






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,
)


a_							=    logging.get_logger(__name__)

a_							=    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'''),
    ]
)

a_							=    _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)


def        _a  (     UpperCamelCase_					:				str   ) ->   str:
						"""simple docstring"""







						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        _a  (     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_					:				Union[str, Any]    ,   ) ->   List[Any]:
						"""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 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      lowercase__     :

	def __init__(							self       )->					Union[str, Any]:







							'''simple docstring'''





							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 		UpperCAmelCase  (							cls		,    __UpperCAmelCase		,    **__UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							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 		UpperCAmelCase  (							__UpperCAmelCase		,    __UpperCAmelCase       )->					Optional[Any]:







							'''simple docstring'''





							IMAGE_PROCESSOR_MAPPING.register(__UpperCAmelCase		,    __UpperCAmelCase       )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''IBertForMaskedLM''',
	    '''IBertForMultipleChoice''',
	    '''IBertForQuestionAnswering''',
	    '''IBertForSequenceClassification''',
	    '''IBertForTokenClassification''',
	    '''IBertModel''',
	    '''IBertPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

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






