

'''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"""







import pytest

from datasets.utils.version import Version
@pytest.mark.parametrize(
    '''other, expected_equality'''			,       [
        (Version('''1.0.0'''						), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''						), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]			,       )
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=Version('''1.0.0'''						)
    assert (version == other) is expected_equality
    assert (version != other) is not expected_equality
    assert (hash(lowercase						) == hash(lowercase						)) is expected_equality