





"""simple docstring"""



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


a_ =			logging.get_logger(__name__)

a_ =			{"""vocab_file""": """spiece.model"""}

a_ =			{
    """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"""
        ),
    }
}

a_ =			{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     VOCAB_FILES_NAMES
 _lowerCamelCase        =     PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase        =     ["""input_ids""", """attention_mask"""]
 _lowerCamelCase        =     []

 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase="<unk>"					,						__lowerCamelCase="<s>"					,						__lowerCamelCase="</s>"					,						__lowerCamelCase="<pad>"					,						__lowerCamelCase="[SEP]"					,						__lowerCamelCase="[MASK]"					,						__lowerCamelCase="[CLS]"					,						__lowerCamelCase = None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Dict						 =     AddedToken(__lowerCamelCase					,						lstrip=__lowerCamelCase					,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) else bos_token
     __A    :						Tuple						 =     AddedToken(__lowerCamelCase					,						lstrip=__lowerCamelCase					,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) else eos_token
     __A    :						int						 =     AddedToken(__lowerCamelCase					,						lstrip=__lowerCamelCase					,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) else unk_token
     __A    :						Dict						 =     AddedToken(__lowerCamelCase					,						lstrip=__lowerCamelCase					,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) else pad_token
     __A    :						str						 =     AddedToken(__lowerCamelCase					,						lstrip=__lowerCamelCase					,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) else cls_token
     __A    :						List[str]						 =     AddedToken(__lowerCamelCase					,						lstrip=__lowerCamelCase					,						rstrip=__lowerCamelCase  ) if isinstance(__lowerCamelCase					,						__lowerCamelCase  ) else sep_token

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

     __A    :						str						 =     {} if sp_model_kwargs is None else sp_model_kwargs

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

     __A    :						Optional[Any]						 =     vocab_file

     __A    :						Optional[int]						 =     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(__lowerCamelCase  )

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return self.sp_model.get_piece_size()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

 def __getstate__(    self  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.__dict__.copy()
     __A    :						Union[str, Any]						 =     None
     return state

 def __setstate__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     d

     # for backward compatibility
     if not hasattr(self					,						'''sp_model_kwargs'''  ):
         __A    :						Tuple						 =     {}

     __A    :						Optional[int]						 =     spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(self.vocab_file  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     return self.sp_model.piece_to_id(__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Dict						 =     self.sp_model.IdToPiece(__lowerCamelCase  )
     return token

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[str]						 =     []
     __A    :						List[Any]						 =     ''''''
     __A    :						List[str]						 =     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(__lowerCamelCase  ) + token
             __A    :						str						 =     True
             __A    :						Tuple						 =     []
         else:
             current_sub_tokens.append(__lowerCamelCase  )
             __A    :						Optional[Any]						 =     False
     out_string += self.sp_model.decode(__lowerCamelCase  )
     return out_string.strip()

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = False					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Dict						 =     kwargs.pop('''use_source_tokenizer'''					,						__lowerCamelCase  )

     __A    :						Optional[int]						 =     self.convert_ids_to_tokens(__lowerCamelCase					,						skip_special_tokens=__lowerCamelCase  )

     # 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
     __A    :						Optional[Any]						 =     []
     __A    :						Union[str, Any]						 =     []
     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(__lowerCamelCase  )  )
                 __A    :						str						 =     []
             sub_texts.append(__lowerCamelCase  )
         else:
             current_sub_text.append(__lowerCamelCase  )
     if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase  )  )

     # Mimic the behavior of the Rust tokenizer:
     # No space before [MASK] and [SEP]
     if spaces_between_special_tokens:
         __A    :						Tuple						 =     re.sub(r''' (\[(MASK|SEP)\])'''					,						r'''\1'''					,						''' '''.join(__lowerCamelCase  )  )
     else:
         __A    :						Tuple						 =     ''''''.join(__lowerCamelCase  )

     __A    :						List[Any]						 =     (
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
         __A    :						Union[str, Any]						 =     self.clean_up_tokenization(__lowerCamelCase  )
         return clean_text
     else:
         return text

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     if not os.path.isdir(__lowerCamelCase  ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
         return
     __A    :						List[str]						 =     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:
             __A    :						str						 =     self.sp_model.serialized_model_proto()
             fi.write(__lowerCamelCase  )

     return (out_vocab_file,)

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = 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 [1] + ([0] * len(__lowerCamelCase  )) + [1]
     return [1] + ([0] * len(__lowerCamelCase  )) + [1] + ([0] * len(__lowerCamelCase  )) + [1]







 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     __A    :						Tuple						 =     [self.sep_token_id]
     __A    :						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]











"""simple docstring"""



def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    if n == 0:
        return 0
    __A    :						int						 =     float('''-inf'''						)
    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max(
            snake_case_				,prices[i - 1] + naive_cut_rod_recursive(n - i				,snake_case_						)						)

    return max_revue







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->int:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)
    __A    :						Dict						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    return _top_down_cut_rod_recursive(snake_case_				,snake_case_				,snake_case_						)







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        __A    :						Any						 =     float('''-inf'''						)
        for i in range(1				,n + 1						):
            __A    :						Union[str, Any]						 =     max(
                snake_case_				,prices[i - 1] + _top_down_cut_rod_recursive(n - i				,snake_case_				,snake_case_						)				,)

        __A    :						Any						 =     max_revenue

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Any:



    '''simple docstring'''







    _enforce_args(snake_case_				,snake_case_						)

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    __A    :						Union[str, Any]						 =     [float('''-inf'''						) for _ in range(n + 1						)]
    __A    :						List[Any]						 =     0

    for i in range(1				,n + 1						):
        __A    :						Union[str, Any]						 =     max_rev[i]
        for j in range(1				,i + 1						):
            __A    :						str						 =     max(snake_case_				,prices[j - 1] + max_rev[i - j]						)

        __A    :						List[str]						 =     max_revenue_i

    return max_rev[n]







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  list						)   ->Union[str, Any]:



    '''simple docstring'''







    if n < 0:
        __A    :						Union[str, Any]						 =     F"""n must be greater than or equal to 0. Got n = {n}"""
        raise ValueError(snake_case_						)

    if n > len(snake_case_						):
        __A    :						List[Any]						 =     (
            '''Each integral piece of rod must have a corresponding price. '''
            F"""Got n = {n} but length of prices = {len(snake_case_						)}"""
        )
        raise ValueError(snake_case_						)







def 		__lowercase     (   )   ->str:



    '''simple docstring'''







    __A    :						Any						 =     [6, 10, 12, 15, 20, 23]
    __A    :						Union[str, Any]						 =     len(snake_case_						)

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    __A    :						str						 =     36

    __A    :						Any						 =     top_down_cut_rod(snake_case_				,snake_case_						)
    __A    :						Any						 =     bottom_up_cut_rod(snake_case_				,snake_case_						)
    __A    :						Optional[Any]						 =     naive_cut_rod_recursive(snake_case_				,snake_case_						)

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
       main()




