





"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def      lowercase__ (					lowerCAmelCase__   :			int			)    ->			Tuple:



			'''simple docstring'''
			a__  :	int		 =			[
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(lowerCAmelCase__							,			lowerCAmelCase__			)






def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]			)    ->			Optional[Any]:



			'''simple docstring'''
			a__ ,	a__  :	str		 =			emb.weight.shape
			a__  :	Any		 =			nn.Linear(lowerCAmelCase__							,			lowerCAmelCase__							,			bias=lowerCAmelCase__			)
			a__  :	Optional[int]		 =			emb.weight.data
			return lin_layer






def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]			)    ->			Optional[int]:



			'''simple docstring'''
			a__  :	Tuple		 =			torch.load(lowerCAmelCase__							,			map_location="cpu"			)
			a__  :	Dict		 =			Namespace(**checkpoint["cfg"]["model"]			)
			a__  :	Optional[int]		 =			checkpoint["model"]
			remove_ignore_keys_(lowerCAmelCase__			)
			a__  :	Dict		 =			state_dict["decoder.embed_tokens.weight"].shape[0]

			a__  :	Optional[Any]		 =			{key.replace("decoder"							,			"model"			): val for key, val in state_dict.items()}

			a__  :	Dict		 =			XGLMConfig(
			    vocab_size=lowerCAmelCase__							,			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							,			)

			a__  :	Any		 =			XGLMForCausalLM(lowerCAmelCase__			)
			a__  :	int		 =			model.load_state_dict(lowerCAmelCase__							,			strict=lowerCAmelCase__			)
			print(lowerCAmelCase__			)
			a__  :	str		 =			make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
		__UpperCAmelCase						=			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.''')
		__UpperCAmelCase						=			parser.parse_args()
		__UpperCAmelCase						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""






__UpperCAmelCase						=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def      lowercase__ (					lowerCAmelCase__   :			Dict							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			Tuple			)    ->			Dict:



			'''simple docstring'''
			# Return True if there is node that has not iterated.
			a__  :	int		 =			[False] * len(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			[s]
			a__  :	Optional[int]		 =			True

			while queue:
						a__  :	Any		 =			queue.pop(0			)
						for ind in range(len(graph[u]			)			):
									if visited[ind] is False and graph[u][ind] > 0:
												queue.append(lowerCAmelCase__			)
												a__  :	Any		 =			True
												a__  :	Union[str, Any]		 =			u

			return visited[t]






def      lowercase__ (					lowerCAmelCase__   :			List[str]							,			lowerCAmelCase__   :			List[Any]							,			lowerCAmelCase__   :			Optional[Any]			)    ->			Any:



			'''simple docstring'''
			a__  :	Any		 =			[-1] * (len(lowerCAmelCase__			))
			a__  :	Any		 =			0
			a__  :	List[str]		 =			[]
			a__  :	Tuple		 =			[i[:] for i in graph]  # Record original cut, copy.
			while bfs(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			):
						a__  :	Any		 =			float("Inf"			)
						a__  :	Optional[int]		 =			sink

						while s != source:
									# Find the minimum value in select path
									a__  :	Union[str, Any]		 =			min(lowerCAmelCase__							,			graph[parent[s]][s]			)
									a__  :	List[str]		 =			parent[s]

						max_flow += path_flow
						a__  :	List[str]		 =			sink

						while v != source:
									a__  :	int		 =			parent[v]
									graph[u][v] -= path_flow
									graph[v][u] += path_flow
									a__  :	List[str]		 =			parent[v]

			for i in range(len(lowerCAmelCase__			)			):
						for j in range(len(graph[0]			)			):
									if graph[i][j] == 0 and temp[i][j] > 0:
												res.append((i, j)			)

			return res


if __name__ == "__main__":
		print(mincut(test_graph, source=0, sink=5))