

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 SPIECE_UNDERLINE, logging


lowercase_			=  logging.get_logger(__name__)

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

lowercase_			=  {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

lowercase_			=  {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
lowercase_			=  0
lowercase_			=  1
lowercase_			=  2
lowercase_			=  3
lowercase_			=  4


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							VOCAB_FILES_NAMES
    lowerCamelCase		     =							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase		     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase		     =							'left'


    def __init__(				self			:	int,lowercase_			:	Tuple,lowercase_			:	Any=False,lowercase_			:	Any=True,lowercase_			:	List[str]=False,lowercase_			:	List[Any]="<s>",lowercase_			:	Optional[Any]="</s>",lowercase_			:	Tuple="<unk>",lowercase_			:	Union[str, Any]="<sep>",lowercase_			:	Any="<pad>",lowercase_			:	int="<cls>",lowercase_			:	List[Any]="<mask>",lowercase_			:	Dict=["<eop>", "<eod>"],lowercase_			:	Optional[Dict[str, Any]] = None,**lowercase_			:	Optional[int],)->   None:





        '''simple docstring'''







        A__   				=       AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else mask_token

        A__   				=       {} if sp_model_kwargs is None else sp_model_kwargs

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

        A__   				=       3

        A__   				=       do_lower_case
        A__   				=       remove_space
        A__   				=       keep_accents
        A__   				=       vocab_file

        A__   				=       spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(lowercase_		)


    @property
    def 						snake_case__					(				self			:	Any		)->   List[str]:





        '''simple docstring'''







        return len(self.sp_model		)


    def 						snake_case__					(				self			:	List[str]		)->   int:





        '''simple docstring'''







        A__   				=       {self.convert_ids_to_tokens(lowercase_		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab


    def __getstate__(				self			:	Optional[Any]		)->   Any:





        '''simple docstring'''







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


    def __setstate__(				self			:	List[str],lowercase_			:	Dict		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       d

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

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


    def 						snake_case__					(				self			:	Optional[int],lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







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

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

        return outputs


    def 						snake_case__					(				self			:	Any,lowercase_			:	str		)->   List[str]:





        '''simple docstring'''







        A__   				=       self.preprocess_text(lowercase_		)
        A__   				=       self.sp_model.encode(lowercase_,out_type=lowercase_		)
        A__   				=       []
        for piece in pieces:
            if len(lowercase_		) > 1 and piece[-1] == str(','		) and piece[-2].isdigit():
                A__   				=       self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase_,''		)		)
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                    if len(cur_pieces[0]		) == 1:
                        A__   				=       cur_pieces[1:]
                    else:
                        A__   				=       cur_pieces[0][1:]
                cur_pieces.append(piece[-1]		)
                new_pieces.extend(lowercase_		)
            else:
                new_pieces.append(lowercase_		)

        return new_pieces


    def 						snake_case__					(				self			:	str,lowercase_			:	Dict		)->   Optional[int]:





        '''simple docstring'''







        return self.sp_model.PieceToId(lowercase_		)


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Tuple		)->   List[Any]:





        '''simple docstring'''







        return self.sp_model.IdToPiece(lowercase_		)


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Union[str, Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       ''.join(lowercase_		).replace(lowercase_,' '		).strip()
        return out_string


    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[int],lowercase_			:	bool = False,lowercase_			:	bool = None,lowercase_			:	bool = True,**lowercase_			:	Union[str, Any],)->   str:





        '''simple docstring'''







        A__   				=       kwargs.pop('use_source_tokenizer',lowercase_		)

        A__   				=       self.convert_ids_to_tokens(lowercase_,skip_special_tokens=lowercase_		)

        # 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
        A__   				=       []
        A__   				=       []
        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(lowercase_		)		)
                    A__   				=       []
                sub_texts.append(lowercase_		)
            else:
                current_sub_text.append(lowercase_		)
        if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(lowercase_		)		)

        # Mimic the behavior of the Rust tokenizer:
        # By default, there are no spaces between special tokens
        A__   				=       ''.join(lowercase_		)

        A__   				=       (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
            A__   				=       self.clean_up_tokenization(lowercase_		)
            return clean_text
        else:
            return text


    def 						snake_case__					(				self			:	int,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [self.cls_token_id]
        if token_ids_a is None:
            return token_ids_a + sep + cls
        return token_ids_a + sep + token_ids_a + sep + cls


    def 						snake_case__					(				self			:	Any,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None,lowercase_			:	bool = False		)->   List[int]:





        '''simple docstring'''







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

        if token_ids_a is not None:
            return ([0] * len(lowercase_		)) + [1] + ([0] * len(lowercase_		)) + [1, 1]
        return ([0] * len(lowercase_		)) + [1, 1]


    def 						snake_case__					(				self			:	Any,lowercase_			:	List[int],lowercase_			:	Optional[List[int]] = None		)->   List[int]:





        '''simple docstring'''







        A__   				=       [self.sep_token_id]
        A__   				=       [2]

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


    def 						snake_case__					(				self			:	List[str],lowercase_			:	str,lowercase_			:	Optional[str] = None		)->   Tuple[str]:





        '''simple docstring'''







        if not os.path.isdir(lowercase_		):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
            return
        A__   				=       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:
                A__   				=       self.sp_model.serialized_model_proto()
                fi.write(lowercase_		)

        return (out_vocab_file,)




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square(SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        A__   				=       update_area_of_max_square(SCREAMING_SNAKE_CASE__   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      col + 1				)
        A__   				=       update_area_of_max_square(row + 1   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    update_area_of_max_square(0   ,      0				)
    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    def update_area_of_max_square_using_dp_array(
        SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        A__   				=       update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       update_area_of_max_square_using_dp_array(row + 1   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

        if mat[row][col]:
            A__   				=       1 + min([right, diagonal, down]				)
            A__   				=       max(largest_square_area[0]   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    A__   				=       [0]
    A__   				=       [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__				)]
    update_area_of_max_square_using_dp_array(0   ,      0   ,      SCREAMING_SNAKE_CASE__				)

    return largest_square_area[0]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [[0] * (cols + 1) for _ in range(rows + 1				)]
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       dp_array[row][col + 1]
            A__   				=       dp_array[row + 1][col + 1]
            A__   				=       dp_array[row + 1][col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(dp_array[row][col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0

    return largest_square_area
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     list[list[int]]				)							->				int:





    '''simple docstring'''






    A__   				=       [0] * (cols + 1)
    A__   				=       [0] * (cols + 1)
    A__   				=       0
    for row in range(rows - 1   ,      -1   ,      -1				):
        for col in range(cols - 1   ,      -1   ,      -1				):
            A__   				=       current_row[col + 1]
            A__   				=       next_row[col + 1]
            A__   				=       next_row[col]

            if mat[row][col] == 1:
                A__   				=       1 + min(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
                A__   				=       max(current_row[col]   ,      SCREAMING_SNAKE_CASE__				)
            else:
                A__   				=       0
        A__   				=       current_row

    return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))

