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 _a       (	unittest.TestCase ):

	'''simple docstring'''



	@property
	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				1
			__A							:   Tuple	  =				3
			__A							:   List[str]	  =				(32, 32)

			__A							:   int	  =				floats_tensor((batch_size, num_channels) + sizes							,			rng=random.Random(0   )   ).to(__UpperCAmelCase   )
			return image

	@property
	def 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   int	  =				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 					__UpperCAmelCase(       self   ):
			torch.manual_seed(0   )
			__A							:   Optional[int]	  =				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 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				"cpu"
			__A							:   List[Any]	  =				self.dummy_uncond_unet
			__A							:   Tuple	  =				DDIMScheduler()
			__A							:   str	  =				self.dummy_vq_model

			__A							:   int	  =				LDMSuperResolutionPipeline(unet=__UpperCAmelCase							,			vqvae=__UpperCAmelCase							,			scheduler=__UpperCAmelCase   )
			ldm.to(__UpperCAmelCase   )
			ldm.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   int	  =				self.dummy_image.to(__UpperCAmelCase   )

			__A							:   str	  =				torch.Generator(device=__UpperCAmelCase   ).manual_seed(0   )
			__A							:   Union[str, Any]	  =				ldm(image=__UpperCAmelCase							,			generator=__UpperCAmelCase							,			num_inference_steps=2							,			output_type="numpy"   ).images

			__A							:   List[Any]	  =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__A							:   Any	  =				np.array([0.86_78, 0.82_45, 0.63_81, 0.68_30, 0.43_85, 0.55_99, 0.46_41, 0.62_01, 0.51_50]   )

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







	@unittest.skipIf(torch_device != "cuda"							,			"This test requires a GPU"   )
	def 					__UpperCAmelCase(       self   ):
			__A							:   List[Any]	  =				self.dummy_uncond_unet
			__A							:   Union[str, Any]	  =				DDIMScheduler()
			__A							:   str	  =				self.dummy_vq_model

			# put models in fp16
			__A							:   Union[str, Any]	  =				unet.half()
			__A							:   str	  =				vqvae.half()

			__A							:   Tuple	  =				LDMSuperResolutionPipeline(unet=__UpperCAmelCase							,			vqvae=__UpperCAmelCase							,			scheduler=__UpperCAmelCase   )
			ldm.to(__UpperCAmelCase   )
			ldm.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   Tuple	  =				self.dummy_image.to(__UpperCAmelCase   )

			__A							:   List[str]	  =				ldm(__UpperCAmelCase							,			num_inference_steps=2							,			output_type="numpy"   ).images

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


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

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
			    "/vq_diffusion/teddy_bear_pool.png"   )
			__A							:   str	  =				init_image.resize((64, 64)							,			resample=PIL_INTERPOLATION["lanczos"]   )

			__A							:   Tuple	  =				LDMSuperResolutionPipeline.from_pretrained("duongna/ldm-super-resolution"							,			device_map="auto"   )
			ldm.set_progress_bar_config(disable=__UpperCAmelCase   )

			__A							:   str	  =				torch.manual_seed(0   )
			__A							:   Optional[int]	  =				ldm(image=__UpperCAmelCase							,			generator=__UpperCAmelCase							,			num_inference_steps=20							,			output_type="numpy"   ).images

			__A							:   Tuple	  =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			__A							:   Optional[int]	  =				np.array([0.76_44, 0.76_79, 0.76_42, 0.76_33, 0.76_66, 0.75_60, 0.74_25, 0.72_57, 0.69_07]   )

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






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .image_encoder import PaintByExampleImageEncoder
					from .pipeline_paint_by_example import PaintByExamplePipeline





