


"""simple docstring"""






from typing import List

from .keymap import KEYMAP, get_character





def 				A_       ( snake_case__		)					->  str:

			def decorator(snake_case__		):
						_UpperCamelCase					:Optional[int]					     =	getattr(snake_case__       ,					'''handle_key'''       ,					[]		)
						handle += [key]
						setattr(snake_case__       ,					'''handle_key'''       ,					snake_case__		)
						return func

			return decorator





def 				A_       ( *snake_case__		)					->  str:

			def decorator(snake_case__		):
						_UpperCamelCase					:Dict					     =	getattr(snake_case__       ,					'''handle_key'''       ,					[]		)
						handle += keys
						setattr(snake_case__       ,					'''handle_key'''       ,					snake_case__		)
						return func

			return decorator






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		def __new__( cls					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	super().__new__(cls					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
					if not hasattr(SCREAMING_SNAKE_CASE__					,				'''key_handler'''   ):
								setattr(SCREAMING_SNAKE_CASE__					,				'''key_handler'''					,				{}   )
					setattr(SCREAMING_SNAKE_CASE__					,				'''handle_input'''					,				KeyHandler.handle_input   )

					for value in attrs.values():
								_UpperCamelCase					:int					     =	getattr(SCREAMING_SNAKE_CASE__					,				'''handle_key'''					,				[]   )
								for key in handled_keys:
											_UpperCamelCase					:Dict					     =	value
					return new_cls





		@staticmethod
		def   _UpperCamelCase( cls   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	get_character()
					if char != KEYMAP["undefined"]:
								_UpperCamelCase					:List[str]					     =	ord(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	cls.key_handler.get(SCREAMING_SNAKE_CASE__   )
					if handler:
								_UpperCamelCase					:Any					     =	char
								return handler(cls   )
					else:
								return None





def 				A_       ( cls		)					->  Any:
			return KeyHandler(cls.__name__       ,					cls.__bases__       ,					cls.__dict__.copy()		)





"""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

