
import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class   UpperCAmelCase		(						A_ ):



   def __init__(self		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Tuple							=      params

          snake_case       :   Union[str, Any]							=      np.array(snake_case__ )
          snake_case       :   str							=      np.array([len(snake_case__ ) for t in data] )

          self.check()
          self.remove_long_sequences()
          self.remove_empty_sequences()
          self.remove_unknown_sequences()
          self.check()
          self.print_statistics()



   def __getitem__(self		:					List[str]    ,						snake_case__		:					Union[str, Any] )		->	Any:


          '''simple docstring'''


          return (self.token_ids[index], self.lengths[index])



   def __len__(self		:					str )		->	int:


          '''simple docstring'''


          return len(self.lengths )



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


          '''simple docstring'''


          assert len(self.token_ids ) == len(self.lengths )
          assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )



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


          '''simple docstring'''


          snake_case       :   str							=      self.params.max_model_input_size
          snake_case       :   int							=      self.lengths > max_len
          logger.info(f"""Splitting {sum(snake_case__ )} too long sequences.""" )

          def divide_chunks(snake_case__		:					Optional[Any]    ,						snake_case__		:					str ):
                 return [l[i : i + n] for i in range(0    ,						len(snake_case__ )    ,						snake_case__ )]

          snake_case       :   List[str]							=      []
          snake_case       :   Tuple							=      []
          if self.params.mlm:
                 snake_case				,					snake_case       :   List[str]							=      self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
          else:
                 snake_case				,					snake_case       :   Optional[int]							=      self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]

          for seq_, len_ in zip(self.token_ids    ,						self.lengths ):
                 assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
                 if len_ <= max_len:
                        new_tok_ids.append(seq_ )
                        new_lengths.append(len_ )
                 else:
                        snake_case       :   List[str]							=      []
                        for sub_s in divide_chunks(seq_    ,						max_len - 2 ):
                               if sub_s[0] != cls_id:
                                      snake_case       :   List[str]							=      np.insert(snake_case__    ,						0    ,						snake_case__ )
                               if sub_s[-1] != sep_id:
                                      snake_case       :   str							=      np.insert(snake_case__    ,						len(snake_case__ )    ,						snake_case__ )
                               assert len(snake_case__ ) <= max_len
                               assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                               sub_seqs.append(snake_case__ )

                        new_tok_ids.extend(snake_case__ )
                        new_lengths.extend([len(snake_case__ ) for l in sub_seqs] )

          snake_case       :   Tuple							=      np.array(snake_case__ )
          snake_case       :   Dict							=      np.array(snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Tuple							=      len(self )
          snake_case       :   List[str]							=      self.lengths > 11
          snake_case       :   str							=      self.token_ids[indices]
          snake_case       :   List[str]							=      self.lengths[indices]
          snake_case       :   Dict							=      len(self )
          logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences.""" )



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


          '''simple docstring'''


          if "unk_token" not in self.params.special_tok_ids:
                 return
          else:
                 snake_case       :   List[str]							=      self.params.special_tok_ids["unk_token"]
          snake_case       :   Tuple							=      len(self )
          snake_case       :   Any							=      np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
          snake_case       :   Dict							=      (unk_occs / self.lengths) < 0.5
          snake_case       :   List[str]							=      self.token_ids[indices]
          snake_case       :   int							=      self.lengths[indices]
          snake_case       :   str							=      len(self )
          logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).""" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	List[str]:


          '''simple docstring'''


          if not self.params.is_master:
                 return
          logger.info(f"""{len(self )} sequences""" )
          # data_len = sum(self.lengths)
          # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
          # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

          # unk_idx = self.params.special_tok_ids['unk_token']
          # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
          # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')



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


          '''simple docstring'''


          snake_case       :   Any							=      [t[0] for t in batch]
          snake_case       :   List[str]							=      [t[1] for t in batch]
          assert len(snake_case__ ) == len(snake_case__ )

          # Max for paddings
          snake_case       :   Dict							=      max(snake_case__ )

          # Pad token ids
          if self.params.mlm:
                 snake_case       :   int							=      self.params.special_tok_ids["pad_token"]
          else:
                 snake_case       :   Optional[int]							=      self.params.special_tok_ids["unk_token"]
          snake_case       :   int							=      [list(t.astype(snake_case__ ) ) + [pad_idx] * (max_seq_len_ - len(snake_case__ )) for t in token_ids]
          assert len(tk_ ) == len(snake_case__ )
          assert all(len(snake_case__ ) == max_seq_len_ for t in tk_ )

          snake_case       :   Tuple							=      torch.tensor(tk_ )  # (bs, max_seq_len_)
          snake_case       :   Optional[int]							=      torch.tensor(snake_case__ )  # (bs)
          return tk_t, lg_t







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





