

from __future__ import annotations

from math import pi, sqrt

def       A_     (							a    ,      a     ):

      """simple docstring"""



      if inductance <= 0:
            raise ValueError('Inductance cannot be 0 or negative'     )

      elif capacitance <= 0:
            raise ValueError('Capacitance cannot be 0 or negative'     )

      else:
            return (
                "Resonant frequency",
                float(1 / (2 * pi * (sqrt(inductance * capacitance     )))     ),
            )


if __name__ == "__main__":
    import doctest

    doctest.testmod()







'''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:
  __a: Tuple          =   None


__a: Tuple          =   logging.get_logger(__name__)

__a: Optional[Any]          =   {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a: Optional[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""",
    },
    """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
__a: Tuple          =   {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE							=    PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE							=    ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE							=    TaTokenizer

       SCREAMING_SNAKE_CASE							=    []

       def __init__(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="</s>"	,   __lowerCAmelCase="<unk>"	,   __lowerCAmelCase="<pad>"	,   __lowerCAmelCase=100	,   __lowerCAmelCase=None	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        # Add extra_ids to the special token list
        if extra_ids > 0 and additional_special_tokens is None:
         lowercase__				:    Union[str, Any]  =					[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 special tokens
         lowercase__				:    Dict  =					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''' )

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

        lowercase__				:    Union[str, Any]  =					vocab_file
        lowercase__				:    Optional[int]  =					False if not self.vocab_file else True
        lowercase__				:    Any  =					extra_ids

       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Union[str, Any]:
        if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowercase__				:    Any  =					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.'''	,   __lowerCAmelCase	,   )

        return max_model_length

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  Tuple[str]:
        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(__lowerCAmelCase ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
         return
        lowercase__				:    List[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 ):
         copyfile(self.vocab_file	,   __lowerCAmelCase )
         logger.info(F"""Copy vocab file to {out_vocab_file}""" )

        return (out_vocab_file,)

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Any  =					token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
        else:
         lowercase__				:    Dict  =					token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[int]:
        lowercase__				:    Optional[int]  =					[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 _lowerCAmelCase(						self )  ->  List[Any]:
        return list(
            set(filter(lambda __lowerCAmelCase : bool(re.search(r'''<extra_id_\d+>'''	,   __lowerCAmelCase ) ) is not None	,   self.additional_special_tokens ) ) )


       def _lowerCAmelCase(						self )  ->  Tuple:
        return [self.convert_tokens_to_ids(__lowerCAmelCase ) for token in self.get_sentinel_tokens()]

