





'''simple docstring'''







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

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






				"""simple docstring"""







				def __init__(  self     :					List[Any]					,				__a     :					Optional[Any]					):
								super().__init__()
								_a								= model
								_a								= 2
								_a								= nn.Linear(self.model.config.hidden_size					,				self.num_labels					)




				def   UpperCamelCase__   (  self     :					List[Any]					):
								pass






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

				_a								= torch.load(lowercase    ,							map_location=torch.device("cpu"						)						)
				lightning_model.load_state_dict(ckpt["state_dict"]						)

				# init longformer question answering model
				_a								= LongformerForQuestionAnswering.from_pretrained(lowercase						)

				# 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(lowercase						)

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


if __name__ == "__main__":
					lowerCAmelCase_					:    List[str]      	=			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.'
					)
					lowerCAmelCase_					:    Dict      	=			parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)








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


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


