
'''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							:   Optional[int]					=     logging.get_logger(__name__)

__lowercase							:   Optional[int]					=     {'''vocab_file''': '''spiece.model'''}

__lowercase							:   Optional[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
__lowercase							:   Tuple					=     {
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}

__lowercase							:   Union[str, Any]					=     '''▁'''

class 	__lowercase				(  __a    ):
			lowerCamelCase			:		Optional[int]	      =			VOCAB_FILES_NAMES
			lowerCamelCase			:		Any	      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase			:		str	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase			:		Dict	      =			["input_ids", "attention_mask"]

			def __init__(self			,   A			,   A="</s>"			,   A="<unk>"			,   A="<pad>"			,   A=1_0_0			,   A=None			,   A = None			,   A=True			,   **A			,   ):







								if extra_ids > 0 and additional_special_tokens is None:
													lowerCamelCase_					:      Any     =		[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
													lowerCamelCase_					:      Optional[int]     =		len(set(filter(lambda A      : 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'''      )

								lowerCamelCase_					:      Union[str, Any]     =		legacy
								lowerCamelCase_					:      Any     =		{} 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_			,   )

								lowerCamelCase_					:      Dict     =		vocab_file
								lowerCamelCase_					:      Any     =		extra_ids

								lowerCamelCase_					:      List[str]     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(lowerCAmelCase_      )

			@staticmethod
			def        UpperCAmelCase__ (A			,   A			,   A      ):







								if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
													lowerCamelCase_					:      Union[str, Any]     =		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        UpperCAmelCase__ (self      ):
								return self.sp_model.get_piece_size() + self._extra_ids

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      str     =		{self.convert_ids_to_tokens(lowerCAmelCase_      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab

			def        UpperCAmelCase__ (self			,   A			,   A = None			,   A = False      ):







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

			def        UpperCAmelCase__ (self      ):
								return [self._convert_token_to_id(lowerCAmelCase_      ) for token in self.get_sentinel_tokens()]

			def        UpperCAmelCase__ (self			,   A      ):







								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        UpperCAmelCase__ (self			,   A			,   A = None      ):
								lowerCamelCase_					:      Dict     =		[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        UpperCAmelCase__ (self			,   A			,   A = None      ):
								lowerCamelCase_					:      int     =		self._add_eos_if_not_present(lowerCAmelCase_      )
								if token_ids_a is None:
													return token_ids_a
								else:
													lowerCamelCase_					:      Optional[Any]     =		self._add_eos_if_not_present(lowerCAmelCase_      )
													return token_ids_a + token_ids_a

			def __getstate__(self      ):
								lowerCamelCase_					:      Dict     =		self.__dict__.copy()
								lowerCamelCase_					:      Tuple     =		None
								return state

			def __setstate__(self			,   A      ):
								lowerCamelCase_					:      Dict     =		d

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

								lowerCamelCase_					:      List[str]     =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(self.vocab_file      )

			def        UpperCAmelCase__ (self			,   A			,   **A      ):







								if not self.legacy:
													lowerCamelCase_					:      List[Any]     =		SPIECE_UNDERLINE + text.replace(lowerCAmelCase_			,   ''' '''      )
								return super().tokenize(lowerCAmelCase_			,   **lowerCAmelCase_      )

			def        UpperCAmelCase__ (self			,   A			,   **A      ):







								if not self.legacy:
													lowerCamelCase_					:      Tuple     =		text.startswith(lowerCAmelCase_      )
													if is_first:
																		lowerCamelCase_					:      List[Any]     =		text[1:]

								lowerCamelCase_					:      List[str]     =		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_      ):
													lowerCamelCase_					:      Dict     =		([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
								return tokens

			def        UpperCAmelCase__ (self			,   A      ):







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

			def        UpperCAmelCase__ (self			,   A      ):







								if index < self.sp_model.get_piece_size():
													lowerCamelCase_					:      List[Any]     =		self.sp_model.IdToPiece(lowerCAmelCase_      )
								else:
													lowerCamelCase_					:      List[Any]     =		F"""<extra_id_{self.vocab_size - 1 - index}>"""
								return token

			def        UpperCAmelCase__ (self			,   A      ):
								lowerCamelCase_					:      Tuple     =		[]
								lowerCamelCase_					:      Optional[int]     =		''''''
								lowerCamelCase_					:      Optional[Any]     =		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
																		lowerCamelCase_					:      List[Any]     =		True
																		lowerCamelCase_					:      Union[str, Any]     =		[]
													else:
																		current_sub_tokens.append(lowerCAmelCase_      )
																		lowerCamelCase_					:      int     =		False
								out_string += self.sp_model.decode(lowerCAmelCase_      )
								return out_string.strip()






			def        UpperCAmelCase__ (self			,   A			,   A = None      ):







								if not os.path.isdir(lowerCAmelCase_      ):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
													return
								lowerCamelCase_					:      List[Any]     =		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:
																		lowerCamelCase_					:      Any     =		self.sp_model.serialized_model_proto()
																		fi.write(lowerCAmelCase_      )

								return (out_vocab_file,)








'''simple docstring'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if (direction == 1 and array[indexa] > array[indexa]) or (
					    direction == 0 and array[indexa] < array[indexa]
					):
										lowerCamelCase_,      lowerCamelCase_					:      str     =		array[indexa], array[indexa]




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      int     =		int(length / 2				)
										for i in range(_lowercase			,       low + middle				):
															comp_and_swap(_lowercase			,       _lowercase			,       i + middle			,       _lowercase				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
										bitonic_merge(_lowercase			,       low + middle			,       _lowercase			,       _lowercase				)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      Dict     =		int(length / 2				)
										bitonic_sort(_lowercase			,       _lowercase			,       _lowercase			,       1				)
										bitonic_sort(_lowercase			,       low + middle			,       _lowercase			,       0				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)


if __name__ == "__main__":
					__lowercase							:   Any					=     input('''Enter numbers separated by a comma:\n''').strip()
					__lowercase							:   Tuple					=     [int(item.strip()) for item in user_input.split(''',''')]

					bitonic_sort(unsorted, 0, len(unsorted), 1)
					print('''\nSorted array in ascending order is: ''', end='''''')
					print(*unsorted, sep=''', ''')

					bitonic_merge(unsorted, 0, len(unsorted), 0)
					print('''Sorted array in descending order is: ''', end='''''')
					print(*unsorted, sep=''', ''')


