

import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch



@require_torch
class 					_a		:


					'''simple docstring'''






					@property
					def 						__A    (				self    ):
						return self.get_dummy_input()






					@property
					def 						__A    (				self    ):
						if self.block_type == "down":
							return (4, 32, 16, 16)
						elif self.block_type == "mid":
							return (4, 32, 32, 32)
						elif self.block_type == "up":
							return (4, 32, 64, 64)

						raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""    )






					def 						__A    (				self					, A__=True					, A__=False					, A__=False					, A__=False					, ):
						A__							:					str       =	4
						A__							:					List[str]       =	32
						A__							:					str       =	(32, 32)

						A__							:					List[str]       =	torch.manual_seed(0    )
						A__							:					Tuple       =	torch.device(A__    )
						A__							:					str       =	(batch_size, num_channels) + sizes
						A__							:					int       =	randn_tensor(A__					, generator=A__					, device=A__    )
						A__							:					Union[str, Any]       =	{"""hidden_states""": hidden_states}

						if include_temb:
							A__							:					List[Any]       =	128
							A__							:					Dict       =	randn_tensor((batch_size, temb_channels)					, generator=A__					, device=A__    )

						if include_res_hidden_states_tuple:
							A__							:					Optional[int]       =	torch.manual_seed(1    )
							A__							:					List[Any]       =	(randn_tensor(A__					, generator=A__					, device=A__    ),)

						if include_encoder_hidden_states:
							A__							:					List[Any]       =	floats_tensor((batch_size, 32, 32)    ).to(A__    )

						if include_skip_sample:
							A__							:					Dict       =	randn_tensor(((batch_size, 3) + sizes)					, generator=A__					, device=A__    )

						return dummy_input






					def 						__A    (				self    ):
						A__							:					Dict       =	{
						    """in_channels""": 32,
						    """out_channels""": 32,
						    """temb_channels""": 128,
						}
						if self.block_type == "up":
							A__							:					Union[str, Any]       =	32

						if self.block_type == "mid":
							init_dict.pop("""out_channels"""    )

						A__							:					Any       =	self.dummy_input
						return init_dict, inputs_dict






					def 						__A    (				self					, A__    ):
						A__     ,     A__							:					Optional[Any]       =	self.prepare_init_args_and_inputs_for_common()
						A__							:					Optional[Any]       =	self.block_class(**A__    )
						unet_block.to(A__    )
						unet_block.eval()

						with torch.no_grad():
							A__							:					Union[str, Any]       =	unet_block(**A__    )

						if isinstance(A__					, A__    ):
							A__							:					Any       =	output[0]

						self.assertEqual(output.shape					, self.output_shape    )

						A__							:					str       =	output[0, -1, -3:, -3:]
						A__							:					List[Any]       =	torch.tensor(A__    ).to(A__    )
						assert torch_all_close(output_slice.flatten()					, A__					, atol=5e-3    )






					@unittest.skipIf(torch_device == """mps"""					, """Training is not supported in mps"""    )
					def 						__A    (				self    ):
						A__     ,     A__							:					Optional[int]       =	self.prepare_init_args_and_inputs_for_common()
						A__							:					str       =	self.block_class(**A__    )
						model.to(A__    )
						model.train()
						A__							:					Optional[int]       =	model(**A__    )

						if isinstance(A__					, A__    ):
							A__							:					Tuple       =	output[0]

						A__							:					Tuple       =	torch.device(A__    )
						A__							:					str       =	randn_tensor(output.shape					, device=A__    )
						A__							:					Union[str, Any]       =	torch.nn.functional.mse_loss(A__					, A__    )
						loss.backward()






def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				str      )  ->   bool:
	A__							:					Union[str, Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	len(lowercase_      )
	A__							:					List[Any]       =	[[False for _ in range(m + 1      )] for _ in range(n + 1      )]
	A__							:					str       =	True
	for i in range(lowercase_      ):
		for j in range(m + 1      ):
			if dp[i][j]:
				if j < m and a[i].upper() == b[j]:
					A__							:					int       =	True
				if a[i].islower():
					A__							:					Dict       =	True
	return dp[n][m]


if __name__ == "__main__":
	import doctest

	doctest.testmod()



