code: 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)
  
  
style_context: 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__)
  
  