'''simple docstring'''

from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def __UpperCamelCase( _A       :					NDArray[floataa]	,      _A       :					NDArray[floataa]	,      _A       :					list[int]	,      _A       :					int	,      ):
   '''simple docstring'''
   UpperCAmelCase__					,   UpperCAmelCase__				:					List[Any] 					=	coefficient_matrix.shape
   UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[int] 					=	constant_matrix.shape

   if rowsa != colsa:
      UpperCAmelCase__				:					List[Any] 					=	F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
      raise ValueError(_A     )

   if colsa != 1:
      UpperCAmelCase__				:					Union[str, Any] 					=	F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
      raise ValueError(_A     )

   if rowsa != rowsa:
      UpperCAmelCase__				:					Optional[int] 					=	(
          '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
          F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
      )
      raise ValueError(_A     )

   if len(_A     ) != rowsa:
      UpperCAmelCase__				:					List[Any] 					=	(
          '''Number of initial values must be equal to number of rows in coefficient '''
          F'''matrix but received {len(_A     )} and {rowsa}'''
      )
      raise ValueError(_A     )

   if iterations <= 0:
      raise ValueError('''Iterations must be at least 1'''     )

   UpperCAmelCase__				:					NDArray[floataa] 					=	np.concatenate(
       (coefficient_matrix, constant_matrix)	,      axis=1     )

   UpperCAmelCase__					,   UpperCAmelCase__				:					Union[str, Any] 					=	table.shape

   strictly_diagonally_dominant(_A     )

   # Iterates the whole matrix for given number of times
   for _ in range(_A     ):
      UpperCAmelCase__				:					List[Any] 					=	[]
      for row in range(_A     ):
         UpperCAmelCase__				:					Any 					=	0
         for col in range(_A     ):
            if col == row:
               UpperCAmelCase__				:					List[Any] 					=	table[row][col]
            elif col == cols - 1:
               UpperCAmelCase__				:					Optional[int] 					=	table[row][col]
            else:
               temp += (-1) * table[row][col] * init_val[col]
         UpperCAmelCase__				:					Dict 					=	(temp + val) / denom
         new_val.append(_A     )
      UpperCAmelCase__				:					Dict 					=	new_val

   return [float(_A     ) for i in new_val]







def __UpperCamelCase( _A       :					NDArray[floataa]     ):
   '''simple docstring'''
   UpperCAmelCase__					,   UpperCAmelCase__				:					List[str] 					=	table.shape

   UpperCAmelCase__				:					Optional[Any] 					=	True

   for i in range(0	,      _A     ):
      UpperCAmelCase__				:					List[str] 					=	0
      for j in range(0	,      cols - 1     ):
         if i == j:
            continue
         else:
            total += table[i][j]

      if table[i][i] <= total:
         raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''     )

   return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
  import doctest

  doctest.testmod()





'''simple docstring'''

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
)
UpperCamelCase__  :						Dict					=      logging.getLogger(__name__)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[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=_A	,      default='''data/dump.txt'''	,      help='''The path to the data.'''     )
   parser.add_argument('''--tokenizer_type'''	,      type=_A	,      default='''bert'''	,      choices=['''bert''', '''roberta''', '''gpt2''']     )
   parser.add_argument('''--tokenizer_name'''	,      type=_A	,      default='''bert-base-uncased'''	,      help='''The tokenizer to use.'''     )
   parser.add_argument('''--dump_file'''	,      type=_A	,      default='''data/dump'''	,      help='''The dump file prefix.'''     )
   UpperCAmelCase__				:					Optional[int] 					=	parser.parse_args()

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

   logger.info('''Start encoding'''     )
   logger.info(F'''{len(_A     )} examples to process.'''     )

   UpperCAmelCase__				:					List[str] 					=	[]
   UpperCAmelCase__				:					Union[str, Any] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1_00_00
   UpperCAmelCase__				:					Tuple 					=	time.time()
   for text in data:
      UpperCAmelCase__				:					Any 					=	F'''{bos} {text.strip()} {sep}'''
      UpperCAmelCase__				:					int 					=	tokenizer.encode(_A	,      add_special_tokens=_A     )
      rslt.append(_A     )

      iter += 1
      if iter % interval == 0:
         UpperCAmelCase__				:					int 					=	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(_A     )} examples processed.'''     )

   UpperCAmelCase__				:					Dict 					=	F'''{args.dump_file}.{args.tokenizer_name}.pickle'''
   UpperCAmelCase__				:					Dict 					=	tokenizer.vocab_size
   if vocab_size < (1 << 16):
      UpperCAmelCase__				:					Any 					=	[np.uintaa(_A     ) for d in rslt]
   else:
      UpperCAmelCase__				:					str 					=	[np.intaa(_A     ) for d in rslt]
   random.shuffle(rslt_     )
   logger.info(F'''Dump to {dp_file}'''     )
   with open(_A	,      '''wb'''     ) as handle:
      pickle.dump(rslt_	,      _A	,      protocol=pickle.HIGHEST_PROTOCOL     )


if __name__ == "__main__":
  main()




