

'''simple docstring'''






def 		_A     (    A__      ):






 """simple docstring"""



 assert (
     isinstance(A__     ,				A__      ) and number_of_steps > 0
 ), F"number_of_steps needs to be positive integer, your input {number_of_steps}"
 if number_of_steps == 1:
  return 1
 __lowercase  ,	__lowercase							  = 1, 1
 for _ in range(number_of_steps - 1      ):
  __lowercase  ,	__lowercase							  = current + previous, current
 return current


if __name__ == "__main__":
 import doctest

 doctest.testmod()



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    SCREAMING_SNAKE_CASE__     :





	'''simple docstring'''



	@property
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				return self.get_dummy_input()



	@property
	def  A							(  self	:    Any	):


				'''simple docstring'''


				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	:    Union[str, Any]			,  lowercase	:    Any=True			,  lowercase	:    List[Any]=False			,  lowercase	:    List[str]=False			,  lowercase	:    Dict=False			,  ):


				'''simple docstring'''


				_snake_case		=			4
				_snake_case		=			32
				_snake_case		=			(32, 32)

				_snake_case		=			torch.manual_seed(0	)
				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			(batch_size, num_channels) + sizes
				_snake_case		=			randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	)
				_snake_case		=			{'hidden_states': hidden_states}

				if include_temb:
							_snake_case		=			128
							_snake_case		=			randn_tensor((batch_size, temb_channels)			,  generator=lowercase			,  device=lowercase	)

				if include_res_hidden_states_tuple:
							_snake_case		=			torch.manual_seed(1	)
							_snake_case		=			(randn_tensor(lowercase			,  generator=lowercase			,  device=lowercase	),)

				if include_encoder_hidden_states:
							_snake_case		=			floats_tensor((batch_size, 32, 32)	).to(lowercase	)

				if include_skip_sample:
							_snake_case		=			randn_tensor(((batch_size, 3) + sizes)			,  generator=lowercase			,  device=lowercase	)

				return dummy_input



	def  A							(  self	:    Any	):


				'''simple docstring'''


				_snake_case		=			{
				    'in_channels': 32,
				    'out_channels': 32,
				    'temb_channels': 128,
				}
				if self.block_type == "up":
							_snake_case		=			32

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

				_snake_case		=			self.dummy_input
				return init_dict, inputs_dict



	def  A							(  self	:    Dict			,  lowercase	:    Optional[int]	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				unet_block.to(lowercase	)
				unet_block.eval()

				with torch.no_grad():
							_snake_case		=			unet_block(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

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

				_snake_case		=			output[0, -1, -3:, -3:]
				_snake_case		=			torch.tensor(lowercase	).to(lowercase	)
				assert torch_all_close(output_slice.flatten()			,  lowercase			,  atol=5E-3	)





	@unittest.skipIf(torch_device == 'mps'			,  'Training is not supported in mps'	)
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				_snake_case	,					_snake_case		=			self.prepare_init_args_and_inputs_for_common()
				_snake_case		=			self.block_class(**lowercase	)
				model.to(lowercase	)
				model.train()
				_snake_case		=			model(**lowercase	)

				if isinstance(lowercase			,  lowercase	):
							_snake_case		=			output[0]

				_snake_case		=			torch.device(lowercase	)
				_snake_case		=			randn_tensor(output.shape			,  device=lowercase	)
				_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  lowercase	)
				loss.backward()