



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      _A				(UpperCamelCase		:      str )  ->List[Any]:



			'''simple docstring'''







			for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
						if class_name in extractors:
									lowerCamelCase__ :      Any								=		model_type_to_module_name(UpperCamelCase )

									lowerCamelCase__ :      Tuple								=		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__ :      List[str]								=		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		:      Any      ,			)  ->List[Any]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		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				):
						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(__magic_name__				)
			def        _snake_case    (cls							,    __magic_name__							,    **__magic_name__				):
						lowerCamelCase__ :      str								=		kwargs.pop("""config"""							,    __magic_name__				)
						lowerCamelCase__ :      int								=		kwargs.pop("""trust_remote_code"""							,    __magic_name__				)
						lowerCamelCase__ :      List[str]								=		True

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

						if image_processor_class is not None:
									lowerCamelCase__ :      Optional[Any]								=		image_processor_class_from_name(__magic_name__				)

						lowerCamelCase__ :      Optional[Any]								=		image_processor_auto_map is not None
						lowerCamelCase__ :      Dict								=		image_processor_class is not None or type(__magic_name__				) in IMAGE_PROCESSOR_MAPPING
						lowerCamelCase__ :      Dict								=		resolve_trust_remote_code(
						    __magic_name__							,    __magic_name__							,    __magic_name__							,    __magic_name__				)

						if has_remote_code and trust_remote_code:
									lowerCamelCase__ :      Union[str, Any]								=		get_class_from_dynamic_module(
									    __magic_name__							,    __magic_name__							,    **__magic_name__				)
									lowerCamelCase__ :      Optional[int]								=		kwargs.pop("""code_revision"""							,    __magic_name__				)
									if os.path.isdir(__magic_name__				):
												image_processor_class.register_for_auto_class()
									return image_processor_class.from_dict(__magic_name__							,    **__magic_name__				)
						elif image_processor_class is not None:
									return image_processor_class.from_dict(__magic_name__							,    **__magic_name__				)
						# Last try: we use the IMAGE_PROCESSOR_MAPPING.
						elif type(__magic_name__				) in IMAGE_PROCESSOR_MAPPING:
									lowerCamelCase__ :      List[Any]								=		IMAGE_PROCESSOR_MAPPING[type(__magic_name__				)]
									return image_processor_class.from_dict(__magic_name__							,    **__magic_name__				)

						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        _snake_case    (__magic_name__							,    __magic_name__				):
						IMAGE_PROCESSOR_MAPPING.register(__magic_name__							,    __magic_name__				)








import itertools
import string
from collections.abc import Generator, Iterable






def      _A				(UpperCamelCase		:      Iterable[str]      ,			UpperCamelCase		:      int )  ->Generator[tuple[str, ...], None, None]:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		iter(UpperCamelCase )
			while True:
						lowerCamelCase__ :      Union[str, Any]								=		tuple(itertools.islice(UpperCamelCase      ,			UpperCamelCase ) )
						if not chunk:
									return
						yield chunk






def      _A				(UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		"""""".join([c.upper() for c in dirty if c in string.ascii_letters] )
			lowerCamelCase__ :      Any								=		""""""

			if len(UpperCamelCase ) < 2:
						return dirty

			for i in range(len(UpperCamelCase ) - 1 ):
						clean += dirty[i]

						if dirty[i] == dirty[i + 1]:
									clean += "X"

			clean += dirty[-1]

			if len(UpperCamelCase ) & 1:
						clean += "X"

			return clean






def      _A				(UpperCamelCase		:      str )  ->list[str]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[Any]								=		"""ABCDEFGHIKLMNOPQRSTUVWXYZ"""
			# we're using a list instead of a '2d' array because it makes the math
			# for setting up the table and doing the actual encoding/decoding simpler
			lowerCamelCase__ :      List[Any]								=		[]

			# copy key chars into the table if they are in `alphabet` ignoring duplicates
			for char in key.upper():
						if char not in table and char in alphabet:
									table.append(UpperCamelCase )

    # fill the rest of the table in with the remaining alphabet chars
			for char in alphabet:
						if char not in table:
									table.append(UpperCamelCase )

			return table






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Tuple								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      int								=		prepare_input(UpperCamelCase )
			lowerCamelCase__ :      List[str]								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Optional[Any]								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
									ciphertext += table[rowa * 5 + (cola + 1) % 5]
						elif cola == cola:
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
									ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						else:  # rectangle
									ciphertext += table[rowa * 5 + cola]
									ciphertext += table[rowa * 5 + cola]

			return ciphertext






def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      str )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      Any								=		generate_table(UpperCamelCase )
			lowerCamelCase__ :      Any								=		""""""

			# https://en.wikipedia.org/wiki/Playfair_cipher#Description
			for chara, chara in chunker(UpperCamelCase      ,			2 ):
						lowerCamelCase__							,lowerCamelCase__ :      int								=		divmod(table.index(UpperCamelCase )      ,			5 )
						lowerCamelCase__							,lowerCamelCase__ :      Any								=		divmod(table.index(UpperCamelCase )      ,			5 )

						if rowa == rowa:
									plaintext += table[rowa * 5 + (cola - 1) % 5]
									plaintext += table[rowa * 5 + (cola - 1) % 5]
						elif cola == cola:
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
									plaintext += table[((rowa - 1) % 5) * 5 + cola]
						else:  # rectangle
									plaintext += table[rowa * 5 + cola]
									plaintext += table[rowa * 5 + cola]

			return plaintext



