



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class  snake_case__(_a							,     unittest.TestCase     ):

					"""simple docstring"""






					lowercase_								=						KandinskyVaaControlnetImgaImgPipeline
					lowercase_								=						["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
					lowercase_								=						["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
					lowercase_								=						[
					    """generator""",
					    """height""",
					    """width""",
					    """strength""",
					    """guidance_scale""",
					    """num_inference_steps""",
					    """return_dict""",
					    """guidance_scale""",
					    """num_images_per_prompt""",
					    """output_type""",
					    """return_dict""",
					]
					lowercase_								=						False







					@property
					def 					snake_case	(				self   :      Any	):
									return 32







					@property
					def 					snake_case	(				self   :      List[Any]	):
									return 32







					@property
					def 					snake_case	(				self   :      Tuple	):
									return self.time_input_dim







					@property
					def 					snake_case	(				self   :      List[str]	):
									return self.time_input_dim * 4







					@property
					def 					snake_case	(				self   :      List[str]	):
									return 100







					@property
					def 					snake_case	(				self   :      str	):
									torch.manual_seed(0	)

									lowercase__	:       Tuple						=	{
									    'in_channels': 8,
									    # Out channels is double in channels because predicts mean and variance
									    'out_channels': 8,
									    'addition_embed_type': 'image_hint',
									    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
									    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
									    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
									    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
									    'layers_per_block': 1,
									    'encoder_hid_dim': self.text_embedder_hidden_size,
									    'encoder_hid_dim_type': 'image_proj',
									    'cross_attention_dim': self.cross_attention_dim,
									    'attention_head_dim': 4,
									    'resnet_time_scale_shift': 'scale_shift',
									    'class_embed_type': None,
									}

									lowercase__	:       Tuple						=	UNetaDConditionModel(**SCREAMING_SNAKE_CASE	)
									return model







					@property
					def 					snake_case	(				self   :      Dict	):
									return {
									    "block_out_channels": [32, 32, 64, 64],
									    "down_block_types": [
									        "DownEncoderBlock2D",
									        "DownEncoderBlock2D",
									        "DownEncoderBlock2D",
									        "AttnDownEncoderBlock2D",
									    ],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "vq_embed_dim": 4,
									}







					@property
					def 					snake_case	(				self   :      Optional[int]	):
									torch.manual_seed(0	)
									lowercase__	:       List[Any]						=	VQModel(**self.dummy_movq_kwargs	)
									return model







					def 					snake_case	(				self   :      Any	):
									lowercase__	:       List[str]						=	self.dummy_unet
									lowercase__	:       List[Any]						=	self.dummy_movq

									lowercase__	:       Union[str, Any]						=	{
									    'num_train_timesteps': 1_000,
									    'beta_schedule': 'linear',
									    'beta_start': 0.00_085,
									    'beta_end': 0.012,
									    'clip_sample': False,
									    'set_alpha_to_one': False,
									    'steps_offset': 0,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									}

									lowercase__	:       Any						=	DDIMScheduler(**SCREAMING_SNAKE_CASE	)

									lowercase__	:       Optional[Any]						=	{
									    'unet': unet,
									    'scheduler': scheduler,
									    'movq': movq,
									}

									return components







					def 					snake_case	(				self   :      Dict ,  SCREAMING_SNAKE_CASE   :      Dict ,  SCREAMING_SNAKE_CASE   :      Optional[int]=0	):
									lowercase__	:       Dict						=	floats_tensor((1, self.text_embedder_hidden_size) ,  rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
									lowercase__	:       Any						=	floats_tensor((1, self.text_embedder_hidden_size) ,  rng=random.Random(seed + 1	)	).to(
									    SCREAMING_SNAKE_CASE	)
									# create init_image
									lowercase__	:       Tuple						=	floats_tensor((1, 3, 64, 64) ,  rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)
									lowercase__	:       str						=	image.cpu().permute(0 ,  2 ,  3 ,  1	)[0]
									lowercase__	:       Optional[int]						=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE	)	).convert("RGB"	).resize((256, 256)	)
									# create hint
									lowercase__	:       int						=	floats_tensor((1, 3, 64, 64) ,  rng=random.Random(SCREAMING_SNAKE_CASE	)	).to(SCREAMING_SNAKE_CASE	)

									if str(SCREAMING_SNAKE_CASE	).startswith("mps"	):
													lowercase__	:       str						=	torch.manual_seed(SCREAMING_SNAKE_CASE	)
									else:
													lowercase__	:       Any						=	torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)
									lowercase__	:       Any						=	{
									    'image': init_image,
									    'image_embeds': image_embeds,
									    'negative_image_embeds': negative_image_embeds,
									    'hint': hint,
									    'generator': generator,
									    'height': 64,
									    'width': 64,
									    'num_inference_steps': 10,
									    'guidance_scale': 7.0,
									    'strength': 0.2,
									    'output_type': 'np',
									}
									return inputs







					def 					snake_case	(				self   :      Union[str, Any]	):
									lowercase__	:       Dict						=	'cpu'

									lowercase__	:       Optional[Any]						=	self.get_dummy_components()

									lowercase__	:       Any						=	self.pipeline_class(**SCREAMING_SNAKE_CASE	)
									lowercase__	:       str						=	pipe.to(SCREAMING_SNAKE_CASE	)

									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

									lowercase__	:       Any						=	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE	)	)
									lowercase__	:       List[str]						=	output.images

									lowercase__	:       List[Any]						=	pipe(
									    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE	) ,  return_dict=SCREAMING_SNAKE_CASE ,  )[0]

									lowercase__	:       Tuple						=	image[0, -3:, -3:, -1]
									lowercase__	:       Optional[Any]						=	image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									lowercase__	:       int						=	np.array(
									    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]	)
									assert (
									    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
									), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
									), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






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

					"""simple docstring"""






					def 					snake_case	(				self   :      Dict	):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







					def 					snake_case	(				self   :      Optional[Any]	):
									lowercase__	:       List[Any]						=	load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"	)

									lowercase__	:       Optional[Any]						=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"	)
									lowercase__	:       Dict						=	init_image.resize((512, 512)	)

									lowercase__	:       Union[str, Any]						=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinskyv22/hint_image_cat.png"	)
									lowercase__	:       List[str]						=	torch.from_numpy(np.array(SCREAMING_SNAKE_CASE	)	).float() / 255.0
									lowercase__	:       Union[str, Any]						=	hint.permute(2 ,  0 ,  1	).unsqueeze(0	)

									lowercase__	:       int						=	'A robot, 4k photo'

									lowercase__	:       List[Any]						=	KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-prior" ,  torch_dtype=torch.floataa	)
									pipe_prior.to(SCREAMING_SNAKE_CASE	)

									lowercase__	:       Dict						=	KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-controlnet-depth" ,  torch_dtype=torch.floataa	)
									lowercase__	:       Union[str, Any]						=	pipeline.to(SCREAMING_SNAKE_CASE	)

									pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

									lowercase__	:       Tuple						=	torch.Generator(device="cpu"	).manual_seed(0	)

									lowercase__	:       str						=	pipe_prior(
									    SCREAMING_SNAKE_CASE ,  image=SCREAMING_SNAKE_CASE ,  strength=0.85 ,  generator=SCREAMING_SNAKE_CASE ,  negative_prompt="" ,  ).to_tuple()

									lowercase__	:       Union[str, Any]						=	pipeline(
									    image=SCREAMING_SNAKE_CASE ,  image_embeds=SCREAMING_SNAKE_CASE ,  negative_image_embeds=SCREAMING_SNAKE_CASE ,  hint=SCREAMING_SNAKE_CASE ,  generator=SCREAMING_SNAKE_CASE ,  num_inference_steps=100 ,  height=512 ,  width=512 ,  strength=0.5 ,  output_type="np" ,  )

									lowercase__	:       Optional[Any]						=	output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE	)








def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    if n == 1 or not isinstance(lowerCamelCase_				,					lowerCamelCase_					):
        return 0
    elif n == 2:
        return 1
    else:
        _lowercase			:				List[str] 						=						[0, 1]
        for i in range(2				,					n + 1					):
            sequence.append(sequence[i - 1] + sequence[i - 2]					)

        return sequence[n]



def 							UpperCamelCase_( lowerCamelCase_					)       ->		int:
    _lowercase			:				Tuple 						=						0
    _lowercase			:				List[str] 						=						2

    while digits < n:
        index += 1
        _lowercase			:				Optional[int] 						=						len(str(fibonacci(lowerCamelCase_					)					)					)

    return index



def 							UpperCamelCase_( lowerCamelCase_ = 1000					)       ->		int:
    return fibonacci_digits_index(lowerCamelCase_					)


if __name__ == "__main__":
 print(solution(int(str(input()).strip())))


