




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


UpperCAmelCase_		:					Tuple          =  logging.get_logger(__name__)




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       ["""pixel_values"""]

						def __init__(		self :Union[str, Any]     ,			__snake_case :bool = True     ,			__snake_case :Dict[str, int] = None     ,			__snake_case :float = None     ,			__snake_case :PILImageResampling = PILImageResampling.BILINEAR     ,			__snake_case :bool = True     ,			__snake_case :Union[int, float] = 1 / 2_55     ,			__snake_case :bool = True     ,			__snake_case :Optional[Union[float, List[float]]] = None     ,			__snake_case :Optional[Union[float, List[float]]] = None     ,			**__snake_case :str     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)
										__magic_name__						:						int										=size if size is not None else {"""shortest_edge""": 3_84}
										__magic_name__						:						str										=get_size_dict(__snake_case     ,			default_to_square=__snake_case			)

										__magic_name__						:						Optional[int]										=do_resize
										__magic_name__						:						str										=size
										# Default value set here for backwards compatibility where the value in config is None
										__magic_name__						:						str										=crop_pct if crop_pct is not None else 2_24 / 2_56
										__magic_name__						:						List[str]										=resample
										__magic_name__						:						Any										=do_rescale
										__magic_name__						:						Optional[int]										=rescale_factor
										__magic_name__						:						Tuple										=do_normalize
										__magic_name__						:						int										=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__magic_name__						:						Optional[int]										=image_std if image_std is not None else IMAGENET_STANDARD_STD

						def      A__    (		self :Optional[Any]     ,			__snake_case :np.ndarray     ,			__snake_case :Dict[str, int]     ,			__snake_case :float     ,			__snake_case :PILImageResampling = PILImageResampling.BICUBIC     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None     ,			**__snake_case :int     ,			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=get_size_dict(__snake_case     ,			default_to_square=__snake_case			)
										if "shortest_edge" not in size:
														raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"			)
										__magic_name__						:						Dict										=size["""shortest_edge"""]

										if shortest_edge < 3_84:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														__magic_name__						:						Optional[int]										=int(shortest_edge / crop_pct			)
														__magic_name__						:						str										=get_resize_output_image_size(__snake_case     ,			size=__snake_case     ,			default_to_square=__snake_case			)
														__magic_name__						:						int										=resize(image=__snake_case     ,			size=__snake_case     ,			resample=__snake_case     ,			data_format=__snake_case     ,			**__snake_case			)
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=__snake_case     ,			size=(shortest_edge, shortest_edge)     ,			data_format=__snake_case     ,			**__snake_case			)
										else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    __snake_case     ,			size=(shortest_edge, shortest_edge)     ,			resample=__snake_case     ,			data_format=__snake_case     ,			**__snake_case			)

						def      A__    (		self :int     ,			__snake_case :np.ndarray     ,			__snake_case :Union[int, float]     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None     ,			**__snake_case :Any     ,			):
										'''simple docstring'''

										return rescale(__snake_case     ,			scale=__snake_case     ,			data_format=__snake_case     ,			**__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :np.ndarray     ,			__snake_case :Union[float, List[float]]     ,			__snake_case :Union[float, List[float]]     ,			__snake_case :Optional[Union[str, ChannelDimension]] = None     ,			**__snake_case :List[Any]     ,			):
										'''simple docstring'''

										return normalize(__snake_case     ,			mean=__snake_case     ,			std=__snake_case     ,			data_format=__snake_case     ,			**__snake_case			)


						def      A__    (		self :List[str]     ,			__snake_case :ImageInput     ,			__snake_case :bool = None     ,			__snake_case :Dict[str, int] = None     ,			__snake_case :float = None     ,			__snake_case :PILImageResampling = None     ,			__snake_case :bool = None     ,			__snake_case :float = None     ,			__snake_case :bool = None     ,			__snake_case :Optional[Union[float, List[float]]] = None     ,			__snake_case :Optional[Union[float, List[float]]] = None     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			__snake_case :ChannelDimension = ChannelDimension.FIRST     ,			**__snake_case :Union[str, Any]     ,			):
										'''simple docstring'''

										__magic_name__						:						str										=do_resize if do_resize is not None else self.do_resize
										__magic_name__						:						Optional[int]										=crop_pct if crop_pct is not None else self.crop_pct
										__magic_name__						:						Optional[int]										=resample if resample is not None else self.resample
										__magic_name__						:						Union[str, Any]										=do_rescale if do_rescale is not None else self.do_rescale
										__magic_name__						:						Optional[Any]										=rescale_factor if rescale_factor is not None else self.rescale_factor
										__magic_name__						:						Tuple										=do_normalize if do_normalize is not None else self.do_normalize
										__magic_name__						:						Union[str, Any]										=image_mean if image_mean is not None else self.image_mean
										__magic_name__						:						Optional[Any]										=image_std if image_std is not None else self.image_std

										__magic_name__						:						Optional[int]										=size if size is not None else self.size
										__magic_name__						:						int										=get_size_dict(__snake_case     ,			default_to_square=__snake_case			)

										__magic_name__						:						Optional[int]										=make_list_of_images(__snake_case			)

										if not valid_images(__snake_case			):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray."""			)

										if do_resize and size is None or resample is None:
														raise ValueError("""Size and resample must be specified if do_resize is True."""			)

										if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
														raise ValueError("""crop_pct must be specified if size < 384."""			)

										if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("""Image mean and std must be specified if do_normalize is True."""			)

										# All transformations expect numpy arrays.
										__magic_name__						:						Union[str, Any]										=[to_numpy_array(__snake_case			) for image in images]

										if do_resize:
														__magic_name__						:						List[str]										=[self.resize(image=__snake_case     ,			size=__snake_case     ,			crop_pct=__snake_case     ,			resample=__snake_case			) for image in images]

										if do_rescale:
														__magic_name__						:						List[str]										=[self.rescale(image=__snake_case     ,			scale=__snake_case			) for image in images]

										if do_normalize:
														__magic_name__						:						Dict										=[self.normalize(image=__snake_case     ,			mean=__snake_case     ,			std=__snake_case			) for image in images]

										__magic_name__						:						int										=[to_channel_dimension_format(__snake_case     ,			__snake_case			) for image in images]

										__magic_name__						:						List[str]										={"""pixel_values""": images}
										return BatchFeature(data=__snake_case     ,			tensor_type=__snake_case			)










'''simple docstring'''







from __future__ import annotations







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a = 0							)      ->				str:
          snake_case			:      List[Any]        =			key







     def 			snake_case_      (				self							,__a							,__a							)      ->				list[str]:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      Tuple        =			key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(__a							) ^ key							) for ch in content]







     def 			snake_case_      (				self							,__a							,__a							)      ->				list[str]:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      List[Any]        =			key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(__a							) ^ key							) for ch in content]







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				str:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      List[Any]        =			key or self.__key or 1

          # make sure key can be any size
          while key > 255:
               key -= 255

          # This will be returned
          snake_case			:      List[str]        =			""""""

          for ch in content:
               ans += chr(ord(__a							) ^ key							)

          return ans







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				str:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      Any        =			key or self.__key or 1

          # make sure key can be any size
          while key > 255:
               key -= 255

          # This will be returned
          snake_case			:      List[str]        =			""""""

          for ch in content:
               ans += chr(ord(__a							) ^ key							)

          return ans







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				bool:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          try:
               with open(__a							) as fin, open("""encrypt.out"""							,"""w+"""							) as fout:
                    # actual encrypt-process
                    for line in fin:
                         fout.write(self.encrypt_string(__a							,__a							)							)

          except OSError:
               return False

          return True







     def 			snake_case_      (				self							,__a							,__a							)      ->				bool:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          try:
               with open(__a							) as fin, open("""decrypt.out"""							,"""w+"""							) as fout:
                    # actual encrypt-process
                    for line in fin:
                         fout.write(self.decrypt_string(__a							,__a							)							)

          except OSError:
               return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")



