






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 				__A     (			a      ,     a      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Tuple			 =StableDiffusionPanoramaPipeline
				UpperCamelCase__ :      Union[str, Any]			 =TEXT_TO_IMAGE_PARAMS
				UpperCamelCase__ :      Union[str, Any]			 =TEXT_TO_IMAGE_BATCH_PARAMS
				UpperCamelCase__ :      List[str]			 =TEXT_TO_IMAGE_IMAGE_PARAMS
				UpperCamelCase__ :      Dict			 =TEXT_TO_IMAGE_IMAGE_PARAMS


				def       __lowercase							( self    ):




									"""simple docstring"""

									torch.manual_seed(0    )
									__UpperCamelCase      :					str       		=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							,  )
									__UpperCamelCase      :					List[Any]       		=DDIMScheduler()
									torch.manual_seed(0    )
									__UpperCamelCase      :					str       		=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    )
									__UpperCamelCase      :					List[str]       		=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							,  )
									__UpperCamelCase      :					Any       		=CLIPTextModel(lowerCamelCase__    )
									__UpperCamelCase      :					List[Any]       		=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

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


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__=0    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=torch.manual_seed(lowerCamelCase__    )
									__UpperCamelCase      :					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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Union[str, Any]       		='cpu'  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase      :					Any       		=self.get_dummy_components()
									__UpperCamelCase      :					Tuple       		=StableDiffusionPanoramaPipeline(**lowerCamelCase__    )
									__UpperCamelCase      :					List[str]       		=sd_pipe.to(lowerCamelCase__    )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									__UpperCamelCase      :					str       		=self.get_dummy_inputs(lowerCamelCase__    )
									__UpperCamelCase      :					Any       		=sd_pipe(**lowerCamelCase__    ).images
									__UpperCamelCase      :					List[Any]       		=image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									__UpperCamelCase      :					Tuple       		=np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									super().test_inference_batch_consistent(batch_sizes=[1, 2]    )


				def       __lowercase							( self    ):




									"""simple docstring"""

									super().test_inference_batch_single_identical(batch_size=2							,  expected_max_diff=3.25E-3    )


				def       __lowercase							( self    ):




									"""simple docstring"""

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

									__UpperCamelCase      :					str       		=self.get_dummy_inputs(lowerCamelCase__    )
									__UpperCamelCase      :					int       		='french fries'
									__UpperCamelCase      :					Optional[Any]       		=sd_pipe(**lowerCamelCase__							,  negative_prompt=lowerCamelCase__    )
									__UpperCamelCase      :					Dict       		=output.images
									__UpperCamelCase      :					Any       		=image[0, -3:, -3:, -1]

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

									__UpperCamelCase      :					List[Any]       		=np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					List[Any]       		='cpu'  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase      :					Optional[Any]       		=self.get_dummy_components()
									__UpperCamelCase      :					Dict       		=StableDiffusionPanoramaPipeline(**lowerCamelCase__    )
									__UpperCamelCase      :					str       		=sd_pipe.to(lowerCamelCase__    )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									__UpperCamelCase      :					Union[str, Any]       		=self.get_dummy_inputs(lowerCamelCase__    )
									__UpperCamelCase      :					str       		=sd_pipe(**lowerCamelCase__							,  view_batch_size=2    )
									__UpperCamelCase      :					Any       		=output.images
									__UpperCamelCase      :					List[str]       		=image[0, -3:, -3:, -1]

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

									__UpperCamelCase      :					List[str]       		=np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		='cpu'  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase      :					Optional[int]       		=self.get_dummy_components()
									__UpperCamelCase      :					List[str]       		=EulerAncestralDiscreteScheduler(
									    beta_start=0.00_085							,  beta_end=0.012							,  beta_schedule='scaled_linear'    )
									__UpperCamelCase      :					Any       		=StableDiffusionPanoramaPipeline(**lowerCamelCase__    )
									__UpperCamelCase      :					int       		=sd_pipe.to(lowerCamelCase__    )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									__UpperCamelCase      :					List[str]       		=self.get_dummy_inputs(lowerCamelCase__    )
									__UpperCamelCase      :					List[str]       		=sd_pipe(**lowerCamelCase__    ).images
									__UpperCamelCase      :					List[Any]       		=image[0, -3:, -3:, -1]

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

									__UpperCamelCase      :					Optional[int]       		=np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952]    )

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







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Optional[Any]       		='cpu'  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase      :					int       		=self.get_dummy_components()
									__UpperCamelCase      :					Tuple       		=PNDMScheduler(
									    beta_start=0.00_085							,  beta_end=0.012							,  beta_schedule='scaled_linear'							,  skip_prk_steps=lowerCamelCase__    )
									__UpperCamelCase      :					Dict       		=StableDiffusionPanoramaPipeline(**lowerCamelCase__    )
									__UpperCamelCase      :					Optional[Any]       		=sd_pipe.to(lowerCamelCase__    )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									__UpperCamelCase      :					List[Any]       		=self.get_dummy_inputs(lowerCamelCase__    )
									__UpperCamelCase      :					List[str]       		=sd_pipe(**lowerCamelCase__    ).images
									__UpperCamelCase      :					Dict       		=image[0, -3:, -3:, -1]

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

									__UpperCamelCase      :					Optional[Any]       		=np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539]    )

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




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





				"""simple docstring"""







				def       __lowercase							( self    ):




									"""simple docstring"""

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


				def       __lowercase							( self							,  lowerCamelCase__=0    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=torch.manual_seed(lowerCamelCase__    )
									__UpperCamelCase      :					Dict       		={
									    'prompt': 'a photo of the dolomites',
									    'generator': generator,
									    'num_inference_steps': 3,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs


				def       __lowercase							( self    ):




									"""simple docstring"""

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

									__UpperCamelCase      :					Optional[int]       		=self.get_inputs()
									__UpperCamelCase      :					str       		=pipe(**lowerCamelCase__    ).images
									__UpperCamelCase      :					str       		=image[0, -3:, -3:, -1].flatten()

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

									__UpperCamelCase      :					Dict       		=np.array(
									    [
									        0.36_968_392,
									        0.27_025_372,
									        0.32_446_766,
									        0.28_379_387,
									        0.36_363_274,
									        0.30_733_347,
									        0.27_100_027,
									        0.27_054_125,
									        0.25_536_096,
									    ]    )

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


				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=StableDiffusionPanoramaPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-base'							,  safety_checker=lowerCamelCase__    )
									__UpperCamelCase      :					List[Any]       		=LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )
									pipe.enable_attention_slicing()

									__UpperCamelCase      :					Tuple       		=self.get_inputs()
									__UpperCamelCase      :					List[Any]       		=pipe(**lowerCamelCase__    ).images
									__UpperCamelCase      :					str       		=image[0, -3:, -3:, -1].flatten()

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

									__UpperCamelCase      :					Dict       		=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       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=0

									def callback_fn(lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ) -> None:
														__UpperCamelCase      :					List[Any]       		=True
														nonlocal number_of_steps
														number_of_steps += 1
														if step == 1:
																			__UpperCamelCase      :					Optional[Any]       		=latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			__UpperCamelCase      :					Tuple       		=latents[0, -3:, -3:, -1]

																			__UpperCamelCase      :					Optional[Any]       		=np.array(
																			    [
																			        0.18_681_869,
																			        0.33_907_816,
																			        0.5_361_276,
																			        0.14_432_865,
																			        -0.02_856_611,
																			        -0.73_941_123,
																			        0.23_397_987,
																			        0.47_322_682,
																			        -0.37_823_164,
																			    ]    )
																			assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5E-2
														elif step == 2:
																			__UpperCamelCase      :					List[Any]       		=latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			__UpperCamelCase      :					Dict       		=latents[0, -3:, -3:, -1]

																			__UpperCamelCase      :					int       		=np.array(
																			    [
																			        0.18_539_645,
																			        0.33_987_248,
																			        0.5_378_559,
																			        0.14_437_142,
																			        -0.02_455_261,
																			        -0.7_338_317,
																			        0.23_990_755,
																			        0.47_356_272,
																			        -0.3_786_505,
																			    ]    )

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

									__UpperCamelCase      :					Any       		=False

									__UpperCamelCase      :					int       		='stabilityai/stable-diffusion-2-base'
									__UpperCamelCase      :					Optional[int]       		=DDIMScheduler.from_pretrained(lowerCamelCase__							,  subfolder='scheduler'    )
									__UpperCamelCase      :					int       		=StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase__							,  scheduler=lowerCamelCase__							,  safety_checker=lowerCamelCase__    )
									__UpperCamelCase      :					Optional[int]       		=pipe.to(lowerCamelCase__    )
									pipe.set_progress_bar_config(disable=lowerCamelCase__    )
									pipe.enable_attention_slicing()

									__UpperCamelCase      :					str       		=self.get_inputs()
									pipe(**lowerCamelCase__							,  callback=lowerCamelCase__							,  callback_steps=1    )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







				def       __lowercase							( self    ):




									"""simple docstring"""

									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									__UpperCamelCase      :					Optional[Any]       		=self.get_inputs()
									__UpperCamelCase      :					List[str]       		=pipe(**lowerCamelCase__    )

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




from math import factorial




def 			UpperCAmelCase__				(			_A      : int = 1_00							):





  '''simple docstring'''







  return sum(int(_A							) for x in str(factorial(_A							)							)							)


if __name__ == "__main__":
       print(solution(int(input('''Enter the Number: ''').strip())))
