


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
__UpperCamelCase       :       List[Any]						       =				data_utils.TransfoXLTokenizer
__UpperCamelCase       :       Dict						       =				data_utils.TransfoXLCorpus
__UpperCamelCase       :       int						       =				data_utils
__UpperCamelCase       :       str						       =				data_utils

def 			_a  (				SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				List[Any]     ):







	"""simple docstring"""



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

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

	if tf_checkpoint_path:
		# Convert a pre-trained TensorFlow model
		UpperCamelCase__					:    List[str]          =   os.path.abspath(SCREAMING_SNAKE_CASE     )
		UpperCamelCase__					:    Optional[int]          =   os.path.abspath(SCREAMING_SNAKE_CASE     )

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

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


if __name__ == "__main__":
							__UpperCamelCase       :       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.",
							)
							__UpperCamelCase       :       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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


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

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [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 + sep + token_ids_a + sep			) * [0]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

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

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


