




'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _UpperCAmelCase							=      DDIMPipeline
   _UpperCAmelCase							=      UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   _UpperCAmelCase							=      PipelineTesterMixin.required_optional_params - {
       """num_images_per_prompt""",
       """latents""",
       """callback""",
       """callback_steps""",
   }
   _UpperCAmelCase							=      UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   _UpperCAmelCase							=      False







   def 		UpperCamelCase__			(	self      )				->    str:
     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			=					DDIMScheduler()
     __a			=					{'unet': unet, 'scheduler': scheduler}
     return components







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    int:
     if str(UpperCamelCase      ).startswith('mps'      ):
       __a			=					torch.manual_seed(UpperCamelCase      )
     else:
       __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
     __a			=					{
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 2,
         'output_type': 'numpy',
     }
     return inputs







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					'cpu'

     __a			=					self.get_dummy_components()
     __a			=					self.pipeline_class(**UpperCamelCase      )
     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					self.get_dummy_inputs(UpperCamelCase      )
     __a			=					pipe(**UpperCamelCase      ).images
     __a			=					image[0, -3:, -3:, -1]

     self.assertEqual(image.shape      ,							(1, 32, 32, 3)      )
     __a			=					np.array(
         [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]      )
     __a			=					np.abs(image_slice.flatten() - expected_slice      ).max()
     self.assertLessEqual(UpperCamelCase      ,							1e-3      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     super().test_save_load_local(expected_max_difference=3e-3      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     super().test_save_load_optional_components(expected_max_difference=3e-3      )







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					'google/ddpm-cifar10-32'

     __a			=					UNetaDModel.from_pretrained(UpperCamelCase      )
     __a			=					DDIMScheduler()

     __a			=					DDIMPipeline(unet=UpperCamelCase      ,							scheduler=UpperCamelCase      )
     ddim.to(UpperCamelCase      )
     ddim.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					torch.manual_seed(0      )
     __a			=					ddim(generator=UpperCamelCase      ,							eta=0.0      ,							output_type='numpy'      ).images

     __a			=					image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __a			=					np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]      )

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







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					'google/ddpm-ema-bedroom-256'

     __a			=					UNetaDModel.from_pretrained(UpperCamelCase      )
     __a			=					DDIMScheduler.from_pretrained(UpperCamelCase      )

     __a			=					DDIMPipeline(unet=UpperCamelCase      ,							scheduler=UpperCamelCase      )
     ddpm.to(UpperCamelCase      )
     ddpm.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					torch.manual_seed(0      )
     __a			=					ddpm(generator=UpperCamelCase      ,							output_type='numpy'      ).images

     __a			=					image[0, -3:, -3:, -1]

     assert image.shape == (1, 256, 256, 3)
     __a			=					np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]      )

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









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					''
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					[chr(i + 65			) for i in range(26			)]
  # Remove duplicate characters from key
  __a			=					remove_duplicates(key.upper()			)
  __a			=					len(a_			)
  # First fill cipher with key characters
  __a			=					{alphabet[i]: char for i, char in enumerate(a_			)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(a_			)				,					26			):
    __a			=					alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      __a			=					alphabet[i - offset]
    __a			=					char
  return cipher_alphabet


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  return "".join(cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  __a			=					{v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					input('Enter message to encode or decode: '			).strip()
  __a			=					input('Enter keyword: '			).strip()
  __a			=					input('Encipher or decipher? E/D:'			).strip()[0].lower()
  try:
    __a			=					{'e': encipher, 'd': decipher}[option]
  except KeyError:
    raise KeyError('invalid input option'			)
  __a			=					create_cipher_map(a_			)
  print(func(a_				,					a_			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



