





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      @property
      def   UpperCAmelCase						(	self				)    ->				Union[str, Any]:






        '''simple docstring'''






        torch.manual_seed(0				)
        __snake_case				:			Any            =    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")			,  )
        return model







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






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    "cpu"
        __snake_case				:			List[str]            =    self.dummy_uncond_unet
        __snake_case				:			Any            =    DDPMScheduler()

        __snake_case				:			Tuple            =    DDPMPipeline(unet=UpperCAmelCase			,  scheduler=UpperCAmelCase				)
        ddpm.to(UpperCAmelCase				)
        ddpm.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Any            =    torch.Generator(device=UpperCAmelCase				).manual_seed(0				)
        __snake_case				:			Dict            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"				).images

        __snake_case				:			Dict            =    torch.Generator(device=UpperCAmelCase				).manual_seed(0				)
        __snake_case				:			Optional[int]            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"			,  return_dict=UpperCAmelCase				)[0]

        __snake_case				:			Tuple            =    image[0, -3:, -3:, -1]
        __snake_case				:			List[str]            =    image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __snake_case				:			Dict            =    np.array(
            [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







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






        '''simple docstring'''






        __snake_case				:			Tuple            =    self.dummy_uncond_unet
        __snake_case				:			Any            =    DDPMScheduler(prediction_type="sample"				)

        __snake_case				:			List[Any]            =    DDPMPipeline(unet=UpperCAmelCase			,  scheduler=UpperCAmelCase				)
        ddpm.to(UpperCAmelCase				)
        ddpm.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Dict            =    torch.manual_seed(0				)
        __snake_case				:			List[Any]            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"				).images

        __snake_case				:			Optional[Any]            =    torch.manual_seed(0				)
        __snake_case				:			int            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"				)[0]

        __snake_case				:			List[Any]            =    image[0, -3:, -3:, -1]
        __snake_case				:			str            =    image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __snake_case				:			Optional[Any]            =    1E-2 if torch_device != "mps" else 3E-2
        assert np.abs(image_slice.flatten() - image_eps_slice.flatten()				).max() < tolerance




@slow
@require_torch_gpu
class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    "google/ddpm-cifar10-32"

        __snake_case				:			Optional[Any]            =    UNetaDModel.from_pretrained(UpperCAmelCase				)
        __snake_case				:			Optional[Any]            =    DDPMScheduler.from_pretrained(UpperCAmelCase				)

        __snake_case				:			Union[str, Any]            =    DDPMPipeline(unet=UpperCAmelCase			,  scheduler=UpperCAmelCase				)
        ddpm.to(UpperCAmelCase				)
        ddpm.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Tuple            =    torch.manual_seed(0				)
        __snake_case				:			Any            =    ddpm(generator=UpperCAmelCase			,  output_type="numpy"				).images

        __snake_case				:			List[Any]            =    image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __snake_case				:			str            =    np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]				)
        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2








_UpperCamelCase    =	[chr(i) for i in range(32, 126)]
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(range(len(alphabets)))
_UpperCamelCase    =	list(reversed(range(len(alphabets))))
_UpperCamelCase    =	[]
_UpperCamelCase    =	_UpperCamelCase    =	_UpperCamelCase    =	0







def  lowerCAmelCase__(		)	->      int:
  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __snake_case				:			int            =    gear_one[0]
  gear_one.append(lowercase			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(lowercase			)			) == 0:
    __snake_case				:			List[str]            =    gear_two[0]
    gear_two.append(lowercase			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(lowercase			)			) == 0:
      __snake_case				:			Any            =    gear_three[0]
      gear_three.append(lowercase			)
      del gear_three[0]
      gear_three_pos += 1







def  lowerCAmelCase__(		lowercase    :      Tuple			)	->      Union[str, Any]:
  __snake_case				:			Union[str, Any]            =    alphabets.index(lowercase			)
  __snake_case				:			Tuple            =    gear_one[target]
  __snake_case				:			Union[str, Any]            =    gear_two[target]
  __snake_case				:			int            =    gear_three[target]
  __snake_case				:			Optional[int]            =    reflector[target]
  __snake_case				:			Union[str, Any]            =    gear_three.index(lowercase			)
  __snake_case				:			Optional[Any]            =    gear_two.index(lowercase			)
  __snake_case				:			int            =    gear_one.index(lowercase			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     _UpperCamelCase    =	list(input('''Type your message:\n'''))
     while True:
          try:
               _UpperCamelCase    =	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!'''
     )

