





class    __SCREAMING_SNAKE_CASE			:







       def __init__(      self     :	Any		,					A     :	str		,					A     :	Any		,					A     :	Union[str, Any]			)   ->Optional[int]:
          lowerCamelCase__   :						List[str]					   =							None
          lowerCamelCase__   :						Tuple					   =							None
          lowerCamelCase__   :						List[Any]					   =							graph

          self._normalize_graph(A		,					A			)
          lowerCamelCase__   :						List[str]					   =							len(A			)
          lowerCamelCase__   :						Union[str, Any]					   =							None







       def 						__lowerCamelCase    (      self     :	Union[str, Any]		,					A     :	List[str]		,					A     :	List[str]			)   ->List[Any]:







          if sources is int:
             lowerCamelCase__   :						Optional[int]					   =							[sources]
          if sinks is int:
             lowerCamelCase__   :						Optional[Any]					   =							[sinks]

          if len(A			) == 0 or len(A			) == 0:
             return

          lowerCamelCase__   :						Union[str, Any]					   =							sources[0]
          lowerCamelCase__   :						Optional[int]					   =							sinks[0]

          # make fake vertex if there are more
          # than one source or sink
          if len(A			) > 1 or len(A			) > 1:
             lowerCamelCase__   :						Optional[Any]					   =							0
             for i in sources:
                max_input_flow += sum(self.graph[i]			)

             lowerCamelCase__   :						Dict					   =							len(self.graph			) + 1
             for room in self.graph:
                room.insert(0		,					0			)
             self.graph.insert(0		,					[0] * size			)
             for i in sources:
                lowerCamelCase__   :						List[str]					   =							max_input_flow
             lowerCamelCase__   :						Optional[int]					   =							0

             lowerCamelCase__   :						Dict					   =							len(self.graph			) + 1
             for room in self.graph:
                room.append(0			)
             self.graph.append([0] * size			)
             for i in sinks:
                lowerCamelCase__   :						List[str]					   =							max_input_flow
             lowerCamelCase__   :						str					   =							size - 1







       def 						__lowerCamelCase    (      self     :	List[Any]			)   ->str:







          if self.maximum_flow_algorithm is None:
             raise Exception('''You need to set maximum flow algorithm before.'''			)
          if self.source_index is None or self.sink_index is None:
             return 0

          self.maximum_flow_algorithm.execute()
          return self.maximum_flow_algorithm.getMaximumFlow()







       def 						__lowerCamelCase    (      self     :	Tuple		,					A     :	Optional[Any]			)   ->List[str]:
          lowerCamelCase__   :						List[str]					   =							algorithm(self			)


class    __SCREAMING_SNAKE_CASE			:







       def __init__(      self     :	Optional[Any]		,					A     :	int			)   ->Tuple:
          lowerCamelCase__   :						Optional[int]					   =							flow_network
          lowerCamelCase__   :						Optional[Any]					   =							flow_network.verticesCount
          lowerCamelCase__   :						List[str]					   =							flow_network.sourceIndex
          lowerCamelCase__   :						Optional[Any]					   =							flow_network.sinkIndex
          # it's just a reference, so you shouldn't change
          # it in your algorithms, use deep copy before doing that
          lowerCamelCase__   :						Optional[Any]					   =							flow_network.graph
          lowerCamelCase__   :						Dict					   =							False







       def 						__lowerCamelCase    (      self     :	Dict			)   ->Dict:







          if not self.executed:
             self._algorithm()
             lowerCamelCase__   :						Tuple					   =							True







       def 						__lowerCamelCase    (      self     :	List[str]			)   ->int:
          pass


class    __SCREAMING_SNAKE_CASE			( lowerCAmelCase_					):







       def __init__(      self     :	Any		,					A     :	Optional[int]			)   ->Any:
          super().__init__(A			)
          # use this to save your result
          lowerCamelCase__   :						int					   =							-1







       def 						__lowerCamelCase    (      self     :	Dict			)   ->int:







          if not self.executed:
             raise Exception('''You should execute algorithm before using its result!'''			)

          return self.maximum_flow


class    __SCREAMING_SNAKE_CASE			( lowerCAmelCase_					):







       def __init__(      self     :	List[str]		,					A     :	Union[str, Any]			)   ->List[Any]:
          super().__init__(A			)

          lowerCamelCase__   :						int					   =							[[0] * self.verticies_count for i in range(self.verticies_count			)]

          lowerCamelCase__   :						Union[str, Any]					   =							[0] * self.verticies_count
          lowerCamelCase__   :						Dict					   =							[0] * self.verticies_count







       def 						__lowerCamelCase    (      self     :	Tuple			)   ->Dict:
          lowerCamelCase__   :						Dict					   =							self.verticies_count

          # push some substance to graph
          for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]			):
             self.preflow[self.source_index][nextvertex_index] += bandwidth
             self.preflow[nextvertex_index][self.source_index] -= bandwidth
             self.excesses[nextvertex_index] += bandwidth

          # Relabel-to-front selection rule
          lowerCamelCase__   :						Union[str, Any]					   =							[
              i
              for i in range(self.verticies_count			)
              if i != self.source_index and i != self.sink_index
          ]

          # move through list
          lowerCamelCase__   :						List[Any]					   =							0
          while i < len(A			):
             lowerCamelCase__   :						int					   =							vertices_list[i]
             lowerCamelCase__   :						Any					   =							self.heights[vertex_index]
             self.process_vertex(A			)
             if self.heights[vertex_index] > previous_height:
                # if it was relabeled, swap elements
                # and start from 0 index
                vertices_list.insert(0		,					vertices_list.pop(A			)			)
                lowerCamelCase__   :						Optional[Any]					   =							0
             else:
                i += 1

          lowerCamelCase__   :						List[Any]					   =							sum(self.preflow[self.source_index]			)







       def 						__lowerCamelCase    (      self     :	Dict		,					A     :	List[Any]			)   ->List[Any]:







          while self.excesses[vertex_index] > 0:
             for neighbour_index in range(self.verticies_count			):
                # if it's neighbour and current vertex is higher
                if (
                    self.graph[vertex_index][neighbour_index]
                    - self.preflow[vertex_index][neighbour_index]
                    > 0
                    and self.heights[vertex_index] > self.heights[neighbour_index]
                ):
                   self.push(A		,					A			)

             self.relabel(A			)







       def 						__lowerCamelCase    (      self     :	str		,					A     :	Dict		,					A     :	List[Any]			)   ->Optional[Any]:
          lowerCamelCase__   :						List[Any]					   =							min(
              self.excesses[from_index]		,					self.graph[from_index][to_index] - self.preflow[from_index][to_index]		,					)
          self.preflow[from_index][to_index] += preflow_delta
          self.preflow[to_index][from_index] -= preflow_delta
          self.excesses[from_index] -= preflow_delta
          self.excesses[to_index] += preflow_delta







       def 						__lowerCamelCase    (      self     :	Any		,					A     :	Dict			)   ->int:
          lowerCamelCase__   :						Optional[int]					   =							None
          for to_index in range(self.verticies_count			):
             if (
                 self.graph[vertex_index][to_index]
                 - self.preflow[vertex_index][to_index]
                 > 0
             ) and (min_height is None or self.heights[to_index] < min_height):
                lowerCamelCase__   :						Tuple					   =							self.heights[to_index]

          if min_height is not None:
             lowerCamelCase__   :						List[str]					   =							min_height + 1


if __name__ == "__main__":
       _A					:    int   =  [0]
       _A					:    Union[str, Any]   =  [3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       _A					:    List[Any]   =  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       _A					:    Union[str, Any]   =  FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       _A					:    Dict   =  flow_network.find_maximum_flow()

       print(F'''maximum flow is {maximum_flow}''')









import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  ShapEImgaImgPipeline
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  ['''image''']
							UpperCamelCase__					     =  [
							    '''num_images_per_prompt''',
							    '''num_inference_steps''',
							    '''generator''',
							    '''latents''',
							    '''guidance_scale''',
							    '''frame_size''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCamelCase__					     =  False
							@property
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								return 32
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								return self.time_input_dim * 4
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								return 8
							@property
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							CLIPVisionConfig(
								    hidden_size=self.text_embedder_hidden_size		,      image_size=64		,      projection_dim=self.text_embedder_hidden_size		,      intermediate_size=37		,      num_attention_heads=4		,      num_channels=3		,      num_hidden_layers=5		,      patch_size=1		,      )

								a										=							CLIPVisionModel(__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								a										=							CLIPImageProcessor(
								    crop_size=224		,      do_center_crop=__magic_name__		,      do_normalize=__magic_name__		,      do_resize=__magic_name__		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

								return image_processor
							@property
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """num_attention_heads""": 2,
								    """attention_head_dim""": 16,
								    """embedding_dim""": self.time_input_dim,
								    """num_embeddings""": 32,
								    """embedding_proj_dim""": self.text_embedder_hidden_size,
								    """time_embed_dim""": self.time_embed_dim,
								    """num_layers""": 1,
								    """clip_embed_dim""": self.time_input_dim * 2,
								    """additional_embeddings""": 0,
								    """time_embed_act_fn""": """gelu""",
								    """norm_in_type""": """layer""",
								    """embedding_proj_norm_type""": """layer""",
								    """encoder_hid_proj_type""": None,
								    """added_emb_type""": None,
								}

								a										=							PriorTransformer(**__magic_name__				)
								return model
							@property
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								torch.manual_seed(0				)

								a										=							{
								    """param_shapes""": (
								        (self.renderer_dim, 93),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								        (self.renderer_dim, 8),
								    ),
								    """d_latent""": self.time_input_dim,
								    """d_hidden""": self.renderer_dim,
								    """n_output""": 12,
								    """background""": (
								        0.1,
								        0.1,
								        0.1,
								    ),
								}
								a										=							ShapERenderer(**__magic_name__				)
								return model
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							self.dummy_prior
								a										=							self.dummy_image_encoder
								a										=							self.dummy_image_processor
								a										=							self.dummy_renderer

								a										=							HeunDiscreteScheduler(
								    beta_schedule="""exp"""		,      num_train_timesteps=1024		,      prediction_type="""sample"""		,      use_karras_sigmas=__magic_name__		,      clip_sample=__magic_name__		,      clip_sample_range=1.0		,      )
								a										=							{
								    """prior""": prior,
								    """image_encoder""": image_encoder,
								    """image_processor""": image_processor,
								    """renderer""": renderer,
								    """scheduler""": scheduler,
								}

								return components
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:str		,      __magic_name__						:Tuple=0				):







								'''simple docstring'''







								a										=							floats_tensor((1, 3, 64, 64)		,      rng=random.Random(__magic_name__				)				).to(__magic_name__				)

								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							{
								    """image""": input_image,
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """frame_size""": 32,
								    """output_type""": """np""",
								}
								return inputs
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()

								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)

								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							pipe(**self.get_dummy_inputs(__magic_name__				)				)
								a										=							output.images[0]
								a										=							image[0, -3:, -3:, -1]

								assert image.shape == (20, 32, 32, 3)

								a										=							np.array(
								    [
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								        0.00039216,
								    ]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							torch_device == """cpu"""
								a										=							True
								self._test_inference_batch_single_identical(
								    batch_size=2		,      test_max_difference=__magic_name__		,      relax_max_difference=__magic_name__		,      )





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							1
								a										=							2

								a										=							self.get_dummy_inputs(__magic_name__				)

								for key in inputs.keys():
									if key in self.batch_params:
										a										=							batch_size * [inputs[key]]

								a										=							pipe(**__magic_name__		,      num_images_per_prompt=__magic_name__				)[0]

								assert images.shape[0] == batch_size * num_images_per_prompt







@slow
@require_torch_gpu
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/shap_e/corgi.png"""				)
								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/shap_e/test_shap_e_img2img_out.npy"""				)
								a										=							ShapEImgaImgPipeline.from_pretrained("""openai/shap-e-img2img"""				)
								a										=							pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							torch.Generator(device=__magic_name__				).manual_seed(0				)

								a										=							pipe(
								    __magic_name__		,      generator=__magic_name__		,      guidance_scale=3.0		,      num_inference_steps=64		,      frame_size=64		,      output_type="""np"""		,      ).images[0]

								assert images.shape == (20, 64, 64, 3)

								assert_mean_pixel_difference(__magic_name__		,      __magic_name__				)

