
'''simple docstring'''







import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					int       =					CycleDiffusionPipeline
		a							:					List[Any]       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
		    """negative_prompt""",
		    """height""",
		    """width""",
		    """negative_prompt_embeds""",
		}
		a							:					List[Any]       =					PipelineTesterMixin.required_optional_params - {"""latents"""}
		a							:					int       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}			)
		a							:					Union[str, Any]       =					IMAGE_TO_IMAGE_IMAGE_PARAMS
		a							:					Optional[int]       =					IMAGE_TO_IMAGE_IMAGE_PARAMS




		def     lowercase       (				self      ):
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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					,						)
								_SCREAMING_SNAKE_CASE				= DDIMScheduler(
								    beta_start=0.0_00_85					,						beta_end=0.0_12					,						beta_schedule="scaled_linear"					,						num_train_timesteps=1_000					,						clip_sample=UpperCamelCase					,						set_alpha_to_one=UpperCamelCase					,						)
								torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= 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      )
								_SCREAMING_SNAKE_CASE				= 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=1_000					,						)
								_SCREAMING_SNAKE_CASE				= CLIPTextModel(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase=0      ):
								_SCREAMING_SNAKE_CASE				= floats_tensor((1, 3, 32, 32)					,						rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= image / 2 + 0.5
								if str(UpperCamelCase      ).startswith("mps"      ):
														_SCREAMING_SNAKE_CASE				= torch.manual_seed(UpperCamelCase      )
								else:
														_SCREAMING_SNAKE_CASE				= torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= {
								    "prompt": "An astronaut riding an elephant",
								    "source_prompt": "An astronaut riding a horse",
								    "image": image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "eta": 0.1,
								    "strength": 0.8,
								    "guidance_scale": 3,
								    "source_guidance_scale": 1,
								    "output_type": "numpy",
								}
								return inputs




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cpu"  # ensure determinism for the device-dependent torch.Generator

								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								_SCREAMING_SNAKE_CASE				= CycleDiffusionPipeline(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								_SCREAMING_SNAKE_CASE				= np.array([0.44_59, 0.49_43, 0.45_44, 0.66_43, 0.54_74, 0.43_27, 0.57_01, 0.59_59, 0.51_79]      )

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




		@unittest.skipIf(torch_device != "cuda"					,						"This test requires a GPU"      )
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_dummy_components()
								for name, module in components.items():
														if hasattr(UpperCamelCase					,						"half"      ):
																				_SCREAMING_SNAKE_CASE				= module.half()
								_SCREAMING_SNAKE_CASE				= CycleDiffusionPipeline(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= self.get_dummy_inputs(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= pipe(**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= output.images

								_SCREAMING_SNAKE_CASE				= images[0, -3:, -3:, -1]

								assert images.shape == (1, 32, 32, 3)
								_SCREAMING_SNAKE_CASE				= np.array([0.35_06, 0.45_43, 0.4_46, 0.45_75, 0.51_95, 0.41_55, 0.52_73, 0.5_18, 0.41_16]      )

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




		@skip_mps
		def     lowercase       (				self      ):
								return super().test_save_load_local()




		@unittest.skip("non-deterministic pipeline"      )
		def     lowercase       (				self      ):
								return super().test_inference_batch_single_identical()




		@skip_mps
		def     lowercase       (				self      ):
								return super().test_dict_tuple_outputs_equivalent()




		@skip_mps
		def     lowercase       (				self      ):
								return super().test_save_load_optional_components()




		@skip_mps
		def     lowercase       (				self      ):
								return super().test_attention_slicing_forward_pass()


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




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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/cycle-diffusion/black_colored_car.png"      )
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"      )
								_SCREAMING_SNAKE_CASE				= init_image.resize((512, 512)      )

								_SCREAMING_SNAKE_CASE				= "CompVis/stable-diffusion-v1-4"
								_SCREAMING_SNAKE_CASE				= DDIMScheduler.from_pretrained(UpperCamelCase					,						subfolder="scheduler"      )
								_SCREAMING_SNAKE_CASE				= CycleDiffusionPipeline.from_pretrained(
								    UpperCamelCase					,						scheduler=UpperCamelCase					,						safety_checker=UpperCamelCase					,						torch_dtype=torch.floataa					,						revision="fp16"      )

								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )
								pipe.enable_attention_slicing()

								_SCREAMING_SNAKE_CASE				= "A black colored car"
								_SCREAMING_SNAKE_CASE				= "A blue colored car"

								_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= pipe(
								    prompt=UpperCamelCase					,						source_prompt=UpperCamelCase					,						image=UpperCamelCase					,						num_inference_steps=100					,						eta=0.1					,						strength=0.85					,						guidance_scale=3					,						source_guidance_scale=1					,						generator=UpperCamelCase					,						output_type="np"					,						)
								_SCREAMING_SNAKE_CASE				= output.images

								# the values aren't exactly equal, but the images look the same visually
								assert np.abs(image - expected_image      ).max() < 5e-1




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
								    "/cycle-diffusion/black_colored_car.png"      )
								_SCREAMING_SNAKE_CASE				= load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"      )
								_SCREAMING_SNAKE_CASE				= init_image.resize((512, 512)      )

								_SCREAMING_SNAKE_CASE				= "CompVis/stable-diffusion-v1-4"
								_SCREAMING_SNAKE_CASE				= DDIMScheduler.from_pretrained(UpperCamelCase					,						subfolder="scheduler"      )
								_SCREAMING_SNAKE_CASE				= CycleDiffusionPipeline.from_pretrained(UpperCamelCase					,						scheduler=UpperCamelCase					,						safety_checker=UpperCamelCase      )

								pipe.to(UpperCamelCase      )
								pipe.set_progress_bar_config(disable=UpperCamelCase      )
								pipe.enable_attention_slicing()

								_SCREAMING_SNAKE_CASE				= "A black colored car"
								_SCREAMING_SNAKE_CASE				= "A blue colored car"

								_SCREAMING_SNAKE_CASE				= torch.manual_seed(0      )
								_SCREAMING_SNAKE_CASE				= pipe(
								    prompt=UpperCamelCase					,						source_prompt=UpperCamelCase					,						image=UpperCamelCase					,						num_inference_steps=100					,						eta=0.1					,						strength=0.85					,						guidance_scale=3					,						source_guidance_scale=1					,						generator=UpperCamelCase					,						output_type="np"					,						)
								_SCREAMING_SNAKE_CASE				= output.images

								assert np.abs(image - expected_image      ).max() < 2e-2

'''simple docstring'''







def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						_SCREAMING_SNAKE_CASE				= int(_SCREAMING_SNAKE_CASE  )
						if n_element < 1:
												_SCREAMING_SNAKE_CASE				= ValueError("a should be a positive number"  )
												raise my_error

						_SCREAMING_SNAKE_CASE				= [1]
						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= (0, 0, 0)
						_SCREAMING_SNAKE_CASE				= 1
						while index < n_element:
												while hamming_list[i] * 2 <= hamming_list[-1]:
																		i += 1
												while hamming_list[j] * 3 <= hamming_list[-1]:
																		j += 1
												while hamming_list[k] * 5 <= hamming_list[-1]:
																		k += 1
												hamming_list.append(
												    min(hamming_list[i] * 2	,			hamming_list[j] * 3	,			hamming_list[k] * 5  )  )
												index += 1
						return hamming_list


if __name__ == "__main__":
						_snake_case				:   int     =				input("""Enter the last number (nth term) of the Hamming Number Series: """)
						print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
						_snake_case				:   str     =				hamming(int(n))
						print("""-----------------------------------------------------""")
						print(F"The list with nth numbers is: {hamming_numbers}")
						print("""-----------------------------------------------------""")