



"""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:
	__snake_case							:     int   =					None


__snake_case							:     Any   =					logging.get_logger(__name__)

__snake_case							:     Union[str, Any]   =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__snake_case							:     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
__snake_case							:     List[Any]   =					{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''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:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	int=None ,    _SCREAMING_SNAKE_CASE:	str=None ,    _SCREAMING_SNAKE_CASE:	Tuple="</s>" ,    _SCREAMING_SNAKE_CASE:	Tuple="<unk>" ,    _SCREAMING_SNAKE_CASE:	Optional[int]="<pad>" ,    _SCREAMING_SNAKE_CASE:	Any=100 ,    _SCREAMING_SNAKE_CASE:	int=None ,    **_SCREAMING_SNAKE_CASE:	Dict ,    )       -> Any:

				"""simple docstring"""

				if extra_ids > 0 and additional_special_tokens is None:
							__lowerCAmelCase   :						Dict				= [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 special tokens
							__lowerCAmelCase   :						int				= 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")

				super().__init__(
				    _SCREAMING_SNAKE_CASE ,    tokenizer_file=_SCREAMING_SNAKE_CASE ,    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 ,    **_SCREAMING_SNAKE_CASE ,    )

				__lowerCAmelCase   :						Optional[Any]				= vocab_file
				__lowerCAmelCase   :						Tuple				= False if not self.vocab_file else True
				__lowerCAmelCase   :						str				= extra_ids

	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Dict:

				"""simple docstring"""

				if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
							__lowerCAmelCase   :						List[str]				= 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." ,    _SCREAMING_SNAKE_CASE ,    )

				return max_model_length

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	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(_SCREAMING_SNAKE_CASE):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
							return
				__lowerCAmelCase   :						List[str]				= 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):
							copyfile(self.vocab_file ,    _SCREAMING_SNAKE_CASE)
							logger.info(F"""Copy vocab file to {out_vocab_file}""")

				return (out_vocab_file,)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	Optional[List[int]] = None)       -> List[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a
				else:
							__lowerCAmelCase   :						List[Any]				= token_ids_a + [self.eos_token_id]
							return self.prefix_tokens + token_ids_a + token_ids_a

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[int] ,    _SCREAMING_SNAKE_CASE:	Optional[List[int]] = None)       -> List[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						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 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

				"""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 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Any:

				"""simple docstring"""

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




"""simple docstring"""






import unittest

from knapsack import knapsack as k



class     A__     (						unittest.TestCase							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= 0
				__lowerCAmelCase   :						str				= [0]
				__lowerCAmelCase   :						Union[str, Any]				= [0]
				__lowerCAmelCase   :						List[Any]				= len(_SCREAMING_SNAKE_CASE)
				self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) ,    0)

				__lowerCAmelCase   :						Dict				= [60]
				__lowerCAmelCase   :						Optional[int]				= [10]
				__lowerCAmelCase   :						Tuple				= len(_SCREAMING_SNAKE_CASE)
				self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) ,    0)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= 3
				__lowerCAmelCase   :						Dict				= [1, 2, 3]
				__lowerCAmelCase   :						str				= [3, 2, 1]
				__lowerCAmelCase   :						Optional[int]				= len(_SCREAMING_SNAKE_CASE)
				self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) ,    5)







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

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= 50
				__lowerCAmelCase   :						Dict				= [60, 100, 120]
				__lowerCAmelCase   :						List[Any]				= [10, 20, 30]
				__lowerCAmelCase   :						int				= len(_SCREAMING_SNAKE_CASE)
				self.assertEqual(k.knapsack(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) ,    220)


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