





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase	:	Tuple		     =       logging.get_logger(__name__)

UpperCAmelCase	:	List[str]		     =       """▁"""

UpperCAmelCase	:	List[Any]		     =       {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase	:	Tuple		     =       {
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

UpperCAmelCase	:	Optional[Any]		     =       {
    """facebook/mbart-large-en-ro""": 1024,
    """facebook/mbart-large-cc25""": 1024,
}

# fmt: off
UpperCAmelCase	:	Dict		     =       ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					List[str]						=			VOCAB_FILES_NAMES
      _lowercase				:					Optional[int]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					Optional[Any]						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					str						=			["""input_ids""", """attention_mask"""]

      _lowercase				:					List[int]						=			[]
      _lowercase				:					List[int]						=			[]







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="<mask>"		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__=None		,      **lowerCAmelCase__		,      )   ->						List[str]:



        '''simple docstring'''




        a__	:					List[Any]										=AddedToken(lowerCAmelCase__		,      lstrip=lowerCAmelCase__		,      rstrip=lowerCAmelCase__							) if isinstance(lowerCAmelCase__		,      lowerCAmelCase__							) else mask_token

        a__	:					int										={} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=lowerCAmelCase__		,      eos_token=lowerCAmelCase__		,      unk_token=lowerCAmelCase__		,      sep_token=lowerCAmelCase__		,      cls_token=lowerCAmelCase__		,      pad_token=lowerCAmelCase__		,      mask_token=lowerCAmelCase__		,      tokenizer_file=lowerCAmelCase__		,      src_lang=lowerCAmelCase__		,      tgt_lang=lowerCAmelCase__		,      additional_special_tokens=lowerCAmelCase__		,      sp_model_kwargs=self.sp_model_kwargs		,      **lowerCAmelCase__		,      )

        a__	:					List[Any]										=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.Load(str(lowerCAmelCase__							)							)
        a__	:					Union[str, Any]										=vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        a__	:					List[Any]										={"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        a__	:					Tuple										=1

        a__	:					int										=len(self.sp_model							)
        a__	:					Optional[int]										={
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase__							)
        }
        a__	:					Tuple										={v: k for k, v in self.lang_code_to_id.items()}
        a__	:					Dict										=len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id							)
        a__	:					Union[str, Any]										={v: k for k, v in self.fairseq_tokens_to_ids.items()}
        a__	:					Union[str, Any]										=list(self.lang_code_to_id.keys()							)

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          self._additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in self._additional_special_tokens]							)

        a__	:					Any										=src_lang if src_lang is not None else "en_XX"
        a__	:					Union[str, Any]										=self.lang_code_to_id[self._src_lang]
        a__	:					Union[str, Any]										=tgt_lang
        self.set_src_lang_special_tokens(self._src_lang							)







      def __getstate__(    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=self.__dict__.copy()
        a__	:					Tuple										=None
        a__	:					int										=self.sp_model.serialized_model_proto()
        return state







      def __setstate__(    self		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        a__	:					List[Any]										=d

        # for backward compatibility
        if not hasattr(self		,      "sp_model_kwargs"							):
          a__	:					str										={}

        a__	:					List[Any]										=spm.SentencePieceProcessor(**self.sp_model_kwargs							)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)







      @property
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return len(self.sp_model							) + len(self.lang_code_to_id							) + self.fairseq_offset + 1  # Plus 1 for the mask token







      @property
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        return self._src_lang







      @src_lang.setter
      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					str										=new_src_lang
        self.set_src_lang_special_tokens(self._src_lang							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False							)   ->						List[int]:



        '''simple docstring'''




        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=lowerCAmelCase__		,      token_ids_a=lowerCAmelCase__		,      already_has_special_tokens=lowerCAmelCase__							)

        a__	:					Any										=[1] * len(self.prefix_tokens							)
        a__	:					Tuple										=[1] * len(self.suffix_tokens							)
        if token_ids_a is None:
          return prefix_ones + ([0] * len(lowerCAmelCase__							)) + suffix_ones
        return prefix_ones + ([0] * len(lowerCAmelCase__							)) + ([0] * len(lowerCAmelCase__							)) + suffix_ones







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						List[int]:



        '''simple docstring'''




        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						List[int]:



        '''simple docstring'''




        a__	:					int										=[self.sep_token_id]
        a__	:					Optional[int]										=[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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        if src_lang is None or tgt_lang is None:
          raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"							)
        a__	:					Dict										=src_lang
        a__	:					Dict										=self(lowerCAmelCase__		,      add_special_tokens=lowerCAmelCase__		,      return_tensors=lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					Union[str, Any]										=self.convert_tokens_to_ids(lowerCAmelCase__							)
        a__	:					Optional[int]										=tgt_lang_id
        return inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										={self.convert_ids_to_tokens(lowerCAmelCase__							): i for i in range(self.vocab_size							)}
        vocab.update(self.added_tokens_encoder							)
        return vocab







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        return self.sp_model.encode(lowerCAmelCase__		,      out_type=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[int]:



        '''simple docstring'''




        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        a__	:					Any										=self.sp_model.PieceToId(lowerCAmelCase__							)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




        a__	:					List[str]										="".join(lowerCAmelCase__							).replace(lowerCAmelCase__		,      " "							).strip()
        return out_string







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						Tuple[str]:



        '''simple docstring'''




        if not os.path.isdir(lowerCAmelCase__							):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
          return
        a__	:					Optional[int]										=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__							) and os.path.isfile(self.vocab_file							):
          copyfile(self.vocab_file		,      lowerCAmelCase__							)
        elif not os.path.isfile(self.vocab_file							):
          with open(lowerCAmelCase__		,      "wb"							) as fi:
            a__	:					Tuple										=self.sp_model.serialized_model_proto()
            fi.write(lowerCAmelCase__							)

        return (out_vocab_file,)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = "en_XX"		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = "ro_RO"		,      **lowerCAmelCase__		,      )   ->						BatchEncoding:



        '''simple docstring'''




        a__	:					Union[str, Any]										=src_lang
        a__	:					str										=tgt_lang
        return super().prepare_seqaseq_batch(lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        return self.set_src_lang_special_tokens(self.src_lang							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        return self.set_tgt_lang_special_tokens(self.tgt_lang							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Dict										=self.lang_code_to_id[src_lang]
        a__	:					int										=[]
        a__	:					Dict										=[self.eos_token_id, self.cur_lang_code]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						None:



        '''simple docstring'''




        a__	:					Any										=self.lang_code_to_id[lang]
        a__	:					str										=[]
        a__	:					str										=[self.eos_token_id, self.cur_lang_code]













import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


UpperCAmelCase	:	int		     =       {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()
def      _A       (		SCREAMING_SNAKE_CASE     :   List[Any]       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Union[str, Any]=None						):





  """simple docstring"""



  a__	:					Optional[int]										=XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE						)

  a__	:					Dict										=finetuning_task.lower() if finetuning_task is not None else ""
  if finetuning_task in GLUE_TASKS_NUM_LABELS:
    print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''						)
    a__	:					List[str]										=finetuning_task
    a__	:					Tuple										=GLUE_TASKS_NUM_LABELS[finetuning_task]
    a__	:					List[Any]										=XLNetForSequenceClassification(SCREAMING_SNAKE_CASE						)
  elif "squad" in finetuning_task:
    a__	:					Optional[int]										=finetuning_task
    a__	:					Dict										=XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE						)
  else:
    a__	:					List[Any]										=XLNetLMHeadModel(SCREAMING_SNAKE_CASE						)

  # Load weights from tf checkpoint
  load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  # Save pytorch-model
  a__	:					Dict										=os.path.join(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
  a__	:					Dict										=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	:	List[str]		     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--xlnet_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained XLNet model. \n"""
             """This specifies the model architecture."""
         ),
     )
     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(
         """--finetuning_task""",
         default=None,
         type=str,
         help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
     )
     UpperCAmelCase	:	int		     =       parser.parse_args()
     print(args)

     convert_xlnet_checkpoint_to_pytorch(
         args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
     )






