




import numpy as np

def   _lowercase   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE="utf-8"				,_SCREAMING_SNAKE_CASE="surrogatepass"					)			->				Optional[Any]:
       lowerCamelCase     :   int    				=      bin(int.from_bytes(text.encode(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)				,"big"					)					)[2:]
       return bits.zfill(8 * ((len(_SCREAMING_SNAKE_CASE					) + 7) // 8)					)

def   _lowercase   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE="utf-8"				,_SCREAMING_SNAKE_CASE="surrogatepass"					)			->				Dict:
       lowerCamelCase     :   Union[str, Any]    				=      int(_SCREAMING_SNAKE_CASE				,2					)
       return n.to_bytes((n.bit_length() + 7) // 8				,"big"					).decode(_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					) or "\0"

def   _lowercase   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				List[str]:
       if size_par + len(_SCREAMING_SNAKE_CASE					) <= 2**size_par - (len(_SCREAMING_SNAKE_CASE					) - 1):
              raise ValueError("size of parity don't match with size of data"					)

       lowerCamelCase     :   Tuple    				=      []
       lowerCamelCase     :   Dict    				=      []
       lowerCamelCase     :   Optional[int]    				=      [bin(_SCREAMING_SNAKE_CASE					)[2:] for x in range(1				,size_par + len(_SCREAMING_SNAKE_CASE					) + 1					)]

       # sorted information data for the size of the output data
       lowerCamelCase     :   Optional[Any]    				=      []
       # data position template + parity
       lowerCamelCase     :   List[Any]    				=      []
       # parity bit counter
       lowerCamelCase     :   Any    				=      0
       # counter position of data bits
       lowerCamelCase     :   Optional[int]    				=      0

       for x in range(1				,size_par + len(_SCREAMING_SNAKE_CASE					) + 1					):
              # Performs a template of bit positions - who should be given,
              # and who should be parity
              if qtd_bp < size_par:
                     if (np.log(_SCREAMING_SNAKE_CASE					) / np.log(2					)).is_integer():
                            data_out_gab.append("P"					)
                            lowerCamelCase     :   Optional[Any]    				=      qtd_bp + 1
                     else:
                            data_out_gab.append("D"					)
              else:
                     data_out_gab.append("D"					)

              # Sorts the data to the new output size
              if data_out_gab[-1] == "D":
                     data_ord.append(data[cont_data]					)
                     cont_data += 1
              else:
                     data_ord.append(_SCREAMING_SNAKE_CASE					)

              # Calculates parity
       lowerCamelCase     :   Any    				=      0  # parity bit counter
       for bp in range(1				,size_par + 1					):
              # Bit counter one for a given parity
              lowerCamelCase     :   str    				=      0
              # counter to control the loop reading
              lowerCamelCase     :   Tuple    				=      0
              for x in data_ord:
                     if x is not None:
                            try:
                                   lowerCamelCase     :   int    				=      (bin_pos[cont_loop])[-1 * (bp)]
                            except IndexError:
                                   lowerCamelCase     :   Optional[int]    				=      "0"
                            if aux == "1" and x == "1":
                                   cont_bo += 1
                     cont_loop += 1
              parity.append(cont_bo % 2					)

              qtd_bp += 1

       # Mount the message
       lowerCamelCase     :   List[str]    				=      0  # parity bit counter
       for x in range(0				,size_par + len(_SCREAMING_SNAKE_CASE					)					):
              if data_ord[x] is None:
                     data_out.append(str(parity[cont_bp]					)					)
                     cont_bp += 1
              else:
                     data_out.append(data_ord[x]					)

       return data_out


def   _lowercase   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Dict:
       lowerCamelCase     :   List[str]    				=      []
       # Parity bit counter
       lowerCamelCase     :   int    				=      0
       # Counter p data bit reading
       lowerCamelCase     :   Optional[Any]    				=      0
       # list of parity received
       lowerCamelCase     :   Tuple    				=      []
       lowerCamelCase     :   Optional[int]    				=      []

       for x in range(1				,len(_SCREAMING_SNAKE_CASE					) + 1					):
              # Performs a template of bit positions - who should be given,
              #  and who should be parity
              if qtd_bp < size_par and (np.log(_SCREAMING_SNAKE_CASE					) / np.log(2					)).is_integer():
                     data_out_gab.append("P"					)
                     lowerCamelCase     :   Union[str, Any]    				=      qtd_bp + 1
              else:
                     data_out_gab.append("D"					)

              # Sorts the data to the new output size
              if data_out_gab[-1] == "D":
                     data_output.append(data[cont_data]					)
              else:
                     parity_received.append(data[cont_data]					)
              cont_data += 1

       # -----------calculates the parity with the data
       lowerCamelCase     :   List[Any]    				=      []
       lowerCamelCase     :   Tuple    				=      []
       lowerCamelCase     :   Tuple    				=      [bin(_SCREAMING_SNAKE_CASE					)[2:] for x in range(1				,size_par + len(_SCREAMING_SNAKE_CASE					) + 1					)]

       #  sorted information data for the size of the output data
       lowerCamelCase     :   List[Any]    				=      []
       # Data position feedback + parity
       lowerCamelCase     :   Dict    				=      []
       # Parity bit counter
       lowerCamelCase     :   Optional[int]    				=      0
       # Counter p data bit reading
       lowerCamelCase     :   Tuple    				=      0

       for x in range(1				,size_par + len(_SCREAMING_SNAKE_CASE					) + 1					):
              # Performs a template position of bits - who should be given,
              # and who should be parity
              if qtd_bp < size_par and (np.log(_SCREAMING_SNAKE_CASE					) / np.log(2					)).is_integer():
                     data_out_gab.append("P"					)
                     lowerCamelCase     :   Tuple    				=      qtd_bp + 1
              else:
                     data_out_gab.append("D"					)

              # Sorts the data to the new output size
              if data_out_gab[-1] == "D":
                     data_ord.append(data_output[cont_data]					)
                     cont_data += 1
              else:
                     data_ord.append(_SCREAMING_SNAKE_CASE					)

              # Calculates parity
       lowerCamelCase     :   Any    				=      0  # parity bit counter
       for bp in range(1				,size_par + 1					):
              # Bit counter one for a certain parity
              lowerCamelCase     :   Optional[int]    				=      0
              # Counter to control loop reading
              lowerCamelCase     :   Optional[Any]    				=      0
              for x in data_ord:
                     if x is not None:
                            try:
                                   lowerCamelCase     :   Union[str, Any]    				=      (bin_pos[cont_loop])[-1 * (bp)]
                            except IndexError:
                                   lowerCamelCase     :   Optional[Any]    				=      "0"
                            if aux == "1" and x == "1":
                                   cont_bo += 1
                     cont_loop += 1
              parity.append(str(cont_bo % 2					)					)

              qtd_bp += 1

       # Mount the message
       lowerCamelCase     :   str    				=      0  # Parity bit counter
       for x in range(0				,size_par + len(_SCREAMING_SNAKE_CASE					)					):
              if data_ord[x] is None:
                     data_out.append(str(parity[cont_bp]					)					)
                     cont_bp += 1
              else:
                     data_out.append(data_ord[x]					)

       lowerCamelCase     :   List[str]    				=      parity_received == parity
       return data_output, ack












import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union




class 	UpperCamelCase__			(enum.Enum  ):

    '''simple docstring'''



    lowerCamelCase_   :			Tuple			  =   """timm"""
    lowerCamelCase_   :			Union[str, Any]			  =   """transformers"""




def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE					)			->				Optional[int]:
       if stage_names is None:
              raise ValueError("Stage_names must be set for transformers backbones"					)

       if out_features is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE				,(list,)					):
                     raise ValueError(f'''out_features must be a list {type(_SCREAMING_SNAKE_CASE					)}'''					)
              if any(feat not in stage_names for feat in out_features					):
                     raise ValueError(f'''out_features must be a subset of stage_names: {stage_names} got {out_features}'''					)

       if out_indices is not None:
              if not isinstance(_SCREAMING_SNAKE_CASE				,(list, tuple)					):
                     raise ValueError(f'''out_indices must be a list or tuple, got {type(_SCREAMING_SNAKE_CASE					)}'''					)
              if any(idx >= len(_SCREAMING_SNAKE_CASE					) for idx in out_indices					):
                     raise ValueError("out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"					)

       if out_features is not None and out_indices is not None:
              if len(_SCREAMING_SNAKE_CASE					) != len(_SCREAMING_SNAKE_CASE					):
                     raise ValueError("out_features and out_indices should have the same length if both are set"					)
              if out_features != [stage_names[idx] for idx in out_indices]:
                     raise ValueError("out_features and out_indices should correspond to the same stages if both are set"					)

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,)			->				Optional[Any]:
       if out_indices is None and out_features is None:
              lowerCamelCase     :   Dict    				=      [len(_SCREAMING_SNAKE_CASE					) - 1]
              lowerCamelCase     :   Tuple    				=      [stage_names[-1]]
       elif out_indices is None and out_features is not None:
              lowerCamelCase     :   List[Any]    				=      [stage_names.index(_SCREAMING_SNAKE_CASE					) for layer in out_features]
       elif out_features is None and out_indices is not None:
              lowerCamelCase     :   Union[str, Any]    				=      [stage_names[idx] for idx in out_indices]
       return out_features, out_indices

def   A   (   _SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,_SCREAMING_SNAKE_CASE				,)			->				Tuple[List[str], List[int]]:
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       lowerCamelCase				,   lowerCamelCase     :   str    				=      _align_output_features_output_indices(
           out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       # Verify that the aligned out_features and out_indices are valid
       verify_out_features_out_indices(out_features=_SCREAMING_SNAKE_CASE				,out_indices=_SCREAMING_SNAKE_CASE				,stage_names=_SCREAMING_SNAKE_CASE					)
       return output_features, output_indices




class 	UpperCamelCase__			:

    '''simple docstring'''



    lowerCamelCase_   :			Optional[BackboneType]			  =   None







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           if getattr(self  ,						"_backbone"  ,						UpperCamelCase__  ) is None:
                  raise ValueError("self._backbone must be set before calling _init_timm_backbone"  )

           # These will diagree with the defaults for the transformers models e.g. for resnet50
           # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
           # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
           lowerCamelCase     :   Any    				=      [stage["module"] for stage in self._backbone.feature_info.info]
           lowerCamelCase     :   Optional[int]    				=      [stage["num_chs"] for stage in self._backbone.feature_info.info]
           lowerCamelCase     :   str    				=      self._backbone.feature_info.out_indices
           lowerCamelCase     :   Any    				=      self._backbone.feature_info.module_name()

           # We verify the out indices and out features are valid
           verify_out_features_out_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )
           lowerCamelCase				,   lowerCamelCase     :   Tuple    				=      out_features, out_indices







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						"stage_names"  )
           lowerCamelCase     :   Optional[int]    				=      getattr(UpperCamelCase__  ,						"out_features"  ,						UpperCamelCase__  )
           lowerCamelCase     :   Union[str, Any]    				=      getattr(UpperCamelCase__  ,						"out_indices"  ,						UpperCamelCase__  )

           lowerCamelCase     :   List[Any]    				=      stage_names
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=UpperCamelCase__  )
           # Number of channels for each stage. This is set in the transformer backbone model init
           lowerCamelCase     :   Tuple    				=      None







    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   None:
           lowerCamelCase     :   Any    				=      config

           lowerCamelCase     :   Any    				=      getattr(UpperCamelCase__  ,						"use_timm_backbone"  ,						UpperCamelCase__  )
           lowerCamelCase     :   int    				=      BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

           if self.backbone_type == BackboneType.TIMM:
                  self._init_timm_backbone(UpperCamelCase__  )
           elif self.backbone_type == BackboneType.TRANSFORMERS:
                  self._init_transformers_backbone(UpperCamelCase__  )
           else:
                  raise ValueError(F'''backbone_type {self.backbone_type} not supported.'''  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_features







    @out_features.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Union[str, Any]:
           lowerCamelCase				,   lowerCamelCase     :   Dict    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_indices







    @out_indices.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   List[str]:
           lowerCamelCase				,   lowerCamelCase     :   int    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Tuple:
           # the current backbones will output the number of channels for each stage
           # even if that stage is not in the out_features list.
           return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names  )}







    @property
    def 					_lowercase  (					self  )  ->   List[Any]:
           return [self.out_feature_channels[name] for name in self.out_features]







    def 					_lowercase  (					self  ,						*UpperCamelCase__  ,						**UpperCamelCase__  )  ->   Tuple:
           lowerCamelCase     :   Union[str, Any]    				=      dict(inspect.signature(self.forward  ).parameters  )
           lowerCamelCase     :   Optional[int]    				=      {k: v for k, v in kwargs.items() if k in signature}
           return self(*UpperCamelCase__  ,						**UpperCamelCase__  )







    def 					_lowercase  (					self  ,						UpperCamelCase__  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						UpperCamelCase__ = None  ,						)  ->   int:
           raise NotImplementedError("This method should be implemented by the derived class."  )







    def 					_lowercase  (					self  )  ->   Dict:
           lowerCamelCase     :   List[Any]    				=      super().to_dict()
           lowerCamelCase     :   Optional[int]    				=      output.pop("_out_features"  )
           lowerCamelCase     :   Dict    				=      output.pop("_out_indices"  )
           return output




class 	UpperCamelCase__			:

    '''simple docstring'''



    @property
    def 					_lowercase  (					self  )  ->   int:
           return self._out_features







    @out_features.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   Optional[Any]:
           lowerCamelCase				,   lowerCamelCase     :   List[Any]    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    @property
    def 					_lowercase  (					self  )  ->   Optional[Any]:
           return self._out_indices







    @out_indices.setter
    def 					_lowercase  (					self  ,						UpperCamelCase__  )  ->   int:
           lowerCamelCase				,   lowerCamelCase     :   Optional[Any]    				=      get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__  ,						out_indices=UpperCamelCase__  ,						stage_names=self.stage_names  )







    def 					_lowercase  (					self  )  ->   Any:
           lowerCamelCase     :   str    				=      super().to_dict()
           lowerCamelCase     :   Union[str, Any]    				=      output.pop("_out_features"  )
           lowerCamelCase     :   Optional[int]    				=      output.pop("_out_indices"  )
           return output






