



'''simple docstring'''
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 				lowercase__      (				unittest.TestCase  ):





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Tuple												=  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       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.dummy_uncond_unet
							_UpperCamelCase   :						str												=  PNDMScheduler()

							_UpperCamelCase   :						Optional[int]												=  PNDMPipeline(unet=lowerCamelCase__					,scheduler=lowerCamelCase__					)
							pndm.to(lowerCamelCase__					)
							pndm.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						int												=  torch.manual_seed(0					)
							_UpperCamelCase   :						Optional[Any]												=  pndm(generator=lowerCamelCase__					,num_inference_steps=20					,output_type='numpy'					).images

							_UpperCamelCase   :						List[Any]												=  torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  pndm(generator=lowerCamelCase__					,num_inference_steps=20					,output_type='numpy'					,return_dict=lowerCamelCase__					)[0]

							_UpperCamelCase   :						Optional[int]												=  image[0, -3:, -3:, -1]
							_UpperCamelCase   :						str												=  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_UpperCamelCase   :						int												=  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 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  'google/ddpm-cifar10-32'

							_UpperCamelCase   :						str												=  UNetaDModel.from_pretrained(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  PNDMScheduler()

							_UpperCamelCase   :						List[str]												=  PNDMPipeline(unet=lowerCamelCase__					,scheduler=lowerCamelCase__					)
							pndm.to(lowerCamelCase__					)
							pndm.set_progress_bar_config(disable=lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(0					)
							_UpperCamelCase   :						Union[str, Any]												=  pndm(generator=lowerCamelCase__					,output_type='numpy'					).images

							_UpperCamelCase   :						Optional[int]												=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_UpperCamelCase   :						Optional[int]												=  np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]					)

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







'''simple docstring'''
from pathlib import Path

import fire






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  Path(UpperCAmelCase_				)
						_UpperCamelCase   :						str												=  Path(UpperCAmelCase_				)
						dest_dir.mkdir(exist_ok=UpperCAmelCase_				)
						for path in src_dir.iterdir():
												_UpperCamelCase   :						int												=  [x.rstrip() for x in list(path.open().readlines()				)][:n]
												_UpperCamelCase   :						Any												=  dest_dir.joinpath(path.name				)
												print(UpperCAmelCase_				)
												dest_path.open('w'				).write('\n'.join(UpperCAmelCase_				)				)


if __name__ == "__main__":
					fire.Fire(minify)


