



'''simple docstring'''





import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      int:
  # Load checkpoint
  __lowerCamelCase       :       str           = torch.load(_lowerCAmelCase       ,map_location='cpu'       )

  __lowerCamelCase       :       Union[str, Any]           = chkpt['model']

  # We have the base model one level deeper than the original XLM repository
  __lowerCamelCase       :       Any           = {}
  for k, v in state_dict.items():
    if "pred_layer" in k:
      __lowerCamelCase       :       Optional[Any]           = v
    else:
      __lowerCamelCase       :       Any           = v

  __lowerCamelCase       :       int           = chkpt['params']
  __lowerCamelCase       :       List[str]           = {n: v for n, v in config.items() if not isinstance(_lowerCAmelCase       ,(torch.FloatTensor, numpy.ndarray)       )}

  __lowerCamelCase       :       List[Any]           = chkpt['dico_word2id']
  __lowerCamelCase       :       str           = {s + '</w>' if s.find('@@'       ) == -1 and i > 13 else s.replace('@@'       ,''       ): i for s, i in vocab.items()}

  # Save pytorch-model
  __lowerCamelCase       :       List[str]           = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  __lowerCamelCase       :       List[str]           = pytorch_dump_folder_path + '/' + CONFIG_NAME
  __lowerCamelCase       :       int           = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

  print(F'Save PyTorch model to {pytorch_weights_dump_path}'       )
  torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

  print(F'Save configuration file to {pytorch_config_dump_path}'       )
  with open(_lowerCAmelCase       ,'w'       ,encoding='utf-8'       ) as f:
    f.write(json.dumps(_lowerCAmelCase       ,indent=2       ) + '\n'       )

  print(F'Save vocab file to {pytorch_config_dump_path}'       )
  with open(_lowerCAmelCase       ,'w'       ,encoding='utf-8'       ) as f:
    f.write(json.dumps(_lowerCAmelCase       ,indent=2       ) + '\n'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase											=						logging.get_logger(__name__)

_UpperCamelCase											=						{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												="""xlm-roberta"""




  def __init__( self      :	Any			,	_a      :	Optional[Any]=3_0522			,	_a      :	Optional[Any]=768			,	_a      :	int=12			,	_a      :	Tuple=12			,	_a      :	str=3072			,	_a      :	List[Any]="gelu"			,	_a      :	int=0.1			,	_a      :	Optional[int]=0.1			,	_a      :	Optional[int]=512			,	_a      :	List[Any]=2			,	_a      :	Optional[Any]=0.02			,	_a      :	str=1e-12			,	_a      :	str=1			,	_a      :	str=0			,	_a      :	Optional[Any]=2			,	_a      :	Optional[int]="absolute"			,	_a      :	int=True			,	_a      :	Tuple=None			,	**_a      :	Any			,	)					->					Dict:
    super().__init__(pad_token_id=_a			,	bos_token_id=_a			,	eos_token_id=_a			,	**_a   )

    __lowerCamelCase       :       int           = vocab_size
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       int           = num_hidden_layers
    __lowerCamelCase       :       Optional[int]           = num_attention_heads
    __lowerCamelCase       :       List[str]           = hidden_act
    __lowerCamelCase       :       Any           = intermediate_size
    __lowerCamelCase       :       int           = hidden_dropout_prob
    __lowerCamelCase       :       List[Any]           = attention_probs_dropout_prob
    __lowerCamelCase       :       Optional[int]           = max_position_embeddings
    __lowerCamelCase       :       Tuple           = type_vocab_size
    __lowerCamelCase       :       Optional[int]           = initializer_range
    __lowerCamelCase       :       Optional[int]           = layer_norm_eps
    __lowerCamelCase       :       str           = position_embedding_type
    __lowerCamelCase       :       List[Any]           = use_cache
    __lowerCamelCase       :       int           = classifier_dropout






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  @property
  def 						_lowercase     ( self      :	Optional[Any]   )					->					Mapping[str, Mapping[int, str]]:
    if self.task == "multiple-choice":
      __lowerCamelCase       :       int           = {0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      __lowerCamelCase       :       Any           = {0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]   )






