


"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def        lowercase							(		lowerCAmelCase__ :     str			) ->      int:
		__a           =			[
		    '''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__ :     Optional[int]			) ->      str:
		__a      ,     __a           =			emb.weight.shape
		__a           =			nn.Linear(lowerCAmelCase__    ,					lowerCAmelCase__    ,					bias=lowerCAmelCase__			)
		__a           =			emb.weight.data
		return lin_layer

def        lowercase							(		lowerCAmelCase__ :     Optional[int]			) ->      Tuple:
		__a           =			torch.load(lowerCAmelCase__    ,					map_location='''cpu'''			)
		__a           =			Namespace(**checkpoint['''cfg''']['''model''']			)
		__a           =			checkpoint['''model''']
		remove_ignore_keys_(lowerCAmelCase__			)
		__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=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           =			XGLMForCausalLM(lowerCAmelCase__			)
		__a           =			model.load_state_dict(lowerCAmelCase__    ,					strict=lowerCAmelCase__			)
		print(lowerCAmelCase__			)
		__a           =			make_linear_from_emb(model.model.embed_tokens			)

		return model


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










"""simple docstring"""




def        lowercase							(		lowerCAmelCase__ :     list			) ->      bool:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		if len(lowerCAmelCase__			) == 1:
				return True
		__a           =			series[1] - series[0]
		for index in range(len(lowerCAmelCase__			) - 1			):
				if series[index + 1] - series[index] != common_diff:
						return False
		return True

def        lowercase							(		lowerCAmelCase__ :     list			) ->      float:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		__a           =			0
		for val in series:
				answer += val
		return answer / len(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






