





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


A    :					List[str] =      logging.get_logger(__name__)

A    :					str =      {'''vocab_file''': '''spiece.model'''}

A    :					List[Any] =      {
    '''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
A    :					List[Any] =      {
    '''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,
}

A    :					List[Any] =      '''▁'''


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       Tuple							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       Union[str, Any]							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       Any							=    ['''input_ids''', '''attention_mask''']






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    List[Any]							,	__lowerCAmelCase   :    Tuple="</s>"							,	__lowerCAmelCase   :    Union[str, Any]="<unk>"							,	__lowerCAmelCase   :    Tuple="<pad>"							,	__lowerCAmelCase   :    str=1_00							,	__lowerCAmelCase   :    Optional[int]=None							,	__lowerCAmelCase   :    Optional[Dict[str, Any]] = None							,	__lowerCAmelCase   :    Optional[int]=True							,	**__lowerCAmelCase   :    List[str]							,	)	->	None:







					"""simple docstring"""






					if extra_ids > 0 and additional_special_tokens is None:
							A__        =   [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_id special tokens
							A__        =   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"""   )
					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"""   )

					A__        =   legacy
					A__        =   {} if sp_model_kwargs is None else sp_model_kwargs

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

					A__        =   vocab_file
					A__        =   extra_ids

					A__        =   spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.Load(__lowerCAmelCase   )






			@staticmethod
			def    a_       (			__lowerCAmelCase   :    List[str]							,	__lowerCAmelCase   :    Optional[int]							,	__lowerCAmelCase   :    Tuple   )	->	Any:







					"""simple docstring"""






					if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
							A__        =   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."""							,	__lowerCAmelCase							,	)

					return max_model_length






			@property
			def    a_       (			self   :    Any   )	->	Tuple:







					"""simple docstring"""






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






			def    a_       (			self   :    int   )	->	List[str]:







					"""simple docstring"""






					A__        =   {self.convert_ids_to_tokens(__lowerCAmelCase   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None							,	__lowerCAmelCase   :    bool = False   )	->	List[int]:







					"""simple docstring"""






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

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






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







					"""simple docstring"""






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






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







					"""simple docstring"""






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






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    List[int]   )	->	List[int]:







					"""simple docstring"""






					if len(__lowerCAmelCase   ) > 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   :    Optional[int]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   [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   :    Dict							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					A__        =   self._add_eos_if_not_present(__lowerCAmelCase   )
					if token_ids_a is None:
							return token_ids_a
					else:
							A__        =   self._add_eos_if_not_present(__lowerCAmelCase   )
							return token_ids_a + token_ids_a






			def __getstate__(			self   :    List[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.__dict__.copy()
					A__        =   None
					return state






			def __setstate__(			self   :    int							,	__lowerCAmelCase   :    str   )	->	int:







					"""simple docstring"""






					A__        =   d

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

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






			def    a_       (			self   :    List[str]							,	__lowerCAmelCase   :    "TextInput"							,	**__lowerCAmelCase   :    Dict   )	->	List[str]:







					"""simple docstring"""






					if not self.legacy:
							A__        =   SPIECE_UNDERLINE + text.replace(__lowerCAmelCase							,	""" """   )
					return super().tokenize(__lowerCAmelCase							,	**__lowerCAmelCase   )






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Dict							,	**__lowerCAmelCase   :    List[str]   )	->	str:







					"""simple docstring"""






					if not self.legacy:
							A__        =   text.startswith(__lowerCAmelCase   )
							if is_first:
									A__        =   text[1:]

					A__        =   self.sp_model.encode(__lowerCAmelCase							,	out_type=__lowerCAmelCase   )

					if not self.legacy and not is_first and not text.startswith(""" """   ) and tokens[0].startswith(__lowerCAmelCase   ):
							A__        =   ([tokens[0][1:]] if len(tokens[0]   ) > 1 else []) + tokens[1:]
					return tokens






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    Dict   )	->	List[str]:







					"""simple docstring"""






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






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Any   )	->	Optional[Any]:







					"""simple docstring"""






					if index < self.sp_model.get_piece_size():
							A__        =   self.sp_model.IdToPiece(__lowerCAmelCase   )
					else:
							A__        =   f'<extra_id_{self.vocab_size - 1 - index}>'
					return token






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Union[str, Any]   )	->	Optional[int]:







					"""simple docstring"""






					A__        =   []
					A__        =   """"""
					A__        =   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(__lowerCAmelCase   ) + token
									A__        =   True
									A__        =   []
							else:
									current_sub_tokens.append(__lowerCAmelCase   )
									A__        =   False
					out_string += self.sp_model.decode(__lowerCAmelCase   )
					return out_string.strip()







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    Optional[str] = None   )	->	Tuple[str]:







					"""simple docstring"""






					if not os.path.isdir(__lowerCAmelCase   ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
							return
					A__        =   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   ) and os.path.isfile(self.vocab_file   ):
							copyfile(self.vocab_file							,	__lowerCAmelCase   )
					elif not os.path.isfile(self.vocab_file   ):
							with open(__lowerCAmelCase							,	"""wb"""   ) as fi:
									A__        =   self.sp_model.serialized_model_proto()
									fi.write(__lowerCAmelCase   )

					return (out_vocab_file,)










class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			pass


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[Any]   )	->	str:







					"""simple docstring"""






					A__        =   [
					    [],
					    [],
					    [],
					]






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					try:
							if len(self.queues[priority]   ) >= 1_00:
									raise OverflowError("""Maximum queue size is 100"""   )
							self.queues[priority].append(__lowerCAmelCase   )
					except IndexError:
							raise ValueError("""Valid priorities are 0, 1, and 2"""   )






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







					"""simple docstring"""






					for queue in self.queues:
							if queue:
									return queue.pop(0   )
					raise UnderFlowError("""All queues are empty"""   )







			def __str__(			self   :    Tuple   )	->	str:







					"""simple docstring"""






					return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues   )   )


class   A			:
			'''simple docstring'''




			def __init__(			self   :    int   )	->	str:







					"""simple docstring"""






					A__        =   []






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					if len(self.queue   ) == 1_00:
							raise OverFlowError("""Maximum queue size is 100"""   )
					self.queue.append(__lowerCAmelCase   )






			def    a_       (			self   :    List[str]   )	->	int:







					"""simple docstring"""






					if not self.queue:
							raise UnderFlowError("""The queue is empty"""   )
					else:
							A__        =   min(self.queue   )
							self.queue.remove(__lowerCAmelCase   )
							return data







			def __str__(			self   :    List[Any]   )	->	str:







					"""simple docstring"""






					return str(self.queue   )







def    __lowerCamelCase					(							)		->				Optional[Any]:






		"""simple docstring"""

		A__        =   FixedPriorityQueue()
		fpq.enqueue(0    ,     1_0    )
		fpq.enqueue(1    ,     7_0    )
		fpq.enqueue(0    ,     1_0_0    )
		fpq.enqueue(2    ,     1    )
		fpq.enqueue(2    ,     5    )
		fpq.enqueue(1    ,     7    )
		fpq.enqueue(2    ,     4    )
		fpq.enqueue(1    ,     6_4    )
		fpq.enqueue(0    ,     1_2_8    )
		print(__a    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(__a    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )
		print(fpq.dequeue()    )







def    __lowerCamelCase					(							)		->				int:






		"""simple docstring"""

		A__        =   ElementPriorityQueue()
		epq.enqueue(1_0    )
		epq.enqueue(7_0    )
		epq.enqueue(1_0_0    )
		epq.enqueue(1    )
		epq.enqueue(5    )
		epq.enqueue(7    )
		epq.enqueue(4    )
		epq.enqueue(6_4    )
		epq.enqueue(1_2_8    )
		print(__a    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(__a    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )
		print(epq.dequeue()    )


if __name__ == "__main__":
					fixed_priority_queue()
					element_priority_queue()



