




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 ConvNextImageProcessor

class 		lowercase_       (	unittest.TestCase				):




					def __init__(   self:		Dict,     _lowercase:		Dict,     _lowercase:		Optional[int]=7,     _lowercase:		Union[str, Any]=3,     _lowercase:		List[Any]=18,     _lowercase:		Optional[int]=30,     _lowercase:		Dict=400,     _lowercase:		List[str]=True,     _lowercase:		Optional[int]=None,     _lowercase:		Tuple=0.875,     _lowercase:		List[str]=True,     _lowercase:		Union[str, Any]=[0.5, 0.5, 0.5],     _lowercase:		Optional[int]=[0.5, 0.5, 0.5],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  size if size is not None else {"""shortest_edge""": 20}
									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  min_resolution
									__lowerCAmelCase					=  max_resolution
									__lowerCAmelCase					=  do_resize
									__lowerCAmelCase					=  size
									__lowerCAmelCase					=  crop_pct
									__lowerCAmelCase					=  do_normalize
									__lowerCAmelCase					=  image_mean
									__lowerCAmelCase					=  image_std







					def     _lowercase						(   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,
									    "crop_pct": self.crop_pct,
									}





@require_torch
@require_vision
class 		lowercase_       (	lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= ConvNextImageProcessor if is_vision_available() else None




					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									__lowerCAmelCase					=  ConvNextImageProcessingTester(self)




					@property
					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									return self.image_processor_tester.prepare_image_processor_dict()




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(_lowercase,     """do_resize"""))
									self.assertTrue(hasattr(_lowercase,     """size"""))
									self.assertTrue(hasattr(_lowercase,     """crop_pct"""))
									self.assertTrue(hasattr(_lowercase,     """do_normalize"""))
									self.assertTrue(hasattr(_lowercase,     """image_mean"""))
									self.assertTrue(hasattr(_lowercase,     """image_std"""))




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size,     {"""shortest_edge""": 20})

									__lowerCAmelCase					=  self.image_processing_class.from_dict(self.image_processor_dict,     size=42)
									self.assertEqual(image_processor.size,     {"""shortest_edge""": 42})




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									__lowerCAmelCase					=  prepare_image_inputs(self.image_processor_tester,     equal_resolution=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     Image.Image)

									# Test not batched input
									__lowerCAmelCase					=  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.size["""shortest_edge"""],
									        self.image_processor_tester.size["""shortest_edge"""],
									    ),     )

									# Test batched
									__lowerCAmelCase					=  image_processing(_lowercase,     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.size["""shortest_edge"""],
									        self.image_processor_tester.size["""shortest_edge"""],
									    ),     )




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


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									__lowerCAmelCase					=  prepare_image_inputs(self.image_processor_tester,     equal_resolution=_lowercase,     numpify=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     np.ndarray)

									# Test not batched input
									__lowerCAmelCase					=  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.size["""shortest_edge"""],
									        self.image_processor_tester.size["""shortest_edge"""],
									    ),     )

									# Test batched
									__lowerCAmelCase					=  image_processing(_lowercase,     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.size["""shortest_edge"""],
									        self.image_processor_tester.size["""shortest_edge"""],
									    ),     )







					def     _lowercase						(   self:		List[str]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									__lowerCAmelCase					=  prepare_image_inputs(self.image_processor_tester,     equal_resolution=_lowercase,     torchify=_lowercase)
									for image in image_inputs:
													self.assertIsInstance(_lowercase,     torch.Tensor)

									# Test not batched input
									__lowerCAmelCase					=  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.size["""shortest_edge"""],
									        self.image_processor_tester.size["""shortest_edge"""],
									    ),     )

									# Test batched
									__lowerCAmelCase					=  image_processing(_lowercase,     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.size["""shortest_edge"""],
									        self.image_processor_tester.size["""shortest_edge"""],
									    ),     )










import itertools
from pathlib import Path







def       lowerCamelCase_  (    _a       :						list[str]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					[tuple(_a    ) for login in logins]

    UpperCAmelCase_     :       str  		=					{char for login in split_logins for char in login}

    for permutation in itertools.permutations(_a    ):
        UpperCAmelCase_     :       str  		=					True
        for login in logins:
            if not (
                permutation.index(login[0]    )
                < permutation.index(login[1]    )
                < permutation.index(login[2]    )
            ):
                UpperCAmelCase_     :       Optional[int]  		=					False
                break

        if satisfied:
            return int("""""".join(_a    )    )

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







def       lowerCamelCase_  (    _a       :						str = "keylog.txt"    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Union[str, Any]  		=					Path(_a    ).parent.joinpath(_a    ).read_text().splitlines()

    return find_secret_passcode(_a    )


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