
'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A__   :		str 		=logging.get_logger(__name__)

A__   :		List[Any] 		={
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''dpt'''
 def __init__(   self			:    Optional[Any]				,   lowerCamelCase			:    List[Any]=7_68				,   lowerCamelCase			:    Union[str, Any]=12				,   lowerCamelCase			:    List[Any]=12				,   lowerCamelCase			:    List[str]=30_72				,   lowerCamelCase			:    Any="gelu"				,   lowerCamelCase			:    List[Any]=0.0				,   lowerCamelCase			:    str=0.0				,   lowerCamelCase			:    Union[str, Any]=0.02				,   lowerCamelCase			:    Tuple=1e-1_2				,   lowerCamelCase			:    Any=3_84				,   lowerCamelCase			:    Tuple=16				,   lowerCamelCase			:    Any=3				,   lowerCamelCase			:    Any=False				,   lowerCamelCase			:    List[str]=True				,   lowerCamelCase			:    Union[str, Any]=[2, 5, 8, 11]				,   lowerCamelCase			:    Any="project"				,   lowerCamelCase			:    Tuple=[4, 2, 1, 0.5]				,   lowerCamelCase			:    List[Any]=[96, 1_92, 3_84, 7_68]				,   lowerCamelCase			:    str=2_56				,   lowerCamelCase			:    int=-1				,   lowerCamelCase			:    List[Any]=False				,   lowerCamelCase			:    Union[str, Any]=True				,   lowerCamelCase			:    Optional[Any]=0.4				,   lowerCamelCase			:    int=2_55				,   lowerCamelCase			:    str=0.1				,   lowerCamelCase			:    Tuple=[1, 10_24, 24, 24]				,   lowerCamelCase			:    Tuple=[0, 1]				,   lowerCamelCase			:    Any=None				,   **lowerCamelCase			:    Any				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )

   __A		:     int												=		hidden_size
   __A		:     Dict												=		is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone."""       )
       __A		:     Optional[int]												=		{
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       __A		:     Tuple												=		BitConfig(**lowerCamelCase       )
     elif isinstance(lowerCamelCase				,   lowerCamelCase       ):
       logger.info("""Initializing the config with a `BiT` backbone."""       )
       __A		:     str												=		BitConfig(**lowerCamelCase       )
     elif isinstance(lowerCamelCase				,   lowerCamelCase       ):
       __A		:     Union[str, Any]												=		backbone_config
     else:
       raise ValueError(
           f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."       )

     __A		:     Tuple												=		backbone_featmap_shape
     __A		:     Dict												=		neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""       )
   else:
     __A		:     List[str]												=		None
     __A		:     Dict												=		None
     __A		:     Optional[int]												=		[]

   __A		:     Union[str, Any]												=		num_hidden_layers
   __A		:     Tuple												=		num_attention_heads
   __A		:     Tuple												=		intermediate_size
   __A		:     Union[str, Any]												=		hidden_act
   __A		:     Optional[int]												=		hidden_dropout_prob
   __A		:     List[str]												=		attention_probs_dropout_prob
   __A		:     str												=		initializer_range
   __A		:     List[str]												=		layer_norm_eps
   __A		:     Any												=		image_size
   __A		:     Dict												=		patch_size
   __A		:     Tuple												=		num_channels
   __A		:     List[Any]												=		qkv_bias
   __A		:     Any												=		backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""       )
   __A		:     Any												=		readout_type
   __A		:     int												=		reassemble_factors
   __A		:     Tuple												=		neck_hidden_sizes
   __A		:     Union[str, Any]												=		fusion_hidden_size
   __A		:     Any												=		head_in_index
   __A		:     int												=		use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   __A		:     List[str]												=		use_auxiliary_head
   __A		:     str												=		auxiliary_loss_weight
   __A		:     Dict												=		semantic_loss_ignore_index
   __A		:     List[str]												=		semantic_classifier_dropout






 def 		lowercase_(   self			:    str       ):



   """simple docstring"""





   __A		:     str												=		copy.deepcopy(self.__dict__       )

   if output["backbone_config"] is not None:
     __A		:     Union[str, Any]												=		self.backbone_config.to_dict()

   __A		:     Tuple												=		self.__class__.model_type
   return output





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[Any]:





  """simple docstring"""

  if not head:
    return True
  # split the list to two parts
  __A							,					__A		:     Any												=		head.next, head
  while fast and fast.next:
    __A		:     List[str]												=		fast.next.next
    __A		:     List[Any]												=		slow.next
  __A		:     int												=		slow.next
  __A		:     List[str]												=		None  # Don't forget here! But forget still works!
  # reverse the second part
  __A		:     Dict												=		None
  while second:
    __A		:     Dict												=		second.next
    __A		:     str												=		node
    __A		:     int												=		second
    __A		:     Optional[int]												=		nxt
  # compare two parts
  # second part has the same or one less node
  while node:
    if node.val != head.val:
      return False
    __A		:     List[Any]												=		node.next
    __A		:     Tuple												=		head.next
  return True







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       Optional[int]:





  """simple docstring"""

  if not head or not head.next:
    return True

  # 1. Get the midpoint (slow)
  __A		:     int												=		head
  while fast and fast.next:
    __A							,					__A		:     Optional[int]												=		fast.next.next, slow.next

  # 2. Push the second half into the stack
  __A		:     Union[str, Any]												=		[slow.val]
  while slow.next:
    __A		:     List[str]												=		slow.next
    stack.append(slow.val )

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

  return True







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Any:





  """simple docstring"""

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



