






class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=data
									__UpperCamelCase :   Any									=None
									__UpperCamelCase :   Any									=None







				def __str__(					self       ):




									"""simple docstring"""

									return f'{self.data}'




class 							__A	:





				"""simple docstring"""







				def __init__(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Union[str, Any]									=None
									__UpperCamelCase :   Union[str, Any]									=None


				def __iter__(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=self.head
									while node:
														yield node.data
														__UpperCamelCase :   int									=node.next


				def __str__(					self       ):




									"""simple docstring"""

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


				def __len__(					self       ):




									"""simple docstring"""

									return sum(1 for _ in self       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									self.insert_at_nth(0				,    _A       )


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									self.insert_at_nth(len(self       )				,    _A       )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   int									=len(self       )

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


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self.delete_at_nth(0       )


				def  __lowercase				(					self       ):




									"""simple docstring"""

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


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=len(self       )

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


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=self.head

									while current.data != data:  # Find the position to delete
														if current.next:
																			__UpperCamelCase :   Tuple									=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
														__UpperCamelCase :   Optional[Any]									=current.next  # 1 --> 3
														__UpperCamelCase :   Union[str, Any]									=current.previous  # 1 <--> 3
									return data







				def  __lowercase				(					self       ):




									"""simple docstring"""

									return len(self       ) == 0






def 				A	(			)	->		None:
					__UpperCamelCase :   Optional[Any]									=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()



'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      UNetaDModel(
           block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,					up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           'image': image,
           'mask_image': mask,
           'generator': generator,
           'num_inference_steps': 5,
           'eta': 0.0,
           'jump_length': 2,
           'jump_n_sample': 2,
           'output_type': 'numpy',
       }
       return inputs






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      sd_pipe(**_A      ).images
       __A													=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __A													=      np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Union[str, Any]:







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


@nightly
@require_torch_gpu
class 			UpperCamelCase__	(     unittest.TestCase):






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

       assert image.shape == (256, 256, 3)
       assert np.abs(expected_image - image      ).mean() < 1E-2






