

"""simple docstring"""


import random
import sys

__A		     =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"






def 					a__					(					)			->			None:
		__lowerCAmelCase: int					   =    input("Enter message: "				)
		__lowerCAmelCase: Any					   =    "LFWOAYUISVKMNXPBDCRJTQEGHZ"
		__lowerCAmelCase: int					   =    input("Encrypt/Decrypt [e/d]: "				)

		check_valid_key(__SCREAMING_SNAKE_CASE				)

		if resp.lower().startswith("e"				):
				__lowerCAmelCase: Tuple					   =    "encrypt"
				__lowerCAmelCase: int					   =    encrypt_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		elif resp.lower().startswith("d"				):
				__lowerCAmelCase: str					   =    "decrypt"
				__lowerCAmelCase: List[str]					   =    decrypt_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)

		print(F"\n{mode.title()}ion: \n{translated}"				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			None:
		__lowerCAmelCase: List[Any]					   =    list(__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Optional[int]					   =    list(__SCREAMING_SNAKE_CASE				)
		key_list.sort()
		letters_list.sort()

		if key_list != letters_list:
				sys.exit("Error in the key or symbol set."				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		return translate_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				"encrypt"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		return translate_message(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				"decrypt"				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			str:
		__lowerCAmelCase: Union[str, Any]					   =    ""
		__lowerCAmelCase: List[str]					   =    LETTERS
		__lowerCAmelCase: Optional[int]					   =    key

		if mode == "decrypt":
				__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    chars_b, chars_a

		for symbol in message:
				if symbol.upper() in chars_a:
						__lowerCAmelCase: Union[str, Any]					   =    chars_a.find(symbol.upper()				)
						if symbol.isupper():
								translated += chars_b[sym_index].upper()
						else:
								translated += chars_b[sym_index].lower()
				else:
						translated += symbol

		return translated






def 					a__					(					)			->			str:
		__lowerCAmelCase: int					   =    list(__SCREAMING_SNAKE_CASE				)
		random.shuffle(__SCREAMING_SNAKE_CASE				)
		return "".join(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			main()





"""simple docstring"""


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__A		     =    logging.get_logger(__name__)


if is_vision_available():
			import PIL





class 					snake_case			(       __snake_case						):
				SCREAMING_SNAKE_CASE_     :   Union[str, Any]        =						["""pixel_values"""]

				def __init__(			self							:       int  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BILINEAR  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Union[int, float] = 1 / 2_5_5  ,						UpperCamelCase__							:       bool = True  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						**UpperCamelCase__							:       Tuple  ,						)->     None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)

						__lowerCAmelCase: List[str]					   =    size if size is not None else {"height": 2_5_6_0, "width": 1_9_2_0}
						if isinstance(UpperCamelCase__  ,						(tuple, list)):
								# The previous feature extractor size parameter was in (width, height) format
								__lowerCAmelCase: str					   =    size[::-1]
						__lowerCAmelCase: int					   =    get_size_dict(UpperCamelCase__)

						__lowerCAmelCase: Union[str, Any]					   =    do_resize
						__lowerCAmelCase: Optional[Any]					   =    size
						__lowerCAmelCase: List[str]					   =    resample
						__lowerCAmelCase: Tuple					   =    do_thumbnail
						__lowerCAmelCase: int					   =    do_align_long_axis
						__lowerCAmelCase: Any					   =    do_pad
						__lowerCAmelCase: Any					   =    do_rescale
						__lowerCAmelCase: int					   =    rescale_factor
						__lowerCAmelCase: str					   =    do_normalize
						__lowerCAmelCase: List[str]					   =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase: List[Any]					   =    image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_  (			self							:       Optional[int]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: Optional[int]					   =    get_image_size(UpperCamelCase__)
						__lowerCAmelCase   ,				__lowerCAmelCase: Union[str, Any]					   =    size["height"], size["width"]

						if (output_width < output_height and input_width > input_height) or (
						    output_width > output_height and input_width < input_height
						):
								__lowerCAmelCase: List[Any]					   =    np.rotaa(UpperCamelCase__  ,						3)

						if data_format is not None:
								__lowerCAmelCase: Dict					   =    to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__)

						return image

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    size["height"], size["width"]
						__lowerCAmelCase   ,				__lowerCAmelCase: Optional[Any]					   =    get_image_size(UpperCamelCase__)

						__lowerCAmelCase: List[Any]					   =    output_width - input_width
						__lowerCAmelCase: str					   =    output_height - input_height

						if random_padding:
								__lowerCAmelCase: Tuple					   =    np.random.randint(low=0  ,						high=delta_height + 1)
								__lowerCAmelCase: Union[str, Any]					   =    np.random.randint(low=0  ,						high=delta_width + 1)
						else:
								__lowerCAmelCase: str					   =    delta_height // 2
								__lowerCAmelCase: Optional[Any]					   =    delta_width // 2

						__lowerCAmelCase: List[Any]					   =    delta_height - pad_top
						__lowerCAmelCase: Dict					   =    delta_width - pad_left

						__lowerCAmelCase: Optional[Any]					   =    ((pad_top, pad_bottom), (pad_left, pad_right))
						return pad(UpperCamelCase__  ,						UpperCamelCase__  ,						data_format=UpperCamelCase__)

				def 							lowercase_  (			self							:       Union[str, Any]  ,						*UpperCamelCase__							:       str  ,						**UpperCamelCase__							:       Optional[int])->     Optional[int]:



						'''simple docstring'''


						logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead.")
						return self.pad_image(*UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BICUBIC  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Optional[int]  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    get_image_size(UpperCamelCase__)
						__lowerCAmelCase   ,				__lowerCAmelCase: Tuple					   =    size["height"], size["width"]

						# We always resize to the smallest of either the input or output size.
						__lowerCAmelCase: Union[str, Any]					   =    min(UpperCamelCase__  ,						UpperCamelCase__)
						__lowerCAmelCase: List[Any]					   =    min(UpperCamelCase__  ,						UpperCamelCase__)

						if height == input_height and width == input_width:
								return image

						if input_height > input_width:
								__lowerCAmelCase: Dict					   =    int(input_width * height / input_height)
						elif input_width > input_height:
								__lowerCAmelCase: Any					   =    int(input_height * width / input_width)

						return resize(
						    UpperCamelCase__  ,						size=(height, width)  ,						resample=UpperCamelCase__  ,						reducing_gap=2.0  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Dict[str, int]  ,						UpperCamelCase__							:       PILImageResampling = PILImageResampling.BICUBIC  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Union[str, Any]  ,						)->     np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase: Tuple					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Dict					   =    min(size["height"]  ,						size["width"])
						__lowerCAmelCase: List[str]					   =    get_resize_output_image_size(UpperCamelCase__  ,						size=UpperCamelCase__  ,						default_to_square=UpperCamelCase__)
						__lowerCAmelCase: Optional[int]					   =    resize(UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__  ,						data_format=UpperCamelCase__  ,						**UpperCamelCase__)
						return resized_image

				def 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Union[int, float]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       Dict  ,						)->     int:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       Any  ,						UpperCamelCase__							:       np.ndarray  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Union[float, List[float]]  ,						UpperCamelCase__							:       Optional[Union[str, ChannelDimension]] = None  ,						**UpperCamelCase__							:       List[str]  ,						)->     np.ndarray:



						'''simple docstring'''


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




				def 							lowercase_  (			self							:       str  ,						UpperCamelCase__							:       ImageInput  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       Dict[str, int] = None  ,						UpperCamelCase__							:       PILImageResampling = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       bool = False  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       float = None  ,						UpperCamelCase__							:       bool = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[float, List[float]]] = None  ,						UpperCamelCase__							:       Optional[Union[str, TensorType]] = None  ,						UpperCamelCase__							:       Optional[ChannelDimension] = ChannelDimension.FIRST  ,						**UpperCamelCase__							:       Optional[Any]  ,						)->     PIL.Image.Image:



						'''simple docstring'''


						__lowerCAmelCase: Optional[int]					   =    do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase: Any					   =    size if size is not None else self.size
						if isinstance(UpperCamelCase__  ,						(tuple, list)):
								# Previous feature extractor had size in (width, height) format
								__lowerCAmelCase: int					   =    size[::-1]
						__lowerCAmelCase: Dict					   =    get_size_dict(UpperCamelCase__)
						__lowerCAmelCase: Optional[Any]					   =    resample if resample is not None else self.resample
						__lowerCAmelCase: int					   =    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
						__lowerCAmelCase: Any					   =    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
						__lowerCAmelCase: str					   =    do_pad if do_pad is not None else self.do_pad
						__lowerCAmelCase: List[Any]					   =    do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase: List[Any]					   =    rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase: List[Any]					   =    do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase: Union[str, Any]					   =    image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase: List[str]					   =    image_std if image_std is not None else self.image_std

						__lowerCAmelCase: Union[str, Any]					   =    make_list_of_images(UpperCamelCase__)

						if not valid_images(UpperCamelCase__):
								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:
								raise ValueError("Size must be specified if do_resize is True.")

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

						if do_pad and size is None:
								raise ValueError("Size must be specified if do_pad 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.
						__lowerCAmelCase: int					   =    [to_numpy_array(UpperCamelCase__) for image in images]

						if do_align_long_axis:
								__lowerCAmelCase: Any					   =    [self.align_long_axis(UpperCamelCase__  ,						size=UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase: Tuple					   =    [self.resize(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						resample=UpperCamelCase__) for image in images]

						if do_thumbnail:
								__lowerCAmelCase: List[Any]					   =    [self.thumbnail(image=UpperCamelCase__  ,						size=UpperCamelCase__) for image in images]

						if do_pad:
								__lowerCAmelCase: Tuple					   =    [self.pad_image(image=UpperCamelCase__  ,						size=UpperCamelCase__  ,						random_padding=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase: Optional[int]					   =    [self.rescale(image=UpperCamelCase__  ,						scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase: Tuple					   =    [self.normalize(image=UpperCamelCase__  ,						mean=UpperCamelCase__  ,						std=UpperCamelCase__) for image in images]

						__lowerCAmelCase: Union[str, Any]					   =    [to_channel_dimension_format(UpperCamelCase__  ,						UpperCamelCase__) for image in images]

						__lowerCAmelCase: Optional[Any]					   =    {"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__  ,						tensor_type=UpperCamelCase__)


