






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


SCREAMING_SNAKE_CASE     :   Any 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     :   Tuple 							=						{
    'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class 							A_    (						UpperCAmelCase__			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					"gpt_bigcode"
 _SCREAMING_SNAKE_CASE							:			Any =					["past_key_values"]
 _SCREAMING_SNAKE_CASE							:			int =					{
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }


 def __init__(	self							,  _A=50257							,  _A=1024							,  _A=768							,  _A=12							,  _A=12							,  _A=None							,  _A="gelu_pytorch_tanh"							,  _A=0.1							,  _A=0.1							,  _A=0.1							,  _A=1e-5							,  _A=0.02							,  _A=True							,  _A=True							,  _A=50256							,  _A=50256							,  _A=True							,  _A=True							,  _A=True							,  **_A							,  ) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				vocab_size
     _UpperCAmelCase				:      Union[str, Any]			  =				n_positions
     _UpperCAmelCase				:      Optional[int]			  =				n_embd
     _UpperCAmelCase				:      Optional[int]			  =				n_layer
     _UpperCAmelCase				:      Tuple			  =				n_head
     _UpperCAmelCase				:      Dict			  =				n_inner
     _UpperCAmelCase				:      Any			  =				activation_function
     _UpperCAmelCase				:      Tuple			  =				resid_pdrop
     _UpperCAmelCase				:      Tuple			  =				embd_pdrop
     _UpperCAmelCase				:      Dict			  =				attn_pdrop
     _UpperCAmelCase				:      List[str]			  =				layer_norm_epsilon
     _UpperCAmelCase				:      Tuple			  =				initializer_range
     _UpperCAmelCase				:      int			  =				scale_attn_weights
     _UpperCAmelCase				:      Union[str, Any]			  =				use_cache
     _UpperCAmelCase				:      List[str]			  =				attention_softmax_in_fpaa
     _UpperCAmelCase				:      List[str]			  =				scale_attention_softmax_in_fpaa
     _UpperCAmelCase				:      Any			  =				multi_query

     _UpperCAmelCase				:      int			  =				bos_token_id
     _UpperCAmelCase				:      List[str]			  =				eos_token_id

     super().__init__(bos_token_id=lowerCamelCase__							,  eos_token_id=lowerCamelCase__							,  **lowerCamelCase__)












import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class 							A_    (						pl.LightningModule			):






 '''simple docstring'''




 def __init__(	self							,  _A) ->		List[str]:

     """simple docstring"""



     super().__init__()
     _UpperCAmelCase				:      Dict			  =				model
     _UpperCAmelCase				:      Union[str, Any]			  =				2
     _UpperCAmelCase				:      List[Any]			  =				nn.Linear(self.model.config.hidden_size							,  self.num_labels)


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				LongformerForQuestionAnswering.from_pretrained(__A )

    # transfer weights
    longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
    longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
    longformer_for_qa.eval()

    # save model
    longformer_for_qa.save_pretrained(__A )

    print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''' )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--longformer_model',
      default=None,
      type=str,
      required=True,
      help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
  )
  parser.add_argument(
      '--longformer_question_answering_ckpt_path',
      default=None,
      type=str,
      required=True,
      help='Path the official PyTorch Lightning Checkpoint.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




