

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


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = '''▁'''

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

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

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

snake_case_			  = {
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]			):



     '''simple docstring'''





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


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       int														=     VOCAB_FILES_NAMES
    __lowerCamelCase						:       List[str]														=     PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase						:       str														=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase						:       List[str]														=     ["""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						,   ):
         lowercase__    :   List[Any]      =       {} 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__    :   Any      =       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__    :   List[Any]      =       {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

         for i in range(10):
              lowercase__    :   List[Any]      =       f"""[unused{i}]"""
              lowercase__    :   List[str]      =       5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         lowercase__    :   Dict      =       12
         lowercase__    :   List[Any]      =       {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):
         lowercase__    :   Dict      =       self.__dict__.copy()
         lowercase__    :   List[str]      =       None
         return state





    def __setstate__(      self						,   a):
         lowercase__    :   int      =       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[Any]      =       {}

         lowercase__    :   List[Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.vocab_file)





    def       snake_case_				(      self						,   a						,   a = None						,   a = False):

         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       snake_case_				(      self						,   a						,   a = None):
         lowercase__    :   Tuple      =       [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       snake_case_				(      self):
         return len(self.sp_model) + self.fairseq_offset





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       {self.convert_ids_to_tokens(a): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab





    def       snake_case_				(      self						,   a):
         return self.sp_model.encode(a						,   out_type=a)





    def       snake_case_				(      self						,   a):
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         lowercase__    :   List[str]      =       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       snake_case_				(      self						,   a):
         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       snake_case_				(      self						,   a):
         lowercase__    :   Optional[int]      =       ''.join(a).replace(a						,   ' ').strip()
         return out_string





    def       snake_case_				(      self						,   a						,   a = None):
         if not os.path.isdir(a):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
              return
         lowercase__    :   Dict      =       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__    :   Optional[Any]      =       self.sp_model.serialized_model_proto()
                   fi.write(a)

         return (out_vocab_file,)





    def       snake_case_				(      self						,   a						,   a = None):

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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

