

def 		UpperCamelCase   (lowercase_:					int = 1000   )	-> int:
	A__  ,  A__				:       Any	  =						1, 1
	A__				:       Dict	  =						[]
	for i in range(1       , n + 1   ):
		A__				:       Union[str, Any]	  =						prev_numerator + 2 * prev_denominator
		A__				:       Optional[Any]	  =						prev_numerator + prev_denominator
		if len(str(lowercase_   )   ) > len(str(lowercase_   )   ):
			result.append(lowercase_   )
		A__				:       List[Any]	  =						numerator
		A__				:       Any	  =						denominator

	return len(lowercase_   )


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






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        _a		(__magic_name__						,  __magic_name__						,  __magic_name__						,  unittest.TestCase							):


					'''simple docstring'''






					UpperCAmelCase__:  int    				=			AltDiffusionPipeline
					UpperCAmelCase__:  Tuple    				=			TEXT_TO_IMAGE_PARAMS
					UpperCAmelCase__:  List[str]    				=			TEXT_TO_IMAGE_BATCH_PARAMS
					UpperCAmelCase__:  Union[str, Any]    				=			TEXT_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__:  Optional[int]    				=			TEXT_TO_IMAGE_IMAGE_PARAMS






					def     __A     (					self     ):
						torch.manual_seed(0     )
						A__				:       Union[str, Any]	  =						UNetaDConditionModel(
						    block_out_channels=(32, 64)       ,			layers_per_block=2       ,			sample_size=32       ,			in_channels=4       ,			out_channels=4       ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")       ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")       ,			cross_attention_dim=32       ,			)
						A__				:       List[str]	  =						DDIMScheduler(
						    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule="""scaled_linear"""       ,			clip_sample=A__       ,			set_alpha_to_one=A__       ,			)
						torch.manual_seed(0     )
						A__				:       int	  =						AutoencoderKL(
						    block_out_channels=[32, 64]       ,			in_channels=3       ,			out_channels=3       ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]       ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]       ,			latent_channels=4       ,			)

						# TODO: address the non-deterministic text encoder (fails for save-load tests)
						# torch.manual_seed(0)
						# text_encoder_config = RobertaSeriesConfig(
						#     hidden_size=32,
						#     project_dim=32,
						#     intermediate_size=37,
						#     layer_norm_eps=1e-05,
						#     num_attention_heads=4,
						#     num_hidden_layers=5,
						#     vocab_size=5002,
						# )
						# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

						torch.manual_seed(0     )
						A__				:       int	  =						CLIPTextConfig(
						    bos_token_id=0       ,			eos_token_id=2       ,			hidden_size=32       ,			projection_dim=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			pad_token_id=1       ,			vocab_size=5002       ,			)
						A__				:       Tuple	  =						CLIPTextModel(A__     )

						A__				:       Union[str, Any]	  =						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""     )
						A__				:       Union[str, Any]	  =						77

						A__				:       Tuple	  =						{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components






					def     __A     (					self       ,			A__       ,			A__=0     ):
						if str(A__     ).startswith("""mps"""     ):
							A__				:       Dict	  =						torch.manual_seed(A__     )
						else:
							A__				:       str	  =						torch.Generator(device=A__     ).manual_seed(A__     )
						A__				:       int	  =						{
						    """prompt""": """A painting of a squirrel eating a burger""",
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 6.0,
						    """output_type""": """numpy""",
						}
						return inputs






					def     __A     (					self     ):
						super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )






					def     __A     (					self     ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

						A__				:       Union[str, Any]	  =						self.get_dummy_components()
						torch.manual_seed(0     )
						A__				:       List[str]	  =						RobertaSeriesConfig(
						    hidden_size=32       ,			project_dim=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			vocab_size=5002       ,			)
						# TODO: remove after fixing the non-deterministic text encoder
						A__				:       Tuple	  =						RobertaSeriesModelWithTransformation(A__     )
						A__				:       Optional[int]	  =						text_encoder

						A__				:       Optional[int]	  =						AltDiffusionPipeline(**A__     )
						A__				:       str	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       List[str]	  =						self.get_dummy_inputs(A__     )
						A__				:       List[str]	  =						"""A photo of an astronaut"""
						A__				:       Optional[int]	  =						alt_pipe(**A__     )
						A__				:       List[Any]	  =						output.images
						A__				:       int	  =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__				:       Optional[Any]	  =						np.array(
						    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]     )

						assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2






					def     __A     (					self     ):
						A__				:       Dict	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator

						A__				:       Union[str, Any]	  =						self.get_dummy_components()
						A__				:       Any	  =						PNDMScheduler(skip_prk_steps=A__     )
						torch.manual_seed(0     )
						A__				:       List[str]	  =						RobertaSeriesConfig(
						    hidden_size=32       ,			project_dim=32       ,			intermediate_size=37       ,			layer_norm_eps=1e-05       ,			num_attention_heads=4       ,			num_hidden_layers=5       ,			vocab_size=5002       ,			)
						# TODO: remove after fixing the non-deterministic text encoder
						A__				:       Optional[Any]	  =						RobertaSeriesModelWithTransformation(A__     )
						A__				:       List[Any]	  =						text_encoder
						A__				:       Optional[Any]	  =						AltDiffusionPipeline(**A__     )
						A__				:       Union[str, Any]	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       Optional[Any]	  =						self.get_dummy_inputs(A__     )
						A__				:       Dict	  =						alt_pipe(**A__     )
						A__				:       str	  =						output.images
						A__				:       Optional[int]	  =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						A__				:       Dict	  =						np.array(
						    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]     )

						assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2



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


					'''simple docstring'''






					def     __A     (					self     ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def     __A     (					self     ):
						# make sure here that pndm scheduler skips prk
						A__				:       Any	  =						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,			safety_checker=A__     )
						A__				:       Union[str, Any]	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       int	  =						"""A painting of a squirrel eating a burger"""
						A__				:       Any	  =						torch.manual_seed(0     )
						A__				:       List[Any]	  =						alt_pipe([prompt]       ,			generator=A__       ,			guidance_scale=6.0       ,			num_inference_steps=20       ,			output_type="""np"""     )

						A__				:       List[Any]	  =						output.images

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

						assert image.shape == (1, 512, 512, 3)
						A__				:       int	  =						np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]     )

						assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""       ,			subfolder="""scheduler"""     )

						A__				:       Optional[Any]	  =						AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""       ,			scheduler=A__       ,			safety_checker=A__     )
						A__				:       Optional[Any]	  =						alt_pipe.to(A__     )
						alt_pipe.set_progress_bar_config(disable=A__     )

						A__				:       Union[str, Any]	  =						"""A painting of a squirrel eating a burger"""
						A__				:       Dict	  =						torch.manual_seed(0     )

						A__				:       List[Any]	  =						alt_pipe([prompt]       ,			generator=A__       ,			num_inference_steps=2       ,			output_type="""numpy"""     )
						A__				:       str	  =						output.images

						A__				:       Union[str, Any]	  =						image[0, -3:, -3:, -1]

						assert image.shape == (1, 512, 512, 3)
						A__				:       List[str]	  =						np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]     )

						assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2



