

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 DeiTImageProcessor



class 		a_	(							unittest.TestCase					):



      """simple docstring"""







      def __init__(				self					:							List[Any]      ,snake_case					:							Tuple      ,snake_case					:							Dict=7      ,snake_case					:							Optional[Any]=3      ,snake_case					:							str=18      ,snake_case					:							Dict=30      ,snake_case					:							List[Any]=400      ,snake_case					:							Dict=True      ,snake_case					:							Optional[Any]=None      ,snake_case					:							Optional[Any]=True      ,snake_case					:							Union[str, Any]=None      ,snake_case					:							Tuple=True      ,snake_case					:							List[str]=[0.5, 0.5, 0.5]      ,snake_case					:							List[str]=[0.5, 0.5, 0.5]      ,):
           SCREAMING_SNAKE_CASE										=size if size is not None else {'height': 20, 'width': 20}
           SCREAMING_SNAKE_CASE										=crop_size if crop_size is not None else {'height': 18, 'width': 18}

           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=num_channels
           SCREAMING_SNAKE_CASE										=image_size
           SCREAMING_SNAKE_CASE										=min_resolution
           SCREAMING_SNAKE_CASE										=max_resolution
           SCREAMING_SNAKE_CASE										=do_resize
           SCREAMING_SNAKE_CASE										=size
           SCREAMING_SNAKE_CASE										=do_center_crop
           SCREAMING_SNAKE_CASE										=crop_size
           SCREAMING_SNAKE_CASE										=do_normalize
           SCREAMING_SNAKE_CASE										=image_mean
           SCREAMING_SNAKE_CASE										=image_std


      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           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,
           }







@require_torch
@require_vision
class 		a_	(							snake_case__			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				DeiTImageProcessor if is_vision_available() else None
      __UpperCAmelCase      		=				True
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=DeiTImageProcessingTester(self    )
      @property
      def 	_lowerCAmelCase					(				self					:							int    ):
           return self.image_processor_tester.prepare_image_processor_dict()
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=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'    )    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.image_processing_class.from_dict(self.image_processor_dict    )
           self.assertEqual(image_processor.size      ,{'height': 20, 'width': 20}    )
           self.assertEqual(image_processor.crop_size      ,{'height': 18, 'width': 18}    )

           SCREAMING_SNAKE_CASE										=self.image_processing_class.from_dict(self.image_processor_dict      ,size=42      ,crop_size=84    )
           self.assertEqual(image_processor.size      ,{'height': 42, 'width': 42}    )
           self.assertEqual(image_processor.crop_size      ,{'height': 84, 'width': 84}    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           pass
      def 	_lowerCAmelCase					(				self					:							int    ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE										=self.image_processing_class(**self.image_processor_dict    )
           # create random PIL images
           SCREAMING_SNAKE_CASE										=prepare_image_inputs(self.image_processor_tester      ,equal_resolution=_A    )
           for image in image_inputs:
                self.assertIsInstance(_A      ,Image.Image    )

           # Test not batched input
           SCREAMING_SNAKE_CASE										=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
           SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							List[Any]    ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE										=self.image_processing_class(**self.image_processor_dict    )
           # create random numpy tensors
           SCREAMING_SNAKE_CASE										=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
           SCREAMING_SNAKE_CASE										=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
           SCREAMING_SNAKE_CASE										=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 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           # Initialize image_processing
           SCREAMING_SNAKE_CASE										=self.image_processing_class(**self.image_processor_dict    )
           # create random PyTorch tensors
           SCREAMING_SNAKE_CASE										=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
           SCREAMING_SNAKE_CASE										=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
           SCREAMING_SNAKE_CASE										=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'],
               )      ,)




import os
import sys
import time

from . import transposition_cipher as trans_cipher







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										='Prehistoric Men.txt'
     SCREAMING_SNAKE_CASE										='Output.txt'
     SCREAMING_SNAKE_CASE										=int(input('Enter key: '      )      )
     SCREAMING_SNAKE_CASE										=input('Encrypt/Decrypt [e/d]: '      )

     if not os.path.exists(lowerCAmelCase_      ):
          print(F'File {input_file} does not exist. Quitting...'      )
          sys.exit()
     if os.path.exists(lowerCAmelCase_      ):
          print(F'Overwrite {output_file}? [y/n]'      )
          SCREAMING_SNAKE_CASE										=input('> '      )
          if not response.lower().startswith('y'      ):
               sys.exit()

     SCREAMING_SNAKE_CASE										=time.time()
     if mode.lower().startswith('e'      ):
          with open(lowerCAmelCase_      ) as f:
               SCREAMING_SNAKE_CASE										=f.read()
          SCREAMING_SNAKE_CASE										=trans_cipher.encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif mode.lower().startswith('d'      ):
          with open(lowerCAmelCase_      ) as f:
               SCREAMING_SNAKE_CASE										=f.read()
          SCREAMING_SNAKE_CASE										=trans_cipher.decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     with open(lowerCAmelCase_,   'w'      ) as output_obj:
          output_obj.write(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=round(time.time() - start_time,   2      )
     print(('Done (', total_time, 'seconds )')      )


if __name__ == "__main__":
 main()

