




"""simple docstring"""







class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			List[str]					):
										lowerCAmelCase  =				None
										lowerCAmelCase  =				None
										lowerCAmelCase  =				graph

										self._normalize_graph(lowerCAmelCase						,						lowerCAmelCase					)
										lowerCAmelCase  =				len(lowerCAmelCase					)
										lowerCAmelCase  =				None


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[Any]					):
										if sources is int:
														lowerCAmelCase  =				[sources]
										if sinks is int:
														lowerCAmelCase  =				[sinks]

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

										lowerCAmelCase  =				sources[0]
										lowerCAmelCase  =				sinks[0]

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

														lowerCAmelCase  =				len(self.graph					) + 1
														for room in self.graph:
																		room.insert(0						,						0					)
														self.graph.insert(0						,						[0] * size					)
														for i in sources:
																		lowerCAmelCase  =				max_input_flow
														lowerCAmelCase  =				0

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


						def   __lowercase (							self				:			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   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				algorithm(self					)






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			str					):
										lowerCAmelCase  =				flow_network
										lowerCAmelCase  =				flow_network.verticesCount
										lowerCAmelCase  =				flow_network.sourceIndex
										lowerCAmelCase  =				flow_network.sinkIndex
										# it's just a reference, so you shouldn't change
										# it in your algorithms, use deep copy before doing that
										lowerCAmelCase  =				flow_network.graph
										lowerCAmelCase  =				False


						def   __lowercase (							self				:			Optional[Any]					):
										if not self.executed:
														self._algorithm()
														lowerCAmelCase  =				True




						def   __lowercase (							self				:			Optional[int]					):
										pass






class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			str					):
										super().__init__(lowerCAmelCase					)
										# use this to save your result
										lowerCAmelCase  =				-1




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

										return self.maximum_flow






class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Dict					):
										super().__init__(lowerCAmelCase					)

										lowerCAmelCase  =				[[0] * self.verticies_count for i in range(self.verticies_count					)]

										lowerCAmelCase  =				[0] * self.verticies_count
										lowerCAmelCase  =				[0] * self.verticies_count


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				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  =				[
										    i
										    for i in range(self.verticies_count					)
										    if i != self.source_index and i != self.sink_index
										]

										# move through list
										lowerCAmelCase  =				0
										while i < len(lowerCAmelCase					):
														lowerCAmelCase  =				vertices_list[i]
														lowerCAmelCase  =				self.heights[vertex_index]
														self.process_vertex(lowerCAmelCase					)
														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(lowerCAmelCase					)					)
																		lowerCAmelCase  =				0
														else:
																		i += 1

										lowerCAmelCase  =				sum(self.preflow[self.source_index]					)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			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(lowerCAmelCase						,						lowerCAmelCase					)

														self.relabel(lowerCAmelCase					)


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[Any]					):
										lowerCAmelCase  =				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   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				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  =				self.heights[to_index]

										if min_height is not None:
														lowerCAmelCase  =				min_height + 1


if __name__ == "__main__":
		a     :   Tuple      =							[0]
		a     :   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     :   int      =							[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

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

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











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """decoder.version""",
				    """decoder.output_projection.weight""",
				    """_float_tensor""",
				    """decoder.embed_positions._float_tensor""",
				]
				for k in ignore_keys:
								state_dict.pop(snake_case__      ,       snake_case__ )





def 							lowercase					(snake_case__   : List[str] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				lowerCAmelCase  =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

				lowerCAmelCase  =				{key.replace("""decoder"""      ,       """model""" ): val for key, val in state_dict.items()}

				lowerCAmelCase  =				XGLMConfig(
				    vocab_size=snake_case__      ,       max_position_embeddings=args.max_target_positions      ,       num_layers=args.decoder_layers      ,       attention_heads=args.decoder_attention_heads      ,       ffn_dim=args.decoder_ffn_embed_dim      ,       d_model=args.decoder_embed_dim      ,       layerdrop=args.decoder_layerdrop      ,       dropout=args.dropout      ,       attention_dropout=args.attention_dropout      ,       activation_dropout=args.activation_dropout      ,       activation_function="""gelu"""      ,       scale_embedding=not args.no_scale_embedding      ,       tie_word_embeddings=args.share_decoder_input_output_embed      ,       )

				lowerCAmelCase  =				XGLMForCausalLM(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
		parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		a      =							parser.parse_args()
		a      =							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





