



"""simple docstring"""
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD





def      __magic_name__				(							lowercase ):
   def wrapper(*lowercase							,			**lowercase ):
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=timeit.default_timer()
      SCREAMING_SNAKE_CASE_: Tuple     			=func(*lowercase							,			**lowercase )
      SCREAMING_SNAKE_CASE_: str     			=timeit.default_timer() - starttime
      return delta

   SCREAMING_SNAKE_CASE_: str     			=func.__name__

   return wrapper





def      __magic_name__				(							lowercase							,			lowercase=100							,			lowercase=None ):
   SCREAMING_SNAKE_CASE_: int     			=[]
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=seq_shapes or {}
   for i in range(lowercase ):
      SCREAMING_SNAKE_CASE_: List[Any]     			={}
      for col_id, (k, v) in enumerate(features.items() ):
         if isinstance(lowercase							,			_ArrayXD ):
            SCREAMING_SNAKE_CASE_: Dict     			=np.random.rand(*v.shape ).astype(v.dtype )
         elif isinstance(lowercase							,			datasets.Value ):
            if v.dtype == "string":
               SCREAMING_SNAKE_CASE_: int     			="""The small grey turtle was surprisingly fast when challenged."""
            else:
               SCREAMING_SNAKE_CASE_: Optional[Any]     			=np.random.randint(10							,			size=1 ).astype(v.dtype ).item()
         elif isinstance(lowercase							,			datasets.Sequence ):
            while isinstance(lowercase							,			datasets.Sequence ):
               SCREAMING_SNAKE_CASE_: Union[str, Any]     			=v.feature
            SCREAMING_SNAKE_CASE_: Optional[int]     			=seq_shapes[k]
            SCREAMING_SNAKE_CASE_: Any     			=np.random.rand(*lowercase ).astype(v.dtype )
         SCREAMING_SNAKE_CASE_: Tuple     			=data

      dummy_data.append((i, example) )

   return dummy_data





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=100							,			lowercase=None ):
   SCREAMING_SNAKE_CASE_: Any     			=generate_examples(lowercase							,			num_examples=lowercase							,			seq_shapes=lowercase )

   with ArrowWriter(features=lowercase							,			path=lowercase ) as writer:
      for key, record in dummy_data:
         SCREAMING_SNAKE_CASE_: Tuple     			=features.encode_example(lowercase )
         writer.write(lowercase )

      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.''' )

   SCREAMING_SNAKE_CASE_: Tuple     			=datasets.Dataset.from_file(filename=lowercase							,			info=datasets.DatasetInfo(features=lowercase ) )

   return dataset








"""simple docstring"""
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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[]
   SCREAMING_SNAKE_CASE_: List[str]     			=[]
   SCREAMING_SNAKE_CASE_: Any     			=[]

   for rt in rc.restypes:
      SCREAMING_SNAKE_CASE_: Optional[int]     			=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] )
      SCREAMING_SNAKE_CASE_: Any     			={name: i for i, name in enumerate(lowercase )}
      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 )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.tensor(
       lowercase							,			dtype=torch.intaa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor(
       lowercase							,			dtype=torch.floataa							,			device=protein["""aatype"""].device							,			)
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=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
   SCREAMING_SNAKE_CASE_: List[str]     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Any     			=restype_atomaa_mask[protein_aatype]

   SCREAMING_SNAKE_CASE_: Tuple     			=residx_atomaa_mask
   SCREAMING_SNAKE_CASE_: Dict     			=residx_atomaa_to_atomaa.long()

   # create the gather indices for mapping back
   SCREAMING_SNAKE_CASE_: Dict     			=restype_atomaa_to_atomaa[protein_aatype]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=residx_atomaa_to_atomaa.long()

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

   SCREAMING_SNAKE_CASE_: List[str]     			=restype_atomaa_mask[protein_aatype]
   SCREAMING_SNAKE_CASE_: List[Any]     			=residx_atomaa_mask

   return protein





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=tree_map(lambda lowercase : torch.tensor(lowercase							,			device=batch["""aatype"""].device )							,			lowercase							,			np.ndarray )
   SCREAMING_SNAKE_CASE_: int     			=tensor_tree_map(lambda lowercase : np.array(lowercase )							,			make_atomaa_masks(lowercase ) )
   return out



