




def 		_A(       UpperCamelCase__							:			list				,       UpperCamelCase__							:			list				,       UpperCamelCase__							:			int				)    -> list:
							'''simple docstring'''


							__lowercase      =    len(UpperCamelCase__				)
							__lowercase      =    [[0] * n for i in range(UpperCamelCase__				)]
							for i in range(UpperCamelCase__				):
														__lowercase      =    y_points[i]

							for i in range(2				,       UpperCamelCase__				):
														for j in range(UpperCamelCase__				,       UpperCamelCase__				):
																					__lowercase      =    (
																					    (xa - x_points[j - i + 1]) * q[j][i - 1]
																					    - (xa - x_points[j]) * q[j - 1][i - 1]
																					) / (x_points[j] - x_points[j - i + 1])

							return [q[n - 1][n - 1], q]


if __name__ == "__main__":
			import doctest

			doctest.testmod()












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 a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		CycleDiffusionPipeline
						UpperCamelCase_	:       List[str]       	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    'negative_prompt',
						    'height',
						    'width',
						    'negative_prompt_embeds',
						}
						UpperCamelCase_	:       int       	=		PipelineTesterMixin.required_optional_params - {'latents'}
						UpperCamelCase_	:       Dict       	=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
						UpperCamelCase_	:       Dict       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_	:       Optional[Any]       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    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				,							)
													__lowercase      =    DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='''scaled_linear'''				,							num_train_timesteps=1_000				,							clip_sample=lowerCamelCase__				,							set_alpha_to_one=lowerCamelCase__				,							)
													torch.manual_seed(0				)
													__lowercase      =    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				)
													__lowercase      =    CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__lowercase      =    CLIPTextModel(lowerCamelCase__				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[str]=0				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													__lowercase      =    image / 2 + 0.5
													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
													__lowercase      =    {
													    '''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     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

													__lowercase      =    self.get_dummy_components()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

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

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

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






						@unittest.skipIf(torch_device != '''cuda'''				,							'''This test requires a GPU'''				)
						def     UpperCAmelCase_      ( self :     str				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(lowerCamelCase__				,							'''half'''				):
																											__lowercase      =    module.half()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

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

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

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






						@skip_mps
						def     UpperCAmelCase_      ( self :     str				)    ->						int:
													"""simple docstring"""


													return super().test_save_load_local()






						@unittest.skip('''non-deterministic pipeline'''				)
						def     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return super().test_inference_batch_single_identical()






						@skip_mps
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													return super().test_save_load_optional_components()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


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

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(
													    lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				,							torch_dtype=torch.floataa				,							revision='''fp16'''				)

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

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    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     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


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

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				)

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

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

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






