


def        _lowerCamelCase(	lowercase__	)						->	float:

		'''simple docstring'''







		if edge <= 0 or not isinstance(lowercase__   , lowercase__	):
				raise ValueError('Length must be a positive.'	)
		return 3 * ((2_5 + 1_0 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)







def        _lowerCamelCase(	lowercase__	)						->	float:

		'''simple docstring'''







		if edge <= 0 or not isinstance(lowercase__   , lowercase__	):
				raise ValueError('Length must be a positive.'	)
		return ((1_5 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
	import doctest

	doctest.testmod()










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           =		None


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

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''',
    },
    '''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           =		{
    '''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_ ):
							UpperCamelCase_   :							Dict       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							Dict       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							str       =['''input_ids''', '''attention_mask''']
							UpperCamelCase_   :							List[str]       =TaTokenizer

							UpperCamelCase_   :							List[int]       =[]


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase=1_0_0      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											__lowercase=			[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=			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=			vocab_file
									__lowercase=			False if not self.vocab_file else True
									__lowercase=			extra_ids


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase=			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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									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=			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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											__lowercase=			token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			[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					):
									return list(
									    set(filter(lambda lowerCAmelCase					: bool(re.search(r'<extra_id_\d+>'      ,				lowerCAmelCase					)					) is not None      ,				self.additional_special_tokens					)					)					)







							def 			_A       (self					):
									return [self.convert_tokens_to_ids(lowerCAmelCase					) for token in self.get_sentinel_tokens()]






