


"""simple docstring"""
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 CLIPImageProcessor



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def __init__(self							,			a_							,			a_=7							,			a_=3							,			a_=18							,			a_=30							,			a_=4_00							,			a_=True							,			a_=None							,			a_=True							,			a_=None							,			a_=True							,			a_=[0.4814_5466, 0.457_8275, 0.4082_1073]							,			a_=[0.2686_2954, 0.2613_0258, 0.2757_7711]							,			a_=True							,			):





			'''simple docstring'''

			__snake_case      :		Any         =							size if size is not None else {'''shortest_edge''': 20}
			__snake_case      :		Optional[int]         =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			__snake_case      :		Dict         =							parent
			__snake_case      :		List[Any]         =							batch_size
			__snake_case      :		str         =							num_channels
			__snake_case      :		Optional[int]         =							image_size
			__snake_case      :		Any         =							min_resolution
			__snake_case      :		Tuple         =							max_resolution
			__snake_case      :		Optional[int]         =							do_resize
			__snake_case      :		int         =							size
			__snake_case      :		int         =							do_center_crop
			__snake_case      :		Optional[Any]         =							crop_size
			__snake_case      :		Union[str, Any]         =							do_normalize
			__snake_case      :		Optional[int]         =							image_mean
			__snake_case      :		Optional[Any]         =							image_std
			__snake_case      :		Optional[int]         =							do_convert_rgb



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_center_crop": self.do_center_crop,
			    "crop_size": self.crop_size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_convert_rgb": self.do_convert_rgb,
			}



		def 							SCREAMING_SNAKE_CASE    (self							,			a_=False							,			a_=False							,			a_=False   ):





			'''simple docstring'''

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

			if equal_resolution:
				__snake_case      :		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:
				__snake_case      :		List[str]         =							[]
				for i in range(self.batch_size   ):
					__snake_case      ,				__snake_case      :		Dict         =							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
				__snake_case      :		Dict         =							[Image.fromarray(np.moveaxis(a_							,			0							,			-1   )   ) for x in image_inputs]

			if torchify:
				__snake_case      :		Dict         =							[torch.from_numpy(a_   ) for x in image_inputs]

			return image_inputs



@require_torch
@require_vision
class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =CLIPImageProcessor if is_vision_available() else None



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							CLIPImageProcessingTester(self   )



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.image_processor_tester.prepare_image_processor_dict()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(a_							,			'''do_resize'''   )   )
			self.assertTrue(hasattr(a_							,			'''size'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_normalize'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_mean'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_std'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_convert_rgb'''   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							self.image_processing_class.from_dict(self.image_processor_dict   )
			self.assertEqual(image_processor.size							,			{'''shortest_edge''': 20}   )
			self.assertEqual(image_processor.crop_size							,			{'''height''': 18, '''width''': 18}   )

			__snake_case      :		int         =							self.image_processing_class.from_dict(self.image_processor_dict							,			size=42							,			crop_size=84   )
			self.assertEqual(image_processor.size							,			{'''shortest_edge''': 42}   )
			self.assertEqual(image_processor.crop_size							,			{'''height''': 84, '''width''': 84}   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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

			# Test not batched input
			__snake_case      :		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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		str         =							image_processing(a_							,			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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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

			# Test not batched input
			__snake_case      :		Optional[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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		Union[str, Any]         =							image_processing(a_							,			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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		str         =							self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			__snake_case      :		List[Any]         =							self.image_processor_tester.prepare_inputs(equal_resolution=a_							,			torchify=a_   )
			for image in image_inputs:
				self.assertIsInstance(a_							,			torch.Tensor   )

			# Test not batched input
			__snake_case      :		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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		Any         =							image_processing(a_							,			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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)



@require_torch
@require_vision
class      _UpperCAmelCase					(				__snake_case,							unittest.TestCase							):






		'''simple docstring'''

		lowerCamelCase__           =CLIPImageProcessor if is_vision_available() else None



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							CLIPImageProcessingTester(self							,			num_channels=4   )
			__snake_case      :		Optional[Any]         =							3



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.image_processor_tester.prepare_image_processor_dict()



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(a_							,			'''do_resize'''   )   )
			self.assertTrue(hasattr(a_							,			'''size'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''center_crop'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_normalize'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_mean'''   )   )
			self.assertTrue(hasattr(a_							,			'''image_std'''   )   )
			self.assertTrue(hasattr(a_							,			'''do_convert_rgb'''   )   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			pass



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

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

			# Test not batched input
			__snake_case      :		int         =							image_processing(image_inputs[0]							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        1,
			        self.expected_encoded_image_num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)

			# Test batched
			__snake_case      :		Optional[int]         =							image_processing(a_							,			return_tensors='''pt'''   ).pixel_values
			self.assertEqual(
			    encoded_images.shape							,			(
			        self.image_processor_tester.batch_size,
			        self.expected_encoded_image_num_channels,
			        self.image_processor_tester.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )							,			)











from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			str:

       """simple docstring"""




       a   ,      a   ,      a			   ="""""", """""", """"""
       a			   =[]

       for character in message_part:
              tmp.append(character_to_number[character]      )

       for each in tmp:
              one += each[0]
              two += each[1]
              three += each[2]

       return one + two + three





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			tuple[str, str, str]:

       """simple docstring"""




       a   ,      a			   ="""""", """"""
       a			   =[]

       for character in message_part:
              this_part += character_to_number[character]

       for digit in this_part:
              tmp += digit
              if len(UpperCAmelCase_      ) == len(UpperCAmelCase_      ):
                     result.append(UpperCAmelCase_      )
                     a			   =""""""

       return result[0], result[1], result[2]





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str      )->			tuple[str, str, dict[str, str], dict[str, str]]:

       """simple docstring"""




       a			   =alphabet.replace(""" """   ,       """"""      ).upper()
       a			   =message.replace(""" """   ,       """"""      ).upper()

       # Check length and characters
       if len(UpperCAmelCase_      ) != 27:
              raise KeyError("""Length of alphabet has to be 27."""      )
       for each in message:
              if each not in alphabet:
                     raise ValueError("""Each message character has to be included in alphabet!"""      )

    # Generate dictionares
       a			   =(
           """111""",
           """112""",
           """113""",
           """121""",
           """122""",
           """123""",
           """131""",
           """132""",
           """133""",
           """211""",
           """212""",
           """213""",
           """221""",
           """222""",
           """223""",
           """231""",
           """232""",
           """233""",
           """311""",
           """312""",
           """313""",
           """321""",
           """322""",
           """323""",
           """331""",
           """332""",
           """333""",
       )
       a			   ={}
       a			   ={}
       for letter, number in zip(UpperCAmelCase_   ,       UpperCAmelCase_      ):
              a			   =number
              a			   =letter

       return message, alphabet, character_to_number, number_to_character





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a   ,      a			   ="""""", """"""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              encrypted_numeric += __encrypt_part(
                  message[i : i + period]   ,       UpperCAmelCase_      )

       for i in range(0   ,       len(UpperCAmelCase_      )   ,       3      ):
              encrypted += number_to_character[encrypted_numeric[i : i + 3]]

       return encrypted





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a			   =[]
       a			   =""""""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              a   ,      a   ,      a			   =__decrypt_part(message[i : i + period]   ,       UpperCAmelCase_      )

              for j in range(0   ,       len(UpperCAmelCase_      )      ):
                     decrypted_numeric.append(a[j] + b[j] + c[j]      )

       for each in decrypted_numeric:
              decrypted += number_to_character[each]

       return decrypted


if __name__ == "__main__":
    _lowerCamelCase							=      '''DEFEND THE EAST WALL OF THE CASTLE.'''
    _lowerCamelCase							=      encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    _lowerCamelCase							=      decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    print(f"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
