
'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block







@dataclass
class UpperCAmelCase__      (					lowerCAmelCase_      ):





  """simple docstring"""

  __UpperCAmelCase							:							torch.FloatTensor







class UpperCAmelCase__      (					lowerCAmelCase_       ,							lowerCAmelCase_      ):





  """simple docstring"""

  @register_to_config
  def __init__(						self :	Any				,_a :	int = 6_5536				,_a :	Optional[int] = None				,_a :	int = 2				,_a :	int = 2				,_a :	int = 0				,_a :	str = "fourier"				,_a :	bool = True				,_a :	bool = False				,_a :	float = 0.0				,_a :	Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,_a :	Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,_a :	Tuple[str] = "UNetMidBlock1D"				,_a :	str = None				,_a :	Tuple[int] = (32, 32, 64)				,_a :	str = None				,_a :	int = 8				,_a :	int = 1				,_a :	bool = False				,):


     '''simple docstring'''






     super().__init__()
     _a							:      Dict     =  sample_size

     # time
     if time_embedding_type == "fourier":
        _a							:      List[str]     =  GaussianFourierProjection(
            embedding_size=8				,set_W_to_weight=__lowerCAmelCase				,log=__lowerCAmelCase				,flip_sin_to_cos=__lowerCAmelCase			)
        _a							:      Tuple     =  2 * block_out_channels[0]
     elif time_embedding_type == "positional":
        _a							:      str     =  Timesteps(
            block_out_channels[0]				,flip_sin_to_cos=__lowerCAmelCase				,downscale_freq_shift=__lowerCAmelCase			)
        _a							:      Any     =  block_out_channels[0]

     if use_timestep_embedding:
        _a							:      Optional[Any]     =  block_out_channels[0] * 4
        _a							:      str     =  TimestepEmbedding(
            in_channels=__lowerCAmelCase				,time_embed_dim=__lowerCAmelCase				,act_fn=__lowerCAmelCase				,out_dim=block_out_channels[0]				,)

     _a							:      Optional[Any]     =  nn.ModuleList([]			)
     _a							:      Union[str, Any]     =  None
     _a							:      List[Any]     =  nn.ModuleList([]			)
     _a							:      int     =  None

     # down
     _a							:      Optional[int]     =  in_channels
     for i, down_block_type in enumerate(__lowerCAmelCase			):
        _a							:      int     =  output_channel
        _a							:      Optional[Any]     =  block_out_channels[i]

        if i == 0:
           input_channel += extra_in_channels

        _a							:      Any     =  i == len(__lowerCAmelCase			) - 1

        _a							:      Tuple     =  get_down_block(
            __lowerCAmelCase				,num_layers=__lowerCAmelCase				,in_channels=__lowerCAmelCase				,out_channels=__lowerCAmelCase				,temb_channels=block_out_channels[0]				,add_downsample=not is_final_block or downsample_each_block				,)
        self.down_blocks.append(__lowerCAmelCase			)

     # mid
     _a							:      Union[str, Any]     =  get_mid_block(
         __lowerCAmelCase				,in_channels=block_out_channels[-1]				,mid_channels=block_out_channels[-1]				,out_channels=block_out_channels[-1]				,embed_dim=block_out_channels[0]				,num_layers=__lowerCAmelCase				,add_downsample=__lowerCAmelCase				,)

     # up
     _a							:      Dict     =  list(reversed(__lowerCAmelCase			)			)
     _a							:      str     =  reversed_block_out_channels[0]
     if out_block_type is None:
        _a							:      Union[str, Any]     =  out_channels
     else:
        _a							:      Any     =  block_out_channels[0]

     for i, up_block_type in enumerate(__lowerCAmelCase			):
        _a							:      str     =  output_channel
        _a							:      Any     =  (
            reversed_block_out_channels[i + 1] if i < len(__lowerCAmelCase			) - 1 else final_upsample_channels
        )

        _a							:      Union[str, Any]     =  i == len(__lowerCAmelCase			) - 1

        _a							:      Optional[Any]     =  get_up_block(
            __lowerCAmelCase				,num_layers=__lowerCAmelCase				,in_channels=__lowerCAmelCase				,out_channels=__lowerCAmelCase				,temb_channels=block_out_channels[0]				,add_upsample=not is_final_block				,)
        self.up_blocks.append(__lowerCAmelCase			)
        _a							:      int     =  output_channel

     # out
     _a							:      Union[str, Any]     =  norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,32			)
     _a							:      List[Any]     =  get_out_block(
         out_block_type=__lowerCAmelCase				,num_groups_out=__lowerCAmelCase				,embed_dim=block_out_channels[0]				,out_channels=__lowerCAmelCase				,act_fn=__lowerCAmelCase				,fc_dim=block_out_channels[-1] // 4				,)


  def     __lowercase						(						self :	Dict				,_a :	torch.FloatTensor				,_a :	Union[torch.Tensor, float, int]				,_a :	bool = True				,):


     '''simple docstring'''






     _a							:      Optional[Any]     =  timestep
     if not torch.is_tensor(__lowerCAmelCase			):
        _a							:      List[Any]     =  torch.tensor([timesteps]				,dtype=torch.long				,device=sample.device			)
     elif torch.is_tensor(__lowerCAmelCase			) and len(timesteps.shape			) == 0:
        _a							:      Optional[Any]     =  timesteps[None].to(sample.device			)

     _a							:      Tuple     =  self.time_proj(__lowerCAmelCase			)
     if self.config.use_timestep_embedding:
        _a							:      Any     =  self.time_mlp(__lowerCAmelCase			)
     else:
        _a							:      List[str]     =  timestep_embed[..., None]
        _a							:      Optional[Any]     =  timestep_embed.repeat([1, 1, sample.shape[2]]			).to(sample.dtype			)
        _a							:      Dict     =  timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])			)

     # 2. down
     _a							:      Union[str, Any]     =  ()
     for downsample_block in self.down_blocks:
        _a,    _a							:      List[Any]     =  downsample_block(hidden_states=__lowerCAmelCase				,temb=__lowerCAmelCase			)
        down_block_res_samples += res_samples

     # 3. mid
     if self.mid_block:
        _a							:      str     =  self.mid_block(__lowerCAmelCase				,__lowerCAmelCase			)

     # 4. up
     for i, upsample_block in enumerate(self.up_blocks			):
        _a							:      Optional[Any]     =  down_block_res_samples[-1:]
        _a							:      str     =  down_block_res_samples[:-1]
        _a							:      List[Any]     =  upsample_block(__lowerCAmelCase				,res_hidden_states_tuple=__lowerCAmelCase				,temb=__lowerCAmelCase			)

     # 5. post-process
     if self.out_block:
        _a							:      Optional[Any]     =  self.out_block(__lowerCAmelCase				,__lowerCAmelCase			)

     if not return_dict:
        return (sample,)

     return UNetaDOutput(sample=__lowerCAmelCase			)




"""simple docstring"""


import copy

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


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """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 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				'dpt'


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any]=768 ,					__lowerCAmelCase						:	str=12 ,					__lowerCAmelCase						:	Optional[int]=12 ,					__lowerCAmelCase						:	Dict=3072 ,					__lowerCAmelCase						:	List[Any]="gelu" ,					__lowerCAmelCase						:	Tuple=0.0 ,					__lowerCAmelCase						:	str=0.0 ,					__lowerCAmelCase						:	List[Any]=0.02 ,					__lowerCAmelCase						:	Optional[Any]=1e-1_2 ,					__lowerCAmelCase						:	List[str]=384 ,					__lowerCAmelCase						:	Tuple=16 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Optional[Any]=True ,					__lowerCAmelCase						:	Optional[Any]=[2, 5, 8, 11] ,					__lowerCAmelCase						:	Any="project" ,					__lowerCAmelCase						:	Dict=[4, 2, 1, 0.5] ,					__lowerCAmelCase						:	int=[96, 192, 384, 768] ,					__lowerCAmelCase						:	Dict=256 ,					__lowerCAmelCase						:	Optional[Any]=-1 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	List[Any]=0.4 ,					__lowerCAmelCase						:	List[str]=255 ,					__lowerCAmelCase						:	List[Any]=0.1 ,					__lowerCAmelCase						:	Optional[int]=[1, 1024, 24, 24] ,					__lowerCAmelCase						:	List[Any]=[0, 1] ,					__lowerCAmelCase						:	Dict=None ,					**__lowerCAmelCase						:	str ,					):
      super().__init__(**__lowerCAmelCase       )

      _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(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          _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[str]       ):
      _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
