


'''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:
	_UpperCAmelCase				: List[str]							    =				None


_UpperCAmelCase				: str							    =				logging.get_logger(__name__)

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

_UpperCAmelCase				: int							    =				{
    """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
_UpperCAmelCase				: List[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      ):







				"""simple docstring"""



				__UpperCamelCase    :		str	=	VOCAB_FILES_NAMES
				__UpperCamelCase    :		str	=	PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase    :		Optional[Any]	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase    :		List[str]	=	['input_ids', 'attention_mask']
				__UpperCamelCase    :		List[str]	=	TaTokenizer

				__UpperCamelCase    :		List[int]	=	[]




				def __init__(self       ,			__lowercase=None       ,			__lowercase=None       ,			__lowercase="</s>"       ,			__lowercase="<unk>"       ,			__lowercase="<pad>"       ,			__lowercase=1_00       ,			__lowercase=None       ,			**__lowercase       ,			):
										# 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(__lowercase		)]
										elif extra_ids > 0 and additional_special_tokens is not None:
																# Check that we have the right number of extra special tokens
																__lowerCAmelCase							       =				len(set(filter(lambda __lowercase		: bool('''extra_id_''' in str(__lowercase		)		)       ,			__lowercase		)		)		)
																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__(
										    __lowercase       ,			tokenizer_file=__lowercase       ,			eos_token=__lowercase       ,			unk_token=__lowercase       ,			pad_token=__lowercase       ,			extra_ids=__lowercase       ,			additional_special_tokens=__lowercase       ,			**__lowercase       ,			)

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




				@staticmethod
				def 					_snake_case  (__lowercase       ,			__lowercase       ,			__lowercase		):
										if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																__lowerCAmelCase							       =				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.'''       ,			__lowercase       ,			)

										return max_model_length




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = 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(__lowercase		):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																return
										__lowerCAmelCase							       =				os.path.join(
										    __lowercase       ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

										if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowercase		):
																copyfile(self.vocab_file       ,			__lowercase		)
																logger.info(F"""Copy vocab file to {out_vocab_file}"""		)

										return (out_vocab_file,)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None		):
										__lowerCAmelCase							       =				token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																return self.prefix_tokens + token_ids_a
										else:
																__lowerCAmelCase							       =				token_ids_a + [self.eos_token_id]
																return self.prefix_tokens + token_ids_a + token_ids_a




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




				def 					_snake_case  (self		):
										return [self.convert_tokens_to_ids(__lowercase		) for token in self.get_sentinel_tokens()]







'''simple docstring'''

def 							__magic_name__(						):
						return [
						    a * b * (1_0_0_0 - a - b)
						    for a in range(1,				9_9_9)
						    for b in range(lowerCamelCase,				9_9_9)
						    if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
						][0]


if __name__ == "__main__":
	print(f"""{solution() = }""")

