






"""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 DeiTImageProcessor


class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(     self			:					Dict,	_lowerCamelCase			:					Any,	_lowerCamelCase			:					Union[str, Any]=7,	_lowerCamelCase			:					Union[str, Any]=3,	_lowerCamelCase			:					Optional[int]=18,	_lowerCamelCase			:					Optional[int]=30,	_lowerCamelCase			:					Optional[Any]=4_00,	_lowerCamelCase			:					List[str]=True,	_lowerCamelCase			:					str=None,	_lowerCamelCase			:					List[Any]=True,	_lowerCamelCase			:					int=None,	_lowerCamelCase			:					str=True,	_lowerCamelCase			:					Optional[Any]=[0.5, 0.5, 0.5],	_lowerCamelCase			:					Optional[int]=[0.5, 0.5, 0.5],	):

     '''simple docstring'''
     __A						      =			size if size is not None else {'''height''': 20, '''width''': 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
     __A						      =			do_normalize
     __A						      =			image_mean
     __A						      =			image_std






   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     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  snake_case		(							_lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     Any									=			DeiTImageProcessor if is_vision_available() else None
   A_						:     str									=			True


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			DeiTImageProcessingTester(self  )


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     return self.image_processor_tester.prepare_image_processor_dict()


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''size'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_std'''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			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}  )

     __A						      =			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     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     pass


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     # 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=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	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(_lowerCamelCase,	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     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     # 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=_lowerCamelCase,	numpify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	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(_lowerCamelCase,	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     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     # 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=_lowerCamelCase,	torchify=_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	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(_lowerCamelCase,	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 itertools
from pathlib import Path



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			[tuple(__UpperCamelCase			) for login in logins]

  __A						      =			{char for login in split_logins for char in login}

  for permutation in itertools.permutations(__UpperCamelCase			):
    __A						      =			True
    for login in logins:
      if not (
          permutation.index(login[0]			)
          < permutation.index(login[1]			)
          < permutation.index(login[2]			)
      ):
        __A						      =			False
        break

    if satisfied:
      return int(''''''.join(__UpperCamelCase			)			)

  raise Exception('''Unable to find the secret passcode'''			)



def  lowerCAmelCase    (   __UpperCamelCase = "keylog.txt"			):




  """simple docstring"""







  __A						      =			Path(__UpperCamelCase			).parent.joinpath(__UpperCamelCase			).read_text().splitlines()

  return find_secret_passcode(__UpperCamelCase			)


if __name__ == "__main__":
       print(F'''{solution() = }''')




