





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        snake_case_		:







		@property
		def 			__lowercase   (		self   )				->   str:
				return self.get_dummy_input()







		@property
		def 			__lowercase   (		self   )				->   Optional[Any]:
				if self.block_type == "down":
						return (4, 3_2, 1_6, 1_6)
				elif self.block_type == "mid":
						return (4, 3_2, 3_2, 3_2)
				elif self.block_type == "up":
						return (4, 3_2, 6_4, 6_4)

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







		def 			__lowercase   (		self			,     __lowercase=True			,     __lowercase=False			,     __lowercase=False			,     __lowercase=False			,     )				->   Dict:
				lowerCamelCase   : Optional[Any]								=4
				lowerCamelCase   : List[str]								=3_2
				lowerCamelCase   : Any								=(3_2, 3_2)

				lowerCamelCase   : str								=torch.manual_seed(0   )
				lowerCamelCase   : int								=torch.device(__lowerCamelCase   )
				lowerCamelCase   : List[str]								=(batch_size, num_channels) + sizes
				lowerCamelCase   : Dict								=randn_tensor(__lowerCamelCase			,     generator=__lowerCamelCase			,     device=__lowerCamelCase   )
				lowerCamelCase   : int								={"hidden_states": hidden_states}

				if include_temb:
						lowerCamelCase   : Any								=1_2_8
						lowerCamelCase   : List[str]								=randn_tensor((batch_size, temb_channels)			,     generator=__lowerCamelCase			,     device=__lowerCamelCase   )

				if include_res_hidden_states_tuple:
						lowerCamelCase   : str								=torch.manual_seed(1   )
						lowerCamelCase   : Tuple								=(randn_tensor(__lowerCamelCase			,     generator=__lowerCamelCase			,     device=__lowerCamelCase   ),)

				if include_encoder_hidden_states:
						lowerCamelCase   : Dict								=floats_tensor((batch_size, 3_2, 3_2)   ).to(__lowerCamelCase   )

				if include_skip_sample:
						lowerCamelCase   : Optional[int]								=randn_tensor(((batch_size, 3) + sizes)			,     generator=__lowerCamelCase			,     device=__lowerCamelCase   )

				return dummy_input







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : Dict								={
				    "in_channels": 3_2,
				    "out_channels": 3_2,
				    "temb_channels": 1_2_8,
				}
				if self.block_type == "up":
						lowerCamelCase   : Dict								=3_2

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

				lowerCamelCase   : str								=self.dummy_input
				return init_dict, inputs_dict







		def 			__lowercase   (		self			,     __lowercase   )				->   Union[str, Any]:
				lowerCamelCase   : str								=self.prepare_init_args_and_inputs_for_common()
				lowerCamelCase   : List[Any]								=self.block_class(**__lowerCamelCase   )
				unet_block.to(__lowerCamelCase   )
				unet_block.eval()

				with torch.no_grad():
						lowerCamelCase   : int								=unet_block(**__lowerCamelCase   )

				if isinstance(__lowerCamelCase			,     __lowerCamelCase   ):
						lowerCamelCase   : Union[str, Any]								=output[0]

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

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







		@unittest.skipIf(torch_device == '''mps'''			,     '''Training is not supported in mps'''   )
		def 			__lowercase   (		self   )				->   Dict:
				lowerCamelCase   : Tuple								=self.prepare_init_args_and_inputs_for_common()
				lowerCamelCase   : str								=self.block_class(**__lowerCamelCase   )
				model.to(__lowerCamelCase   )
				model.train()
				lowerCamelCase   : Optional[int]								=model(**__lowerCamelCase   )

				if isinstance(__lowerCamelCase			,     __lowerCamelCase   ):
						lowerCamelCase   : Optional[Any]								=output[0]

				lowerCamelCase   : List[str]								=torch.device(__lowerCamelCase   )
				lowerCamelCase   : List[str]								=randn_tensor(output.shape			,     device=__lowerCamelCase   )
				lowerCamelCase   : Dict								=torch.nn.functional.mse_loss(__lowerCamelCase			,     __lowerCamelCase   )
				loss.backward()








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

