

import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    1_0

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    [1, 2, 3, 4]
      _lowerCAmelCase   :					int        =    [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
      self.assertEqual(truncate_or_pad(A_     ,						self.block_size     ,						0       )     ,						A_       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      _lowerCAmelCase   :					Dict        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      self.assertEqual(truncate_or_pad(A_     ,						self.block_size     ,						0       )     ,						A_       )

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
      _lowerCAmelCase   :					Any        =    [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
      self.assertEqual(truncate_or_pad(A_     ,						self.block_size     ,						0       )     ,						A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this."
      _lowerCAmelCase   :					List[Any]        =    process_story(A_       )
      self.assertEqual(A_     ,						[]       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    ""
      _lowerCAmelCase   :					Optional[Any]        =    process_story(A_       )
      self.assertEqual(A_     ,						[]       )
      self.assertEqual(A_     ,						[]       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    (
          "It was the year of Our Lord one thousand seven hundred and "
          "seventy-five\n\nSpiritual revelations were conceded to England "
          "at that favoured period, as at this.\n@highlight\n\nIt was the best of times"
      )
      _lowerCAmelCase   :					Dict        =    process_story(A_       )

      _lowerCAmelCase   :					Any        =    [
          "It was the year of Our Lord one thousand seven hundred and seventy-five.",
          "Spiritual revelations were conceded to England at that favoured period, as at this.",
      ]
      self.assertEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Any        =    ["It was the best of times."]
      self.assertEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    torch.tensor([1, 2, 3, 4]       )
      _lowerCAmelCase   :					Any        =    torch.tensor([1, 1, 1, 1]       )
      np.testing.assert_array_equal(build_mask(A_     ,						0       ).numpy()     ,						expected.numpy()       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]       )
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
      np.testing.assert_array_equal(build_mask(A_     ,						2_3       ).numpy()     ,						expected.numpy()       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    torch.tensor([8, 2, 3, 4, 1, 1, 1]       )
      _lowerCAmelCase   :					str        =    torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
      np.testing.assert_array_equal(build_mask(A_     ,						1       ).numpy()     ,						expected.numpy()       )


 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					int        =    1_0_1
      _lowerCAmelCase   :					Any        =    torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]       )
      _lowerCAmelCase   :					int        =    torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]       )

      _lowerCAmelCase   :					Any        =    compute_token_type_ids(A_     ,						A_       )
      np.testing.assert_array_equal(A_     ,						A_       )









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 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, torch.Tensor]:

     """simple docstring"""







     _lowerCAmelCase   :					int        =    []
     _lowerCAmelCase   :					Tuple        =    []
     _lowerCAmelCase   :					List[Any]        =    []

     for rt in rc.restypes:
          _lowerCAmelCase   :					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]				)
          _lowerCAmelCase   :					str        =    {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE				)}
          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				)

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

     _lowerCAmelCase   :					Any        =    residx_atomaa_mask
     _lowerCAmelCase   :					Dict        =    residx_atomaa_to_atomaa.long()

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

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

     _lowerCAmelCase   :					Tuple        =    restype_atomaa_mask[protein_aatype]
     _lowerCAmelCase   :					List[str]        =    residx_atomaa_mask

     return protein







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Dict[str, np.ndarray]:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    tree_map(lambda SCREAMING_SNAKE_CASE				: torch.tensor(SCREAMING_SNAKE_CASE		,							device=batch["aatype"].device				)		,							SCREAMING_SNAKE_CASE		,							np.ndarray				)
     _lowerCAmelCase   :					Optional[int]        =    tensor_tree_map(lambda SCREAMING_SNAKE_CASE				: np.array(SCREAMING_SNAKE_CASE				)		,							make_atomaa_masks(SCREAMING_SNAKE_CASE				)				)
     return out






