


'''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, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import MobileNetVaImageProcessor





class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def __init__(      self  ,				__UpperCamelCase  ,				__UpperCamelCase=7  ,				__UpperCamelCase=3  ,				__UpperCamelCase=18  ,				__UpperCamelCase=30  ,				__UpperCamelCase=400  ,				__UpperCamelCase=True  ,				__UpperCamelCase=None  ,				__UpperCamelCase=True  ,				__UpperCamelCase=None  ,				)							->     str:
          _a            =		size if size is not None else {"shortest_edge": 20}
          _a            =		crop_size if crop_size is not None else {"height": 18, "width": 18}
          _a            =		parent
          _a            =		batch_size
          _a            =		num_channels
          _a            =		image_size
          _a            =		min_resolution
          _a            =		max_resolution
          _a            =		do_resize
          _a            =		size
          _a            =		do_center_crop
          _a            =		crop_size






     def 			a_       (      self		)							->     int:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					MobileNetVaImageProcessor if is_vision_available() else None






     def 			a_       (      self		)							->     Tuple:
          _a            =		MobileNetVaImageProcessingTester(self		)






     @property
     def 			a_       (      self		)							->     Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()






     def 			a_       (      self		)							->     Dict:
          _a            =		self.image_processing_class(**self.image_processor_dict		)
          self.assertTrue(hasattr(__UpperCamelCase  ,				"do_resize"		)		)
          self.assertTrue(hasattr(__UpperCamelCase  ,				"size"		)		)
          self.assertTrue(hasattr(__UpperCamelCase  ,				"do_center_crop"		)		)
          self.assertTrue(hasattr(__UpperCamelCase  ,				"center_crop"		)		)






     def 			a_       (      self		)							->     int:
          _a            =		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}		)

          _a            =		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]:
          pass






     def 			a_       (      self		)							->     List[Any]:
          # Initialize image_processing
          _a            =		self.image_processing_class(**self.image_processor_dict		)
          # create random PIL images
          _a            =		prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase  ,				Image.Image		)

          # Test not batched input
          _a            =		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
          _a            =		image_processing(__UpperCamelCase  ,				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		)							->     Dict:
          # Initialize image_processing
          _a            =		self.image_processing_class(**self.image_processor_dict		)
          # create random numpy tensors
          _a            =		prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__UpperCamelCase  ,				numpify=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase  ,				np.ndarray		)

          # Test not batched input
          _a            =		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
          _a            =		image_processing(__UpperCamelCase  ,				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		)							->     Tuple:
          # Initialize image_processing
          _a            =		self.image_processing_class(**self.image_processor_dict		)
          # create random PyTorch tensors
          _a            =		prepare_image_inputs(self.image_processor_tester  ,				equal_resolution=__UpperCamelCase  ,				torchify=__UpperCamelCase		)
          for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase  ,				torch.Tensor		)

          # Test not batched input
          _a            =		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
          _a            =		image_processing(__UpperCamelCase  ,				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"],
              )  ,				)





'''simple docstring'''

import os
import sys
import time

from . import transposition_cipher as trans_cipher






def __UpperCamelCase (      )   ->  None:







     '''simple docstring'''






     _a            =		"Prehistoric Men.txt"
     _a            =		"Output.txt"
     _a            =		int(input("Enter key: "					)					)
     _a            =		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]"					)
          _a            =		input("> "					)
          if not response.lower().startswith("y"					):
               sys.exit()

     _a            =		time.time()
     if mode.lower().startswith("e"					):
          with open(__lowerCamelCase					) as f:
               _a            =		f.read()
          _a            =		trans_cipher.encrypt_message(__lowerCamelCase   ,	__lowerCamelCase					)
     elif mode.lower().startswith("d"					):
          with open(__lowerCamelCase					) as f:
               _a            =		f.read()
          _a            =		trans_cipher.decrypt_message(__lowerCamelCase   ,	__lowerCamelCase					)

     with open(__lowerCamelCase   ,	"w"					) as output_obj:
          output_obj.write(__lowerCamelCase					)

     _a            =		round(time.time() - start_time   ,	2					)
     print(("Done (", total_time, "seconds )")					)


if __name__ == "__main__":
   main()

