

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 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Union[str, Any]											=							CONFIG_NAME

						@register_to_config
						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = 8 , __UpperCamelCase = "lanczos" , __UpperCamelCase = True , __UpperCamelCase = False , )							->	Dict:




									'''simple docstring'''


									super().__init__()

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	PIL.Image.Image:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    str								=      images[None, ...]
									__UpperCamelCase    :    int								=      (images * 2_55).round().astype("uint8"    )
									if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												__UpperCamelCase    :    int								=      [Image.fromarray(image.squeeze() , mode="L"    ) for image in images]
									else:
												__UpperCamelCase    :    Any								=      [Image.fromarray(__UpperCamelCase    ) for image in images]

									return pil_images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	np.ndarray:




									'''simple docstring'''


									if not isinstance(__UpperCamelCase , __UpperCamelCase    ):
												__UpperCamelCase    :    Tuple								=      [images]
									__UpperCamelCase    :    Any								=      [np.array(__UpperCamelCase    ).astype(np.floataa    ) / 255.0 for image in images]
									__UpperCamelCase    :    str								=      np.stack(__UpperCamelCase , axis=0    )

									return images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	torch.FloatTensor:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    Dict								=      images[..., None]

									__UpperCamelCase    :    List[Any]								=      torch.from_numpy(images.transpose(0 , 3 , 1 , 2    )    )
									return images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	np.ndarray:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      images.cpu().permute(0 , 2 , 3 , 1    ).float().numpy()
									return images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									return 2.0 * images - 1.0

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	List[Any]:




									'''simple docstring'''


									return (images / 2 + 0.5).clamp(0 , 1    )

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	PIL.Image.Image:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      image.convert("RGB"    )
									return image

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , )							->	PIL.Image.Image:




									'''simple docstring'''


									if height is None:
												__UpperCamelCase    :    Dict								=      image.height
									if width is None:
												__UpperCamelCase    :    Tuple								=      image.width

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

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None , __UpperCamelCase = None , )							->	torch.Tensor:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      (PIL.Image.Image, np.ndarray, torch.Tensor)
									if isinstance(__UpperCamelCase , __UpperCamelCase    ):
												__UpperCamelCase    :    Tuple								=      [image]
									elif not (isinstance(__UpperCamelCase , __UpperCamelCase    ) and all(isinstance(__UpperCamelCase , __UpperCamelCase    ) for i in image    )):
												raise ValueError(
												    f'''Input is in incorrect format: {[type(__UpperCamelCase    ) for i in image]}. Currently, we only support {", ".join(__UpperCamelCase    )}'''    )

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

									elif isinstance(image[0] , np.ndarray    ):
												__UpperCamelCase    :    List[str]								=      np.concatenate(__UpperCamelCase , axis=0    ) if image[0].ndim == 4 else np.stack(__UpperCamelCase , axis=0    )
												__UpperCamelCase    :    List[Any]								=      self.numpy_to_pt(__UpperCamelCase    )
												__UpperCamelCase    :    Optional[Any]								=      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    :    List[str]								=      torch.cat(__UpperCamelCase , axis=0    ) if image[0].ndim == 4 else torch.stack(__UpperCamelCase , axis=0    )
												__UpperCamelCase    :    Dict								=      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    :    str								=      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()}]''' , __UpperCamelCase , )
												__UpperCamelCase    :    List[Any]								=      False

									if do_normalize:
												__UpperCamelCase    :    List[Any]								=      self.normalize(__UpperCamelCase    )

									return image




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = "pil" , __UpperCamelCase = None , )							->	List[Any]:




									'''simple docstring'''


									if not isinstance(__UpperCamelCase , torch.Tensor    ):
												raise ValueError(
												    f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase    )}. We only support pytorch tensor'''    )
									if output_type not in ["latent", "pt", "np", "pil"]:
												__UpperCamelCase    :    List[Any]								=      (
												    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" , __UpperCamelCase , standard_warn=__UpperCamelCase    )
												__UpperCamelCase    :    Optional[int]								=      "np"

									if output_type == "latent":
												return image

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

									__UpperCamelCase    :    str								=      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    :    Any								=      self.pt_to_numpy(__UpperCamelCase    )

									if output_type == "np":
												return image

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







class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						lowercase  :				Optional[Any]											=							CONFIG_NAME

						@register_to_config
						def __init__(				self , __UpperCamelCase = True , __UpperCamelCase = 8 , __UpperCamelCase = "lanczos" , __UpperCamelCase = True , )							->	Optional[Any]:




									'''simple docstring'''


									super().__init__()

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	int:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    Dict								=      images[None, ...]
									__UpperCamelCase    :    Dict								=      (images * 2_55).round().astype("uint8"    )
									if images.shape[-1] == 1:
												# special case for grayscale (single channel) images
												__UpperCamelCase    :    Tuple								=      [Image.fromarray(image.squeeze() , mode="L"    ) for image in images]
									else:
												__UpperCamelCase    :    Dict								=      [Image.fromarray(image[:, :, :3]    ) for image in images]

									return pil_images

						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase    )							->	Dict:




									'''simple docstring'''


									return image[:, :, 1] * 2**8 + image[:, :, 2]

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	Optional[int]:




									'''simple docstring'''


									if images.ndim == 3:
												__UpperCamelCase    :    str								=      images[None, ...]
									__UpperCamelCase    :    Tuple								=      images[:, :, :, 3:]
									if images.shape[-1] == 6:
												__UpperCamelCase    :    Optional[Any]								=      (images_depth * 2_55).round().astype("uint8"    )
												__UpperCamelCase    :    str								=      [
												    Image.fromarray(self.rgblike_to_depthmap(__UpperCamelCase    ) , mode="I;16"    ) for image_depth in images_depth
												]
									elif images.shape[-1] == 4:
												__UpperCamelCase    :    str								=      (images_depth * 65535.0).astype(np.uintaa    )
												__UpperCamelCase    :    Optional[int]								=      [Image.fromarray(__UpperCamelCase , mode="I;16"    ) for image_depth in images_depth]
									else:
												raise Exception("Not supported"    )

									return pil_images




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = "pil" , __UpperCamelCase = None , )							->	List[Any]:




									'''simple docstring'''


									if not isinstance(__UpperCamelCase , torch.Tensor    ):
												raise ValueError(
												    f'''Input for postprocessing is in incorrect format: {type(__UpperCamelCase    )}. We only support pytorch tensor'''    )
									if output_type not in ["latent", "pt", "np", "pil"]:
												__UpperCamelCase    :    int								=      (
												    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" , __UpperCamelCase , standard_warn=__UpperCamelCase    )
												__UpperCamelCase    :    List[str]								=      "np"

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

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

									__UpperCamelCase    :    Tuple								=      self.pt_to_numpy(__UpperCamelCase    )

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

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


import random
import sys

from . import cryptomath_module as cryptomath

lowercase     :       Optional[Any]        =       (
    r" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"
    r"abcdefghijklmnopqrstuvwxyz{|}~"
)







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			if mode == "encrypt":
						if key_a == 1:
									sys.exit(
									    "The affine cipher becomes weak when key "
									    "A is set to 1. Choose different key"		)
						if key_b == 0:
									sys.exit(
									    "The affine cipher becomes weak when key "
									    "B is set to 0. Choose different key"		)
			if key_a < 0 or key_b < 0 or key_b > len(_lowerCAmelCase		) - 1:
						sys.exit(
						    "Key A must be greater than 0 and key B must "
						    F'''be between 0 and {len(_lowerCAmelCase		) - 1}.'''		)
			if cryptomath.gcd(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		) != 1:
						sys.exit(
						    F'''Key A {key_a} and the symbol set size {len(_lowerCAmelCase		)} '''
						    "are not relatively prime. Choose a different key."		)







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase							,				__UpperCamelCase    :    int								=      divmod(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			check_keys(_lowerCAmelCase     ,			_lowerCAmelCase     ,			"encrypt"		)
			__UpperCamelCase    :    List[Any]								=      ""
			for symbol in message:
						if symbol in SYMBOLS:
									__UpperCamelCase    :    Tuple								=      SYMBOLS.find(_lowerCAmelCase		)
									cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_lowerCAmelCase		)]
						else:
									cipher_text += symbol
			return cipher_text







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		str		):
			__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      divmod(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			check_keys(_lowerCAmelCase     ,			_lowerCAmelCase     ,			"decrypt"		)
			__UpperCamelCase    :    List[Any]								=      ""
			__UpperCamelCase    :    Tuple								=      cryptomath.find_mod_inverse(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		)
			for symbol in message:
						if symbol in SYMBOLS:
									__UpperCamelCase    :    List[Any]								=      SYMBOLS.find(_lowerCAmelCase		)
									plain_text += SYMBOLS[
									    (sym_index - key_b) * mod_inverse_of_key_a % len(_lowerCAmelCase		)
									]
						else:
									plain_text += symbol
			return plain_text







def  UpperCAmelCase_   ():
			while True:
						__UpperCamelCase    :    Optional[int]								=      random.randint(2     ,			len(_lowerCAmelCase		)		)
						__UpperCamelCase    :    Optional[int]								=      random.randint(2     ,			len(_lowerCAmelCase		)		)
						if cryptomath.gcd(_lowerCAmelCase     ,			len(_lowerCAmelCase		)		) == 1 and key_b % len(_lowerCAmelCase		) != 0:
									return key_b * len(_lowerCAmelCase		) + key_b







def  UpperCAmelCase_   ():
			__UpperCamelCase    :    List[Any]								=      input("Enter message: "		).strip()
			__UpperCamelCase    :    List[Any]								=      int(input("Enter key [2000 - 9000]: "		).strip()		)
			__UpperCamelCase    :    Optional[int]								=      input("Encrypt/Decrypt [E/D]: "		).strip().lower()

			if mode.startswith("e"		):
						__UpperCamelCase    :    str								=      "encrypt"
						__UpperCamelCase    :    str								=      encrypt_message(_lowerCAmelCase     ,			_lowerCAmelCase		)
			elif mode.startswith("d"		):
						__UpperCamelCase    :    int								=      "decrypt"
						__UpperCamelCase    :    Dict								=      decrypt_message(_lowerCAmelCase     ,			_lowerCAmelCase		)
			print(F'''\n{mode.title()}ed text: \n{translated}'''		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					# main()