





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

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


lowercase				:	Optional[Any]      						=  logging.get_logger(__name__)


lowercase				:	List[str]      						=  {
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

lowercase				:	List[Any]      						=  {
    """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"""
        ),
    },
}

lowercase				:	Union[str, Any]      						=  """</w>"""
lowercase				:	List[Any]      						=  """@@ """



def A_     (		A__    )  ->		Optional[int]:
       a__						:     Any			=    set()
       a__						:     List[Any]			=    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)    )
              a__						:     Dict			=    char
       return pairs


# Speech2Text2 has no max input length
lowercase				:	Union[str, Any]      						=  {"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Any	  =  VOCAB_FILES_NAMES
   __A       :    Union[str, Any]	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    List[Any]	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    List[str]	  =  ['''input_ids''', '''attention_mask''']

   def __init__( self ,  lowercase ,  lowercase="<s>" ,  lowercase="<pad>" ,  lowercase="</s>" ,  lowercase="<unk>" ,  lowercase=False ,  lowercase=None ,  **lowercase ,  )      ->						Optional[Any]:







          '''simple docstring'''

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

          a__						:     int			=    do_lower_case

          with open(lowercase ,  encoding='utf-8') as vocab_handle:
                 a__						:     Optional[int]			=    json.load(lowercase)
          a__						:     Any			=    {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.')

                 a__						:     Union[str, Any]			=    None
                 a__						:     str			=    None
          else:
                 with open(lowercase ,  encoding='utf-8') as merges_handle:
                        a__						:     List[Any]			=    merges_handle.read().split('\n')[:-1]

                 a__						:     List[str]			=    [tuple(merge.split()[:2]) for merge in merges]
                 a__						:     Optional[Any]			=    dict(zip(lowercase ,  range(len(lowercase))))
                 a__						:     int			=    {}

   @property
   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          return len(self.decoder)

   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Tuple			=    tuple(token[:-1]) + (token[-1] + BPE_TOKEN_MERGES,)
          if token in self.cache:
                 return self.cache[token]
          a__						:     Any			=    get_pairs(lowercase)

          if not pairs:
                 return token

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

                        if word[i] == first and i < len(lowercase) - 1 and word[i + 1] == second:
                               new_word.append(first + second)
                               i += 2
                        else:
                               new_word.append(word[i])
                               i += 1
                 a__						:     int			=    tuple(lowercase)
                 a__						:     Tuple			=    new_word
                 if len(lowercase) == 1:
                        break
                 else:
                        a__						:     Dict			=    get_pairs(lowercase)
          a__						:     Any			=    ' '.join(lowercase)
          if word == "\n  " + BPE_TOKEN_MERGES:
                 a__						:     str			=    '\n' + BPE_TOKEN_MERGES

          if word.endswith(lowercase):
                 a__						:     Optional[int]			=    word.replace(lowercase ,  '')

          a__						:     int			=    word.replace(' ' ,  lowercase)
          a__						:     int			=    word
          return word

   def 							__lowercase		( self ,  lowercase)      ->						Optional[int]:







          '''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:
                 a__						:     Any			=    text.lower()

          a__						:     List[Any]			=    text.split()

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

          return split_tokens

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

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

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     Tuple			=    self.decoder.get(lowercase ,  self.unk_token)
          return result

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     Any			=    ' '.join(lowercase)

          # make sure @@ tokens are concatenated
          a__						:     Union[str, Any]			=    ''.join(string.split(lowercase))

          return string

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

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

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

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

          with open(lowercase ,  'w' ,  encoding='utf-8') as writer:
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,  key=lambda lowercase: 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!')
                               a__						:     Any			=    token_index
                        writer.write(' '.join(lowercase) + '\n')
                        index += 1

          return (vocab_file, merges_file)









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase				:	Union[str, Any]      						=  data_utils.TransfoXLTokenizer
lowercase				:	Optional[int]      						=  data_utils.TransfoXLCorpus
lowercase				:	List[Any]      						=  data_utils
lowercase				:	Tuple      						=  data_utils



def A_     (		A__		,				A__		,				A__		,				A__    )  ->		Optional[Any]:
       if transfo_xl_dataset_file:
              # Convert a pre-processed corpus (see original TensorFlow repo)
              with open(A__		,				'rb'    ) as fp:
                     a__						:     int			=    pickle.load(A__		,				encoding='latin1'    )
              # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
              a__						:     int			=    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
              print(F'Save vocabulary to {pytorch_vocab_dump_path}'    )
              a__						:     List[Any]			=    corpus.vocab.__dict__
              torch.save(A__		,				A__    )

              a__						:     Dict			=    corpus.__dict__
              corpus_dict_no_vocab.pop('vocab'		,				A__    )
              a__						:     Optional[int]			=    pytorch_dump_folder_path + '/' + CORPUS_NAME
              print(F'Save dataset to {pytorch_dataset_dump_path}'    )
              torch.save(A__		,				A__    )

       if tf_checkpoint_path:
              # Convert a pre-trained TensorFlow model
              a__						:     Union[str, Any]			=    os.path.abspath(A__    )
              a__						:     Optional[Any]			=    os.path.abspath(A__    )

              print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'    )
              # Initialise PyTorch model
              if transfo_xl_config_file == "":
                     a__						:     Dict			=    TransfoXLConfig()
              else:
                     a__						:     Dict			=    TransfoXLConfig.from_json_file(A__    )
              print(F'Building PyTorch model from configuration: {config}'    )
              a__						:     Optional[int]			=    TransfoXLLMHeadModel(A__    )

              a__						:     int			=    load_tf_weights_in_transfo_xl(A__		,				A__		,				A__    )
              # Save pytorch-model
              a__						:     Any			=    os.path.join(A__		,				A__    )
              a__						:     Dict			=    os.path.join(A__		,				A__    )
              print(F'Save PyTorch model to {os.path.abspath(A__    )}'    )
              torch.save(model.state_dict()		,				A__    )
              print(F'Save configuration file to {os.path.abspath(A__    )}'    )
              with open(A__		,				'w'		,				encoding='utf-8'    ) as f:
                     f.write(config.to_json_string()    )


if __name__ == "__main__":
    lowercase				:	List[Any]      						=  argparse.ArgumentParser()
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        required=True,
        help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
    )
    parser.add_argument(
        """--tf_checkpoint_path""",
        default="""""",
        type=str,
        help="""An optional path to a TensorFlow checkpoint path to be converted.""",
    )
    parser.add_argument(
        """--transfo_xl_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained BERT model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument(
        """--transfo_xl_dataset_file""",
        default="""""",
        type=str,
        help="""An optional dataset file to be converted in a vocabulary.""",
    )
    lowercase				:	Any      						=  parser.parse_args()
    convert_transfo_xl_checkpoint_to_pytorch(
        args.tf_checkpoint_path,
        args.transfo_xl_config_file,
        args.pytorch_dump_folder_path,
        args.transfo_xl_dataset_file,
    )


