



"""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   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					int				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		3
          lowerCamelCase__           =		(32, 32)

          lowerCamelCase__           =		floats_tensor((batch_size, num_channels) + sizes							,					rng=random.Random(0				)				).to(lowerCamelCase_				)
          return image
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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=1_00							,					)
          return model
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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							:					Tuple				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		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(lowerCamelCase_				)
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
          lowerCamelCase__           =		self.dummy_cond_unet_upscale
          lowerCamelCase__           =		DDPMScheduler()
          lowerCamelCase__           =		DDIMScheduler(prediction_type='v_prediction'				)
          lowerCamelCase__           =		self.dummy_vae
          lowerCamelCase__           =		self.dummy_text_encoder
          lowerCamelCase__           =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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

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

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

          lowerCamelCase__           =		output.images

          lowerCamelCase__           =		torch.Generator(device=lowerCamelCase_				).manual_seed(0				)
          lowerCamelCase__           =		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]

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

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

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

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

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

          lowerCamelCase__           =		torch.Generator(device=lowerCamelCase_				).manual_seed(0				)
          lowerCamelCase__           =		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'							,					)
          lowerCamelCase__           =		output.images
          assert image.shape[0] == 2


    @unittest.skipIf(torch_device != 'cuda'							,					'This test requires a GPU'				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.dummy_cond_unet_upscale
          lowerCamelCase__           =		DDPMScheduler()
          lowerCamelCase__           =		DDIMScheduler(prediction_type='v_prediction'				)
          lowerCamelCase__           =		self.dummy_vae
          lowerCamelCase__           =		self.dummy_text_encoder
          lowerCamelCase__           =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)

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

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

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

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

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





@slow
@require_torch_gpu
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png'				)
          lowerCamelCase__           =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat.npy'				)

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

          lowerCamelCase__           =		'''a cat sitting on a park bench'''

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

          assert image.shape == (5_12, 5_12, 3)
          assert np.abs(expected_image - image				).max() < 1e-3
    def      _UpperCamelCase       ( self							:					Any				):
          lowerCamelCase__           =		load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-upscale/low_res_cat.png'				)
          lowerCamelCase__           =		load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
              '/upsampled_cat_fp16.npy'				)

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

          lowerCamelCase__           =		'''a cat sitting on a park bench'''

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

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


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

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

          lowerCamelCase__           =		'''stabilityai/stable-diffusion-x4-upscaler'''
          lowerCamelCase__           =		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()

          lowerCamelCase__           =		'''a cat sitting on a park bench'''

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

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







"""simple docstring"""


import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    "The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
    " StableDiffusionImg2ImgPipeline` instead."
)



