code: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE       = {
      "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
      "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
      "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
      "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
  }
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
      lowercase__		      =   '''longt5'''
      lowercase__		      =   ['''past_key_values''']
      lowercase__		      =   {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
  
  
  
  
      def __init__(					self		: List[str]			,				snake_case_		: List[str]=32_128			,				snake_case_		: str=512			,				snake_case_		: Union[str, Any]=64			,				snake_case_		: str=2_048			,				snake_case_		: Optional[int]=6			,				snake_case_		: Union[str, Any]=None			,				snake_case_		: Union[str, Any]=8			,				snake_case_		: List[Any]=127			,				snake_case_		: Dict=16			,				snake_case_		: List[str]=32			,				snake_case_		: Dict=128			,				snake_case_		: Optional[int]=0.1			,				snake_case_		: int=1e-6			,				snake_case_		: List[Any]=1.0			,				snake_case_		: int="relu"			,				snake_case_		: List[str]=True			,				snake_case_		: Optional[int]="local"			,				snake_case_		: Optional[int]=True			,				snake_case_		: Any=0			,				snake_case_		: int=1			,				**snake_case_		: Optional[int]			,				)   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       vocab_size
       A__ =       d_model
       A__ =       d_kv
       A__ =       d_ff
       A__ =       num_layers
       # default = symmetry
       A__ =       num_decoder_layers if num_decoder_layers is not None else self.num_layers
       A__ =       num_heads
       A__ =       local_radius
       A__ =       global_block_size
       A__ =       relative_attention_num_buckets
       A__ =       relative_attention_max_distance
       A__ =       dropout_rate
       A__ =       layer_norm_epsilon
       A__ =       initializer_factor
       A__ =       feed_forward_proj
       A__ =       encoder_attention_type
       A__ =       use_cache
  
       A__ =       self.feed_forward_proj.split("-"   )
       A__ =       act_info[-1]
       A__ =       act_info[0] == "gated"
  
       if len(snake_case_   ) > 1 and act_info[0] != "gated" or len(snake_case_   ) > 2:
        raise ValueError(
            F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
            "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
            "'gated-gelu' or 'relu'"   )
  
       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
        A__ =       "gelu_new"
  
       super().__init__(
           pad_token_id=snake_case_			,				eos_token_id=snake_case_			,				is_encoder_decoder=snake_case_			,				**snake_case_			,				)
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		A_							):
  
  
  
  
      @property
      def 							__magic_name__							(					self		: str   )   ->							Mapping[str, Mapping[int, str]]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       {
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
        A__ =       "past_encoder_sequence + sequence"
        A__ =       {0: "batch"}
        A__ =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
        A__ =       {0: "batch", 1: "decoder_sequence"}
        A__ =       {0: "batch", 1: "decoder_sequence"}
  
       if self.use_past:
        self.fill_with_past_key_values_(snake_case_			,				direction="inputs"   )
  
       return common_inputs
  
  
  
  
      @property
      def 							__magic_name__							(					self		: Any   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       return 13
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import Dict, Tuple, overload
  
  import torch
  import torch.types
  from torch import nn
  
  from . import residue_constants as rc
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import batched_gather
  
  
  
  
  
  
  
  @overload
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	torch.Tensor:
   ...
  
  
  
  
  
  
  
  @overload
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Tuple[torch.Tensor, torch.Tensor]:
   ...
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Dict:
   A__ =       aatype == rc.restype_order["G"]
   A__ =       rc.atom_order["CA"]
   A__ =       rc.atom_order["CB"]
   A__ =       torch.where(
       is_gly[..., None].expand(*((-1,) * len(is_gly.shape		))    ,				3		)    ,				all_atom_positions[..., ca_idx, :]    ,				all_atom_positions[..., cb_idx, :]    ,				)
  
   if all_atom_masks is not None:
    A__ =       torch.where(
        lowercase_    ,				all_atom_masks[..., ca_idx]    ,				all_atom_masks[..., cb_idx]    ,				)
    return pseudo_beta, pseudo_beta_mask
   else:
    return pseudo_beta
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	torch.Tensor:
   A__ =       batched_gather(
       lowercase_    ,				batch["residx_atom37_to_atom14"]    ,				dim=-2    ,				no_batch_dims=len(atomaa.shape[:-2]		)    ,				)
  
   A__ =       atomaa_data * batch["atom37_atom_exists"][..., None]
  
   return atomaa_data
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	torch.Tensor:
   A__ =       template_feats["template_aatype"]
   A__ =       template_feats["template_torsion_angles_sin_cos"]
   A__ =       template_feats["template_alt_torsion_angles_sin_cos"]
   A__ =       template_feats["template_torsion_angles_mask"]
   A__ =       torch.cat(
       [
           nn.functional.one_hot(lowercase_    ,				22		),
           torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]    ,				14		),
           alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]    ,				14		),
           torsion_angles_mask,
       ]    ,				dim=-1    ,				)
  
   return template_angle_feat
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_ = False    ,				lowercase_ = 1E-20    ,				lowercase_ = 1E8    ,				)		->	torch.Tensor:
   A__ =       batch["template_pseudo_beta_mask"]
   A__ =       template_mask[..., None] * template_mask[..., None, :]
  
   # Compute distogram (this seems to differ slightly from Alg. 5)
   A__ =       batch["template_pseudo_beta"]
   A__ =       torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2    ,				dim=-1    ,				keepdim=lowercase_		)
   A__ =       torch.linspace(lowercase_    ,				lowercase_    ,				lowercase_    ,				device=tpb.device		) ** 2
   A__ =       torch.cat([lower[1:], lower.new_tensor([inf]		)]    ,				dim=-1		)
   A__ =       ((dgram > lower) * (dgram < upper)).type(dgram.dtype		)
  
   A__ =       [dgram, template_mask_ad[..., None]]
  
   A__ =       nn.functional.one_hot(
       batch["template_aatype"]    ,				rc.restype_num + 2    ,				)
  
   A__ =       batch["template_aatype"].shape[-1]
   to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]    ,				lowercase_    ,				-1    ,				-1		)		)
   to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]    ,				-1    ,				lowercase_    ,				-1		)		)
  
   A__,   A__,   A__ =       [rc.atom_order[a] for a in ["N", "CA", "C"]]
   A__ =       Rigid.make_transform_from_reference(
       n_xyz=batch["template_all_atom_positions"][..., n, :]    ,				ca_xyz=batch["template_all_atom_positions"][..., ca, :]    ,				c_xyz=batch["template_all_atom_positions"][..., c, :]    ,				eps=lowercase_    ,				)
   A__ =       rigids.get_trans()[..., None, :, :]
   A__ =       rigids[..., None].invert_apply(lowercase_		)
  
   A__ =       torch.rsqrt(eps + torch.sum(rigid_vec**2    ,				dim=-1		)		)
  
   A__ =       batch["template_all_atom_mask"]
   A__ =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
   A__ =       template_mask[..., None] * template_mask[..., None, :]
  
   A__ =       inv_distance_scalar * template_mask_ad
   A__ =       rigid_vec * inv_distance_scalar[..., None]
  
   if not use_unit_vector:
    A__ =       unit_vector * 0.0
  
   to_concat.extend(torch.unbind(unit_vector[..., None, :]    ,				dim=-1		)		)
   to_concat.append(template_mask_ad[..., None]		)
  
   A__ =       torch.cat(lowercase_    ,				dim=-1		)
   A__ =       act * template_mask_ad[..., None]
  
   return act
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	torch.Tensor:
   A__ =       nn.functional.one_hot(batch["extra_msa"]    ,				23		)
   A__ =       [
       msa_ahot,
       batch["extra_has_deletion"].unsqueeze(-1		),
       batch["extra_deletion_value"].unsqueeze(-1		),
   ]
   return torch.cat(lowercase_    ,				dim=-1		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				)		->	Rigid:
   # [*, N, 8, 4, 4]
   A__ =       rrgdf[aatype, ...]
  
   # [*, N, 8] transformations, i.e.
   #   One [*, N, 8, 3, 3] rotation matrix and
   #   One [*, N, 8, 3]    translation matrix
   A__ =       r.from_tensor_axa(lowercase_		)
  
   A__ =       alpha.new_zeros((*((1,) * len(alpha.shape[:-1]		)), 2)		)
   A__ =       1
  
   # [*, N, 8, 2]
   A__ =       torch.cat([bb_rot.expand(*alpha.shape[:-2]    ,				-1    ,				-1		), alpha]    ,				dim=-2		)
  
   # [*, N, 8, 3, 3]
   # Produces rotation matrices of the form:
   # [
   #   [1, 0  , 0  ],
   #   [0, a_2,-a_1],
   #   [0, a_1, a_2]
   # ]
   # This follows the original code rather than the supplement, which uses
   # different indices.
  
   A__ =       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape		)
   A__ =       1
   A__ =       alpha[..., 1]
   A__ =       -alpha[..., 0]
   A__ =       alpha
  
   A__ =       default_r.compose(Rigid(Rotation(rot_mats=lowercase_		)    ,				lowercase_		)		)
  
   A__ =       all_frames[..., 5]
   A__ =       all_frames[..., 6]
   A__ =       all_frames[..., 7]
  
   A__ =       all_frames[..., 4]
   A__ =       chia_frame_to_bb.compose(lowercase_		)
   A__ =       chia_frame_to_bb.compose(lowercase_		)
   A__ =       chia_frame_to_bb.compose(lowercase_		)
  
   A__ =       Rigid.cat(
       [
           all_frames[..., :5],
           chia_frame_to_bb.unsqueeze(-1		),
           chia_frame_to_bb.unsqueeze(-1		),
           chia_frame_to_bb.unsqueeze(-1		),
       ]    ,				dim=-1    ,				)
  
   A__ =       r[..., None].compose(lowercase_		)
  
   return all_frames_to_global
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				)		->	torch.Tensor:
   # [*, N, 14]
   A__ =       group_idx[aatype, ...]
  
   # [*, N, 14, 8]
   A__ =       nn.functional.one_hot(
       lowercase_    ,				num_classes=default_frames.shape[-3]    ,				)
  
   # [*, N, 14, 8]
   A__ =       r[..., None, :] * group_mask_one_hot
  
   # [*, N, 14]
   A__ =       t_atoms_to_global.map_tensor_fn(lambda lowercase_		: torch.sum(lowercase_    ,				dim=-1		)		)
  
   # [*, N, 14, 1]
   A__ =       atom_mask[aatype, ...].unsqueeze(-1		)
  
   # [*, N, 14, 3]
   A__ =       lit_positions[aatype, ...]
   A__ =       t_atoms_to_global.apply(lowercase_		)
   A__ =       pred_positions * atom_mask
  
   return pred_positions
  
  
  
  
  
  