



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,
)


__snake_case    :		Optional[Any]  =logging.get_logger(__name__)

__snake_case    :		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'),
    ]
)

__snake_case    :		List[str]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
								if class_name in extractors:
												lowerCAmelCase__					:    List[str]												=					model_type_to_module_name(lowerCamelCase_)

												lowerCAmelCase__					:    List[Any]												=					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__					:    Optional[Any]												=					importlib.import_module('''transformers''')
				if hasattr(lowerCamelCase_	,lowerCamelCase_):
								return getattr(lowerCamelCase_	,lowerCamelCase_)

				return None






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, os.PathLike]	,lowerCamelCase_		:		Optional[Union[str, os.PathLike]] = None	,lowerCamelCase_		:		bool = False	,lowerCamelCase_		:		bool = False	,lowerCamelCase_		:		Optional[Dict[str, str]] = None	,lowerCamelCase_		:		Optional[Union[bool, str]] = None	,lowerCamelCase_		:		Optional[str] = None	,lowerCamelCase_		:		bool = False	,**lowerCamelCase_		:		Union[str, Any]	,):





				'''simple docstring'''

				lowerCAmelCase__					:    str												=					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 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self			)					->      str:

						"""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(__lowerCamelCase			)
		def 		lowerCAmelCase__				(cls		,__lowerCamelCase		,**__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					kwargs.pop('''config'''		,__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					kwargs.pop('''trust_remote_code'''		,__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					True

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

						if image_processor_class is not None:
										lowerCAmelCase__					:    Union[str, Any]												=					image_processor_class_from_name(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					image_processor_auto_map is not None
						lowerCAmelCase__					:    Dict												=					image_processor_class is not None or type(__lowerCamelCase			) in IMAGE_PROCESSOR_MAPPING
						lowerCAmelCase__					:    Any												=					resolve_trust_remote_code(
						    __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)

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

						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__				(__lowerCamelCase		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						IMAGE_PROCESSOR_MAPPING.register(__lowerCamelCase		,__lowerCamelCase			)






import string

import numpy






def      lowerCAmelCase__		(   lowerCamelCase_		:		int	,lowerCamelCase_		:		int):





				'''simple docstring'''

				return b if a == 0 else greatest_common_divisor(b % a	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		snake_case_     =string.ascii_uppercase + string.digits
		# This cipher takes alphanumerics into account
		# i.e. a total of 36 characters

		# take x and return x % len(key_string)
		snake_case_     =numpy.vectorize(lambda lowerCamelCase__: x % 36)

		snake_case_     =numpy.vectorize(lowerCamelCase__)



		def __init__(self		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					self.modulus(__lowerCamelCase			)  # mod36 calc's on the encrypt key
						self.check_determinant()  # validate the determinant of the encryption key
						lowerCAmelCase__					:    Optional[int]												=					encrypt_key.shape[0]



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						return self.key_string.index(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						return self.key_string[round(__lowerCamelCase			)]



		def 		lowerCAmelCase__				(self			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					round(numpy.linalg.det(self.encrypt_key			)			)

						if det < 0:
										lowerCAmelCase__					:    str												=					det % len(self.key_string			)

						lowerCAmelCase__					:    Optional[Any]												=					len(self.key_string			)
						if greatest_common_divisor(__lowerCamelCase		,len(self.key_string			)			) != 1:
										lowerCAmelCase__					:    List[str]												=					(
										    f"""determinant modular {req_l} of encryption key({det}) """
										    f"""is not co prime w.r.t {req_l}.\nTry another key."""
										)
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					[char for char in text.upper() if char in self.key_string]

						lowerCAmelCase__					:    int												=					chars[-1]
						while len(__lowerCamelCase			) % self.break_key != 0:
										chars.append(__lowerCamelCase			)

						return "".join(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.process_text(text.upper()			)
						lowerCAmelCase__					:    str												=					''''''

						for i in range(0		,len(__lowerCamelCase			) - self.break_key + 1		,self.break_key			):
										lowerCAmelCase__					:    Any												=					text[i : i + self.break_key]
										lowerCAmelCase__					:    Dict												=					[self.replace_letters(__lowerCamelCase			) for char in batch]
										lowerCAmelCase__					:    int												=					numpy.array([vec]			).T
										lowerCAmelCase__					:    Union[str, Any]												=					self.modulus(self.encrypt_key.dot(__lowerCamelCase			)			).T.tolist()[
										    0
										]
										lowerCAmelCase__					:    Union[str, Any]												=					''''''.join(
										    self.replace_digits(__lowerCamelCase			) for num in batch_encrypted			)
										encrypted += encrypted_batch

						return encrypted



		def 		lowerCAmelCase__				(self			)					->      numpy.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					round(numpy.linalg.det(self.encrypt_key			)			)

						if det < 0:
										lowerCAmelCase__					:    int												=					det % len(self.key_string			)
						lowerCAmelCase__					:    Dict												=					None
						for i in range(len(self.key_string			)			):
										if (det * i) % len(self.key_string			) == 1:
														lowerCAmelCase__					:    Optional[Any]												=					i
														break

						lowerCAmelCase__					:    Optional[Any]												=					(
						    det_inv
						    * numpy.linalg.det(self.encrypt_key			)
						    * numpy.linalg.inv(self.encrypt_key			)
						)

						return self.to_int(self.modulus(__lowerCamelCase			)			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.make_decrypt_key()
						lowerCAmelCase__					:    List[str]												=					self.process_text(text.upper()			)
						lowerCAmelCase__					:    Optional[Any]												=					''''''

						for i in range(0		,len(__lowerCamelCase			) - self.break_key + 1		,self.break_key			):
										lowerCAmelCase__					:    List[Any]												=					text[i : i + self.break_key]
										lowerCAmelCase__					:    Tuple												=					[self.replace_letters(__lowerCamelCase			) for char in batch]
										lowerCAmelCase__					:    Optional[Any]												=					numpy.array([vec]			).T
										lowerCAmelCase__					:    Tuple												=					self.modulus(decrypt_key.dot(__lowerCamelCase			)			).T.tolist()[0]
										lowerCAmelCase__					:    Optional[int]												=					''''''.join(
										    self.replace_digits(__lowerCamelCase			) for num in batch_decrypted			)
										decrypted += decrypted_batch

						return decrypted






def      lowerCAmelCase__		(   ):





				'''simple docstring'''

				lowerCAmelCase__					:    Any												=					int(input('''Enter the order of the encryption key: '''))
				lowerCAmelCase__					:    Union[str, Any]												=					[]

				print('''Enter each row of the encryption key with space separated integers''')
				for _ in range(lowerCamelCase_):
								lowerCAmelCase__					:    int												=					[int(lowerCamelCase_) for x in input().split()]
								hill_matrix.append(lowerCamelCase_)

				lowerCAmelCase__					:    List[str]												=					HillCipher(numpy.array(lowerCamelCase_))

				print('''Would you like to encrypt or decrypt some text? (1 or 2)''')
				lowerCAmelCase__					:    List[Any]												=					input('''\n1. Encrypt\n2. Decrypt\n''')
				if option == "1":
								lowerCAmelCase__					:    Optional[int]												=					input('''What text would you like to encrypt?: ''')
								print('''Your encrypted text is:''')
								print(hc.encrypt(lowerCamelCase_))
				elif option == "2":
								lowerCAmelCase__					:    Dict												=					input('''What text would you like to decrypt?: ''')
								print('''Your decrypted text is:''')
								print(hc.decrypt(lowerCamelCase_))


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							main()

