'''simple docstring'''






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 lowercase_					(		a__						,						unittest.TestCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      PaintByExamplePipeline
   __lowerCAmelCase    						=      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCAmelCase    						=      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCAmelCase    						=      frozenset([] )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
   def   __UpperCAmelCase      (			self			: Dict    )     -> Dict:
        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=__A    )
        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(__A,   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			: Any,   UpperCamelCase__			: Tuple    )     -> List[Any]:
        _A								=     np.array(image.convert('RGB'    )    )
        _A								=     image[None].transpose(0,   3,   1,   2    )
        _A								=     torch.from_numpy(__A    ).to(dtype=torch.floataa    ) / 127.5 - 1.0
        return image
   def   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: str="cpu",   UpperCamelCase__			: List[Any]=0    )     -> str:
        _A								=     floats_tensor((1, 3, 32, 32),   rng=random.Random(__A    )    ).to(__A    )
        _A								=     image.cpu().permute(0,   2,   3,   1    )[0]
        _A								=     Image.fromarray(np.uinta(__A    )    ).convert('RGB'    ).resize((64, 64)    )
        _A								=     Image.fromarray(np.uinta(image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
        _A								=     Image.fromarray(np.uinta(__A    )    ).convert('RGB'    ).resize((32, 32)    )

        if str(__A    ).startswith('mps'    ):
             _A								=     torch.manual_seed(__A    )
        else:
             _A								=     torch.Generator(device=__A    ).manual_seed(__A    )
        _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			: int    )     -> Optional[int]:
        _A								=     self.get_dummy_components()

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

        _A								=     self.get_dummy_inputs()
        _A								=     pipe(**__A    )
        _A								=     output.images

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

        assert image.shape == (1, 64, 64, 3)
        _A								=     np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
   def   __UpperCAmelCase      (			self			: List[str]    )     -> Dict:
        _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(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        _A								=     pipe(image=__A,   mask_image=mask_image[:, 0],   **__A    )
        _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(__A    )    ).convert('RGB'    )
        _A								=     Image.fromarray(np.uinta(__A    )    ).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			: int    )     -> List[str]:
        super().test_inference_batch_single_identical(expected_max_diff=3e-3    )







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







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: List[Any]    )     -> Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Tuple:
        _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(__A    )
        pipe.set_progress_bar_config(disable=__A    )

        _A								=     torch.manual_seed(3_21    )
        _A								=     pipe(
            image=__A,   mask_image=__A,   example_image=__A,   generator=__A,   guidance_scale=5.0,   num_inference_steps=50,   output_type='np',   )

        _A								=     output.images

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

        assert image.shape == (1, 5_12, 5_12, 3)
        _A								=     np.array([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]    )

        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


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase						:    Dict     					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



