


'''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    ):


  '''simple docstring'''







  def        _a							(							self :       int ):

        '''simple docstring'''




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






  @property
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  Any          =				1
        A_						:  int          =				3
        A_						:  List[Any]          =				(32, 32)

        A_						:  Optional[Any]          =				floats_tensor((batch_size, num_channels) + sizes	,rng=random.Random(0 ) ).to(_a )
        return image






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  int          =				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=_a	,only_cross_attention=(True, True, False)	,num_class_embeds=100	,)
        return model






  @property
  def        _a							(							self :       Dict ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  str          =				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        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        torch.manual_seed(0 )
        A_						:  Union[str, Any]          =				CLIPTextConfig(
            bos_token_id=0	,eos_token_id=2	,hidden_size=32	,intermediate_size=37	,layer_norm_eps=1e-05	,num_attention_heads=4	,num_hidden_layers=5	,pad_token_id=1	,vocab_size=1000	,hidden_act="""gelu"""	,projection_dim=512	,)
        return CLIPTextModel(_a )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




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

        A_						:  List[str]          =				self.dummy_image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  Dict          =				Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((64, 64) )

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

        A_						:  Tuple          =				"""A painting of a squirrel eating a burger"""
        A_						:  Any          =				torch.Generator(device=_a ).manual_seed(0 )
        A_						:  Optional[Any]          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,)

        A_						:  List[str]          =				output.images

        A_						:  str          =				torch.Generator(device=_a ).manual_seed(0 )
        A_						:  Union[str, Any]          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,return_dict=_a	,)[0]

        A_						:  Union[str, Any]          =				image[0, -3:, -3:, -1]
        A_						:  Dict          =				image_from_tuple[0, -3:, -3:, -1]

        A_						:  Union[str, Any]          =				low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        A_						:  List[str]          =				np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )

        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        _a							(							self :       Tuple ):

        '''simple docstring'''




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

        A_						:  List[str]          =				self.dummy_image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  Optional[Any]          =				Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((64, 64) )

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

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

        A_						:  Tuple          =				torch.Generator(device=_a ).manual_seed(0 )
        A_						:  List[str]          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,num_images_per_prompt=2	,guidance_scale=6.0	,noise_level=20	,num_inference_steps=2	,output_type="""np"""	,)
        A_						:  Any          =				output.images
        assert image.shape[0] == 2






  @unittest.skipIf(torch_device != """cuda"""	,"""This test requires a GPU""" )
  def        _a							(							self :       Union[str, Any] ):

        '''simple docstring'''




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

        A_						:  Tuple          =				self.dummy_image.cpu().permute(0	,2	,3	,1 )[0]
        A_						:  Tuple          =				Image.fromarray(np.uinta(_a ) ).convert("""RGB""" ).resize((64, 64) )

        # put models in fp16, except vae as it overflows in fp16
        A_						:  Optional[Any]          =				unet.half()
        A_						:  Tuple          =				text_encoder.half()

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

        A_						:  Optional[int]          =				"""A painting of a squirrel eating a burger"""
        A_						:  List[Any]          =				torch.manual_seed(0 )
        A_						:  str          =				sd_pipe(
            [prompt]	,image=_a	,generator=_a	,num_inference_steps=2	,output_type="""np"""	,).images

        A_						:  str          =				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    ):


  '''simple docstring'''







  def        _a							(							self :       str ):

        '''simple docstring'''




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






  def        _a							(							self :       Dict ):

        '''simple docstring'''




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

        A_						:  List[str]          =				"""stabilityai/stable-diffusion-x4-upscaler"""
        A_						:  List[str]          =				StableDiffusionUpscalePipeline.from_pretrained(_a )
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )
        pipe.enable_attention_slicing()

        A_						:  Tuple          =				"""a cat sitting on a park bench"""

        A_						:  int          =				torch.manual_seed(0 )
        A_						:  int          =				pipe(
            prompt=_a	,image=_a	,generator=_a	,output_type="""np"""	,)
        A_						:  Any          =				output.images[0]

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






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




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

        A_						:  Tuple          =				"""stabilityai/stable-diffusion-x4-upscaler"""
        A_						:  int          =				StableDiffusionUpscalePipeline.from_pretrained(
            _a	,torch_dtype=torch.floataa	,)
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )
        pipe.enable_attention_slicing()

        A_						:  Any          =				"""a cat sitting on a park bench"""

        A_						:  Union[str, Any]          =				torch.manual_seed(0 )
        A_						:  List[str]          =				pipe(
            prompt=_a	,image=_a	,generator=_a	,output_type="""np"""	,)
        A_						:  Optional[int]          =				output.images[0]

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






  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




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

        A_						:  Dict          =				load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/sd2-upscale/low_res_cat.png""" )

        A_						:  Any          =				"""stabilityai/stable-diffusion-x4-upscaler"""
        A_						:  Tuple          =				StableDiffusionUpscalePipeline.from_pretrained(
            _a	,torch_dtype=torch.floataa	,)
        pipe.to(_a )
        pipe.set_progress_bar_config(disable=_a )
        pipe.enable_attention_slicing(1 )
        pipe.enable_sequential_cpu_offload()

        A_						:  Tuple          =				"""a cat sitting on a park bench"""

        A_						:  Any          =				torch.manual_seed(0 )
        A_						:  Optional[Any]          =				pipe(
            prompt=_a	,image=_a	,generator=_a	,num_inference_steps=5	,output_type="""np"""	,)

        A_						:  List[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 DummyObject, requires_backends



class 	__lowerCAmelCase		(							metaclass=__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""torch""", """torchsde"""]






  def __init__(							self :       Any	,*_a :       Union[str, Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        requires_backends(self	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       Optional[int]	,*_a :       List[Any]	,**_a :       Any ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )






  @classmethod
  def        _a							(							cls :       List[Any]	,*_a :       Tuple	,**_a :       Union[str, Any] ):

        '''simple docstring'''




        requires_backends(cls	,["""torch""", """torchsde"""] )
