




'''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 		lowercase_   (			A					,  A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''pixel_values'''
   lowerCamelCase_      =						False
   lowerCamelCase_      =						TimmBackboneConfig



   def __init__(   self :     int  ,				__lowerCamelCase :     List[str]  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      requires_backends(self  ,				"timm"   )
      super().__init__(__lowerCamelCase   )
      _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(__lowerCamelCase  ,				"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(__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.
      _SCREAMING_SNAKE_CASE	=				config.out_indices if getattr(__lowerCamelCase  ,				"out_indices"  ,				__lowerCamelCase   ) is not None else (-1,)

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



   @classmethod
   def 							lowerCAmelCase_      (   cls :     Optional[int]  ,				__lowerCamelCase :     Tuple  ,				*__lowerCamelCase :     str  ,				**__lowerCamelCase :     Any   ):

      """simple docstring"""
      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"  ,				__lowerCamelCase   )
      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=__lowerCamelCase  ,				num_channels=__lowerCamelCase  ,				features_only=__lowerCamelCase  ,				use_pretrained_backbone=__lowerCamelCase  ,				out_indices=__lowerCamelCase  ,				)
      return super()._from_config(__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      pass



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str=None  ,				__lowerCamelCase :     str=None  ,				__lowerCamelCase :     Union[str, Any]=None  ,				**__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _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(__lowerCamelCase  ,				**__lowerCamelCase   )
         _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(__lowerCamelCase  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				None

      _SCREAMING_SNAKE_CASE	=				tuple(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tuple(__lowerCamelCase   ) 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=__lowerCamelCase  ,				hidden_states=__lowerCamelCase  ,				attentions=__lowerCamelCase   )







'''simple docstring'''



lowerCamelCase_			=						'Tobias Carryer'

from time import time







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Dict=int(time()   )   ):  # noqa: B008

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				multiplier
      _SCREAMING_SNAKE_CASE	=				increment
      _SCREAMING_SNAKE_CASE	=				modulo
      _SCREAMING_SNAKE_CASE	=				seed



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				(self.multiplier * self.seed + self.increment) % self.modulo
      return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 lowerCamelCase_			=						LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
 while True:
  print(lcg.next_number())

