




'''simple docstring'''






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()






@skip_mps
class        lowercase_      (						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase							):
     A_											=			StableDiffusionPanoramaPipeline
     A_											=			TEXT_TO_IMAGE_PARAMS
     A_											=			TEXT_TO_IMAGE_BATCH_PARAMS
     A_											=			TEXT_TO_IMAGE_IMAGE_PARAMS
     A_											=			TEXT_TO_IMAGE_IMAGE_PARAMS





     def 			_lowerCAmelCase      (     self     :					List[str]			):
           torch.manual_seed(0			)
           snake_case__       :   List[Any]	      =    UNetaDConditionModel(
               block_out_channels=(32, 64)     ,							layers_per_block=1     ,							sample_size=32     ,							in_channels=4     ,							out_channels=4     ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')     ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')     ,							cross_attention_dim=32     ,							)
           snake_case__       :   Optional[Any]	      =    DDIMScheduler()
           torch.manual_seed(0			)
           snake_case__       :   Any	      =    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     ,							)
           torch.manual_seed(0			)
           snake_case__       :   Tuple	      =    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     ,							)
           snake_case__       :   Any	      =    CLIPTextModel(__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

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





     def 			_lowerCAmelCase      (     self     :					Dict     ,							__lowerCamelCase     :					Optional[int]     ,							__lowerCamelCase     :					Optional[int]=0			):
           snake_case__       :   Optional[int]	      =    torch.manual_seed(__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    {
               'prompt': 'a photo of the dolomites',
               'generator': generator,
               # Setting height and width to None to prevent OOMs on CPU.
               'height': None,
               'width': None,
               'num_inference_steps': 1,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
           }
           return inputs





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Tuple	      =    'cpu'  # ensure determinism for the device-dependent torch.Generator
           snake_case__       :   Dict	      =    self.get_dummy_components()
           snake_case__       :   Dict	      =    StableDiffusionPanoramaPipeline(**__lowerCamelCase			)
           snake_case__       :   Tuple	      =    sd_pipe.to(__lowerCamelCase			)
           sd_pipe.set_progress_bar_config(disable=__lowerCamelCase			)

           snake_case__       :   Union[str, Any]	      =    self.get_dummy_inputs(__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    sd_pipe(**__lowerCamelCase			).images
           snake_case__       :   int	      =    image[0, -3:, -3:, -1]
           assert image.shape == (1, 64, 64, 3)

           snake_case__       :   int	      =    np.array([0.6_1_8_6, 0.5_3_7_4, 0.4_9_1_5, 0.4_1_3_5, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_7, 0.4_7_5_7]			)

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





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           super().test_inference_batch_consistent(batch_sizes=[1, 2]			)





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           super().test_inference_batch_single_identical(batch_size=2     ,							expected_max_diff=3.25E-3			)





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Tuple	      =    'cpu'  # ensure determinism for the device-dependent torch.Generator
           snake_case__       :   Union[str, Any]	      =    self.get_dummy_components()
           snake_case__       :   int	      =    StableDiffusionPanoramaPipeline(**__lowerCamelCase			)
           snake_case__       :   Tuple	      =    sd_pipe.to(__lowerCamelCase			)
           sd_pipe.set_progress_bar_config(disable=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    self.get_dummy_inputs(__lowerCamelCase			)
           snake_case__       :   int	      =    'french fries'
           snake_case__       :   Tuple	      =    sd_pipe(**__lowerCamelCase     ,							negative_prompt=__lowerCamelCase			)
           snake_case__       :   List[str]	      =    output.images
           snake_case__       :   Any	      =    image[0, -3:, -3:, -1]

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

           snake_case__       :   Any	      =    np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]			)

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





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   int	      =    'cpu'  # ensure determinism for the device-dependent torch.Generator
           snake_case__       :   int	      =    self.get_dummy_components()
           snake_case__       :   List[Any]	      =    StableDiffusionPanoramaPipeline(**__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    sd_pipe.to(__lowerCamelCase			)
           sd_pipe.set_progress_bar_config(disable=__lowerCamelCase			)

           snake_case__       :   Tuple	      =    self.get_dummy_inputs(__lowerCamelCase			)
           snake_case__       :   str	      =    sd_pipe(**__lowerCamelCase     ,							view_batch_size=2			)
           snake_case__       :   int	      =    output.images
           snake_case__       :   Dict	      =    image[0, -3:, -3:, -1]

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

           snake_case__       :   str	      =    np.array([0.6_1_8_7, 0.5_3_7_5, 0.4_9_1_5, 0.4_1_3_6, 0.4_1_1_4, 0.4_5_6_3, 0.5_1_2_8, 0.4_9_7_6, 0.4_7_5_7]			)

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





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Optional[int]	      =    'cpu'  # ensure determinism for the device-dependent torch.Generator
           snake_case__       :   Tuple	      =    self.get_dummy_components()
           snake_case__       :   int	      =    EulerAncestralDiscreteScheduler(
               beta_start=0.0_0_0_8_5     ,							beta_end=0.0_1_2     ,							beta_schedule='scaled_linear'			)
           snake_case__       :   Dict	      =    StableDiffusionPanoramaPipeline(**__lowerCamelCase			)
           snake_case__       :   Tuple	      =    sd_pipe.to(__lowerCamelCase			)
           sd_pipe.set_progress_bar_config(disable=__lowerCamelCase			)

           snake_case__       :   int	      =    self.get_dummy_inputs(__lowerCamelCase			)
           snake_case__       :   List[str]	      =    sd_pipe(**__lowerCamelCase			).images
           snake_case__       :   int	      =    image[0, -3:, -3:, -1]

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

           snake_case__       :   Dict	      =    np.array([0.4_0_2_4, 0.6_5_1_0, 0.4_9_0_1, 0.5_3_7_8, 0.5_8_1_3, 0.5_6_2_2, 0.4_7_9_5, 0.4_4_6_7, 0.4_9_5_2]			)

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





     def 			_lowerCAmelCase      (     self     :					Dict			):
           snake_case__       :   Any	      =    'cpu'  # ensure determinism for the device-dependent torch.Generator
           snake_case__       :   List[Any]	      =    self.get_dummy_components()
           snake_case__       :   List[Any]	      =    PNDMScheduler(
               beta_start=0.0_0_0_8_5     ,							beta_end=0.0_1_2     ,							beta_schedule='scaled_linear'     ,							skip_prk_steps=__lowerCamelCase			)
           snake_case__       :   Optional[int]	      =    StableDiffusionPanoramaPipeline(**__lowerCamelCase			)
           snake_case__       :   str	      =    sd_pipe.to(__lowerCamelCase			)
           sd_pipe.set_progress_bar_config(disable=__lowerCamelCase			)

           snake_case__       :   Union[str, Any]	      =    self.get_dummy_inputs(__lowerCamelCase			)
           snake_case__       :   Tuple	      =    sd_pipe(**__lowerCamelCase			).images
           snake_case__       :   Tuple	      =    image[0, -3:, -3:, -1]

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

           snake_case__       :   Dict	      =    np.array([0.6_3_9_1, 0.6_2_9_1, 0.4_8_6_1, 0.5_1_3_4, 0.5_5_5_2, 0.4_5_7_8, 0.5_0_3_2, 0.5_0_2_3, 0.4_5_3_9]			)

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






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





     def 			_lowerCAmelCase      (     self     :					Tuple			):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





     def 			_lowerCAmelCase      (     self     :					Union[str, Any]     ,							__lowerCamelCase     :					int=0			):
           snake_case__       :   Tuple	      =    torch.manual_seed(__lowerCamelCase			)
           snake_case__       :   List[Any]	      =    {
               'prompt': 'a photo of the dolomites',
               'generator': generator,
               'num_inference_steps': 3,
               'guidance_scale': 7.5,
               'output_type': 'numpy',
           }
           return inputs





     def 			_lowerCAmelCase      (     self     :					int			):
           snake_case__       :   Optional[Any]	      =    'stabilityai/stable-diffusion-2-base'
           snake_case__       :   Any	      =    DDIMScheduler.from_pretrained(__lowerCamelCase     ,							subfolder='scheduler'			)
           snake_case__       :   List[Any]	      =    StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase     ,							scheduler=__lowerCamelCase     ,							safety_checker=__lowerCamelCase			)
           pipe.to(__lowerCamelCase			)
           pipe.set_progress_bar_config(disable=__lowerCamelCase			)
           pipe.enable_attention_slicing()

           snake_case__       :   List[Any]	      =    self.get_inputs()
           snake_case__       :   Any	      =    pipe(**__lowerCamelCase			).images
           snake_case__       :   Dict	      =    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 2048, 3)

           snake_case__       :   Any	      =    np.array(
               [
                   0.3_6_9_6_8_3_9_2,
                   0.2_7_0_2_5_3_7_2,
                   0.3_2_4_4_6_7_6_6,
                   0.2_8_3_7_9_3_8_7,
                   0.3_6_3_6_3_2_7_4,
                   0.3_0_7_3_3_3_4_7,
                   0.2_7_1_0_0_0_2_7,
                   0.2_7_0_5_4_1_2_5,
                   0.2_5_5_3_6_0_9_6,
               ]			)

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





     def 			_lowerCAmelCase      (     self     :					List[Any]			):
           snake_case__       :   Union[str, Any]	      =    StableDiffusionPanoramaPipeline.from_pretrained(
               'stabilityai/stable-diffusion-2-base'     ,							safety_checker=__lowerCamelCase			)
           snake_case__       :   Union[str, Any]	      =    LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
           pipe.to(__lowerCamelCase			)
           pipe.set_progress_bar_config(disable=__lowerCamelCase			)
           pipe.enable_attention_slicing()

           snake_case__       :   Any	      =    self.get_inputs()
           snake_case__       :   Dict	      =    pipe(**__lowerCamelCase			).images
           snake_case__       :   Optional[Any]	      =    image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 2048, 3)

           snake_case__       :   Any	      =    np.array(
               [
                   [
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                       0.0,
                   ]
               ]			)

           assert np.abs(expected_slice - image_slice			).max() < 1E-3





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           snake_case__       :   Optional[Any]	      =    0

           def callback_fn(__lowerCamelCase     :					int     ,							__lowerCamelCase     :					int     ,							__lowerCamelCase     :					torch.FloatTensor			) -> None:
                 snake_case__       :   str	      =    True
                 nonlocal number_of_steps
                 number_of_steps += 1
                 if step == 1:
                       snake_case__       :   int	      =    latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 64, 256)
                       snake_case__       :   Tuple	      =    latents[0, -3:, -3:, -1]

                       snake_case__       :   Union[str, Any]	      =    np.array(
                           [
                               0.1_8_6_8_1_8_6_9,
                               0.3_3_9_0_7_8_1_6,
                               0.5_3_6_1_2_7_6,
                               0.1_4_4_3_2_8_6_5,
                               -0.0_2_8_5_6_6_1_1,
                               -0.7_3_9_4_1_1_2_3,
                               0.2_3_3_9_7_9_8_7,
                               0.4_7_3_2_2_6_8_2,
                               -0.3_7_8_2_3_1_6_4,
                           ]			)
                       assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2
                 elif step == 2:
                       snake_case__       :   Union[str, Any]	      =    latents.detach().cpu().numpy()
                       assert latents.shape == (1, 4, 64, 256)
                       snake_case__       :   List[Any]	      =    latents[0, -3:, -3:, -1]

                       snake_case__       :   Optional[Any]	      =    np.array(
                           [
                               0.1_8_5_3_9_6_4_5,
                               0.3_3_9_8_7_2_4_8,
                               0.5_3_7_8_5_5_9,
                               0.1_4_4_3_7_1_4_2,
                               -0.0_2_4_5_5_2_6_1,
                               -0.7_3_3_8_3_1_7,
                               0.2_3_9_9_0_7_5_5,
                               0.4_7_3_5_6_2_7_2,
                               -0.3_7_8_6_5_0_5,
                           ]			)

                       assert np.abs(latents_slice.flatten() - expected_slice			).max() < 5E-2

           snake_case__       :   str	      =    False

           snake_case__       :   str	      =    'stabilityai/stable-diffusion-2-base'
           snake_case__       :   int	      =    DDIMScheduler.from_pretrained(__lowerCamelCase     ,							subfolder='scheduler'			)
           snake_case__       :   int	      =    StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase     ,							scheduler=__lowerCamelCase     ,							safety_checker=__lowerCamelCase			)
           snake_case__       :   Dict	      =    pipe.to(__lowerCamelCase			)
           pipe.set_progress_bar_config(disable=__lowerCamelCase			)
           pipe.enable_attention_slicing()

           snake_case__       :   Dict	      =    self.get_inputs()
           pipe(**__lowerCamelCase     ,							callback=__lowerCamelCase     ,							callback_steps=1			)
           assert callback_fn.has_been_called
           assert number_of_steps == 3





     def 			_lowerCAmelCase      (     self     :					Optional[Any]			):
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           snake_case__       :   List[Any]	      =    'stabilityai/stable-diffusion-2-base'
           snake_case__       :   Union[str, Any]	      =    DDIMScheduler.from_pretrained(__lowerCamelCase     ,							subfolder='scheduler'			)
           snake_case__       :   Optional[Any]	      =    StableDiffusionPanoramaPipeline.from_pretrained(__lowerCamelCase     ,							scheduler=__lowerCamelCase     ,							safety_checker=__lowerCamelCase			)
           snake_case__       :   int	      =    pipe.to(__lowerCamelCase			)
           pipe.set_progress_bar_config(disable=__lowerCamelCase			)
           pipe.enable_attention_slicing(1			)
           pipe.enable_sequential_cpu_offload()

           snake_case__       :   Any	      =    self.get_inputs()
           snake_case__       :   Optional[int]	      =    pipe(**__lowerCamelCase			)

           snake_case__       :   List[str]	      =    torch.cuda.max_memory_allocated()
           # make sure that less than 5.2 GB is allocated
           assert mem_bytes < 5.5 * 10**9






'''simple docstring'''






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      if n == 1 or not isinstance(__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		):
            return 0
      elif n == 2:
            return 1
      else:
            snake_case__       :   int	      =    [0, 1]
            for i in range(2				,      n + 1		):
                  sequence.append(sequence[i - 1] + sequence[i - 2]		)

            return sequence[n]






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   int:
      snake_case__       :   Optional[int]	      =    0
      snake_case__       :   Dict	      =    2

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

      return index






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE = 1000		)    ->   int:
      return fibonacci_digits_index(__SCREAMING_SNAKE_CASE		)


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