"""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 SwinaSRImageProcessor
  from transformers.image_transforms import get_image_size
class   UpperCAmelCase_	(				unittest.TestCase   ):
    def __init__(	self			:       int   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Any=7   ,						_lowercase			:       Dict=3   ,						_lowercase			:       Union[str, Any]=1_8   ,						_lowercase			:       int=3_0   ,						_lowercase			:       Optional[int]=4_0_0   ,						_lowercase			:       Optional[int]=True   ,						_lowercase			:       Optional[Any]=1 / 2_5_5   ,						_lowercase			:       int=True   ,						_lowercase			:       Dict=8   ,						)						->    Optional[int]:
       _lowercase							     =       parent
       _lowercase							     =       batch_size
       _lowercase							     =       num_channels
       _lowercase							     =       image_size
       _lowercase							     =       min_resolution
       _lowercase							     =       max_resolution
       _lowercase							     =       do_rescale
       _lowercase							     =       rescale_factor
       _lowercase							     =       do_pad
       _lowercase							     =       pad_size
    def 		_lowerCamelCase      (	self			:       Any		)						->    Union[str, Any]:
       return {
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
           "pad_size": self.pad_size,
       }




    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       str=False   ,						_lowercase			:       Optional[Any]=False   ,						_lowercase			:       Tuple=False		)						->    Tuple:
       assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

       if equal_resolution:
          _lowercase							     =       []
          for i in range(self.batch_size		):
             image_inputs.append(
                 np.random.randint(
                     2_5_5   ,						size=(self.num_channels, self.max_resolution, self.max_resolution)   ,						dtype=np.uinta		)		)
       else:
          _lowercase							     =       []
          for i in range(self.batch_size		):
             _lowercase     ,  _lowercase							     =       np.random.choice(np.arange(self.min_resolution   ,						self.max_resolution		)   ,						2		)
             image_inputs.append(np.random.randint(2_5_5   ,						size=(self.num_channels, width, height)   ,						dtype=np.uinta		)		)

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

       if torchify:
          _lowercase							     =       [torch.from_numpy(_lowercase		) for x in image_inputs]

       return image_inputs
@require_torch
@require_vision
class   UpperCAmelCase_	(				lowercase__,    unittest.TestCase   ):
    snake_case_											=      SwinaSRImageProcessor if is_vision_available() else None
    def 		_lowerCamelCase      (	self			:       Dict		)						->    List[str]:
       _lowercase							     =       SwinaSRImageProcessingTester(self		)
    @property
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    Optional[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_lowercase   ,						"do_rescale"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"rescale_factor"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"do_pad"		)		)
       self.assertTrue(hasattr(_lowercase   ,						"pad_size"		)		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[str]:
       pass
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       Optional[int]		)						->    int:
       _lowercase     ,  _lowercase							     =       get_image_size(_lowercase		)
       _lowercase							     =       self.image_processor_tester.pad_size

       _lowercase							     =       (old_height // size + 1) * size - old_height
       _lowercase							     =       (old_width // size + 1) * size - old_width
       return old_height + pad_height, old_width + pad_width
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[Any]:
       # Initialize image_processor
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _lowercase							     =       self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase		)
       for image in image_inputs:
          self.assertIsInstance(_lowercase   ,						Image.Image		)

       # Test not batched input
       _lowercase							     =       image_processor(image_inputs[0]   ,						return_tensors="pt"		).pixel_values
       _lowercase     ,  _lowercase							     =       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			:       List[Any]		)						->    Tuple:
       # Initialize image_processor
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       _lowercase							     =       self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase   ,						numpify=_lowercase		)
       for image in image_inputs:
          self.assertIsInstance(_lowercase   ,						np.ndarray		)

       # Test not batched input
       _lowercase							     =       image_processor(image_inputs[0]   ,						return_tensors="pt"		).pixel_values
       _lowercase     ,  _lowercase							     =       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			:       Tuple		)						->    Union[str, Any]:
       # Initialize image_processor
       _lowercase							     =       self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       _lowercase							     =       self.image_processor_tester.prepare_inputs(equal_resolution=_lowercase   ,						torchify=_lowercase		)
       for image in image_inputs:
          self.assertIsInstance(_lowercase   ,						torch.Tensor		)

       # Test not batched input
       _lowercase							     =       image_processor(image_inputs[0]   ,						return_tensors="pt"		).pixel_values
       _lowercase     ,  _lowercase							     =       self.calculate_expected_size(image_inputs[0]		)
       self.assertEqual(
           encoded_images.shape   ,						(
               1,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,						)
"""simple docstring"""


from typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()