




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:
				__a			:	int							     =		None




class     A					(				unittest.TestCase       ):







							def __init__(    self   :	str   ,     __UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	Union[str, Any]=7   ,     __UpperCAmelCase   :	Any=3   ,     __UpperCAmelCase   :	Any=30   ,     __UpperCAmelCase   :	Tuple=400   ,     __UpperCAmelCase   :	Any=True   ,     __UpperCAmelCase   :	Optional[int]=None   ,     __UpperCAmelCase   :	List[Any]=True   ,     __UpperCAmelCase   :	Optional[Any]=None   ,     __UpperCAmelCase   :	List[Any]=None   ,     __UpperCAmelCase   :	Union[str, Any]=True   ,     __UpperCAmelCase   :	Dict=1 / 255   ,     __UpperCAmelCase   :	Union[str, Any]=True   ,     __UpperCAmelCase   :	List[str]=FLAVA_IMAGE_MEAN   ,     __UpperCAmelCase   :	Tuple=FLAVA_IMAGE_STD   ,     __UpperCAmelCase   :	Dict=14   ,     __UpperCAmelCase   :	Dict=75   ,     __UpperCAmelCase   :	Any=None   ,     __UpperCAmelCase   :	int=16   ,     __UpperCAmelCase   :	Union[str, Any]=0.3   ,     __UpperCAmelCase   :	Any=None   ,     __UpperCAmelCase   :	Any=True   ,     __UpperCAmelCase   :	Optional[Any]=None   ,     __UpperCAmelCase   :	str=None   ,     __UpperCAmelCase   :	Tuple=True   ,     __UpperCAmelCase   :	int=None   ,     __UpperCAmelCase   :	Optional[Any]=True   ,     __UpperCAmelCase   :	List[str]=True   ,     __UpperCAmelCase   :	Dict=FLAVA_CODEBOOK_MEAN   ,     __UpperCAmelCase   :	List[Any]=FLAVA_CODEBOOK_STD   ,     )		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= size if size is not None else {'height': 224, 'width': 224}
										UpperCamelCase_     				= crop_size if crop_size is not None else {'height': 224, 'width': 224}
										UpperCamelCase_     				= codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
										UpperCamelCase_     				= codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

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

										UpperCamelCase_     				= input_size_patches
										UpperCamelCase_     				= total_mask_patches
										UpperCamelCase_     				= mask_group_max_patches
										UpperCamelCase_     				= mask_group_min_patches
										UpperCamelCase_     				= mask_group_min_aspect_ratio
										UpperCamelCase_     				= mask_group_max_aspect_ratio

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







							def        lowercase__							(    self   :	Optional[Any]				)		->					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        lowercase__							(    self   :	Tuple				)		->					str:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Any				)		->					Optional[int]:

										"""simple docstring"""



										return (
										    (self.input_size_patches, self.input_size_patches)
										    if not isinstance(self.input_size_patches   ,     __UpperCAmelCase				)
										    else self.input_size_patches
										)







							def        lowercase__							(    self   :	Union[str, Any]				)		->					Optional[int]:

										"""simple docstring"""



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







@require_torch
@require_vision
class     A					(				lowerCamelCase_  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		List[str]			  =       FlavaImageProcessor if is_vision_available() else None
							_SCREAMING_SNAKE_CASE  :		Tuple			  =       None







							def        lowercase__							(    self   :	str				)		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= FlavaImageProcessingTester(self				)







							@property
							def        lowercase__							(    self   :	Optional[int]				)		->					Optional[Any]:

										"""simple docstring"""



										return self.image_processor_tester.prepare_image_processor_dict()







							def        lowercase__							(    self   :	List[Any]				)		->					Dict:

										"""simple docstring"""



										UpperCamelCase_     				= 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   ,     'resample'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'crop_size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'do_rescale'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'rescale_factor'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'masking_generator'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_do_resize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_resample'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_do_center_crop'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_crop_size'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_do_map_pixels'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_do_normalize'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_image_mean'				)				)
										self.assertTrue(hasattr(__UpperCAmelCase   ,     'codebook_image_std'				)				)







							def        lowercase__							(    self   :	List[str]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size   ,     {'height': 224, 'width': 224}				)
										self.assertEqual(image_processor.crop_size   ,     {'height': 224, 'width': 224}				)
										self.assertEqual(image_processor.codebook_size   ,     {'height': 112, 'width': 112}				)
										self.assertEqual(image_processor.codebook_crop_size   ,     {'height': 112, 'width': 112}				)

										UpperCamelCase_     				= 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        lowercase__							(    self   :	List[str]				)		->					Dict:

										"""simple docstring"""



										pass







							def        lowercase__							(    self   :	List[Any]				)		->					Any:

										"""simple docstring"""



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

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

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

										UpperCamelCase_				,   UpperCamelCase_     				= 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
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				)
										UpperCamelCase_				,   UpperCamelCase_     				= 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        lowercase__							(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	Optional[Any]   ,     __UpperCAmelCase   :	int				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										# create random tensors
										UpperCamelCase_     				= prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=__UpperCAmelCase   ,     **__UpperCAmelCase				)
										for image in image_inputs:
													self.assertIsInstance(__UpperCAmelCase   ,     __UpperCAmelCase				)

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

										UpperCamelCase_				,   UpperCamelCase_     				= 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)   ,     )

										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_image_mask=__UpperCAmelCase   ,     return_tensors='pt'				)

										UpperCamelCase_				,   UpperCamelCase_     				= 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,
										    )   ,     )

										UpperCamelCase_				,   UpperCamelCase_     				= 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
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_tensors='pt'				).pixel_values

										UpperCamelCase_				,   UpperCamelCase_     				= 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
										UpperCamelCase_     				= image_processing(__UpperCAmelCase   ,     return_image_mask=__UpperCAmelCase   ,     return_tensors='pt'				)

										UpperCamelCase_				,   UpperCamelCase_     				= 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,
										    )   ,     )

										UpperCamelCase_				,   UpperCamelCase_     				= 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        lowercase__							(    self   :	List[str]				)		->					Optional[Any]:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Union[str, Any]				)		->					int:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	str				)		->					int:

										"""simple docstring"""



										random.seed(1234				)
										UpperCamelCase_     				= self.image_processing_class(**self.image_processor_dict				)
										UpperCamelCase_     				= prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=__UpperCAmelCase   ,     torchify=__UpperCAmelCase				)

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







							def        lowercase__							(    self   :	List[str]				)		->					Optional[Any]:

										"""simple docstring"""



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

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






__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCamelCase_     				= gear_one[0]
			gear_one.append(__snake_case					)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(__snake_case					)					) == 0:
						UpperCamelCase_     				= gear_two[0]
						gear_two.append(__snake_case					)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(__snake_case					)					) == 0:
									UpperCamelCase_     				= gear_three[0]
									gear_three.append(__snake_case					)
									del gear_three[0]
									gear_three_pos += 1




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= alphabets.index(__snake_case					)
			UpperCamelCase_     				= gear_one[target]
			UpperCamelCase_     				= gear_two[target]
			UpperCamelCase_     				= gear_three[target]
			UpperCamelCase_     				= reflector[target]
			UpperCamelCase_     				= gear_three.index(__snake_case					)
			UpperCamelCase_     				= gear_two.index(__snake_case					)
			UpperCamelCase_     				= gear_one.index(__snake_case					)
			code.append(alphabets[target]					)
			rotator()


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[Any]							     =		int(input("""Please set token:(must be only digits)\n"""))
												break
								except Exception as error:
												print(error)
				for _ in range(token):
								rotator()
				for j in decode:
								engine(j)
				print("""\n""" + """""".join(code))
				print(
				    f"""\nYour Token is {token} please write it down.\nIf you want to decode """
				    """this message again you should input same digits as token!"""
				)
