



"""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


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''': 5_1_2,
}




def 			snake_case		(		lowerCAmelCase_	)						->				List[str]:
  _snake_case   	=      collections.OrderedDict()
  with open(lowerCAmelCase_  ,						'''r'''  ,						encoding='''utf-8'''	) as reader:
    _snake_case   	=      reader.readlines()
  for index, token in enumerate(lowerCAmelCase_	):
    _snake_case   	=      token.rstrip('''\n'''	)
    _snake_case   	=      index
  return vocab







class     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE  ):
   A__		:	Optional[int]           =  VOCAB_FILES_NAMES
   A__		:	Tuple           =  PRETRAINED_VOCAB_FILES_MAP
   A__		:	int           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__		:	Optional[int]           =  ['''input_ids''', '''attention_mask''']



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



     """simple docstring"""
     _snake_case   	=      {} if sp_model_kwargs is None else sp_model_kwargs

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

     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

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

     for i in range(1_0				):
       _snake_case   	=      f"""[unused{i}]"""
       _snake_case   	=      5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     _snake_case   	=      1_2
     _snake_case   	=      {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(__lowerCamelCase				)



   def __getstate__(       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      self.__dict__.copy()
     _snake_case   	=      None
     return state



   def __setstate__(       self    :	Optional[int]							,       __lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      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'''				):
       _snake_case   	=      {}

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



   def    __UpperCAmelCase    (       self    :	int							,       __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 None:
       return ([0] * len(__lowerCamelCase				)) + [1]
     return ([0] * len(__lowerCamelCase				)) + [1] + ([0] * len(__lowerCamelCase				)) + [1]



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""
     _snake_case   	=      [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    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     return len(self.sp_model				) + self.fairseq_offset



   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      {self.convert_ids_to_tokens(__lowerCamelCase				): i for i in range(self.vocab_size				)}
     vocab.update(self.added_tokens_encoder				)
     return vocab



   def    __UpperCAmelCase    (       self    :	List[Any]							,       __lowerCamelCase    :	str				):



     """simple docstring"""
     return self.sp_model.encode(__lowerCamelCase							,       out_type=__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     _snake_case   	=      self.sp_model.PieceToId(__lowerCamelCase				)

     # 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    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Tuple				):



     """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    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      ''''''.join(__lowerCamelCase				).replace(__lowerCamelCase							,       ''' '''				).strip()
     return out_string



   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __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
     _snake_case   	=      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:
         _snake_case   	=      self.sp_model.serialized_model_proto()
         fi.write(__lowerCamelCase				)

     return (out_vocab_file,)





   def    __UpperCAmelCase    (       self    :	Union[str, Any]							,       __lowerCamelCase    :	List[int]							,       __lowerCamelCase    :	Optional[List[int]] = None				):



     """simple docstring"""

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













class 					UpperCamelCase_ :



	'''simple docstring'''




	def __init__(			self			,     a   )			->       Tuple:
								snake_case_           =				n
								snake_case_           =				[None] * self.n
								snake_case_           =				0  # index of the first element
								snake_case_           =				0
								snake_case_           =				0




	def __len__(			self   )			->       int:
								return self.size




	def 				_UpperCamelCase     (			self   )			->       bool:
								return self.size == 0




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								return False if self.is_empty() else self.array[self.front]




	def 				_UpperCamelCase     (			self			,     a   )			->       Dict:
								if self.size >= self.n:
															raise Exception('QUEUE IS FULL'   )

								snake_case_           =				data
								snake_case_           =				(self.rear + 1) % self.n
								self.size += 1
								return self






	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								if self.size == 0:
															raise Exception('UNDERFLOW'   )

								snake_case_           =				self.array[self.front]
								snake_case_           =				None
								snake_case_           =				(self.front + 1) % self.n
								self.size -= 1
								return temp





