
from __future__ import annotations

import math





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if len(__lowerCAmelCase							) != 2 or len(a[0]							) != 2 or len(__lowerCAmelCase							) != 2 or len(b[0]							) != 2:
        raise Exception("Matrices are not 2x2"							)
    snake_case__						=				[
        [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
        [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
    ]
    return new_matrix





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return [
        [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
        for row in range(len(__lowerCAmelCase							)							)
    ]





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return [
        [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
        for row in range(len(__lowerCAmelCase							)							)
    ]





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if len(__lowerCAmelCase							) % 2 != 0 or len(a[0]							) % 2 != 0:
        raise Exception("Odd matrices are not supported!"							)

    snake_case__						=				len(__lowerCAmelCase							)
    snake_case__						=				matrix_length // 2

    snake_case__						=				[[a[i][j] for j in range(__lowerCAmelCase    ,		__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
    snake_case__						=				[
        [a[i][j] for j in range(__lowerCAmelCase    ,		__lowerCAmelCase							)] for i in range(__lowerCAmelCase    ,		__lowerCAmelCase							)
    ]

    snake_case__						=				[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
    snake_case__						=				[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase    ,		__lowerCAmelCase							)]

    return top_left, top_right, bot_left, bot_right





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    return len(__lowerCAmelCase							), len(matrix[0]							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    print("\n".join(str(__lowerCAmelCase							) for line in matrix							)							)





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if matrix_dimensions(__lowerCAmelCase							) == (2, 2):
        return default_matrix_multiplication(__lowerCAmelCase    ,		__lowerCAmelCase							)

    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				split_matrix(__lowerCAmelCase							)
    snake_case__						,					snake_case__						,					snake_case__						,					snake_case__						=				split_matrix(__lowerCAmelCase							)

    snake_case__						=				actual_strassen(__lowerCAmelCase    ,		matrix_subtraction(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
    snake_case__						=				actual_strassen(matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		__lowerCAmelCase							)
    snake_case__						=				actual_strassen(matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		__lowerCAmelCase							)
    snake_case__						=				actual_strassen(__lowerCAmelCase    ,		matrix_subtraction(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
    snake_case__						=				actual_strassen(matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
    snake_case__						=				actual_strassen(matrix_subtraction(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)							)
    snake_case__						=				actual_strassen(matrix_subtraction(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)							)

    snake_case__						=				matrix_addition(matrix_subtraction(matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		__lowerCAmelCase							)    ,		__lowerCAmelCase							)
    snake_case__						=				matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)
    snake_case__						=				matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)
    snake_case__						=				matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCAmelCase    ,		__lowerCAmelCase							)    ,		__lowerCAmelCase							)    ,		__lowerCAmelCase							)

    # construct the new matrix from our 4 quadrants
    snake_case__						=				[]
    for i in range(len(__lowerCAmelCase							)							):
        new_matrix.append(top_left[i] + top_right[i]							)
    for i in range(len(__lowerCAmelCase							)							):
        new_matrix.append(bot_left[i] + bot_right[i]							)
    return new_matrix





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if matrix_dimensions(__lowerCAmelCase							)[1] != matrix_dimensions(__lowerCAmelCase							)[0]:
        snake_case__						=				(
            "Unable to multiply these matrices, please check the dimensions.\n"
            F"""Matrix A: {matrixa}\n"""
            F"""Matrix B: {matrixa}"""
        )
        raise Exception(__lowerCAmelCase							)
    snake_case__						=				matrix_dimensions(__lowerCAmelCase							)
    snake_case__						=				matrix_dimensions(__lowerCAmelCase							)

    if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
        return [matrixa, matrixa]

    snake_case__						=				max(*__lowerCAmelCase    ,		*__lowerCAmelCase							)
    snake_case__						=				int(math.pow(2    ,		math.ceil(math.loga(__lowerCAmelCase							)							)							)							)
    snake_case__						=				matrixa
    snake_case__						=				matrixa

    # Adding zeros to the matrices so that the arrays dimensions are the same and also
    # power of 2
    for i in range(0    ,		__lowerCAmelCase							):
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]    ,		__lowerCAmelCase							):
                new_matrixa[i].append(0							)
        else:
            new_matrixa.append([0] * maxim							)
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]    ,		__lowerCAmelCase							):
                new_matrixa[i].append(0							)
        else:
            new_matrixa.append([0] * maxim							)

    snake_case__						=				actual_strassen(__lowerCAmelCase    ,		__lowerCAmelCase							)

    # Removing the additional zeros
    for i in range(0    ,		__lowerCAmelCase							):
        if i < dimensiona[0]:
            for _ in range(dimensiona[1]    ,		__lowerCAmelCase							):
                final_matrix[i].pop()
        else:
            final_matrix.pop()
    return final_matrix


if __name__ == "__main__":
     __magic_name__            =  [
         [2, 3, 4, 5],
         [6, 4, 3, 1],
         [2, 3, 6, 7],
         [3, 1, 2, 4],
         [2, 3, 4, 5],
         [6, 4, 3, 1],
         [2, 3, 6, 7],
         [3, 1, 2, 4],
         [2, 3, 4, 5],
         [6, 2, 3, 1],
     ]
     __magic_name__            =  [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
     print(strassen(matrixa, matrixa))





import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {'''vocab_file''': '''sentencepiece.model'''}

__magic_name__            =  {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__magic_name__            =  {
    '''google/rembert''': 256,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Optional[Any]    	=     VOCAB_FILES_NAMES
      _A						:		Optional[int]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=False		,	lowerCamelCase=True		,	lowerCamelCase=True		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[UNK]"		,	lowerCamelCase="[SEP]"		,	lowerCamelCase="[PAD]"		,	lowerCamelCase="[CLS]"		,	lowerCamelCase="[MASK]"		,	**lowerCamelCase		,	):
          super().__init__(
              do_lower_case=lowerCamelCase		,	remove_space=lowerCamelCase		,	keep_accents=lowerCamelCase		,	bos_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	sep_token=lowerCamelCase		,	pad_token=lowerCamelCase		,	cls_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	**lowerCamelCase		,	)

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

          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(lowerCamelCase )
      @property
      def A_			(			self ):
          return len(self.sp_model )
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(lowerCamelCase ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          snake_case__						=				d
          snake_case__						=				spm.SentencePieceProcessor()
          self.sp_model.Load(self.vocab_file )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=False ):
          snake_case__						=				self.sp_model.EncodeAsPieces(lowerCamelCase )
          return pieces
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.PieceToId(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.IdToPiece(lowerCamelCase )
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				self.sp_model.decode_pieces(lowerCamelCase )
          return out_string
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):

          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is not None:
              return [1] + ([0] * len(lowerCamelCase )) + [1] + ([0] * len(lowerCamelCase )) + [1]
          return [1] + ([0] * len(lowerCamelCase )) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(lowerCamelCase ):
              logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase ) )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
              copyfile(self.vocab_file		,	lowerCamelCase )

          return (out_vocab_file,)



