

"""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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Union[str, Any]  =    torch.load(__A ,   map_location='cpu'   )

    _lowerCAmelCase       :   List[Any]  =    chkpt['model']

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

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

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

    # Save pytorch-model
    _lowerCAmelCase       :   int  =    pytorch_dump_folder_path + '/' + WEIGHTS_NAME
    _lowerCAmelCase       :   Any  =    pytorch_dump_folder_path + '/' + CONFIG_NAME
    _lowerCAmelCase       :   Optional[Any]  =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

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

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

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


if __name__ == "__main__":
 _lowerCAmelCase						=		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."""
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""






import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

_lowerCAmelCase						=		"""</w>"""
_lowerCAmelCase						=		"""@@ """

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    set()
    _lowerCAmelCase       :   Dict  =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)   )
        _lowerCAmelCase       :   Any  =    char
    return pairs


# Speech2Text2 has no max input length
_lowerCAmelCase						=		{"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _UpperCAmelCase          =		["input_ids", "attention_mask"]

  def __init__(		self    ,_A    ,_A="<s>"    ,_A="<pad>"    ,_A="</s>"    ,_A="<unk>"    ,_A=False    ,_A=None    ,**_A    ,):
      '''simple docstring'''

      super().__init__(
          unk_token=_A    ,bos_token=_A    ,eos_token=_A    ,pad_token=_A    ,do_lower_case=_A    ,**_A    ,)

      _lowerCAmelCase       :   List[Any]  =    do_lower_case

      with open(_A    ,encoding='utf-8'      ) as vocab_handle:
          _lowerCAmelCase       :   Optional[int]  =    json.load(_A      )
      _lowerCAmelCase       :   Tuple  =    {v: k for k, v in self.encoder.items()}

      if merges_file is None:
          logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""      )

          _lowerCAmelCase       :   Optional[Any]  =    None
          _lowerCAmelCase       :   Tuple  =    None
      else:
          with open(_A    ,encoding='utf-8'      ) as merges_handle:
              _lowerCAmelCase       :   Optional[Any]  =    merges_handle.read().split('\n'      )[:-1]

          _lowerCAmelCase       :   List[str]  =    [tuple(merge.split()[:2]      ) for merge in merges]
          _lowerCAmelCase       :   List[Any]  =    dict(zip(_A    ,range(len(_A      )      )      )      )
          _lowerCAmelCase       :   Union[str, Any]  =    {}

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.decoder      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return dict(self.encoder    ,**self.added_tokens_encoder      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
      if token in self.cache:
          return self.cache[token]
      _lowerCAmelCase       :   str  =    get_pairs(_A      )

      if not pairs:
          return token

      while True:
          _lowerCAmelCase       :   List[str]  =    min(_A    ,key=lambda _A      : self.bpe_ranks.get(_A    ,float('inf'      )      )      )
          if bigram not in self.bpe_ranks:
              break
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    bigram
          _lowerCAmelCase       :   Union[str, Any]  =    []
          _lowerCAmelCase       :   Dict  =    0
          while i < len(_A      ):
              try:
                  _lowerCAmelCase       :   Dict  =    word.index(_A    ,_A      )
              except ValueError:
                  new_word.extend(word[i:]      )
                  break
              else:
                  new_word.extend(word[i:j]      )
                  _lowerCAmelCase       :   Optional[Any]  =    j

              if word[i] == first and i < len(_A      ) - 1 and word[i + 1] == second:
                  new_word.append(first + second      )
                  i += 2
              else:
                  new_word.append(word[i]      )
                  i += 1
          _lowerCAmelCase       :   Optional[Any]  =    tuple(_A      )
          _lowerCAmelCase       :   List[str]  =    new_word
          if len(_A      ) == 1:
              break
          else:
              _lowerCAmelCase       :   List[str]  =    get_pairs(_A      )
      _lowerCAmelCase       :   Any  =    ' '.join(_A      )
      if word == "\n  " + BPE_TOKEN_MERGES:
          _lowerCAmelCase       :   str  =    '\n' + BPE_TOKEN_MERGES

      if word.endswith(_A      ):
          _lowerCAmelCase       :   Dict  =    word.replace(_A    ,''      )

      _lowerCAmelCase       :   str  =    word.replace(' '    ,_A      )
      _lowerCAmelCase       :   str  =    word
      return word

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if self.bpe_ranks is None:
          raise ValueError(
              'This tokenizer was instantiated without a `merges.txt` file, so'
              ' that it can only be used for decoding, not for encoding.'
              'Make sure to provide `merges.txt` file at instantiation to enable '
              'encoding.'      )

      if self.do_lower_case:
          _lowerCAmelCase       :   Optional[Any]  =    text.lower()

      _lowerCAmelCase       :   Tuple  =    text.split()

      _lowerCAmelCase       :   Union[str, Any]  =    []
      for token in text:
          if token:
              split_tokens.extend(list(self.bpe(_A      ).split(' '      )      )      )

      return split_tokens

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.encoder.get(_A    ,self.encoder.get(self.unk_token      )      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.decoder.get(_A    ,self.unk_token      )
      return result

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    ' '.join(_A      )

      # make sure @@ tokens are concatenated
      _lowerCAmelCase       :   int  =    ''.join(string.split(_A      )      )

      return string







  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      _lowerCAmelCase       :   List[Any]  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
      _lowerCAmelCase       :   str  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

      with open(_A    ,'w'    ,encoding='utf-8'      ) as f:
          f.write(json.dumps(self.encoder    ,indent=2    ,sort_keys=_A    ,ensure_ascii=_A      ) + '\n'      )

      _lowerCAmelCase       :   str  =    0
      if self.bpe_ranks is None:
          return (vocab_file,)

      with open(_A    ,'w'    ,encoding='utf-8'      ) as writer:
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,key=lambda _A      : kv[1]      ):
              if index != token_index:
                  logger.warning(
                      F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                      ' Please check that the tokenizer is not corrupted!'      )
                  _lowerCAmelCase       :   Dict  =    token_index
              writer.write(' '.join(_A      ) + '\n'      )
              index += 1

      return (vocab_file, merges_file)





