



import numpy as np

snake_case__       :  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 :


						'''simple docstring'''





						def __init__(						self						:				Optional[Any]						):






							'''simple docstring'''



							__lowercase									=      np.array(SCREAMING_SNAKE_CASE__						)



						def      _snake_case						(						self						:				Union[str, Any]				,   lowerCamelCase						:				Tuple						):






							'''simple docstring'''



							__lowercase									=      np.where(letter == self.SQUARE						)
							__lowercase									=      np.concatenate([indexa + 1, indexa + 1]						)
							return indexes



						def      _snake_case						(						self						:				int				,   lowerCamelCase						:				Tuple				,   lowerCamelCase						:				Optional[int]						):






							'''simple docstring'''



							__lowercase									=      self.SQUARE[indexa - 1, indexa - 1]
							return letter



						def      _snake_case						(						self						:				Any				,   lowerCamelCase						:				Dict						):






							'''simple docstring'''



							__lowercase									=      message.lower()
							__lowercase									=      message.replace(" "				,   ""						)
							__lowercase									=      message.replace("j"				,   "i"						)

							__lowercase									=      np.empty((2, len(SCREAMING_SNAKE_CASE__						))						)
							for letter_index in range(len(SCREAMING_SNAKE_CASE__						)						):
								__lowercase									=      self.letter_to_numbers(message[letter_index]						)

								__lowercase									=      numbers[0]
								__lowercase									=      numbers[1]

							__lowercase									=      first_step.reshape(2 * len(SCREAMING_SNAKE_CASE__						)						)
							__lowercase									=      ''''''
							for numbers_index in range(len(SCREAMING_SNAKE_CASE__						)						):
								__lowercase									=      int(second_step[numbers_index * 2]						)
								__lowercase									=      int(second_step[(numbers_index * 2) + 1]						)
								__lowercase									=      self.numbers_to_letter(SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__						)
								__lowercase									=      encoded_message + letter

							return encoded_message



						def      _snake_case						(						self						:				Optional[Any]				,   lowerCamelCase						:				Tuple						):






							'''simple docstring'''



							__lowercase									=      message.lower()
							message.replace(" "				,   ""						)
							__lowercase									=      np.empty(2 * len(SCREAMING_SNAKE_CASE__						)						)
							for letter_index in range(len(SCREAMING_SNAKE_CASE__						)						):
								__lowercase									=      self.letter_to_numbers(message[letter_index]						)
								__lowercase									=      numbers[0]
								__lowercase									=      numbers[1]

							__lowercase									=      first_step.reshape((2, len(SCREAMING_SNAKE_CASE__						))						)
							__lowercase									=      ''''''
							for numbers_index in range(len(SCREAMING_SNAKE_CASE__						)						):
								__lowercase									=      int(second_step[0, numbers_index]						)
								__lowercase									=      int(second_step[1, numbers_index]						)
								__lowercase									=      self.numbers_to_letter(SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__						)
								__lowercase									=      decoded_message + letter

							return decoded_message







"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class      A(  unittest.TestCase		):

		"""simple docstring"""





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

					"""simple docstring"""



					torch.manual_seed(0   )
					_UpperCamelCase					:str					     =	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   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	self.dummy_uncond_unet
					_UpperCamelCase					:List[Any]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[Any]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Dict					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:List[Any]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:Dict					     =	pipe(num_inference_steps=2					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''					,				return_dict=SCREAMING_SNAKE_CASE__   )[0]

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					_UpperCamelCase					:List[Any]					     =	image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					_UpperCamelCase					:List[Any]					     =	np.array([0.0, 1.0, 0.0, 0.0, 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		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[Any]					     =	'''google/ncsnpp-celebahq-256'''
					_UpperCamelCase					:Any					     =	UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	KarrasVeScheduler()

					_UpperCamelCase					:Optional[int]					     =	KarrasVePipeline(unet=SCREAMING_SNAKE_CASE__					,				scheduler=SCREAMING_SNAKE_CASE__   )
					pipe.to(SCREAMING_SNAKE_CASE__   )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[str]					     =	torch.manual_seed(0   )
					_UpperCamelCase					:List[Any]					     =	pipe(num_inference_steps=20					,				generator=SCREAMING_SNAKE_CASE__					,				output_type='''numpy'''   ).images

					_UpperCamelCase					:Dict					     =	image[0, -3:, -3:, -1]
					assert image.shape == (1, 2_56, 2_56, 3)
					_UpperCamelCase					:Any					     =	np.array([0.5_7_8, 0.5_8_1_1, 0.5_9_2_4, 0.5_8_0_9, 0.5_8_7, 0.5_8_8_6, 0.5_8_6_1, 0.5_8_0_2, 0.5_8_6]   )

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

