




import warnings

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


_A     =			logging.get_logger(__name__)

_A     =			{
    '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   'mvp'
		__snake_case									=   ['past_key_values']
		__snake_case									=   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



		def __init__(     self,	UpperCamelCase__=5_0267,	UpperCamelCase__=1024,	UpperCamelCase__=12,	UpperCamelCase__=4096,	UpperCamelCase__=16,	UpperCamelCase__=12,	UpperCamelCase__=4096,	UpperCamelCase__=16,	UpperCamelCase__=0.0,	UpperCamelCase__=0.0,	UpperCamelCase__="gelu",	UpperCamelCase__=1024,	UpperCamelCase__=0.1,	UpperCamelCase__=0.0,	UpperCamelCase__=0.0,	UpperCamelCase__=0.02,	UpperCamelCase__=0.0,	UpperCamelCase__=False,	UpperCamelCase__=True,	UpperCamelCase__=1,	UpperCamelCase__=0,	UpperCamelCase__=2,	UpperCamelCase__=True,	UpperCamelCase__=2,	UpperCamelCase__=2,	UpperCamelCase__=False,	UpperCamelCase__=100,	UpperCamelCase__=800,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  vocab_size
								lowerCAmelCase_      			=  max_position_embeddings
								lowerCAmelCase_      			=  d_model
								lowerCAmelCase_      			=  encoder_ffn_dim
								lowerCAmelCase_      			=  encoder_layers
								lowerCAmelCase_      			=  encoder_attention_heads
								lowerCAmelCase_      			=  decoder_ffn_dim
								lowerCAmelCase_      			=  decoder_layers
								lowerCAmelCase_      			=  decoder_attention_heads
								lowerCAmelCase_      			=  dropout
								lowerCAmelCase_      			=  attention_dropout
								lowerCAmelCase_      			=  activation_dropout
								lowerCAmelCase_      			=  activation_function
								lowerCAmelCase_      			=  init_std
								lowerCAmelCase_      			=  encoder_layerdrop
								lowerCAmelCase_      			=  decoder_layerdrop
								lowerCAmelCase_      			=  classifier_dropout
								lowerCAmelCase_      			=  use_cache
								lowerCAmelCase_      			=  encoder_layers
								lowerCAmelCase_      			=  scale_embedding  # scale factor will be sqrt(d_model) if True
								lowerCAmelCase_      			=  use_prompt
								lowerCAmelCase_      			=  prompt_length
								lowerCAmelCase_      			=  prompt_mid_dim

								super().__init__(
								    pad_token_id=UpperCamelCase__,	bos_token_id=UpperCamelCase__,	eos_token_id=UpperCamelCase__,	is_encoder_decoder=UpperCamelCase__,	decoder_start_token_id=UpperCamelCase__,	forced_eos_token_id=UpperCamelCase__,	**UpperCamelCase__,	)

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''',	UpperCamelCase__   ):
														lowerCAmelCase_      			=  self.bos_token_id
														warnings.warn(
														    f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
														    '''The config can simply be saved and uploaded again to be fixed.'''   )










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  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_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


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




