



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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


snake_case    :						Optional[int]  =						logging.get_logger(__name__)




class 							lowerCAmelCase__  (	UpperCAmelCase__						):
     __A     :    List[str]           =						["pixel_values"]







     def __init__(       self      :     Optional[Any]			,		_A      :     bool = True			,		_A      :     Dict[str, int] = None			,		_A      :     PILImageResampling = PILImageResampling.BICUBIC			,		_A      :     bool = True			,		_A      :     Union[int, float] = 1 / 255			,		_A      :     bool = True			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     bool = True			,		**_A      :     Optional[int]			,		):
           super().__init__(**lowerCamelCase__)
           A__	:							Optional[int]						    = size if size is not None else {"height": 384, "width": 384}
           A__	:							str						    = get_size_dict(lowerCamelCase__			,		default_to_square=lowerCamelCase__)

           A__	:							Dict						    = do_resize
           A__	:							List[str]						    = size
           A__	:							List[str]						    = resample
           A__	:							Optional[Any]						    = do_rescale
           A__	:							List[str]						    = rescale_factor
           A__	:							Optional[int]						    = do_normalize
           A__	:							Union[str, Any]						    = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
           A__	:							Tuple						    = image_std if image_std is not None else OPENAI_CLIP_STD
           A__	:							Tuple						    = do_convert_rgb







     def  _lowercase			(       self      :     Optional[int]			,		_A      :     np.ndarray			,		_A      :     Dict[str, int]			,		_A      :     PILImageResampling = PILImageResampling.BICUBIC			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     str			,		):
           A__	:							Optional[int]						    = get_size_dict(lowerCamelCase__			,		default_to_square=lowerCamelCase__)
           if "height" not in size or "width" not in size:
                 raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}')
           A__	:							Optional[Any]						    = (size["height"], size["width"])
           return resize(lowerCamelCase__			,		size=lowerCamelCase__			,		resample=lowerCamelCase__			,		data_format=lowerCamelCase__			,		**lowerCamelCase__)







     def  _lowercase			(       self      :     Dict			,		_A      :     np.ndarray			,		_A      :     Union[int, float]			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     Tuple			,		):
           return rescale(lowerCamelCase__			,		scale=lowerCamelCase__			,		data_format=lowerCamelCase__			,		**lowerCamelCase__)







     def  _lowercase			(       self      :     str			,		_A      :     np.ndarray			,		_A      :     Union[float, List[float]]			,		_A      :     Union[float, List[float]]			,		_A      :     Optional[Union[str, ChannelDimension]] = None			,		**_A      :     List[str]			,		):
           return normalize(lowerCamelCase__			,		mean=lowerCamelCase__			,		std=lowerCamelCase__			,		data_format=lowerCamelCase__			,		**lowerCamelCase__)







     def  _lowercase			(       self      :     Optional[int]			,		_A      :     ImageInput			,		_A      :     Optional[bool] = None			,		_A      :     Optional[Dict[str, int]] = None			,		_A      :     PILImageResampling = None			,		_A      :     Optional[bool] = None			,		_A      :     Optional[float] = None			,		_A      :     Optional[bool] = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[float, List[float]]] = None			,		_A      :     Optional[Union[str, TensorType]] = None			,		_A      :     bool = None			,		_A      :     ChannelDimension = ChannelDimension.FIRST			,		**_A      :     Dict			,		):
           A__	:							Any						    = do_resize if do_resize is not None else self.do_resize
           A__	:							Any						    = resample if resample is not None else self.resample
           A__	:							Any						    = do_rescale if do_rescale is not None else self.do_rescale
           A__	:							Tuple						    = rescale_factor if rescale_factor is not None else self.rescale_factor
           A__	:							List[Any]						    = do_normalize if do_normalize is not None else self.do_normalize
           A__	:							Union[str, Any]						    = image_mean if image_mean is not None else self.image_mean
           A__	:							Dict						    = image_std if image_std is not None else self.image_std
           A__	:							Optional[Any]						    = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           A__	:							List[Any]						    = size if size is not None else self.size
           A__	:							Dict						    = get_size_dict(lowerCamelCase__			,		default_to_square=lowerCamelCase__)

           A__	:							List[Any]						    = make_list_of_images(lowerCamelCase__)

           if not valid_images(lowerCamelCase__):
                 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_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.")

           # PIL RGBA images are converted to RGB
           if do_convert_rgb:
                 A__	:							List[Any]						    = [convert_to_rgb(lowerCamelCase__) for image in images]

           # All transformations expect numpy arrays.
           A__	:							Any						    = [to_numpy_array(lowerCamelCase__) for image in images]

           if do_resize:
                 A__	:							Union[str, Any]						    = [self.resize(image=lowerCamelCase__			,		size=lowerCamelCase__			,		resample=lowerCamelCase__) for image in images]

           if do_rescale:
                 A__	:							List[Any]						    = [self.rescale(image=lowerCamelCase__			,		scale=lowerCamelCase__) for image in images]

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

           A__	:							List[str]						    = [to_channel_dimension_format(lowerCamelCase__			,		lowerCamelCase__) for image in images]

           A__	:							List[str]						    = BatchFeature(data={"pixel_values": images}			,		tensor_type=lowerCamelCase__)

           return encoded_outputs




snake_case    :						Tuple  =						'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def 	snake_case__       (					)		->		None:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = input("Enter message: "       )
						A__	:							Tuple						    = input("Enter key [alphanumeric]: "       )
						A__	:							Optional[Any]						    = input("Encrypt/Decrypt [e/d]: "       )

						if mode.lower().startswith("e"       ):
												A__	:							Tuple						    = "encrypt"
												A__	:							str						    = encrypt_message(__lowercase							,     __lowercase       )
						elif mode.lower().startswith("d"       ):
												A__	:							Optional[Any]						    = "decrypt"
												A__	:							Union[str, Any]						    = decrypt_message(__lowercase							,     __lowercase       )

						print(F'\n{mode.title()}ed message:'       )
						print(__lowercase       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "encrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						return translate_message(__lowercase							,     __lowercase							,     "decrypt"       )
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		str:







						"""simple docstring"""

						A__	:							Dict						    = []
						A__	:							Union[str, Any]						    = 0
						A__	:							List[Any]						    = key.upper()

						for symbol in message:
												A__	:							List[str]						    = LETTERS.find(symbol.upper()       )
												if num != -1:
																		if mode == "encrypt":
																								num += LETTERS.find(key[key_index]       )
																		elif mode == "decrypt":
																								num -= LETTERS.find(key[key_index]       )

																		num %= len(__lowercase       )

																		if symbol.isupper():
																								translated.append(LETTERS[num]       )
																		elif symbol.islower():
																								translated.append(LETTERS[num].lower()       )

																		key_index += 1
																		if key_index == len(__lowercase       ):
																								A__	:							Optional[int]						    = 0
												else:
																		translated.append(__lowercase       )
						return "".join(__lowercase       )


if __name__ == "__main__":
		main()