






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 			__a  (    A__       ,   A__       ):
 _lowerCAmelCase       :       List[Any]        = '''pixel_values'''
 _lowerCAmelCase       :       Tuple        = False
 _lowerCAmelCase       :       str        = TimmBackboneConfig







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   **SCREAMING_SNAKE_CASE					:   List[str]						):


      '''simple docstring'''






      requires_backends(self							,   "timm"						)
      super().__init__(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  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(SCREAMING_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__			:   Optional[int]       				=  getattr(SCREAMING_SNAKE_CASE							,   "use_pretrained_backbone"							,   SCREAMING_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__			:   List[str]       				=  config.out_indices if getattr(SCREAMING_SNAKE_CASE							,   "out_indices"							,   SCREAMING_SNAKE_CASE						) is not None else (-1,)

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







 @classmethod
 def 					__lowercase					(		cls					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   *SCREAMING_SNAKE_CASE					:   List[Any]							,   **SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






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

      UpperCamelCase__			:   List[str]       				=  kwargs.pop("config"							,   TimmBackboneConfig()						)

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

      UpperCamelCase__			:   Any       				=  kwargs.pop("num_channels"							,   config.num_channels						)
      UpperCamelCase__			:   List[Any]       				=  kwargs.pop("features_only"							,   config.features_only						)
      UpperCamelCase__			:   Optional[int]       				=  kwargs.pop("use_pretrained_backbone"							,   config.use_pretrained_backbone						)
      UpperCamelCase__			:   List[Any]       				=  kwargs.pop("out_indices"							,   config.out_indices						)
      UpperCamelCase__			:   Union[str, Any]       				=  TimmBackboneConfig(
          backbone=SCREAMING_SNAKE_CASE							,   num_channels=SCREAMING_SNAKE_CASE							,   features_only=SCREAMING_SNAKE_CASE							,   use_pretrained_backbone=SCREAMING_SNAKE_CASE							,   out_indices=SCREAMING_SNAKE_CASE							,   )
      return super()._from_config(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      pass







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


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  return_dict if return_dict is not None else self.config.use_return_dict
      UpperCamelCase__			:   str       				=  (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      UpperCamelCase__			:   Any       				=  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__			:   Dict       				=  self._all_layers
           UpperCamelCase__			:   Optional[Any]       				=  self._backbone(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   List[str]       				=  self._return_layers
           UpperCamelCase__			:   Dict       				=  tuple(hidden_states[i] for i in self.out_indices						)
      else:
           UpperCamelCase__			:   int       				=  self._backbone(SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Tuple       				=  None

      UpperCamelCase__			:   Dict       				=  tuple(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[int]       				=  tuple(SCREAMING_SNAKE_CASE						) if hidden_states is not None else None

      if not return_dict:
           UpperCamelCase__			:   Optional[int]       				=  (feature_maps,)
           if output_hidden_states:
                UpperCamelCase__			:   Optional[Any]       				=  output + (hidden_states,)
           return output

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







from math import factorial





class 			__a  :







 def __init__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  real
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Union[str, Any]       				=  [1] * rank
      else:
           UpperCamelCase__			:   int       				=  rank







 def __repr__(		self					:   Tuple						):


      '''simple docstring'''






      return (
          F'{self.real}+'
          F'{"+".join(str(SCREAMING_SNAKE_CASE						)+"E"+str(n+1						)for n,dual in enumerate(self.duals						)						)}'
      )







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.duals.copy()
      while cur[-1] == 0:
           cur.pop(-1						)
      return Dual(self.real							,   SCREAMING_SNAKE_CASE						)







 def __add__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           return Dual(self.real + other							,   self.duals						)
      UpperCamelCase__			:   Optional[int]       				=  self.duals.copy()
      UpperCamelCase__			:   Any       				=  other.duals.copy()
      if len(SCREAMING_SNAKE_CASE						) > len(SCREAMING_SNAKE_CASE						):
           o_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      elif len(SCREAMING_SNAKE_CASE						) < len(SCREAMING_SNAKE_CASE						):
           s_dual.extend([1] * (len(SCREAMING_SNAKE_CASE						) - len(SCREAMING_SNAKE_CASE						))						)
      UpperCamelCase__			:   Optional[int]       				=  []
      for i in range(len(SCREAMING_SNAKE_CASE						)						):
           new_duals.append(s_dual[i] + o_dual[i]						)
      return Dual(self.real + other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Dict        = __add__







 def __sub__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      return self + other * -1







 def __mul__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Dict						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i * other						)
           return Dual(self.real * other							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[Any]       				=  [0] * (len(self.duals						) + len(other.duals						) + 1)
      for i, item in enumerate(self.duals						):
           for j, jtem in enumerate(other.duals						):
                new_duals[i + j + 1] += item * jtem
      for k in range(len(self.duals						)						):
           new_duals[k] += self.duals[k] * other.real
      for index in range(len(other.duals						)						):
           new_duals[index] += other.duals[index] * self.real
      return Dual(self.real * other.real							,   SCREAMING_SNAKE_CASE						)

 _lowerCAmelCase       :       Union[str, Any]        = __mul__







 def __truediv__(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Any						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  []
           for i in self.duals:
                new_duals.append(i / other						)
           return Dual(self.real / other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __floordiv__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      if not isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  []
           for i in self.duals:
                new_duals.append(i // other						)
           return Dual(self.real // other							,   SCREAMING_SNAKE_CASE						)
      raise ValueError







 def __pow__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      if n < 0 or isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           raise ValueError("power must be a positive integer"						)
      if n == 0:
           return 1
      if n == 1:
           return self
      UpperCamelCase__			:   str       				=  self
      for _ in range(n - 1						):
           x *= self
      return x







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     if not callable(__lowerCAmelCase			):
          raise ValueError("differentiate() requires a function as input for func"			)
     if not isinstance(__lowerCAmelCase					,    (float, int)			):
          raise ValueError("differentiate() requires a float as input for position"			)
     if not isinstance(__lowerCAmelCase					,    __lowerCAmelCase			):
          raise ValueError("differentiate() requires an int as input for order"			)
     UpperCamelCase__			:   Optional[Any]       				=  Dual(__lowerCAmelCase					,    1			)
     UpperCamelCase__			:   Any       				=  func(__lowerCAmelCase			)
     if order == 0:
          return result.real
     return result.duals[order - 1] * factorial(__lowerCAmelCase			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()







 def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	List[Any]:
      return y**2 * y**4




 print(differentiate(f, 9, 2))