


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 					lowerCamelCase_   (	_a     ,       _a=1	):




		"""simple docstring"""






		if n_shave_prefix_segments >= 0:
				return ".".join(path.split('''.'''	)[n_shave_prefix_segments:]	)
		else:
				return ".".join(path.split('''.'''	)[:n_shave_prefix_segments]	)






def 					lowerCamelCase_   (	_a     ,       _a=0	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= []
		for old_item in old_list:
				lowerCAmelCase__       :      Any   					= old_item.replace('''in_layers.0'''     ,       '''norm1'''	)
				lowerCAmelCase__       :      Dict   					= new_item.replace('''in_layers.2'''     ,       '''conv1'''	)

				lowerCAmelCase__       :      List[Any]   					= new_item.replace('''out_layers.0'''     ,       '''norm2'''	)
				lowerCAmelCase__       :      Dict   					= new_item.replace('''out_layers.3'''     ,       '''conv2'''	)

				lowerCAmelCase__       :      Dict   					= new_item.replace('''emb_layers.1'''     ,       '''time_emb_proj'''	)
				lowerCAmelCase__       :      List[Any]   					= new_item.replace('''skip_connection'''     ,       '''conv_shortcut'''	)

				lowerCAmelCase__       :      int   					= shave_segments(_a     ,       n_shave_prefix_segments=_a	)

				mapping.append({'''old''': old_item, '''new''': new_item}	)

		return mapping






def 					lowerCamelCase_   (	_a     ,       _a=0	):




		"""simple docstring"""






		lowerCAmelCase__       :      int   					= []
		for old_item in old_list:
				lowerCAmelCase__       :      Any   					= old_item

				lowerCAmelCase__       :      str   					= new_item.replace('''norm.weight'''     ,       '''group_norm.weight'''	)
				lowerCAmelCase__       :      Union[str, Any]   					= new_item.replace('''norm.bias'''     ,       '''group_norm.bias'''	)

				lowerCAmelCase__       :      Dict   					= new_item.replace('''proj_out.weight'''     ,       '''proj_attn.weight'''	)
				lowerCAmelCase__       :      List[str]   					= new_item.replace('''proj_out.bias'''     ,       '''proj_attn.bias'''	)

				lowerCAmelCase__       :      Optional[int]   					= shave_segments(_a     ,       n_shave_prefix_segments=_a	)

				mapping.append({'''old''': old_item, '''new''': new_item}	)

		return mapping






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None     ,       _a=None     ,       _a=None	):




		"""simple docstring"""






		assert isinstance(_a     ,       _a	), "Paths should be a list of dicts containing 'old' and 'new' keys."

		# Splits the attention layers into three variables.
		if attention_paths_to_split is not None:
				for path, path_map in attention_paths_to_split.items():
						lowerCAmelCase__       :      Optional[Any]   					= old_checkpoint[path]
						lowerCAmelCase__       :      Union[str, Any]   					= old_tensor.shape[0] // 3

						lowerCAmelCase__       :      List[Any]   					= (-1, channels) if len(old_tensor.shape	) == 3 else (-1)

						lowerCAmelCase__       :      Dict   					= old_tensor.shape[0] // config['''num_head_channels'''] // 3

						lowerCAmelCase__       :      Optional[int]   					= old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]	)
						lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__       :      int   					= old_tensor.split(channels // num_heads     ,       dim=1	)

						lowerCAmelCase__       :      Optional[Any]   					= query.reshape(_a	)
						lowerCAmelCase__       :      int   					= key.reshape(_a	)
						lowerCAmelCase__       :      str   					= value.reshape(_a	)

		for path in paths:
				lowerCAmelCase__       :      str   					= path['''new''']

				# These have already been assigned
				if attention_paths_to_split is not None and new_path in attention_paths_to_split:
						continue

				# Global renaming happens here
				lowerCAmelCase__       :      Optional[int]   					= new_path.replace('''middle_block.0'''     ,       '''mid_block.resnets.0'''	)
				lowerCAmelCase__       :      Any   					= new_path.replace('''middle_block.1'''     ,       '''mid_block.attentions.0'''	)
				lowerCAmelCase__       :      int   					= new_path.replace('''middle_block.2'''     ,       '''mid_block.resnets.1'''	)

				if additional_replacements is not None:
						for replacement in additional_replacements:
								lowerCAmelCase__       :      Dict   					= new_path.replace(replacement['''old''']     ,       replacement['''new''']	)

        # proj_attn.weight has to be converted from conv 1D to linear
				if "proj_attn.weight" in new_path:
						lowerCAmelCase__       :      int   					= old_checkpoint[path['''old''']][:, :, 0]
				else:
						lowerCAmelCase__       :      int   					= old_checkpoint[path['''old''']]






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= {}

		lowerCAmelCase__       :      Optional[int]   					= checkpoint['''time_embed.0.weight''']
		lowerCAmelCase__       :      Any   					= checkpoint['''time_embed.0.bias''']
		lowerCAmelCase__       :      str   					= checkpoint['''time_embed.2.weight''']
		lowerCAmelCase__       :      Dict   					= checkpoint['''time_embed.2.bias''']

		lowerCAmelCase__       :      Union[str, Any]   					= checkpoint['''input_blocks.0.0.weight''']
		lowerCAmelCase__       :      str   					= checkpoint['''input_blocks.0.0.bias''']

		lowerCAmelCase__       :      str   					= checkpoint['''out.0.weight''']
		lowerCAmelCase__       :      Union[str, Any]   					= checkpoint['''out.0.bias''']
		lowerCAmelCase__       :      Optional[Any]   					= checkpoint['''out.2.weight''']
		lowerCAmelCase__       :      List[Any]   					= checkpoint['''out.2.bias''']

		# Retrieves the keys for the input blocks only
		lowerCAmelCase__       :      int   					= len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''input_blocks''' in layer}	)
		lowerCAmelCase__       :      Dict   					= {
		    layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
		    for layer_id in range(_a	)
		}

		# Retrieves the keys for the middle blocks only
		lowerCAmelCase__       :      List[Any]   					= len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''middle_block''' in layer}	)
		lowerCAmelCase__       :      str   					= {
		    layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
		    for layer_id in range(_a	)
		}

		# Retrieves the keys for the output blocks only
		lowerCAmelCase__       :      Optional[Any]   					= len({'''.'''.join(layer.split('''.'''	)[:2]	) for layer in checkpoint if '''output_blocks''' in layer}	)
		lowerCAmelCase__       :      Optional[int]   					= {
		    layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
		    for layer_id in range(_a	)
		}

		for i in range(1     ,       _a	):
				lowerCAmelCase__       :      Any   					= (i - 1) // (config['''num_res_blocks'''] + 1)
				lowerCAmelCase__       :      Any   					= (i - 1) % (config['''num_res_blocks'''] + 1)

				lowerCAmelCase__       :      Tuple   					= [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
				lowerCAmelCase__       :      str   					= [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

				if f'input_blocks.{i}.0.op.weight' in checkpoint:
						lowerCAmelCase__       :      Any   					= checkpoint[
						    f'input_blocks.{i}.0.op.weight'
						]
						lowerCAmelCase__       :      List[Any]   					= checkpoint[
						    f'input_blocks.{i}.0.op.bias'
						]
						continue

				lowerCAmelCase__       :      List[str]   					= renew_resnet_paths(_a	)
				lowerCAmelCase__       :      Optional[Any]   					= {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
				lowerCAmelCase__       :      int   					= {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
				assign_to_checkpoint(
				    _a     ,       _a     ,       _a     ,       additional_replacements=[meta_path, resnet_op]     ,       config=_a	)

				if len(_a	):
						lowerCAmelCase__       :      Tuple   					= renew_attention_paths(_a	)
						lowerCAmelCase__       :      Optional[Any]   					= {
						    '''old''': f'input_blocks.{i}.1',
						    '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
						}
						lowerCAmelCase__       :      List[str]   					= {
						    f'input_blocks.{i}.1.qkv.bias': {
						        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
						        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
						        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
						    },
						    f'input_blocks.{i}.1.qkv.weight': {
						        '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
						        '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
						        '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
						    },
						}
						assign_to_checkpoint(
						    _a     ,       _a     ,       _a     ,       additional_replacements=[meta_path]     ,       attention_paths_to_split=_a     ,       config=_a     ,       )

		lowerCAmelCase__       :      int   					= middle_blocks[0]
		lowerCAmelCase__       :      int   					= middle_blocks[1]
		lowerCAmelCase__       :      Optional[int]   					= middle_blocks[2]

		lowerCAmelCase__       :      Dict   					= renew_resnet_paths(_a	)
		assign_to_checkpoint(_a     ,       _a     ,       _a     ,       config=_a	)

		lowerCAmelCase__       :      str   					= renew_resnet_paths(_a	)
		assign_to_checkpoint(_a     ,       _a     ,       _a     ,       config=_a	)

		lowerCAmelCase__       :      Union[str, Any]   					= renew_attention_paths(_a	)
		lowerCAmelCase__       :      List[str]   					= {
		    '''middle_block.1.qkv.bias''': {
		        '''key''': '''mid_block.attentions.0.key.bias''',
		        '''query''': '''mid_block.attentions.0.query.bias''',
		        '''value''': '''mid_block.attentions.0.value.bias''',
		    },
		    '''middle_block.1.qkv.weight''': {
		        '''key''': '''mid_block.attentions.0.key.weight''',
		        '''query''': '''mid_block.attentions.0.query.weight''',
		        '''value''': '''mid_block.attentions.0.value.weight''',
		    },
		}
		assign_to_checkpoint(
		    _a     ,       _a     ,       _a     ,       attention_paths_to_split=_a     ,       config=_a	)

		for i in range(_a	):
				lowerCAmelCase__       :      Tuple   					= i // (config['''num_res_blocks'''] + 1)
				lowerCAmelCase__       :      List[str]   					= i % (config['''num_res_blocks'''] + 1)
				lowerCAmelCase__       :      Optional[Any]   					= [shave_segments(_a     ,       2	) for name in output_blocks[i]]
				lowerCAmelCase__       :      Union[str, Any]   					= {}

				for layer in output_block_layers:
						lowerCAmelCase__					,     lowerCAmelCase__       :      Any   					= layer.split('''.'''	)[0], shave_segments(_a     ,       1	)
						if layer_id in output_block_list:
								output_block_list[layer_id].append(_a	)
						else:
								lowerCAmelCase__       :      Optional[Any]   					= [layer_name]

				if len(_a	) > 1:
						lowerCAmelCase__       :      List[Any]   					= [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
						lowerCAmelCase__       :      Optional[int]   					= [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

						lowerCAmelCase__       :      Union[str, Any]   					= renew_resnet_paths(_a	)
						lowerCAmelCase__       :      Dict   					= renew_resnet_paths(_a	)

						lowerCAmelCase__       :      Optional[Any]   					= {'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
						assign_to_checkpoint(_a     ,       _a     ,       _a     ,       additional_replacements=[meta_path]     ,       config=_a	)

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								lowerCAmelCase__       :      Union[str, Any]   					= list(output_block_list.values()	).index(['''conv.weight''', '''conv.bias''']	)
								lowerCAmelCase__       :      Optional[Any]   					= checkpoint[
								    f'output_blocks.{i}.{index}.conv.weight'
								]
								lowerCAmelCase__       :      Union[str, Any]   					= checkpoint[
								    f'output_blocks.{i}.{index}.conv.bias'
								]

								# Clear attentions as they have been attributed above.
								if len(_a	) == 2:
										lowerCAmelCase__       :      int   					= []

						if len(_a	):
								lowerCAmelCase__       :      Union[str, Any]   					= renew_attention_paths(_a	)
								lowerCAmelCase__       :      str   					= {
								    '''old''': f'output_blocks.{i}.1',
								    '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
								}
								lowerCAmelCase__       :      List[str]   					= {
								    f'output_blocks.{i}.1.qkv.bias': {
								        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
								        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
								        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
								    },
								    f'output_blocks.{i}.1.qkv.weight': {
								        '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
								        '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
								        '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
								    },
								}
								assign_to_checkpoint(
								    _a     ,       _a     ,       _a     ,       additional_replacements=[meta_path]     ,       attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions	) else None     ,       config=_a     ,       )
				else:
						lowerCAmelCase__       :      Tuple   					= renew_resnet_paths(_a     ,       n_shave_prefix_segments=1	)
						for path in resnet_0_paths:
								lowerCAmelCase__       :      Union[str, Any]   					= '''.'''.join(['''output_blocks''', str(_a	), path['''old''']]	)
								lowerCAmelCase__       :      Optional[int]   					= '''.'''.join(['''up_blocks''', str(_a	), '''resnets''', str(_a	), path['''new''']]	)

								lowerCAmelCase__       :      Optional[int]   					= checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
		lowerCamelCase						=		argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)

		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the architecture.''',
		)

		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

		lowerCamelCase						=		parser.parse_args()

		lowerCamelCase						=		torch.load(args.checkpoint_path)

		with open(args.config_file) as f:
				lowerCamelCase						=		json.loads(f.read())

		lowerCamelCase						=		convert_ldm_checkpoint(checkpoint, config)

		if "ldm" in config:
				del config["ldm"]

		lowerCamelCase						=		UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		try:
				lowerCamelCase						=		DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
				lowerCamelCase						=		VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

				lowerCamelCase						=		LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
				pipe.save_pretrained(args.dump_path)
		except:  # noqa: E722
				model.save_pretrained(args.dump_path)








class  _a							:




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

			self._normalize_graph(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Tuple   					= len(_SCREAMING_SNAKE_CASE		)
			lowerCAmelCase__       :      Optional[int]   					= None




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			if sources is int:
					lowerCAmelCase__       :      List[Any]   					= [sources]
			if sinks is int:
					lowerCAmelCase__       :      Optional[Any]   					= [sinks]

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

			lowerCAmelCase__       :      Union[str, Any]   					= sources[0]
			lowerCAmelCase__       :      Dict   					= sinks[0]

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

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

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




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, Any]:
			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 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	int:
			lowerCAmelCase__       :      int   					= algorithm(self		)





class  _a							:




	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple		)->	Union[str, Any]:
			lowerCAmelCase__       :      Tuple   					= flow_network
			lowerCAmelCase__       :      Dict   					= flow_network.verticesCount
			lowerCAmelCase__       :      Optional[Any]   					= 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__       :      str   					= flow_network.graph
			lowerCAmelCase__       :      Optional[int]   					= False




	def 							UpperCAmelCase__( self						:		List[str]		)->	Dict:
			if not self.executed:
					self._algorithm()
					lowerCAmelCase__       :      Any   					= True




	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	int:
			pass





class  _a							(    _lowercase):




	def __init__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		List[Any]		)->	Union[str, Any]:
			super().__init__(_SCREAMING_SNAKE_CASE		)
			# use this to save your result
			lowerCAmelCase__       :      Dict   					= -1




	def 							UpperCAmelCase__( self						:		Any		)->	Optional[Any]:
			if not self.executed:
					raise Exception('''You should execute algorithm before using its result!'''		)

			return self.maximum_flow





class  _a							(    _lowercase):




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

			lowerCAmelCase__       :      Any   					= [[0] * self.verticies_count for i in range(self.verticies_count		)]

			lowerCAmelCase__       :      Optional[Any]   					= [0] * self.verticies_count
			lowerCAmelCase__       :      str   					= [0] * self.verticies_count




	def 							UpperCAmelCase__( self						:		Any		)->	List[Any]:
			lowerCAmelCase__       :      Optional[Any]   					= 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__       :      Any   					= [
			    i
			    for i in range(self.verticies_count		)
			    if i != self.source_index and i != self.sink_index
			]

			# move through list
			lowerCAmelCase__       :      str   					= 0
			while i < len(_SCREAMING_SNAKE_CASE		):
					lowerCAmelCase__       :      Union[str, Any]   					= vertices_list[i]
					lowerCAmelCase__       :      Any   					= self.heights[vertex_index]
					self.process_vertex(_SCREAMING_SNAKE_CASE		)
					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(_SCREAMING_SNAKE_CASE		)		)
							lowerCAmelCase__       :      Optional[Any]   					= 0
					else:
							i += 1

			lowerCAmelCase__       :      Optional[Any]   					= sum(self.preflow[self.source_index]		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		)->	Optional[int]:
			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(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)

					self.relabel(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      Union[str, 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 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[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__       :      List[Any]   					= self.heights[to_index]

			if min_height is not None:
					lowerCAmelCase__       :      Optional[Any]   					= min_height + 1


if __name__ == "__main__":
		lowerCamelCase						=		[0]
		lowerCamelCase						=		[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],
		# ]
		lowerCamelCase						=		[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

		# prepare our network
		lowerCamelCase						=		FlowNetwork(graph, entrances, exits)
		# set algorithm
		flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
		# and calculate
		lowerCamelCase						=		flow_network.find_maximum_flow()

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




