





'''simple docstring'''
import copy

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


a__							:							List[str] =logging.get_logger(__name__)

a__							:							str ={
    '''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					="dpt"






 def __init__(       self       :							int							,	__A       :							Optional[int]=7_6_8							,	__A       :							Any=1_2							,	__A       :							Tuple=1_2							,	__A       :							Optional[Any]=3_0_7_2							,	__A       :							List[Any]="gelu"							,	__A       :							Tuple=0.0							,	__A       :							List[Any]=0.0							,	__A       :							List[Any]=0.02							,	__A       :							Dict=1e-12							,	__A       :							List[Any]=3_8_4							,	__A       :							List[str]=1_6							,	__A       :							Union[str, Any]=3							,	__A       :							Optional[int]=False							,	__A       :							str=True							,	__A       :							Union[str, Any]=[2, 5, 8, 1_1]							,	__A       :							Any="project"							,	__A       :							List[str]=[4, 2, 1, 0.5]							,	__A       :							Union[str, Any]=[9_6, 1_9_2, 3_8_4, 7_6_8]							,	__A       :							List[str]=2_5_6							,	__A       :							str=-1							,	__A       :							Dict=False							,	__A       :							Tuple=True							,	__A       :							Union[str, Any]=0.4							,	__A       :							int=2_5_5							,	__A       :							Tuple=0.1							,	__A       :							Optional[Any]=[1, 1_0_2_4, 2_4, 2_4]							,	__A       :							List[str]=[0, 1]							,	__A       :							Optional[Any]=None							,	**__A       :							Tuple							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  is_hybrid

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

               __UpperCamelCase       =  backbone_featmap_shape
               __UpperCamelCase       =  neck_ignore_stages

               if readout_type != "project":
                      raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'	)
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  []

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






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)

        if output["backbone_config"] is not None:
               __UpperCamelCase       =  self.backbone_config.to_dict()

        __UpperCamelCase       =  self.__class__.model_type
        return output







'''simple docstring'''
from __future__ import annotations

from typing import Any

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 pass






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Any	):
        __UpperCamelCase       =  data
        __UpperCamelCase       =  None






 def __iter__(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self
        __UpperCamelCase       =  []
        while node:
               if node in visited:
                      raise ContainsLoopError
               visited.append(__A	)
               yield node.data
               __UpperCamelCase       =  node.next_node






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        try:
               list(self	)
               return False
        except ContainsLoopError:
               return True


if __name__ == "__main__":
    a__							:							Dict =Node(1)
    a__							:							Optional[int] =Node(2)
    a__							:							List[str] =Node(3)
    a__							:							Optional[int] =Node(4)
    print(root_node.has_loop)  # False
    a__							:							str =root_node.next_node
    print(root_node.has_loop)  # True

    a__							:							Optional[int] =Node(5)
    a__							:							List[Any] =Node(6)
    a__							:							int =Node(5)
    a__							:							Tuple =Node(6)
    print(root_node.has_loop)  # False

    a__							:							str =Node(1)
    print(root_node.has_loop)  # False
