




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


__lowerCAmelCase		:				List[Any]       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				str       			=		{'vocab_file': 'spiece.model'}

__lowerCAmelCase		:				str       			=		{
    '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'
        ),
    }
}

__lowerCAmelCase		:				Union[str, Any]       			=		{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							Dict							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]





				def __init__(      self				:     Dict					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Optional[int]="<unk>"					,      __lowerCamelCase				:     int="<s>"					,      __lowerCamelCase				:     int="</s>"					,      __lowerCamelCase				:     Optional[Any]="<pad>"					,      __lowerCamelCase				:     List[str]="[SEP]"					,      __lowerCamelCase				:     Optional[Any]="[MASK]"					,      __lowerCamelCase				:     Tuple="[CLS]"					,      __lowerCamelCase				:     Optional[Dict[str, Any]] = None					,      **__lowerCamelCase				:     Optional[int]					,      )					->     None:
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else bos_token
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else eos_token
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else unk_token
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else pad_token
							a    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else cls_token
							a    =			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    =			AddedToken(__lowerCamelCase					,      lstrip=__lowerCamelCase					,      rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase					,      __lowerCamelCase							) else mask_token

							a    =			{} 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    =			vocab_file

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     List[Any]							)					->     str:
							return self.sp_model.get_piece_size()





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Union[str, Any]:
							a    =			{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				:     int							)					->     Dict:
							a    =			self.__dict__.copy()
							a    =			None
							return state





				def __setstate__(      self				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]							)					->     List[str]:
							a    =			d

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

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





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     str							)					->     List[str]:
							return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]					,      __lowerCamelCase				:     str							)					->     Any:
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Tuple							)					->     Dict:
							a    =			self.sp_model.IdToPiece(__lowerCamelCase							)
							return token





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Any							)					->     str:
							a    =			[]
							a    =			""
							a    =			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    =			True
													a    =			[]
										else:
													current_sub_tokens.append(__lowerCamelCase							)
													a    =			False
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = None					,      __lowerCamelCase				:     bool = True					,      **__lowerCamelCase				:     str					,      )					->     str:
							a    =			kwargs.pop("use_source_tokenizer"					,      __lowerCamelCase							)

							a    =			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    =			[]
							a    =			[]
							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    =			[]
													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    =			re.sub(r" (\[(MASK|SEP)\])"					,      r"\1"					,      " ".join(__lowerCamelCase							)							)
							else:
										a    =			"".join(__lowerCamelCase							)

							a    =			(
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
										a    =			self.clean_up_tokenization(__lowerCamelCase							)
										return clean_text
							else:
										return text





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a    =			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    =			self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None							)					->     List[int]:
							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a    =			[self.cls_token_id]
							a    =			[self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None					,      __lowerCamelCase				:     bool = False							)					->     List[int]:
							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				:     List[Any]					,      __lowerCamelCase				:     List[int]					,      __lowerCamelCase				:     Optional[List[int]] = None							)					->     List[int]:
							a    =			[self.sep_token_id]
							a    =			[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]













from itertools import count


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 50 )			->     int:
    lowerCamelCase__    :      Optional[Any]					 =		[1] * min_block_length

    for n in count(_UpperCAmelCase ):
        fill_count_functions.append(1 )

        for block_length in range(_UpperCAmelCase						,	n + 1 ):
            for block_start in range(n - block_length ):
                fill_count_functions[n] += fill_count_functions[
                    n - block_start - block_length - 1
                ]

            fill_count_functions[n] += 1

        if fill_count_functions[n] > 100_0000:
            break

    return n


if __name__ == "__main__":
    print(F"""{solution() = }""")





