

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


_UpperCAmelCase		:       Tuple						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{"""vocab_file""": """spiece.model"""}

_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""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
_UpperCAmelCase		:       Optional[int]						    =	{
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

_UpperCAmelCase		:       Dict						    =	"""▁"""







class 			lowercase    (	lowerCAmelCase_      ):
				__SCREAMING_SNAKE_CASE :			int	      =      VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE :			int	      =      PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE :			Any	      =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      ["""input_ids""", """attention_mask"""]




				def __init__( self				,       snake_case				,       snake_case="</s>"				,       snake_case="<unk>"				,       snake_case="<pad>"				,       snake_case=100				,       snake_case=None				,       snake_case = None				,       snake_case=True				,       **snake_case				,       ):

											if extra_ids > 0 and additional_special_tokens is None:
																		snake_case_          =   [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
																		snake_case_          =   len(set(filter(lambda 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'	)

											snake_case_          =   legacy
											snake_case_          =   {} 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				,       )

											snake_case_          =   vocab_file
											snake_case_          =   extra_ids

											snake_case_          =   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(__SCREAMING_SNAKE_CASE	)




				@staticmethod
				def    a							( snake_case				,       snake_case				,       snake_case	):

											if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
																		snake_case_          =   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    a							( self	):
											return self.sp_model.get_piece_size() + self._extra_ids




				def    a							( self	):
											snake_case_          =   {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    a							( self				,       snake_case				,       snake_case = None				,       snake_case = False	):

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




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




				def    a							( self				,       snake_case	):

											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    a							( self				,       snake_case				,       snake_case = None	):
											snake_case_          =   [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				,       snake_case				,       snake_case = None	):
											snake_case_          =   self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE	)
											if token_ids_a is None:
																		return token_ids_a
											else:
																		snake_case_          =   self._add_eos_if_not_present(__SCREAMING_SNAKE_CASE	)
																		return token_ids_a + token_ids_a




				def __getstate__( self	):
											snake_case_          =   self.__dict__.copy()
											snake_case_          =   None
											return state




				def __setstate__( self				,       snake_case	):
											snake_case_          =   d

											# for backward compatibility
											if not hasattr(self				,       'sp_model_kwargs'	):
																		snake_case_          =   {}

											snake_case_          =   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(self.vocab_file	)




				def    a							( self				,       snake_case				,       **snake_case	):

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




				def    a							( self				,       snake_case				,       **snake_case	):

											if not self.legacy:
																		snake_case_          =   text.startswith(__SCREAMING_SNAKE_CASE	)
																		if is_first:
																									snake_case_          =   text[1:]

											snake_case_          =   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	):
																		snake_case_          =   ([tokens[0][1:]] if len(tokens[0]	) > 1 else []) + tokens[1:]
											return tokens




				def    a							( self				,       snake_case	):

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




				def    a							( self				,       snake_case	):

											if index < self.sp_model.get_piece_size():
																		snake_case_          =   self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE	)
											else:
																		snake_case_          =   F'''<extra_id_{self.vocab_size - 1 - index}>'''
											return token




				def    a							( self				,       snake_case	):
											snake_case_          =   []
											snake_case_          =   ''
											snake_case_          =   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
																									snake_case_          =   True
																									snake_case_          =   []
																		else:
																									current_sub_tokens.append(__SCREAMING_SNAKE_CASE	)
																									snake_case_          =   False
											out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	)
											return out_string.strip()




				def    a							( self				,       snake_case				,       snake_case = None	):

											if not os.path.isdir(__SCREAMING_SNAKE_CASE	):
																		logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
																		return
											snake_case_          =   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:
																									snake_case_          =   self.sp_model.serialized_model_proto()
																									fi.write(__SCREAMING_SNAKE_CASE	)

											return (out_vocab_file,)







def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							global f  # a global dp table for knapsack
							if f[i][j] < 0:
														if j < wt[i - 1]:
																					snake_case_          =   mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					snake_case_          =   max(
																					    mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)       ,							mf_knapsack(i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							j - wt[i - 1]				) + val[i - 1]       ,							)
														snake_case_          =   val
							return f[i][j]





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   [[0] * (w + 1) for _ in range(n + 1				)]

							for i in range(1       ,							n + 1				):
														for w_ in range(1       ,							w + 1				):
																					if wt[i - 1] <= w_:
																												snake_case_          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]       ,							dp[i - 1][w_]				)
																					else:
																												snake_case_          =   dp[i - 1][w_]

							return dp[n][w_], dp





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if not (isinstance(UpperCamelCase__       ,							(list, tuple)				) and isinstance(UpperCamelCase__       ,							(list, tuple)				)):
														raise ValueError(
														    'Both the weights and values vectors must be either lists or tuples'				)

							snake_case_          =   len(UpperCamelCase__				)
							if num_items != len(UpperCamelCase__				):
														snake_case_          =   (
														    'The number of weights must be the same as the number of values.\n'
														    F'''But got {num_items} weights and {len(UpperCamelCase__				)} values'''
														)
														raise ValueError(UpperCamelCase__				)
							for i in range(UpperCamelCase__				):
														if not isinstance(wt[i]       ,							UpperCamelCase__				):
																					snake_case_          =   (
																					    'All weights must be integers but got weight of '
																					    F'''type {type(wt[i]				)} at index {i}'''
																					)
																					raise TypeError(UpperCamelCase__				)

							snake_case_    ,   snake_case_          =   knapsack(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   set()
							_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

							return optimal_val, example_optional_set





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if i > 0 and j > 0:
														if dp[i - 1][j] == dp[i][j]:
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							UpperCamelCase__       ,							UpperCamelCase__				)
														else:
																					optimal_set.add(UpperCamelCase__				)
																					_construct_solution(UpperCamelCase__       ,							UpperCamelCase__       ,							i - 1       ,							j - wt[i - 1]       ,							UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       int						    =	[3, 2, 4, 4]
					_UpperCAmelCase		:       Tuple						    =	[4, 3, 2, 3]
					_UpperCAmelCase		:       Dict						    =	4
					_UpperCAmelCase		:       int						    =	6
					_UpperCAmelCase		:       Union[str, Any]						    =	[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					_UpperCAmelCase    ,						_UpperCAmelCase		:       Optional[int]						    =	knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)




