

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import SwinaSRImageProcessor
					from transformers.image_transforms import get_image_size
class 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase , __UpperCamelCase=7 , __UpperCamelCase=3 , __UpperCamelCase=18 , __UpperCamelCase=30 , __UpperCamelCase=4_00 , __UpperCamelCase=True , __UpperCamelCase=1 / 2_55 , __UpperCamelCase=True , __UpperCamelCase=8 , )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      parent
									__UpperCamelCase    :    List[str]								=      batch_size
									__UpperCamelCase    :    Optional[int]								=      num_channels
									__UpperCamelCase    :    Union[str, Any]								=      image_size
									__UpperCamelCase    :    Tuple								=      min_resolution
									__UpperCamelCase    :    List[Any]								=      max_resolution
									__UpperCamelCase    :    Any								=      do_rescale
									__UpperCamelCase    :    Optional[int]								=      rescale_factor
									__UpperCamelCase    :    Optional[int]								=      do_pad
									__UpperCamelCase    :    int								=      pad_size

						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									return {
									    "do_rescale": self.do_rescale,
									    "rescale_factor": self.rescale_factor,
									    "do_pad": self.do_pad,
									    "pad_size": self.pad_size,
									}




						def __lowerCamelCase					(				self , __UpperCamelCase=False , __UpperCamelCase=False , __UpperCamelCase=False    )							->	Any:




									'''simple docstring'''


									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
												__UpperCamelCase    :    Dict								=      []
												for i in range(self.batch_size    ):
															image_inputs.append(
															    np.random.randint(
															        2_55 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta    )    )
									else:
												__UpperCamelCase    :    int								=      []
												for i in range(self.batch_size    ):
															__UpperCamelCase							,				__UpperCamelCase    :    int								=      np.random.choice(np.arange(self.min_resolution , self.max_resolution    ) , 2    )
															image_inputs.append(np.random.randint(2_55 , size=(self.num_channels, width, height) , dtype=np.uinta    )    )

									if not numpify and not torchify:
												# PIL expects the channel dimension as last dimension
												__UpperCamelCase    :    Union[str, Any]								=      [Image.fromarray(np.moveaxis(__UpperCamelCase , 0 , -1    )    ) for x in image_inputs]

									if torchify:
												__UpperCamelCase    :    Optional[int]								=      [torch.from_numpy(__UpperCamelCase    ) for x in image_inputs]

									return image_inputs







@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				Union[str, Any]											=							SwinaSRImageProcessor if is_vision_available() else None

						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      SwinaSRImageProcessingTester(self    )

						@property
						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(__UpperCamelCase , "do_rescale"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "rescale_factor"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "do_pad"    )    )
									self.assertTrue(hasattr(__UpperCamelCase , "pad_size"    )    )

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




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Any								=      get_image_size(__UpperCamelCase    )
									__UpperCamelCase    :    Dict								=      self.image_processor_tester.pad_size

									__UpperCamelCase    :    List[Any]								=      (old_height // size + 1) * size - old_height
									__UpperCamelCase    :    List[str]								=      (old_width // size + 1) * size - old_width
									return old_height + pad_height, old_width + pad_width

						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									__UpperCamelCase    :    Union[str, Any]								=      self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase    )
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , Image.Image    )

									# Test not batched input
									__UpperCamelCase    :    Optional[int]								=      image_processor(image_inputs[0] , return_tensors="pt"    ).pixel_values
									__UpperCamelCase							,				__UpperCamelCase    :    Optional[int]								=      self.calculate_expected_size(np.array(image_inputs[0]    )    )
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    ) , )

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									__UpperCamelCase    :    int								=      self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase , numpify=__UpperCamelCase    )
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , np.ndarray    )

									# Test not batched input
									__UpperCamelCase    :    str								=      image_processor(image_inputs[0] , return_tensors="pt"    ).pixel_values
									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      self.calculate_expected_size(image_inputs[0]    )
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    ) , )




						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									__UpperCamelCase    :    Dict								=      self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCamelCase , torchify=__UpperCamelCase    )
									for image in image_inputs:
												self.assertIsInstance(__UpperCamelCase , torch.Tensor    )

									# Test not batched input
									__UpperCamelCase    :    Optional[int]								=      image_processor(image_inputs[0] , return_tensors="pt"    ).pixel_values
									__UpperCamelCase							,				__UpperCamelCase    :    Any								=      self.calculate_expected_size(image_inputs[0]    )
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    ) , )


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()