






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 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	Dict						):
  # Load checkpoint
  __UpperCAmelCase				:      Optional[int]	 =    torch.load(_UpperCAmelCase    ,						map_location="""cpu"""						)

  __UpperCAmelCase				:      List[str]	 =    chkpt["""model"""]

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

  __UpperCAmelCase				:      str	 =    chkpt["""params"""]
  __UpperCAmelCase				:      Any	 =    {n: v for n, v in config.items() if not isinstance(_UpperCAmelCase    ,						(torch.FloatTensor, numpy.ndarray)						)}

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

  # Save pytorch-model
  __UpperCAmelCase				:      str	 =    pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __UpperCAmelCase				:      Optional[Any]	 =    pytorch_dump_folder_path + """/""" + CONFIG_NAME
  __UpperCAmelCase				:      Optional[Any]	 =    pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

  print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""						)
  torch.save(_UpperCAmelCase    ,						_UpperCAmelCase						)

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

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


if __name__ == "__main__":
       a						:			str     	=					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."
       )
       a						:			List[Any]     	=					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     :						Union[str, Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
    '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
    '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
    '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
    '''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
    '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
    '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
    '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
    '''bert-large-uncased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
    '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
    '''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-cased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-uncased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
    ),
    '''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class       a							(   a__  ):
 snake_case__             =       '''bert'''



 def __init__(     self				,       _snake_case=3_05_22				,       _snake_case=7_68				,       _snake_case=12				,       _snake_case=12				,       _snake_case=30_72				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case="absolute"				,       _snake_case=True				,       _snake_case=None				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case				,       **_snake_case     )

  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache
  lowerCAmelCase    				= classifier_dropout







class       a							(   a__  ):



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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





