from __future__ import annotations
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->tuple[float, float]:

  '''simple docstring'''




  assert (
      c % greatest_common_divisor(lowerCamelCase__ ,				lowerCamelCase__					) == 0
  )  # greatest_common_divisor(a,b) function implemented below
  ((a__)		,(a__)		,(a__))     		=     extended_gcd(lowerCamelCase__ ,				lowerCamelCase__					)  # extended_gcd(a,b) function implemented below
  a__     		=     c / d
  return (r * x, r * y)
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ = 2					)				->None:

  '''simple docstring'''




  ((a__)		,(a__))     		=     diophantine(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)  # Initial value
  a__     		=     greatest_common_divisor(lowerCamelCase__ ,				lowerCamelCase__					)
  a__     		=     a // d
  a__     		=     b // d

  for i in range(lowerCamelCase__					):
    a__     		=     xa + i * q
    a__     		=     ya - i * p
    print(lowerCamelCase__ ,				lowerCamelCase__					)
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->int:

  '''simple docstring'''




  if a < b:
    a__		,a__     		=     b, a

  while a % b != 0:
    a__		,a__     		=     b, a % b

  return b
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->tuple[int, int, int]:

  '''simple docstring'''




  assert a >= 0 and b >= 0

  if b == 0:
    a__		,a__		,a__     		=     a, 1, 0
  else:
    ((a__)		,(a__)		,(a__))     		=     extended_gcd(lowerCamelCase__ ,				a % b					)
    a__     		=     q
    a__     		=     p - q * (a // b)

  assert a % d == 0 and b % d == 0
  assert d == a * x + b * y

  return (d, x, y)


if __name__ == "__main__":
     from doctest import testmod

     testmod(name="""diophantine""", verbose=True)
     testmod(name="""diophantine_all_soln""", verbose=True)
     testmod(name="""extended_gcd""", verbose=True)
     testmod(name="""greatest_common_divisor""", verbose=True)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   						=    {
    """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   						=    [
         """OpenLlamaForCausalLM""",
         """OpenLlamaModel""",
         """OpenLlamaPreTrainedModel""",
         """OpenLlamaForSequenceClassification""",
     ]


if TYPE_CHECKING:
     from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from transformers import LlamaTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_open_llama import (
              OpenLlamaForCausalLM,
              OpenLlamaForSequenceClassification,
              OpenLlamaModel,
              OpenLlamaPreTrainedModel,
          )


else:
     import sys

     _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

