
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        =  logging.get_logger(__name__)

lowercase        =  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        =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str       )  ->    Dict:
							for module_name, extractors in IMAGE_PROCESSOR_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 IMAGE_PROCESSOR_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__				:      Union[str, os.PathLike]			,				UpperCAmelCase__				:      Optional[Union[str, os.PathLike]] = None			,				UpperCAmelCase__				:      bool = False			,				UpperCAmelCase__				:      bool = False			,				UpperCAmelCase__				:      Optional[Dict[str, str]] = None			,				UpperCAmelCase__				:      Optional[Union[bool, str]] = None			,				UpperCAmelCase__				:      Optional[str] = None			,				UpperCAmelCase__				:      bool = False			,				**UpperCAmelCase__				:      Dict			,				)  ->    Optional[Any]:
							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 image processor configuration file, will try to use the model config instead."""       )
														return {}

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





class       __A:

					def __init__(	self					:     List[str]   ):
												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    lowercase__      (	cls					:     Tuple   ,					__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					kwargs.pop("""config"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					kwargs.pop("""trust_remote_code"""   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					True

												lowerCamelCase_  ,   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    lowercase__      (	__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Tuple   ):
												IMAGE_PROCESSOR_MAPPING.register(__UpperCamelCase   ,					__UpperCamelCase   )







from typing import TYPE_CHECKING

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


lowercase        =  {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''DeiTFeatureExtractor''']
		lowercase        =  ['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  [
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

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





