





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin



@dataclass
class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42





class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  @register_to_config
  def __init__(    self			:		Optional[int] ,  lowerCAmelCase_			:		int = 1_6 ,  lowerCAmelCase_			:		int = 8_8 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		int = 1 ,  lowerCAmelCase_			:		float = 0.0 ,  lowerCAmelCase_			:		int = 3_2 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		str = "geglu" ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		bool = True ,  )							->						Dict:
     super().__init__()
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     attention_head_dim
     __lowerCAmelCase							     =     num_attention_heads * attention_head_dim

     __lowerCAmelCase							     =     in_channels

     __lowerCAmelCase							     =     torch.nn.GroupNorm(num_groups=lowerCAmelCase_ ,  num_channels=lowerCAmelCase_ ,  eps=1e-6 ,  affine=lowerCAmelCase_					)
     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)

     # 3. Define transformers blocks
     __lowerCAmelCase							     =     nn.ModuleList(
         [
             BasicTransformerBlock(
                 lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  dropout=lowerCAmelCase_ ,  cross_attention_dim=lowerCAmelCase_ ,  activation_fn=lowerCAmelCase_ ,  attention_bias=lowerCAmelCase_ ,  double_self_attention=lowerCAmelCase_ ,  norm_elementwise_affine=lowerCAmelCase_ ,  )
             for d in range(lowerCAmelCase_					)
         ]					)

     __lowerCAmelCase							     =     nn.Linear(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[int]=1 ,  lowerCAmelCase_			:		Optional[int]=None ,  lowerCAmelCase_			:		bool = True ,  )							->						str:
     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase     ,     __lowerCAmelCase							     =     hidden_states.shape
     __lowerCAmelCase							     =     batch_frames // num_frames

     __lowerCAmelCase							     =     hidden_states

     __lowerCAmelCase							     =     hidden_states[None, :].reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  2 ,  1 ,  3 ,  4					)

     __lowerCAmelCase							     =     self.norm(lowerCAmelCase_					)
     __lowerCAmelCase							     =     hidden_states.permute(0 ,  3 ,  4 ,  2 ,  1					).reshape(batch_size * height * width ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.proj_in(lowerCAmelCase_					)

     # 2. Blocks
     for block in self.transformer_blocks:
        __lowerCAmelCase							     =     block(
            lowerCAmelCase_ ,  encoder_hidden_states=lowerCAmelCase_ ,  timestep=lowerCAmelCase_ ,  cross_attention_kwargs=lowerCAmelCase_ ,  class_labels=lowerCAmelCase_ ,  )

     # 3. Output
     __lowerCAmelCase							     =     self.proj_out(lowerCAmelCase_					)
     __lowerCAmelCase							     =     (
         hidden_states[None, None, :]
         .reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
         .permute(0 ,  3 ,  4 ,  1 ,  2					)
         .contiguous()
     )
     __lowerCAmelCase							     =     hidden_states.reshape(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     hidden_states + residual

     if not return_dict:
        return (output,)

     return TransformerTemporalModelOutput(sample=lowerCAmelCase_					)








from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map

def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []
   __lowerCAmelCase							     =     []

   for rt in rc.restypes:
      __lowerCAmelCase							     =     rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
      restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
      __lowerCAmelCase							     =     {name: i for i, name in enumerate(lowerCAmelCase_     )}
      restype_atomaa_to_atomaa_list.append(
          [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

      restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

   # Add dummy mapping for restype 'UNK'
   restype_atomaa_to_atomaa_list.append([0] * 14     )
   restype_atomaa_to_atomaa_list.append([0] * 37     )
   restype_atomaa_mask_list.append([0.0] * 14     )

   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.intaa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     torch.tensor(
       lowerCAmelCase_,     dtype=torch.floataa,     device=protein['aatype'].device,     )
   __lowerCAmelCase							     =     protein['aatype'].to(torch.long     )

   # create the mapping for (residx, atom14) --> atom37, i.e. an array
   # with shape (num_res, 14) containing the atom37 indices for this protein
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]

   __lowerCAmelCase							     =     residx_atomaa_mask
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   __lowerCAmelCase							     =     restype_atomaa_to_atomaa[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_to_atomaa.long()

   # create the corresponding mask
   __lowerCAmelCase							     =     torch.zeros([21, 37],     dtype=torch.floataa,     device=protein['aatype'].device     )
   for restype, restype_letter in enumerate(rc.restypes     ):
      __lowerCAmelCase							     =     rc.restype_atoa[restype_letter]
      __lowerCAmelCase							     =     rc.residue_atoms[restype_name]
      for atom_name in atom_names:
         __lowerCAmelCase							     =     rc.atom_order[atom_name]
         __lowerCAmelCase							     =     1

   __lowerCAmelCase							     =     restype_atomaa_mask[protein_aatype]
   __lowerCAmelCase							     =     residx_atomaa_mask

   return protein




def   a_   (			lowerCAmelCase_				:      Dict[str, torch.Tensor]     ):
   __lowerCAmelCase							     =     tree_map(lambda lowerCAmelCase_     : torch.tensor(lowerCAmelCase_,     device=batch['aatype'].device     ),     lowerCAmelCase_,     np.ndarray     )
   __lowerCAmelCase							     =     tensor_tree_map(lambda lowerCAmelCase_     : np.array(lowerCAmelCase_     ),     make_atomaa_masks(lowerCAmelCase_     )     )
   return out

