




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      snake_case	(						unittest.TestCase				):


   '''simple docstring'''







   def 						UpperCAmelCase							(       self  :						str      )       ->int:
     '''simple docstring'''







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




   @property
   def 						UpperCAmelCase							(       self  :						int      )       ->int:
     '''simple docstring'''







     UpperCAmelCase__         = 1
     UpperCAmelCase__         = 3
     UpperCAmelCase__         = (32, 32)

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




   @property
   def 						UpperCAmelCase							(       self  :						List[Any]      )       ->str:
     '''simple docstring'''







     torch.manual_seed(0      )
     UpperCAmelCase__         = 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=lowerCamelCase_    ,     only_cross_attention=(True, True, False)    ,     num_class_embeds=100    ,     )
     return model




   @property
   def 						UpperCAmelCase							(       self  :						str      )       ->Union[str, Any]:
     '''simple docstring'''







     torch.manual_seed(0      )
     UpperCAmelCase__         = 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  :						int      )       ->Any:
     '''simple docstring'''







     torch.manual_seed(0      )
     UpperCAmelCase__         = 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(lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						Dict      )       ->Dict:
     '''simple docstring'''







     UpperCAmelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator
     UpperCAmelCase__         = self.dummy_cond_unet_upscale
     UpperCAmelCase__         = DDPMScheduler()
     UpperCAmelCase__         = DDIMScheduler(prediction_type="""v_prediction"""      )
     UpperCAmelCase__         = self.dummy_vae
     UpperCAmelCase__         = self.dummy_text_encoder
     UpperCAmelCase__         = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

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

     # make sure here that pndm scheduler skips prk
     UpperCAmelCase__         = StableDiffusionUpscalePipeline(
         unet=lowerCamelCase_    ,     low_res_scheduler=lowerCamelCase_    ,     scheduler=lowerCamelCase_    ,     vae=lowerCamelCase_    ,     text_encoder=lowerCamelCase_    ,     tokenizer=lowerCamelCase_    ,     max_noise_level=350    ,     )
     UpperCAmelCase__         = sd_pipe.to(lowerCamelCase_      )
     sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

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

     UpperCAmelCase__         = output.images

     UpperCAmelCase__         = torch.Generator(device=lowerCamelCase_      ).manual_seed(0      )
     UpperCAmelCase__         = sd_pipe(
         [prompt]    ,     image=lowerCamelCase_    ,     generator=lowerCamelCase_    ,     guidance_scale=6.0    ,     noise_level=20    ,     num_inference_steps=2    ,     output_type="""np"""    ,     return_dict=lowerCamelCase_    ,     )[0]

     UpperCAmelCase__         = image[0, -3:, -3:, -1]
     UpperCAmelCase__         = image_from_tuple[0, -3:, -3:, -1]

     UpperCAmelCase__         = low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     UpperCAmelCase__         = 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 						UpperCAmelCase							(       self  :						Tuple      )       ->Optional[int]:
     '''simple docstring'''







     UpperCAmelCase__         = """cpu"""  # ensure determinism for the device-dependent torch.Generator
     UpperCAmelCase__         = self.dummy_cond_unet_upscale
     UpperCAmelCase__         = DDPMScheduler()
     UpperCAmelCase__         = DDIMScheduler(prediction_type="""v_prediction"""      )
     UpperCAmelCase__         = self.dummy_vae
     UpperCAmelCase__         = self.dummy_text_encoder
     UpperCAmelCase__         = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

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

     # make sure here that pndm scheduler skips prk
     UpperCAmelCase__         = StableDiffusionUpscalePipeline(
         unet=lowerCamelCase_    ,     low_res_scheduler=lowerCamelCase_    ,     scheduler=lowerCamelCase_    ,     vae=lowerCamelCase_    ,     text_encoder=lowerCamelCase_    ,     tokenizer=lowerCamelCase_    ,     max_noise_level=350    ,     )
     UpperCAmelCase__         = sd_pipe.to(lowerCamelCase_      )
     sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

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

     UpperCAmelCase__         = torch.Generator(device=lowerCamelCase_      ).manual_seed(0      )
     UpperCAmelCase__         = sd_pipe(
         [prompt]    ,     image=lowerCamelCase_    ,     generator=lowerCamelCase_    ,     num_images_per_prompt=2    ,     guidance_scale=6.0    ,     noise_level=20    ,     num_inference_steps=2    ,     output_type="""np"""    ,     )
     UpperCAmelCase__         = output.images
     assert image.shape[0] == 2







   @unittest.skipIf(torch_device != """cuda"""    ,     """This test requires a GPU"""      )
   def 						UpperCAmelCase							(       self  :						Any      )       ->str:
     '''simple docstring'''







     UpperCAmelCase__         = self.dummy_cond_unet_upscale
     UpperCAmelCase__         = DDPMScheduler()
     UpperCAmelCase__         = DDIMScheduler(prediction_type="""v_prediction"""      )
     UpperCAmelCase__         = self.dummy_vae
     UpperCAmelCase__         = self.dummy_text_encoder
     UpperCAmelCase__         = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

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

     # put models in fp16, except vae as it overflows in fp16
     UpperCAmelCase__         = unet.half()
     UpperCAmelCase__         = text_encoder.half()

     # make sure here that pndm scheduler skips prk
     UpperCAmelCase__         = StableDiffusionUpscalePipeline(
         unet=lowerCamelCase_    ,     low_res_scheduler=lowerCamelCase_    ,     scheduler=lowerCamelCase_    ,     vae=lowerCamelCase_    ,     text_encoder=lowerCamelCase_    ,     tokenizer=lowerCamelCase_    ,     max_noise_level=350    ,     )
     UpperCAmelCase__         = sd_pipe.to(lowerCamelCase_      )
     sd_pipe.set_progress_bar_config(disable=lowerCamelCase_      )

     UpperCAmelCase__         = """A painting of a squirrel eating a burger"""
     UpperCAmelCase__         = torch.manual_seed(0      )
     UpperCAmelCase__         = sd_pipe(
         [prompt]    ,     image=lowerCamelCase_    ,     generator=lowerCamelCase_    ,     num_inference_steps=2    ,     output_type="""np"""    ,     ).images

     UpperCAmelCase__         = low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)





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


   '''simple docstring'''







   def 						UpperCAmelCase							(       self  :						str      )       ->List[str]:
     '''simple docstring'''







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




   def 						UpperCAmelCase							(       self  :						List[Any]      )       ->Union[str, Any]:
     '''simple docstring'''







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

     UpperCAmelCase__         = """stabilityai/stable-diffusion-x4-upscaler"""
     UpperCAmelCase__         = StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase_      )
     pipe.to(lowerCamelCase_      )
     pipe.set_progress_bar_config(disable=lowerCamelCase_      )
     pipe.enable_attention_slicing()

     UpperCAmelCase__         = """a cat sitting on a park bench"""

     UpperCAmelCase__         = torch.manual_seed(0      )
     UpperCAmelCase__         = pipe(
         prompt=lowerCamelCase_    ,     image=lowerCamelCase_    ,     generator=lowerCamelCase_    ,     output_type="""np"""    ,     )
     UpperCAmelCase__         = output.images[0]

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




   def 						UpperCAmelCase							(       self  :						List[Any]      )       ->Optional[Any]:
     '''simple docstring'''







     UpperCAmelCase__         = load_image(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/sd2-upscale/low_res_cat.png"""      )
     UpperCAmelCase__         = load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
         """/upsampled_cat_fp16.npy"""      )

     UpperCAmelCase__         = """stabilityai/stable-diffusion-x4-upscaler"""
     UpperCAmelCase__         = StableDiffusionUpscalePipeline.from_pretrained(
         lowerCamelCase_    ,     torch_dtype=torch.floataa    ,     )
     pipe.to(lowerCamelCase_      )
     pipe.set_progress_bar_config(disable=lowerCamelCase_      )
     pipe.enable_attention_slicing()

     UpperCAmelCase__         = """a cat sitting on a park bench"""

     UpperCAmelCase__         = torch.manual_seed(0      )
     UpperCAmelCase__         = pipe(
         prompt=lowerCamelCase_    ,     image=lowerCamelCase_    ,     generator=lowerCamelCase_    ,     output_type="""np"""    ,     )
     UpperCAmelCase__         = output.images[0]

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







   def 						UpperCAmelCase							(       self  :						int      )       ->Dict:
     '''simple docstring'''







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

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

     UpperCAmelCase__         = """stabilityai/stable-diffusion-x4-upscaler"""
     UpperCAmelCase__         = StableDiffusionUpscalePipeline.from_pretrained(
         lowerCamelCase_    ,     torch_dtype=torch.floataa    ,     )
     pipe.to(lowerCamelCase_      )
     pipe.set_progress_bar_config(disable=lowerCamelCase_      )
     pipe.enable_attention_slicing(1      )
     pipe.enable_sequential_cpu_offload()

     UpperCAmelCase__         = """a cat sitting on a park bench"""

     UpperCAmelCase__         = torch.manual_seed(0      )
     UpperCAmelCase__         = pipe(
         prompt=lowerCamelCase_    ,     image=lowerCamelCase_    ,     generator=lowerCamelCase_    ,     num_inference_steps=5    ,     output_type="""np"""    ,     )

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












import gc
import threading
import time

import psutil
import torch
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Union[str, Any]							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    psutil.Process()
         _UpperCAmelCase							=    False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    -1

         while True:
                _UpperCAmelCase							=    max(self.process.memory_info().rss       ,				self.cpu_memory_peak							)

                # can't sleep or will not catch the peak right (this comment is here on purpose)
                if not self.peak_monitoring:
                       break


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    threading.Thread(target=self.peak_monitor							)
         _UpperCAmelCase							=    True
         self.thread.start()





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    False
         self.thread.join()
         return self.cpu_memory_peak



__a:     Union[str, Any]   					=					PeakCPUMemory()




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time()}

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

       # CPU mem
       _UpperCAmelCase							=    psutil.Process().memory_info().rss
       cpu_peak_tracker.start()

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    torch.cuda.memory_allocated(__snake_case )
       torch.cuda.reset_peak_memory_stats()

       return measures




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time() - start_measures["""time"""]}

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

       # CPU mem
       _UpperCAmelCase							=    (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
       _UpperCAmelCase							=    (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
              _UpperCAmelCase							=    (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0

       return measures





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       print(f"""{description}:""" )
       print(f"""- Time: {measures["time"]:.2f}s""" )
       for i in range(torch.cuda.device_count() ):
              print(f"""- GPU {i} allocated: {measures[str(__snake_case )]:.2f}MiB""" )
              _UpperCAmelCase							=    measures[f"""{i}-peak"""]
              print(f"""- GPU {i} peak: {peak:.2f}MiB""" )
       print(f"""- CPU RAM allocated: {measures["cpu"]:.2f}MiB""" )
       print(f"""- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB""" )