


"""simple docstring"""






lowercase  		=			{
    """A""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """B""": ("""ABCDEFGHIJKLM""", """NOPQRSTUVWXYZ"""),
    """C""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """D""": ("""ABCDEFGHIJKLM""", """ZNOPQRSTUVWXY"""),
    """E""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """F""": ("""ABCDEFGHIJKLM""", """YZNOPQRSTUVWX"""),
    """G""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """H""": ("""ABCDEFGHIJKLM""", """XYZNOPQRSTUVW"""),
    """I""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """J""": ("""ABCDEFGHIJKLM""", """WXYZNOPQRSTUV"""),
    """K""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """L""": ("""ABCDEFGHIJKLM""", """VWXYZNOPQRSTU"""),
    """M""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """N""": ("""ABCDEFGHIJKLM""", """UVWXYZNOPQRST"""),
    """O""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """P""": ("""ABCDEFGHIJKLM""", """TUVWXYZNOPQRS"""),
    """Q""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """R""": ("""ABCDEFGHIJKLM""", """STUVWXYZNOPQR"""),
    """S""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """T""": ("""ABCDEFGHIJKLM""", """RSTUVWXYZNOPQ"""),
    """U""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """V""": ("""ABCDEFGHIJKLM""", """QRSTUVWXYZNOP"""),
    """W""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """X""": ("""ABCDEFGHIJKLM""", """PQRSTUVWXYZNO"""),
    """Y""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
    """Z""": ("""ABCDEFGHIJKLM""", """OPQRSTUVWXYZN"""),
}



def 			A__		(					_UpperCAmelCase    :      str	)							->	list[tuple[str, str]]:

	'''simple docstring'''


	return [alphabet[char] for char in key.upper()]



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      str	)							->	str:

	'''simple docstring'''


	snake_case__  :						Tuple		=      ""
	snake_case__  :						Dict		=      0
	snake_case__  :						List[str]		=      generate_table(_UpperCAmelCase	)
	for char in words.upper():
		cipher += get_opponent(table[count]				,    _UpperCAmelCase	)
		snake_case__  :						List[Any]		=      (count + 1) % len(_UpperCAmelCase	)
	return cipher



def 			A__		(					_UpperCAmelCase    :      str				,    _UpperCAmelCase    :      str	)							->	str:

	'''simple docstring'''


	return encrypt(_UpperCAmelCase				,    _UpperCAmelCase	)



def 			A__		(					_UpperCAmelCase    :      tuple[str, str]				,    _UpperCAmelCase    :      str	)							->	tuple[int, int]:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      0 if char in table[0] else 1
	snake_case__  :						List[Any]		=      table[row].index(_UpperCAmelCase	)
	return row, col



def 			A__		(					_UpperCAmelCase    :      tuple[str, str]				,    _UpperCAmelCase    :      str	)							->	str:

	'''simple docstring'''


	snake_case__,					snake_case__  :						Union[str, Any]		=      get_position(_UpperCAmelCase				,    char.upper()	)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
					import doctest

					doctest.testmod()  # Fist ensure that all our tests are passing...
					lowercase  		=			input("""Enter key: """).strip()
					lowercase  		=			input("""Enter text to encrypt: """).strip()
					lowercase  		=			encrypt(key, text)

					print(f"Encrypted: {cipher_text}")
					print(f"Decrypted with key: {decrypt(key, cipher_text)}")









'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					RePaintPipeline
     UpperCAmelCase			       =					IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {
         '''latents''',
         '''num_images_per_prompt''',
         '''callback''',
         '''callback_steps''',
     }
     UpperCAmelCase			       =					IMAGE_INPAINTING_BATCH_PARAMS
     UpperCAmelCase			       =					False






     def 			a_       (      self		)							->     int:
          torch.manual_seed(0		)
          torch.manual_seed(0		)
          _a            =		UNetaDModel(
              block_out_channels=(32, 64)  ,				layers_per_block=2  ,				sample_size=32  ,				in_channels=3  ,				out_channels=3  ,				down_block_types=("DownBlock2D", "AttnDownBlock2D")  ,				up_block_types=("AttnUpBlock2D", "UpBlock2D")  ,				)
          _a            =		RePaintScheduler()
          _a            =		{"unet": unet, "scheduler": scheduler}
          return components






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Tuple:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)
          _a            =		np.random.RandomState(__UpperCamelCase		).standard_normal((1, 3, 32, 32)		)
          _a            =		torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		(image > 0).to(device=__UpperCamelCase  ,				dtype=torch.floataa		)
          _a            =		{
              "image": image,
              "mask_image": mask,
              "generator": generator,
              "num_inference_steps": 5,
              "eta": 0.0,
              "jump_length": 2,
              "jump_n_sample": 2,
              "output_type": "numpy",
          }
          return inputs






     def 			a_       (      self		)							->     Optional[Any]:
          _a            =		"cpu"  # ensure determinism for the device-dependent torch.Generator
          _a            =		self.get_dummy_components()
          _a            =		RePaintPipeline(**__UpperCamelCase		)
          _a            =		sd_pipe.to(__UpperCamelCase		)
          sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		self.get_dummy_inputs(__UpperCamelCase		)
          _a            =		sd_pipe(**__UpperCamelCase		).images
          _a            =		image[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _a            =		np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]		)

          assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3






     @skip_mps
     def 			a_       (      self		)							->     Union[str, Any]:
          return super().test_save_load_local()






     @unittest.skip("non-deterministic pipeline"		)
     def 			a_       (      self		)							->     int:
          return super().test_inference_batch_single_identical()






     @skip_mps
     def 			a_       (      self		)							->     str:
          return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def 			a_       (      self		)							->     int:
          return super().test_save_load_optional_components()






     @skip_mps
     def 			a_       (      self		)							->     Optional[int]:
          return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     List[str]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Union[str, Any]:
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256.png"		)
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"		)
          _a            =		load_numpy(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
              "repaint/celeba_hq_256_result.npy"		)

          _a            =		"google/ddpm-ema-celebahq-256"
          _a            =		UNetaDModel.from_pretrained(__UpperCamelCase		)
          _a            =		RePaintScheduler.from_pretrained(__UpperCamelCase		)

          _a            =		RePaintPipeline(unet=__UpperCamelCase  ,				scheduler=__UpperCamelCase		).to(__UpperCamelCase		)
          repaint.set_progress_bar_config(disable=__UpperCamelCase		)
          repaint.enable_attention_slicing()

          _a            =		torch.manual_seed(0		)
          _a            =		repaint(
              __UpperCamelCase  ,				__UpperCamelCase  ,				num_inference_steps=250  ,				eta=0.0  ,				jump_length=10  ,				jump_n_sample=10  ,				generator=__UpperCamelCase  ,				output_type="np"  ,				)
          _a            =		output.images[0]

          assert image.shape == (256, 256, 3)
          assert np.abs(expected_image - image		).mean() < 1e-2

