



"""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__      =    logging.get_logger(__name__)

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

lowerCAmelCase__      =    {
    '''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__      =    {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

lowerCAmelCase__      =    '''▁'''






class 	__snake_case	(     _lowercase):
  snake_case__    :       Union[str, Any]        =						VOCAB_FILES_NAMES
  snake_case__    :       int        =						PRETRAINED_VOCAB_FILES_MAP
  snake_case__    :       List[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case__    :       Union[str, Any]        =						["input_ids", "attention_mask"]




  def __init__(			self					:						Any						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Optional[int]="</s>"						, __lowerCAmelCase					:						Tuple="<unk>"						, __lowerCAmelCase					:						Tuple="<pad>"						, __lowerCAmelCase					:						Tuple=1_0_0						, __lowerCAmelCase					:						int=None						, __lowerCAmelCase					:						Optional[Dict[str, Any]] = None						, __lowerCAmelCase					:						Union[str, Any]=True						, **__lowerCAmelCase					:						int						, ):






         """simple docstring"""






         if extra_ids > 0 and additional_special_tokens is None:
                _lowerCamelCase						:    Optional[int]         =			[f'''<extra_id_{i}>''' for i in range(__lowerCAmelCase		)]
         elif extra_ids > 0 and additional_special_tokens is not None:
                # Check that we have the right number of extra_id special tokens
                _lowerCamelCase						:    List[str]         =			len(set(filter(lambda __lowerCAmelCase		: bool('''extra_id''' in str(__lowerCAmelCase		)		)						, __lowerCAmelCase		)		)		)
                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						:    List[str]         =			legacy
         _lowerCamelCase						:    Union[str, Any]         =			{} if sp_model_kwargs is None else sp_model_kwargs

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

         _lowerCamelCase						:    Any         =			vocab_file
         _lowerCamelCase						:    List[str]         =			extra_ids

         _lowerCamelCase						:    Optional[Any]         =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(__lowerCAmelCase		)




  @staticmethod
  def 						SCREAMING_SNAKE_CASE    (			__lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
                _lowerCamelCase						:    Optional[Any]         =			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.'''						, __lowerCAmelCase						, )

         return max_model_length




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         return self.sp_model.get_piece_size() + self._extra_ids




  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[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		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         return list(
             set(filter(lambda __lowerCAmelCase		: bool(re.search(R'''<extra_id_\d+>'''						, __lowerCAmelCase		)		) is not None						, self.additional_special_tokens		)		)		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         return [self._convert_token_to_id(__lowerCAmelCase		) for token in self.get_sentinel_tokens()]




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						List[int]		):






         """simple docstring"""






         if len(__lowerCAmelCase		) > 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					:						Optional[Any]						, __lowerCAmelCase					:						List[int]						, __lowerCAmelCase					:						Optional[List[int]] = None		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			[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					:						int						, __lowerCAmelCase					:						List[int]						, __lowerCAmelCase					:						Optional[List[int]] = None		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self._add_eos_if_not_present(__lowerCAmelCase		)
         if token_ids_a is None:
                return token_ids_a
         else:
                _lowerCamelCase						:    Optional[int]         =			self._add_eos_if_not_present(__lowerCAmelCase		)
                return token_ids_a + token_ids_a




  def __getstate__(			self					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    Tuple         =			self.__dict__.copy()
         _lowerCamelCase						:    str         =			None
         return state




  def __setstate__(			self					:						Union[str, Any]						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         _lowerCamelCase						:    int         =			d

         # for backward compatibility
         if not hasattr(self						, '''sp_model_kwargs'''		):
                _lowerCamelCase						:    List[Any]         =			{}

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]						, __lowerCAmelCase					:						"TextInput"						, **__lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         if not self.legacy:
                _lowerCamelCase						:    Dict         =			SPIECE_UNDERLINE + text.replace(__lowerCAmelCase						, ''' '''		)
         return super().tokenize(__lowerCAmelCase						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						int						, **__lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         if not self.legacy:
                _lowerCamelCase						:    Dict         =			text.startswith(__lowerCAmelCase		)
                if is_first:
                       _lowerCamelCase						:    Optional[int]         =			text[1:]

         _lowerCamelCase						:    Optional[Any]         =			self.sp_model.encode(__lowerCAmelCase						, out_type=__lowerCAmelCase		)

         if not self.legacy and not is_first and not text.startswith(''' '''		) and tokens[0].startswith(__lowerCAmelCase		):
                _lowerCamelCase						:    Union[str, Any]         =			([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
         return tokens




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         if token.startswith('''<extra_id_'''		):
                _lowerCamelCase						:    Optional[int]         =			re.match(R'''<extra_id_(\d+)>'''						, __lowerCAmelCase		)
                _lowerCamelCase						:    Any         =			int(match.group(1		)		)
                return self.vocab_size - num - 1
         return self.sp_model.piece_to_id(__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         if index < self.sp_model.get_piece_size():
                _lowerCamelCase						:    str         =			self.sp_model.IdToPiece(__lowerCAmelCase		)
         else:
                _lowerCamelCase						:    Dict         =			f'''<extra_id_{self.vocab_size - 1 - index}>'''
         return token




  def 						SCREAMING_SNAKE_CASE    (			self					:						int						, __lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			[]
         _lowerCamelCase						:    Union[str, Any]         =			''''''
         _lowerCamelCase						:    Optional[int]         =			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
                       _lowerCamelCase						:    int         =			True
                       _lowerCamelCase						:    Optional[int]         =			[]
                else:
                       current_sub_tokens.append(__lowerCAmelCase		)
                       _lowerCamelCase						:    Optional[int]         =			False
         out_string += self.sp_model.decode(__lowerCAmelCase		)
         return out_string.strip()




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __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
         _lowerCamelCase						:    Union[str, Any]         =			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:
                       _lowerCamelCase						:    int         =			self.sp_model.serialized_model_proto()
                       fi.write(__lowerCAmelCase		)

         return (out_vocab_file,)







"""simple docstring"""






def 	snake_case_  (				A_ :		float						):


       '''simple docstring'''





       if edge <= 0 or not isinstance(A_,    A_						):
              raise ValueError('''Length must be a positive.'''						)
       return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)






def 	snake_case_  (				A_ :		float						):


       '''simple docstring'''





       if edge <= 0 or not isinstance(A_,    A_						):
              raise ValueError('''Length must be a positive.'''						)
       return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
   import doctest

   doctest.testmod()


