
import unittest

import numpy as np

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

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 DPTImageProcessor

class __snake_case						( unittest.TestCase	):
    def __init__(    self						,snake_case						,snake_case=7						,snake_case=3						,snake_case=18						,snake_case=30						,snake_case=400						,snake_case=True						,snake_case=None						,snake_case=True						,snake_case=[0.5, 0.5, 0.5]						,snake_case=[0.5, 0.5, 0.5]						,):



           '''simple docstring'''







           lowercase				:	str			      = size if size is not None else {"""height""": 18, """width""": 18}
           lowercase				:	Optional[int]			      = parent
           lowercase				:	List[Any]			      = batch_size
           lowercase				:	Dict			      = num_channels
           lowercase				:	List[Any]			      = image_size
           lowercase				:	List[str]			      = min_resolution
           lowercase				:	str			      = max_resolution
           lowercase				:	Union[str, Any]			      = do_resize
           lowercase				:	Any			      = size
           lowercase				:	List[str]			      = do_normalize
           lowercase				:	Tuple			      = image_mean
           lowercase				:	str			      = image_std
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''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,
           }






@require_torch
@require_vision
class __snake_case						( lowercase__   ,      unittest.TestCase	):
    _a						:		List[Any]=							DPTImageProcessor if is_vision_available() else None
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	str			      = DPTImageProcessingTester(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'''







           lowercase				:	Optional[Any]			      = self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(lowercase_						,"""image_mean""" ) )
           self.assertTrue(hasattr(lowercase_						,"""image_std""" ) )
           self.assertTrue(hasattr(lowercase_						,"""do_normalize""" ) )
           self.assertTrue(hasattr(lowercase_						,"""do_resize""" ) )
           self.assertTrue(hasattr(lowercase_						,"""size""" ) )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	Optional[Any]			      = self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size						,{"""height""": 18, """width""": 18} )

           lowercase				:	Union[str, Any]			      = self.image_processing_class.from_dict(self.image_processor_dict						,size=42 )
           self.assertEqual(image_processor.size						,{"""height""": 42, """width""": 42} )
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







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

           # Test not batched input
           lowercase				:	Dict			      = 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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           lowercase				:	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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	str			      = self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           lowercase				:	str			      = 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
           lowercase				:	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.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           lowercase				:	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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)
    def  _SCREAMING_SNAKE_CASE			(    self ):



           '''simple docstring'''







           lowercase				:	List[Any]			      = self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           lowercase				:	List[Any]			      = 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
           lowercase				:	Dict			      = 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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)

           # Test batched
           lowercase				:	Dict			      = 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["""height"""],
                   self.image_processor_tester.size["""width"""],
               )						,)




_lowerCAmelCase:						List[str]   				=       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def   _lowercase(					):
							a__      =input('Enter message: '     )
							a__      =input('Enter key [alphanumeric]: '     )
							a__      =input('Encrypt/Decrypt [e/d]: '     )

							if mode.lower().startswith('e'     ):
														a__      ='encrypt'
														a__      =encrypt_message(__a    ,     __a     )
							elif mode.lower().startswith('d'     ):
														a__      ='decrypt'
														a__      =decrypt_message(__a    ,     __a     )

							print(f"""\n{mode.title()}ed message:"""     )
							print(__a     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'encrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str     ):
							return translate_message(__a    ,     __a    ,     'decrypt'     )






def   _lowercase(					__a				:						str    ,     __a				:						str    ,     __a				:						str     ):
							a__      =[]
							a__      =0
							a__      =key.upper()

							for symbol in message:
														a__      =LETTERS.find(symbol.upper()     )
														if num != -1:
																					if mode == "encrypt":
																												num += LETTERS.find(key[key_index]     )
																					elif mode == "decrypt":
																												num -= LETTERS.find(key[key_index]     )

																					num %= len(__a     )

																					if symbol.isupper():
																												translated.append(LETTERS[num]     )
																					elif symbol.islower():
																												translated.append(LETTERS[num].lower()     )

																					key_index += 1
																					if key_index == len(__a     ):
																												a__      =0
														else:
																					translated.append(__a     )
							return "".join(__a     )


if __name__ == "__main__":
	main()

