






"""simple docstring"""



import copy

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


A__	:    Union[str, Any]			 = 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__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			"dpt"


			def __init__(				self					:  Any       ,	snake_case__					:  Dict=768       ,	snake_case__					:  Optional[Any]=12       ,	snake_case__					:  int=12       ,	snake_case__					:  Optional[Any]=3072       ,	snake_case__					:  Tuple="gelu"       ,	snake_case__					:  Union[str, Any]=0.0       ,	snake_case__					:  Union[str, Any]=0.0       ,	snake_case__					:  Optional[int]=0.02       ,	snake_case__					:  Optional[int]=1E-12       ,	snake_case__					:  Optional[int]=384       ,	snake_case__					:  Union[str, Any]=16       ,	snake_case__					:  Optional[Any]=3       ,	snake_case__					:  List[Any]=False       ,	snake_case__					:  Tuple=True       ,	snake_case__					:  Optional[Any]=[2, 5, 8, 11]       ,	snake_case__					:  List[Any]="project"       ,	snake_case__					:  Optional[int]=[4, 2, 1, 0.5]       ,	snake_case__					:  Tuple=[96, 192, 384, 768]       ,	snake_case__					:  Union[str, Any]=256       ,	snake_case__					:  Optional[int]=-1       ,	snake_case__					:  List[str]=False       ,	snake_case__					:  Optional[Any]=True       ,	snake_case__					:  Optional[int]=0.4       ,	snake_case__					:  int=255       ,	snake_case__					:  Optional[int]=0.1       ,	snake_case__					:  str=[1, 1024, 24, 24]       ,	snake_case__					:  Union[str, Any]=[0, 1]       ,	snake_case__					:  Union[str, Any]=None       ,	**snake_case__					:  List[Any]       ,	):
										super().__init__(**snake_case__ )

										lowerCamelCase_	:							Dict									=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[Any]									={
																								    "global_padding": "same",
																								    "layer_type": "bottleneck",
																								    "depths": [3, 4, 9],
																								    "out_features": ["stage1", "stage2", "stage3"],
																								    "embedding_dynamic_padding": True,
																								}
																								lowerCamelCase_	:							Any									=BitConfig(**snake_case__ )
																	elif isinstance(snake_case__       ,	snake_case__ ):
																								logger.info("Initializing the config with a `BiT` backbone." )
																								lowerCamelCase_	:							List[str]									=BitConfig(**snake_case__ )
																	elif isinstance(snake_case__       ,	snake_case__ ):
																								lowerCamelCase_	:							Tuple									=backbone_config
																	else:
																								raise ValueError(
																								    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )

																	lowerCamelCase_	:							Optional[int]									=backbone_featmap_shape
																	lowerCamelCase_	:							Union[str, Any]									=neck_ignore_stages

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

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


			def      UpperCAmelCase__						(				self					:  Optional[int] ):
										lowerCamelCase_	:							Union[str, Any]									=copy.deepcopy(self.__dict__ )

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

										lowerCamelCase_	:							Optional[int]									=self.__class__.model_type
										return output











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import _LazyModule


A__	:    Tuple			 = {'tokenization_byt5': ['ByT5Tokenizer']}


if TYPE_CHECKING:
						from .tokenization_byta import ByTaTokenizer
else:
						import sys

						A__	:    Dict			 = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



