
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,
)


lowerCamelCase_						: List[Any]         =  logging.get_logger(__name__)

lowerCamelCase_						: Dict         =  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"""),
    ]
)

lowerCamelCase_						: List[Any]         =  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def        A__					(						lowerCamelCase						)  ->	str:
				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												UpperCamelCase_:							int	=						model_type_to_module_name(lowerCamelCase						)

												UpperCamelCase_:							int	=						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.
				UpperCamelCase_:							List[str]	=						importlib.import_module("""transformers"""						)
				if hasattr(lowerCamelCase     ,    lowerCamelCase						):
								return getattr(lowerCamelCase     ,    lowerCamelCase						)

				return None







def        A__					(						lowerCamelCase     ,    lowerCamelCase = None     ,    lowerCamelCase = False     ,    lowerCamelCase = False     ,    lowerCamelCase = None     ,    lowerCamelCase = None     ,    lowerCamelCase = None     ,    lowerCamelCase = False     ,    **lowerCamelCase     ,    )  ->	Tuple:
				UpperCamelCase_:							Dict	=						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        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       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(snake_case_      )
			def       lowerCAmelCase__	(       cls :       Dict						,	snake_case_ :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						kwargs.pop("""config"""						,	snake_case_      )
							UpperCamelCase_:							List[Any]	=						kwargs.pop("""trust_remote_code"""						,	snake_case_      )
							UpperCamelCase_:							Optional[int]	=						True

							UpperCamelCase_,   UpperCamelCase_:							str	=						ImageProcessingMixin.get_image_processor_dict(snake_case_						,	**snake_case_      )
							UpperCamelCase_:							Any	=						config_dict.get("""image_processor_type"""						,	snake_case_      )
							UpperCamelCase_:							str	=						None
							if "AutoImageProcessor" in config_dict.get("""auto_map"""						,	{}      ):
											UpperCamelCase_:							List[str]	=						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:
											UpperCamelCase_:							List[Any]	=						config_dict.pop("""feature_extractor_type"""						,	snake_case_      )
											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."""      )
															UpperCamelCase_:							List[str]	=						feature_extractor_class.replace("""FeatureExtractor"""						,	"""ImageProcessor"""      )
											if "AutoFeatureExtractor" in config_dict.get("""auto_map"""						,	{}      ):
															UpperCamelCase_:							int	=						config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
															UpperCamelCase_:							Any	=						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(snake_case_						,	snake_case_      ):
															UpperCamelCase_:							Any	=						AutoConfig.from_pretrained(snake_case_						,	**snake_case_      )
											# It could be in `config.image_processor_type``
											UpperCamelCase_:							Any	=						getattr(snake_case_						,	"""image_processor_type"""						,	snake_case_      )
											if hasattr(snake_case_						,	"""auto_map"""      ) and "AutoImageProcessor" in config.auto_map:
															UpperCamelCase_:							Tuple	=						config.auto_map["""AutoImageProcessor"""]

							if image_processor_class is not None:
											UpperCamelCase_:							Dict	=						image_processor_class_from_name(snake_case_      )

							UpperCamelCase_:							str	=						image_processor_auto_map is not None
							UpperCamelCase_:							int	=						image_processor_class is not None or type(snake_case_      ) in IMAGE_PROCESSOR_MAPPING
							UpperCamelCase_:							Tuple	=						resolve_trust_remote_code(
							    snake_case_						,	snake_case_						,	snake_case_						,	snake_case_      )

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

							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       lowerCAmelCase__	(       snake_case_ :       Union[str, Any]						,	snake_case_ :       Union[str, Any]      ):
							IMAGE_PROCESSOR_MAPPING.register(snake_case_						,	snake_case_      )








class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       int						,	snake_case_ :       Optional[Any]=None						,	snake_case_ :       List[str]=None      ):
							UpperCamelCase_:							List[Any]	=						data
							UpperCamelCase_:							List[Any]	=						previous
							UpperCamelCase_:							Tuple	=						next_node

			def __str__(       self :       Dict      ):
							return f'''{self.data}'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							return self.data

			def       lowerCAmelCase__	(       self :       Any      ):
							return self.next




			def       lowerCAmelCase__	(       self :       List[str]      ):
							return self.previous




class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       Optional[Any]						,	snake_case_ :       int      ):
							UpperCamelCase_:							Union[str, Any]	=						head

			def __iter__(       self :       Union[str, Any]      ):
							return self




			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							if not self.current:
											raise StopIteration
							else:
											UpperCamelCase_:							Dict	=						self.current.get_data()
											UpperCamelCase_:							Tuple	=						self.current.get_next()
											return value




class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       int      ):
							UpperCamelCase_:							Optional[int]	=						None  # First node in list
							UpperCamelCase_:							Dict	=						None  # Last node in list

			def __str__(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.head
							UpperCamelCase_:							Tuple	=						[]
							while current is not None:
											nodes.append(current.get_data()      )
											UpperCamelCase_:							List[str]	=						current.get_next()
							return " ".join(str(snake_case_      ) for node in nodes      )

			def __contains__(       self :       int						,	snake_case_ :       int      ):
							UpperCamelCase_:							Optional[Any]	=						self.head
							while current:
											if current.get_data() == value:
															return True
											UpperCamelCase_:							Any	=						current.get_next()
							return False

			def __iter__(       self :       Any      ):
							return LinkedListIterator(self.head      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							if self.head:
											return self.head.get_data()
							return None

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							if self.tail:
											return self.tail.get_data()
							return None

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Node      ):
							if self.head is None:
											UpperCamelCase_:							Tuple	=						node
											UpperCamelCase_:							Optional[int]	=						node
							else:
											self.insert_before_node(self.head						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Node      ):
							if self.head is None:
											self.set_head(snake_case_      )
							else:
											self.insert_after_node(self.tail						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       int      ):
							UpperCamelCase_:							Any	=						Node(snake_case_      )
							if self.head is None:
											self.set_head(snake_case_      )
							else:
											self.set_tail(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Node						,	snake_case_ :       Node      ):
							UpperCamelCase_:							str	=						node
							UpperCamelCase_:							int	=						node.previous

							if node.get_previous() is None:
											UpperCamelCase_:							int	=						node_to_insert
							else:
											UpperCamelCase_:							Dict	=						node_to_insert

							UpperCamelCase_:							int	=						node_to_insert

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Node						,	snake_case_ :       Node      ):
							UpperCamelCase_:							Tuple	=						node
							UpperCamelCase_:							Dict	=						node.next

							if node.get_next() is None:
											UpperCamelCase_:							Union[str, Any]	=						node_to_insert
							else:
											UpperCamelCase_:							str	=						node_to_insert

							UpperCamelCase_:							int	=						node_to_insert

			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       int						,	snake_case_ :       int      ):
							UpperCamelCase_:							Union[str, Any]	=						1
							UpperCamelCase_:							List[str]	=						Node(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						self.head
							while node:
											if current_position == position:
															self.insert_before_node(snake_case_						,	snake_case_      )
															return
											current_position += 1
											UpperCamelCase_:							Dict	=						node.next
							self.insert_after_node(self.tail						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       int      ):
							UpperCamelCase_:							Union[str, Any]	=						self.head
							while node:
											if node.get_data() == item:
															return node
											UpperCamelCase_:							List[Any]	=						node.get_next()
							raise Exception("""Node not found"""      )

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       List[str]      ):
							if (node := self.get_node(snake_case_      )) is not None:
											if node == self.head:
															UpperCamelCase_:							Optional[int]	=						self.head.get_next()

											if node == self.tail:
															UpperCamelCase_:							Union[str, Any]	=						self.tail.get_previous()

											self.remove_node_pointers(snake_case_      )

			@staticmethod
			def       lowerCAmelCase__	(       snake_case_ :       Node      ):
							if node.get_next():
											UpperCamelCase_:							str	=						node.previous

							if node.get_previous():
											UpperCamelCase_:							int	=						node.next

							UpperCamelCase_:							List[str]	=						None
							UpperCamelCase_:							int	=						None




			def       lowerCAmelCase__	(       self :       str      ):
							return self.head is None







def        A__					(						)  ->	None:
				pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()






