




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						 =					logging.get_logger(__name__)

lowerCAmelCase_						 =					{
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}
class 		_A   (    a__ ):
  _UpperCamelCase  :     Optional[int]					=     '''xlm'''
  _UpperCamelCase  :     List[Any]					=     {
      '''hidden_size''': '''emb_dim''',
      '''num_attention_heads''': '''n_heads''',
      '''num_hidden_layers''': '''n_layers''',
      '''n_words''': '''vocab_size''',  # For backward compatibility
  }






  def __init__(    self						:      Dict ,		_A						:      Tuple=30_145 ,		_A						:      Union[str, Any]=2_048 ,		_A						:      Tuple=12 ,		_A						:      Tuple=16 ,		_A						:      str=0.1 ,		_A						:      List[Any]=0.1 ,		_A						:      List[str]=True ,		_A						:      List[str]=False ,		_A						:      str=False ,		_A						:      int=False ,		_A						:      Optional[Any]=1 ,		_A						:      List[str]=True ,		_A						:      Union[str, Any]=512 ,		_A						:      List[str]=2_048**-0.5 ,		_A						:      Optional[int]=1E-12 ,		_A						:      Any=0.02 ,		_A						:      Optional[int]=0 ,		_A						:      str=1 ,		_A						:      Any=2 ,		_A						:      Optional[Any]=3 ,		_A						:      str=5 ,		_A						:      Optional[Any]=True ,		_A						:      int="first" ,		_A						:      int=True ,		_A						:      Union[str, Any]=None ,		_A						:      List[Any]=True ,		_A						:      List[str]=0.1 ,		_A						:      List[Any]=5 ,		_A						:      List[Any]=5 ,		_A						:      Optional[Any]=0 ,		_A						:      Optional[int]=0 ,		_A						:      List[Any]=2 ,		_A						:      List[Any]=0 ,		**_A						:      Dict ,		)						->       Any:






       """simple docstring"""







       lowercase   :  int        =					vocab_size
       lowercase   :  Optional[int]        =					emb_dim
       lowercase   :  Any        =					n_layers
       lowercase   :  Optional[Any]        =					n_heads
       lowercase   :  Optional[Any]        =					dropout
       lowercase   :  Optional[int]        =					attention_dropout
       lowercase   :  List[str]        =					gelu_activation
       lowercase   :  Any        =					sinusoidal_embeddings
       lowercase   :  List[Any]        =					causal
       lowercase   :  Any        =					asm
       lowercase   :  int        =					n_langs
       lowercase   :  List[Any]        =					use_lang_emb
       lowercase   :  Tuple        =					layer_norm_eps
       lowercase   :  Any        =					bos_index
       lowercase   :  Any        =					eos_index
       lowercase   :  Optional[Any]        =					pad_index
       lowercase   :  int        =					unk_index
       lowercase   :  List[Any]        =					mask_index
       lowercase   :  List[str]        =					is_encoder
       lowercase   :  Dict        =					max_position_embeddings
       lowercase   :  Any        =					embed_init_std
       lowercase   :  Tuple        =					init_std
       lowercase   :  Any        =					summary_type
       lowercase   :  Dict        =					summary_use_proj
       lowercase   :  Dict        =					summary_activation
       lowercase   :  Dict        =					summary_proj_to_labels
       lowercase   :  List[Any]        =					summary_first_dropout
       lowercase   :  str        =					start_n_top
       lowercase   :  Any        =					end_n_top
       lowercase   :  Tuple        =					mask_token_id
       lowercase   :  Tuple        =					lang_id

       if "n_words" in kwargs:
            lowercase   :  Dict        =					kwargs['n_words']

       super().__init__(pad_token_id=_UpperCAmelCase ,		bos_token_id=_UpperCAmelCase ,		**_UpperCAmelCase )


class 		_A   (    a__ ):






  @property
  def    __a     (    self						:      Tuple )						->       List[str]:






       """simple docstring"""







       if self.task == "multiple-choice":
            lowercase   :  List[Any]        =					{0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            lowercase   :  List[str]        =					{0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''token_type_ids''', dynamic_axis),
           ] )






'''simple docstring'''




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Tuple         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Union[str, Any]:
     __A		:					int						=			RobertaPreLayerNormConfig.from_pretrained(
         __snake_case					,				architectures=['RobertaPreLayerNormForMaskedLM']     )

     # convert state_dict
     __A		:					Tuple						=			torch.load(hf_hub_download(repo_id=__snake_case					,				filename='pytorch_model.bin'     )     )
     __A		:					str						=			{}
     for tensor_key, tensor_value in original_state_dict.items():
          # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
          if tensor_key.startswith('roberta.'     ):
               __A		:					Dict						=			'roberta_prelayernorm.' + tensor_key[len('roberta.'     ) :]

          # The original implementation contains weights which are not used, remove them from the state_dict
          if tensor_key.endswith('.self.LayerNorm.weight'     ) or tensor_key.endswith('.self.LayerNorm.bias'     ):
               continue

          __A		:					str						=			tensor_value

     __A		:					Union[str, Any]						=			RobertaPreLayerNormForMaskedLM.from_pretrained(
         pretrained_model_name_or_path=__snake_case					,				config=__snake_case					,				state_dict=__snake_case     )
     model.save_pretrained(__snake_case     )

     # convert tokenizer
     __A		:					List[Any]						=			AutoTokenizer.from_pretrained(__snake_case     )
     tokenizer.save_pretrained(__snake_case     )


if __name__ == "__main__":
  lowercase__       : Dict         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint-repo''',
      default=None,
      type=str,
      required=True,
      help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Optional[Any]         =       parser.parse_args()
  convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)