


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal





def __UpperCAmelCase       (		__A ,						__A			)						->					str | Literal[False]:



       '''simple docstring'''





       UpperCAmelCase__										=     list(__A			)
       UpperCAmelCase__										=     list(__A			)
       UpperCAmelCase__										=     0
       for i in range(len(__A			)			):
              if lista[i] != lista[i]:
                     count += 1
                     UpperCAmelCase__										=     "_"
       if count > 1:
              return False
       else:
              return "".join(__A			)





def __UpperCAmelCase       (		__A			)						->					list[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     []
       while True:
              UpperCAmelCase__										=     ["$"] * len(__A			)
              UpperCAmelCase__										=     []
              for i in range(len(__A			)			):
                     for j in range(i + 1 ,						len(__A			)			):
                            UpperCAmelCase__										=     compare_string(binary[i] ,						binary[j]			)
                            if k is False:
                                   UpperCAmelCase__										=     "*"
                                   UpperCAmelCase__										=     "*"
                                   temp.append("X"			)
              for i in range(len(__A			)			):
                     if checka[i] == "$":
                            pi.append(binary[i]			)
              if len(__A			) == 0:
                     return pi
              UpperCAmelCase__										=     list(set(__A			)			)





def __UpperCAmelCase       (		__A ,						__A			)						->					list[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     []
       for minterm in minterms:
              UpperCAmelCase__										=     ""
              for _ in range(__A			):
                     UpperCAmelCase__										=     str(minterm % 2			) + string
                     minterm //= 2
              temp.append(__A			)
       return temp





def __UpperCAmelCase       (		__A ,						__A ,						__A			)						->					bool:



       '''simple docstring'''





       UpperCAmelCase__										=     list(__A			)
       UpperCAmelCase__										=     list(__A			)
       UpperCAmelCase__										=     0
       for i in range(len(__A			)			):
              if lista[i] != lista[i]:
                     count_n += 1
       return count_n == count





def __UpperCAmelCase       (		__A ,						__A			)						->					list[str]:



       '''simple docstring'''





       UpperCAmelCase__										=     []
       UpperCAmelCase__										=     [0] * len(__A			)
       for i in range(len(chart[0]			)			):
              UpperCAmelCase__										=     0
              UpperCAmelCase__										=     -1
              for j in range(len(__A			)			):
                     if chart[j][i] == 1:
                            count += 1
                            UpperCAmelCase__										=     j
              if count == 1:
                     UpperCAmelCase__										=     1
       for i in range(len(__A			)			):
              if select[i] == 1:
                     for j in range(len(chart[0]			)			):
                            if chart[i][j] == 1:
                                   for k in range(len(__A			)			):
                                          UpperCAmelCase__										=     0
                     temp.append(prime_implicants[i]			)
       while True:
              UpperCAmelCase__										=     0
              UpperCAmelCase__										=     -1
              UpperCAmelCase__										=     0
              for i in range(len(__A			)			):
                     UpperCAmelCase__										=     chart[i].count(1			)
                     if count_n > max_n:
                            UpperCAmelCase__										=     count_n
                            UpperCAmelCase__										=     i

              if max_n == 0:
                     return temp

              temp.append(prime_implicants[rem]			)

              for i in range(len(chart[0]			)			):
                     if chart[rem][i] == 1:
                            for j in range(len(__A			)			):
                                   UpperCAmelCase__										=     0





def __UpperCAmelCase       (		__A ,						__A			)						->					list[list[int]]:



       '''simple docstring'''





       UpperCAmelCase__										=     [[0 for x in range(len(__A			)			)] for x in range(len(__A			)			)]
       for i in range(len(__A			)			):
              UpperCAmelCase__										=     prime_implicants[i].count("_"			)
              for j in range(len(__A			)			):
                     if is_for_table(prime_implicants[i] ,						binary[j] ,						__A			):
                            UpperCAmelCase__										=     1

       return chart





def __UpperCAmelCase       (		)						->					None:



       '''simple docstring'''





       UpperCAmelCase__										=     int(input("Enter the no. of variables\n"			)			)
       UpperCAmelCase__										=     [
           float(__A			)
           for x in input(
               "Enter the decimal representation of Minterms 'Spaces Separated'\n"			).split()
       ]
       UpperCAmelCase__										=     decimal_to_binary(__A ,						__A			)

       UpperCAmelCase__										=     check(__A			)
       print("Prime Implicants are:"			)
       print(__A			)
       UpperCAmelCase__										=     prime_implicant_chart(__A ,						__A			)

       UpperCAmelCase__										=     selection(__A ,						__A			)
       print("Essential Prime Implicants are:"			)
       print(__A			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





