


'''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 						__UpperCamelCase	(						unittest.TestCase							):





							def 		a__   (  self		:int			):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





							@property
							def 		a__   (  self		:Any			):
									snake_case_		:							Any			       =						1
									snake_case_		:							Any			       =						3
									snake_case_		:							str			       =						(3_2, 3_2)

									snake_case_		:							List[Any]			       =						floats_tensor((batch_size, num_channels) + sizes  ,rng=random.Random(0			)			).to(lowerCamelCase_			)
									return image





							@property
							def 		a__   (  self		:List[str]			):
									torch.manual_seed(0			)
									snake_case_		:							Any			       =						UNetaDConditionModel(
									    block_out_channels=(3_2, 3_2, 6_4)  ,layers_per_block=2  ,sample_size=3_2  ,in_channels=7  ,out_channels=4  ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")  ,up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")  ,cross_attention_dim=3_2  ,attention_head_dim=8  ,use_linear_projection=lowerCamelCase_  ,only_cross_attention=(True, True, False)  ,num_class_embeds=1_0_0  ,)
									return model





							@property
							def 		a__   (  self		:List[str]			):
									torch.manual_seed(0			)
									snake_case_		:							Dict			       =						AutoencoderKL(
									    block_out_channels=[3_2, 3_2, 6_4]  ,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		:Tuple			):
									torch.manual_seed(0			)
									snake_case_		:							Optional[Any]			       =						CLIPTextConfig(
									    bos_token_id=0  ,eos_token_id=2  ,hidden_size=3_2  ,intermediate_size=3_7  ,layer_norm_eps=1E-0_5  ,num_attention_heads=4  ,num_hidden_layers=5  ,pad_token_id=1  ,vocab_size=1_0_0_0  ,hidden_act="""gelu"""  ,projection_dim=5_1_2  ,)
									return CLIPTextModel(lowerCamelCase_			)





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

									snake_case_		:							List[Any]			       =						self.dummy_image.cpu().permute(0  ,2  ,3  ,1			)[0]
									snake_case_		:							Union[str, Any]			       =						Image.fromarray(np.uinta(lowerCamelCase_			)			).convert("""RGB"""			).resize((6_4, 6_4)			)

									# make sure here that pndm scheduler skips prk
									snake_case_		:							int			       =						StableDiffusionUpscalePipeline(
									    unet=lowerCamelCase_  ,low_res_scheduler=lowerCamelCase_  ,scheduler=lowerCamelCase_  ,vae=lowerCamelCase_  ,text_encoder=lowerCamelCase_  ,tokenizer=lowerCamelCase_  ,max_noise_level=3_5_0  ,)
									snake_case_		:							str			       =						sd_pipe.to(lowerCamelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase_			)

									snake_case_		:							Union[str, Any]			       =						'''A painting of a squirrel eating a burger'''
									snake_case_		:							Union[str, Any]			       =						torch.Generator(device=lowerCamelCase_			).manual_seed(0			)
									snake_case_		:							List[Any]			       =						sd_pipe(
									    [prompt]  ,image=lowerCamelCase_  ,generator=lowerCamelCase_  ,guidance_scale=6.0  ,noise_level=2_0  ,num_inference_steps=2  ,output_type="""np"""  ,)

									snake_case_		:							Union[str, Any]			       =						output.images

									snake_case_		:							List[Any]			       =						torch.Generator(device=lowerCamelCase_			).manual_seed(0			)
									snake_case_		:							Tuple			       =						sd_pipe(
									    [prompt]  ,image=lowerCamelCase_  ,generator=lowerCamelCase_  ,guidance_scale=6.0  ,noise_level=2_0  ,num_inference_steps=2  ,output_type="""np"""  ,return_dict=lowerCamelCase_  ,)[0]

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

									snake_case_		:							List[str]			       =						low_res_image.size[0] * 4
									assert image.shape == (1, expected_height_width, expected_height_width, 3)
									snake_case_		:							List[Any]			       =						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		:List[str]			):
									snake_case_		:							Dict			       =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
									snake_case_		:							Union[str, Any]			       =						self.dummy_cond_unet_upscale
									snake_case_		:							Any			       =						DDPMScheduler()
									snake_case_		:							Optional[int]			       =						DDIMScheduler(prediction_type="""v_prediction"""			)
									snake_case_		:							List[str]			       =						self.dummy_vae
									snake_case_		:							int			       =						self.dummy_text_encoder
									snake_case_		:							Optional[Any]			       =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

									snake_case_		:							Optional[Any]			       =						self.dummy_image.cpu().permute(0  ,2  ,3  ,1			)[0]
									snake_case_		:							List[Any]			       =						Image.fromarray(np.uinta(lowerCamelCase_			)			).convert("""RGB"""			).resize((6_4, 6_4)			)

									# make sure here that pndm scheduler skips prk
									snake_case_		:							int			       =						StableDiffusionUpscalePipeline(
									    unet=lowerCamelCase_  ,low_res_scheduler=lowerCamelCase_  ,scheduler=lowerCamelCase_  ,vae=lowerCamelCase_  ,text_encoder=lowerCamelCase_  ,tokenizer=lowerCamelCase_  ,max_noise_level=3_5_0  ,)
									snake_case_		:							Any			       =						sd_pipe.to(lowerCamelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase_			)

									snake_case_		:							int			       =						'''A painting of a squirrel eating a burger'''
									snake_case_		:							List[str]			       =						sd_pipe(
									    2 * [prompt]  ,image=2 * [low_res_image]  ,guidance_scale=6.0  ,noise_level=2_0  ,num_inference_steps=2  ,output_type="""np"""  ,)
									snake_case_		:							Union[str, Any]			       =						output.images
									assert image.shape[0] == 2

									snake_case_		:							Optional[Any]			       =						torch.Generator(device=lowerCamelCase_			).manual_seed(0			)
									snake_case_		:							Optional[Any]			       =						sd_pipe(
									    [prompt]  ,image=lowerCamelCase_  ,generator=lowerCamelCase_  ,num_images_per_prompt=2  ,guidance_scale=6.0  ,noise_level=2_0  ,num_inference_steps=2  ,output_type="""np"""  ,)
									snake_case_		:							Optional[Any]			       =						output.images
									assert image.shape[0] == 2





							@unittest.skipIf(torch_device != """cuda"""  ,"""This test requires a GPU"""			)
							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							List[str]			       =						self.dummy_cond_unet_upscale
									snake_case_		:							Dict			       =						DDPMScheduler()
									snake_case_		:							str			       =						DDIMScheduler(prediction_type="""v_prediction"""			)
									snake_case_		:							int			       =						self.dummy_vae
									snake_case_		:							Optional[Any]			       =						self.dummy_text_encoder
									snake_case_		:							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_		:							Union[str, Any]			       =						Image.fromarray(np.uinta(lowerCamelCase_			)			).convert("""RGB"""			).resize((6_4, 6_4)			)

									# put models in fp16, except vae as it overflows in fp16
									snake_case_		:							List[str]			       =						unet.half()
									snake_case_		:							List[Any]			       =						text_encoder.half()

									# make sure here that pndm scheduler skips prk
									snake_case_		:							int			       =						StableDiffusionUpscalePipeline(
									    unet=lowerCamelCase_  ,low_res_scheduler=lowerCamelCase_  ,scheduler=lowerCamelCase_  ,vae=lowerCamelCase_  ,text_encoder=lowerCamelCase_  ,tokenizer=lowerCamelCase_  ,max_noise_level=3_5_0  ,)
									snake_case_		:							Any			       =						sd_pipe.to(lowerCamelCase_			)
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase_			)

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

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





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





							def 		a__   (  self		:Optional[int]			):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





							def 		a__   (  self		:List[Any]			):
									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_		:							Union[str, Any]			       =						load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
									    """/upsampled_cat.npy"""			)

									snake_case_		:							Dict			       =						'''stabilityai/stable-diffusion-x4-upscaler'''
									snake_case_		:							Tuple			       =						StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase_			)
									pipe.to(lowerCamelCase_			)
									pipe.set_progress_bar_config(disable=lowerCamelCase_			)
									pipe.enable_attention_slicing()

									snake_case_		:							Dict			       =						'''a cat sitting on a park bench'''

									snake_case_		:							str			       =						torch.manual_seed(0			)
									snake_case_		:							Any			       =						pipe(
									    prompt=lowerCamelCase_  ,image=lowerCamelCase_  ,generator=lowerCamelCase_  ,output_type="""np"""  ,)
									snake_case_		:							Dict			       =						output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image			).max() < 1E-3





							def 		a__   (  self		:Any			):
									snake_case_		:							Dict			       =						load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/sd2-upscale/low_res_cat.png"""			)
									snake_case_		:							int			       =						load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
									    """/upsampled_cat_fp16.npy"""			)

									snake_case_		:							List[Any]			       =						'''stabilityai/stable-diffusion-x4-upscaler'''
									snake_case_		:							Any			       =						StableDiffusionUpscalePipeline.from_pretrained(
									    lowerCamelCase_  ,torch_dtype=torch.floataa  ,)
									pipe.to(lowerCamelCase_			)
									pipe.set_progress_bar_config(disable=lowerCamelCase_			)
									pipe.enable_attention_slicing()

									snake_case_		:							Tuple			       =						'''a cat sitting on a park bench'''

									snake_case_		:							Optional[Any]			       =						torch.manual_seed(0			)
									snake_case_		:							Union[str, Any]			       =						pipe(
									    prompt=lowerCamelCase_  ,image=lowerCamelCase_  ,generator=lowerCamelCase_  ,output_type="""np"""  ,)
									snake_case_		:							Tuple			       =						output.images[0]

									assert image.shape == (5_1_2, 5_1_2, 3)
									assert np.abs(expected_image - image			).max() < 5E-1





							def 		a__   (  self		:Tuple			):
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									snake_case_		:							List[str]			       =						'''stabilityai/stable-diffusion-x4-upscaler'''
									snake_case_		:							List[Any]			       =						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()

									snake_case_		:							Optional[int]			       =						'''a cat sitting on a park bench'''

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

									snake_case_		:							Dict			       =						torch.cuda.max_memory_allocated()
									# make sure that less than 2.9 GB is allocated
									assert mem_bytes < 2.9 * 1_0**9



'''simple docstring'''






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						__UpperCamelCase	(						lowercase__							):





							@staticmethod
							@abstractmethod
							def 		a__   (  _UpperCamelCase		:ArgumentParser			):
									raise NotImplementedError()





							@abstractmethod
							def 		a__   (  self		:Optional[Any]			):
									raise NotImplementedError()