



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 		_snake_case   (      lowerCamelCase       ,lowerCamelCase       ,lowerCamelCase       ,unittest.TestCase					):





 """simple docstring"""



 lowerCamelCase_        =      StableDiffusionInpaintPipeline
 lowerCamelCase_        =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCamelCase_        =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCamelCase_        =      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCamelCase_        =      frozenset([]					)
 def lowercase_		(    self       )						->				Any:




   """simple docstring"""







   torch.manual_seed(0       )
   _A							     =     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=A_						,     )
   _A							     =     PNDMScheduler(skip_prk_steps=A_       )
   torch.manual_seed(0       )
   _A							     =     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       )
   _A							     =     CLIPTextConfig(
       bos_token_id=0						,     eos_token_id=2						,     hidden_size=3_2						,     intermediate_size=3_7						,     layer_norm_eps=1e-05						,     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						,     )
   _A							     =     CLIPTextModel(A_       )
   _A							     =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

   _A							     =     {
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''vae''': vae,
       '''text_encoder''': text_encoder,
       '''tokenizer''': tokenizer,
       '''safety_checker''': None,
       '''feature_extractor''': None,
   }
   return components
 def lowercase_		(    self						,     a						,     a=0       )						->				List[Any]:




   """simple docstring"""







   _A							     =     floats_tensor((1, 3, 3_2, 3_2)						,     rng=random.Random(A_       )       ).to(A_       )
   _A							     =     image.cpu().permute(0						,     2						,     3						,     1       )[0]
   _A							     =     Image.fromarray(np.uinta(A_       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )
   _A							     =     Image.fromarray(np.uinta(image + 4       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )
   if str(A_       ).startswith('''mps'''       ):
     _A							     =     torch.manual_seed(A_       )
   else:
     _A							     =     torch.Generator(device=A_       ).manual_seed(A_       )
   _A							     =     {
       '''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 lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   _A							     =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
   _A							     =     self.get_dummy_components()
   _A							     =     StableDiffusionInpaintPipeline(**A_       )
   _A							     =     sd_pipe.to(A_       )
   sd_pipe.set_progress_bar_config(disable=A_       )

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

   assert image.shape == (1, 6_4, 6_4, 3)
   _A							     =     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 lowercase_		(    self       )						->				Tuple:




   """simple docstring"""







   super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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





 """simple docstring"""



 def lowercase_		(    self       )						->				Optional[int]:




   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def lowercase_		(    self       )						->				List[str]:




   """simple docstring"""







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

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

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

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

   assert image.shape == (5_1_2, 5_1_2, 3)
   assert np.abs(expected_image - image       ).max() < 9e-3
 def lowercase_		(    self       )						->				Optional[int]:




   """simple docstring"""







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

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

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

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

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



 def lowercase_		(    self       )						->				int:




   """simple docstring"""







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

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

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

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

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

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






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



