
"""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  ( lowerCAmelCase						,	lowerCAmelCase			):
				a__:      Dict          =    'pixel_values'
				a__:      Any          =    False
				a__:      List[Any]          =    TimmBackboneConfig




				def __init__(				self							,						UpperCAmelCase							,						**UpperCAmelCase   ):
							requires_backends(self							,						'''timm'''   )
							super().__init__(UpperCAmelCase   )
							lowerCamelCase_					=  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.'''   )

							lowerCamelCase_					=  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.
							lowerCamelCase_					=  config.out_indices if getattr(UpperCAmelCase							,						'''out_indices'''							,						UpperCAmelCase   ) is not None else (-1,)

							lowerCamelCase_					=  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.
							lowerCamelCase_					=  self._backbone.return_layers
							lowerCamelCase_					=  {layer['''module''']: str(UpperCAmelCase   ) for i, layer in enumerate(self._backbone.feature_info.info   )}
							super()._init_backbone(UpperCAmelCase   )




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

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

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

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




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							pass





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

							lowerCamelCase_					=  tuple(UpperCAmelCase   )
							lowerCamelCase_					=  tuple(UpperCAmelCase   ) if hidden_states is not None else None

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

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










def       SCREAMING_SNAKE_CASE__  (       _lowercase     : str			)   ->							str:




		'''simple docstring'''
		if not all(char in '01' for char in bin_string			):
				raise ValueError('Non-binary value was passed to the function'			)
		if not bin_string:
				raise ValueError('Empty string was passed to the function'			)
		lowercase__					:      Tuple      =		''
		while len(_lowercase			) % 3 != 0:
				lowercase__					:      Optional[int]      =		'0' + bin_string
		lowercase__					:      List[Any]      =		[
		    bin_string[index : index + 3]
		    for index in range(len(_lowercase			)			)
		    if index % 3 == 0
		]
		for bin_group in bin_string_in_3_list:
				lowercase__					:      int      =		0
				for index, val in enumerate(_lowercase			):
						oct_val += int(2 ** (2 - index) * int(_lowercase			)			)
				oct_string += str(_lowercase			)
		return oct_string


if __name__ == "__main__":
			from doctest import testmod

			testmod()


