



"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def __lowercase			(							_a    ):
    snake_case_ :     Any				=	[
        """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			(							_a    ):
    snake_case_ :     Union[str, Any]				=	emb.weight.shape
    snake_case_ :     Optional[int]				=	nn.Linear(lowerCAmelCase_		,			lowerCAmelCase_		,			bias=lowerCAmelCase_    )
    snake_case_ :     int				=	emb.weight.data
    return lin_layer




def __lowercase			(							_a    ):
    snake_case_ :     Tuple				=	torch.load(lowerCAmelCase_		,			map_location='''cpu'''    )
    snake_case_ :     List[Any]				=	Namespace(**checkpoint['''cfg''']['''model''']    )
    snake_case_ :     str				=	checkpoint["""model"""]
    remove_ignore_keys_(lowerCAmelCase_    )
    snake_case_ :     int				=	state_dict["""decoder.embed_tokens.weight"""].shape[0]

    snake_case_ :     List[Any]				=	{key.replace('''decoder'''		,			'''model'''    ): val for key, val in state_dict.items()}

    snake_case_ :     Union[str, Any]				=	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		,			)

    snake_case_ :     Dict				=	XGLMForCausalLM(lowerCAmelCase_    )
    snake_case_ :     List[str]				=	model.load_state_dict(lowerCAmelCase_		,			strict=lowerCAmelCase_    )
    print(lowerCAmelCase_    )
    snake_case_ :     List[Any]				=	make_linear_from_emb(model.model.embed_tokens    )

    return model


if __name__ == "__main__":
     lowercase__    :  int							    =	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__    :  List[str]							    =	parser.parse_args()
     lowercase__    :  Dict							    =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
     model.save_pretrained(args.pytorch_dump_folder_path)










'''simple docstring'''






from torch import nn



class 							lowercase       (  nn.Module				):


 """simple docstring"""


 def __init__(							self  ,a_  ,a_							)		->   List[Any]:
        super().__init__()
        _UpperCAmelCase  :						Dict			     = class_size
        _UpperCAmelCase  :						Union[str, Any]			     = embed_size
        # self.mlp1 = nn.Linear(embed_size, embed_size)
        # self.mlp2 = (nn.Linear(embed_size, class_size))
        _UpperCAmelCase  :						List[Any]			     = nn.Linear(a_  ,a_							)







 def _snake_case (							self  ,a_							)		->   Tuple:
        # hidden_state = nn.functional.relu(self.mlp1(hidden_state))
        # hidden_state = self.mlp2(hidden_state)
        _UpperCAmelCase  :						Optional[int]			     = self.mlp(a_							)
        return logits



