




lowerCamelCase__				:     List[Any]			       =							float('inf')





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	Tuple       ,       _lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_					=    [0] * n
       SCREAMING_SNAKE_CASE_					=    [0] * n
       SCREAMING_SNAKE_CASE_					=    [0] * n
       SCREAMING_SNAKE_CASE_					=    [[] for _ in range(_lowerCAmelCase		)]



 def    lowerCAmelCase_						(							self						:	str       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	Tuple=0		):
       self.adj[a].append([b, len(self.adj[b]		), c, 0]		)
       self.adj[b].append([a, len(self.adj[a]		) - 1, rcap, 0]		)



 def    lowerCAmelCase_						(							self						:	Optional[Any]       ,       _lowerCAmelCase						:	Optional[int]       ,       _lowerCAmelCase						:	Optional[Any]       ,       _lowerCAmelCase						:	List[str]		):
       if vertex == sink or not flow:
             return flow

       for i in range(self.ptr[vertex]       ,       len(self.adj[vertex]		)		):
             SCREAMING_SNAKE_CASE_					=    self.adj[vertex][i]
             if self.lvl[e[0]] == self.lvl[vertex] + 1:
                   SCREAMING_SNAKE_CASE_					=    self.depth_first_search(e[0]       ,       _lowerCAmelCase       ,       min(_lowerCAmelCase       ,       e[2] - e[3]		)		)
                   if p:
                         self.adj[vertex][i][3] += p
                         self.adj[e[0]][e[1]][3] -= p
                         return p
             SCREAMING_SNAKE_CASE_					=    self.ptr[vertex] + 1
       return 0




 def    lowerCAmelCase_						(							self						:	Dict       ,       _lowerCAmelCase						:	Dict       ,       _lowerCAmelCase						:	Union[str, Any]		):
       SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    0, source
       for l in range(31		):  # noqa: E741  l = 30 maybe faster for random data
             while True:
                   SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    [0] * len(self.q		), [0] * len(self.q		)
                   SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    0, 1, 1
                   while qi < qe and not self.lvl[sink]:
                         SCREAMING_SNAKE_CASE_					=    self.q[qi]
                         qi += 1
                         for e in self.adj[v]:
                               if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                                     SCREAMING_SNAKE_CASE_					=    e[0]
                                     qe += 1
                                     SCREAMING_SNAKE_CASE_					=    self.lvl[v] + 1

                   SCREAMING_SNAKE_CASE_					=    self.depth_first_search(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)
                   while p:
                         flow += p
                         SCREAMING_SNAKE_CASE_					=    self.depth_first_search(_lowerCAmelCase       ,       _lowerCAmelCase       ,       _lowerCAmelCase		)

                   if not self.lvl[sink]:
                         break

       return flow


lowerCamelCase__				:     List[Any]			       =							Dinic(10)
lowerCamelCase__				:     Optional[Any]			       =							0
lowerCamelCase__				:     List[Any]			       =							9
for vertex in range(1, 5):
   graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
   graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
   graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))





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  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE ,     unittest.TestCase      ):


 '''simple docstring'''







 lowercase_       					=					RePaintPipeline
 lowercase_       					=					IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
 lowercase_       					=					PipelineTesterMixin.required_optional_params - {
     "latents",
     "num_images_per_prompt",
     "callback",
     "callback_steps",
 }
 lowercase_       					=					IMAGE_INPAINTING_BATCH_PARAMS
 lowercase_       					=					False



 def    lowerCAmelCase_						(							self						:	int		):
       torch.manual_seed(0		)
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_					=    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')       ,       )
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler()
       SCREAMING_SNAKE_CASE_					=    {'unet': unet, 'scheduler': scheduler}
       return components



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Tuple       ,       _lowerCAmelCase						:	str=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_					=    torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_					=    torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    np.random.RandomState(_lowerCAmelCase		).standard_normal((1, 3, 32, 32)		)
       SCREAMING_SNAKE_CASE_					=    torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    (image > 0).to(device=_lowerCAmelCase       ,       dtype=torch.floataa		)
       SCREAMING_SNAKE_CASE_					=    {
           '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    lowerCAmelCase_						(							self						:	Optional[Any]		):
       SCREAMING_SNAKE_CASE_					=    'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_					=    self.get_dummy_components()
       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    sd_pipe.to(_lowerCAmelCase		)
       sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

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

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_					=    np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]		)

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



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_local()



 @unittest.skip('non-deterministic pipeline'		)
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_inference_batch_single_identical()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	List[Any]		):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def    lowerCAmelCase_						(							self						:	str		):
       return super().test_save_load_optional_components()




 @skip_mps
 def    lowerCAmelCase_						(							self						:	Tuple		):
       return super().test_attention_slicing_forward_pass()





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


 '''simple docstring'''







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




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

       SCREAMING_SNAKE_CASE_					=    'google/ddpm-ema-celebahq-256'
       SCREAMING_SNAKE_CASE_					=    UNetaDModel.from_pretrained(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    RePaintScheduler.from_pretrained(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    RePaintPipeline(unet=_lowerCAmelCase       ,       scheduler=_lowerCAmelCase		).to(_lowerCAmelCase		)
       repaint.set_progress_bar_config(disable=_lowerCAmelCase		)
       repaint.enable_attention_slicing()

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

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