






'''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     A    (      SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Optional[int]      =						StableDiffusionInpaintPipeline
 __a			:						Optional[int]      =						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 __a			:						Optional[int]      =						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __a			:						Tuple      =						frozenset(
     []						)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 __a			:						Tuple      =						frozenset([]						)
 def   _UpperCAmelCase       (							self ):
       torch.manual_seed(0 )
       UpperCamelCase_				:   str					    =							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=__lowerCAmelCase			,     )
       UpperCamelCase_				:   Dict					    =							PNDMScheduler(skip_prk_steps=__lowerCAmelCase )
       torch.manual_seed(0 )
       UpperCamelCase_				:   int					    =							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 )
       UpperCamelCase_				:   Union[str, Any]					    =							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			,     )
       UpperCamelCase_				:   Optional[int]					    =							CLIPTextModel(__lowerCAmelCase )
       UpperCamelCase_				:   List[Any]					    =							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

       UpperCamelCase_				:   Tuple					    =							{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=0 ):
       UpperCamelCase_				:   Any					    =							floats_tensor((1, 3, 32, 32)			,     rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							image.cpu().permute(0			,     2			,     3			,     1 )[0]
       UpperCamelCase_				:   Dict					    =							Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )
       UpperCamelCase_				:   Optional[Any]					    =							Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
       if str(__lowerCAmelCase ).startswith("""mps""" ):
             UpperCamelCase_				:   Optional[int]					    =							torch.manual_seed(__lowerCAmelCase )
       else:
             UpperCamelCase_				:   Union[str, Any]					    =							torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
       UpperCamelCase_				:   Optional[int]					    =							{
           '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 ):
       UpperCamelCase_				:   Tuple					    =							'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase_				:   Optional[Any]					    =							self.get_dummy_components()
       UpperCamelCase_				:   List[str]					    =							StableDiffusionInpaintPipeline(**__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							sd_pipe.to(__lowerCAmelCase )
       sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

       UpperCamelCase_				:   Optional[int]					    =							self.get_dummy_inputs(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							sd_pipe(**__lowerCAmelCase ).images
       UpperCamelCase_				:   Optional[Any]					    =							image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       UpperCamelCase_				:   Union[str, Any]					    =							np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76] )

       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     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png""" )
       UpperCamelCase_				:   List[str]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
       UpperCamelCase_				:   List[str]					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
           """/yellow_cat_sitting_on_a_park_bench.npy""" )

       UpperCamelCase_				:   List[str]					    =							'stabilityai/stable-diffusion-2-inpainting'
       UpperCamelCase_				:   str					    =							StableDiffusionInpaintPipeline.from_pretrained(__lowerCAmelCase			,     safety_checker=__lowerCAmelCase )
       pipe.to(__lowerCAmelCase )
       pipe.set_progress_bar_config(disable=__lowerCAmelCase )
       pipe.enable_attention_slicing()

       UpperCamelCase_				:   Dict					    =							'Face of a yellow cat, high resolution, sitting on a park bench'

       UpperCamelCase_				:   Tuple					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   List[str]					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     mask_image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )
       UpperCamelCase_				:   List[str]					    =							output.images[0]

       assert image.shape == (5_12, 5_12, 3)
       assert np.abs(expected_image - image ).max() < 9E-3
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png""" )
       UpperCamelCase_				:   Union[str, Any]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
       UpperCamelCase_				:   str					    =							load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
           """/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )

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

       UpperCamelCase_				:   Optional[int]					    =							'Face of a yellow cat, high resolution, sitting on a park bench'

       UpperCamelCase_				:   int					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   List[Any]					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     mask_image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     output_type="""np"""			,     )
       UpperCamelCase_				:   Tuple					    =							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()

       UpperCamelCase_				:   List[Any]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/sd2-inpaint/init_image.png""" )
       UpperCamelCase_				:   List[Any]					    =							load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )

       UpperCamelCase_				:   Tuple					    =							'stabilityai/stable-diffusion-2-inpainting'
       UpperCamelCase_				:   List[str]					    =							PNDMScheduler.from_pretrained(__lowerCAmelCase			,     subfolder="""scheduler""" )
       UpperCamelCase_				:   List[Any]					    =							StableDiffusionInpaintPipeline.from_pretrained(
           __lowerCAmelCase			,     safety_checker=__lowerCAmelCase			,     scheduler=__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()

       UpperCamelCase_				:   List[str]					    =							'Face of a yellow cat, high resolution, sitting on a park bench'

       UpperCamelCase_				:   List[str]					    =							torch.manual_seed(0 )
       UpperCamelCase_				:   Union[str, Any]					    =							pipe(
           prompt=__lowerCAmelCase			,     image=__lowerCAmelCase			,     mask_image=__lowerCAmelCase			,     generator=__lowerCAmelCase			,     num_inference_steps=2			,     output_type="""np"""			,     )

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














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase							=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase							=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase							=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase							=re.compile(R"(_{2,})")

UpperCamelCase							=R"^\w+(\.\w+)*$"

UpperCamelCase							=R"<>:/\|?*"



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							_uppercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      UpperCamelCase_				:   List[Any]					    =							_lowercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      return name.lower()



def 							snake_case					(     a_							: Any	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							_single_underscore_re.split(a_	)
      UpperCamelCase_				:   Tuple					    =							[_multiple_underscores_re.split(a_	) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_	) if n != """"""	)



def 							snake_case					(     a_							: Optional[int]	)       ->				str:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      return camelcase_to_snakecase(a_	)



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      if not re.match(_split_re    ,   a_	):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."	)
      return f"{filename_prefix_for_name(a_	)}-{split}"



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: int    ,   a_							: List[Any]    ,   a_							: Union[str, Any]=None	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							filename_prefix_for_split(a_    ,   a_	)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_				:   str					    =							os.path.join(a_    ,   a_	)
      return f"{filepath}*"



def 							snake_case					(     a_							: Tuple    ,   a_							: Any    ,   a_							: Optional[int]    ,   a_							: Union[str, Any]=None    ,   a_							: Optional[Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							filename_prefix_for_split(a_    ,   a_	)
      UpperCamelCase_				:   Dict					    =							os.path.join(a_    ,   a_	)

      if shard_lengths:
            UpperCamelCase_				:   Optional[int]					    =							len(a_	)
            UpperCamelCase_				:   Tuple					    =							[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_	)]
            if filetype_suffix:
                  UpperCamelCase_				:   Tuple					    =							[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_				:   Dict					    =							prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






