






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase					:		List[str]	  =						logging.get_logger(__name__)
__UpperCamelCase					:		str	  =						{"""vocab_file""": """spiece.model"""}

__UpperCamelCase					:		Dict	  =						{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

__UpperCamelCase					:		int	  =						{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

__UpperCamelCase					:		Any	  =						"""▁"""


class   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       VOCAB_FILES_NAMES
							_UpperCAmelCase     =       PRETRAINED_VOCAB_FILES_MAP
							_UpperCAmelCase     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							def __init__(     self:      str       ,	UpperCamelCase:      Union[str, Any]       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      Any=True       ,	UpperCamelCase:      int=False       ,	UpperCamelCase:      Dict="[CLS]"       ,	UpperCamelCase:      List[Any]="[SEP]"       ,	UpperCamelCase:      Dict="<unk>"       ,	UpperCamelCase:      str="[SEP]"       ,	UpperCamelCase:      int="<pad>"       ,	UpperCamelCase:      List[Any]="[CLS]"       ,	UpperCamelCase:      Any="[MASK]"       ,	UpperCamelCase:      Optional[Dict[str, Any]] = None       ,	**UpperCamelCase:      int       ,	)		->		None:
											# Mask token behave like a normal word, i.e. include the space before it and
											# is included in the raw text, there should be a match in a non-normalized sentence.
											snake_case__									=     (
											    AddedToken(UpperCamelCase       ,	lstrip=UpperCamelCase       ,	rstrip=UpperCamelCase       ,	normalized=UpperCamelCase						)
											    if isinstance(UpperCamelCase       ,	UpperCamelCase						)
											    else mask_token
											)

											snake_case__									=     {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=UpperCamelCase       ,	remove_space=UpperCamelCase       ,	keep_accents=UpperCamelCase       ,	bos_token=UpperCamelCase       ,	eos_token=UpperCamelCase       ,	unk_token=UpperCamelCase       ,	sep_token=UpperCamelCase       ,	pad_token=UpperCamelCase       ,	cls_token=UpperCamelCase       ,	mask_token=UpperCamelCase       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**UpperCamelCase       ,	)

											snake_case__									=     do_lower_case
											snake_case__									=     remove_space
											snake_case__									=     keep_accents
											snake_case__									=     vocab_file

											snake_case__									=     spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(UpperCamelCase						)
							@property
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[int]:
											return len(self.sp_model						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											snake_case__									=     {self.convert_ids_to_tokens(UpperCamelCase						): i for i in range(self.vocab_size						)}
											vocab.update(self.added_tokens_encoder						)
											return vocab
							def __getstate__(     self:      int						)		->		List[Any]:
											snake_case__									=     self.__dict__.copy()
											snake_case__									=     None
											return state
							def __setstate__(     self:      str       ,	UpperCamelCase:      Optional[int]						)		->		int:
											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 lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      List[Any]						)		->		int:
											if self.remove_space:
															snake_case__									=     ' '.join(inputs.strip().split()						)
											else:
															snake_case__									=     inputs
											snake_case__									=     outputs.replace('``'       ,	'"'						).replace('\'\''       ,	'"'						)

											if not self.keep_accents:
															snake_case__									=     unicodedata.normalize('NFKD'       ,	UpperCamelCase						)
															snake_case__									=     ''.join([c for c in outputs if not unicodedata.combining(UpperCamelCase						)]						)
											if self.do_lower_case:
															snake_case__									=     outputs.lower()

											return outputs
							def lowerCAmelCase_ (     self:      Dict       ,	UpperCamelCase:      str						)		->		List[str]:
											snake_case__									=     self.preprocess_text(UpperCamelCase						)
											snake_case__									=     self.sp_model.encode(UpperCamelCase       ,	out_type=UpperCamelCase						)
											snake_case__									=     []
											for piece in pieces:
															if len(UpperCamelCase						) > 1 and piece[-1] == str(','						) and piece[-2].isdigit():
																			snake_case__									=     self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase       ,	''						)						)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																							if len(cur_pieces[0]						) == 1:
																											snake_case__									=     cur_pieces[1:]
																							else:
																											snake_case__									=     cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]						)
																			new_pieces.extend(UpperCamelCase						)
															else:
																			new_pieces.append(UpperCamelCase						)

											return new_pieces
							def lowerCAmelCase_ (     self:      Any       ,	UpperCamelCase:      Optional[int]						)		->		Any:
											return self.sp_model.PieceToId(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      int						)		->		str:
											return self.sp_model.IdToPiece(UpperCamelCase						)
							def lowerCAmelCase_ (     self:      int       ,	UpperCamelCase:      Optional[int]						)		->		Optional[int]:
											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(UpperCamelCase						) + token
																			snake_case__									=     True
																			snake_case__									=     []
															else:
																			current_sub_tokens.append(UpperCamelCase						)
																			snake_case__									=     False
											out_string += self.sp_model.decode(UpperCamelCase						)
											return out_string.strip()
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      List[int]       ,	UpperCamelCase:      Optional[List[int]] = None						)		->		List[int]:
											snake_case__									=     [self.sep_token_id]
											snake_case__									=     [self.cls_token_id]
											if token_ids_a is None:
															return cls + token_ids_a + sep
											return cls + token_ids_a + sep + token_ids_a + sep
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      List[int]       ,	UpperCamelCase:      Optional[List[int]] = None       ,	UpperCamelCase:      bool = False						)		->		List[int]:

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

											if token_ids_a is not None:
															return [1] + ([0] * len(UpperCamelCase						)) + [1] + ([0] * len(UpperCamelCase						)) + [1]
											return [1] + ([0] * len(UpperCamelCase						)) + [1]
							def lowerCAmelCase_ (     self:      List[Any]       ,	UpperCamelCase:      List[int]       ,	UpperCamelCase:      Optional[List[int]] = None						)		->		List[int]:
											snake_case__									=     [self.sep_token_id]
											snake_case__									=     [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep						) * [0]
											return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]


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

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

											return (out_vocab_file,)












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




