



"""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 ViTImageProcessor





class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_=7				,UpperCAmelCase_=3				,UpperCAmelCase_=18				,UpperCAmelCase_=30				,UpperCAmelCase_=4_00				,UpperCAmelCase_=True				,UpperCAmelCase_=None				,UpperCAmelCase_=True				,UpperCAmelCase_=[0.5, 0.5, 0.5]				,UpperCAmelCase_=[0.5, 0.5, 0.5]				,):
			_lowercase				: Optional[Any]     =   size if size is not None else {"""height""": 18, """width""": 18}
			_lowercase				: Tuple     =   parent
			_lowercase				: str     =   batch_size
			_lowercase				: str     =   num_channels
			_lowercase				: int     =   image_size
			_lowercase				: Union[str, Any]     =   min_resolution
			_lowercase				: Tuple     =   max_resolution
			_lowercase				: Tuple     =   do_resize
			_lowercase				: Optional[Any]     =   size
			_lowercase				: Tuple     =   do_normalize
			_lowercase				: int     =   image_mean
			_lowercase				: Tuple     =   image_std






	def    lowerCamelCase__					(     self   ):
			return {
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_normalize": self.do_normalize,
			    "do_resize": self.do_resize,
			    "size": self.size,
			}







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




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  ViTImageProcessor if is_vision_available() else None






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   ViTImageProcessingTester(self   )






	@property
	def    lowerCamelCase__					(     self   ):
			return self.image_processor_tester.prepare_image_processor_dict()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   self.image_processing_class(**self.image_processor_dict   )
			self.assertTrue(hasattr(UpperCAmelCase_				,"""image_mean"""   )   )
			self.assertTrue(hasattr(UpperCAmelCase_				,"""image_std"""   )   )
			self.assertTrue(hasattr(UpperCAmelCase_				,"""do_normalize"""   )   )
			self.assertTrue(hasattr(UpperCAmelCase_				,"""do_resize"""   )   )
			self.assertTrue(hasattr(UpperCAmelCase_				,"""size"""   )   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   self.image_processing_class.from_dict(self.image_processor_dict   )
			self.assertEqual(image_processor.size				,{"""height""": 18, """width""": 18}   )

			_lowercase				: str     =   self.image_processing_class.from_dict(self.image_processor_dict				,size=42   )
			self.assertEqual(image_processor.size				,{"""height""": 42, """width""": 42}   )






	def    lowerCamelCase__					(     self   ):
			pass






	def    lowerCamelCase__					(     self   ):
			# Initialize image_processing
			_lowercase				: int     =   self.image_processing_class(**self.image_processor_dict   )
			# create random PIL images
			_lowercase				: Optional[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
			_lowercase				: List[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.size["""height"""],
			        self.image_processor_tester.size["""width"""],
			    )				,)

			# Test batched
			_lowercase				: Tuple     =   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.size["""height"""],
			        self.image_processor_tester.size["""width"""],
			    )				,)






	def    lowerCamelCase__					(     self   ):
			# Initialize image_processing
			_lowercase				: List[Any]     =   self.image_processing_class(**self.image_processor_dict   )
			# create random numpy tensors
			_lowercase				: Tuple     =   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
			_lowercase				: Union[str, 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.size["""height"""],
			        self.image_processor_tester.size["""width"""],
			    )				,)

			# Test batched
			_lowercase				: Tuple     =   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.size["""height"""],
			        self.image_processor_tester.size["""width"""],
			    )				,)






	def    lowerCamelCase__					(     self   ):
			# Initialize image_processing
			_lowercase				: Dict     =   self.image_processing_class(**self.image_processor_dict   )
			# create random PyTorch tensors
			_lowercase				: Optional[Any]     =   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
			_lowercase				: 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.size["""height"""],
			        self.image_processor_tester.size["""width"""],
			    )				,)

			# Test batched
			_lowercase				: int     =   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.size["""height"""],
			        self.image_processor_tester.size["""width"""],
			    )				,)







"""simple docstring"""







import itertools
from pathlib import Path
def _A		(		lowercase						):







    """simple docstring"""
    a											=[tuple(lowercase						) for login in logins]

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

    for permutation in itertools.permutations(lowercase						):
        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(lowercase						)						)

    raise Exception('''Unable to find the secret passcode'''						)
def _A		(		lowercase = "keylog.txt"						):







    """simple docstring"""
    a											=Path(lowercase						).parent.joinpath(lowercase						).read_text().splitlines()

    return find_secret_passcode(lowercase						)


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