from typing import List, Union

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


UpperCamelCase    				=		logging.get_logger(__name__)

UpperCamelCase    				=		{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}



class     snake_case_			(						a__			):
   __A      :						Dict   =							"led"
   __A      :						int   =							{
       "num_attention_heads": "encoder_attention_heads",
       "hidden_size": "d_model",
       "attention_probs_dropout_prob": "attention_dropout",
       "initializer_range": "init_std",
   }






   def __init__( self							:	Any      ,		lowercase_							:	Optional[int]=5_02_65      ,		lowercase_							:	Union[str, Any]=1_63_84      ,		lowercase_							:	Dict=10_24      ,		lowercase_							:	Dict=12      ,		lowercase_							:	int=40_96      ,		lowercase_							:	Union[str, Any]=16      ,		lowercase_							:	str=12      ,		lowercase_							:	Union[str, Any]=40_96      ,		lowercase_							:	Union[str, Any]=16      ,		lowercase_							:	Dict=0.0      ,		lowercase_							:	Optional[int]=0.0      ,		lowercase_							:	int=True      ,		lowercase_							:	Tuple=True      ,		lowercase_							:	List[str]="gelu"      ,		lowercase_							:	List[Any]=10_24      ,		lowercase_							:	List[str]=0.1      ,		lowercase_							:	Union[str, Any]=0.0      ,		lowercase_							:	Optional[int]=0.0      ,		lowercase_							:	Union[str, Any]=0.02      ,		lowercase_							:	List[str]=2      ,		lowercase_							:	Tuple=0.0      ,		lowercase_							:	List[Any]=1      ,		lowercase_							:	int=0      ,		lowercase_							:	Optional[int]=2      ,		lowercase_							:	str = 5_12      ,		**lowercase_							:	Optional[int]      ,		)   ->		List[Any]:
       lowercase__					:  Union[str, Any]          =							vocab_size
       lowercase__					:  List[Any]          =							max_encoder_position_embeddings
       lowercase__					:  Optional[int]          =							max_decoder_position_embeddings
       lowercase__					:  List[Any]          =							d_model
       lowercase__					:  int          =							encoder_ffn_dim
       lowercase__					:  str          =							encoder_layers
       lowercase__					:  str          =							encoder_attention_heads
       lowercase__					:  Any          =							decoder_ffn_dim
       lowercase__					:  List[Any]          =							decoder_layers
       lowercase__					:  int          =							decoder_attention_heads
       lowercase__					:  Any          =							dropout
       lowercase__					:  Tuple          =							attention_dropout
       lowercase__					:  int          =							activation_dropout
       lowercase__					:  Any          =							activation_function
       lowercase__					:  Tuple          =							init_std
       lowercase__					:  Optional[Any]          =							encoder_layerdrop
       lowercase__					:  Dict          =							decoder_layerdrop
       lowercase__					:  Any          =							classifier_dropout
       lowercase__					:  str          =							use_cache
       lowercase__					:  List[str]          =							encoder_layers
       lowercase__					:  Union[str, Any]          =							attention_window

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







'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(__A ,     __A ,     __A       )

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
      torch.save(model.state_dict() ,     __A ,     _use_new_zipfile_serialization=__A       )


if __name__ == "__main__":
  a__    :   str								=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--bert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  a__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





