


"""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 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      str      							=					"pixel_values"
				_lowerCAmelCase      :      str      							=					False
				_lowerCAmelCase      :      int      							=					TimmBackboneConfig



				def __init__(						self       ,		UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								requires_backends(self       ,		"timm"     )
								super().__init__(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				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(UpperCAmelCase__       ,		"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__     							=				getattr(UpperCAmelCase__       ,		"use_pretrained_backbone"       ,		UpperCAmelCase__     )
								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__     							=				config.out_indices if getattr(UpperCAmelCase__       ,		"out_indices"       ,		UpperCAmelCase__     ) is not None else (-1,)

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



				@classmethod
				def   lowerCAmelCase__		(						cls       ,		UpperCAmelCase__       ,		*UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								requires_backends(cls       ,		["vision", "timm"]     )
								from ...models.timm_backbone import TimmBackboneConfig

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

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

								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("num_channels"       ,		config.num_channels     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("features_only"       ,		config.features_only     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("use_pretrained_backbone"       ,		config.use_pretrained_backbone     )
								SCREAMING_SNAKE_CASE__     							=				kwargs.pop("out_indices"       ,		config.out_indices     )
								SCREAMING_SNAKE_CASE__     							=				TimmBackboneConfig(
								    backbone=UpperCAmelCase__       ,		num_channels=UpperCAmelCase__       ,		features_only=UpperCAmelCase__       ,		use_pretrained_backbone=UpperCAmelCase__       ,		out_indices=UpperCAmelCase__       ,		)
								return super()._from_config(UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								pass






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

								SCREAMING_SNAKE_CASE__     							=				tuple(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tuple(UpperCAmelCase__     ) if hidden_states is not None else None

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

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




"""simple docstring"""






from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors


def __lowercase     (      lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				prime_factors(lowerCamelCase_  )
				if is_square_free(lowerCamelCase_  ):
								return -1 if len(lowerCamelCase_  ) % 2 else 1
				return 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()
