






import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class       snake_case__ (				lowerCamelCase__						,     lowerCamelCase__						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					Dict								=			StableDiffusionXLImgaImgPipeline
       lowerCamelCase				:					Any								=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
       lowerCamelCase				:					int								=			PipelineTesterMixin.required_optional_params - {"""latents"""}
       lowerCamelCase				:					Any								=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       lowerCamelCase				:					Dict								=			IMAGE_TO_IMAGE_IMAGE_PARAMS
       lowerCamelCase				:					str								=			IMAGE_TO_IMAGE_IMAGE_PARAMS



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            torch.manual_seed(0     )
            __snake_case      :Optional[int] 							=    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""")      ,    attention_head_dim=(2, 4)      ,    use_linear_projection=snake_case__      ,    addition_embed_type="""text_time"""      ,    addition_time_embed_dim=8      ,    transformer_layers_per_block=(1, 2)      ,    projection_class_embeddings_input_dim=80      ,    cross_attention_dim=64      ,    )
            __snake_case      :Any 							=    EulerDiscreteScheduler(
                beta_start=0.0_00_85      ,    beta_end=0.0_12      ,    steps_offset=1      ,    beta_schedule="""scaled_linear"""      ,    timestep_spacing="""leading"""      ,    )
            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      ,    sample_size=1_28      ,    )
            torch.manual_seed(0     )
            __snake_case      :Optional[Any] 							=    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=10_00      ,    hidden_act="""gelu"""      ,    projection_dim=32      ,    )
            __snake_case      :List[Any] 							=    CLIPTextModel(snake_case__     )
            __snake_case      :Optional[Any] 							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      ,    local_files_only=snake_case__     )

            __snake_case      :Optional[Any] 							=    CLIPTextModelWithProjection(snake_case__     )
            __snake_case      :Union[str, Any] 							=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      ,    local_files_only=snake_case__     )

            __snake_case      :List[str] 							=    {
                """unet""": unet,
                """scheduler""": scheduler,
                """vae""": vae,
                """text_encoder""": text_encoder,
                """tokenizer""": tokenizer,
                """text_encoder_2""": text_encoder_a,
                """tokenizer_2""": tokenizer_a,
                # "safety_checker": None,
                # "feature_extractor": None,
            }
            return components



       def        __lowercase    (				self      ,    a__      ,    a__=0     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    floats_tensor((1, 3, 32, 32)      ,    rng=random.Random(snake_case__     )     ).to(snake_case__     )
            __snake_case      :List[str] 							=    image / 2 + 0.5
            if str(snake_case__     ).startswith("""mps"""     ):
                 __snake_case      :Dict 							=    torch.manual_seed(snake_case__     )
            else:
                 __snake_case      :str 							=    torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
            __snake_case      :List[Any] 							=    {
                """prompt""": """A painting of a squirrel eating a burger""",
                """image""": image,
                """generator""": generator,
                """num_inference_steps""": 2,
                """guidance_scale""": 5.0,
                """output_type""": """numpy""",
                """strength""": 0.75,
            }
            return inputs



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
            __snake_case      :List[str] 							=    self.get_dummy_components()
            __snake_case      :Any 							=    StableDiffusionXLImgaImgPipeline(**snake_case__     )
            __snake_case      :Optional[int] 							=    sd_pipe.to(snake_case__     )
            sd_pipe.set_progress_bar_config(disable=snake_case__     )

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

            assert image.shape == (1, 32, 32, 3)

            __snake_case      :List[str] 							=    np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]     )

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



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            super().test_attention_slicing_forward_pass(expected_max_diff=3e-3     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            super().test_inference_batch_single_identical(expected_max_diff=3e-3     )



       def        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :str 							=    self.get_dummy_components()
            __snake_case      :int 							=    StableDiffusionXLImgaImgPipeline(**snake_case__     )
            __snake_case      :str 							=    sd_pipe.to(snake_case__     )
            __snake_case      :List[Any] 							=    sd_pipe.to(snake_case__     )
            sd_pipe.set_progress_bar_config(disable=snake_case__     )

            # forward without prompt embeds
            __snake_case      :Any 							=    self.get_dummy_inputs(snake_case__     )
            __snake_case      :str 							=    3 * ["""this is a negative prompt"""]
            __snake_case      :List[str] 							=    negative_prompt
            __snake_case      :Dict 							=    3 * [inputs["""prompt"""]]

            __snake_case      :Union[str, Any] 							=    sd_pipe(**snake_case__     )
            __snake_case      :Union[str, Any] 							=    output.images[0, -3:, -3:, -1]

            # forward with prompt embeds
            __snake_case      :Optional[int] 							=    self.get_dummy_inputs(snake_case__     )
            __snake_case      :str 							=    3 * ["""this is a negative prompt"""]
            __snake_case      :List[Any] 							=    3 * [inputs.pop("""prompt"""     )]

            (
                __snake_case		
            )      :Any 							=    sd_pipe.encode_prompt(snake_case__      ,    negative_prompt=snake_case__     )

            __snake_case      :Any 							=    sd_pipe(
                **snake_case__      ,    prompt_embeds=snake_case__      ,    negative_prompt_embeds=snake_case__      ,    pooled_prompt_embeds=snake_case__      ,    negative_pooled_prompt_embeds=snake_case__      ,    )
            __snake_case      :Union[str, Any] 							=    output.images[0, -3:, -3:, -1]

            # make sure that it's equal
            assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()     ).max() < 1e-4




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

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()



       def        __lowercase    (				self      ,    a__      ,    a__="cpu"      ,    a__=torch.floataa      ,    a__=0     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    torch.Generator(device=snake_case__     ).manual_seed(snake_case__     )
            __snake_case      :Optional[Any] 							=    np.random.RandomState(snake_case__     ).standard_normal((1, 4, 64, 64)     )
            __snake_case      :Tuple 							=    torch.from_numpy(snake_case__     ).to(device=snake_case__      ,    dtype=snake_case__     )
            __snake_case      :int 							=    {
                """prompt""": """a photograph of an astronaut riding a horse""",
                """latents""": latents,
                """generator""": generator,
                """num_inference_steps""": 3,
                """guidance_scale""": 7.5,
                """output_type""": """numpy""",
            }
            return inputs



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""     )
            pipe.to(snake_case__     )
            pipe.set_progress_bar_config(disable=snake_case__     )

            __snake_case      :Union[str, Any] 							=    self.get_inputs(snake_case__     )
            __snake_case      :Optional[int] 							=    pipe(**snake_case__     ).images
            __snake_case      :Optional[Any] 							=    image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 5_12, 5_12, 3)
            __snake_case      :int 							=    np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]     )
            assert np.abs(image_slice - expected_slice     ).max() < 7e-3









def  __magic_name__  (				lowercase = 1000				)				-> int:

							"""simple docstring"""



							lowercase_				,		lowercase_	:   Optional[Any]  				=     1, 1
							lowercase_	:   Tuple  				=     2
							while True:
														lowercase_	:   Dict  				=     0
														lowercase_	:   List[Any]  				=     fa + fa
														lowercase_				,		lowercase_	:   List[str]  				=     fa, f
														index += 1
														for _ in str(lowercase				):
																					i += 1
														if i == n:
																					break
							return index


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