




"""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"""

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







      def  snake_case		(       self   :	Union[str, Any]			):
          torch.manual_seed(0			)
          __UpperCamelCase											=  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	,    )
          __UpperCamelCase											=  PNDMScheduler(skip_prk_steps=_UpperCamelCase			)
          torch.manual_seed(0			)
          __UpperCamelCase											=  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			)
          __UpperCamelCase											=  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	,    )
          __UpperCamelCase											=  PaintByExampleImageEncoder(_UpperCamelCase	,    proj_size=32			)
          __UpperCamelCase											=  CLIPImageProcessor(crop_size=32	,    size=32			)

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







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	int			):
          __UpperCamelCase											=  np.array(image.convert('''RGB'''			)			)
          __UpperCamelCase											=  image[None].transpose(0	,    3	,    1	,    2			)
          __UpperCamelCase											=  torch.from_numpy(_UpperCamelCase			).to(dtype=torch.floataa			) / 1_27.5 - 1.0
          return image







      def  snake_case		(       self   :	List[Any]	,    snake_case   :	Any="cpu"	,    snake_case   :	int=0			):
          __UpperCamelCase											=  floats_tensor((1, 3, 32, 32)	,    rng=random.Random(_UpperCamelCase			)			).to(_UpperCamelCase			)
          __UpperCamelCase											=  image.cpu().permute(0	,    2	,    3	,    1			)[0]
          __UpperCamelCase											=  Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			).resize((64, 64)			)
          __UpperCamelCase											=  Image.fromarray(np.uinta(image + 4			)			).convert('''RGB'''			).resize((64, 64)			)
          __UpperCamelCase											=  Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			).resize((32, 32)			)

          if str(_UpperCamelCase			).startswith('''mps'''			):
              __UpperCamelCase											=  torch.manual_seed(_UpperCamelCase			)
          else:
              __UpperCamelCase											=  torch.Generator(device=_UpperCamelCase			).manual_seed(_UpperCamelCase			)
          __UpperCamelCase											=  {
              '''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  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  self.get_dummy_components()

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

          __UpperCamelCase											=  self.get_dummy_inputs()
          __UpperCamelCase											=  pipe(**_UpperCamelCase			)
          __UpperCamelCase											=  output.images

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

          assert image.shape == (1, 64, 64, 3)
          __UpperCamelCase											=  np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]			)

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







      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  '''cpu'''
          __UpperCamelCase											=  self.get_dummy_inputs()
          inputs.pop('''mask_image'''			)
          __UpperCamelCase											=  self.convert_to_pt(inputs.pop('''image'''			)			)
          __UpperCamelCase											=  image.clamp(0	,    1			) / 2

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

          __UpperCamelCase											=  pipe(image=_UpperCamelCase	,    mask_image=mask_image[:, 0]	,    **_UpperCamelCase			)
          __UpperCamelCase											=  output.images

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

          __UpperCamelCase											=  Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			)
          __UpperCamelCase											=  Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			)

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

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







      def  snake_case		(       self   :	List[str]			):
          super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







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

      """simple docstring"""

      def  snake_case		(       self   :	List[str]			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def  snake_case		(       self   :	Optional[Any]			):
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/paint_by_example/dog_in_bucket.png'''			)
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/paint_by_example/mask.png'''			)
          __UpperCamelCase											=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/paint_by_example/panda.jpg'''			)

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

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

          __UpperCamelCase											=  output.images

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

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase											=  np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]			)

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

 a_										=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






