

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, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import ConvNextImageProcessor
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=None , __UpperCamelCase=0.875 , __UpperCamelCase=True , __UpperCamelCase=[0.5, 0.5, 0.5] , __UpperCamelCase=[0.5, 0.5, 0.5] , )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      size if size is not None else {'''shortest_edge''': 20}
									__UpperCamelCase    :    List[Any]								=      parent
									__UpperCamelCase    :    Optional[int]								=      batch_size
									__UpperCamelCase    :    str								=      num_channels
									__UpperCamelCase    :    Union[str, Any]								=      image_size
									__UpperCamelCase    :    Optional[Any]								=      min_resolution
									__UpperCamelCase    :    Union[str, Any]								=      max_resolution
									__UpperCamelCase    :    Tuple								=      do_resize
									__UpperCamelCase    :    List[Any]								=      size
									__UpperCamelCase    :    Optional[int]								=      crop_pct
									__UpperCamelCase    :    Any								=      do_normalize
									__UpperCamelCase    :    Optional[int]								=      image_mean
									__UpperCamelCase    :    Union[str, Any]								=      image_std




						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_pct": self.crop_pct,
									}







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







						"""simple docstring"""




						lowercase  :				int											=							ConvNextImageProcessor if is_vision_available() else None

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      ConvNextImageProcessingTester(self    )

						@property
						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()

						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(lowercase_ , "do_resize"    )    )
									self.assertTrue(hasattr(lowercase_ , "size"    )    )
									self.assertTrue(hasattr(lowercase_ , "crop_pct"    )    )
									self.assertTrue(hasattr(lowercase_ , "do_normalize"    )    )
									self.assertTrue(hasattr(lowercase_ , "image_mean"    )    )
									self.assertTrue(hasattr(lowercase_ , "image_std"    )    )

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




									'''simple docstring'''


									__UpperCamelCase    :    int								=      self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size , {"shortest_edge": 20}    )

									__UpperCamelCase    :    Tuple								=      self.image_processing_class.from_dict(self.image_processor_dict , size=42    )
									self.assertEqual(image_processor.size , {"shortest_edge": 42}    )

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




									'''simple docstring'''


									pass

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									__UpperCamelCase    :    List[str]								=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_    )
									for image in image_inputs:
												self.assertIsInstance(lowercase_ , Image.Image    )

									# Test not batched input
									__UpperCamelCase    :    List[str]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["shortest_edge"],
									        self.image_processor_tester.size["shortest_edge"],
									    ) , )

									# Test batched
									__UpperCamelCase    :    int								=      image_processing(lowercase_ , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["shortest_edge"],
									        self.image_processor_tester.size["shortest_edge"],
									    ) , )

						def __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


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

									# Test not batched input
									__UpperCamelCase    :    List[str]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["shortest_edge"],
									        self.image_processor_tester.size["shortest_edge"],
									    ) , )

									# Test batched
									__UpperCamelCase    :    Union[str, Any]								=      image_processing(lowercase_ , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["shortest_edge"],
									        self.image_processor_tester.size["shortest_edge"],
									    ) , )




						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									__UpperCamelCase    :    Dict								=      prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase_ , torchify=lowercase_    )
									for image in image_inputs:
												self.assertIsInstance(lowercase_ , torch.Tensor    )

									# Test not batched input
									__UpperCamelCase    :    Union[str, Any]								=      image_processing(image_inputs[0] , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["shortest_edge"],
									        self.image_processor_tester.size["shortest_edge"],
									    ) , )

									# Test batched
									__UpperCamelCase    :    List[Any]								=      image_processing(lowercase_ , return_tensors="pt"    ).pixel_values
									self.assertEqual(
									    encoded_images.shape , (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["shortest_edge"],
									        self.image_processor_tester.size["shortest_edge"],
									    ) , )


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()