															if not isinstance(_UpperCAmelCase		,			_UpperCAmelCase				):
																						lowerCAmelCase			=		AutoConfig.from_pretrained(_UpperCAmelCase		,			**_UpperCAmelCase				)
															# It could be in `config.image_processor_type``
															lowerCAmelCase			=		getattr(_UpperCAmelCase		,			'''image_processor_type'''		,			_UpperCAmelCase				)
															if hasattr(_UpperCAmelCase		,			'''auto_map'''				) and "AutoImageProcessor" in config.auto_map:
																						lowerCAmelCase			=		config.auto_map['AutoImageProcessor']

								if image_processor_class is not None:
															lowerCAmelCase			=		image_processor_class_from_name(_UpperCAmelCase				)

								lowerCAmelCase			=		image_processor_auto_map is not None
								lowerCAmelCase			=		image_processor_class is not None or type(_UpperCAmelCase				) in IMAGE_PROCESSOR_MAPPING
								lowerCAmelCase			=		resolve_trust_remote_code(
								    _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)

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

								raise ValueError(
								    F"Unrecognized image processor in {pretrained_model_name_or_path}. Should have a "
								    F"`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following "
								    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()				)}"				)




	@staticmethod
	def 			SCREAMING_SNAKE_CASE_				(							__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)						->Any:
								IMAGE_PROCESSOR_MAPPING.register(_UpperCAmelCase		,			_UpperCAmelCase				)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''SwinForImageClassification''',
							    '''SwinForMaskedImageModeling''',
							    '''SwinModel''',
							    '''SwinPreTrainedModel''',
							    '''SwinBackbone''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFSwinForImageClassification''',
							    '''TFSwinForMaskedImageModeling''',
							    '''TFSwinModel''',
							    '''TFSwinPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

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




