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

import sentencepiece as spm

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


__lowerCamelCase			: List[Any]							   =		logging.get_logger(__name__)

__lowerCamelCase			: str							   =		'''▁'''

__lowerCamelCase			: Dict							   =		{'''vocab_file''': '''sentencepiece.bpe.model'''}

__lowerCamelCase			: Optional[Any]							   =		{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

__lowerCamelCase			: List[str]							   =		{
    '''facebook/xglm-564M''': 2048,
}







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]




   def __init__(    self			:				Tuple       ,	_lowercase			:				int       ,	_lowercase			:				str="<s>"       ,	_lowercase			:				Any="</s>"       ,	_lowercase			:				int="</s>"       ,	_lowercase			:				Dict="<s>"       ,	_lowercase			:				Optional[int]="<unk>"       ,	_lowercase			:				str="<pad>"       ,	_lowercase			:				Optional[Dict[str, Any]] = None       ,	**_lowercase			:				Dict       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {} if sp_model_kwargs is None else sp_model_kwargs

     # Compatibility with the original tokenizer
     SCREAMING_SNAKE_CASE__										=      7
     SCREAMING_SNAKE_CASE__										=      [f"""<madeupword{i}>""" for i in range(self.num_madeup_words      )]

     SCREAMING_SNAKE_CASE__										=      kwargs.get("""additional_special_tokens"""       ,	[]      )
     kwargs["additional_special_tokens"] += [
         word for word in madeup_words if word not in kwargs["additional_special_tokens"]
     ]

     super().__init__(
         bos_token=_lowercase       ,	eos_token=_lowercase       ,	unk_token=_lowercase       ,	sep_token=_lowercase       ,	cls_token=_lowercase       ,	pad_token=_lowercase       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**_lowercase       ,	)

     SCREAMING_SNAKE_CASE__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(str(_lowercase      )      )
     SCREAMING_SNAKE_CASE__										=      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'

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

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

     SCREAMING_SNAKE_CASE__										=      len(self.sp_model      )
     SCREAMING_SNAKE_CASE__										=      {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words      )}
     self.fairseq_tokens_to_ids.update(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}




   def __getstate__(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.__dict__.copy()
     SCREAMING_SNAKE_CASE__										=      None
     SCREAMING_SNAKE_CASE__										=      self.sp_model.serialized_model_proto()
     return state




   def __setstate__(    self			:				Optional[Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      d

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

     SCREAMING_SNAKE_CASE__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )




   def 	__a						(    self			:				Dict       ,	_lowercase			:				List[int]       ,	_lowercase			:				Optional[List[int]] = None      ):



     """simple docstring"""






     if token_ids_a is None:
       return [self.sep_token_id] + token_ids_a
     SCREAMING_SNAKE_CASE__										=      [self.sep_token_id]
     return sep + token_ids_a + sep + sep + token_ids_a




   def 	__a						(    self			:				int       ,	_lowercase			:				List[int]       ,	_lowercase			:				Optional[List[int]] = None       ,	_lowercase			:				bool = False      ):



     """simple docstring"""






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

     if token_ids_a is None:
       return [1] + ([0] * len(_lowercase      ))
     return [1] + ([0] * len(_lowercase      )) + [1, 1] + ([0] * len(_lowercase      ))




   def 	__a						(    self			:				Any       ,	_lowercase			:				List[int]       ,	_lowercase			:				Optional[List[int]] = None      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [self.sep_token_id]

     if token_ids_a is None:
       return len(sep + token_ids_a      ) * [0]
     return len(sep + token_ids_a + sep + sep + token_ids_a      ) * [0]




   @property
   def 	__a						(    self			:				Dict      ):



     """simple docstring"""






     return len(self.sp_model      ) + self.fairseq_offset + self.num_madeup_words




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {self.convert_ids_to_tokens(_lowercase      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				str      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int       ,	_lowercase			:				Optional[Any]      ):



     """simple docstring"""






     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     SCREAMING_SNAKE_CASE__										=      self.sp_model.PieceToId(_lowercase      )

     # 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 	__a						(    self			:				Optional[Any]       ,	_lowercase			:				Dict      ):



     """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 	__a						(    self			:				List[Any]       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """""".join(_lowercase      ).replace(_lowercase       ,	""" """      ).strip()
     return out_string




   def 	__a						(    self			:				Optional[int]       ,	_lowercase			:				str       ,	_lowercase			:				Optional[str] = None      ):



     """simple docstring"""






     if not os.path.isdir(_lowercase      ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
       return
     SCREAMING_SNAKE_CASE__										=      os.path.join(
         _lowercase       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

     if os.path.abspath(self.vocab_file      ) != os.path.abspath(_lowercase      ) and os.path.isfile(self.vocab_file      ):
       copyfile(self.vocab_file       ,	_lowercase      )
     elif not os.path.isfile(self.vocab_file      ):
       with open(_lowercase       ,	"""wb"""      ) as fi:
         SCREAMING_SNAKE_CASE__										=      self.sp_model.serialized_model_proto()
         fi.write(_lowercase      )

     return (out_vocab_file,)

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def 	__SCREAMING_SNAKE_CASE  (					)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ArgumentParser(
      description=(
          """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
      )	)

  # Optional arguments for the launch helper
  parser.add_argument("""--num_cores""" ,     type=__UpperCamelCase ,     default=1 ,     help="""Number of TPU cores to use (1 or 8)."""	)

  # positional
  parser.add_argument(
      """training_script""" ,     type=__UpperCamelCase ,     help=(
          """The full path to the single TPU training """
          """program/script to be launched in parallel, """
          """followed by all the arguments for the """
          """training script"""
      ) ,     )

  # rest from the training program
  parser.add_argument("""training_script_args""" ,     nargs=__UpperCamelCase	)

  return parser.parse_args()



def 	__SCREAMING_SNAKE_CASE  (					)							->					Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      parse_args()

  # Import training_script as a module.
  SCREAMING_SNAKE_CASE__										=      Path(args.training_script	)
  sys.path.append(str(script_fpath.parent.resolve()	)	)
  SCREAMING_SNAKE_CASE__										=      script_fpath.stem
  SCREAMING_SNAKE_CASE__										=      importlib.import_module(__UpperCamelCase	)

  # Patch sys.argv
  SCREAMING_SNAKE_CASE__										=      [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores	)]

  xmp.spawn(mod._mp_fn ,     args=() ,     nprocs=args.num_cores	)


if __name__ == "__main__":
   main()
