






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@property
							def lowerCAmelCase_ (     self:      Dict						)		->		str:
											torch.manual_seed(0						)
											snake_case__									=     UNetaDModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')       ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')       ,	)
											return model


							def lowerCAmelCase_ (     self:      Any						)		->		Union[str, Any]:
											snake_case__									=     self.dummy_uncond_unet
											snake_case__									=     PNDMScheduler()

											snake_case__									=     PNDMPipeline(unet=UpperCamelCase       ,	scheduler=UpperCamelCase						)
											pndm.to(UpperCamelCase						)
											pndm.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     pndm(generator=UpperCamelCase       ,	num_inference_steps=20       ,	output_type='numpy'						).images

											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     pndm(generator=UpperCamelCase       ,	num_inference_steps=20       ,	output_type='numpy'       ,	return_dict=UpperCamelCase						)[0]

											snake_case__									=     image[0, -3:, -3:, -1]
											snake_case__									=     image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case__									=     np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

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


@slow
@require_torch
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      str						)		->		Union[str, Any]:
											snake_case__									=     'google/ddpm-cifar10-32'

											snake_case__									=     UNetaDModel.from_pretrained(UpperCamelCase						)
											snake_case__									=     PNDMScheduler()

											snake_case__									=     PNDMPipeline(unet=UpperCamelCase       ,	scheduler=UpperCamelCase						)
											pndm.to(UpperCamelCase						)
											pndm.set_progress_bar_config(disable=UpperCamelCase						)
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     pndm(generator=UpperCamelCase       ,	output_type='numpy'						).images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case__									=     np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]						)

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












from collections.abc import Sequence
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				return sum(c * (x**i) for i, c in enumerate(_A  )  )
def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				snake_case__									=     0.0
				for coeff in reversed(_A  ):
								snake_case__									=     result * x + coeff
				return result


if __name__ == "__main__":
							__UpperCamelCase					:		str	  =						(0.0, 0.0, 5.0, 9.3, 7.0)
							__UpperCamelCase					:		Dict	  =						1_0.0
							print(evaluate_poly(poly, x))
							print(horner(poly, x))




