



import copy

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


_lowercase    	=		logging.get_logger(__name__)

_lowercase    	=		{
    "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      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''dpt'''




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

         A__					=       hidden_size
         A__					=       is_hybrid

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

             A__					=       backbone_featmap_shape
             A__					=       neck_ignore_stages

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

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




     def 		snake_case_		(					self):
         A__					=       copy.deepcopy(self.__dict__)

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

         A__					=       self.__class__.model_type
         return output





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase    	=		logging.get_logger(__name__)



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''timm_backbone'''




     def __init__(					self			,    a__=None			,    a__=3			,    a__=True			,    a__=True			,    a__=None			,    **a__			,    ):
         super().__init__(**a__)
         A__					=       backbone
         A__					=       num_channels
         A__					=       features_only
         A__					=       use_pretrained_backbone
         A__					=       True
         A__					=       out_indices if out_indices is not None else (-1,)
