




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
_lowerCAmelCase  :      Optional[int]						=      data_utils.TransfoXLTokenizer
_lowerCAmelCase  :      Union[str, Any]						=      data_utils.TransfoXLCorpus
_lowerCAmelCase  :      int						=      data_utils
_lowerCAmelCase  :      Dict						=      data_utils



def   lowerCAmelCase						(						_lowerCAmelCase							:      Optional[Any]  ,							_lowerCAmelCase							:      str  ,							_lowerCAmelCase							:      Tuple  ,							_lowerCAmelCase							:      Dict		):




 """simple docstring"""







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

  UpperCAmelCase__							       =  corpus.__dict__
  corpus_dict_no_vocab.pop("vocab"  ,							__UpperCAmelCase		)
  UpperCAmelCase__							       =  pytorch_dump_folder_path + "/" + CORPUS_NAME
  print(F'''Save dataset to {pytorch_dataset_dump_path}'''		)
  torch.save(__UpperCAmelCase  ,							__UpperCAmelCase		)

 if tf_checkpoint_path:
  # Convert a pre-trained TensorFlow model
  UpperCAmelCase__							       =  os.path.abspath(__UpperCAmelCase		)
  UpperCAmelCase__							       =  os.path.abspath(__UpperCAmelCase		)

  print(F'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'''		)
  # Initialise PyTorch model
  if transfo_xl_config_file == "":
   UpperCAmelCase__							       =  TransfoXLConfig()
  else:
   UpperCAmelCase__							       =  TransfoXLConfig.from_json_file(__UpperCAmelCase		)
  print(F'''Building PyTorch model from configuration: {config}'''		)
  UpperCAmelCase__							       =  TransfoXLLMHeadModel(__UpperCAmelCase		)

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


if __name__ == "__main__":
  _lowerCAmelCase  :      Union[str, 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.",
  )
  _lowerCAmelCase  :      Optional[int]						=      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,
  )











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowerCamelCase__ :   Union[str, Any]					=							logging.get_logger(__name__)

lowerCamelCase__ :   Dict					=							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__ :   Tuple					=							{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowerCamelCase__ :   int					=							{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


lowerCamelCase__ :   str					=							{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    VOCAB_FILES_NAMES
 lowercase_				    =    PRETRAINED_VOCAB_FILES_MAP
 lowercase_				    =    PRETRAINED_INIT_CONFIGURATION
 lowercase_				    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_				    =    SqueezeBertTokenizer



 def __init__(							self  :		Tuple				,				_lowerCAmelCase  :		Dict=None				,				_lowerCAmelCase  :		int=None				,				_lowerCAmelCase  :		List[str]=True				,				_lowerCAmelCase  :		str="[UNK]"				,				_lowerCAmelCase  :		Union[str, Any]="[SEP]"				,				_lowerCAmelCase  :		List[Any]="[PAD]"				,				_lowerCAmelCase  :		str="[CLS]"				,				_lowerCAmelCase  :		Dict="[MASK]"				,				_lowerCAmelCase  :		Optional[int]=True				,				_lowerCAmelCase  :		Dict=None				,				**_lowerCAmelCase  :		str				,				):
       super().__init__(
           _lowerCAmelCase				,				tokenizer_file=_lowerCAmelCase				,				do_lower_case=_lowerCAmelCase				,				unk_token=_lowerCAmelCase				,				sep_token=_lowerCAmelCase				,				pad_token=_lowerCAmelCase				,				cls_token=_lowerCAmelCase				,				mask_token=_lowerCAmelCase				,				tokenize_chinese_chars=_lowerCAmelCase				,				strip_accents=_lowerCAmelCase				,				**_lowerCAmelCase				,				)

       SCREAMING_SNAKE_CASE_							=			json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
       if (
           normalizer_state.get('lowercase'				,				_lowerCAmelCase						) != do_lower_case
           or normalizer_state.get('strip_accents'				,				_lowerCAmelCase						) != strip_accents
           or normalizer_state.get('handle_chinese_chars'				,				_lowerCAmelCase						) != tokenize_chinese_chars
       ):
             SCREAMING_SNAKE_CASE_							=			getattr(_lowerCAmelCase				,				normalizer_state.pop('type'						)						)
             SCREAMING_SNAKE_CASE_							=			do_lower_case
             SCREAMING_SNAKE_CASE_							=			strip_accents
             SCREAMING_SNAKE_CASE_							=			tokenize_chinese_chars
             SCREAMING_SNAKE_CASE_							=			normalizer_class(**_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			do_lower_case



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[int]=None						):
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

       return output



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		List[int]				,				_lowerCAmelCase  :		Optional[List[int]] = None						):
       SCREAMING_SNAKE_CASE_							=			[self.sep_token_id]
       SCREAMING_SNAKE_CASE_							=			[self.cls_token_id]
       if token_ids_a is None:
             return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]




 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[str] = None						):
       SCREAMING_SNAKE_CASE_							=			self._tokenizer.model.save(_lowerCAmelCase				,				name=_lowerCAmelCase						)
       return tuple(_lowerCAmelCase						)