





import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class        _a(    unittest.TestCase    ):







		@property
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							torch.manual_seed(0				)
							_snake_case       :					Optional[int]				       =   UNetaDModel(
							    block_out_channels=(3_2, 6_4) ,						layers_per_block=2 ,						sample_size=3_2 ,						in_channels=3 ,						out_channels=3 ,						down_block_types=("DownBlock2D", "AttnDownBlock2D") ,						up_block_types=("AttnUpBlock2D", "UpBlock2D") ,						)
							return model







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   self.dummy_uncond_unet
							_snake_case       :					Tuple				       =   PNDMScheduler()

							_snake_case       :					Dict				       =   PNDMPipeline(unet=__snake_case ,						scheduler=__snake_case				)
							pndm.to(__snake_case				)
							pndm.set_progress_bar_config(disable=__snake_case				)

							_snake_case       :					Optional[int]				       =   torch.manual_seed(0				)
							_snake_case       :					Optional[int]				       =   pndm(generator=__snake_case ,						num_inference_steps=2_0 ,						output_type="numpy"				).images

							_snake_case       :					Optional[Any]				       =   torch.manual_seed(0				)
							_snake_case       :					str				       =   pndm(generator=__snake_case ,						num_inference_steps=2_0 ,						output_type="numpy" ,						return_dict=__snake_case				)[0]

							_snake_case       :					Optional[int]				       =   image[0, -3:, -3:, -1]
							_snake_case       :					List[Any]				       =   image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)
							_snake_case       :					int				       =   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]				)

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






@slow
@require_torch
class        _a(    unittest.TestCase    ):







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Tuple				       =   "google/ddpm-cifar10-32"

							_snake_case       :					Any				       =   UNetaDModel.from_pretrained(__snake_case				)
							_snake_case       :					Optional[int]				       =   PNDMScheduler()

							_snake_case       :					Optional[Any]				       =   PNDMPipeline(unet=__snake_case ,						scheduler=__snake_case				)
							pndm.to(__snake_case				)
							pndm.set_progress_bar_config(disable=__snake_case				)
							_snake_case       :					Optional[Any]				       =   torch.manual_seed(0				)
							_snake_case       :					int				       =   pndm(generator=__snake_case ,						output_type="numpy"				).images

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

							assert image.shape == (1, 3_2, 3_2, 3)
							_snake_case       :					str				       =   np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]				)

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






import numpy as np

__lowerCAmelCase :Dict     =    [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class        _a:







		def __init__(    self				)      ->      None:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.array(__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      np.ndarray:


							'''simple docstring'''

							_snake_case       , _snake_case       :					List[Any]				       =   np.where(letter == self.SQUARE				)
							_snake_case       :					Any				       =   np.concatenate([indexa + 1, indexa + 1]				)
							return indexes







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   self.SQUARE[indexa - 1, indexa - 1]
							return letter







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   message.lower()
							_snake_case       :					List[Any]				       =   message.replace(" " ,						""				)
							_snake_case       :					Any				       =   message.replace("j" ,						"i"				)

							_snake_case       :					Tuple				       =   np.empty((2, len(__snake_case				))				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   self.letter_to_numbers(message[letter_index]				)

												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					List[Any]				       =   numbers[1]

							_snake_case       :					Any				       =   first_step.reshape(2 * len(__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Optional[int]				       =   int(second_step[numbers_index * 2]				)
												_snake_case       :					List[str]				       =   int(second_step[(numbers_index * 2) + 1]				)
												_snake_case       :					Any				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					Any				       =   encoded_message + letter

							return encoded_message







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   message.lower()
							message.replace(" " ,						""				)
							_snake_case       :					Tuple				       =   np.empty(2 * len(__snake_case				)				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Union[str, Any]				       =   self.letter_to_numbers(message[letter_index]				)
												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					Any				       =   numbers[1]

							_snake_case       :					Optional[Any]				       =   first_step.reshape((2, len(__snake_case				))				)
							_snake_case       :					Union[str, Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   int(second_step[0, numbers_index]				)
												_snake_case       :					Optional[Any]				       =   int(second_step[1, numbers_index]				)
												_snake_case       :					Union[str, Any]				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					str				       =   decoded_message + letter

							return decoded_message