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


_lowerCamelCase   :				Tuple     			=						logging.get_logger(__name__)
_lowerCamelCase   :				Optional[Any]     			=						{'''vocab_file''': '''spiece.model'''}

_lowerCamelCase   :				Optional[int]     			=						{
    '''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''',
    }
}

_lowerCamelCase   :				int     			=						{
    '''albert-base-v1''': 5_12,
    '''albert-large-v1''': 5_12,
    '''albert-xlarge-v1''': 5_12,
    '''albert-xxlarge-v1''': 5_12,
    '''albert-base-v2''': 5_12,
    '''albert-large-v2''': 5_12,
    '''albert-xlarge-v2''': 5_12,
    '''albert-xxlarge-v2''': 5_12,
}

_lowerCamelCase   :				str     			=						'''▁'''


class   lowercase      (       a       ):
							lowercase__  :    int									=							VOCAB_FILES_NAMES
							lowercase__  :    Tuple									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Dict									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




							def __init__(  self				:   Optional[Any]		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   Tuple=False		,							_UpperCamelCase				:   Optional[Any]="[CLS]"		,							_UpperCamelCase				:   Tuple="[SEP]"		,							_UpperCamelCase				:   Tuple="<unk>"		,							_UpperCamelCase				:   str="[SEP]"		,							_UpperCamelCase				:   Union[str, Any]="<pad>"		,							_UpperCamelCase				:   Any="[CLS]"		,							_UpperCamelCase				:   str="[MASK]"		,							_UpperCamelCase				:   Optional[Dict[str, Any]] = None		,							**_UpperCamelCase				:   List[str]		,							)   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    (
												    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase		,							normalized=_UpperCamelCase )
												    if isinstance(_UpperCamelCase		,							_UpperCamelCase )
												    else mask_token
												)

												SCREAMING_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		,							)

												SCREAMING_SNAKE_CASE           =    do_lower_case
												SCREAMING_SNAKE_CASE           =    remove_space
												SCREAMING_SNAKE_CASE           =    keep_accents
												SCREAMING_SNAKE_CASE           =    vocab_file

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




							@property
							def 		__snake_case(  self				:   Dict )   ->			Dict:



												'''simple docstring'''

												return len(self.sp_model )




							def 		__snake_case(  self				:   int )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_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				:   Dict )   ->			Tuple:



												'''simple docstring'''

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




							def __setstate__(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any] )   ->			Any:



												'''simple docstring'''

												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 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Dict )   ->			Any:



												'''simple docstring'''

												if self.remove_space:
																	SCREAMING_SNAKE_CASE           =    " ".join(inputs.strip().split() )
												else:
																	SCREAMING_SNAKE_CASE           =    inputs
												SCREAMING_SNAKE_CASE           =    outputs.replace("``"		,							"\"" ).replace("''"		,							"\"" )

												if not self.keep_accents:
																	SCREAMING_SNAKE_CASE           =    unicodedata.normalize("NFKD"		,							_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    "".join([c for c in outputs if not unicodedata.combining(_UpperCamelCase )] )
												if self.do_lower_case:
																	SCREAMING_SNAKE_CASE           =    outputs.lower()

												return outputs




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   str )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.preprocess_text(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.sp_model.encode(_UpperCamelCase		,							out_type=_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    []
												for piece in pieces:
																	if len(_UpperCamelCase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
																						SCREAMING_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:
																																SCREAMING_SNAKE_CASE           =    cur_pieces[1:]
																											else:
																																SCREAMING_SNAKE_CASE           =    cur_pieces[0][1:]
																						cur_pieces.append(piece[-1] )
																						new_pieces.extend(_UpperCamelCase )
																	else:
																						new_pieces.append(_UpperCamelCase )

												return new_pieces




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Dict )   ->			str:



												'''simple docstring'''

												return self.sp_model.PieceToId(_UpperCamelCase )




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   Dict )   ->			Tuple:



												'''simple docstring'''

												return self.sp_model.IdToPiece(_UpperCamelCase )




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any] )   ->			Any:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_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 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''simple docstring'''

												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 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_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 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												if not os.path.isdir(_UpperCamelCase ):
																	logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
																	return
												SCREAMING_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:
																						SCREAMING_SNAKE_CASE           =    self.sp_model.serialized_model_proto()
																						fi.write(_UpperCamelCase )

												return (out_vocab_file,)





from __future__ import annotations

from collections.abc import Iterator


class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   int )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    value
												SCREAMING_SNAKE_CASE           =    None
												SCREAMING_SNAKE_CASE           =    None



class   lowercase      :




							def __init__(  self				:   str		,							_UpperCamelCase				:   Node )   ->			None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    tree




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   Node | None )   ->			int:



												'''simple docstring'''

												if node is None:
																	return 0
												return node.value + (
												    self.depth_first_search(node.left ) + self.depth_first_search(node.right )
												)





							def __iter__(  self				:   List[Any] )   ->			Iterator[int]:



												'''simple docstring'''

												yield self.depth_first_search(self.tree )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




