






"""simple docstring"""


import random
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():
       import PIL

       from transformers import FlavaImageProcessor
       from transformers.image_utils import PILImageResampling
       from transformers.models.flava.image_processing_flava import (
           FLAVA_CODEBOOK_MEAN,
           FLAVA_CODEBOOK_STD,
           FLAVA_IMAGE_MEAN,
           FLAVA_IMAGE_STD,
       )
else:
       lowercase_    					=					lowercase_    					=					lowercase_    					=					lowercase_    					=					None


class  snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(     self			:					Dict,	_lowerCamelCase			:					Optional[int],	_lowerCamelCase			:					Any=7,	_lowerCamelCase			:					Tuple=3,	_lowerCamelCase			:					int=30,	_lowerCamelCase			:					str=4_00,	_lowerCamelCase			:					int=True,	_lowerCamelCase			:					List[str]=None,	_lowerCamelCase			:					Optional[Any]=True,	_lowerCamelCase			:					int=None,	_lowerCamelCase			:					Any=None,	_lowerCamelCase			:					Optional[int]=True,	_lowerCamelCase			:					int=1 / 2_55,	_lowerCamelCase			:					Optional[Any]=True,	_lowerCamelCase			:					Dict=FLAVA_IMAGE_MEAN,	_lowerCamelCase			:					int=FLAVA_IMAGE_STD,	_lowerCamelCase			:					Any=14,	_lowerCamelCase			:					Union[str, Any]=75,	_lowerCamelCase			:					Optional[int]=None,	_lowerCamelCase			:					List[Any]=16,	_lowerCamelCase			:					Tuple=0.3,	_lowerCamelCase			:					Union[str, Any]=None,	_lowerCamelCase			:					Optional[Any]=True,	_lowerCamelCase			:					List[Any]=None,	_lowerCamelCase			:					Optional[int]=None,	_lowerCamelCase			:					Dict=True,	_lowerCamelCase			:					int=None,	_lowerCamelCase			:					Dict=True,	_lowerCamelCase			:					int=True,	_lowerCamelCase			:					Optional[int]=FLAVA_CODEBOOK_MEAN,	_lowerCamelCase			:					Union[str, Any]=FLAVA_CODEBOOK_STD,	):

     '''simple docstring'''
     __A						      =			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
     __A						      =			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
     __A						      =			codebook_size if codebook_size is not None else {'''height''': 1_12, '''width''': 1_12}
     __A						      =			codebook_crop_size if codebook_crop_size is not None else {'''height''': 1_12, '''width''': 1_12}

     __A						      =			parent
     __A						      =			batch_size
     __A						      =			num_channels
     __A						      =			do_resize
     __A						      =			do_rescale
     __A						      =			rescale_factor
     __A						      =			min_resolution
     __A						      =			max_resolution
     __A						      =			size
     __A						      =			resample if resample is not None else PILImageResampling.BICUBIC
     __A						      =			do_normalize
     __A						      =			image_mean
     __A						      =			image_std
     __A						      =			do_center_crop
     __A						      =			crop_size

     __A						      =			input_size_patches
     __A						      =			total_mask_patches
     __A						      =			mask_group_max_patches
     __A						      =			mask_group_min_patches
     __A						      =			mask_group_min_aspect_ratio
     __A						      =			mask_group_max_aspect_ratio

     __A						      =			codebook_do_resize
     __A						      =			codebook_size
     __A						      =			codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
     __A						      =			codebook_do_center_crop
     __A						      =			codebook_crop_size
     __A						      =			codebook_do_map_pixels
     __A						      =			codebook_do_normalize
     __A						      =			codebook_image_mean
     __A						      =			codebook_image_std


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''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,
         "resample": self.resample,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "input_size_patches": self.input_size_patches,
         "total_mask_patches": self.total_mask_patches,
         "mask_group_max_patches": self.mask_group_max_patches,
         "mask_group_min_patches": self.mask_group_min_patches,
         "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
         "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
         "codebook_do_resize": self.codebook_do_resize,
         "codebook_size": self.codebook_size,
         "codebook_resample": self.codebook_resample,
         "codebook_do_center_crop": self.codebook_do_center_crop,
         "codebook_crop_size": self.codebook_crop_size,
         "codebook_do_map_pixels": self.codebook_do_map_pixels,
         "codebook_do_normalize": self.codebook_do_normalize,
         "codebook_image_mean": self.codebook_image_mean,
         "codebook_image_std": self.codebook_image_std,
     }


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return (self.size["height"], self.size["width"])


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     return (
         (self.input_size_patches, self.input_size_patches)
         if not isinstance(self.input_size_patches,	_lowerCamelCase  )
         else self.input_size_patches
     )






   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class  snake_case		(							_lowerCAmelCase       ,     unittest.TestCase						):


   '''simple docstring'''

   A_						:     int									=			FlavaImageProcessor if is_vision_available() else None
   A_						:     Dict									=			None


   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

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


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

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


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''image_std'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''resample'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''crop_size'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''do_rescale'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''rescale_factor'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''masking_generator'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_do_resize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_size'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_resample'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_do_center_crop'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_crop_size'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_do_map_pixels'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_do_normalize'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_image_mean'''  )  )
     self.assertTrue(hasattr(_lowerCamelCase,	'''codebook_image_std'''  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			self.image_processing_class.from_dict(self.image_processor_dict  )
     self.assertEqual(image_processor.size,	{'''height''': 2_24, '''width''': 2_24}  )
     self.assertEqual(image_processor.crop_size,	{'''height''': 2_24, '''width''': 2_24}  )
     self.assertEqual(image_processor.codebook_size,	{'''height''': 1_12, '''width''': 1_12}  )
     self.assertEqual(image_processor.codebook_crop_size,	{'''height''': 1_12, '''width''': 1_12}  )

     __A						      =			self.image_processing_class.from_dict(
         self.image_processor_dict,	size=42,	crop_size=84,	codebook_size=33,	codebook_crop_size=66  )
     self.assertEqual(image_processor.size,	{'''height''': 42, '''width''': 42}  )
     self.assertEqual(image_processor.crop_size,	{'''height''': 84, '''width''': 84}  )
     self.assertEqual(image_processor.codebook_size,	{'''height''': 33, '''width''': 33}  )
     self.assertEqual(image_processor.codebook_crop_size,	{'''height''': 66, '''width''': 66}  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     pass


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''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,	PIL.Image.Image  )

     # Test not batched input
     __A						      =			image_processing(image_inputs[0],	return_tensors='''pt'''  )

     # Test no bool masked pos
     self.assertFalse('''bool_masked_pos''' in encoded_images  )

     __A      ,   __A						      =			self.image_processor_tester.get_expected_image_size()

     self.assertEqual(
         encoded_images.pixel_values.shape,	(1, self.image_processor_tester.num_channels, expected_height, expected_width),	)

     # Test batched
     __A						      =			image_processing(_lowerCamelCase,	return_tensors='''pt'''  )
     __A      ,   __A						      =			self.image_processor_tester.get_expected_image_size()

     # Test no bool masked pos
     self.assertFalse('''bool_masked_pos''' in encoded_images  )

     self.assertEqual(
         encoded_images.pixel_values.shape,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ),	)


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

     '''simple docstring'''
     # Initialize image_processing
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     # create random tensors
     __A						      =			prepare_image_inputs(self.image_processor_tester,	equal_resolution=_lowerCamelCase,	**_lowerCamelCase  )
     for image in image_inputs:
       self.assertIsInstance(_lowerCamelCase,	_lowerCamelCase  )

     # Test not batched input
     __A						      =			image_processing(image_inputs[0],	return_tensors='''pt'''  )

     __A      ,   __A						      =			self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.pixel_values.shape,	(1, self.image_processor_tester.num_channels, expected_height, expected_width),	)

     __A						      =			image_processing(_lowerCamelCase,	return_image_mask=_lowerCamelCase,	return_tensors='''pt'''  )

     __A      ,   __A						      =			self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.pixel_values.shape,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ),	)

     __A      ,   __A						      =			self.image_processor_tester.get_expected_mask_size()
     self.assertEqual(
         encoded_images.bool_masked_pos.shape,	(
             self.image_processor_tester.batch_size,
             expected_height,
             expected_width,
         ),	)

     # Test batched
     __A						      =			image_processing(_lowerCamelCase,	return_tensors='''pt'''  ).pixel_values

     __A      ,   __A						      =			self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.shape,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ),	)

     # Test masking
     __A						      =			image_processing(_lowerCamelCase,	return_image_mask=_lowerCamelCase,	return_tensors='''pt'''  )

     __A      ,   __A						      =			self.image_processor_tester.get_expected_image_size()
     self.assertEqual(
         encoded_images.pixel_values.shape,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ),	)

     __A      ,   __A						      =			self.image_processor_tester.get_expected_mask_size()
     self.assertEqual(
         encoded_images.bool_masked_pos.shape,	(
             self.image_processor_tester.batch_size,
             expected_height,
             expected_width,
         ),	)


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any]  ):

     '''simple docstring'''
     self._test_call_framework(np.ndarray,	prepare_kwargs={'''numpify''': True}  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     self._test_call_framework(torch.Tensor,	prepare_kwargs={'''torchify''': True}  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     # Initialize image_processing
     random.seed(12_34  )
     __A						      =			self.image_processing_class(**self.image_processor_dict  )
     __A						      =			prepare_image_inputs(self.image_processor_tester,	equal_resolution=_lowerCamelCase,	torchify=_lowerCamelCase  )

     # Test not batched input
     __A						      =			image_processing(image_inputs[0],	return_image_mask=_lowerCamelCase,	return_tensors='''pt'''  )
     self.assertEqual(encoded_images.bool_masked_pos.sum().item(),	75  )






   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''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,	PIL.Image.Image  )

     # Test not batched input
     __A						      =			image_processing(image_inputs[0],	return_codebook_pixels=_lowerCamelCase,	return_tensors='''pt'''  )
     __A      ,   __A						      =			self.image_processor_tester.get_expected_codebook_image_size()
     self.assertEqual(
         encoded_images.codebook_pixel_values.shape,	(1, self.image_processor_tester.num_channels, expected_height, expected_width),	)

     # Test batched
     __A						      =			image_processing(_lowerCamelCase,	return_codebook_pixels=_lowerCamelCase,	return_tensors='''pt'''  )
     __A      ,   __A						      =			self.image_processor_tester.get_expected_codebook_image_size()
     self.assertEqual(
         encoded_images.codebook_pixel_values.shape,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_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() = }''')




