



'''simple docstring'''





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase							       =		logging.get_logger(__name__)

_UpperCamelCase							       =		{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  a_							      ="""oneformer"""
  a_							      ={"""hidden_size""": """hidden_dim"""}




  def __init__(				self   :			List[Any]    ,			_a   :			Optional[Dict] = None    ,			_a   :			int = 255    ,			_a   :			int = 150    ,			_a   :			int = 0.1    ,			_a   :			float = 2.0    ,			_a   :			float = 5.0    ,			_a   :			float = 5.0    ,			_a   :			float = 0.5    ,			_a   :			float = 0.07    ,			_a   :			int = 1_2544    ,			_a   :			float = 3.0    ,			_a   :			float = 0.75    ,			_a   :			float = 0.02    ,			_a   :			float = 1.0    ,			_a   :			float = 1e-05    ,			_a   :			bool = False    ,			_a   :			bool = True    ,			_a   :			bool = True    ,			_a   :			Optional[list] = [4, 8, 16, 32]    ,			_a   :			int = 77    ,			_a   :			int = 256    ,			_a   :			int = 77    ,			_a   :			int = 6    ,			_a   :			int = 4_9408    ,			_a   :			int = 2    ,			_a   :			int = 16    ,			_a   :			int = 256    ,			_a   :			int = 256    ,			_a   :			int = 256    ,			_a   :			int = 1024    ,			_a   :			str = "GN"    ,			_a   :			int = 6    ,			_a   :			int = 10    ,			_a   :			bool = True    ,			_a   :			int = 8    ,			_a   :			float = 0.1    ,			_a   :			int = 2048    ,			_a   :			bool = False    ,			_a   :			bool = False    ,			_a   :			int = 2    ,			_a   :			int = 4    ,			**_a   :			List[Any]    ,			)      ->      Dict:
    if backbone_config is None:
      logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'     )
      __lowerCamelCase : Optional[Any]					 =				CONFIG_MAPPING['swin'](
          image_size=224    ,			in_channels=3    ,			patch_size=4    ,			embed_dim=96    ,			depths=[2, 2, 6, 2]    ,			num_heads=[3, 6, 12, 24]    ,			window_size=7    ,			drop_path_rate=0.3    ,			use_absolute_embeddings=A_    ,			out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,			)
    elif isinstance(A_    ,			A_     ):
      __lowerCamelCase : int					 =				backbone_config.get('model_type'     )
      __lowerCamelCase : Dict					 =				CONFIG_MAPPING[backbone_model_type]
      __lowerCamelCase : List[Any]					 =				config_class.from_dict(A_     )

    __lowerCamelCase : Any					 =				backbone_config

    __lowerCamelCase : Dict					 =				ignore_value
    __lowerCamelCase : Optional[Any]					 =				num_queries
    __lowerCamelCase : str					 =				no_object_weight
    __lowerCamelCase : Optional[Any]					 =				class_weight
    __lowerCamelCase : str					 =				mask_weight
    __lowerCamelCase : Any					 =				dice_weight
    __lowerCamelCase : List[str]					 =				contrastive_weight
    __lowerCamelCase : Tuple					 =				contrastive_temperature
    __lowerCamelCase : Dict					 =				train_num_points
    __lowerCamelCase : Dict					 =				oversample_ratio
    __lowerCamelCase : List[str]					 =				importance_sample_ratio
    __lowerCamelCase : Tuple					 =				init_std
    __lowerCamelCase : Union[str, Any]					 =				init_xavier_std
    __lowerCamelCase : List[Any]					 =				layer_norm_eps
    __lowerCamelCase : List[Any]					 =				is_training
    __lowerCamelCase : Optional[Any]					 =				use_auxiliary_loss
    __lowerCamelCase : Optional[int]					 =				output_auxiliary_logits
    __lowerCamelCase : Dict					 =				strides
    __lowerCamelCase : str					 =				task_seq_len
    __lowerCamelCase : str					 =				text_encoder_width
    __lowerCamelCase : Optional[Any]					 =				text_encoder_context_length
    __lowerCamelCase : Optional[Any]					 =				text_encoder_num_layers
    __lowerCamelCase : List[Any]					 =				text_encoder_vocab_size
    __lowerCamelCase : Optional[int]					 =				text_encoder_proj_layers
    __lowerCamelCase : str					 =				text_encoder_n_ctx
    __lowerCamelCase : Tuple					 =				conv_dim
    __lowerCamelCase : Tuple					 =				mask_dim
    __lowerCamelCase : Optional[int]					 =				hidden_dim
    __lowerCamelCase : List[str]					 =				encoder_feedforward_dim
    __lowerCamelCase : int					 =				norm
    __lowerCamelCase : int					 =				encoder_layers
    __lowerCamelCase : Any					 =				decoder_layers
    __lowerCamelCase : Tuple					 =				use_task_norm
    __lowerCamelCase : Optional[Any]					 =				num_attention_heads
    __lowerCamelCase : str					 =				dropout
    __lowerCamelCase : Tuple					 =				dim_feedforward
    __lowerCamelCase : List[str]					 =				pre_norm
    __lowerCamelCase : Tuple					 =				enforce_input_proj
    __lowerCamelCase : Optional[int]					 =				query_dec_layers
    __lowerCamelCase : Any					 =				common_stride
    __lowerCamelCase : Any					 =				decoder_layers

    super().__init__(**A_     )




  def 		_lowercase  (				self   :			List[str]     )      ->      Any:
    __lowerCamelCase : Optional[Any]					 =				copy.deepcopy(self.__dict__     )
    __lowerCamelCase : List[Any]					 =				self.backbone_config.to_dict()
    __lowerCamelCase : List[str]					 =				self.__class__.model_type
    return output











'''simple docstring'''





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class       lowerCamelCase_					(       SCREAMING_SNAKE_CASE_  ):
  """simple docstring"""




  @abstractmethod
  def __lt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __gt__(				self   :			T    ,			_a   :			T     )      ->      bool:
    pass




  @abstractmethod
  def __eq__(				self   :			T    ,			_a   :			object     )      ->      bool:
    pass




_UpperCamelCase							       =		TypeVar('T', bound=Comparable)






class       lowerCamelCase_					(       Generic[T]  ):
  """simple docstring"""




  def __init__(				self   :			List[str]     )      ->      None:
    __lowerCamelCase : list[T]					 =				[]
    __lowerCamelCase : int					 =				0




  def __repr__(				self   :			int     )      ->      str:
    return str(self.h     )




  def 		_lowercase  (				self   :			str    ,			_a   :			int     )      ->      int | None:
    if child_idx > 0:
      return (child_idx - 1) // 2
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Optional[Any]					 =				2 * parent_idx + 1
    if left_child_index < self.heap_size:
      return left_child_index
    return None




  def 		_lowercase  (				self   :			Tuple    ,			_a   :			int     )      ->      int | None:
    __lowerCamelCase : Any					 =				2 * parent_idx + 2
    if right_child_index < self.heap_size:
      return right_child_index
    return None




  def 		_lowercase  (				self   :			int    ,			_a   :			int     )      ->      None:
    if index < self.heap_size:
      __lowerCamelCase : int					 =				index
      __lowerCamelCase : Dict					 =				self.left_child_idx(_a     )
      __lowerCamelCase : str					 =				self.right_child_idx(_a     )
      # check which child is larger than its parent
      if left_child is not None and self.h[left_child] > self.h[violation]:
        __lowerCamelCase : Optional[int]					 =				left_child
      if right_child is not None and self.h[right_child] > self.h[violation]:
        __lowerCamelCase : Union[str, Any]					 =				right_child
      # if violation indeed exists
      if violation != index:
        # swap to fix the violation
        __lowerCamelCase ,__lowerCamelCase : int					 =				self.h[index], self.h[violation]
        # fix the subsequent violation recursively if any
        self.max_heapify(_a     )




  def 		_lowercase  (				self   :			int    ,			_a   :			Iterable[T]     )      ->      None:
    __lowerCamelCase : Dict					 =				list(_a     )
    __lowerCamelCase : Any					 =				len(self.h     )
    if self.heap_size > 1:
      # max_heapify from right to left but exclude leaves (last level)
      for i in range(self.heap_size // 2 - 1    ,			-1    ,			-1     ):
        self.max_heapify(_a     )




  def 		_lowercase  (				self   :			int     )      ->      T:
    if self.heap_size >= 2:
      __lowerCamelCase : Any					 =				self.h[0]
      __lowerCamelCase : Dict					 =				self.h.pop(-1     )
      self.heap_size -= 1
      self.max_heapify(0     )
      return me
    elif self.heap_size == 1:
      self.heap_size -= 1
      return self.h.pop(-1     )
    else:
      raise Exception('Empty heap'     )




  def 		_lowercase  (				self   :			Optional[Any]    ,			_a   :			T     )      ->      None:
    self.h.append(_a     )
    __lowerCamelCase : List[Any]					 =				(self.heap_size - 1) // 2
    self.heap_size += 1
    while idx >= 0:
      self.max_heapify(_a     )
      __lowerCamelCase : Union[str, Any]					 =				(idx - 1) // 2




  def 		_lowercase  (				self   :			Tuple     )      ->      None:
    __lowerCamelCase : Optional[Any]					 =				self.heap_size
    for j in range(size - 1    ,			0    ,			-1     ):
      __lowerCamelCase ,__lowerCamelCase : List[str]					 =				self.h[j], self.h[0]
      self.heap_size -= 1
      self.max_heapify(0     )
    __lowerCamelCase : Tuple					 =				size


if __name__ == "__main__":
   import doctest

   # run doc test
   doctest.testmod()

   # demo
   for unsorted in [
       [0],
       [2],
       [3, 5],
       [5, 3],
       [5, 5],
       [0, 0, 0, 0],
       [1, 1, 1, 1],
       [2, 2, 3, 5],
       [0, 2, 2, 3, 5],
       [2, 5, 3, 0, 2, 3, 0, 3],
       [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
       [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
       [-45, -2, -5],
   ]:
      print(f'''unsorted array: {unsorted}''')

      _UpperCamelCase							       =		Heap()
      heap.build_max_heap(unsorted)
      print(f'''after build heap: {heap}''')

      print(f'''max value: {heap.extract_max()}''')
      print(f'''after max value removed: {heap}''')

      heap.insert(100)
      print(f'''after new value 100 inserted: {heap}''')

      heap.heap_sort()
      print(f'''heap-sorted array: {heap}\n''')






