

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		__magic_name__     (				lowerCamelCase__						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      Tuple        =  PaintByExamplePipeline
   lowerCamelCase__					:      str        =  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase__					:      Any        =  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase__					:      Optional[Any]        =  frozenset([]				)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



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






     """simple docstring"""






     torch.manual_seed(0			)
     a__    =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,       )
     a__    =PNDMScheduler(skip_prk_steps=lowercase_			)
     torch.manual_seed(0			)
     a__    =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,       )
     torch.manual_seed(0			)
     a__    =CLIPVisionConfig(
         hidden_size=32,       projection_dim=32,       intermediate_size=37,       layer_norm_eps=1E-05,       num_attention_heads=4,       num_hidden_layers=5,       image_size=32,       patch_size=4,       )
     a__    =PaintByExampleImageEncoder(lowercase_,       proj_size=32			)
     a__    =CLIPImageProcessor(crop_size=32,       size=32			)

     a__    ={
         '''unet''': unet,
         '''scheduler''': scheduler,
         '''vae''': vae,
         '''image_encoder''': image_encoder,
         '''safety_checker''': None,
         '''feature_extractor''': feature_extractor,
     }
     return components



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[Any]:






     """simple docstring"""






     a__    =np.array(image.convert('''RGB'''			)			)
     a__    =image[None].transpose(0,       3,       1,       2			)
     a__    =torch.from_numpy(lowercase_			).to(dtype=torch.floataa			) / 127.5 - 1.0
     return image



   def 					_UpperCAmelCase					(							self,       lowercase_="cpu",       lowercase_=0			)				->       str:






     """simple docstring"""






     a__    =floats_tensor((1, 3, 32, 32),       rng=random.Random(lowercase_			)			).to(lowercase_			)
     a__    =image.cpu().permute(0,       2,       3,       1			)[0]
     a__    =Image.fromarray(np.uinta(lowercase_			)			).convert('''RGB'''			).resize((64, 64)			)
     a__    =Image.fromarray(np.uinta(image + 4			)			).convert('''RGB'''			).resize((64, 64)			)
     a__    =Image.fromarray(np.uinta(lowercase_			)			).convert('''RGB'''			).resize((32, 32)			)

     if str(lowercase_			).startswith('''mps'''			):
       a__    =torch.manual_seed(lowercase_			)
     else:
       a__    =torch.Generator(device=lowercase_			).manual_seed(lowercase_			)
     a__    ={
         '''example_image''': example_image,
         '''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			)				->       Union[str, Any]:






     """simple docstring"""






     a__    =self.get_dummy_components()

     # make sure here that pndm scheduler skips prk
     a__    =PaintByExamplePipeline(**lowercase_			)
     a__    =pipe.to('''cpu'''			)
     pipe.set_progress_bar_config(disable=lowercase_			)

     a__    =self.get_dummy_inputs()
     a__    =pipe(**lowercase_			)
     a__    =output.images

     a__    =image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)
     a__    =np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]			)

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



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    ='''cpu'''
     a__    =self.get_dummy_inputs()
     inputs.pop('''mask_image'''			)
     a__    =self.convert_to_pt(inputs.pop('''image'''			)			)
     a__    =image.clamp(0,       1			) / 2

     # make sure here that pndm scheduler skips prk
     a__    =PaintByExamplePipeline(**self.get_dummy_components()			)
     a__    =pipe.to(lowercase_			)
     pipe.set_progress_bar_config(disable=lowercase_			)

     a__    =pipe(image=lowercase_,       mask_image=mask_image[:, 0],       **lowercase_			)
     a__    =output.images

     a__    =image.cpu().permute(0,       2,       3,       1			)[0]
     a__    =mask_image.cpu().permute(0,       2,       3,       1			)[0]

     a__    =Image.fromarray(np.uinta(lowercase_			)			).convert('''RGB'''			)
     a__    =Image.fromarray(np.uinta(lowercase_			)			).convert('''RGB'''			)

     a__    =pipe(**self.get_dummy_inputs()			)
     a__    =output.images

     assert out_a.shape == (1, 64, 64, 3)
     assert np.abs(out_a.flatten() - out_a.flatten()			).max() < 5E-2



   def 					_UpperCAmelCase					(							self			)				->       Dict:






     """simple docstring"""






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







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

   '''simple docstring'''




   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






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



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






     """simple docstring"""






     a__    =load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/paint_by_example/dog_in_bucket.png'''			)
     a__    =load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/paint_by_example/mask.png'''			)
     a__    =load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/paint_by_example/panda.jpg'''			)

     a__    =PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''			)
     a__    =pipe.to(lowercase_			)
     pipe.set_progress_bar_config(disable=lowercase_			)

     a__    =torch.manual_seed(321			)
     a__    =pipe(
         image=lowercase_,       mask_image=lowercase_,       example_image=lowercase_,       generator=lowercase_,       guidance_scale=5.0,       num_inference_steps=50,       output_type='''np''',       )

     a__    =output.images

     a__    =image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     a__    =np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]			)

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

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_A		:      Optional[Any]						   ={
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A		:      Union[str, Any]						   =[
     '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MobileViTV2ForImageClassification''',
     '''MobileViTV2ForSemanticSegmentation''',
     '''MobileViTV2Model''',
     '''MobileViTV2PreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

 _A		:      Tuple						   =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)