

"""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							(UpperCamelCase_   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Optional[int]       = """pixel_values"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = False
     _SCREAMING_SNAKE_CASE						:str       = TimmBackboneConfig

     def __init__(						self	,    _a	,    **_a  )  ->      Optional[Any]:


           """simple docstring"""


           requires_backends(self	,    """timm"""  )
           super().__init__(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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(_a	,    """out_features"""  ) and config.out_features is not None:
                 raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	getattr(_a	,    """use_pretrained_backbone"""	,    _a  )
           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.
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	config.out_indices if getattr(_a	,    """out_indices"""	,    _a  ) is not None else (-1,)

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	timm.create_model(
               config.backbone	,    pretrained=_a	,    features_only=config.features_only	,    in_chans=config.num_channels	,    out_indices=_a	,    **_a	,    )
           # 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.
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self._backbone.return_layers
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{layer["""module"""]: str(_a  ) for i, layer in enumerate(self._backbone.feature_info.info  )}
           super()._init_backbone(_a  )

     @classmethod
     def 					_a			(						cls	,    _a	,    *_a	,    **_a  )  ->      Any:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs.pop("""config"""	,    TimmBackboneConfig()  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs.pop("""use_timm_backbone"""	,    _a  )
           if not use_timm:
                 raise ValueError("""use_timm_backbone must be True for timm backbones"""  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	kwargs.pop("""num_channels"""	,    config.num_channels  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	kwargs.pop("""features_only"""	,    config.features_only  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	kwargs.pop("""use_pretrained_backbone"""	,    config.use_pretrained_backbone  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	kwargs.pop("""out_indices"""	,    config.out_indices  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	TimmBackboneConfig(
               backbone=_a	,    num_channels=_a	,    features_only=_a	,    use_pretrained_backbone=_a	,    out_indices=_a	,    )
           return super()._from_config(_a	,    **_a  )

     def 					_a			(						self	,    _a  )  ->      List[Any]:


           """simple docstring"""


           pass





     def 					_a			(						self	,    _a	,    _a=None	,    _a=None	,    _a=None	,    **_a  )  ->      Union[BackboneOutput, Tuple[Tensor, ...]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	return_dict if return_dict is not None else self.config.use_return_dict
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self._all_layers
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	self._backbone(_a	,    **_a  )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	self._return_layers
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tuple(hidden_states[i] for i in self.out_indices  )
           else:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self._backbone(_a	,    **_a  )
                 SCREAMING_SNAKE_CASE__   :      Any      		=	None

           SCREAMING_SNAKE_CASE__   :      str      		=	tuple(_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tuple(_a  ) if hidden_states is not None else None

           if not return_dict:
                 SCREAMING_SNAKE_CASE__   :      Any      		=	(feature_maps,)
                 if output_hidden_states:
                       SCREAMING_SNAKE_CASE__   :      Any      		=	output + (hidden_states,)
                 return output

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







"""simple docstring"""


from math import sqrt




def  _lowercase					(    __lowerCAmelCase							)       ->				bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5  ,					int(sqrt(__lowerCAmelCase							) + 1							)  ,					6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True




def  _lowercase					(    __lowerCAmelCase = 1_0001							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	1
      while count != nth and number < 3:
            number += 1
            if is_prime(__lowerCAmelCase							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(__lowerCAmelCase							):
                  count += 1
      return number


if __name__ == "__main__":
  print(f'{solution() = }')




