
import os
import re
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_						:     Optional[int]	       =							logging.get_logger(__name__)

UpperCamelCase_						:     Tuple	       =							{"""vocab_file""": """spiece.model"""}

UpperCamelCase_						:     int	       =							{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    }
}

UpperCamelCase_						:     Any	       =							{
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}




class   __lowercase						(						__snake_case       ):
	_A       =   VOCAB_FILES_NAMES
	_A       =   PRETRAINED_VOCAB_FILES_MAP
	_A       =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A       =   ["input_ids", "attention_mask"]
	_A       =   []






	def __init__(self				: str      ,	snake_case				: Dict      ,	snake_case				: int="<unk>"      ,	snake_case				: Dict="<s>"      ,	snake_case				: Union[str, Any]="</s>"      ,	snake_case				: Optional[Any]="<pad>"      ,	snake_case				: int="[SEP]"      ,	snake_case				: List[str]="[MASK]"      ,	snake_case				: Optional[int]="[CLS]"      ,	snake_case				: Optional[Dict[str, Any]] = None      ,	**snake_case				: Union[str, Any]      ,	) ->			None:
							_lowercase       :	Union[str, Any]           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else bos_token
							_lowercase       :	Tuple           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else eos_token
							_lowercase       :	Union[str, Any]           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else unk_token
							_lowercase       :	int           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else pad_token
							_lowercase       :	List[str]           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else cls_token
							_lowercase       :	Optional[int]           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else sep_token

							# Mask token behave like a normal word, i.e. include the space before it
							_lowercase       :	int           =   AddedToken(snake_case      ,	lstrip=snake_case      ,	rstrip=snake_case    ) if isinstance(snake_case      ,	snake_case    ) else mask_token

							_lowercase       :	Dict           =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=snake_case      ,	eos_token=snake_case      ,	unk_token=snake_case      ,	pad_token=snake_case      ,	sep_token=snake_case      ,	mask_token=snake_case      ,	cls_token=snake_case      ,	sp_model_kwargs=self.sp_model_kwargs      ,	**snake_case      ,	)

							_lowercase       :	Dict           =   vocab_file

							_lowercase       :	int           =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(snake_case    )






	@property
	def       _a(self				: int    ) ->			Optional[Any]:
							return self.sp_model.get_piece_size()






	def       _a(self				: Optional[Any]    ) ->			List[str]:
							_lowercase       :	str           =   {self.convert_ids_to_tokens(snake_case    ): i for i in range(self.vocab_size    )}
							vocab.update(self.added_tokens_encoder    )
							return vocab






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






	def __setstate__(self				: Optional[Any]      ,	snake_case				: Union[str, Any]    ) ->			List[str]:
							_lowercase       :	Optional[Any]           =   d

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

							_lowercase       :	int           =   spm.SentencePieceProcessor(**self.sp_model_kwargs    )
							self.sp_model.Load(self.vocab_file    )






	def       _a(self				: List[str]      ,	snake_case				: str    ) ->			List[str]:
							return self.sp_model.encode(snake_case      ,	out_type=snake_case    )






	def       _a(self				: List[Any]      ,	snake_case				: Dict    ) ->			Dict:
							return self.sp_model.piece_to_id(snake_case    )






	def       _a(self				: Any      ,	snake_case				: Optional[Any]    ) ->			Any:
							_lowercase       :	Any           =   self.sp_model.IdToPiece(snake_case    )
							return token






	def       _a(self				: Optional[Any]      ,	snake_case				: Dict    ) ->			int:
							_lowercase       :	Optional[Any]           =   []
							_lowercase       :	Optional[int]           =   ""
							_lowercase       :	Union[str, 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(snake_case    ) + token
																			_lowercase       :	List[Any]           =   True
																			_lowercase       :	List[str]           =   []
													else:
																			current_sub_tokens.append(snake_case    )
																			_lowercase       :	List[Any]           =   False
							out_string += self.sp_model.decode(snake_case    )
							return out_string.strip()






	def       _a(self				: Optional[Any]      ,	snake_case				: List[int]      ,	snake_case				: bool = False      ,	snake_case				: bool = None      ,	snake_case				: bool = True      ,	**snake_case				: Union[str, Any]      ,	) ->			str:
							_lowercase       :	Optional[Any]           =   kwargs.pop("use_source_tokenizer"      ,	snake_case    )

							_lowercase       :	Dict           =   self.convert_ids_to_tokens(snake_case      ,	skip_special_tokens=snake_case    )

							# To avoid mixing byte-level and unicode for byte-level BPT
							# we need to build string separately for added tokens and byte-level tokens
							# cf. https://github.com/huggingface/transformers/issues/1133
							_lowercase       :	List[str]           =   []
							_lowercase       :	int           =   []
							for token in filtered_tokens:
													if skip_special_tokens and token in self.all_special_ids:
																			continue
													if token in self.added_tokens_encoder:
																			if current_sub_text:
																									sub_texts.append(self.convert_tokens_to_string(snake_case    )    )
																									_lowercase       :	int           =   []
																			sub_texts.append(snake_case    )
													else:
																			current_sub_text.append(snake_case    )
							if current_sub_text:
													sub_texts.append(self.convert_tokens_to_string(snake_case    )    )

							# Mimic the behavior of the Rust tokenizer:
							# No space before [MASK] and [SEP]
							if spaces_between_special_tokens:
													_lowercase       :	Tuple           =   re.sub(R" (\[(MASK|SEP)\])"      ,	R"\1"      ,	" ".join(snake_case    )    )
							else:
													_lowercase       :	Dict           =   "".join(snake_case    )

							_lowercase       :	Optional[int]           =   (
							    clean_up_tokenization_spaces
							    if clean_up_tokenization_spaces is not None
							    else self.clean_up_tokenization_spaces
							)
							if clean_up_tokenization_spaces:
													_lowercase       :	Dict           =   self.clean_up_tokenization(snake_case    )
													return clean_text
							else:
													return text






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

							if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case    ) and os.path.isfile(self.vocab_file    ):
													copyfile(self.vocab_file      ,	snake_case    )
							elif not os.path.isfile(self.vocab_file    ):
													with open(snake_case      ,	"wb"    ) as fi:
																			_lowercase       :	Optional[int]           =   self.sp_model.serialized_model_proto()
																			fi.write(snake_case    )

							return (out_vocab_file,)






	def       _a(self				: Union[str, Any]      ,	snake_case				: List[int]      ,	snake_case				: Optional[List[int]] = None    ) ->			List[int]:
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_lowercase       :	str           =   [self.cls_token_id]
							_lowercase       :	Union[str, Any]           =   [self.sep_token_id]
							return cls + token_ids_a + sep + token_ids_a + sep






	def       _a(self				: List[Any]      ,	snake_case				: List[int]      ,	snake_case				: Optional[List[int]] = None      ,	snake_case				: bool = False    ) ->			List[int]:
							if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=snake_case      ,	token_ids_a=snake_case      ,	already_has_special_tokens=snake_case    )

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






	def       _a(self				: str      ,	snake_case				: List[int]      ,	snake_case				: Optional[List[int]] = None    ) ->			List[int]:
							_lowercase       :	List[str]           =   [self.sep_token_id]
							_lowercase       :	Optional[int]           =   [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]






from __future__ import annotations



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   list[str] | None = None  )   ->			list[list[str]]:






						'''simple docstring'''

						_lowercase       :	Dict           =   word_bank or []
						# create a table
						_lowercase       :	int           =   len(_UpperCAmelCase  ) + 1

						_lowercase       :	list[list[list[str]]]           =   []
						for _ in range(_UpperCAmelCase  ):
												table.append([]  )
						# seed value
						_lowercase       :	Optional[int]           =   [[]]  # because empty string has empty combination

						# iterate through the indices
						for i in range(_UpperCAmelCase  ):
												# condition
												if table[i] != []:
																		for word in word_bank:
																								# slice condition
																								if target[i : i + len(_UpperCAmelCase  )] == word:
																														_lowercase       :	list[list[str]]           =   [
																														    [word, *way] for way in table[i]
																														]
																														# adds the word to every combination the current position holds
																														# now,push that combination to the table[i+len(word)]
																														table[i + len(_UpperCAmelCase  )] += new_combinations

    # combinations are in reverse order so reverse for better output
						for combination in table[len(_UpperCAmelCase  )]:
												combination.reverse()

						return table[len(_UpperCAmelCase  )]


if __name__ == "__main__":
							print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
							print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
							print(
							    all_construct(
							        """hexagonosaurus""",
							        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
							    )
							)




