




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 MobileViTImageProcessor
class 						_A (  unittest.TestCase				):






  def __init__(  self		:     Dict				,						_A		:     Union[str, Any]				,						_A		:     str=7				,						_A		:     Any=3				,						_A		:     Optional[int]=18				,						_A		:     Optional[int]=30				,						_A		:     Optional[Any]=400				,						_A		:     Optional[Any]=True				,						_A		:     List[str]=None				,						_A		:     List[Any]=True				,						_A		:     Dict=None				,						_A		:     Optional[Any]=True				,						)		->   Any:






       """simple docstring"""







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






  def  __a      (  self		:     Any				)		->   int:






       """simple docstring"""







       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_flip_channel_order": self.do_flip_channel_order,
       }


@require_torch
@require_vision
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      Dict			=      MobileViTImageProcessor if is_vision_available() else None






  def  __a      (  self		:     Dict				)		->   Tuple:






       """simple docstring"""







       lowercase :       List[Any]       =			MobileViTImageProcessingTester(self				)






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






       """simple docstring"""







       return self.image_processor_tester.prepare_image_processor_dict()






  def  __a      (  self		:     Dict				)		->   Dict:






       """simple docstring"""







       lowercase :       Tuple       =			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_flip_channel_order'''				)				)






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






       """simple docstring"""







       lowercase :       List[str]       =			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}				)

       lowercase :       List[Any]       =			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  __a      (  self		:     Union[str, Any]				)		->   List[Any]:






       """simple docstring"""







       pass






  def  __a      (  self		:     int				)		->   str:






       """simple docstring"""







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

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

       # Test batched
       lowercase :       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  __a      (  self		:     List[str]				)		->   Optional[int]:






       """simple docstring"""







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

       # Test not batched input
       lowercase :       int       =			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
       lowercase :       Tuple       =			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  __a      (  self		:     Optional[int]				)		->   Union[str, Any]:






       """simple docstring"""







       lowercase :       Any       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       lowercase :       Optional[int]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				,						torchify=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						torch.Tensor				)

       # Test not batched input
       lowercase :       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
       lowercase :       Optional[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'''],
           )				,						)





from string import ascii_uppercase

lowerCAmelCase_						=			{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_						=			dict(enumerate(ascii_uppercase))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			len(__magic_name__   )
     lowercase :       Any       =			0
     while True:
          if x == i:
               lowercase :       Any       =			0
          if len(__magic_name__   ) == len(__magic_name__   ):
               break
          key += key[i]
          i += 1
     return key






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       str       =			''''''
     lowercase :       Dict       =			0
     for letter in message:
          if letter == " ":
               cipher_text += " "
          else:
               lowercase :       Dict       =			(dicta[letter] - dicta[key_new[i]]) % 26
               i += 1
               cipher_text += dicta[x]
     return cipher_text






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Any       =			''''''
     lowercase :       str       =			0
     for letter in cipher_text:
          if letter == " ":
               or_txt += " "
          else:
               lowercase :       Any       =			(dicta[letter] + dicta[key_new[i]] + 26) % 26
               i += 1
               or_txt += dicta[x]
     return or_txt






def  snake_case(    )			->							None:





     '''simple docstring'''


     lowercase :       Dict       =			'''THE GERMAN ATTACK'''
     lowercase :       Dict       =			'''SECRET'''
     lowercase :       Union[str, Any]       =			generate_key(__magic_name__			,		__magic_name__   )
     lowercase :       List[str]       =			cipher_text(__magic_name__			,		__magic_name__   )
     print(F"""Encrypted Text = {s}"""   )
     print(F"""Original Text = {original_text(__magic_name__			,		__magic_name__   )}"""   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()