





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class lowerCamelCase						(unittest.TestCase					):







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  @property
  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     1
        _snake_case		:	Union[str, Any]					     =     3
        _snake_case		:	List[Any]					     =     (32, 32)

        _snake_case		:	Dict					     =     floats_tensor((batch_size, num_channels) + sizes     ,     rng=random.Random(0		)		).to(lowercase__		)
        return image







  @property
  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:	List[str]					     =     UNetaDConditionModel(
            block_out_channels=(32, 32, 64)     ,     layers_per_block=2     ,     sample_size=32     ,     in_channels=7     ,     out_channels=4     ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')     ,     up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,     cross_attention_dim=32     ,     attention_head_dim=8     ,     use_linear_projection=lowercase__     ,     only_cross_attention=(True, True, False)     ,     num_class_embeds=100     ,     )
        return model







  @property
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:	Any					     =     AutoencoderKL(
            block_out_channels=[32, 32, 64]     ,     in_channels=3     ,     out_channels=3     ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,     latent_channels=4     ,     )
        return model







  @property
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:	Union[str, Any]					     =     CLIPTextConfig(
            bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=32     ,     intermediate_size=37     ,     layer_norm_eps=1E-0_5     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     pad_token_id=1     ,     vocab_size=1_000     ,     hidden_act='''gelu'''     ,     projection_dim=512     ,     )
        return CLIPTextModel(lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	List[str]					     =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        _snake_case		:	Union[str, Any]					     =     self.dummy_cond_unet_upscale
        _snake_case		:	List[Any]					     =     DDPMScheduler()
        _snake_case		:	Dict					     =     DDIMScheduler(prediction_type='''v_prediction'''		)
        _snake_case		:	Union[str, Any]					     =     self.dummy_vae
        _snake_case		:	Optional[Any]					     =     self.dummy_text_encoder
        _snake_case		:	List[Any]					     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        _snake_case		:	int					     =     self.dummy_image.cpu().permute(0     ,     2     ,     3     ,     1		)[0]
        _snake_case		:	List[str]					     =     Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		).resize((64, 64)		)

        # make sure here that pndm scheduler skips prk
        _snake_case		:	int					     =     StableDiffusionUpscalePipeline(
            unet=lowercase__     ,     low_res_scheduler=lowercase__     ,     scheduler=lowercase__     ,     vae=lowercase__     ,     text_encoder=lowercase__     ,     tokenizer=lowercase__     ,     max_noise_level=350     ,     )
        _snake_case		:	Tuple					     =     sd_pipe.to(lowercase__		)
        sd_pipe.set_progress_bar_config(disable=lowercase__		)

        _snake_case		:	Union[str, Any]					     =     '''A painting of a squirrel eating a burger'''
        _snake_case		:	Any					     =     torch.Generator(device=lowercase__		).manual_seed(0		)
        _snake_case		:	int					     =     sd_pipe(
            [prompt]     ,     image=lowercase__     ,     generator=lowercase__     ,     guidance_scale=6.0     ,     noise_level=20     ,     num_inference_steps=2     ,     output_type='''np'''     ,     )

        _snake_case		:	Dict					     =     output.images

        _snake_case		:	Optional[int]					     =     torch.Generator(device=lowercase__		).manual_seed(0		)
        _snake_case		:	List[str]					     =     sd_pipe(
            [prompt]     ,     image=lowercase__     ,     generator=lowercase__     ,     guidance_scale=6.0     ,     noise_level=20     ,     num_inference_steps=2     ,     output_type='''np'''     ,     return_dict=lowercase__     ,     )[0]

        _snake_case		:	Optional[Any]					     =     image[0, -3:, -3:, -1]
        _snake_case		:	Union[str, Any]					     =     image_from_tuple[0, -3:, -3:, -1]

        _snake_case		:	Dict					     =     low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        _snake_case		:	int					     =     np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]		)

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







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	Any					     =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        _snake_case		:	Dict					     =     self.dummy_cond_unet_upscale
        _snake_case		:	Any					     =     DDPMScheduler()
        _snake_case		:	List[str]					     =     DDIMScheduler(prediction_type='''v_prediction'''		)
        _snake_case		:	Optional[Any]					     =     self.dummy_vae
        _snake_case		:	List[str]					     =     self.dummy_text_encoder
        _snake_case		:	Optional[Any]					     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        _snake_case		:	str					     =     self.dummy_image.cpu().permute(0     ,     2     ,     3     ,     1		)[0]
        _snake_case		:	Optional[Any]					     =     Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		).resize((64, 64)		)

        # make sure here that pndm scheduler skips prk
        _snake_case		:	str					     =     StableDiffusionUpscalePipeline(
            unet=lowercase__     ,     low_res_scheduler=lowercase__     ,     scheduler=lowercase__     ,     vae=lowercase__     ,     text_encoder=lowercase__     ,     tokenizer=lowercase__     ,     max_noise_level=350     ,     )
        _snake_case		:	List[Any]					     =     sd_pipe.to(lowercase__		)
        sd_pipe.set_progress_bar_config(disable=lowercase__		)

        _snake_case		:	str					     =     '''A painting of a squirrel eating a burger'''
        _snake_case		:	Optional[Any]					     =     sd_pipe(
            2 * [prompt]     ,     image=2 * [low_res_image]     ,     guidance_scale=6.0     ,     noise_level=20     ,     num_inference_steps=2     ,     output_type='''np'''     ,     )
        _snake_case		:	Any					     =     output.images
        assert image.shape[0] == 2

        _snake_case		:	str					     =     torch.Generator(device=lowercase__		).manual_seed(0		)
        _snake_case		:	Optional[Any]					     =     sd_pipe(
            [prompt]     ,     image=lowercase__     ,     generator=lowercase__     ,     num_images_per_prompt=2     ,     guidance_scale=6.0     ,     noise_level=20     ,     num_inference_steps=2     ,     output_type='''np'''     ,     )
        _snake_case		:	str					     =     output.images
        assert image.shape[0] == 2







  @unittest.skipIf(torch_device != '''cuda'''     ,     '''This test requires a GPU'''		)
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	str					     =     self.dummy_cond_unet_upscale
        _snake_case		:	Dict					     =     DDPMScheduler()
        _snake_case		:	int					     =     DDIMScheduler(prediction_type='''v_prediction'''		)
        _snake_case		:	Dict					     =     self.dummy_vae
        _snake_case		:	Optional[int]					     =     self.dummy_text_encoder
        _snake_case		:	List[Any]					     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        _snake_case		:	Dict					     =     self.dummy_image.cpu().permute(0     ,     2     ,     3     ,     1		)[0]
        _snake_case		:	Dict					     =     Image.fromarray(np.uinta(lowercase__		)		).convert('''RGB'''		).resize((64, 64)		)

        # put models in fp16, except vae as it overflows in fp16
        _snake_case		:	Tuple					     =     unet.half()
        _snake_case		:	int					     =     text_encoder.half()

        # make sure here that pndm scheduler skips prk
        _snake_case		:	Optional[Any]					     =     StableDiffusionUpscalePipeline(
            unet=lowercase__     ,     low_res_scheduler=lowercase__     ,     scheduler=lowercase__     ,     vae=lowercase__     ,     text_encoder=lowercase__     ,     tokenizer=lowercase__     ,     max_noise_level=350     ,     )
        _snake_case		:	List[Any]					     =     sd_pipe.to(lowercase__		)
        sd_pipe.set_progress_bar_config(disable=lowercase__		)

        _snake_case		:	List[Any]					     =     '''A painting of a squirrel eating a burger'''
        _snake_case		:	int					     =     torch.manual_seed(0		)
        _snake_case		:	Tuple					     =     sd_pipe(
            [prompt]     ,     image=lowercase__     ,     generator=lowercase__     ,     num_inference_steps=2     ,     output_type='''np'''     ,     ).images

        _snake_case		:	List[Any]					     =     low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)





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







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-upscale/low_res_cat.png'''		)
        _snake_case		:	Optional[Any]					     =     load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
            '''/upsampled_cat.npy'''		)

        _snake_case		:	Union[str, Any]					     =     '''stabilityai/stable-diffusion-x4-upscaler'''
        _snake_case		:	Tuple					     =     StableDiffusionUpscalePipeline.from_pretrained(lowercase__		)
        pipe.to(lowercase__		)
        pipe.set_progress_bar_config(disable=lowercase__		)
        pipe.enable_attention_slicing()

        _snake_case		:	Union[str, Any]					     =     '''a cat sitting on a park bench'''

        _snake_case		:	Any					     =     torch.manual_seed(0		)
        _snake_case		:	Tuple					     =     pipe(
            prompt=lowercase__     ,     image=lowercase__     ,     generator=lowercase__     ,     output_type='''np'''     ,     )
        _snake_case		:	Optional[Any]					     =     output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image		).max() < 1E-3







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	str					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-upscale/low_res_cat.png'''		)
        _snake_case		:	Optional[Any]					     =     load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
            '''/upsampled_cat_fp16.npy'''		)

        _snake_case		:	int					     =     '''stabilityai/stable-diffusion-x4-upscaler'''
        _snake_case		:	Union[str, Any]					     =     StableDiffusionUpscalePipeline.from_pretrained(
            lowercase__     ,     torch_dtype=torch.floataa     ,     )
        pipe.to(lowercase__		)
        pipe.set_progress_bar_config(disable=lowercase__		)
        pipe.enable_attention_slicing()

        _snake_case		:	List[str]					     =     '''a cat sitting on a park bench'''

        _snake_case		:	List[str]					     =     torch.manual_seed(0		)
        _snake_case		:	str					     =     pipe(
            prompt=lowercase__     ,     image=lowercase__     ,     generator=lowercase__     ,     output_type='''np'''     ,     )
        _snake_case		:	Optional[int]					     =     output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image		).max() < 5E-1







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _snake_case		:	List[Any]					     =     load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-upscale/low_res_cat.png'''		)

        _snake_case		:	Optional[int]					     =     '''stabilityai/stable-diffusion-x4-upscaler'''
        _snake_case		:	Union[str, Any]					     =     StableDiffusionUpscalePipeline.from_pretrained(
            lowercase__     ,     torch_dtype=torch.floataa     ,     )
        pipe.to(lowercase__		)
        pipe.set_progress_bar_config(disable=lowercase__		)
        pipe.enable_attention_slicing(1		)
        pipe.enable_sequential_cpu_offload()

        _snake_case		:	str					     =     '''a cat sitting on a park bench'''

        _snake_case		:	Tuple					     =     torch.manual_seed(0		)
        _snake_case		:	List[str]					     =     pipe(
            prompt=lowercase__     ,     image=lowercase__     ,     generator=lowercase__     ,     num_inference_steps=5     ,     output_type='''np'''     ,     )

        _snake_case		:	Any					     =     torch.cuda.max_memory_allocated()
        # make sure that less than 2.9 GB is allocated
        assert mem_bytes < 2.9 * 10**9









'''simple docstring'''

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
   from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


