
"""simple docstring"""




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

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


A__   :					str							=   logging.get_logger(__name__)

A__   :					Optional[Any]							=   '▁'

A__   :					Union[str, Any]							=   {'vocab_file': 'prophetnet.tokenizer'}

A__   :					Tuple							=   {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

A__   :					int							=   {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

A__   :					Optional[Any]							=   {
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Optional[int]       =     collections.OrderedDict()
  with open(_UpperCamelCase ,				'''r''' ,				encoding='''utf-8'''						) as reader:
    _lowercase:			Optional[Any]       =     reader.readlines()
  for index, token in enumerate(_UpperCamelCase						):
    _lowercase:			Any       =     token.rstrip('''\n'''						)
    _lowercase:			Tuple       =     index
  return vocab







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       def __init__(     self ,				A_ ,				A_="[SEP]" ,				A_="[SEP]" ,				A_="[SEP]" ,				A_="[UNK]" ,				A_="[PAD]" ,				A_="[CLS]" ,				A_="[MASK]" ,				A_ = None ,				**A_ ,				)     ->	None:





         """simple docstring"""




         _lowercase:			Dict       =     {} if sp_model_kwargs is None else sp_model_kwargs

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

         try:
           import sentencepiece as spm
         except ImportError:
           logger.warning(
               '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
               ''' pip install sentencepiece'''       )
           raise

         _lowercase:			Any       =     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(str(A_       )       )
         _lowercase:			List[str]       =     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'

         # put special tokens and [unused] tokens into the vocab
         _lowercase:			int       =     {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

         for i in range(10       ):
           _lowercase:			Tuple       =     f'''[unused{i}]'''
           _lowercase:			List[Any]       =     5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         _lowercase:			Optional[int]       =     12
         _lowercase:			Tuple       =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         for k in self.fairseq_tokens_to_ids.keys():
           self.unique_no_split_tokens.append(A_       )
       def __getstate__(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     self.__dict__.copy()
         _lowercase:			List[str]       =     None
         return state
       def __setstate__(     self ,				A_       )     ->	int:





         """simple docstring"""




         _lowercase:			List[Any]       =     d
         try:
           import sentencepiece as spm
         except ImportError:
           logger.warning(
               '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
               ''' pip install sentencepiece'''       )
           raise

         # for backward compatibility
         if not hasattr(self ,				'''sp_model_kwargs'''       ):
           _lowercase:			List[str]       =     {}

         _lowercase:			Optional[Any]       =     spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(self.vocab_file       )
       def 							lowercase_							(     self ,				A_ ,				A_ = None ,				A_ = False       )     ->	List[int]:





         """simple docstring"""




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

         if token_ids_a is None:
           return ([0] * len(A_       )) + [1]
         return ([0] * len(A_       )) + [1] + ([0] * len(A_       )) + [1]
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			str       =     [self.sep_token_id]

         if token_ids_a is None:
           return len(token_ids_a + sep       ) * [0]
         return len(token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         return len(self.sp_model       ) + self.fairseq_offset
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab
       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         return self.sp_model.encode(A_ ,				out_type=A_       )
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




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

         # 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 ,				A_       )     ->	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 							lowercase_							(     self ,				A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     ''''''.join(A_       ).replace(A_ ,				''' '''       ).strip()
         return out_string
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




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

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(A_       ) and os.path.isfile(self.vocab_file       ):
           copyfile(self.vocab_file ,				A_       )
         elif not os.path.isfile(self.vocab_file       ):
           with open(A_ ,				'''wb'''       ) as fi:
             _lowercase:			List[str]       =     self.sp_model.serialized_model_proto()
             fi.write(A_       )

         return (out_vocab_file,)



       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




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


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  if len(_UpperCamelCase						) == 1:
    return True
  _lowercase:			Any       =     series[1] - series[0]
  for index in range(len(_UpperCamelCase						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  _lowercase:			Tuple       =     0
  for val in series:
    answer += val
  return answer / len(_UpperCamelCase						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
