
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
   import sqlitea

   import sqlalchemy






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					Tuple    ,						snake_case__		:					Union[str, "sqlalchemy.sql.Selectable"]    ,						snake_case__		:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]    ,						snake_case__		:					Optional[Features] = None    ,						snake_case__		:					str = None    ,						snake_case__		:					bool = False    ,						**snake_case__		:					Dict    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(features=snake_case__    ,						cache_dir=snake_case__    ,						keep_in_memory=snake_case__    ,						**snake_case__ )
          snake_case       :   Tuple							=      Sql(
              cache_dir=snake_case__    ,						features=snake_case__    ,						sql=snake_case__    ,						con=snake_case__    ,						**snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      None
          snake_case       :   int							=      None
          snake_case       :   List[str]							=      None
          snake_case       :   int							=      None

          self.builder.download_and_prepare(
              download_config=snake_case__    ,						download_mode=snake_case__    ,						verification_mode=snake_case__    ,						base_path=snake_case__    ,						)

          # Build dataset for splits
          snake_case       :   Optional[int]							=      self.builder.as_dataset(
              split="train"    ,						verification_mode=snake_case__    ,						in_memory=self.keep_in_memory )
          return dataset






class   UpperCAmelCase		:



   def __init__(self		:					Union[str, Any]    ,						snake_case__		:					Dataset    ,						snake_case__		:					str    ,						snake_case__		:					Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					Optional[int] = None    ,						**snake_case__		:					Dict    ,						)		->	Optional[Any]:


          '''simple docstring'''


          if num_proc is not None and num_proc <= 0:
                 raise ValueError(f"""num_proc {num_proc} must be an integer > 0.""" )

          snake_case       :   Any							=      dataset
          snake_case       :   Dict							=      name
          snake_case       :   Optional[Any]							=      con
          snake_case       :   Any							=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          snake_case       :   Optional[int]							=      num_proc
          snake_case       :   str							=      to_sql_kwargs



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   int							=      self.to_sql_kwargs.pop("sql"    ,						snake_case__ )
          snake_case       :   Tuple							=      self.to_sql_kwargs.pop("con"    ,						snake_case__ )
          snake_case       :   Tuple							=      self.to_sql_kwargs.pop("index"    ,						snake_case__ )

          snake_case       :   Union[str, Any]							=      self._write(index=snake_case__    ,						**self.to_sql_kwargs )
          return written



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Tuple )		->	Optional[Any]:


          '''simple docstring'''


          snake_case				,					snake_case				,					snake_case       :   Tuple							=      args
          snake_case       :   Optional[int]							=      {**to_sql_kwargs, "if_exists": "append"} if offset > 0 else to_sql_kwargs
          snake_case       :   List[Any]							=      query_table(
              table=self.dataset.data    ,						key=slice(snake_case__    ,						offset + self.batch_size )    ,						indices=self.dataset._indices    ,						)
          snake_case       :   List[Any]							=      batch.to_pandas()
          snake_case       :   Optional[Any]							=      df.to_sql(self.name    ,						self.con    ,						index=snake_case__    ,						**snake_case__ )
          return num_rows or len(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str    ,						**snake_case__		:					Union[str, Any] )		->	int:


          '''simple docstring'''


          snake_case       :   List[str]							=      0

          if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0    ,						len(self.dataset )    ,						self.batch_size )    ,						unit="ba"    ,						disable=not logging.is_progress_bar_enabled()    ,						desc="Creating SQL from Arrow format"    ,						):
                        written += self._batch_sql((offset, index, to_sql_kwargs) )
          else:
                 snake_case				,					snake_case       :   List[str]							=      len(self.dataset ), self.batch_size
                 with multiprocessing.Pool(self.num_proc ) as pool:
                        for num_rows in logging.tqdm(
                            pool.imap(
                                self._batch_sql    ,						[(offset, index, to_sql_kwargs) for offset in range(0    ,						snake_case__    ,						snake_case__ )]    ,						)    ,						total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size    ,						unit="ba"    ,						disable=not logging.is_progress_bar_enabled()    ,						desc="Creating SQL from Arrow format"    ,						):
                               written += num_rows

          return written







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 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   List[str]							=      []
       snake_case       :   Optional[int]							=      []
       snake_case       :   Any							=      []

       for rt in rc.restypes:
              snake_case       :   List[Any]							=      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]    )
              snake_case       :   str							=      {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    )

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

       snake_case       :   str							=      residx_atomaa_mask
       snake_case       :   Any							=      residx_atomaa_to_atomaa.long()

       # create the gather indices for mapping back
       snake_case       :   List[str]							=      restype_atomaa_to_atomaa[protein_aatype]
       snake_case       :   List[Any]							=      residx_atomaa_to_atomaa.long()

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

       snake_case       :   List[Any]							=      restype_atomaa_mask[protein_aatype]
       snake_case       :   int							=      residx_atomaa_mask

       return protein




def 			UpperCamelCase					(    __lowerCamelCase     : Dict[str, torch.Tensor]    ):
       snake_case       :   Dict							=      tree_map(lambda __lowerCamelCase    : torch.tensor(__lowerCamelCase				,					device=batch["aatype"].device    )				,					__lowerCamelCase				,					np.ndarray    )
       snake_case       :   List[str]							=      tensor_tree_map(lambda __lowerCamelCase    : np.array(__lowerCamelCase    )				,					make_atomaa_masks(__lowerCamelCase    )    )
       return out





