


"""simple docstring"""


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def  __snake_case       (   UpperCamelCase__	)      ->		List[Any]:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A       , A					       = emb.weight.shape
		A					       = nn.Linear(UpperCamelCase__ ,			UpperCamelCase__ ,			bias=UpperCamelCase__	)
		A					       = emb.weight.data
		return lin_layer







def  __snake_case       (   UpperCamelCase__	)      ->		str:







		"""simple docstring"""





		A					       = torch.load(UpperCamelCase__ ,			map_location='cpu'	)
		A					       = Namespace(**checkpoint['cfg']['model']	)
		A					       = checkpoint['model']
		remove_ignore_keys_(UpperCamelCase__	)
		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=UpperCamelCase__ ,			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(UpperCamelCase__	)
		A					       = model.load_state_dict(UpperCamelCase__ ,			strict=UpperCamelCase__	)
		print(UpperCamelCase__	)
		A					       = make_linear_from_emb(model.model.embed_tokens	)

		return model


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









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





