

'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()







class lowerCamelCase   (    unittest.TestCase				):

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					1
					snake_case							=					3
					snake_case							=					(32, 32)

					snake_case							=					floats_tensor((batch_size, num_channels) + sizes,						rng=random.Random(0 ) ).to(lowercase_ )
					return image

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					torch.manual_seed(0 )
					snake_case							=					UNetaDModel(
					    block_out_channels=(32, 64),						layers_per_block=2,						sample_size=32,						in_channels=6,						out_channels=3,						down_block_types=('DownBlock2D', 'AttnDownBlock2D'),						up_block_types=('AttnUpBlock2D', 'UpBlock2D'),						)
					return model

				@property
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					torch.manual_seed(0 )
					snake_case							=					VQModel(
					    block_out_channels=[32, 64],						in_channels=3,						out_channels=3,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'],						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'],						latent_channels=3,						)
					return model

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case							=					'cpu'
					snake_case							=					self.dummy_uncond_unet
					snake_case							=					DDIMScheduler()
					snake_case							=					self.dummy_vq_model

					snake_case							=					LDMSuperResolutionPipeline(unet=lowercase_,						vqvae=lowercase_,						scheduler=lowercase_ )
					ldm.to(lowercase_ )
					ldm.set_progress_bar_config(disable=lowercase_ )

					snake_case							=					self.dummy_image.to(lowercase_ )

					snake_case							=					torch.Generator(device=lowercase_ ).manual_seed(0 )
					snake_case							=					ldm(image=lowercase_,						generator=lowercase_,						num_inference_steps=2,						output_type='numpy' ).images

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

					assert image.shape == (1, 64, 64, 3)
					snake_case							=					np.array([0.8_678, 0.8_245, 0.6_381, 0.6_830, 0.4_385, 0.5_599, 0.4_641, 0.6_201, 0.5_150] )

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






				@unittest.skipIf(torch_device != 'cuda',						'This test requires a GPU' )
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.dummy_uncond_unet
					snake_case							=					DDIMScheduler()
					snake_case							=					self.dummy_vq_model

					# put models in fp16
					snake_case							=					unet.half()
					snake_case							=					vqvae.half()

					snake_case							=					LDMSuperResolutionPipeline(unet=lowercase_,						vqvae=lowercase_,						scheduler=lowercase_ )
					ldm.to(lowercase_ )
					ldm.set_progress_bar_config(disable=lowercase_ )

					snake_case							=					self.dummy_image.to(lowercase_ )

					snake_case							=					ldm(lowercase_,						num_inference_steps=2,						output_type='numpy' ).images

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







@slow
@require_torch
class lowerCamelCase   (    unittest.TestCase				):

				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case							=					load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/vq_diffusion/teddy_bear_pool.png' )
					snake_case							=					init_image.resize((64, 64),						resample=PIL_INTERPOLATION['lanczos'] )

					snake_case							=					LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution',						device_map='auto' )
					ldm.set_progress_bar_config(disable=lowercase_ )

					snake_case							=					torch.manual_seed(0 )
					snake_case							=					ldm(image=lowercase_,						generator=lowercase_,						num_inference_steps=20,						output_type='numpy' ).images

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

					assert image.shape == (1, 256, 256, 3)
					snake_case							=					np.array([0.7_644, 0.7_679, 0.7_642, 0.7_633, 0.7_666, 0.7_560, 0.7_425, 0.7_257, 0.6_907] )

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












"""simple docstring"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						sorted(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[2]	,						reverse=_SCREAMING_SNAKE_CASE     )

		# Create a list of size equal to the maximum deadline
		# and initialize it with -1
		UpperCamelCase =						max(_SCREAMING_SNAKE_CASE	,						key=lambda _SCREAMING_SNAKE_CASE     : value[1]     )[1]
		UpperCamelCase =						[-1] * max_deadline

		# Finding the maximum profit and the count of jobs
		UpperCamelCase =						0
		UpperCamelCase =						0
		for job in jobs:
				# Find a free time slot for this job
				# (Note that we start from the last possible slot)
				for i in range(job[1] - 1	,						-1	,						-1     ):
						if time_slots[i] == -1:
								UpperCamelCase =						job[0]
								count += 1
								max_profit += job[2]
								break
		return [count, max_profit]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






