

'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_ta import TaTokenizer
else:
   lowerCamelCase			:				Tuple 			=					None


lowerCamelCase			:				Dict 			=					logging.get_logger(__name__)

lowerCamelCase			:				str 			=					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase			:				Tuple 			=					{
    "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",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCamelCase			:				str 			=					{
    "t5-small": 5_1_2,
    "t5-base": 5_1_2,
    "t5-large": 5_1_2,
    "t5-3b": 5_1_2,
    "t5-11b": 5_1_2,
}







class 							A__   (	A__  ):
  A__					=    VOCAB_FILES_NAMES
  A__					=    PRETRAINED_VOCAB_FILES_MAP
  A__					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A__					=    ['input_ids', 'attention_mask']
  A__					=    TaTokenizer

  A__					=    []





  def __init__(		self				:     List[str]							, _a				:     str=None							, _a				:     int=None							, _a				:     Optional[int]="</s>"							, _a				:     Tuple="<unk>"							, _a				:     List[str]="<pad>"							, _a				:     Any=100							, _a				:     Optional[Any]=None							, **_a				:     List[str]							, )     ->   Optional[Any]:





       '''simple docstring'''






       if extra_ids > 0 and additional_special_tokens is None:
            _SCREAMING_SNAKE_CASE							   =[f"<extra_id_{i}>" for i in range(_a			)]
       elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra special tokens
            _SCREAMING_SNAKE_CASE							   =len(set(filter(lambda _a			: bool('extra_id_' in str(_a			)			)							, _a			)			)			)
            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'			)

       super().__init__(
           _a							, tokenizer_file=_a							, eos_token=_a							, unk_token=_a							, pad_token=_a							, extra_ids=_a							, additional_special_tokens=_a							, **_a							, )

       _SCREAMING_SNAKE_CASE							   =vocab_file
       _SCREAMING_SNAKE_CASE							   =False if not self.vocab_file else True
       _SCREAMING_SNAKE_CASE							   =extra_ids





  @staticmethod
  def 						A  (		_a				:     Any							, _a				:     Optional[Any]							, _a				:     List[str]			)     ->   Tuple:





       '''simple docstring'''






       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
            _SCREAMING_SNAKE_CASE							   =TaTokenizerFast.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.'							, _a							, )

       return max_model_length





  def 						A  (		self				:     List[str]							, _a				:     str							, _a				:     Optional[str] = None			)     ->   Tuple[str]:





       '''simple docstring'''






       if not self.can_save_slow_tokenizer:
            raise ValueError(
                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                'tokenizer.'			)

       if not os.path.isdir(_a			):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
            return
       _SCREAMING_SNAKE_CASE							   =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			):
            copyfile(self.vocab_file							, _a			)
            logger.info(f"Copy vocab file to {out_vocab_file}"			)

       return (out_vocab_file,)





  def 						A  (		self				:     Tuple							, _a				:     List[int]							, _a				:     Optional[List[int]] = None			)     ->   List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a
       else:
            _SCREAMING_SNAKE_CASE							   =token_ids_a + [self.eos_token_id]
            return self.prefix_tokens + token_ids_a + token_ids_a





  def 						A  (		self				:     str							, _a				:     List[int]							, _a				:     Optional[List[int]] = None			)     ->   List[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =[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 						A  (		self				:     Any			)     ->   Optional[int]:





       '''simple docstring'''






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





  def 						A  (		self				:     Any			)     ->   List[Any]:





       '''simple docstring'''






       return [self.convert_tokens_to_ids(_a			) for token in self.get_sentinel_tokens()]



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					float							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if mass < 0:
          raise ValueError('The mass of a body cannot be negative'    )
     return 0.5 * mass * abs(_UpperCamelCase    ) * abs(_UpperCamelCase    )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
