
'''simple docstring'''







from __future__ import annotations

import sys







def   lowerCAmelCase(  a__    :  int      , a__    :  int      ):







       '''simple docstring'''






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

       lowerCamelCase__   ,		lowerCamelCase__				   =      a, b
       lowerCamelCase__   ,		lowerCamelCase__				   =      1, 0
       lowerCamelCase__   ,		lowerCamelCase__				   =      0, 1

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

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

       return old_coeff_a, old_coeff_b







def   lowerCAmelCase(  ):







       '''simple docstring'''






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


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



'''simple docstring'''







from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase_      =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):







    def   lowerCAmelCase(  cls    :  Dict      ):







           '''simple docstring'''






           return cls







def   lowerCAmelCase(  a__    :  Union[Tuple, List]      , a__    :  Optional[Union[List["torch.Generator"], "torch.Generator"]] = None      , a__    :  Optional["torch.device"] = None      , a__    :  Optional["torch.dtype"] = None      , a__    :  Optional["torch.layout"] = None      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      device
       lowerCamelCase__				   =      shape[0]

       lowerCamelCase__				   =      layout or torch.strided
       lowerCamelCase__				   =      device or torch.device("cpu"      )

       if generator is not None:
              lowerCamelCase__				   =      generator.device.type if not isinstance(a__      , a__      ) else generator[0].device.type
              if gen_device_type != device.type and gen_device_type == "cpu":
                     lowerCamelCase__				   =      "cpu"
                     if device != "mps":
                            logger.info(
                                f"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
                                f""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
                                f""" slighly speed up this function by passing a generator that was created on the {device} device."""      )
              elif gen_device_type != device.type and gen_device_type == "cuda":
                     raise ValueError(f"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""      )

       if isinstance(a__      , a__      ):
              lowerCamelCase__				   =      (1,) + shape[1:]
              lowerCamelCase__				   =      [
                  torch.randn(a__      , generator=generator[i]      , device=a__      , dtype=a__      , layout=a__      )
                  for i in range(a__      )
              ]
              lowerCamelCase__				   =      torch.cat(a__      , dim=0      ).to(a__      )
       else:
              lowerCamelCase__				   =      torch.randn(a__      , generator=a__      , device=a__      , dtype=a__      , layout=a__      ).to(a__      )

       return latents







def   lowerCAmelCase(  a__    :  Union[str, Any]      ):







       '''simple docstring'''






       if is_torch_version("<"      , "2.0.0"      ) or not hasattr(a__      , "_dynamo"      ):
              return False
       return isinstance(a__      , torch._dynamo.eval_frame.OptimizedModule      )

