

import os
import re
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


__UpperCAmelCase    				=     logging.get_logger(__name__)

__UpperCAmelCase    				=     {'vocab_file': 'spiece.model'}

__UpperCAmelCase    				=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

__UpperCAmelCase    				=     {
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Any      =						VOCAB_FILES_NAMES
 _lowercase     :					int      =						PRETRAINED_VOCAB_FILES_MAP
 _lowercase     :					List[Any]      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase     :					Union[str, Any]      =						['''input_ids''', '''attention_mask''']
 _lowercase     :					List[int]      =						[]

 def __init__(		self :	int     ,						A_ :	Any     ,						A_ :	Union[str, Any]="<unk>"     ,						A_ :	Tuple="<s>"     ,						A_ :	int="</s>"     ,						A_ :	Tuple="<pad>"     ,						A_ :	Dict="[SEP]"     ,						A_ :	Optional[int]="[MASK]"     ,						A_ :	str="[CLS]"     ,						A_ :	Optional[Dict[str, Any]] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else bos_token
      _lowerCAmelCase   :					int        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else eos_token
      _lowerCAmelCase   :					Dict        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else unk_token
      _lowerCAmelCase   :					Any        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else pad_token
      _lowerCAmelCase   :					List[Any]        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else cls_token
      _lowerCAmelCase   :					Union[str, Any]        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      _lowerCAmelCase   :					Any        =    AddedToken(A_     ,						lstrip=A_     ,						rstrip=A_       ) if isinstance(A_     ,						A_       ) else mask_token

      _lowerCAmelCase   :					Optional[Any]        =    {} if sp_model_kwargs is None else sp_model_kwargs

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

      _lowerCAmelCase   :					Tuple        =    vocab_file

      _lowerCAmelCase   :					Union[str, Any]        =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(A_       )

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return self.sp_model.get_piece_size()

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    {self.convert_ids_to_tokens(A_       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab

 def __getstate__(		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.__dict__.copy()
      _lowerCAmelCase   :					List[str]        =    None
      return state

 def __setstate__(		self :	Dict     ,						A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    d

      # for backward compatibility
      if not hasattr(self     ,						"sp_model_kwargs"       ):
           _lowerCAmelCase   :					Any        =    {}

      _lowerCAmelCase   :					Any        =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(self.vocab_file       )

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	str       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Any     ,						A_ :	int       ):






      '''simple docstring'''




      return self.sp_model.piece_to_id(A_       )

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.sp_model.IdToPiece(A_       )
      return token

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    []
      _lowerCAmelCase   :					Optional[int]        =    ""
      _lowerCAmelCase   :					Optional[Any]        =    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(A_       ) + token
                _lowerCAmelCase   :					List[Any]        =    True
                _lowerCAmelCase   :					str        =    []
           else:
                current_sub_tokens.append(A_       )
                _lowerCAmelCase   :					int        =    False
      out_string += self.sp_model.decode(A_       )
      return out_string.strip()

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






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    kwargs.pop("use_source_tokenizer"     ,						A_       )

      _lowerCAmelCase   :					Tuple        =    self.convert_ids_to_tokens(A_     ,						skip_special_tokens=A_       )

      # 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
      _lowerCAmelCase   :					str        =    []
      _lowerCAmelCase   :					int        =    []
      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(A_       )       )
                     _lowerCAmelCase   :					Any        =    []
                sub_texts.append(A_       )
           else:
                current_sub_text.append(A_       )
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(A_       )       )

      # Mimic the behavior of the Rust tokenizer:
      # No space before [MASK] and [SEP]
      if spaces_between_special_tokens:
           _lowerCAmelCase   :					List[Any]        =    re.sub(r" (\[(MASK|SEP)\])"     ,						r"\1"     ,						" ".join(A_       )       )
      else:
           _lowerCAmelCase   :					int        =    "".join(A_       )

      _lowerCAmelCase   :					List[str]        =    (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
           _lowerCAmelCase   :					Optional[Any]        =    self.clean_up_tokenization(A_       )
           return clean_text
      else:
           return text

 def    __magic_name__       (		self :	Any     ,						A_ :	str     ,						A_ :	Optional[str] = None       ):






      '''simple docstring'''




      if not os.path.isdir(A_       ):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
      _lowerCAmelCase   :					Optional[Any]        =    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:
                _lowerCAmelCase   :					int        =    self.sp_model.serialized_model_proto()
                fi.write(A_       )

      return (out_vocab_file,)

 def    __magic_name__       (		self :	int     ,						A_ :	List[int]     ,						A_ :	Optional[List[int]] = None       ):






      '''simple docstring'''




      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _lowerCAmelCase   :					Dict        =    [self.cls_token_id]
      _lowerCAmelCase   :					int        =    [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep

 def    __magic_name__       (		self :	Tuple     ,						A_ :	List[int]     ,						A_ :	Optional[List[int]] = None     ,						A_ :	bool = False       ):






      '''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 [1] + ([0] * len(A_       )) + [1]
      return [1] + ([0] * len(A_       )) + [1] + ([0] * len(A_       )) + [1]


 def    __magic_name__       (		self :	int     ,						A_ :	List[int]     ,						A_ :	Optional[List[int]] = None       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    [self.sep_token_id]
      _lowerCAmelCase   :					Union[str, Any]        =    [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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      "list[int]":

     """simple docstring"""







     if upper_limit < 0:
          raise ValueError("Limit for the Catalan sequence must be ≥ 0"				)

     _lowerCAmelCase   :					Optional[Any]        =    [0] * (upper_limit + 1)

     # Base case: C(0) = C(1) = 1
     _lowerCAmelCase   :					Optional[int]        =    1
     if upper_limit > 0:
          _lowerCAmelCase   :					Any        =    1

     # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
     for i in range(2		,							upper_limit + 1				):
          for j in range(SCREAMING_SNAKE_CASE				):
               catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

     return catalan_list


if __name__ == "__main__":
  print('\n********* Catalan Numbers Using Dynamic Programming ************\n')
  print('\n*** Enter -1 at any time to quit ***')
  print('\nEnter the upper limit (≥ 0) for the Catalan number sequence: ', end='')
  try:
    while True:
      __UpperCAmelCase    				=     int(input().strip())
      if N < 0:
        print('\n********* Goodbye!! ************')
        break
      else:
        print(f'''The Catalan numbers from 0 through {N} are:''')
        print(catalan_numbers(N))
        print('Try another upper limit for the sequence: ', end='')
  except (NameError, ValueError):
    print('\n********* Invalid input, goodbye! ************\n')

  import doctest

  doctest.testmod()






