




import warnings
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       CONFIG_NAME







      @register_to_config
      def __init__(  self       :			int			,							snake_case       :			bool = True			,							snake_case       :			int = 8			,							snake_case       :			str = "lanczos"			,							snake_case       :			bool = True			,							snake_case       :			bool = False			,							):
          super().__init__()







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    (images * 255).round().astype('''uint8'''					)
          if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              __UpperCamelCase			=    [Image.fromarray(image.squeeze()			,							mode='''L'''					) for image in images]
          else:
              __UpperCamelCase			=    [Image.fromarray(snake_case					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			Union[List[PIL.Image.Image], PIL.Image.Image]					):
          if not isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [images]
          __UpperCamelCase			=    [np.array(snake_case					).astype(np.floataa					) / 2_55.0 for image in images]
          __UpperCamelCase			=    np.stack(snake_case			,							axis=0					)

          return images







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[..., None]

          __UpperCamelCase			=    torch.from_numpy(images.transpose(0			,							3			,							1			,							2					)					)
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			torch.FloatTensor					):
          __UpperCamelCase			=    images.cpu().permute(0			,							2			,							3			,							1					).float().numpy()
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			List[Any]					):
          return 2.0 * images - 1.0







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return (images / 2 + 0.5).clamp(0			,							1					)







      @staticmethod
      def    snake_case						(  snake_case       :			PIL.Image.Image					):
          __UpperCamelCase			=    image.convert('''RGB'''					)
          return image







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			PIL.Image.Image			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          if height is None:
              __UpperCamelCase			=    image.height
          if width is None:
              __UpperCamelCase			=    image.width

          __UpperCamelCase		,   __UpperCamelCase			=    (
              x - x % self.config.vae_scale_factor for x in (width, height)
          )  # resize to integer multiple of vae_scale_factor
          __UpperCamelCase			=    image.resize((width, height)			,							resample=PIL_INTERPOLATION[self.config.resample]					)
          return image







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          __UpperCamelCase			=    (PIL.Image.Image, np.ndarray, torch.Tensor)
          if isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [image]
          elif not (isinstance(snake_case			,							snake_case					) and all(isinstance(snake_case			,							snake_case					) for i in image					)):
              raise ValueError(
                  F"Input is in incorrect format: {[type(snake_case					) for i in image]}. Currently, we only support {', '.join(snake_case					)}"					)

          if isinstance(image[0]			,							PIL.Image.Image					):
              if self.config.do_convert_rgb:
                  __UpperCamelCase			=    [self.convert_to_rgb(snake_case					) for i in image]
              if self.config.do_resize:
                  __UpperCamelCase			=    [self.resize(snake_case			,							snake_case			,							snake_case					) for i in image]
              __UpperCamelCase			=    self.pil_to_numpy(snake_case					)  # to np
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)  # to pt

          elif isinstance(image[0]			,							np.ndarray					):
              __UpperCamelCase			=    np.concatenate(snake_case			,							axis=0					) if image[0].ndim == 4 else np.stack(snake_case			,							axis=0					)
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    image.shape
              if self.config.do_resize and (
                  height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
              ):
                  raise ValueError(
                      F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
                      F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"					)

          elif isinstance(image[0]			,							torch.Tensor					):
              __UpperCamelCase			=    torch.cat(snake_case			,							axis=0					) if image[0].ndim == 4 else torch.stack(snake_case			,							axis=0					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    image.shape

              # don't need any preprocess if the image is latents
              if channel == 4:
                  return image

              if self.config.do_resize and (
                  height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
              ):
                  raise ValueError(
                      F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
                      F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"					)

        # expected range [0,1], normalize to [-1,1]
          __UpperCamelCase			=    self.config.do_normalize
          if image.min() < 0:
              warnings.warn(
                  '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                  F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"			,							snake_case			,							)
              __UpperCamelCase			=    False

          if do_normalize:
              __UpperCamelCase			=    self.normalize(snake_case					)

          return image







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. We only support pytorch tensor"					)
          if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase			=    (
                  F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                  '''`pil`, `np`, `pt`, `latent`'''
              )
              deprecate('''Unsupported output_type'''			,							'''1.0.0'''			,							snake_case			,							standard_warn=snake_case					)
              __UpperCamelCase			=    '''np'''

          if output_type == "latent":
              return image

          if do_denormalize is None:
              __UpperCamelCase			=    [self.config.do_normalize] * image.shape[0]

          __UpperCamelCase			=    torch.stack(
              [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

          if output_type == "pt":
              return image

          __UpperCamelCase			=    self.pt_to_numpy(snake_case					)

          if output_type == "np":
              return image

          if output_type == "pil":
              return self.numpy_to_pil(snake_case					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[Any]     =       CONFIG_NAME







      @register_to_config
      def __init__(  self       :			Any			,							snake_case       :			bool = True			,							snake_case       :			int = 8			,							snake_case       :			str = "lanczos"			,							snake_case       :			bool = True			,							):
          super().__init__()







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    (images * 255).round().astype('''uint8'''					)
          if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              __UpperCamelCase			=    [Image.fromarray(image.squeeze()			,							mode='''L'''					) for image in images]
          else:
              __UpperCamelCase			=    [Image.fromarray(image[:, :, :3]					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return image[:, :, 1] * 2**8 + image[:, :, 2]







      def    snake_case						(  self       :			Dict			,							snake_case       :			List[str]					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    images[:, :, :, 3:]
          if images.shape[-1] == 6:
              __UpperCamelCase			=    (images_depth * 255).round().astype('''uint8'''					)
              __UpperCamelCase			=    [
                  Image.fromarray(self.rgblike_to_depthmap(snake_case					)			,							mode='''I;16'''					) for image_depth in images_depth
              ]
          elif images.shape[-1] == 4:
              __UpperCamelCase			=    (images_depth * 6_55_35.0).astype(np.uintaa					)
              __UpperCamelCase			=    [Image.fromarray(snake_case			,							mode='''I;16'''					) for image_depth in images_depth]
          else:
              raise Exception('''Not supported'''					)

          return pil_images







      def    snake_case						(  self       :			Dict			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. We only support pytorch tensor"					)
          if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase			=    (
                  F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                  '''`pil`, `np`, `pt`, `latent`'''
              )
              deprecate('''Unsupported output_type'''			,							'''1.0.0'''			,							snake_case			,							standard_warn=snake_case					)
              __UpperCamelCase			=    '''np'''

          if do_denormalize is None:
              __UpperCamelCase			=    [self.config.do_normalize] * image.shape[0]

          __UpperCamelCase			=    torch.stack(
              [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

          __UpperCamelCase			=    self.pt_to_numpy(snake_case					)

          if output_type == "np":
              if image.shape[-1] == 6:
                  __UpperCamelCase			=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]					) for im in image]			,							axis=0					)
              else:
                  __UpperCamelCase			=    image[:, :, :, 3:]
              return image[:, :, :, :3], image_depth

          if output_type == "pil":
              return self.numpy_to_pil(snake_case					), self.numpy_to_depth(snake_case					)
          else:
              raise Exception(F"This type {output_type} is not supported"					)








'''simple docstring'''

import random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

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



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		list(snake_case_							)
 key_list.sort()
 letters_list.sort()

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



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[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        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


