from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class __snake_case    (						snake_case_		):




   def __init__(   self      :    List[Any]	,     _lowercase      :    str	,     _lowercase      :    Any				):



     """simple docstring"""






     super().__init__()

     # make sure scheduler can always be converted to DDIM
     SCREAMING_SNAKE_CASE__      							=				DDIMScheduler.from_config(scheduler.config				)

     self.register_modules(unet=_lowercase	,     scheduler=_lowercase				)




   @torch.no_grad()
   def __call__(   self      :    int	,     _lowercase      :    Tuple = 1	,     _lowercase      :    Union[str, Any] = None	,     _lowercase      :    Tuple = 0.0	,     _lowercase      :    Tuple = 50	,     _lowercase      :    Tuple = None	,     _lowercase      :    str = "pil"	,     _lowercase      :    List[str] = True	,     ):



     """simple docstring"""






     if isinstance(self.unet.config.sample_size	,     _lowercase				):
       SCREAMING_SNAKE_CASE__      							=				(
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
     else:
       SCREAMING_SNAKE_CASE__      							=				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if isinstance(_lowercase	,     _lowercase				) and len(_lowercase				) != batch_size:
       raise ValueError(
           f"""You have passed a list of generators of length {len(_lowercase				)}, but requested an effective batch"""
           f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

     SCREAMING_SNAKE_CASE__      							=				randn_tensor(_lowercase	,     generator=_lowercase	,     device=self.device	,     dtype=self.unet.dtype				)

     # set step values
     self.scheduler.set_timesteps(_lowercase				)

     for t in self.progress_bar(self.scheduler.timesteps				):
       # 1. predict noise model_output
       SCREAMING_SNAKE_CASE__      							=				self.unet(_lowercase	,     _lowercase				).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       SCREAMING_SNAKE_CASE__      							=				self.scheduler.step(
           _lowercase	,     _lowercase	,     _lowercase	,     eta=_lowercase	,     use_clipped_model_output=_lowercase	,     generator=_lowercase				).prev_sample

     SCREAMING_SNAKE_CASE__      							=				(image / 2 + 0.5).clamp(0	,     1				)
     SCREAMING_SNAKE_CASE__      							=				image.cpu().permute(0	,     2	,     3	,     1				).numpy()
     if output_type == "pil":
       SCREAMING_SNAKE_CASE__      							=				self.numpy_to_pil(_lowercase				)

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=_lowercase				)

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		):




   def      __a						(   self      :    str				):



     """simple docstring"""






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




   @property
   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				1
     SCREAMING_SNAKE_CASE__      							=				3
     SCREAMING_SNAKE_CASE__      							=				(32, 32)

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




   @property
   def      __a						(   self      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				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=1_00	,     )
     return model




   @property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				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      :    Dict				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				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=10_00	,     hidden_act="""gelu"""	,     projection_dim=5_12	,     )
     return CLIPTextModel(_lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






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

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

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

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

     SCREAMING_SNAKE_CASE__      							=				output.images

     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				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]

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

     SCREAMING_SNAKE_CASE__      							=				low_res_image.size[0] * 4
     assert image.shape == (1, expected_height_width, expected_height_width, 3)
     SCREAMING_SNAKE_CASE__      							=				np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]				)

     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      :    Dict				):



     """simple docstring"""






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

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

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

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

     SCREAMING_SNAKE_CASE__      							=				torch.Generator(device=_lowercase				).manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				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"""	,     )
     SCREAMING_SNAKE_CASE__      							=				output.images
     assert image.shape[0] == 2




   @unittest.skipIf(torch_device != """cuda"""	,     """This test requires a GPU"""				)
   def      __a						(   self      :    str				):



     """simple docstring"""






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

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

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

     # make sure here that pndm scheduler skips prk
     SCREAMING_SNAKE_CASE__      							=				StableDiffusionUpscalePipeline(
         unet=_lowercase	,     low_res_scheduler=_lowercase	,     scheduler=_lowercase	,     vae=_lowercase	,     text_encoder=_lowercase	,     tokenizer=_lowercase	,     max_noise_level=3_50	,     )
     SCREAMING_SNAKE_CASE__      							=				sd_pipe.to(_lowercase				)
     sd_pipe.set_progress_bar_config(disable=_lowercase				)

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

     SCREAMING_SNAKE_CASE__      							=				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		):




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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




   def      __a						(   self      :    str				):



     """simple docstring"""






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

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

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

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

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image				).max() < 1E-3




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






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

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

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

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

     assert image.shape == (5_12, 5_12, 3)
     assert np.abs(expected_image - image				).max() < 5E-1




   def      __a						(   self      :    Any				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__      							=				"""stabilityai/stable-diffusion-x4-upscaler"""
     SCREAMING_SNAKE_CASE__      							=				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()

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

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

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