

'''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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			StableDiffusionInpaintPipeline
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  SCREAMING_SNAKE_CASE    :  Tuple							=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  SCREAMING_SNAKE_CASE    :  str							=			frozenset(
      []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
  SCREAMING_SNAKE_CASE    :  List[str]							=			frozenset([]      )






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   torch.manual_seed(0			)
   __lowercase							  = UNetaDConditionModel(
       block_out_channels=(3_2, 6_4)					,layers_per_block=2					,sample_size=3_2					,in_channels=9					,out_channels=4					,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,cross_attention_dim=3_2					,attention_head_dim=(2, 4)					,use_linear_projection=lowercase__					,)
   __lowercase							  = PNDMScheduler(skip_prk_steps=lowercase__			)
   torch.manual_seed(0			)
   __lowercase							  = AutoencoderKL(
       block_out_channels=[3_2, 6_4]					,in_channels=3					,out_channels=3					,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,latent_channels=4					,sample_size=1_2_8					,)
   torch.manual_seed(0			)
   __lowercase							  = 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					,)
   __lowercase							  = CLIPTextModel(lowercase__			)
   __lowercase							  = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

   __lowercase							  = {
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''vae''': vae,
       '''text_encoder''': text_encoder,
       '''tokenizer''': tokenizer,
       '''safety_checker''': None,
       '''feature_extractor''': None,
   }
   return components






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     List[str]					,lowercase__   :     List[str]=0			):
   # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
   __lowercase							  = floats_tensor((1, 3, 3_2, 3_2)					,rng=random.Random(lowercase__			)			).to(lowercase__			)
   __lowercase							  = image.cpu().permute(0					,2					,3					,1			)[0]
   __lowercase							  = Image.fromarray(np.uinta(lowercase__			)			).convert('''RGB'''			).resize((6_4, 6_4)			)
   __lowercase							  = Image.fromarray(np.uinta(image + 4			)			).convert('''RGB'''			).resize((6_4, 6_4)			)
   if str(lowercase__			).startswith('''mps'''			):
    __lowercase							  = torch.manual_seed(lowercase__			)
   else:
    __lowercase							  = torch.Generator(device=lowercase__			).manual_seed(lowercase__			)
   __lowercase							  = {
       '''prompt''': '''A painting of a squirrel eating a burger''',
       '''image''': init_image,
       '''mask_image''': mask_image,
       '''generator''': generator,
       '''num_inference_steps''': 2,
       '''guidance_scale''': 6.0,
       '''output_type''': '''numpy''',
   }
   return inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   __lowercase							  = self.get_dummy_components()
   __lowercase							  = StableDiffusionInpaintPipeline(**lowercase__			)
   __lowercase							  = sd_pipe.to(lowercase__			)
   sd_pipe.set_progress_bar_config(disable=lowercase__			)

   __lowercase							  = self.get_dummy_inputs(lowercase__			)
   __lowercase							  = sd_pipe(**lowercase__			).images
   __lowercase							  = image[0, -3:, -3:, -1]

   assert image.shape == (1, 6_4, 6_4, 3)
   __lowercase							  = np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]			)

   assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   super().test_inference_batch_single_identical(expected_max_diff=3e-3			)






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




  """simple docstring"""




  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/sd2-inpaint/init_image.png'''			)
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)
   __lowercase							  = load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
       '''/yellow_cat_sitting_on_a_park_bench.npy'''			)

   __lowercase							  = '''stabilityai/stable-diffusion-2-inpainting'''
   __lowercase							  = StableDiffusionInpaintPipeline.from_pretrained(lowercase__					,safety_checker=lowercase__			)
   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)
   pipe.enable_attention_slicing()

   __lowercase							  = '''Face of a yellow cat, high resolution, sitting on a park bench'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,image=lowercase__					,mask_image=lowercase__					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images[0]

   assert image.shape == (5_1_2, 5_1_2, 3)
   assert np.abs(expected_image - image			).max() < 9e-3






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/sd2-inpaint/init_image.png'''			)
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)
   __lowercase							  = load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
       '''/yellow_cat_sitting_on_a_park_bench_fp16.npy'''			)

   __lowercase							  = '''stabilityai/stable-diffusion-2-inpainting'''
   __lowercase							  = StableDiffusionInpaintPipeline.from_pretrained(
       lowercase__					,torch_dtype=torch.floataa					,safety_checker=lowercase__					,)
   pipe.to(lowercase__			)
   pipe.set_progress_bar_config(disable=lowercase__			)
   pipe.enable_attention_slicing()

   __lowercase							  = '''Face of a yellow cat, high resolution, sitting on a park bench'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,image=lowercase__					,mask_image=lowercase__					,generator=lowercase__					,output_type='''np'''					,)
   __lowercase							  = output.images[0]

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()

   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/sd2-inpaint/init_image.png'''			)
   __lowercase							  = load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''			)

   __lowercase							  = '''stabilityai/stable-diffusion-2-inpainting'''
   __lowercase							  = PNDMScheduler.from_pretrained(lowercase__					,subfolder='''scheduler'''			)
   __lowercase							  = StableDiffusionInpaintPipeline.from_pretrained(
       lowercase__					,safety_checker=lowercase__					,scheduler=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()

   __lowercase							  = '''Face of a yellow cat, high resolution, sitting on a park bench'''

   __lowercase							  = torch.manual_seed(0			)
   __lowercase							  = pipe(
       prompt=lowercase__					,image=lowercase__					,mask_image=lowercase__					,generator=lowercase__					,num_inference_steps=2					,output_type='''np'''					,)

   __lowercase							  = torch.cuda.max_memory_allocated()
   # make sure that less than 2.65 GB is allocated
   assert mem_bytes < 2.6_5 * 1_0**9




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 parser.add_argument(
     '''--txt2img_unclip''',
     default='''kakaobrain/karlo-v1-alpha''',
     type=str,
     required=False,
     help='''The pretrained txt2img unclip.''',
 )

 lowerCAmelCase__											=	parser.parse_args()

 lowerCAmelCase__											=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 lowerCAmelCase__											=	CLIPImageProcessor()
 lowerCAmelCase__											=	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

 lowerCAmelCase__											=	UnCLIPImageVariationPipeline(
     decoder=txtaimg.decoder,
     text_encoder=txtaimg.text_encoder,
     tokenizer=txtaimg.tokenizer,
     text_proj=txtaimg.text_proj,
     feature_extractor=feature_extractor,
     image_encoder=image_encoder,
     super_res_first=txtaimg.super_res_first,
     super_res_last=txtaimg.super_res_last,
     decoder_scheduler=txtaimg.decoder_scheduler,
     super_res_scheduler=txtaimg.super_res_scheduler,
 )

 imgaimg.save_pretrained(args.dump_path)

