





"""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  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__			,   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : str	    =   StableDiffusionInpaintPipeline
      lowercase_      : Any	    =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowercase_      : int	    =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      lowercase_      : int	    =   frozenset(
          []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      lowercase_      : Optional[int]	    =   frozenset([]		)

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        torch.manual_seed(0  )
        A_  :       Optional[Any]										=  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=snake_case_					,      )
        A_  :       Optional[Any]										=  PNDMScheduler(skip_prk_steps=snake_case_  )
        torch.manual_seed(0  )
        A_  :       List[str]										=  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_  :       str										=  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_  :       Any										=  CLIPTextModel(snake_case_  )
        A_  :       Union[str, Any]										=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

        A_  :       Union[str, Any]										=  {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=0  ):

        """simple docstring"""

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

        """simple docstring"""

        A_  :       Union[str, Any]										=  'cpu'  # ensure determinism for the device-dependent torch.Generator
        A_  :       Tuple										=  self.get_dummy_components()
        A_  :       str										=  StableDiffusionInpaintPipeline(**snake_case_  )
        A_  :       str										=  sd_pipe.to(snake_case_  )
        sd_pipe.set_progress_bar_config(disable=snake_case_  )

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

        assert image.shape == (1, 6_4, 6_4, 3)
        A_  :       Optional[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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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


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



      '''simple docstring'''

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

        A_  :       Optional[int]										=  'stabilityai/stable-diffusion-2-inpainting'
        A_  :       Any										=  StableDiffusionInpaintPipeline.from_pretrained(snake_case_					,      safety_checker=snake_case_  )
        pipe.to(snake_case_  )
        pipe.set_progress_bar_config(disable=snake_case_  )
        pipe.enable_attention_slicing()

        A_  :       str										=  'Face of a yellow cat, high resolution, sitting on a park bench'

        A_  :       str										=  torch.manual_seed(0  )
        A_  :       Optional[int]										=  pipe(
            prompt=snake_case_					,      image=snake_case_					,      mask_image=snake_case_					,      generator=snake_case_					,      output_type='np'					,      )
        A_  :       Union[str, Any]										=  output.images[0]

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/sd2-inpaint/init_image.png'  )
        A_  :       Optional[int]										=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )
        A_  :       Any										=  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_  :       Dict										=  'stabilityai/stable-diffusion-2-inpainting'
        A_  :       List[Any]										=  StableDiffusionInpaintPipeline.from_pretrained(
            snake_case_					,      torch_dtype=torch.floataa					,      safety_checker=snake_case_					,      )
        pipe.to(snake_case_  )
        pipe.set_progress_bar_config(disable=snake_case_  )
        pipe.enable_attention_slicing()

        A_  :       Union[str, Any]										=  'Face of a yellow cat, high resolution, sitting on a park bench'

        A_  :       Tuple										=  torch.manual_seed(0  )
        A_  :       Union[str, Any]										=  pipe(
            prompt=snake_case_					,      image=snake_case_					,      mask_image=snake_case_					,      generator=snake_case_					,      output_type='np'					,      )
        A_  :       Optional[Any]										=  output.images[0]

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

        A_  :       List[str]										=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/sd2-inpaint/init_image.png'  )
        A_  :       Tuple										=  load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'  )

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

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

        A_  :       Dict										=  torch.manual_seed(0  )
        A_  :       int										=  pipe(
            prompt=snake_case_					,      image=snake_case_					,      mask_image=snake_case_					,      generator=snake_case_					,      num_inference_steps=2					,      output_type='np'					,      )

        A_  :       Optional[Any]										=  torch.cuda.max_memory_allocated()
        # make sure that less than 2.65 GB is allocated
        assert mem_bytes < 2.65 * 1_0**9






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCamelCase_     :						int						=					{}


try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Tuple						=					['MLukeTokenizer']

if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mluke import MLukeTokenizer


else:
   import sys

   lowerCamelCase_     :						List[str]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)