'''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_			(snake_case__							,				unittest.TestCase					):
      '''simple docstring'''

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




      def   _A			( self		:       Dict ):
             torch.manual_seed(0 )
             _UpperCAmelCase  :						str							   =					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  :						Optional[Any]							   =					PNDMScheduler(skip_prk_steps=A )
             torch.manual_seed(0 )
             _UpperCAmelCase  :						Union[str, Any]							   =					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  :						Tuple							   =					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  :						Union[str, Any]							   =					PaintByExampleImageEncoder(A		,      proj_size=32 )
             _UpperCAmelCase  :						Optional[Any]							   =					CLIPImageProcessor(crop_size=32		,      size=32 )

             _UpperCAmelCase  :						Union[str, Any]							   =					{
                 "unet": unet,
                 "scheduler": scheduler,
                 "vae": vae,
                 "image_encoder": image_encoder,
                 "safety_checker": None,
                 "feature_extractor": feature_extractor,
             }
             return components




      def   _A			( self		:       Union[str, Any]		,      A		:       Union[str, Any] ):
             _UpperCAmelCase  :						str							   =					np.array(image.convert("RGB" ) )
             _UpperCAmelCase  :						Any							   =					image[None].transpose(0		,      3		,      1		,      2 )
             _UpperCAmelCase  :						str							   =					torch.from_numpy(A ).to(dtype=torch.floataa ) / 127.5 - 1.0
             return image




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

             if str(A ).startswith("mps" ):
                    _UpperCAmelCase  :						Any							   =					torch.manual_seed(A )
             else:
                    _UpperCAmelCase  :						Optional[Any]							   =					torch.Generator(device=A ).manual_seed(A )
             _UpperCAmelCase  :						Optional[int]							   =					{
                 "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   _A			( self		:       str ):
             _UpperCAmelCase  :						Optional[Any]							   =					self.get_dummy_components()

             # make sure here that pndm scheduler skips prk
             _UpperCAmelCase  :						Any							   =					PaintByExamplePipeline(**A )
             _UpperCAmelCase  :						Any							   =					pipe.to("cpu" )
             pipe.set_progress_bar_config(disable=A )

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

             _UpperCAmelCase  :						Dict							   =					image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             _UpperCAmelCase  :						int							   =					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   _A			( self		:       Any ):
             _UpperCAmelCase  :						int							   =					"cpu"
             _UpperCAmelCase  :						Optional[int]							   =					self.get_dummy_inputs()
             inputs.pop("mask_image" )
             _UpperCAmelCase  :						List[Any]							   =					self.convert_to_pt(inputs.pop("image" ) )
             _UpperCAmelCase  :						Optional[int]							   =					image.clamp(0		,      1 ) / 2

             # make sure here that pndm scheduler skips prk
             _UpperCAmelCase  :						Optional[Any]							   =					PaintByExamplePipeline(**self.get_dummy_components() )
             _UpperCAmelCase  :						Optional[Any]							   =					pipe.to(A )
             pipe.set_progress_bar_config(disable=A )

             _UpperCAmelCase  :						str							   =					pipe(image=A		,      mask_image=mask_image[:, 0]		,      **A )
             _UpperCAmelCase  :						Union[str, Any]							   =					output.images

             _UpperCAmelCase  :						str							   =					image.cpu().permute(0		,      2		,      3		,      1 )[0]
             _UpperCAmelCase  :						str							   =					mask_image.cpu().permute(0		,      2		,      3		,      1 )[0]

             _UpperCAmelCase  :						List[str]							   =					Image.fromarray(np.uinta(A ) ).convert("RGB" )
             _UpperCAmelCase  :						Optional[Any]							   =					Image.fromarray(np.uinta(A ) ).convert("RGB" )

             _UpperCAmelCase  :						int							   =					pipe(**self.get_dummy_inputs() )
             _UpperCAmelCase  :						Tuple							   =					output.images

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





      def   _A			( 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   _A			( self		:       Tuple ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()





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

             _UpperCAmelCase  :						List[Any]							   =					PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example" )
             _UpperCAmelCase  :						Dict							   =					pipe.to(A )
             pipe.set_progress_bar_config(disable=A )

             _UpperCAmelCase  :						Tuple							   =					torch.manual_seed(321 )
             _UpperCAmelCase  :						Tuple							   =					pipe(
                 image=A		,      mask_image=A		,      example_image=A		,      generator=A		,      guidance_scale=5.0		,      num_inference_steps=50		,      output_type="np"		,      )

             _UpperCAmelCase  :						int							   =					output.images

             _UpperCAmelCase  :						str							   =					image[0, -3:, -3:, -1]

             assert image.shape == (1, 512, 512, 3)
             _UpperCAmelCase  :						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 TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

