

'''simple docstring'''



import math

def 	lowerCamelCase__							(   )		->     None:



    """simple docstring"""







    _snake_case			:			str				       =		input('Enter message: ')
    _snake_case			:			Dict				       =		int(input(F"""Enter key [2-{len(a__) - 1}]: """))
    _snake_case			:			Optional[int]				       =		input('Encryption/Decryption [e/d]: ')

    if mode.lower().startswith('e'):
        _snake_case			:			List[Any]				       =		encrypt_message(a__      ,   a__)
    elif mode.lower().startswith('d'):
        _snake_case			:			List[str]				       =		decrypt_message(a__      ,   a__)

    # Append pipe symbol (vertical bar) to identify spaces at the end.
    print(F"""Output:\n{text + '|'}""")

def 	lowerCamelCase__							(   a__      ,   a__)		->     str:



    """simple docstring"""







    _snake_case			:			Dict				       =		[''] * key
    for col in range(a__):
        _snake_case			:			int				       =		col
        while pointer < len(a__):
            cipher_text[col] += message[pointer]
            pointer += key
    return "".join(a__)

def 	lowerCamelCase__							(   a__      ,   a__)		->     str:



    """simple docstring"""







    _snake_case			:			Any				       =		math.ceil(len(a__) / key)
    _snake_case			:			List[Any]				       =		key
    _snake_case			:			Dict				       =		(num_cols * num_rows) - len(a__)
    _snake_case			:			Union[str, Any]				       =		[''] * num_cols
    _snake_case			:			List[str]				       =		0
    _snake_case			:			Union[str, Any]				       =		0

    for symbol in message:
        plain_text[col] += symbol
        col += 1

        if (
            (col == num_cols)
            or (col == num_cols - 1)
            and (row >= num_rows - num_shaded_boxes)
        ):
            _snake_case			:			List[Any]				       =		0
            row += 1

    return "".join(a__)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()





"""simple docstring"""




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class     lowercase(_lowercase    ,						_lowercase    ,						unittest.TestCase       ):
     __snake_case:	Optional[int]						=     IFImgaImgSuperResolutionPipeline
     __snake_case:	Dict						=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
     __snake_case:	Any						=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}       )
     __snake_case:	List[Any]						=     PipelineTesterMixin.required_optional_params - {'latents'}



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         return self._get_superresolution_dummy_components()



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=0   )		->     Union[str, Any]:







         """simple docstring"""



         if str(__SCREAMING_SNAKE_CASE   ).startswith('mps'   ):
             a__		=			torch.manual_seed(__SCREAMING_SNAKE_CASE   )
         else:
             a__		=			torch.Generator(device=__SCREAMING_SNAKE_CASE   ).manual_seed(__SCREAMING_SNAKE_CASE   )

         a__		=			floats_tensor((1, 3, 3_2, 3_2)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			floats_tensor((1, 3, 1_6, 1_6)     ,				rng=random.Random(__SCREAMING_SNAKE_CASE   )   ).to(__SCREAMING_SNAKE_CASE   )

         a__		=			{
             'prompt': 'A painting of a squirrel eating a burger',
             'image': image,
             'original_image': original_image,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs



     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()     ,				reason='XFormers attention is only available with CUDA and `xformers` installed'     ,				)
     def 			lowercase__  (							self   )		->     Optional[int]:







         """simple docstring"""



         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3   )



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != 'cuda'     ,				reason='float16 requires CUDA'   )
     def 			lowercase__  (							self   )		->     List[Any]:







         """simple docstring"""



         super().test_save_load_floataa(expected_max_diff=1e-1   )



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







         """simple docstring"""



         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2   )



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







         """simple docstring"""



         self._test_save_load_local()




     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2     ,				)


