
'''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


_lowercase :  Any    							=       logging.get_logger(__name__)

_lowercase :  int    							=       {"vocab_file": "spiece.model"}

_lowercase :  List[str]    							=       {
    "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
_lowercase :  Optional[int]    							=       {
    "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,
}

_lowercase :  Tuple    							=       "▁"






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     VOCAB_FILES_NAMES
					lowerCAmelCase_        =     PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_        =     ['''input_ids''', '''attention_mask''']






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE="</s>"				,							__SCREAMING_SNAKE_CASE="<unk>"				,							__SCREAMING_SNAKE_CASE="<pad>"				,							__SCREAMING_SNAKE_CASE=1_00				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=True				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											if extra_ids > 0 and additional_special_tokens is None:
																	lowercase_	:     str													=       [F'''<extra_id_{i}>''' for i in range(__SCREAMING_SNAKE_CASE		)]
											elif extra_ids > 0 and additional_special_tokens is not None:
																	# Check that we have the right number of extra_id special tokens
																	lowercase_	:     List[Any]													=       len(set(filter(lambda __SCREAMING_SNAKE_CASE		: bool('''extra_id''' in str(__SCREAMING_SNAKE_CASE		)		)				,							__SCREAMING_SNAKE_CASE		)		)		)
																	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'''		)

											lowercase_	:     Tuple													=       legacy
											lowercase_	:     int													=       {} if sp_model_kwargs is None else sp_model_kwargs

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

											lowercase_	:     str													=       vocab_file
											lowercase_	:     Union[str, Any]													=       extra_ids

											lowercase_	:     Tuple													=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(__SCREAMING_SNAKE_CASE		)






					@staticmethod
					def 	_snake_case    (   __SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																	lowercase_	:     int													=       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.'''				,							__SCREAMING_SNAKE_CASE				,							)

											return max_model_length






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__SCREAMING_SNAKE_CASE				,							token_ids_a=__SCREAMING_SNAKE_CASE				,							already_has_special_tokens=__SCREAMING_SNAKE_CASE		)

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if len(__SCREAMING_SNAKE_CASE		) > 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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_	:     int													=       self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE		)
											if token_ids_a is None:
																	return token_ids_a
											else:
																	lowercase_	:     str													=       self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE		)
																	return token_ids_a + token_ids_a






					def __getstate__(   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.__dict__.copy()
											lowercase_	:     Any													=       None
											return state






					def __setstate__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       d

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

											lowercase_	:     Tuple													=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not self.legacy:
																	lowercase_	:     Any													=       SPIECE_UNDERLINE + text.replace(__SCREAMING_SNAKE_CASE				,							''' '''		)
											return super().tokenize(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not self.legacy:
																	lowercase_	:     Optional[int]													=       text.startswith(__SCREAMING_SNAKE_CASE		)
																	if is_first:
																							lowercase_	:     List[Any]													=       text[1:]

											lowercase_	:     Dict													=       self.sp_model.encode(__SCREAMING_SNAKE_CASE				,							out_type=__SCREAMING_SNAKE_CASE		)

											if not self.legacy and not is_first and not text.startswith(''' '''		) and tokens[0].startswith(__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[int]													=       ([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
											return tokens






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index < self.sp_model.get_piece_size():
																	lowercase_	:     List[str]													=       self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_	:     Optional[int]													=       F'''<extra_id_{self.vocab_size - 1 - index}>'''
											return token






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       []
											lowercase_	:     str													=       ''''''
											lowercase_	:     List[str]													=       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(__SCREAMING_SNAKE_CASE		) + token
																							lowercase_	:     List[Any]													=       True
																							lowercase_	:     Union[str, Any]													=       []
																	else:
																							current_sub_tokens.append(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       False
											out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE		)
											return out_string.strip()






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if not os.path.isdir(__SCREAMING_SNAKE_CASE		):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
																	return
											lowercase_	:     List[Any]													=       os.path.join(
											    __SCREAMING_SNAKE_CASE				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		) and os.path.isfile(self.vocab_file		):
																	copyfile(self.vocab_file				,							__SCREAMING_SNAKE_CASE		)
											elif not os.path.isfile(self.vocab_file		):
																	with open(__SCREAMING_SNAKE_CASE				,							'''wb'''		) as fi:
																							lowercase_	:     List[Any]													=       self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)





'''simple docstring'''







import unittest

from knapsack import greedy_knapsack as kp






class  lowerCAmelCase__   (  unittest.TestCase     ):






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       [10, 20, 30, 40, 50, 60]
											lowercase_	:     Optional[Any]													=       [2, 4, 6, 8, 10, 12]
											lowercase_	:     Union[str, Any]													=       1_00
											self.assertEqual(kp.calc_profit(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)				,							2_10		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''max_weight must greater than zero.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''Weight can not be negative.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''Profit can not be negative.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(__SCREAMING_SNAKE_CASE				,							'''max_weight must greater than zero.'''		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.assertRaisesRegex(
											    __SCREAMING_SNAKE_CASE				,							'''The length of profit and weight must be same.'''		)


if __name__ == "__main__":
							unittest.main()



