





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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'▁'

_UpperCAmelCase        =				{'vocab_file': 'prophetnet.tokenizer'}

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

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

_UpperCAmelCase        =				{
    'microsoft/xprophetnet-large-wiki100-cased': 5_1_2,
}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:
       UpperCamelCase_          =  collections.OrderedDict()
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as reader:
              UpperCamelCase_          =  reader.readlines()
       for index, token in enumerate(UpperCamelCase_  ):
              UpperCamelCase_          =  token.rstrip("\n"  )
              UpperCamelCase_          =  index
       return vocab







class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Union[str, Any]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					Any         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					List[Any]         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					Any         =   ['''input_ids''', '''attention_mask''']

       def __init__(		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: List[str]="[SEP]"					,   _SCREAMING_SNAKE_CASE: List[str]="[SEP]"					,   _SCREAMING_SNAKE_CASE: Optional[int]="[SEP]"					,   _SCREAMING_SNAKE_CASE: Tuple="[UNK]"					,   _SCREAMING_SNAKE_CASE: str="[PAD]"					,   _SCREAMING_SNAKE_CASE: int="[CLS]"					,   _SCREAMING_SNAKE_CASE: Union[str, Any]="[MASK]"					,   _SCREAMING_SNAKE_CASE: Optional[Dict[str, Any]] = None					,   **_SCREAMING_SNAKE_CASE: Tuple					,   )       -> None:




              """simple docstring"""


              UpperCamelCase_          =  {} if sp_model_kwargs is None else sp_model_kwargs

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

              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

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  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
              UpperCamelCase_          =  {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

              for i in range(10		):
                     UpperCamelCase_          =  f'''[unused{i}]'''
                     UpperCamelCase_          =  5 + i

              # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
              UpperCamelCase_          =  12
              UpperCamelCase_          =  {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(_SCREAMING_SNAKE_CASE		)

       def __getstate__(		self: Any		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  self.__dict__.copy()
              UpperCamelCase_          =  None
              return state

       def __setstate__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  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"		):
                     UpperCamelCase_          =  {}

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

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> List[int]:




              """simple docstring"""


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

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

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [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		)       -> Optional[int]:




              """simple docstring"""


              return len(self.sp_model		) + self.fairseq_offset

       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


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

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> str:




              """simple docstring"""


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

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              UpperCamelCase_          =  self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

              # 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: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[int]:




              """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: str					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE					,   " "		).strip()
              return out_string

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Tuple[str]:




              """simple docstring"""


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

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

              return (out_vocab_file,)



       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


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








_UpperCAmelCase        =				{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


_UpperCAmelCase        =				{value: key for key, value in encode_dict.items()}




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       UpperCamelCase_          =  ""
       for letter in word.lower():
              if letter.isalpha() or letter == " ":
                     encoded += encode_dict[letter]
              else:
                     raise Exception("encode() accepts only letters of the alphabet and spaces"  )
       return encoded




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		str:
       if set(UpperCamelCase_  ) - {"A", "B", " "} != set():
              raise Exception("decode() accepts only 'A', 'B' and spaces"  )
       UpperCamelCase_          =  ""
       for word in coded.split():
              while len(UpperCamelCase_  ) != 0:
                     decoded += decode_dict[word[:5]]
                     UpperCamelCase_          =  word[5:]
              decoded += " "
       return decoded.strip()


if __name__ == "__main__":
     from doctest import testmod

     testmod()

