import copy
from typing import Dict, List, Optional

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


a_							=      {
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

a_							=      logging.get_logger(__name__)


class 			UpperCAmelCase__    (		_lowercase					):



  """simple docstring"""






  lowerCAmelCase__		:	List[Any]    		= '''mask2former'''
  lowerCAmelCase__		:	Dict    		= ['''swin''']
  lowerCAmelCase__		:	Union[str, Any]    		= {'''hidden_size''': '''hidden_dim'''}






  def __init__(				self:			Any				,				__lowerCAmelCase:			Optional[Dict] = None				,				__lowerCAmelCase:			int = 256				,				__lowerCAmelCase:			int = 256				,				__lowerCAmelCase:			int = 256				,				__lowerCAmelCase:			int = 1_024				,				__lowerCAmelCase:			str = "relu"				,				__lowerCAmelCase:			int = 6				,				__lowerCAmelCase:			int = 10				,				__lowerCAmelCase:			int = 8				,				__lowerCAmelCase:			float = 0.0				,				__lowerCAmelCase:			int = 2_048				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			int = 4				,				__lowerCAmelCase:			int = 255				,				__lowerCAmelCase:			int = 100				,				__lowerCAmelCase:			float = 0.1				,				__lowerCAmelCase:			float = 2.0				,				__lowerCAmelCase:			float = 5.0				,				__lowerCAmelCase:			float = 5.0				,				__lowerCAmelCase:			int = 12_544				,				__lowerCAmelCase:			float = 3.0				,				__lowerCAmelCase:			float = 0.75				,				__lowerCAmelCase:			float = 0.02				,				__lowerCAmelCase:			float = 1.0				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			List[int] = [4, 8, 16, 32]				,				__lowerCAmelCase:			bool = None				,				**__lowerCAmelCase:			Optional[int]				,				) -> Optional[int]:




    '''simple docstring'''




    if backbone_config is None:
      logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."   )
      __UpperCAmelCase						    =       CONFIG_MAPPING["swin"](
          image_size=224				,				in_channels=3				,				patch_size=4				,				embed_dim=96				,				depths=[2, 2, 18, 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"]				,				)

    if isinstance(A_				,				A_   ):
      __UpperCAmelCase						    =       backbone_config.pop("model_type"   )
      __UpperCAmelCase						    =       CONFIG_MAPPING[backbone_model_type]
      __UpperCAmelCase						    =       config_class.from_dict(A_   )

    # verify that the backbone is supported
    if backbone_config.model_type not in self.backbones_supported:
      logger.warning_once(
          F'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '''
          F'''Supported model types: {",".join(self.backbones_supported   )}'''   )

    __UpperCAmelCase						    =       backbone_config
    __UpperCAmelCase						    =       feature_size
    __UpperCAmelCase						    =       mask_feature_size
    __UpperCAmelCase						    =       hidden_dim
    __UpperCAmelCase						    =       encoder_feedforward_dim
    __UpperCAmelCase						    =       activation_function
    __UpperCAmelCase						    =       encoder_layers
    __UpperCAmelCase						    =       decoder_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       dropout
    __UpperCAmelCase						    =       dim_feedforward
    __UpperCAmelCase						    =       pre_norm
    __UpperCAmelCase						    =       enforce_input_projection
    __UpperCAmelCase						    =       common_stride
    __UpperCAmelCase						    =       ignore_value
    __UpperCAmelCase						    =       num_queries
    __UpperCAmelCase						    =       no_object_weight
    __UpperCAmelCase						    =       class_weight
    __UpperCAmelCase						    =       mask_weight
    __UpperCAmelCase						    =       dice_weight
    __UpperCAmelCase						    =       train_num_points
    __UpperCAmelCase						    =       oversample_ratio
    __UpperCAmelCase						    =       importance_sample_ratio
    __UpperCAmelCase						    =       init_std
    __UpperCAmelCase						    =       init_xavier_std
    __UpperCAmelCase						    =       use_auxiliary_loss
    __UpperCAmelCase						    =       feature_strides
    __UpperCAmelCase						    =       output_auxiliary_logits
    __UpperCAmelCase						    =       decoder_layers

    super().__init__(**A_   )






  @classmethod
  def 	_UpperCAmelCase				(				cls:			List[str]				,				__lowerCAmelCase:			PretrainedConfig				,				**__lowerCAmelCase:			Dict   ) -> Union[str, Any]:




    '''simple docstring'''




    return cls(
        backbone_config=A_				,				**A_				,				)






  def 	_UpperCAmelCase				(				self:			List[str]   ) -> Dict[str, any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       copy.deepcopy(self.__dict__   )
    __UpperCAmelCase						    =       self.backbone_config.to_dict()
    __UpperCAmelCase						    =       self.__class__.model_type
    return output




def       __lowerCAmelCase      (						A_   : str					)    ->  Any:
  if not head:
    return True
  # split the list to two parts
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       head.next, head
  while fast and fast.next:
    __UpperCAmelCase						    =       fast.next.next
    __UpperCAmelCase						    =       slow.next
  __UpperCAmelCase						    =       slow.next
  __UpperCAmelCase						    =       None  # Don't forget here! But forget still works!
  # reverse the second part
  __UpperCAmelCase						    =       None
  while second:
    __UpperCAmelCase						    =       second.next
    __UpperCAmelCase						    =       node
    __UpperCAmelCase						    =       second
    __UpperCAmelCase						    =       nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    __UpperCAmelCase						    =       node.next
    __UpperCAmelCase						    =       head.next
  return True




def       __lowerCAmelCase      (						A_   : Optional[Any]					)    ->  Optional[Any]:
  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  __UpperCAmelCase						    =       __UpperCAmelCase						    =       __UpperCAmelCase						    =       head
  while fast and fast.next:
    __UpperCAmelCase    ,		__UpperCAmelCase						    =       fast.next.next, slow.next

  # 2. Push the second half into the stack
  __UpperCAmelCase						    =       [slow.val]
  while slow.next:
    __UpperCAmelCase						    =       slow.next
    stack.append(slow.val					)

  # 3. Comparison
  while stack:
    if stack.pop() != cur.val:
      return False
    __UpperCAmelCase						    =       cur.next

  return True





def       __lowerCAmelCase      (						A_   : int					)    ->  Optional[int]:
  if not head or not head.next:
    return True
  __UpperCAmelCase						    =       {}
  __UpperCAmelCase						    =       0
  while head:
    if head.val in d:
      d[head.val].append(A_					)
    else:
      __UpperCAmelCase						    =       [pos]
    __UpperCAmelCase						    =       head.next
    pos += 1
  __UpperCAmelCase						    =       pos - 1
  __UpperCAmelCase						    =       0
  for v in d.values():
    if len(A_					) % 2 != 0:
      middle += 1
    else:
      __UpperCAmelCase						    =       0
      for i in range(0						,	len(A_					)					):
        if v[i] + v[len(A_					) - 1 - step] != checksum:
          return False
        step += 1
    if middle > 1:
      return False
  return True



