'''simple docstring'''

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


_A			:      int					      =				logging.get_logger(__name__)

_A			:      Dict					      =				{
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Tuple      	=					"""lilt"""
 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Tuple=3_05_22      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=7_68      ,       SCREAMING_SNAKE_CASE__   :	Dict=12      ,       SCREAMING_SNAKE_CASE__   :	Tuple=12      ,       SCREAMING_SNAKE_CASE__   :	int=30_72      ,       SCREAMING_SNAKE_CASE__   :	Dict="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	str=0.1      ,       SCREAMING_SNAKE_CASE__   :	str=5_12      ,       SCREAMING_SNAKE_CASE__   :	int=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=1e-1_2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	List[Any]="absolute"      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=None      ,       SCREAMING_SNAKE_CASE__   :	Any=4      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=10_24      ,       **SCREAMING_SNAKE_CASE__   :	Any      ,       )  ->    Dict:
  super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__      ,       **SCREAMING_SNAKE_CASE__							)

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







'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """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      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		max_position_embeddings=10_24						,		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											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, 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.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






