



import copy

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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						{
    "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 	lowerCamelCase      (						_UpperCamelCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Optional[Any]									=  "dpt"



					def __init__(			self    ,	lowerCAmelCase=768    ,	lowerCAmelCase=12    ,	lowerCAmelCase=12    ,	lowerCAmelCase=3072    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=0.0    ,	lowerCAmelCase=0.02    ,	lowerCAmelCase=1e-1_2    ,	lowerCAmelCase=384    ,	lowerCAmelCase=16    ,	lowerCAmelCase=3    ,	lowerCAmelCase=False    ,	lowerCAmelCase=True    ,	lowerCAmelCase=[2, 5, 8, 11]    ,	lowerCAmelCase="project"    ,	lowerCAmelCase=[4, 2, 1, 0.5]    ,	lowerCAmelCase=[96, 192, 384, 768]    ,	lowerCAmelCase=256    ,	lowerCAmelCase=-1    ,	lowerCAmelCase=False    ,	lowerCAmelCase=True    ,	lowerCAmelCase=0.4    ,	lowerCAmelCase=255    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=[1, 1024, 24, 24]    ,	lowerCAmelCase=[0, 1]    ,	lowerCAmelCase=None    ,	**lowerCAmelCase    ,	):
							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    A__		(			self     ):
							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






from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin



class 	lowerCamelCase      (						lowercase__,     lowercase__  ):







					'''simple docstring'''







					@register_to_config
					def __init__(			self    ,	lowerCAmelCase = 768    ,	):
							super().__init__()

							UpperCAmelCase_						=				nn.Parameter(torch.zeros(1    ,	lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(torch.ones(1    ,	lowerCAmelCase     )     )



					def    A__		(			self    ,	lowerCAmelCase = None    ,	lowerCAmelCase = None    ,	):
							UpperCAmelCase_						=				nn.Parameter(self.mean.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							UpperCAmelCase_						=				nn.Parameter(self.std.to(lowerCAmelCase     ).to(lowerCAmelCase     )     )
							return self



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds - self.mean) * 1.0 / self.std
							return embeds



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				(embeds * self.std) + self.mean
							return embeds

