






"""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			(  lowerCAmelCase		,	unittest.TestCase    ):
      _a :  Tuple										= PaintByExamplePipeline
      _a :  Optional[int]										= IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
      _a :  Any										= IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      _a :  List[str]										= frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            torch.manual_seed(0    )
            __snake_case   :     str	      =UNetaDConditionModel(
                block_out_channels=(3_2, 6_4)				, layers_per_block=2				, sample_size=3_2				, in_channels=9				, out_channels=4				, down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				, up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				, cross_attention_dim=3_2				, )
            __snake_case   :     Optional[Any]	      =PNDMScheduler(skip_prk_steps=a    )
            torch.manual_seed(0    )
            __snake_case   :     str	      =AutoencoderKL(
                block_out_channels=[3_2, 6_4]				, in_channels=3				, out_channels=3				, down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				, up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				, latent_channels=4				, )
            torch.manual_seed(0    )
            __snake_case   :     Any	      =CLIPVisionConfig(
                hidden_size=3_2				, projection_dim=3_2				, intermediate_size=3_7				, layer_norm_eps=1e-05				, num_attention_heads=4				, num_hidden_layers=5				, image_size=3_2				, patch_size=4				, )
            __snake_case   :     Tuple	      =PaintByExampleImageEncoder(a				, proj_size=3_2    )
            __snake_case   :     int	      =CLIPImageProcessor(crop_size=3_2				, size=3_2    )

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


      def _UpperCamelCase    (     self					:						Union[str, Any]				, a					:						int    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =np.array(image.convert('''RGB'''    )    )
            __snake_case   :     int	      =image[None].transpose(0				, 3				, 1				, 2    )
            __snake_case   :     List[Any]	      =torch.from_numpy(a    ).to(dtype=torch.floataa    ) / 1_2_7.5 - 1.0
            return image


      def _UpperCamelCase    (     self					:						int				, a					:						List[str]="cpu"				, a					:						Any=0    ):





            """simple docstring"""







            __snake_case   :     int	      =floats_tensor((1, 3, 3_2, 3_2)				, rng=random.Random(a    )    ).to(a    )
            __snake_case   :     Optional[Any]	      =image.cpu().permute(0				, 2				, 3				, 1    )[0]
            __snake_case   :     int	      =Image.fromarray(np.uinta(a    )    ).convert('''RGB'''    ).resize((6_4, 6_4)    )
            __snake_case   :     str	      =Image.fromarray(np.uinta(image + 4    )    ).convert('''RGB'''    ).resize((6_4, 6_4)    )
            __snake_case   :     List[Any]	      =Image.fromarray(np.uinta(a    )    ).convert('''RGB'''    ).resize((3_2, 3_2)    )

            if str(a    ).startswith('''mps'''    ):
                  __snake_case   :     str	      =torch.manual_seed(a    )
            else:
                  __snake_case   :     Dict	      =torch.Generator(device=a    ).manual_seed(a    )
            __snake_case   :     Dict	      ={
                '''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					:						Optional[Any]    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =self.get_dummy_components()

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

            __snake_case   :     List[str]	      =self.get_dummy_inputs()
            __snake_case   :     Dict	      =pipe(**a    )
            __snake_case   :     Dict	      =output.images

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

            assert image.shape == (1, 6_4, 6_4, 3)
            __snake_case   :     List[Any]	      =np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]    )

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


      def _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







            __snake_case   :     Dict	      ='''cpu'''
            __snake_case   :     Dict	      =self.get_dummy_inputs()
            inputs.pop('''mask_image'''    )
            __snake_case   :     Dict	      =self.convert_to_pt(inputs.pop('''image'''    )    )
            __snake_case   :     Optional[Any]	      =image.clamp(0				, 1    ) / 2

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

            __snake_case   :     str	      =pipe(image=a				, mask_image=mask_image[:, 0]				, **a    )
            __snake_case   :     Optional[Any]	      =output.images

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

            __snake_case   :     Dict	      =Image.fromarray(np.uinta(a    )    ).convert('''RGB'''    )
            __snake_case   :     str	      =Image.fromarray(np.uinta(a    )    ).convert('''RGB'''    )

            __snake_case   :     List[str]	      =pipe(**self.get_dummy_inputs()    )
            __snake_case   :     List[str]	      =output.images

            assert out_a.shape == (1, 6_4, 6_4, 3)
            assert np.abs(out_a.flatten() - out_a.flatten()    ).max() < 5e-2







      def _UpperCamelCase    (     self					:						str    ):





            """simple docstring"""







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



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


      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







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







      def _UpperCamelCase    (     self					:						Union[str, Any]    ):





            """simple docstring"""







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

            __snake_case   :     Optional[Any]	      =PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''    )
            __snake_case   :     int	      =pipe.to(a    )
            pipe.set_progress_bar_config(disable=a    )

            __snake_case   :     Optional[int]	      =torch.manual_seed(3_2_1    )
            __snake_case   :     Tuple	      =pipe(
                image=a				, mask_image=a				, example_image=a				, generator=a				, guidance_scale=5.0				, num_inference_steps=5_0				, output_type='''np'''				, )

            __snake_case   :     Dict	      =output.images

            __snake_case   :     List[Any]	      =image[0, -3:, -3:, -1]

            assert image.shape == (1, 5_1_2, 5_1_2, 3)
            __snake_case   :     List[str]	      =np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]    )

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










"""simple docstring"""





class   _lowercase			:


      def __init__(     self					:						Optional[Any]				, a					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =data
            __snake_case   :     Optional[Any]	      =None
            __snake_case   :     Any	      =None







      def __str__(     self					:						Any    ):





            """simple docstring"""







            return f'''{self.data}'''



class   _lowercase			:


      def __init__(     self					:						Optional[int]    ):





            """simple docstring"""







            __snake_case   :     Any	      =None
            __snake_case   :     int	      =None


      def __iter__(     self					:						int    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =self.head
            while node:
                  yield node.data
                  __snake_case   :     str	      =node.next


      def __str__(     self					:						Optional[int]    ):





            """simple docstring"""







            return "->".join([str(a    ) for item in self]    )


      def __len__(     self					:						str    ):





            """simple docstring"""







            return sum(1 for _ in self    )


      def _UpperCamelCase    (     self					:						int				, a					:						List[Any]    ):





            """simple docstring"""







            self.insert_at_nth(0				, a    )


      def _UpperCamelCase    (     self					:						str				, a					:						Optional[int]    ):





            """simple docstring"""







            self.insert_at_nth(len(self    )				, a    )


      def _UpperCamelCase    (     self					:						Optional[Any]				, a					:						int				, a					:						Tuple    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =len(self    )

            if not 0 <= index <= length:
                  raise IndexError('''list index out of range'''    )
            __snake_case   :     List[Any]	      =Node(a    )
            if self.head is None:
                  __snake_case   :     Dict	      =new_node
            elif index == 0:
                  __snake_case   :     Union[str, Any]	      =new_node
                  __snake_case   :     Any	      =self.head
                  __snake_case   :     List[Any]	      =new_node
            elif index == length:
                  __snake_case   :     int	      =new_node
                  __snake_case   :     Optional[Any]	      =self.tail
                  __snake_case   :     Optional[Any]	      =new_node
            else:
                  __snake_case   :     Any	      =self.head
                  for _ in range(0				, a    ):
                        __snake_case   :     Optional[Any]	      =temp.next
                  __snake_case   :     Optional[Any]	      =new_node
                  __snake_case   :     Union[str, Any]	      =temp.previous
                  __snake_case   :     List[Any]	      =temp
                  __snake_case   :     str	      =new_node


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            return self.delete_at_nth(0    )


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            return self.delete_at_nth(len(self    ) - 1    )


      def _UpperCamelCase    (     self					:						Optional[int]				, a					:						int    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =len(self    )

            if not 0 <= index <= length - 1:
                  raise IndexError('''list index out of range'''    )
            __snake_case   :     str	      =self.head  # default first node
            if length == 1:
                  __snake_case   :     List[Any]	      =None
            elif index == 0:
                  __snake_case   :     List[Any]	      =self.head.next
                  __snake_case   :     Optional[int]	      =None
            elif index == length - 1:
                  __snake_case   :     str	      =self.tail
                  __snake_case   :     Union[str, Any]	      =self.tail.previous
                  __snake_case   :     Optional[Any]	      =None
            else:
                  __snake_case   :     Optional[int]	      =self.head
                  for _ in range(0				, a    ):
                        __snake_case   :     List[Any]	      =temp.next
                  __snake_case   :     Union[str, Any]	      =temp
                  __snake_case   :     Optional[int]	      =temp.previous
                  __snake_case   :     int	      =temp.next
            return delete_node.data


      def _UpperCamelCase    (     self					:						List[Any]				, a					:						Any    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =self.head

            while current.data != data:  # Find the position to delete
                  if current.next:
                        __snake_case   :     Dict	      =current.next
                  else:  # We have reached the end an no value matches
                        raise ValueError('''No data matching given value'''    )

            if current == self.head:
                  self.delete_head()

            elif current == self.tail:
                  self.delete_tail()

            else:  # Before: 1 <--> 2(current) <--> 3
                  __snake_case   :     Tuple	      =current.next  # 1 --> 3
                  __snake_case   :     Optional[Any]	      =current.previous  # 1 <--> 3
            return data







      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return len(self    ) == 0



def      __lowercase       (     )       ->       None:
      __snake_case   :     List[str]	      =DoublyLinkedList()
      assert linked_list.is_empty() is True
      assert str(a			) == ""

      try:
            linked_list.delete_head()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      try:
            linked_list.delete_tail()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      for i in range(10			):
            assert len(a			) == i
            linked_list.insert_at_nth(a							,		i + 1			)
      assert str(a			) == "->".join(str(a			) for i in range(1							,		11			)			)

      linked_list.insert_at_head(0			)
      linked_list.insert_at_tail(11			)
      assert str(a			) == "->".join(str(a			) for i in range(0							,		12			)			)

      assert linked_list.delete_head() == 0
      assert linked_list.delete_at_nth(9			) == 10
      assert linked_list.delete_tail() == 11
      assert len(a			) == 9
      assert str(a			) == "->".join(str(a			) for i in range(1							,		10			)			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


