'''simple docstring'''
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
     import timm


if is_torch_available():
     from torch import Tensor







class  __A				( __lowerCamelCase    ,						__lowerCamelCase			):
 lowerCamelCase				       ='''pixel_values'''
 lowerCamelCase				       =False
 lowerCamelCase				       =TimmBackboneConfig
 def __init__(   self			:    Tuple				,   lowerCamelCase			:    str				,   **lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   requires_backends(self				,   """timm"""       )
   super().__init__(SCREAMING_SNAKE_CASE_       )
   __A		:     Optional[Any]												=		config

   if config.backbone is None:
     raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""       )

   if config.backbone not in timm.list_models():
     raise ValueError(f"backbone {config.backbone} is not supported by timm."       )

   if hasattr(SCREAMING_SNAKE_CASE_				,   """out_features"""       ) and config.out_features is not None:
     raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""       )

   __A		:     Any												=		getattr(SCREAMING_SNAKE_CASE_				,   """use_pretrained_backbone"""				,   SCREAMING_SNAKE_CASE_       )
   if pretrained is None:
     raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""       )

   # We just take the final layer by default. This matches the default for the transformers models.
   __A		:     int												=		config.out_indices if getattr(SCREAMING_SNAKE_CASE_				,   """out_indices"""				,   SCREAMING_SNAKE_CASE_       ) is not None else (-1,)

   __A		:     int												=		timm.create_model(
       config.backbone				,   pretrained=SCREAMING_SNAKE_CASE_				,   features_only=config.features_only				,   in_chans=config.num_channels				,   out_indices=SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_				,   )
   # These are used to control the output of the model when called. If output_hidden_states is True, then
   # return_layers is modified to include all layers.
   __A		:     Union[str, Any]												=		self._backbone.return_layers
   __A		:     Tuple												=		{layer["""module"""]: str(SCREAMING_SNAKE_CASE_       ) for i, layer in enumerate(self._backbone.feature_info.info       )}
   super()._init_backbone(SCREAMING_SNAKE_CASE_       )
 @classmethod
 def 		lowercase_(   cls			:    Any				,   lowerCamelCase			:    List[Any]				,   *lowerCamelCase			:    Optional[Any]				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   requires_backends(cls				,   ["""vision""", """timm"""]       )
   from ...models.timm_backbone import TimmBackboneConfig

   __A		:     int												=		kwargs.pop("""config"""				,   TimmBackboneConfig()       )

   __A		:     Optional[int]												=		kwargs.pop("""use_timm_backbone"""				,   SCREAMING_SNAKE_CASE_       )
   if not use_timm:
     raise ValueError("""use_timm_backbone must be True for timm backbones"""       )

   __A		:     str												=		kwargs.pop("""num_channels"""				,   config.num_channels       )
   __A		:     str												=		kwargs.pop("""features_only"""				,   config.features_only       )
   __A		:     Dict												=		kwargs.pop("""use_pretrained_backbone"""				,   config.use_pretrained_backbone       )
   __A		:     Tuple												=		kwargs.pop("""out_indices"""				,   config.out_indices       )
   __A		:     Optional[Any]												=		TimmBackboneConfig(
       backbone=SCREAMING_SNAKE_CASE_				,   num_channels=SCREAMING_SNAKE_CASE_				,   features_only=SCREAMING_SNAKE_CASE_				,   use_pretrained_backbone=SCREAMING_SNAKE_CASE_				,   out_indices=SCREAMING_SNAKE_CASE_				,   )
   return super()._from_config(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_       )
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Any       ):



   """simple docstring"""





   pass






 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Optional[Any]=None				,   lowerCamelCase			:    int=None				,   lowerCamelCase			:    Optional[int]=None				,   **lowerCamelCase			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		return_dict if return_dict is not None else self.config.use_return_dict
   __A		:     List[Any]												=		(
       output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
   )
   __A		:     Optional[int]												=		output_attentions if output_attentions is not None else self.config.output_attentions

   if output_attentions:
     raise ValueError("""Cannot output attentions for timm backbones at the moment"""       )

   if output_hidden_states:
     # We modify the return layers to include all the stages of the backbone
     __A		:     Union[str, Any]												=		self._all_layers
     __A		:     str												=		self._backbone(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_       )
     __A		:     Optional[int]												=		self._return_layers
     __A		:     Optional[int]												=		tuple(hidden_states[i] for i in self.out_indices       )
   else:
     __A		:     int												=		self._backbone(SCREAMING_SNAKE_CASE_				,   **SCREAMING_SNAKE_CASE_       )
     __A		:     Union[str, Any]												=		None

   __A		:     List[str]												=		tuple(SCREAMING_SNAKE_CASE_       )
   __A		:     List[Any]												=		tuple(SCREAMING_SNAKE_CASE_       ) if hidden_states is not None else None

   if not return_dict:
     __A		:     Optional[Any]												=		(feature_maps,)
     if output_hidden_states:
       __A		:     List[Any]												=		output + (hidden_states,)
     return output

   return BackboneOutput(feature_maps=SCREAMING_SNAKE_CASE_				,   hidden_states=SCREAMING_SNAKE_CASE_				,   attentions=SCREAMING_SNAKE_CASE_       )





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       bool:





  """simple docstring"""

  if n == 2:
    return True
  if not n % 2 or n < 2:
    return False
  if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
    return False
  if n > 3_3170_4406_4679_8873_8596_1981 and not allow_probable:
    raise ValueError(
        """Warning: upper bound of deterministic test is exceeded. """
        """Pass allow_probable=True to allow probabilistic test. """
        """A return value of True indicates a probable prime.""" )
  # array bounds provided by analysis
  __A		:     Optional[int]												=		[
      2047,
      137_3653,
      2532_6001,
      32_1503_1751,
      2_1523_0289_8747,
      3_4747_4966_0383,
      341_5500_7172_8321,
      1,
      382_5123_0565_4641_3051,
      1,
      1,
      3186_6585_7834_0311_5116_7461,
      3_3170_4406_4679_8873_8596_1981,
  ]

  __A		:     Optional[int]												=		[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
  for idx, _p in enumerate(__SCREAMING_SNAKE_CASE			,     1 ):
    if n < _p:
      # then we have our last prime to check
      __A		:     Union[str, Any]												=		primes[:idx]
      break
  __A							,					__A		:     int												=		n - 1, 0
  # break up n -1 into a power of 2 (s) and
  # remaining odd component
  # essentially, solve for d * 2 ** s == n - 1
  while d % 2 == 0:
    d //= 2
    s += 1
  for prime in plist:
    __A		:     int												=		False
    for r in range(__SCREAMING_SNAKE_CASE ):
      __A		:     Optional[Any]												=		pow(__SCREAMING_SNAKE_CASE			,     d * 2**r			,     __SCREAMING_SNAKE_CASE )
      # see article for analysis explanation for m
      if (r == 0 and m == 1) or ((m + 1) % n == 0):
        __A		:     Optional[int]												=		True
        # this loop will not determine compositeness
        break
    if pr:
      continue
    # if pr is False, then the above loop never evaluated to true,
    # and the n MUST be composite
    return False
  return True







def        A_     (						)				->       None:





  """simple docstring"""

  assert not miller_rabin(561 )
  assert miller_rabin(563 )
  # 2047

  assert not miller_rabin(83_8201 )
  assert miller_rabin(83_8207 )
  # 1_373_653

  assert not miller_rabin(1731_6001 )
  assert miller_rabin(1731_6017 )
  # 25_326_001

  assert not miller_rabin(30_7838_6641 )
  assert miller_rabin(30_7838_6653 )
  # 3_215_031_751

  assert not miller_rabin(1_7130_4557_4801 )
  assert miller_rabin(1_7130_4557_4819 )
  # 2_152_302_898_747

  assert not miller_rabin(2_7797_9972_8307 )
  assert miller_rabin(2_7797_9972_8327 )
  # 3_474_749_660_383

  assert not miller_rabin(113_8500_2390_9441 )
  assert miller_rabin(113_8500_2390_9527 )
  # 341_550_071_728_321

  assert not miller_rabin(127_5041_0188_4880_4351 )
  assert miller_rabin(127_5041_0188_4880_4391 )
  # 3_825_123_056_546_413_051

  assert not miller_rabin(796_6646_4458_5077_8779_1867 )
  assert miller_rabin(796_6646_4458_5077_8779_1951 )
  # 318_665_857_834_031_151_167_461

  assert not miller_rabin(5528_4067_7446_6478_9766_0333 )
  assert miller_rabin(5528_4067_7446_6478_9766_0359 )
  # 3_317_044_064_679_887_385_961_981
  # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()



