



'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


__lowerCAmelCase									=   logging.get_logger(__name__)

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

__lowerCAmelCase									=   {
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCAmelCase									=   {
    '''yjernite/retribert-base-uncased''': 512,
}


__lowerCAmelCase									=   {
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Optional[Any]   				=					VOCAB_FILES_NAMES
							lowerCAmelCase       :    Optional[int]   				=					PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase       :    Optional[Any]   				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase       :    Any   				=					PRETRAINED_INIT_CONFIGURATION
							lowerCAmelCase       :    List[str]   				=					RetriBertTokenizer
							lowerCAmelCase       :    Tuple   				=					['input_ids', 'attention_mask']







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

									_a     :      List[Any]        =							json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
									if (
									    normalizer_state.get('lowercase'	,_UpperCAmelCase		) != do_lower_case
									    or normalizer_state.get('strip_accents'	,_UpperCAmelCase		) != strip_accents
									    or normalizer_state.get('handle_chinese_chars'	,_UpperCAmelCase		) != tokenize_chinese_chars
									):
											_a     :      List[Any]        =							getattr(_UpperCAmelCase	,normalizer_state.pop('type'		)		)
											_a     :      Union[str, Any]        =							do_lower_case
											_a     :      Optional[int]        =							strip_accents
											_a     :      Dict        =							tokenize_chinese_chars
											_a     :      Dict        =							normalizer_class(**_UpperCAmelCase		)

									_a     :      Dict        =							do_lower_case







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Union[str, Any]=None		):
									_a     :      Any        =							[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 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									_a     :      Optional[int]        =							[self.sep_token_id]
									_a     :      Optional[Any]        =							[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 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[str] = None		):
									_a     :      Tuple        =							self._tokenizer.model.save(_UpperCAmelCase	,name=_UpperCAmelCase		)
									return tuple(_UpperCAmelCase		)





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()
