



from __future__ import annotations


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		TypeError(
                 "Matrices must be formed from a list of zero or more lists containing at "
                 "least one and the same number of values, each of which must be of type "
                 "int or float.")
             if len(lowercase) != 0:
                   UpperCAmelCase_  :	str				      =		len(rows[0])
                   if cols == 0:
                         raise error
                   for row in rows:
                         if len(lowercase) != cols:
                               raise error
                         for value in row:
                               if not isinstance(lowercase     ,(int, float)):
                                     raise error
                   UpperCAmelCase_  :	int				      =		rows
             else:
                   UpperCAmelCase_  :	Union[str, Any]				      =		[]



       def  A_   (     self):

             """simple docstring"""





             return [[row[i] for row in self.rows] for i in range(len(self.rows[0]))]



       @property
       def  A_   (     self):

             """simple docstring"""





             return len(self.rows)



       @property
       def  A_   (     self):

             """simple docstring"""





             return len(self.rows[0])



       @property
       def  A_   (     self):

             """simple docstring"""





             return (self.num_rows, self.num_columns)



       @property
       def  A_   (     self):

             """simple docstring"""





             return self.order[0] == self.order[1]



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		[
                 [0 if column_num != row_num else 1 for column_num in range(self.num_rows)]
                 for row_num in range(self.num_rows)
             ]
             return Matrix(lowercase)



       def  A_   (     self):

             """simple docstring"""





             if not self.is_square:
                   return 0
             if self.order == (0, 0):
                   return 1
             if self.order == (1, 1):
                   return int(self.rows[0][0])
             if self.order == (2, 2):
                   return int(
                       (self.rows[0][0] * self.rows[1][1])
                       - (self.rows[0][1] * self.rows[1][0]))
             else:
                   return sum(
                       self.rows[0][column] * self.cofactors().rows[0][column]
                       for column in range(self.num_columns))



       def  A_   (     self):

             """simple docstring"""





             return bool(self.determinant())



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		[
                 [
                     self.rows[other_row][other_column]
                     for other_column in range(self.num_columns)
                     if other_column != column
                 ]
                 for other_row in range(self.num_rows)
                 if other_row != row
             ]
             return Matrix(lowercase).determinant()



       def  A_   (     self     ,lowercase     ,lowercase):

             """simple docstring"""





             if (row + column) % 2 == 0:
                   return self.get_minor(lowercase     ,lowercase)
             return -1 * self.get_minor(lowercase     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             return Matrix(
                 [
                     [self.get_minor(lowercase     ,lowercase) for column in range(self.num_columns)]
                     for row in range(self.num_rows)
                 ])



       def  A_   (     self):

             """simple docstring"""





             return Matrix(
                 [
                     [
                         self.minors().rows[row][column]
                         if (row + column) % 2 == 0
                         else self.minors().rows[row][column] * -1
                         for column in range(self.minors().num_columns)
                     ]
                     for row in range(self.minors().num_rows)
                 ])



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		[
                 [self.cofactors().rows[column][row] for column in range(self.num_columns)]
                 for row in range(self.num_rows)
             ]
             return Matrix(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		self.determinant()
             if not determinant:
                   raise TypeError("Only matrices with a non-zero determinant have an inverse")
             return self.adjugate() * (1 / determinant)



       def __repr__(     self):

             """simple docstring"""





             return str(self.rows)



       def __str__(     self):

             """simple docstring"""





             if self.num_rows == 0:
                   return "[]"
             if self.num_rows == 1:
                   return "[[" + ". ".join(str(self.rows[0])) + "]]"
             return (
                 "["
                 + "\n ".join(
                     [
                         "[" + ". ".join([str(lowercase) for value in row]) + ".]"
                         for row in self.rows
                     ])
                 + "]"
             )



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		TypeError("Row must be a list containing all ints and/or floats")
             if not isinstance(lowercase     ,lowercase):
                   raise type_error
             for value in row:
                   if not isinstance(lowercase     ,(int, float)):
                         raise type_error
             if len(lowercase) != self.num_columns:
                   raise ValueError(
                       "Row must be equal in length to the other rows in the matrix")
             if position is None:
                   self.rows.append(lowercase)
             else:
                   UpperCAmelCase_  :	Optional[int]				      =		self.rows[0:position] + [row] + self.rows[position:]



       def  A_   (     self     ,lowercase     ,lowercase = None):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		TypeError(
                 "Column must be a list containing all ints and/or floats")
             if not isinstance(lowercase     ,lowercase):
                   raise type_error
             for value in column:
                   if not isinstance(lowercase     ,(int, float)):
                         raise type_error
             if len(lowercase) != self.num_rows:
                   raise ValueError(
                       "Column must be equal in length to the other columns in the matrix")
             if position is None:
                   UpperCAmelCase_  :	List[str]				      =		[self.rows[i] + [column[i]] for i in range(self.num_rows)]
             else:
                   UpperCAmelCase_  :	List[str]				      =		[
                       self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
                       for i in range(self.num_rows)
                   ]



       def __eq__(     self     ,lowercase):

             """simple docstring"""





             if not isinstance(lowercase     ,lowercase):
                   return NotImplemented
             return self.rows == other.rows



       def __ne__(     self     ,lowercase):

             """simple docstring"""





             return not self == other



       def __neg__(     self):

             """simple docstring"""





             return self * -1



       def __add__(     self     ,lowercase):

             """simple docstring"""





             if self.order != other.order:
                   raise ValueError("Addition requires matrices of the same order")
             return Matrix(
                 [
                     [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns)]
                     for i in range(self.num_rows)
                 ])



       def __sub__(     self     ,lowercase):

             """simple docstring"""





             if self.order != other.order:
                   raise ValueError("Subtraction requires matrices of the same order")
             return Matrix(
                 [
                     [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns)]
                     for i in range(self.num_rows)
                 ])



       def __mul__(     self     ,lowercase):

             """simple docstring"""





             if isinstance(lowercase     ,(int, float)):
                   return Matrix(
                       [[int(element * other) for element in row] for row in self.rows])
             elif isinstance(lowercase     ,lowercase):
                   if self.num_columns != other.num_rows:
                         raise ValueError(
                             "The number of columns in the first matrix must "
                             "be equal to the number of rows in the second")
                   return Matrix(
                       [
                           [Matrix.dot_product(lowercase     ,lowercase) for column in other.columns()]
                           for row in self.rows
                       ])
             else:
                   raise TypeError(
                       "A Matrix can only be multiplied by an int, float, or another matrix")



       def __pow__(     self     ,lowercase):

             """simple docstring"""





             if not isinstance(lowercase     ,lowercase):
                   raise TypeError("A Matrix can only be raised to the power of an int")
             if not self.is_square:
                   raise ValueError("Only square matrices can be raised to a power")
             if other == 0:
                   return self.identity()
             if other < 0:
                   if self.is_invertable():
                         return self.inverse() ** (-other)
                   raise ValueError(
                       "Only invertable matrices can be raised to a negative power")
             UpperCAmelCase_  :	int				      =		self
             for _ in range(other - 1):
                   result *= self
             return result



       @classmethod
       def  A_   (     cls     ,lowercase     ,lowercase):

             """simple docstring"""





             return sum(row[i] * column[i] for i in range(len(lowercase)))


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCamelCase         =			logging.getLogger(__name__)







def    _snake_case (       )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		argparse.ArgumentParser(
          description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."    )
      parser.add_argument("--file_path"					,  type=__snake_case					,  default="data/dump.txt"					,  help="The path to the data."    )
      parser.add_argument("--tokenizer_type"					,  type=__snake_case					,  default="bert"					,  choices=["bert", "roberta", "gpt2"]    )
      parser.add_argument("--tokenizer_name"					,  type=__snake_case					,  default="bert-base-uncased"					,  help="The tokenizer to use."    )
      parser.add_argument("--dump_file"					,  type=__snake_case					,  default="data/dump"					,  help="The dump file prefix."    )
      UpperCAmelCase_  :	str				      =		parser.parse_args()

      logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""    )
      if args.tokenizer_type == "bert":
            UpperCAmelCase_  :	int				      =		BertTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase_  :	Dict				      =		tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase_  :	str				      =		RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	int				      =		tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase_  :	Tuple				      =		GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	List[Any]				      =		tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase_  :	List[str]				      =		tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

      logger.info(F"""Loading text from {args.file_path}"""    )
      with open(args.file_path					,  "r"					,  encoding="utf8"    ) as fp:
            UpperCAmelCase_  :	Union[str, Any]				      =		fp.readlines()

      logger.info("Start encoding"    )
      logger.info(F"""{len(__snake_case    )} examples to process."""    )

      UpperCAmelCase_  :	List[str]				      =		[]
      UpperCAmelCase_  :	str				      =		0
      UpperCAmelCase_  :	Union[str, Any]				      =		1_0_0_0_0
      UpperCAmelCase_  :	int				      =		time.time()
      for text in data:
            UpperCAmelCase_  :	str				      =		F"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase_  :	Any				      =		tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case    )
            rslt.append(__snake_case    )

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase_  :	List[str]				      =		time.time()
                  logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""    )
                  UpperCAmelCase_  :	Optional[Any]				      =		time.time()
      logger.info("Finished binarization"    )
      logger.info(F"""{len(__snake_case    )} examples processed."""    )

      UpperCAmelCase_  :	List[Any]				      =		F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase_  :	Optional[int]				      =		tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            UpperCAmelCase_  :	List[Any]				      =		[np.uintaa(__snake_case    ) for d in rslt]
      else:
            UpperCAmelCase_  :	str				      =		[np.intaa(__snake_case    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(F"""Dump to {dp_file}"""    )
      with open(__snake_case					,  "wb"    ) as handle:
            pickle.dump(rslt_					,  __snake_case					,  protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
   main()
