

from itertools import count

def 						UpperCamelCase					(lowercase_: int = 50					)     ->      int:
	A__							:    List[Any]        =		[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() = }''')






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
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   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		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






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    Dict        =		VQModel(
						    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=3    ,       )
						return model






					@property
					def   __A			( self			):
						torch.manual_seed(0			)
						A__							:    int        =		CLIPTextConfig(
						    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1e-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=1000    ,       )
						return CLIPTextModel(A__			)






					def   __A			( self			):
						A__							:    int        =		self.dummy_uncond_unet
						A__							:    Optional[int]        =		DDIMScheduler()
						A__							:    List[Any]        =		self.dummy_vq_model

						A__							:    Optional[int]        =		LDMPipeline(unet=A__    ,       vqvae=A__    ,       scheduler=A__			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""			).images

						A__							:    Optional[int]        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=2    ,       output_type="""numpy"""    ,       return_dict=A__			)[0]

						A__							:    str        =		image[0, -3:, -3:, -1]
						A__							:    Tuple        =		image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__							:    Dict        =		np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]			)
						A__							:    List[Any]        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < tolerance



@slow
@require_torch
class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""			)
						ldm.to(A__			)
						ldm.set_progress_bar_config(disable=A__			)

						A__							:    Tuple        =		torch.manual_seed(0			)
						A__							:    List[Any]        =		ldm(generator=A__    ,       num_inference_steps=5    ,       output_type="""numpy"""			).images

						A__							:    int        =		image[0, -3:, -3:, -1]

						assert image.shape == (1, 256, 256, 3)
						A__							:    Any        =		np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]			)
						A__							:    int        =		1e-2 if torch_device != """mps""" else 3e-2

						assert np.abs(image_slice.flatten() - expected_slice			).max() < tolerance



