



"""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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__      ,			UpperCAmelCase__      ,			UpperCAmelCase__      ,			unittest.TestCase ):
   __lowerCAmelCase  :   Optional[int]						=						StableDiffusionInpaintPipeline
   __lowerCAmelCase  :   str						=						TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCAmelCase  :   str						=						TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCAmelCase  :   Tuple						=						frozenset(
       [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   __lowerCAmelCase  :   Optional[Any]						=						frozenset([] )

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    torch.manual_seed(0						)
    UpperCAmelCase	:					Optional[Any]	=	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=_SCREAMING_SNAKE_CASE      ,     )
    UpperCAmelCase	:					Optional[int]	=	PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE						)
    torch.manual_seed(0						)
    UpperCAmelCase	:					Union[str, Any]	=	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=128      ,     )
    torch.manual_seed(0						)
    UpperCAmelCase	:					Dict	=	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=1000      ,     hidden_act="""gelu"""      ,     projection_dim=512      ,     )
    UpperCAmelCase	:					str	=	CLIPTextModel(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""						)

    UpperCAmelCase	:					List[Any]	=	{
        """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      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=0						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	floats_tensor((1, 3, 32, 32)      ,     rng=random.Random(_SCREAMING_SNAKE_CASE						)						).to(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	image.cpu().permute(0      ,     2      ,     3      ,     1						)[0]
    UpperCAmelCase	:					Optional[int]	=	Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE						)						).convert("""RGB"""						).resize((64, 64)						)
    UpperCAmelCase	:					int	=	Image.fromarray(np.uinta(image + 4						)						).convert("""RGB"""						).resize((64, 64)						)
    if str(_SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
     UpperCAmelCase	:					Optional[Any]	=	torch.manual_seed(_SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					List[str]	=	torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					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 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
    UpperCAmelCase	:					Any	=	self.get_dummy_components()
    UpperCAmelCase	:					Optional[int]	=	StableDiffusionInpaintPipeline(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					int	=	sd_pipe.to(_SCREAMING_SNAKE_CASE						)
    sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[Any]	=	self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	sd_pipe(**_SCREAMING_SNAKE_CASE						).images
    UpperCAmelCase	:					Dict	=	image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    UpperCAmelCase	:					Any	=	np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]						)

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




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Optional[Any]:





    '''simple docstring'''




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



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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Union[str, Any]:





    '''simple docstring'''




    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[str]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	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	:					Union[str, Any]	=	load_numpy(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
        """/yellow_cat_sitting_on_a_park_bench.npy"""						)

    UpperCAmelCase	:					Tuple	=	"""stabilityai/stable-diffusion-2-inpainting"""
    UpperCAmelCase	:					int	=	StableDiffusionInpaintPipeline.from_pretrained(_SCREAMING_SNAKE_CASE      ,     safety_checker=_SCREAMING_SNAKE_CASE						)
    pipe.to(_SCREAMING_SNAKE_CASE						)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
    pipe.enable_attention_slicing()

    UpperCAmelCase	:					Dict	=	"""Face of a yellow cat, high resolution, sitting on a park bench"""

    UpperCAmelCase	:					Dict	=	torch.manual_seed(0						)
    UpperCAmelCase	:					Union[str, Any]	=	pipe(
        prompt=_SCREAMING_SNAKE_CASE      ,     image=_SCREAMING_SNAKE_CASE      ,     mask_image=_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     output_type="""np"""      ,     )
    UpperCAmelCase	:					Union[str, Any]	=	output.images[0]

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image						).max() < 9E-3

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
        """/sd2-inpaint/init_image.png"""						)
    UpperCAmelCase	:					Tuple	=	load_image(
        """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""						)
    UpperCAmelCase	:					Optional[int]	=	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	:					List[str]	=	StableDiffusionInpaintPipeline.from_pretrained(
        _SCREAMING_SNAKE_CASE      ,     torch_dtype=torch.floataa      ,     safety_checker=_SCREAMING_SNAKE_CASE      ,     )
    pipe.to(_SCREAMING_SNAKE_CASE						)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
    pipe.enable_attention_slicing()

    UpperCAmelCase	:					List[Any]	=	"""Face of a yellow cat, high resolution, sitting on a park bench"""

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

    assert image.shape == (512, 512, 3)
    assert np.abs(expected_image - image						).max() < 5E-1




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




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

    UpperCAmelCase	:					Any	=	"""stabilityai/stable-diffusion-2-inpainting"""
    UpperCAmelCase	:					Dict	=	PNDMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE      ,     subfolder="""scheduler"""						)
    UpperCAmelCase	:					Union[str, Any]	=	StableDiffusionInpaintPipeline.from_pretrained(
        _SCREAMING_SNAKE_CASE      ,     safety_checker=_SCREAMING_SNAKE_CASE      ,     scheduler=_SCREAMING_SNAKE_CASE      ,     torch_dtype=torch.floataa      ,     )
    pipe.to(_SCREAMING_SNAKE_CASE						)
    pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
    pipe.enable_attention_slicing(1						)
    pipe.enable_sequential_cpu_offload()

    UpperCAmelCase	:					List[Any]	=	"""Face of a yellow cat, high resolution, sitting on a park bench"""

    UpperCAmelCase	:					Union[str, Any]	=	torch.manual_seed(0						)
    UpperCAmelCase	:					Union[str, Any]	=	pipe(
        prompt=_SCREAMING_SNAKE_CASE      ,     image=_SCREAMING_SNAKE_CASE      ,     mask_image=_SCREAMING_SNAKE_CASE      ,     generator=_SCREAMING_SNAKE_CASE      ,     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"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
