"""simple docstring"""




import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
			from ...tokenization_utils_base import TextInput
from ...utils import logging


__lowerCAmelCase    :							Optional[int]													=						logging.get_logger(__name__)

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

__lowerCAmelCase    :							Union[str, Any]													=						{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
__lowerCAmelCase    :							str													=						{
    "t5-small": 5_12,
    "t5-base": 5_12,
    "t5-large": 5_12,
    "t5-3b": 5_12,
    "t5-11b": 5_12,
}

__lowerCAmelCase    :							int													=						"▁"

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	str			=    VOCAB_FILES_NAMES
					UpperCamelCase_ :	int			=    PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase_ :	Optional[Any]			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase_ :	List[Any]			=    ["input_ids", "attention_mask"]




					def __init__(	self						:     str  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Optional[int]="</s>"  ,						snake_case__						:     List[str]="<unk>"  ,						snake_case__						:     Union[str, Any]="<pad>"  ,						snake_case__						:     Tuple=100  ,						snake_case__						:     Dict=None  ,						snake_case__						:     Optional[Dict[str, Any]] = None  ,						snake_case__						:     Union[str, Any]=True  ,						**snake_case__						:     Tuple  ,						):
							# Add extra_ids to the special token list
							if extra_ids > 0 and additional_special_tokens is None:
									lowerCAmelCase__        =   [F"""<extra_id_{i}>""" for i in range(snake_case__     )]
							elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra_id special tokens
									lowerCAmelCase__        =   len(set(filter(lambda snake_case__     : bool("""extra_id""" in str(snake_case__     )     )  ,						snake_case__     )     )     )
									if extra_tokens != extra_ids:
											raise ValueError(
											    F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
											    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
											    """ tokens"""     )
							if legacy:
									logger.warning_once(
									    F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
									    """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""     )

							lowerCAmelCase__        =   legacy
							lowerCAmelCase__        =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=snake_case__  ,						unk_token=snake_case__  ,						pad_token=snake_case__  ,						extra_ids=snake_case__  ,						additional_special_tokens=snake_case__  ,						sp_model_kwargs=self.sp_model_kwargs  ,						legacy=snake_case__  ,						**snake_case__  ,						)

							lowerCAmelCase__        =   vocab_file
							lowerCAmelCase__        =   extra_ids

							lowerCAmelCase__        =   spm.SentencePieceProcessor(**self.sp_model_kwargs     )
							self.sp_model.Load(snake_case__     )




					@staticmethod
					def 						_SCREAMING_SNAKE_CASE						(	snake_case__						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     str     ):
							if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
									lowerCAmelCase__        =   TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
									if init_max_model_length is not None and init_max_model_length != max_model_length:
											return init_max_model_length
									elif init_max_model_length is None:
											warnings.warn(
											    """This tokenizer was incorrectly instantiated with a model max length of"""
											    F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
											    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
											    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
											    F""" {pretrained_model_name_or_path} automatically truncating your input to"""
											    F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
											    F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
											    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
											    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""  ,						snake_case__  ,						)

							return max_model_length




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							return self.sp_model.get_piece_size() + self._extra_ids




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None  ,						snake_case__						:     bool = False     ):
							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=snake_case__  ,						token_ids_a=snake_case__  ,						already_has_special_tokens=snake_case__     )

							# normal case: some special tokens
							if token_ids_a is None:
									return ([0] * len(snake_case__     )) + [1]
							return ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							return list(
							    set(filter(lambda snake_case__     : bool(re.search(R"""<extra_id_\d+>"""  ,						snake_case__     )     ) is not None  ,						self.additional_special_tokens     )     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return [self._convert_token_to_id(snake_case__     ) for token in self.get_sentinel_tokens()]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     List[int]     ):
							if len(snake_case__     ) > 0 and token_ids[-1] == self.eos_token_id:
									warnings.warn(
									    F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
									    """ eos tokens being added."""     )
									return token_ids
							else:
									return token_ids + [self.eos_token_id]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None     ):
							lowerCAmelCase__        =   [self.eos_token_id]

							if token_ids_a is None:
									return len(token_ids_a + eos     ) * [0]
							return len(token_ids_a + eos + token_ids_a + eos     ) * [0]




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     List[int]  ,						snake_case__						:     Optional[List[int]] = None     ):
							lowerCAmelCase__        =   self._add_eos_if_not_present(snake_case__     )
							if token_ids_a is None:
									return token_ids_a
							else:
									lowerCAmelCase__        =   self._add_eos_if_not_present(snake_case__     )
									return token_ids_a + token_ids_a




					def __getstate__(	self						:     str     ):
							lowerCAmelCase__        =   self.__dict__.copy()
							lowerCAmelCase__        =   None
							return state




					def __setstate__(	self						:     Dict  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   d

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     "TextInput"  ,						**snake_case__						:     Any     ):
							# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
							# the beginning of the text
							if not self.legacy:
									lowerCAmelCase__        =   SPIECE_UNDERLINE + text.replace(snake_case__  ,						""" """     )
							return super().tokenize(snake_case__  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Dict  ,						**snake_case__						:     Optional[Any]     ):
							if not self.legacy:
									lowerCAmelCase__        =   text.startswith(snake_case__     )
									if is_first:
											lowerCAmelCase__        =   text[1:]

							lowerCAmelCase__        =   self.sp_model.encode(snake_case__  ,						out_type=snake_case__     )

							if not self.legacy and not is_first and not text.startswith(""" """     ) and tokens[0].startswith(snake_case__     ):
									lowerCAmelCase__        =   ([tokens[0][1:]] if len(tokens[0]     ) > 1 else []) + tokens[1:]
							return tokens




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     str     ):
							if token.startswith("""<extra_id_"""     ):
									lowerCAmelCase__        =   re.match(R"""<extra_id_(\d+)>"""  ,						snake_case__     )
									lowerCAmelCase__        =   int(match.group(1     )     )
									return self.vocab_size - num - 1
							return self.sp_model.piece_to_id(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Dict     ):
							if index < self.sp_model.get_piece_size():
									lowerCAmelCase__        =   self.sp_model.IdToPiece(snake_case__     )
							else:
									lowerCAmelCase__        =   F"""<extra_id_{self.vocab_size - 1 - index}>"""
							return token




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   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(snake_case__     ) + token
											lowerCAmelCase__        =   True
											lowerCAmelCase__        =   []
									else:
											current_sub_tokens.append(snake_case__     )
											lowerCAmelCase__        =   False
							out_string += self.sp_model.decode(snake_case__     )
							return out_string.strip()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     str  ,						snake_case__						:     Optional[str] = None     ):
							if not os.path.isdir(snake_case__     ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
									return
							lowerCAmelCase__        =   os.path.join(
							    snake_case__  ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

							if os.path.abspath(self.vocab_file     ) != os.path.abspath(snake_case__     ) and os.path.isfile(self.vocab_file     ):
									copyfile(self.vocab_file  ,						snake_case__     )
							elif not os.path.isfile(self.vocab_file     ):
									with open(snake_case__  ,						"""wb"""     ) as fi:
											lowerCAmelCase__        =   self.sp_model.serialized_model_proto()
											fi.write(snake_case__     )

							return (out_vocab_file,)





"""simple docstring"""




import os
from math import logaa







def 							_UpperCAmelCase		(       lowerCamelCase__ = "base_exp.txt"     ):





		"""simple docstring"""




		lowerCAmelCase__        =   0
		lowerCAmelCase__        =   0
		for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__     )			,  lowerCamelCase__     )     )     ):
				lowerCAmelCase__   ,					lowerCAmelCase__        =   list(map(lowerCamelCase__			,  line.split(""","""     )     )     )
				if x * logaa(lowerCamelCase__     ) > largest:
						lowerCAmelCase__        =   x * logaa(lowerCamelCase__     )
						lowerCAmelCase__        =   i + 1
		return result


if __name__ == "__main__":
			print(solution())




