

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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.get_dummy_input()


			@property
			def       __lowerCAmelCase			(     self				):

				"""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       __lowerCAmelCase			(     self			,					__UpperCamelCase=True			,					__UpperCamelCase=False			,					__UpperCamelCase=False			,					__UpperCamelCase=False			,					):

				"""simple docstring"""


				snake_case_   		=			4
				snake_case_   		=			32
				snake_case_   		=			(32, 32)

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

				if include_temb:
					snake_case_   		=			1_28
					snake_case_   		=			randn_tensor((batch_size, temb_channels)			,					generator=__UpperCamelCase			,					device=__UpperCamelCase				)

				if include_res_hidden_states_tuple:
					snake_case_   		=			torch.manual_seed(1				)
					snake_case_   		=			(randn_tensor(__UpperCamelCase			,					generator=__UpperCamelCase			,					device=__UpperCamelCase				),)

				if include_encoder_hidden_states:
					snake_case_   		=			floats_tensor((batch_size, 32, 32)				).to(__UpperCamelCase				)

				if include_skip_sample:
					snake_case_   		=			randn_tensor(((batch_size, 3) + sizes)			,					generator=__UpperCamelCase			,					device=__UpperCamelCase				)

				return dummy_input


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			{
				    'in_channels': 32,
				    'out_channels': 32,
				    'temb_channels': 1_28,
				}
				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       __lowerCAmelCase			(     self			,					__UpperCamelCase				):

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self.prepare_init_args_and_inputs_for_common()
				snake_case_   		=			self.block_class(**__UpperCamelCase				)
				unet_block.to(__UpperCamelCase				)
				unet_block.eval()

				with torch.no_grad():
					snake_case_   		=			unet_block(**__UpperCamelCase				)

				if isinstance(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			output[0]

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

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







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

				"""simple docstring"""


				snake_case_		,							snake_case_   		=			self.prepare_init_args_and_inputs_for_common()
				snake_case_   		=			self.block_class(**__UpperCamelCase				)
				model.to(__UpperCamelCase				)
				model.train()
				snake_case_   		=			model(**__UpperCamelCase				)

				if isinstance(__UpperCamelCase			,					__UpperCamelCase				):
					snake_case_   		=			output[0]

				snake_case_   		=			torch.device(__UpperCamelCase				)
				snake_case_   		=			randn_tensor(output.shape			,					device=__UpperCamelCase				)
				snake_case_   		=			torch.nn.functional.mse_loss(__UpperCamelCase			,					__UpperCamelCase				)
				loss.backward()



from itertools import count





def a(lowercase__ = 50						):





	'''simple docstring'''
	snake_case_   		=			[1] * min_block_length

	for n in count(lowercase__						):
		fill_count_functions.append(1						)

		for block_length in range(lowercase__      ,			n + 1						):
			for block_start in range(n - block_length						):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1000000:
			break

	return n


if __name__ == "__main__":
		print(f"""{solution() = }""")
