






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  _UpperCAmelCase   (			A__						,A__      ):

				"""simple docstring"""




				lowercase__											=			"""pixel_values"""
				lowercase__											=			False
				lowercase__											=			TimmBackboneConfig




				def __init__(	self		:       Tuple,    lowerCamelCase		:       List[Any],    **lowerCamelCase		:       int    ):

								'''simple docstring'''
								requires_backends(self,    '''timm'''    )
								super().__init__(lowerCamelCase    )
								lowercase__  					=     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(lowerCamelCase,    '''out_features'''    ) and config.out_features is not None:
												raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''    )

								lowercase__  					=     getattr(lowerCamelCase,    '''use_pretrained_backbone''',    lowerCamelCase    )
								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.
								lowercase__  					=     config.out_indices if getattr(lowerCamelCase,    '''out_indices''',    lowerCamelCase    ) is not None else (-1,)

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




				@classmethod
				def 							lowercase__  (	cls		:       str,    lowerCamelCase		:       int,    *lowerCamelCase		:       List[Any],    **lowerCamelCase		:       List[str]    ):

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

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

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

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




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								pass




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       List[str]=None,    lowerCamelCase		:       Any=None,    **lowerCamelCase		:       Any    ):

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

								lowercase__  					=     tuple(lowerCamelCase    )
								lowercase__  					=     tuple(lowerCamelCase    ) if hidden_states is not None else None

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

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












import sys
import turtle






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  ):






				'''simple docstring'''
				my_pen.up()
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.down()
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.goto(vertexa[0]						,  vertexa[1]							)
				my_pen.goto(vertexa[0]						,  vertexa[1]							)

				if depth == 0:
								return

				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)
				triangle(lowerCamelCase_						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  get_mid(lowerCamelCase_						,  lowerCamelCase_							)						,  depth - 1							)


if __name__ == "__main__":
							if len(sys.argv) != 2:
														raise ValueError(
														    'Correct format for using this script: '
														    'python fractals.py <int:depth_for_fractal>'
														)
							A__      :     Dict       =					turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							A__      :     Union[str, Any]       =					[(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))




