






"""simple docstring"""


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

import sentencepiece as spm

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


lowercase_    					=					logging.get_logger(__name__)
lowercase_    					=					{'vocab_file': 'spiece.model'}

lowercase_    					=					{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
}

lowercase_    					=					{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowercase_    					=					'▁'


class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     Optional[Any]									=			VOCAB_FILES_NAMES
   A_						:     int									=			PRETRAINED_VOCAB_FILES_MAP
   A_						:     Union[str, Any]									=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_						:     List[str]									=			["input_ids", "token_type_ids"]


   def __init__(     self			:					str,	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					List[Any]=False,	_lowerCamelCase			:					List[str]=True,	_lowerCamelCase			:					Dict=True,	_lowerCamelCase			:					Dict="<unk>",	_lowerCamelCase			:					Tuple="[SEP]",	_lowerCamelCase			:					Optional[Any]="<pad>",	_lowerCamelCase			:					List[Any]="[CLS]",	_lowerCamelCase			:					str="[MASK]",	_lowerCamelCase			:					Optional[Dict[str, Any]] = None,	**_lowerCamelCase			:					Any,	):

     '''simple docstring'''
     # Mask token behave like a normal word, i.e. include the space before it and
     # is included in the raw text, there should be a match in a non-normalized sentence.
     __A						      =			(
         AddedToken(_lowerCamelCase,	lstrip=_lowerCamelCase,	rstrip=_lowerCamelCase,	normalized=_lowerCamelCase  )
         if isinstance(_lowerCamelCase,	_lowerCamelCase  )
         else mask_token
     )

     __A						      =			{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         do_lower_case=_lowerCamelCase,	remove_space=_lowerCamelCase,	keep_accents=_lowerCamelCase,	unk_token=_lowerCamelCase,	sep_token=_lowerCamelCase,	pad_token=_lowerCamelCase,	cls_token=_lowerCamelCase,	mask_token=_lowerCamelCase,	sp_model_kwargs=self.sp_model_kwargs,	**_lowerCamelCase,	)

     __A						      =			do_lower_case
     __A						      =			remove_space
     __A						      =			keep_accents
     __A						      =			vocab_file

     __A						      =			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(_lowerCamelCase  )


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return len(self.sp_model  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     __A						      =			{self.convert_ids_to_tokens(_lowerCamelCase  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab


   def __getstate__(     self			:					int  ):

     '''simple docstring'''
     __A						      =			self.__dict__.copy()
     __A						      =			None
     return state


   def __setstate__(     self			:					Dict,	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     __A						      =			d

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

     __A						      =			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(self.vocab_file  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[Any],	_lowerCamelCase			:					Tuple  ):

     '''simple docstring'''
     if self.remove_space:
       __A						      =			''' '''.join(inputs.strip().split()  )
     else:
       __A						      =			inputs
     __A						      =			outputs.replace('''``''',	'''"'''  ).replace('''\'\'''',	'''"'''  )

     if not self.keep_accents:
       __A						      =			unicodedata.normalize('''NFKD''',	_lowerCamelCase  )
       __A						      =			''''''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase  )]  )
     if self.do_lower_case:
       __A						      =			outputs.lower()

     return outputs


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any],	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     __A						      =			self.preprocess_text(_lowerCamelCase  )
     __A						      =			self.sp_model.encode(_lowerCamelCase,	out_type=_lowerCamelCase  )
     __A						      =			[]
     for piece in pieces:
       if len(_lowerCamelCase  ) > 1 and piece[-1] == str(''','''  ) and piece[-2].isdigit():
         __A						      =			self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase,	''''''  )  )
         if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
           if len(cur_pieces[0]  ) == 1:
             __A						      =			cur_pieces[1:]
           else:
             __A						      =			cur_pieces[0][1:]
         cur_pieces.append(piece[-1]  )
         new_pieces.extend(_lowerCamelCase  )
       else:
         new_pieces.append(_lowerCamelCase  )

     return new_pieces


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					str  ):

     '''simple docstring'''
     return self.sp_model.PieceToId(_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					int  ):

     '''simple docstring'''
     return self.sp_model.IdToPiece(_lowerCamelCase  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	_lowerCamelCase			:					Optional[int]  ):

     '''simple docstring'''
     __A						      =			[]
     __A						      =			''''''
     __A						      =			False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(_lowerCamelCase  ) + token
         __A						      =			True
         __A						      =			[]
       else:
         current_sub_tokens.append(_lowerCamelCase  )
         __A						      =			False
     out_string += self.sp_model.decode(_lowerCamelCase  )
     return out_string.strip()


   def     _SCREAMING_SNAKE_CASE    (     self			:					Any,	_lowerCamelCase			:					List[int],	_lowerCamelCase			:					bool = False,	_lowerCamelCase			:					bool = None,	_lowerCamelCase			:					bool = True,	**_lowerCamelCase			:					Optional[Any],	):

     '''simple docstring'''
     __A						      =			kwargs.pop('''use_source_tokenizer''',	_lowerCamelCase  )

     __A						      =			self.convert_ids_to_tokens(_lowerCamelCase,	skip_special_tokens=_lowerCamelCase  )

     # To avoid mixing byte-level and unicode for byte-level BPT
     # we need to build string separately for added tokens and byte-level tokens
     # cf. https://github.com/huggingface/transformers/issues/1133
     __A						      =			[]
     __A						      =			[]
     for token in filtered_tokens:
       if skip_special_tokens and token in self.all_special_ids:
         continue
       if token in self.added_tokens_encoder:
         if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase  )  )
           __A						      =			[]
         sub_texts.append(_lowerCamelCase  )
       else:
         current_sub_text.append(_lowerCamelCase  )
     if current_sub_text:
       sub_texts.append(self.convert_tokens_to_string(_lowerCamelCase  )  )

     # Mimic the behavior of the Rust tokenizer:
     # No space after <unk>
     if spaces_between_special_tokens:
       __A						      =			re.sub(R'''(<unk>) ''',	R'''\1''',	''' '''.join(_lowerCamelCase  )  )
     else:
       __A						      =			''''''.join(_lowerCamelCase  )

     __A						      =			(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
       __A						      =			self.clean_up_tokenization(_lowerCamelCase  )
       return clean_text
     else:
       return text


   def     _SCREAMING_SNAKE_CASE    (     self			:					str,	_lowerCamelCase			:					List[int],	_lowerCamelCase			:					Optional[List[int]] = None  ):

     '''simple docstring'''
     __A						      =			[self.sep_token_id]
     __A						      =			[self.cls_token_id]
     if token_ids_a is None:
       return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict,	_lowerCamelCase			:					List[int],	_lowerCamelCase			:					Optional[List[int]] = None,	_lowerCamelCase			:					bool = False  ):

     '''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  )

     if token_ids_a is not None:
       return [1] + ([0] * len(_lowerCamelCase  )) + [1] + ([0] * len(_lowerCamelCase  )) + [1]
     return [1] + ([0] * len(_lowerCamelCase  )) + [1]


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					List[int],	_lowerCamelCase			:					Optional[List[int]] = None  ):

     '''simple docstring'''
     __A						      =			[self.sep_token_id]
     __A						      =			[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     _SCREAMING_SNAKE_CASE    (     self			:					List[str],	_lowerCamelCase			:					str,	_lowerCamelCase			:					Optional[str] = None  ):

     '''simple docstring'''
     if not os.path.isdir(_lowerCamelCase  ):
       logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
       return
     __A						      =			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						      =			self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase  )

     return (out_vocab_file,)












"""simple docstring"""


# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


lowercase_    					=					logging.get_logger(__name__)


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       from transformers import Trainer


       class  snake_case		(							_lowerCAmelCase						):


          '''simple docstring'''

          def __init__(     self			:					List[Any],	_lowerCamelCase			:					int = 1_01  ):

            '''simple docstring'''
            __A						      =			length


          def __len__(     self			:					Optional[int]  ):

            '''simple docstring'''
            return self.length






          def __getitem__(     self			:					List[str],	_lowerCamelCase			:					Union[str, Any]  ):

            '''simple docstring'''
            return i





       class  snake_case		:


          '''simple docstring'''

          def __call__(     self			:					Tuple,	_lowerCamelCase			:					Dict  ):

            '''simple docstring'''
            return {"input_ids": torch.tensor(_lowerCamelCase  ), "labels": torch.tensor(_lowerCamelCase  )}





       class  snake_case		(							nn.Module						):


          '''simple docstring'''

          def __init__(     self			:					List[str]  ):

            '''simple docstring'''
            super().__init__()
            # Add some (unused) params otherwise DDP will complain.
            __A						      =			nn.Linear(1_20,	80  )






          def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Optional[Any]=None  ):

            '''simple docstring'''
            if labels is not None:
              return torch.tensor(0.0,	device=input_ids.device  ), input_ids
            else:
              return input_ids



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			HfArgumentParser((TrainingArguments,)			)
  sys.argv += ["--output_dir", "./examples"]
  __A						      =			parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f'Process rank: {training_args.local_rank}, device: {training_args.device}, '
      f'tpu_num_cores: {training_args.tpu_num_cores}'		,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
    __A						      =			DummyDataset(__UpperCamelCase			)

    def compute_metrics(__UpperCamelCase			) -> Dict:
      __A						      =			list(range(len(__UpperCamelCase			)			)			)
      __A						      =			p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    __A						      =			Trainer(
        model=DummyModel()		,	args=__UpperCamelCase		,	data_collator=DummyDataCollator()		,	eval_dataset=__UpperCamelCase		,	compute_metrics=__UpperCamelCase		,	)
    __A						      =			trainer.evaluate()
    logger.info(__UpperCamelCase			)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase			)
      exit(1			)

    __A						      =			trainer.predict(__UpperCamelCase			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    __A						      =			2

    __A						      =			trainer.evaluate()
    logger.info(__UpperCamelCase			)
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase			)
      exit(1			)

    __A						      =			trainer.predict(__UpperCamelCase			)
    logger.info(p.metrics			)
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics			)
      exit(1			)

    __A						      =			None

  logger.info('''🔥 All distributed tests successful'''			)



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  main()


if __name__ == "__main__":
       main()




