


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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     ...
@overload
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     ...
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       aatype == rc.restype_order['''G''']
     snake_case					=       rc.atom_order['''CA''']
     snake_case					=       rc.atom_order['''CB''']
     snake_case					=       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:
          snake_case					=       torch.where(
              UpperCamelCase_							,all_atom_masks[..., ca_idx]							,all_atom_masks[..., cb_idx]							,)
          return pseudo_beta, pseudo_beta_mask
     else:
          return pseudo_beta
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       batched_gather(
         UpperCamelCase_							,batch['''residx_atom37_to_atom14''']							,dim=-2							,no_batch_dims=len(atomaa.shape[:-2]       )							,)

     snake_case					=       atomaa_data * batch['''atom37_atom_exists'''][..., None]

     return atomaa_data
def     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       template_feats['''template_aatype''']
     snake_case					=       template_feats['''template_torsion_angles_sin_cos''']
     snake_case					=       template_feats['''template_alt_torsion_angles_sin_cos''']
     snake_case					=       template_feats['''template_torsion_angles_mask''']
     snake_case					=       torch.cat(
         [
             nn.functional.one_hot(UpperCamelCase_							,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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_ = False							,UpperCamelCase_ = 1e-2_0							,UpperCamelCase_ = 1e8							,):



     """simple docstring"""






     snake_case					=       batch['''template_pseudo_beta_mask''']
     snake_case					=       template_mask[..., None] * template_mask[..., None, :]

     # Compute distogram (this seems to differ slightly from Alg. 5)
     snake_case					=       batch['''template_pseudo_beta''']
     snake_case					=       torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2							,dim=-1							,keepdim=UpperCamelCase_       )
     snake_case					=       torch.linspace(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,device=tpb.device       ) ** 2
     snake_case					=       torch.cat([lower[1:], lower.new_tensor([inf]       )]							,dim=-1       )
     snake_case					=       ((dgram > lower) * (dgram < upper)).type(dgram.dtype       )

     snake_case					=       [dgram, template_mask_ad[..., None]]

     snake_case					=       nn.functional.one_hot(
         batch['''template_aatype''']							,rc.restype_num + 2							,)

     snake_case					=       batch['''template_aatype'''].shape[-1]
     to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]							,UpperCamelCase_							,-1							,-1       )       )
     to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]							,-1							,UpperCamelCase_							,-1       )       )

     snake_case				,						snake_case				,						snake_case					=       [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
     snake_case					=       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=UpperCamelCase_							,)
     snake_case					=       rigids.get_trans()[..., None, :, :]
     snake_case					=       rigids[..., None].invert_apply(UpperCamelCase_       )

     snake_case					=       torch.rsqrt(eps + torch.sum(rigid_vec**2							,dim=-1       )       )

     snake_case					=       batch['''template_all_atom_mask''']
     snake_case					=       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
     snake_case					=       template_mask[..., None] * template_mask[..., None, :]

     snake_case					=       inv_distance_scalar * template_mask_ad
     snake_case					=       rigid_vec * inv_distance_scalar[..., None]

     if not use_unit_vector:
          snake_case					=       unit_vector * 0.0

     to_concat.extend(torch.unbind(unit_vector[..., None, :]							,dim=-1       )       )
     to_concat.append(template_mask_ad[..., None]       )

     snake_case					=       torch.cat(UpperCamelCase_							,dim=-1       )
     snake_case					=       act * template_mask_ad[..., None]

     return act
def     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       nn.functional.one_hot(batch['''extra_msa''']							,23       )
     snake_case					=       [
         msa_ahot,
         batch['''extra_has_deletion'''].unsqueeze(-1       ),
         batch['''extra_deletion_value'''].unsqueeze(-1       ),
     ]
     return torch.cat(UpperCamelCase_							,dim=-1       )
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):



     """simple docstring"""






     snake_case					=       rrgdf[aatype, ...]

     # [*, N, 8] transformations, i.e.
     #   One [*, N, 8, 3, 3] rotation matrix and
     #   One [*, N, 8, 3]    translation matrix
     snake_case					=       r.from_tensor_axa(UpperCamelCase_       )

     snake_case					=       alpha.new_zeros((*((1,) * len(alpha.shape[:-1]       )), 2)       )
     snake_case					=       1

     # [*, N, 8, 2]
     snake_case					=       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.

     snake_case					=       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape       )
     snake_case					=       1
     snake_case					=       alpha[..., 1]
     snake_case					=       -alpha[..., 0]
     snake_case					=       alpha

     snake_case					=       default_r.compose(Rigid(Rotation(rot_mats=UpperCamelCase_       )							,UpperCamelCase_       )       )

     snake_case					=       all_frames[..., 5]
     snake_case					=       all_frames[..., 6]
     snake_case					=       all_frames[..., 7]

     snake_case					=       all_frames[..., 4]
     snake_case					=       chia_frame_to_bb.compose(UpperCamelCase_       )
     snake_case					=       chia_frame_to_bb.compose(UpperCamelCase_       )
     snake_case					=       chia_frame_to_bb.compose(UpperCamelCase_       )

     snake_case					=       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							,)

     snake_case					=       r[..., None].compose(UpperCamelCase_       )

     return all_frames_to_global

def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,):



     """simple docstring"""






     snake_case					=       group_idx[aatype, ...]

     # [*, N, 14, 8]
     snake_case					=       nn.functional.one_hot(
         UpperCamelCase_							,num_classes=default_frames.shape[-3]							,)

     # [*, N, 14, 8]
     snake_case					=       r[..., None, :] * group_mask_one_hot

     # [*, N, 14]
     snake_case					=       t_atoms_to_global.map_tensor_fn(lambda UpperCamelCase_       : torch.sum(UpperCamelCase_							,dim=-1       )       )

     # [*, N, 14, 1]
     snake_case					=       atom_mask[aatype, ...].unsqueeze(-1       )

     # [*, N, 14, 3]
     snake_case					=       lit_positions[aatype, ...]
     snake_case					=       t_atoms_to_global.apply(UpperCamelCase_       )
     snake_case					=       pred_positions * atom_mask

     return pred_positions








import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)

@dataclass
class 						A__      :







    """simple docstring"""

    __magic_name__    						=	42
    __magic_name__    						=	42
    __magic_name__    						=	None
    __magic_name__    						=	None
    def 	a_    ( self					):
         return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"






@dataclass(frozen=snake_case__			)
class 						A__      :







    """simple docstring"""

    __magic_name__    						=	42
    __magic_name__    						=	None
    __magic_name__    						=	None
    __magic_name__    						=	None
    def 	a_    ( self					):
         return json.dumps(dataclasses.asdict(self					)					) + "\n"






class 						A__      :







    """simple docstring"""

    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         raise NotImplementedError()
    def 	a_    ( self					):
         raise NotImplementedError()
    def 	a_    ( self    ,      __snake_case					):
         if len(self.get_labels()					) > 1:
              snake_case					=       self.get_labels()[int(example.label					)]
         return example




    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
         with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
              return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
         snake_case					=       [] if labels is None else labels
         snake_case					=       [] if examples is None else examples
         snake_case					=       mode
         snake_case					=       verbose
    def __len__( self					):
         return len(self.examples					)
    def __getitem__( self    ,      __snake_case					):
         if isinstance(__snake_case    ,      __snake_case					):
              return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
         return self.examples[idx]
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
         snake_case					=       cls(**__snake_case					)
         processor.add_examples_from_csv(
             __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
         return processor
    @classmethod
    def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
         snake_case					=       cls(**__snake_case					)
         processor.add_examples(__snake_case    ,      labels=__snake_case					)
         return processor
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
         snake_case					=       self._read_tsv(__snake_case					)
         if skip_first_row:
              snake_case					=       lines[1:]
         snake_case					=       []
         snake_case					=       []
         snake_case					=       []
         for i, line in enumerate(__snake_case					):
              texts.append(line[column_text]					)
              labels.append(line[column_label]					)
              if column_id is not None:
                   ids.append(line[column_id]					)
              else:
                   snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                   ids.append(__snake_case					)

         return self.add_examples(
             __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
         if labels is not None and len(__snake_case					) != len(__snake_case					):
              raise ValueError(
                  F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
         if ids is not None and len(__snake_case					) != len(__snake_case					):
              raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
         if ids is None:
              snake_case					=       [None] * len(__snake_case					)
         if labels is None:
              snake_case					=       [None] * len(__snake_case					)
         snake_case					=       []
         snake_case					=       set()
         for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
              if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                   snake_case				,						snake_case					=       text_or_text_and_label
              else:
                   snake_case					=       text_or_text_and_label
              added_labels.add(__snake_case					)
              examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)

         # Update examples
         if overwrite_examples:
              snake_case					=       examples
         else:
              self.examples.extend(__snake_case					)

         # Update labels
         if overwrite_labels:
              snake_case					=       list(__snake_case					)
         else:
              snake_case					=       list(set(self.labels					).union(__snake_case					)					)

         return self.examples




    def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
         if max_length is None:
              snake_case					=       tokenizer.max_len

         snake_case					=       {label: i for i, label in enumerate(self.labels					)}

         snake_case					=       []
         for ex_index, example in enumerate(self.examples					):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info(F'''Tokenizing example {ex_index}'''					)

              snake_case					=       tokenizer.encode(
                  example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
              all_input_ids.append(__snake_case					)

         snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)

         snake_case					=       []
         for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
              if ex_index % 1_0_0_0_0 == 0:
                   logger.info(F'''Writing example {ex_index}/{len(self.examples					)}'''					)
              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)

              # Zero-pad up to the sequence length.
              snake_case					=       batch_length - len(__snake_case					)
              if pad_on_left:
                   snake_case					=       ([pad_token] * padding_length) + input_ids
                   snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
              else:
                   snake_case					=       input_ids + ([pad_token] * padding_length)
                   snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

              if len(__snake_case					) != batch_length:
                   raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
              if len(__snake_case					) != batch_length:
                   raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)

              if self.mode == "classification":
                   snake_case					=       label_map[example.label]
              elif self.mode == "regression":
                   snake_case					=       float(example.label					)
              else:
                   raise ValueError(self.mode					)

              if ex_index < 5 and self.verbose:
                   logger.info('''*** Example ***'''					)
                   logger.info(F'''guid: {example.guid}'''					)
                   logger.info(F'''input_ids: {" ".join([str(__snake_case					) for x in input_ids]					)}'''					)
                   logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                   logger.info(F'''label: {example.label} (id = {label})'''					)

              features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)

         if return_tensors is None:
              return features
         elif return_tensors == "tf":
              if not is_tf_available():
                   raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''					)
              import tensorflow as tf

              def gen():
                   for ex in features:
                        yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

              snake_case					=       tf.data.Dataset.from_generator(
                  __snake_case    ,      ({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,      ({'''input_ids''': tf.TensorShape([None]					), '''attention_mask''': tf.TensorShape([None]					)}, tf.TensorShape([]					))    ,      )
              return dataset
         elif return_tensors == "pt":
              if not is_torch_available():
                   raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''					)
              import torch
              from torch.utils.data import TensorDataset

              snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
              snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
              if self.mode == "classification":
                   snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
              elif self.mode == "regression":
                   snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)

              snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
              return dataset
         else:
              raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)




