from __future__ import annotations

import sys


def 							lowerCAmelCase__    (       a__  ,			a__      )	->tuple[int, int]:




  '''simple docstring'''

  if abs(a__      ) == 1:
    return a, 0
  elif abs(a__      ) == 1:
    return 0, b

  _UpperCamelCase	,		_UpperCamelCase				=  a, b
  _UpperCamelCase	,		_UpperCamelCase				=  1, 0
  _UpperCamelCase	,		_UpperCamelCase				=  0, 1

  while remainder != 0:
    _UpperCamelCase				=  old_remainder // remainder
    _UpperCamelCase	,		_UpperCamelCase				=  remainder, old_remainder - quotient * remainder
    _UpperCamelCase	,		_UpperCamelCase				=  coeff_a, old_coeff_a - quotient * coeff_a
    _UpperCamelCase	,		_UpperCamelCase				=  coeff_b, old_coeff_b - quotient * coeff_b

  # sign correction for negative numbers
  if a < 0:
    _UpperCamelCase				=  -old_coeff_a
  if b < 0:
    _UpperCamelCase				=  -old_coeff_b

  return old_coeff_a, old_coeff_b


def 							lowerCAmelCase__    (       )	->int:




  '''simple docstring'''

  if len(sys.argv      ) < 3:
    print("2 integer arguments required"      )
    return 1
  _UpperCamelCase				=  int(sys.argv[1]      )
  _UpperCamelCase				=  int(sys.argv[2]      )
  print(extended_euclidean_algorithm(a__  ,			a__      )      )
  return 0


if __name__ == "__main__":
   raise SystemExit(main())





lowerCamelCase__   = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase__   = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase__   = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




