






"""simple docstring"""



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

class 			a	(  enum.Enum      ):
    UpperCAmelCase_					:	Tuple      	="timm"
    UpperCAmelCase_					:	Any      	="transformers"


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[Iterable[str]]						,   __snake_case     :    Optional[Iterable[int]]						,   __snake_case     :    Optional[Iterable[str]]       ):





  '''simple docstring'''





  if stage_names is None:
    raise ValueError('Stage_names must be set for transformers backbones'       )

  if out_features is not None:
    if not isinstance(__snake_case						,   (list,)       ):
      raise ValueError(f'out_features must be a list {type(__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(__snake_case						,   (list, tuple)       ):
      raise ValueError(f'out_indices must be a list or tuple, got {type(__snake_case       )}'       )
    if any(idx >= len(__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(__snake_case       ) != len(__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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[List[str]]						,   __snake_case     :    Optional[Union[List[int], Tuple[int]]]						,   __snake_case     :    List[str]						,   ):





  '''simple docstring'''





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





  '''simple docstring'''





  verify_out_features_out_indices(out_features=__snake_case						,   out_indices=__snake_case						,   stage_names=__snake_case       )
  lowercase       ,	lowercase				       =			_align_output_features_output_indices(
      out_features=__snake_case						,   out_indices=__snake_case						,   stage_names=__snake_case       )
  # Verify that the aligned out_features and out_indices are valid
  verify_out_features_out_indices(out_features=__snake_case						,   out_indices=__snake_case						,   stage_names=__snake_case       )
  return output_features, output_indices

class 			a	:
    UpperCAmelCase_					:	Optional[BackboneType]      	=None







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      if getattr(self				,						'_backbone'				,						_lowerCamelCase			) 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']
      lowercase				       =			[stage['module'] for stage in self._backbone.feature_info.info]
      lowercase				       =			[stage['num_chs'] for stage in self._backbone.feature_info.info]
      lowercase				       =			self._backbone.feature_info.out_indices
      lowercase				       =			self._backbone.feature_info.module_name()

      # We verify the out indices and out features are valid
      verify_out_features_out_indices(
          out_features=_lowerCamelCase				,						out_indices=_lowerCamelCase				,						stage_names=self.stage_names			)
      lowercase       ,	lowercase				       =			out_features, out_indices







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			getattr(_lowerCamelCase				,						'stage_names'			)
      lowercase				       =			getattr(_lowerCamelCase				,						'out_features'				,						_lowerCamelCase			)
      lowercase				       =			getattr(_lowerCamelCase				,						'out_indices'				,						_lowerCamelCase			)

      lowercase				       =			stage_names
      lowercase       ,	lowercase				       =			get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase				,						out_indices=_lowerCamelCase				,						stage_names=_lowerCamelCase			)
      # Number of channels for each stage. This is set in the transformer backbone model init
      lowercase				       =			None







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			config

      lowercase				       =			getattr(_lowerCamelCase				,						'use_timm_backbone'				,						_lowerCamelCase			)
      lowercase				       =			BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

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







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_features







    @out_features.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase       ,	lowercase				       =			get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase				,						out_indices=_lowerCamelCase				,						stage_names=self.stage_names			)







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_indices







    @out_indices.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase       ,	lowercase				       =			get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase				,						out_indices=_lowerCamelCase				,						stage_names=self.stage_names			)







    @property
    def      UpperCamelCase_ (     self			):
      # 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      UpperCamelCase_ (     self			):
      return [self.out_feature_channels[name] for name in self.out_features]







    def      UpperCamelCase_ (     self				,						*_lowerCamelCase				,						**_lowerCamelCase			):
      lowercase				       =			dict(inspect.signature(self.forward			).parameters			)
      lowercase				       =			{k: v for k, v in kwargs.items() if k in signature}
      return self(*_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						_lowerCamelCase = None				,						):
      raise NotImplementedError('This method should be implemented by the derived class.'			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			super().to_dict()
      lowercase				       =			output.pop('_out_features'			)
      lowercase				       =			output.pop('_out_indices'			)
      return output


class 			a	:







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_features







    @out_features.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase       ,	lowercase				       =			get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase				,						out_indices=_lowerCamelCase				,						stage_names=self.stage_names			)







    @property
    def      UpperCamelCase_ (     self			):
      return self._out_indices







    @out_indices.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase       ,	lowercase				       =			get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase				,						out_indices=_lowerCamelCase				,						stage_names=self.stage_names			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			super().to_dict()
      lowercase				       =			output.pop('_out_features'			)
      lowercase				       =			output.pop('_out_indices'			)
      return output










"""simple docstring"""



from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase						:    Optional[int]		=						TypeVar('T', bound=bool)

class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			value
      lowercase				       =			None
      lowercase				       =			None







    @property
    def      UpperCamelCase_ (     self			):
      return self._value







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						_lowerCamelCase			):
      if not roota:
        return roota

      if not roota:
        return roota

      if roota.value > roota.value:
        lowercase       ,	lowercase				       =			roota, roota

      if random.choice([True, False]			):
        lowercase       ,	lowercase				       =			roota.right, roota.left

      lowercase				       =			RandomizedHeapNode.merge(roota.left				,						_lowerCamelCase			)

      return roota


class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase = ()			):
      lowercase				       =			None

      if data:
        for item in data:
          self.insert(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			RandomizedHeapNode.merge(self._root				,						RandomizedHeapNode(_lowerCamelCase			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.top()

      if self._root is None:
        return None

      lowercase				       =			RandomizedHeapNode.merge(self._root.left				,						self._root.right			)

      return result







    def      UpperCamelCase_ (     self			):
      if not self._root:
        raise IndexError('Can\'t get top element for the empty heap.'			)
      return self._root.value







    def      UpperCamelCase_ (     self			):
      lowercase				       =			None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[]
      while self:
        result.append(self.pop()			)

      return result







    def __bool__(     self			):
      return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


