




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 __snake_case			(			a ,  a		):
   UpperCAmelCase__  :    str          =  '''pixel_values'''
   UpperCAmelCase__  :    str          =  False
   UpperCAmelCase__  :    List[Any]          =  TimmBackboneConfig







   def __init__(      self		:						Optional[int]					,						_snake_case		:						int					,						**_snake_case		:						Optional[int]):

        """simple docstring"""


        requires_backends(self					,						'''timm''')
        super().__init__(_snake_case)
        UpperCAmelCase_          =							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(_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.''')

        UpperCAmelCase_          =							getattr(_snake_case					,						'''use_pretrained_backbone'''					,						_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.
        UpperCAmelCase_          =							config.out_indices if getattr(_snake_case					,						'''out_indices'''					,						_snake_case) is not None else (-1,)

        UpperCAmelCase_          =							timm.create_model(
            config.backbone					,						pretrained=_snake_case					,						features_only=config.features_only					,						in_chans=config.num_channels					,						out_indices=_snake_case					,						**_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.
        UpperCAmelCase_          =							self._backbone.return_layers
        UpperCAmelCase_          =							{layer['''module''']: str(_snake_case) for i, layer in enumerate(self._backbone.feature_info.info)}
        super()._init_backbone(_snake_case)







   @classmethod
   def lowerCamelCase						(      cls		:						Dict					,						_snake_case		:						str					,						*_snake_case		:						List[str]					,						**_snake_case		:						Optional[Any]):

        """simple docstring"""


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

        UpperCAmelCase_          =							kwargs.pop('''config'''					,						TimmBackboneConfig())

        UpperCAmelCase_          =							kwargs.pop('''use_timm_backbone'''					,						_snake_case)
        if not use_timm:
             raise ValueError('''use_timm_backbone must be True for timm backbones''')

        UpperCAmelCase_          =							kwargs.pop('''num_channels'''					,						config.num_channels)
        UpperCAmelCase_          =							kwargs.pop('''features_only'''					,						config.features_only)
        UpperCAmelCase_          =							kwargs.pop('''use_pretrained_backbone'''					,						config.use_pretrained_backbone)
        UpperCAmelCase_          =							kwargs.pop('''out_indices'''					,						config.out_indices)
        UpperCAmelCase_          =							TimmBackboneConfig(
            backbone=_snake_case					,						num_channels=_snake_case					,						features_only=_snake_case					,						use_pretrained_backbone=_snake_case					,						out_indices=_snake_case					,						)
        return super()._from_config(_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        pass







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						Tuple					,						_snake_case		:						int=None					,						_snake_case		:						Any=None					,						_snake_case		:						int=None					,						**_snake_case		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							return_dict if return_dict is not None else self.config.use_return_dict
        UpperCAmelCase_          =							(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        UpperCAmelCase_          =							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
             UpperCAmelCase_          =							self._all_layers
             UpperCAmelCase_          =							self._backbone(_snake_case					,						**_snake_case)
             UpperCAmelCase_          =							self._return_layers
             UpperCAmelCase_          =							tuple(hidden_states[i] for i in self.out_indices)
        else:
             UpperCAmelCase_          =							self._backbone(_snake_case					,						**_snake_case)
             UpperCAmelCase_          =							None

        UpperCAmelCase_          =							tuple(_snake_case)
        UpperCAmelCase_          =							tuple(_snake_case) if hidden_states is not None else None

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

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













"""simple docstring"""




from decimal import Decimal, getcontext
from math import ceil, factorial






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    int					) ->str:






	'''simple docstring'''






	if not isinstance(_lowercase    ,       _lowercase					):
		raise TypeError("Undefined for non-integers"					)
	elif precision < 1:
		raise ValueError("Undefined for non-natural numbers"					)

	a      :		Tuple	=				precision
	a      :		str	=				ceil(precision / 14					)
	a      :		List[Any]	=				42_6880 * Decimal(1_0005					).sqrt()
	a      :		Union[str, Any]	=				1
	a      :		Dict	=				1359_1409
	a      :		Optional[int]	=				Decimal(_lowercase					)
	for k in range(1    ,       _lowercase					):
		a      :		int	=				factorial(6 * k					) // (factorial(3 * k					) * factorial(_lowercase					) ** 3)
		linear_term += 5_4514_0134
		exponential_term *= -26_2537_4126_4076_8000
		partial_sum += Decimal(multinomial_term * linear_term					) / exponential_term
	return str(constant_term / partial_sum					)[:-1]


if __name__ == "__main__":
		a						: Optional[Any]     	=			50
		print(F'''The first {n} digits of pi is: {pi(n)}''')


