"""simple docstring"""


import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD


class 							a					(		nn.Module ):
    _snake_case  :	int
    _snake_case  :	int
    _snake_case  :	float  				=				0.0
    _snake_case  :	int  				=				1
    _snake_case  :	int  				=				1
    _snake_case  :	bool  				=				True
    _snake_case  :	bool  				=				False
    _snake_case  :	bool  				=				False
    _snake_case  :	bool  				=				False
    _snake_case  :	jnp.dtype  				=				jnp.floataa


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _UpperCAmelCase    			=   []
      _UpperCAmelCase    			=   []

      for i in range(self.num_layers       ):
        _UpperCAmelCase    			=   self.in_channels if i == 0 else self.out_channels

        _UpperCAmelCase    			=   FlaxResnetBlockaD(
            in_channels=__lowerCAmelCase ,					out_channels=self.out_channels ,					dropout_prob=self.dropout ,					dtype=self.dtype ,					)
        resnets.append(__lowerCAmelCase       )

        _UpperCAmelCase    			=   FlaxTransformeraDModel(
            in_channels=self.out_channels ,					n_heads=self.num_attention_heads ,					d_head=self.out_channels // self.num_attention_heads ,					depth=1 ,					use_linear_projection=self.use_linear_projection ,					only_cross_attention=self.only_cross_attention ,					use_memory_efficient_attention=self.use_memory_efficient_attention ,					dtype=self.dtype ,					)
        attentions.append(__lowerCAmelCase       )

      _UpperCAmelCase    			=   resnets
      _UpperCAmelCase    			=   attentions

      if self.add_downsample:
        _UpperCAmelCase    			=   FlaxDownsampleaD(self.out_channels ,					dtype=self.dtype       )


    def __call__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	List[Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	List[str]=True       ):
      _UpperCAmelCase    			=   ()

      for resnet, attn in zip(self.resnets ,					self.attentions       ):
        _UpperCAmelCase    			=   resnet(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )
        _UpperCAmelCase    			=   attn(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )
        output_states += (hidden_states,)

      if self.add_downsample:
        _UpperCAmelCase    			=   self.downsamplers_a(__lowerCAmelCase       )
        output_states += (hidden_states,)

      return hidden_states, output_states



class 							a					(		nn.Module ):
    _snake_case  :	int
    _snake_case  :	int
    _snake_case  :	float  				=				0.0
    _snake_case  :	int  				=				1
    _snake_case  :	bool  				=				True
    _snake_case  :	jnp.dtype  				=				jnp.floataa


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   []

      for i in range(self.num_layers       ):
        _UpperCAmelCase    			=   self.in_channels if i == 0 else self.out_channels

        _UpperCAmelCase    			=   FlaxResnetBlockaD(
            in_channels=__lowerCAmelCase ,					out_channels=self.out_channels ,					dropout_prob=self.dropout ,					dtype=self.dtype ,					)
        resnets.append(__lowerCAmelCase       )
      _UpperCAmelCase    			=   resnets

      if self.add_downsample:
        _UpperCAmelCase    			=   FlaxDownsampleaD(self.out_channels ,					dtype=self.dtype       )


    def __call__(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	Union[str, Any] ,					__lowerCAmelCase						:	Tuple ,					__lowerCAmelCase						:	int=True       ):
      _UpperCAmelCase    			=   ()

      for resnet in self.resnets:
        _UpperCAmelCase    			=   resnet(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )
        output_states += (hidden_states,)

      if self.add_downsample:
        _UpperCAmelCase    			=   self.downsamplers_a(__lowerCAmelCase       )
        output_states += (hidden_states,)

      return hidden_states, output_states



class 							a					(		nn.Module ):
    _snake_case  :	int
    _snake_case  :	int
    _snake_case  :	int
    _snake_case  :	float  				=				0.0
    _snake_case  :	int  				=				1
    _snake_case  :	int  				=				1
    _snake_case  :	bool  				=				True
    _snake_case  :	bool  				=				False
    _snake_case  :	bool  				=				False
    _snake_case  :	bool  				=				False
    _snake_case  :	jnp.dtype  				=				jnp.floataa


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   []
      _UpperCAmelCase    			=   []

      for i in range(self.num_layers       ):
        _UpperCAmelCase    			=   self.in_channels if (i == self.num_layers - 1) else self.out_channels
        _UpperCAmelCase    			=   self.prev_output_channel if i == 0 else self.out_channels

        _UpperCAmelCase    			=   FlaxResnetBlockaD(
            in_channels=resnet_in_channels + res_skip_channels ,					out_channels=self.out_channels ,					dropout_prob=self.dropout ,					dtype=self.dtype ,					)
        resnets.append(__lowerCAmelCase       )

        _UpperCAmelCase    			=   FlaxTransformeraDModel(
            in_channels=self.out_channels ,					n_heads=self.num_attention_heads ,					d_head=self.out_channels // self.num_attention_heads ,					depth=1 ,					use_linear_projection=self.use_linear_projection ,					only_cross_attention=self.only_cross_attention ,					use_memory_efficient_attention=self.use_memory_efficient_attention ,					dtype=self.dtype ,					)
        attentions.append(__lowerCAmelCase       )

      _UpperCAmelCase    			=   resnets
      _UpperCAmelCase    			=   attentions

      if self.add_upsample:
        _UpperCAmelCase    			=   FlaxUpsampleaD(self.out_channels ,					dtype=self.dtype       )


    def __call__(       self						:	Optional[int] ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	Tuple=True       ):
      for resnet, attn in zip(self.resnets ,					self.attentions       ):
        # pop res hidden states
        _UpperCAmelCase    			=   res_hidden_states_tuple[-1]
        _UpperCAmelCase    			=   res_hidden_states_tuple[:-1]
        _UpperCAmelCase    			=   jnp.concatenate((hidden_states, res_hidden_states) ,					axis=-1       )

        _UpperCAmelCase    			=   resnet(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )
        _UpperCAmelCase    			=   attn(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )

      if self.add_upsample:
        _UpperCAmelCase    			=   self.upsamplers_a(__lowerCAmelCase       )

      return hidden_states



class 							a					(		nn.Module ):
    _snake_case  :	int
    _snake_case  :	int
    _snake_case  :	int
    _snake_case  :	float  				=				0.0
    _snake_case  :	int  				=				1
    _snake_case  :	bool  				=				True
    _snake_case  :	jnp.dtype  				=				jnp.floataa


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   []

      for i in range(self.num_layers       ):
        _UpperCAmelCase    			=   self.in_channels if (i == self.num_layers - 1) else self.out_channels
        _UpperCAmelCase    			=   self.prev_output_channel if i == 0 else self.out_channels

        _UpperCAmelCase    			=   FlaxResnetBlockaD(
            in_channels=resnet_in_channels + res_skip_channels ,					out_channels=self.out_channels ,					dropout_prob=self.dropout ,					dtype=self.dtype ,					)
        resnets.append(__lowerCAmelCase       )

      _UpperCAmelCase    			=   resnets

      if self.add_upsample:
        _UpperCAmelCase    			=   FlaxUpsampleaD(self.out_channels ,					dtype=self.dtype       )


    def __call__(       self						:	Optional[Any] ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	Any ,					__lowerCAmelCase						:	Optional[Any] ,					__lowerCAmelCase						:	Any=True       ):
      for resnet in self.resnets:
        # pop res hidden states
        _UpperCAmelCase    			=   res_hidden_states_tuple[-1]
        _UpperCAmelCase    			=   res_hidden_states_tuple[:-1]
        _UpperCAmelCase    			=   jnp.concatenate((hidden_states, res_hidden_states) ,					axis=-1       )

        _UpperCAmelCase    			=   resnet(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )

      if self.add_upsample:
        _UpperCAmelCase    			=   self.upsamplers_a(__lowerCAmelCase       )

      return hidden_states



class 							a					(		nn.Module ):
    _snake_case  :	int
    _snake_case  :	float  				=				0.0
    _snake_case  :	int  				=				1
    _snake_case  :	int  				=				1
    _snake_case  :	bool  				=				False
    _snake_case  :	bool  				=				False
    _snake_case  :	jnp.dtype  				=				jnp.floataa


    def   lowerCAmelCase_	(       self						:	int       ):
      # there is always at least one resnet
      _UpperCAmelCase    			=   [
          FlaxResnetBlockaD(
              in_channels=self.in_channels ,					out_channels=self.in_channels ,					dropout_prob=self.dropout ,					dtype=self.dtype ,					)
      ]

      _UpperCAmelCase    			=   []

      for _ in range(self.num_layers       ):
        _UpperCAmelCase    			=   FlaxTransformeraDModel(
            in_channels=self.in_channels ,					n_heads=self.num_attention_heads ,					d_head=self.in_channels // self.num_attention_heads ,					depth=1 ,					use_linear_projection=self.use_linear_projection ,					use_memory_efficient_attention=self.use_memory_efficient_attention ,					dtype=self.dtype ,					)
        attentions.append(__lowerCAmelCase       )

        _UpperCAmelCase    			=   FlaxResnetBlockaD(
            in_channels=self.in_channels ,					out_channels=self.in_channels ,					dropout_prob=self.dropout ,					dtype=self.dtype ,					)
        resnets.append(__lowerCAmelCase       )

      _UpperCAmelCase    			=   resnets
      _UpperCAmelCase    			=   attentions


    def __call__(       self						:	int ,					__lowerCAmelCase						:	Dict ,					__lowerCAmelCase						:	Optional[int] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Dict=True       ):
      _UpperCAmelCase    			=   self.resnets[0](__lowerCAmelCase ,					__lowerCAmelCase       )
      for attn, resnet in zip(self.attentions ,					self.resnets[1:]       ):
        _UpperCAmelCase    			=   attn(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )
        _UpperCAmelCase    			=   resnet(__lowerCAmelCase ,					__lowerCAmelCase ,					deterministic=__lowerCAmelCase       )

      return hidden_states

"""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
