






'''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 							lowerCAmelCase					(							lowerCamelCase__							,      unittest.TestCase   ):







   """simple docstring"""







   _A						=   PaintByExamplePipeline
   _A						=   IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   _A						=   IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _A						=   frozenset([]   )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



   def    __magic_name__    ( self			)    ->   List[str]:
    torch.manual_seed(0			)
    __a    :	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   ,   )
    __a    :	Dict									=						PNDMScheduler(skip_prk_steps=_A			)
    torch.manual_seed(0			)
    __a    :	str									=						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    :	int									=						CLIPVisionConfig(
        hidden_size=32   ,   projection_dim=32   ,   intermediate_size=37   ,   layer_norm_eps=1E-0_5   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   image_size=32   ,   patch_size=4   ,   )
    __a    :	Optional[int]									=						PaintByExampleImageEncoder(_A   ,   proj_size=32			)
    __a    :	int									=						CLIPImageProcessor(crop_size=32   ,   size=32			)

    __a    :	List[str]									=						{
        'unet': unet,
        'scheduler': scheduler,
        'vae': vae,
        'image_encoder': image_encoder,
        'safety_checker': None,
        'feature_extractor': feature_extractor,
    }
    return components



   def    __magic_name__    ( self   ,   _A			)    ->   Optional[Any]:
    __a    :	Any									=						np.array(image.convert('RGB'			)			)
    __a    :	str									=						image[None].transpose(0   ,   3   ,   1   ,   2			)
    __a    :	List[str]									=						torch.from_numpy(_A			).to(dtype=torch.floataa			) / 127.5 - 1.0
    return image



   def    __magic_name__    ( self   ,   _A="cpu"   ,   _A=0			)    ->   List[str]:
    # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
    __a    :	Tuple									=						floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(_A			)			).to(_A			)
    __a    :	Tuple									=						image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]
    __a    :	Dict									=						Image.fromarray(np.uinta(_A			)			).convert('RGB'			).resize((64, 64)			)
    __a    :	Tuple									=						Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
    __a    :	Optional[Any]									=						Image.fromarray(np.uinta(_A			)			).convert('RGB'			).resize((32, 32)			)

    if str(_A			).startswith('mps'			):
     __a    :	Tuple									=						torch.manual_seed(_A			)
    else:
     __a    :	List[str]									=						torch.Generator(device=_A			).manual_seed(_A			)
    __a    :	List[Any]									=						{
        '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    __magic_name__    ( self			)    ->   Dict:
    __a    :	Dict									=						self.get_dummy_components()

    # make sure here that pndm scheduler skips prk
    __a    :	Union[str, Any]									=						PaintByExamplePipeline(**_A			)
    __a    :	Union[str, Any]									=						pipe.to('cpu'			)
    pipe.set_progress_bar_config(disable=_A			)

    __a    :	int									=						self.get_dummy_inputs()
    __a    :	List[Any]									=						pipe(**_A			)
    __a    :	List[Any]									=						output.images

    __a    :	Tuple									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 64, 64, 3)
    __a    :	str									=						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    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	int									=						'cpu'
    __a    :	Tuple									=						self.get_dummy_inputs()
    inputs.pop('mask_image'			)
    __a    :	Tuple									=						self.convert_to_pt(inputs.pop('image'			)			)
    __a    :	Union[str, Any]									=						image.clamp(0   ,   1			) / 2

    # make sure here that pndm scheduler skips prk
    __a    :	str									=						PaintByExamplePipeline(**self.get_dummy_components()			)
    __a    :	Union[str, Any]									=						pipe.to(_A			)
    pipe.set_progress_bar_config(disable=_A			)

    __a    :	List[Any]									=						pipe(image=_A   ,   mask_image=mask_image[:, 0]   ,   **_A			)
    __a    :	List[Any]									=						output.images

    __a    :	List[Any]									=						image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]
    __a    :	Any									=						mask_image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]

    __a    :	Dict									=						Image.fromarray(np.uinta(_A			)			).convert('RGB'			)
    __a    :	Optional[Any]									=						Image.fromarray(np.uinta(_A			)			).convert('RGB'			)

    __a    :	int									=						pipe(**self.get_dummy_inputs()			)
    __a    :	Union[str, Any]									=						output.images

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



   def    __magic_name__    ( self			)    ->   str:
    super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def    __magic_name__    ( self			)    ->   List[Any]:
    # make sure here that pndm scheduler skips prk
    __a    :	int									=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/paint_by_example/dog_in_bucket.png'			)
    __a    :	Optional[Any]									=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/paint_by_example/mask.png'			)
    __a    :	Tuple									=						load_image(
        'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
        '/paint_by_example/panda.jpg'			)

    __a    :	Tuple									=						PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'			)
    __a    :	str									=						pipe.to(_A			)
    pipe.set_progress_bar_config(disable=_A			)

    __a    :	Tuple									=						torch.manual_seed(321			)
    __a    :	int									=						pipe(
        image=_A   ,   mask_image=_A   ,   example_image=_A   ,   generator=_A   ,   guidance_scale=5.0   ,   num_inference_steps=50   ,   output_type='np'   ,   )

    __a    :	Union[str, Any]									=						output.images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 512, 512, 3)
    __a    :	List[str]									=						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 List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def   lowerCAmelCase__			(					cls		):
   return cls






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		):
 __a    :	int									=						device
 __a    :	Union[str, Any]									=						shape[0]

 __a    :	Union[str, Any]									=						layout or torch.strided
 __a    :	Any									=						device or torch.device('cpu'		)

 if generator is not None:
  __a    :	Optional[int]									=						generator.device.type if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   __a    :	int									=						'cpu'
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''		)
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''		)

 if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  __a    :	Optional[int]									=						(1,) + shape[1:]
  __a    :	Dict									=						[
      torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=generator[i]       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		)
      for i in range(SCREAMING_SNAKE_CASE__		)
  ]
  __a    :	List[str]									=						torch.cat(SCREAMING_SNAKE_CASE__       ,		dim=0		).to(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	Tuple									=						torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

 return latents






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if is_torch_version('<'       ,		'2.0.0'		) or not hasattr(SCREAMING_SNAKE_CASE__       ,		'_dynamo'		):
  return False
 return isinstance(SCREAMING_SNAKE_CASE__       ,		torch._dynamo.eval_frame.OptimizedModule		)




