






import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 	lowerCamelCase__				(							__UpperCAmelCase,   unittest.TestCase ):
      lowerCamelCase_							:   Optional[int]											=	RePaintPipeline
      lowerCamelCase_							:   Optional[int]											=	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
      lowerCamelCase_							:   Optional[Any]											=	PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      lowerCamelCase_							:   str											=	IMAGE_INPAINTING_BATCH_PARAMS
      lowerCamelCase_							:   List[Any]											=	False






      def      UpperCAmelCase_     (self					:    Tuple		)       ->   str:


       """simple docstring"""






       torch.manual_seed(0		)
       torch.manual_seed(0		)
       lowerCamelCase_							:    Tuple							=     UNetaDModel(
           block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=3		,		out_channels=3		,		down_block_types=('DownBlock2D', 'AttnDownBlock2D')		,		up_block_types=('AttnUpBlock2D', 'UpBlock2D')		,		)
       lowerCamelCase_							:    Optional[int]							=     RePaintScheduler()
       lowerCamelCase_							:    Any							=     {'unet': unet, 'scheduler': scheduler}
       return components






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    Optional[Any]		,		_snake_case					:    Optional[int]=0		)       ->   Dict:


       """simple docstring"""






       if str(_snake_case		).startswith('mps'		):
        lowerCamelCase_							:    Optional[Any]							=     torch.manual_seed(_snake_case		)
       else:
        lowerCamelCase_							:    Any							=     torch.Generator(device=_snake_case		).manual_seed(_snake_case		)
       lowerCamelCase_							:    List[Any]							=     np.random.RandomState(_snake_case		).standard_normal((1, 3, 32, 32)		)
       lowerCamelCase_							:    List[Any]							=     torch.from_numpy(_snake_case		).to(device=_snake_case		,		dtype=torch.floataa		)
       lowerCamelCase_							:    Union[str, Any]							=     (image > 0).to(device=_snake_case		,		dtype=torch.floataa		)
       lowerCamelCase_							:    Optional[int]							=     {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs






      def      UpperCAmelCase_     (self					:    int		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_							:    Any							=     self.get_dummy_components()
       lowerCamelCase_							:    str							=     RePaintPipeline(**_snake_case		)
       lowerCamelCase_							:    Any							=     sd_pipe.to(_snake_case		)
       sd_pipe.set_progress_bar_config(disable=_snake_case		)

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

       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_							:    int							=     np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-3






      @skip_mps
      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   List[Any]:


       """simple docstring"""






       return super().test_save_load_local()






      @unittest.skip('non-deterministic pipeline'		)
      def      UpperCAmelCase_     (self					:    str		)       ->   Optional[Any]:


       """simple docstring"""






       return super().test_inference_batch_single_identical()






      @skip_mps
      def      UpperCAmelCase_     (self					:    Dict		)       ->   Tuple:


       """simple docstring"""






       return super().test_dict_tuple_outputs_equivalent()






      @skip_mps
      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   Tuple:


       """simple docstring"""






       return super().test_save_load_optional_components()






      @skip_mps
      def      UpperCAmelCase_     (self					:    Any		)       ->   Any:


       """simple docstring"""






       return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class 	lowerCamelCase__				(							unittest.TestCase ):






      def      UpperCAmelCase_     (self					:    Any		)       ->   List[str]:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   str:


       """simple docstring"""






       lowerCamelCase_							:    Optional[int]							=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'		)
       lowerCamelCase_							:    Optional[Any]							=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'		)
       lowerCamelCase_							:    str							=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'		)

       lowerCamelCase_							:    Any							=     'google/ddpm-ema-celebahq-256'
       lowerCamelCase_							:    Tuple							=     UNetaDModel.from_pretrained(_snake_case		)
       lowerCamelCase_							:    Tuple							=     RePaintScheduler.from_pretrained(_snake_case		)

       lowerCamelCase_							:    List[Any]							=     RePaintPipeline(unet=_snake_case		,		scheduler=_snake_case		).to(_snake_case		)
       repaint.set_progress_bar_config(disable=_snake_case		)
       repaint.enable_attention_slicing()

       lowerCamelCase_							:    int							=     torch.manual_seed(0		)
       lowerCamelCase_							:    Dict							=     repaint(
           _snake_case		,		_snake_case		,		num_inference_steps=250		,		eta=0.0		,		jump_length=10		,		jump_n_sample=10		,		generator=_snake_case		,		output_type='np'		,		)
       lowerCamelCase_							:    Union[str, Any]							=     output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image		).mean() < 1e-2







import warnings

from ...utils import logging
from .image_processing_mobilenet_va import MobileNetVaImageProcessor


lowercase     :			str     						=	logging.get_logger(__name__)

class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """simple docstring"""

            warnings.warn(
                '''The class MobileNetV2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MobileNetV2ImageProcessor instead.''',							A,							)
            super().__init__(*A,							**A   )






