






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= [
					    '''decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(_lowercase					,			_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A							,_A								= emb.weight.shape
					_A								= nn.Linear(_lowercase					,			_lowercase					,			bias=_lowercase )
					_A								= emb.weight.data
					return lin_layer




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= torch.load(_lowercase					,			map_location='''cpu''' )
					_A								= Namespace(**checkpoint['''cfg''']['''model'''] )
					_A								= checkpoint['''model''']
					remove_ignore_keys_(_lowercase )
					_A								= state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

					_A								= XGLMConfig(
					    vocab_size=_lowercase					,			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								= XGLMForCausalLM(_lowercase )
					_A								= model.load_state_dict(_lowercase					,			strict=_lowercase )
					print(_lowercase )
					_A								= 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)











__A      =				{0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}

__A      =				{0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= []

					for neighbour in graph[vert]:
										if not visited[neighbour]:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					order.append(_lowercase )

					return order




def      __A					(     _lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= True
					_A								= [vert]

					for neighbour in reversed_graph[vert]:
										if not visited[neighbour]:
															component += find_components(_lowercase					,			_lowercase					,			_lowercase )

					return component




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= len(_lowercase ) * [False]
					_A								= {vert: [] for vert in range(len(_lowercase ) )}

					for vert, neighbours in graph.items():
										for neighbour in neighbours:
															reversed_graph[neighbour].append(_lowercase )

					_A								= []
					for i, was_visited in enumerate(_lowercase ):
										if not was_visited:
															order += topology_sort(_lowercase					,			_lowercase					,			_lowercase )

					_A								= []
					_A								= len(_lowercase ) * [False]

					for i in range(len(_lowercase ) ):
										_A								= order[len(_lowercase ) - i - 1]
										if not visited[vert]:
															_A								= find_components(_lowercase					,			_lowercase					,			_lowercase )
															components_list.append(_lowercase )

					return components_list



