



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			List[str]:  # noqa: E741






						'''simple docstring'''



						UpperCAmelCase_=       len(lowerCAmelCase_    )
						UpperCAmelCase_=       0
						UpperCAmelCase_=       [0] * n
						UpperCAmelCase_=       [False] * n
						UpperCAmelCase_=       [False] * n

						def dfs(lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Tuple      ,lowerCAmelCase_       : Tuple    ):
												if parent == root:
																		out_edge_count += 1
												UpperCAmelCase_=       True
												UpperCAmelCase_=       at

												for to in l[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								UpperCAmelCase_=       dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )

																								# AP found via bridge
																								if at < low[to]:
																														UpperCAmelCase_=       True
																								# AP found via cycle
																								if at == low[to]:
																														UpperCAmelCase_=       True
																		else:
																								UpperCAmelCase_=       min(low[at]      ,lowerCAmelCase_    )
												return out_edge_count

						for i in range(lowerCAmelCase_    ):
												if not visited[i]:
																		UpperCAmelCase_=       0
																		UpperCAmelCase_=       dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,-1      ,lowerCAmelCase_    )
																		UpperCAmelCase_=       out_edge_count > 1

						for x in range(len(lowerCAmelCase_    )    ):
												if is_art[x] is True:
																		print(lowerCAmelCase_    )


# Adjacency list of graph
__A       =						{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}



compute_ap(data)









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



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

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

						UpperCAmelCase_=       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      ,)

						UpperCAmelCase_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       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)




