






'''simple docstring'''







import copy

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


_A			:    int					  =logging.get_logger(__name__)

_A			:    Tuple					  ={
    '''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 _lowercase			(       _lowercase	):
							a          = """dpt"""




							def __init__(							self:       Union[str, Any]    ,							UpperCamelCase__:       List[str]=768    ,							UpperCamelCase__:       List[str]=12    ,							UpperCamelCase__:       Optional[Any]=12    ,							UpperCamelCase__:       int=3_072    ,							UpperCamelCase__:       List[Any]="gelu"    ,							UpperCamelCase__:       Optional[int]=0.0    ,							UpperCamelCase__:       Dict=0.0    ,							UpperCamelCase__:       List[str]=0.02    ,							UpperCamelCase__:       int=1e-12    ,							UpperCamelCase__:       Dict=384    ,							UpperCamelCase__:       int=16    ,							UpperCamelCase__:       str=3    ,							UpperCamelCase__:       Any=False    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Optional[Any]=[2, 5, 8, 11]    ,							UpperCamelCase__:       Optional[Any]="project"    ,							UpperCamelCase__:       Tuple=[4, 2, 1, 0.5]    ,							UpperCamelCase__:       Union[str, Any]=[96, 192, 384, 768]    ,							UpperCamelCase__:       Optional[Any]=256    ,							UpperCamelCase__:       List[Any]=-1    ,							UpperCamelCase__:       Optional[Any]=False    ,							UpperCamelCase__:       List[str]=True    ,							UpperCamelCase__:       List[Any]=0.4    ,							UpperCamelCase__:       Union[str, Any]=255    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       Any=[1, 1_024, 24, 24]    ,							UpperCamelCase__:       Optional[int]=[0, 1]    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														super().__init__(**UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= hidden_size
														lowerCamelCase__      :			str 						= is_hybrid

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

																					lowerCamelCase__      :			List[str] 						= backbone_featmap_shape
																					lowerCamelCase__      :			List[str] 						= neck_ignore_stages

																					if readout_type != "project":
																												raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""  )
														else:
																					lowerCamelCase__      :			Union[str, Any] 						= None
																					lowerCamelCase__      :			str 						= None
																					lowerCamelCase__      :			List[Any] 						= []

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




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[str] 						= copy.deepcopy(self.__dict__  )

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

														lowerCamelCase__      :			Any 						= self.__class__.model_type
														return output









'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

