



"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def     _lowercase       (						__snake_case			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     Optional[int]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[Any]				= emb.weight.shape
			__lowerCAmelCase   :						List[Any]				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						List[str]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Optional[int]:
			__lowerCAmelCase   :						Tuple				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						str				= mam_aaa["args"] or mam_aaa["cfg"]["model"]
			__lowerCAmelCase   :						Union[str, Any]				= mam_aaa["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Tuple				= state_dict["encoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						List[Any]				= MaMaaaConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=1_024      ,encoder_layers=args.encoder_layers      ,decoder_layers=args.decoder_layers      ,encoder_attention_heads=args.encoder_attention_heads      ,decoder_attention_heads=args.decoder_attention_heads      ,encoder_ffn_dim=args.encoder_ffn_embed_dim      ,decoder_ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.encoder_embed_dim      ,encoder_layerdrop=args.encoder_layerdrop      ,decoder_layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="relu"      ,)

			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"]
			__lowerCAmelCase   :						Tuple				= MaMaaaForConditionalGeneration(__snake_case			)
			model.model.load_state_dict(__snake_case      ,strict=__snake_case			)
			__lowerCAmelCase   :						Dict				= make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
	__snake_case							:     Optional[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.')
	__snake_case							:     Tuple   =					parser.parse_args()
	__snake_case							:     List[str]   =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

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

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,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      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					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.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)