


"""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


__UpperCamelCase		= logging.get_logger(__name__)

__UpperCamelCase		= {"vocab_file": "spiece.model"}

__UpperCamelCase		= {
    "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		= {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

__UpperCamelCase		= "▁"
class    lowerCAmelCase      (   lowercase_       ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_		:	Union[str, Any]														=	VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE_		:	List[Any]														=	PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE_		:	int														=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE_		:	Dict														=	["""input_ids""", """attention_mask"""]







		def __init__(      self		,			lowerCAmelCase__		,			lowerCAmelCase__="</s>"		,			lowerCAmelCase__="<unk>"		,			lowerCAmelCase__="<pad>"		,			lowerCAmelCase__=100		,			lowerCAmelCase__=None		,			lowerCAmelCase__ = None		,			lowerCAmelCase__=True		,			**lowerCAmelCase__		,			)	->				None:
								# Add extra_ids to the special token list
								if extra_ids > 0 and additional_special_tokens is None:
														SCREAMING_SNAKE_CASE						=   [F'<extra_id_{i}>' for i in range(_lowercase							)]
								elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra_id special tokens
														SCREAMING_SNAKE_CASE						=   len(set(filter(lambda lowerCAmelCase__							: bool('extra_id' in str(_lowercase							)							)		,			_lowercase							)							)							)
														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'							)

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

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

								SCREAMING_SNAKE_CASE						=   vocab_file
								SCREAMING_SNAKE_CASE						=   extra_ids

								SCREAMING_SNAKE_CASE						=   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
								self.sp_model.Load(_lowercase							)







		@staticmethod
		def 				__A		(      lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__							)	->				Union[str, Any]:
								if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
														SCREAMING_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.'		,			_lowercase		,			)

								return max_model_length







		@property
		def 				__A		(      self							)	->				Dict:
								return self.sp_model.get_piece_size() + self._extra_ids







		def 				__A		(      self							)	->				Any:
								SCREAMING_SNAKE_CASE						=   {self.convert_ids_to_tokens(_lowercase							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__ = None		,			lowerCAmelCase__ = False							)	->				List[int]:
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowercase		,			token_ids_a=_lowercase		,			already_has_special_tokens=_lowercase							)

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







		def 				__A		(      self							)	->				str:
								return list(
								    set(filter(lambda lowerCAmelCase__							: bool(re.search(r'<extra_id_\d+>'		,			_lowercase							)							) is not None		,			self.additional_special_tokens							)							)							)







		def 				__A		(      self							)	->				Union[str, Any]:
								return [self._convert_token_to_id(_lowercase							) for token in self.get_sentinel_tokens()]







		def 				__A		(      self		,			lowerCAmelCase__							)	->				List[int]:
								if len(_lowercase							) > 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		,			lowerCAmelCase__		,			lowerCAmelCase__ = None							)	->				List[int]:
								SCREAMING_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		,			lowerCAmelCase__		,			lowerCAmelCase__ = None							)	->				List[int]:
								SCREAMING_SNAKE_CASE						=   self._add_eos_if_not_present(_lowercase							)
								if token_ids_a is None:
														return token_ids_a
								else:
														SCREAMING_SNAKE_CASE						=   self._add_eos_if_not_present(_lowercase							)
														return token_ids_a + token_ids_a







		def __getstate__(      self							)	->				Union[str, Any]:
								SCREAMING_SNAKE_CASE						=   self.__dict__.copy()
								SCREAMING_SNAKE_CASE						=   None
								return state







		def __setstate__(      self		,			lowerCAmelCase__							)	->				Any:
								SCREAMING_SNAKE_CASE						=   d

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

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







		def 				__A		(      self		,			lowerCAmelCase__		,			**lowerCAmelCase__							)	->				List[str]:
								# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
								# the beginning of the text
								if not self.legacy:
														SCREAMING_SNAKE_CASE						=   SPIECE_UNDERLINE + text.replace(_lowercase		,			' '							)
								return super().tokenize(_lowercase		,			**_lowercase							)







		def 				__A		(      self		,			lowerCAmelCase__		,			**lowerCAmelCase__							)	->				int:
								if not self.legacy:
														SCREAMING_SNAKE_CASE						=   text.startswith(_lowercase							)
														if is_first:
																				SCREAMING_SNAKE_CASE						=   text[1:]

								SCREAMING_SNAKE_CASE						=   self.sp_model.encode(_lowercase		,			out_type=_lowercase							)

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







		def 				__A		(      self		,			lowerCAmelCase__							)	->				int:
								if token.startswith('<extra_id_'							):
														SCREAMING_SNAKE_CASE						=   re.match(r'<extra_id_(\d+)>'		,			_lowercase							)
														SCREAMING_SNAKE_CASE						=   int(match.group(1							)							)
														return self.vocab_size - num - 1
								return self.sp_model.piece_to_id(_lowercase							)







		def 				__A		(      self		,			lowerCAmelCase__							)	->				int:
								if index < self.sp_model.get_piece_size():
														SCREAMING_SNAKE_CASE						=   self.sp_model.IdToPiece(_lowercase							)
								else:
														SCREAMING_SNAKE_CASE						=   F'<extra_id_{self.vocab_size - 1 - index}>'
								return token







		def 				__A		(      self		,			lowerCAmelCase__							)	->				str:
								SCREAMING_SNAKE_CASE						=   []
								SCREAMING_SNAKE_CASE						=   ''
								SCREAMING_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(_lowercase							) + token
																				SCREAMING_SNAKE_CASE						=   True
																				SCREAMING_SNAKE_CASE						=   []
														else:
																				current_sub_tokens.append(_lowercase							)
																				SCREAMING_SNAKE_CASE						=   False
								out_string += self.sp_model.decode(_lowercase							)
								return out_string.strip()







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__ = None							)	->				Tuple[str]:
								if not os.path.isdir(_lowercase							):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
														return
								SCREAMING_SNAKE_CASE						=   os.path.join(
								    _lowercase		,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

								if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowercase							) and os.path.isfile(self.vocab_file							):
														copyfile(self.vocab_file		,			_lowercase							)
								elif not os.path.isfile(self.vocab_file							):
														with open(_lowercase		,			'wb'							) as fi:
																				SCREAMING_SNAKE_CASE						=   self.sp_model.serialized_model_proto()
																				fi.write(_lowercase							)

								return (out_vocab_file,)





'''simple docstring'''

def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					int   )      -> str:
							if a < 0 or b < 0:
														raise ValueError('the value of both inputs must be positive'   )
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]  # remove the leading "0b"
							SCREAMING_SNAKE_CASE_				=				str(bin(lowerCAmelCase__   )   )[2:]
							SCREAMING_SNAKE_CASE_				=				max(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
							return "0b" + "".join(
							    str(int('1' in (char_a, char_b)   )   )
							    for char_a, char_b in zip(a_binary.zfill(lowerCAmelCase__   )		,b_binary.zfill(lowerCAmelCase__   )   )   )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





