


"""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 MobileViTImageProcessor







class _lowerCAmelCase    (				unittest.TestCase     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__=7						,     UpperCamelCase__=3						,     UpperCamelCase__=18						,     UpperCamelCase__=30						,     UpperCamelCase__=400						,     UpperCamelCase__=True						,     UpperCamelCase__=None						,     UpperCamelCase__=True						,     UpperCamelCase__=None						,     UpperCamelCase__=True						,     )     ->		List[Any]:

       '''simple docstring'''




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







   def 			lowerCamelCase		(     self						)     ->		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 _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[str]							  =  MobileViTImageProcessor if is_vision_available() else None







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Union[str, Any]				 =   MobileViTImageProcessingTester(self						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   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"						)						)
       self.assertTrue(hasattr(UpperCamelCase__						,     "do_flip_channel_order"						)						)







   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   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}						)

       snake_case   :				Tuple				 =   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 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       pass







   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       snake_case   :				List[Any]				 =   prepare_image_inputs(self.image_processor_tester						,     equal_resolution=UpperCamelCase__						)
       for image in image_inputs:
           self.assertIsInstance(UpperCamelCase__						,     Image.Image						)

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

       # Test batched
       snake_case   :				Dict				 =   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 			lowerCamelCase		(     self						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Dict				 =   self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       snake_case   :				Optional[int]				 =   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
       snake_case   :				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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )						,     )

       # Test batched
       snake_case   :				List[Any]				 =   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 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

       '''simple docstring'''




       snake_case   :				Tuple				 =   self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       snake_case   :				List[str]				 =   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
       snake_case   :				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
       snake_case   :				List[Any]				 =   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 string







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				List[str]				 =   ""
    for i in sequence:
        snake_case   :				Optional[Any]				 =   ord(lowercase			)
        if 65 <= extract <= 90:
            output += chr(155 - extract			)
        elif 97 <= extract <= 122:
            output += chr(219 - extract			)
        else:
            output += i
    return output







def   __lowerCAmelCase						(						lowercase  :  str			)     ->						str:


    """simple docstring"""





    snake_case   :				Dict				 =   string.ascii_letters
    snake_case   :				List[Any]				 =   string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
    return "".join(
        letters_reversed[letters.index(lowercase			)] if c in letters else c for c in sequence			)







def   __lowerCAmelCase						(						)     ->						None:


    """simple docstring"""





    from timeit import timeit

    print("Running performance benchmarks..."			)
    snake_case   :				Optional[int]				 =   "from string import printable ; from __main__ import atbash, atbash_slow"
    print(F'> atbash_slow(): {timeit("atbash_slow(printable)"						,							setup=lowercase			)} seconds'			)
    print(F'>      atbash(): {timeit("atbash(printable)"						,							setup=lowercase			)} seconds'			)


if __name__ == "__main__":
  for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
    print(F'''{example} encrypted in atbash: {atbash(example)}''')
  benchmark()





