
'''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 ConvNextImageProcessor


class       lowerCAmelCase			(					unittest.TestCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase=7       ,    UpperCamelCase=3       ,    UpperCamelCase=18       ,    UpperCamelCase=30       ,    UpperCamelCase=400       ,    UpperCamelCase=True       ,    UpperCamelCase=None       ,    UpperCamelCase=0.8_75       ,    UpperCamelCase=True       ,    UpperCamelCase=[0.5, 0.5, 0.5]       ,    UpperCamelCase=[0.5, 0.5, 0.5]       ,    ):
								_SCREAMING_SNAKE_CASE					=			size if size is not None else {"shortest_edge": 20}
								_SCREAMING_SNAKE_CASE					=			parent
								_SCREAMING_SNAKE_CASE					=			batch_size
								_SCREAMING_SNAKE_CASE					=			num_channels
								_SCREAMING_SNAKE_CASE					=			image_size
								_SCREAMING_SNAKE_CASE					=			min_resolution
								_SCREAMING_SNAKE_CASE					=			max_resolution
								_SCREAMING_SNAKE_CASE					=			do_resize
								_SCREAMING_SNAKE_CASE					=			size
								_SCREAMING_SNAKE_CASE					=			crop_pct
								_SCREAMING_SNAKE_CASE					=			do_normalize
								_SCREAMING_SNAKE_CASE					=			image_mean
								_SCREAMING_SNAKE_CASE					=			image_std




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


@require_torch
@require_vision
class       lowerCAmelCase			(					__UpperCAmelCase		,  unittest.TestCase      ):
		a				:       Union[str, Any]							=       ConvNextImageProcessor if is_vision_available() else None




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			ConvNextImageProcessingTester(self   )




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




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(UpperCamelCase       ,    "do_resize"   )   )
								self.assertTrue(hasattr(UpperCamelCase       ,    "size"   )   )
								self.assertTrue(hasattr(UpperCamelCase       ,    "crop_pct"   )   )
								self.assertTrue(hasattr(UpperCamelCase       ,    "do_normalize"   )   )
								self.assertTrue(hasattr(UpperCamelCase       ,    "image_mean"   )   )
								self.assertTrue(hasattr(UpperCamelCase       ,    "image_std"   )   )




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

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




		def      lowercase							(					self   ):
								pass




		def      lowercase							(					self   ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE					=			self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								_SCREAMING_SNAKE_CASE					=			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=UpperCamelCase   )
								for image in image_inputs:
														self.assertIsInstance(UpperCamelCase       ,    Image.Image   )

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




		def      lowercase							(					self   ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE					=			self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )       ,    )




		def      lowercase							(					self   ):
								# Initialize image_processing
								_SCREAMING_SNAKE_CASE					=			self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			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
								_SCREAMING_SNAKE_CASE					=			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["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )       ,    )

'''simple docstring'''







from __future__ import annotations

import random
import string


class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase = None   ):
								_SCREAMING_SNAKE_CASE					=			passcode or self.__passcode_creator()
								_SCREAMING_SNAKE_CASE					=			self.__make_key_list()
								_SCREAMING_SNAKE_CASE					=			self.__make_shift_key()




		def __str__(					self   ):
								return "".join(self.__passcode   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								for i in range(1       ,    len(UpperCamelCase   )       ,    2   ):
														iterlist[i] *= -1
								return iterlist




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			string.ascii_letters + string.digits
								_SCREAMING_SNAKE_CASE					=			[random.choice(UpperCamelCase   ) for _ in range(random.randint(10       ,    20   )   )]
								return password




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			(
								    string.ascii_letters + string.digits + string.punctuation + " \t\n"
								)

								_SCREAMING_SNAKE_CASE					=			[]

								# creates points known as breakpoints to break the key_list_options at those
								# points and pivot each substring
								_SCREAMING_SNAKE_CASE					=			sorted(set(self.__passcode   )   )
								_SCREAMING_SNAKE_CASE					=			[]

								# algorithm for creating a new shuffled list, keys_l, out of key_list_options
								for i in key_list_options:
														temp_list.extend(UpperCamelCase   )

														# checking breakpoints at which to pivot temporary sublist and add it into
														# keys_l
														if i in breakpoints or i == key_list_options[-1]:
																				keys_l.extend(temp_list[::-1]   )
																				temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
								return keys_l




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			sum(self.__neg_pos([ord(UpperCamelCase   ) for x in self.__passcode]   )   )
								return num if num > 0 else len(self.__passcode   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			""

								# decoding shift like Caesar cipher algorithm implementing negative shift or
								# reverse shift or left shift
								for i in encoded_message:
														_SCREAMING_SNAKE_CASE					=			self.__key_list.index(UpperCamelCase   )
														decoded_message += self.__key_list[
														    (position - self.__shift_key) % -len(self.__key_list   )
														]

								return decoded_message




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			""

								# encoding shift like Caesar cipher algorithm implementing positive shift or
								# forward shift or right shift
								for i in plaintext:
														_SCREAMING_SNAKE_CASE					=			self.__key_list.index(UpperCamelCase   )
														encoded_message += self.__key_list[
														    (position + self.__shift_key) % len(self.__key_list   )
														]

								return encoded_message



def       _a		(						_SCREAMING_SNAKE_CASE		: str = "Hello, this is a modified Caesar cipher"	):
						_SCREAMING_SNAKE_CASE					=			ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(_SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()