






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def  a__ (__lowercase							:int   )		-> int:
				_A	:				int   =      [
				    '''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							:Any   )		-> int:
				_A  ,				_A	:				Optional[Any]   =      emb.weight.shape
				_A	:				Optional[int]   =      nn.Linear(__lowercase					,  __lowercase					,  bias=__lowercase   )
				_A	:				Optional[int]   =      emb.weight.data
				return lin_layer



def  a__ (__lowercase							:str   )		-> List[str]:
				_A	:				Any   =      torch.load(__lowercase					,  map_location='''cpu'''   )
				_A	:				Optional[Any]   =      Namespace(**checkpoint['''cfg''']['''model''']   )
				_A	:				Optional[int]   =      checkpoint['''model''']
				remove_ignore_keys_(__lowercase   )
				_A	:				Any   =      state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

				_A	:				int   =      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	:				Optional[Any]   =      XGLMForCausalLM(__lowercase   )
				_A	:				Dict   =      model.load_state_dict(__lowercase					,  strict=__lowercase   )
				print(__lowercase   )
				_A	:				Dict   =      make_linear_from_emb(model.model.embed_tokens   )

				return model


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











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


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



