


import math
from typing import Callable, List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=[] )->  str:




      """simple docstring"""

      snake_case_	=				size[0] - overlap_pixels * 2
      snake_case_	=				size[1] - overlap_pixels * 2
      for letter in ["l", "r"]:
            if letter in remove_borders:
                  size_x += overlap_pixels
      for letter in ["t", "b"]:
            if letter in remove_borders:
                  size_y += overlap_pixels
      snake_case_	=				np.ones((size_y, size_x) , dtype=np.uinta ) * 255
      snake_case_	=				np.pad(SCREAMING_SNAKE_CASE , mode='''linear_ramp''' , pad_width=SCREAMING_SNAKE_CASE , end_values=0 )

      if "l" in remove_borders:
            snake_case_	=				mask[:, overlap_pixels : mask.shape[1]]
      if "r" in remove_borders:
            snake_case_	=				mask[:, 0 : mask.shape[1] - overlap_pixels]
      if "t" in remove_borders:
            snake_case_	=				mask[overlap_pixels : mask.shape[0], :]
      if "b" in remove_borders:
            snake_case_	=				mask[0 : mask.shape[0] - overlap_pixels, :]
      return mask






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[str]:




      """simple docstring"""

      return max(SCREAMING_SNAKE_CASE , min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      return (
          clamp(rect[0] , min[0] , max[0] ),
          clamp(rect[1] , min[1] , max[1] ),
          clamp(rect[2] , min[0] , max[0] ),
          clamp(rect[3] , min[1] , max[1] ),
      )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				list(SCREAMING_SNAKE_CASE )
      rect[0] -= overlap
      rect[1] -= overlap
      rect[2] += overlap
      rect[3] += overlap
      snake_case_	=				clamp_rect(SCREAMING_SNAKE_CASE , [0, 0] , [image_size[0], image_size[1]] )
      return rect






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				Image.new('''RGB''' , (tile.size[0] + original_slice, tile.size[1]) )
      result.paste(
          original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
              (slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
      result.paste(SCREAMING_SNAKE_CASE , (original_slice, 0) )
      return result






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Optional[int]:




      """simple docstring"""

      snake_case_	=				(original_image_slice * 4, 0, tile.size[0], tile.size[1])
      snake_case_	=				tile.crop(SCREAMING_SNAKE_CASE )
      return tile






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      snake_case_	=				n % d
      return n - divisor







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = 3_50		,				):
       super().__init__(
           vae=_UpperCAmelCase		,				text_encoder=_UpperCAmelCase		,				tokenizer=_UpperCAmelCase		,				unet=_UpperCAmelCase		,				low_res_scheduler=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				max_noise_level=_UpperCAmelCase		,				)



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				**_UpperCAmelCase ):
       torch.manual_seed(0 )
       snake_case_	=				(
           min(image.size[0] - (tile_size + original_image_slice)		,				x * tile_size ),
           min(image.size[1] - (tile_size + original_image_slice)		,				y * tile_size ),
           min(image.size[0]		,				(x + 1) * tile_size ),
           min(image.size[1]		,				(y + 1) * tile_size ),
       )
       snake_case_	=				add_overlap_rect(_UpperCAmelCase		,				_UpperCAmelCase		,				image.size )
       snake_case_	=				image.crop(_UpperCAmelCase )
       snake_case_	=				((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
       snake_case_	=				translated_slice_x - (original_image_slice / 2)
       snake_case_	=				max(0		,				_UpperCAmelCase )
       snake_case_	=				squeeze_tile(_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				to_input.size
       snake_case_	=				to_input.resize((tile_size, tile_size)		,				Image.BICUBIC )
       snake_case_	=				super(_UpperCAmelCase		,				self ).__call__(image=_UpperCAmelCase		,				**_UpperCAmelCase ).images[0]
       snake_case_	=				upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)		,				Image.BICUBIC )
       snake_case_	=				unsqueeze_tile(_UpperCAmelCase		,				_UpperCAmelCase )
       snake_case_	=				upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)		,				Image.BICUBIC )
       snake_case_	=				[]
       if x == 0:
             remove_borders.append('''l''' )
       elif crop_rect[2] == image.size[0]:
             remove_borders.append('''r''' )
       if y == 0:
             remove_borders.append('''t''' )
       elif crop_rect[3] == image.size[1]:
             remove_borders.append('''b''' )
       snake_case_	=				Image.fromarray(
           make_transparency_mask(
               (upscaled_tile.size[0], upscaled_tile.size[1])		,				tile_border * 4		,				remove_borders=_UpperCAmelCase )		,				mode='''L'''		,				)
       final_image.paste(
           _UpperCAmelCase		,				(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)		,				_UpperCAmelCase )





 @torch.no_grad()
 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase = 75		,				_UpperCAmelCase = 9.0		,				_UpperCAmelCase = 50		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 1		,				_UpperCAmelCase = 0.0		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = 1		,				_UpperCAmelCase = 1_28		,				_UpperCAmelCase = 32		,				_UpperCAmelCase = 32		,				):
       snake_case_	=				Image.new('''RGB'''		,				(image.size[0] * 4, image.size[1] * 4) )
       snake_case_	=				math.ceil(image.size[0] / tile_size )
       snake_case_	=				math.ceil(image.size[1] / tile_size )
       snake_case_	=				tcx * tcy
       snake_case_	=				0
       for y in range(_UpperCAmelCase ):
             for x in range(_UpperCAmelCase ):
                   self._process_tile(
                       _UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				prompt=_UpperCAmelCase		,				num_inference_steps=_UpperCAmelCase		,				guidance_scale=_UpperCAmelCase		,				noise_level=_UpperCAmelCase		,				negative_prompt=_UpperCAmelCase		,				num_images_per_prompt=_UpperCAmelCase		,				eta=_UpperCAmelCase		,				generator=_UpperCAmelCase		,				latents=_UpperCAmelCase		,				)
                   current_count += 1
                   if callback is not None:
                         callback({'''progress''': current_count / total_tile_count, '''image''': final_image} )
       return final_image






def 	__lowerCAmelCase   ()->  int:




      """simple docstring"""

      snake_case_	=				'''stabilityai/stable-diffusion-x4-upscaler'''
      snake_case_	=				StableDiffusionTiledUpscalePipeline.from_pretrained(SCREAMING_SNAKE_CASE , revision='''fp16''' , torch_dtype=torch.floataa )
      snake_case_	=				pipe.to('''cuda''' )
      snake_case_	=				Image.open('''../../docs/source/imgs/diffusers_library.jpg''' )

      def callback(SCREAMING_SNAKE_CASE ):
            print(f'''progress: {obj["progress"]:.4f}''' )
            obj["image"].save('''diffusers_library_progress.jpg''' )

      snake_case_	=				pipe(image=SCREAMING_SNAKE_CASE , prompt='''Black font, white background, vector''' , noise_level=40 , callback=SCREAMING_SNAKE_CASE )
      final_image.save('''diffusers_library.jpg''' )


if __name__ == "__main__":
   main()



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 lowerCAmelCase_						(			lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

 __snake_case												=  StableDiffusionInpaintPipeline
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __snake_case												=  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __snake_case												=  frozenset(
     []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __snake_case												=  frozenset([]	)



 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=9		,				out_channels=4		,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,				cross_attention_dim=32		,				attention_head_dim=(2, 4)		,				use_linear_projection=_UpperCAmelCase		,				)
       snake_case_	=				PNDMScheduler(skip_prk_steps=_UpperCAmelCase )
       torch.manual_seed(0 )
       snake_case_	=				AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=3		,				out_channels=3		,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']		,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']		,				latent_channels=4		,				sample_size=1_28		,				)
       torch.manual_seed(0 )
       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		,				)
       snake_case_	=				CLIPTextModel(_UpperCAmelCase )
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

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



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0 ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       snake_case_	=				floats_tensor((1, 3, 32, 32)		,				rng=random.Random(_UpperCAmelCase ) ).to(_UpperCAmelCase )
       snake_case_	=				image.cpu().permute(0		,				2		,				3		,				1 )[0]
       snake_case_	=				Image.fromarray(np.uinta(_UpperCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
       snake_case_	=				Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
       if str(_UpperCAmelCase ).startswith('''mps''' ):
             snake_case_	=				torch.manual_seed(_UpperCAmelCase )
       else:
             snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(_UpperCAmelCase )
       snake_case_	=				{
           '''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 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case_	=				self.get_dummy_components()
       snake_case_	=				StableDiffusionInpaintPipeline(**_UpperCAmelCase )
       snake_case_	=				sd_pipe.to(_UpperCAmelCase )
       sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase )

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

       assert image.shape == (1, 64, 64, 3)
       snake_case_	=				np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

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





 def 						UpperCamelCase__					(     self ):
       super().test_inference_batch_single_identical(expected_max_diff=3E-3 )







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

 '''simple docstring'''

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



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

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

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

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

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-inpaint/init_image.png''' )
       snake_case_	=				load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
           '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

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

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

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

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





 def 						UpperCamelCase__					(     self ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       snake_case_	=				'''stabilityai/stable-diffusion-2-inpainting'''
       snake_case_	=				PNDMScheduler.from_pretrained(_UpperCAmelCase		,				subfolder='''scheduler''' )
       snake_case_	=				StableDiffusionInpaintPipeline.from_pretrained(
           _UpperCAmelCase		,				safety_checker=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				torch_dtype=torch.floataa		,				)
       pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )
       pipe.enable_attention_slicing(1 )
       pipe.enable_sequential_cpu_offload()

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

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

       snake_case_	=				torch.cuda.max_memory_allocated()
       # make sure that less than 2.65 GB is allocated
       assert mem_bytes < 2.65 * 10**9