






"""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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []

  for rt in rc.restypes:
    lowercase							    =       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]    )
    lowercase							    =       {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    )

  lowercase							    =       torch.tensor(
      lowerCamelCase_		,					dtype=torch.intaa		,					device=protein['aatype'].device		,					)
  lowercase							    =       torch.tensor(
      lowerCamelCase_		,					dtype=torch.intaa		,					device=protein['aatype'].device		,					)
  lowercase							    =       torch.tensor(
      lowerCamelCase_		,					dtype=torch.floataa		,					device=protein['aatype'].device		,					)
  lowercase							    =       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
  lowercase							    =       restype_atomaa_to_atomaa[protein_aatype]
  lowercase							    =       restype_atomaa_mask[protein_aatype]

  lowercase							    =       residx_atomaa_mask
  lowercase							    =       residx_atomaa_to_atomaa.long()

  # create the gather indices for mapping back
  lowercase							    =       restype_atomaa_to_atomaa[protein_aatype]
  lowercase							    =       residx_atomaa_to_atomaa.long()

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

  lowercase							    =       restype_atomaa_mask[protein_aatype]
  lowercase							    =       residx_atomaa_mask

  return protein



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       tree_map(lambda __snake_case    : torch.tensor(lowerCamelCase_		,					device=batch['aatype'].device    )		,					lowerCamelCase_		,					np.ndarray    )
  lowercase							    =       tensor_tree_map(lambda __snake_case    : np.array(lowerCamelCase_    )		,					make_atomaa_masks(lowerCamelCase_    )    )
  return out










import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class       _UpperCAmelCase					(       A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       CTRLTokenizer
				lowercase__											=       False
				lowercase__											=       False




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

								'''simple docstring'''
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowercase__    =			['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
								lowercase__    =			dict(zip(lowerCamelCase,  range(len(lowerCamelCase  )  )  )  )
								lowercase__    =			['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
								lowercase__    =			{'''unk_token''': '''<unk>'''}

								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''vocab_file''']  )
								lowercase__    =			os.path.join(self.tmpdirname,  VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write(json.dumps(lowerCamelCase  ) + '''\n'''  )
								with open(self.merges_file,  '''w''',  encoding='''utf-8'''  ) as fp:
												fp.write('''\n'''.join(lowerCamelCase  )  )




				def 				lowercase__   (     self						:    Union[str, Any],  **lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								kwargs.update(self.special_tokens_map  )
								return CTRLTokenizer.from_pretrained(self.tmpdirname,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt react readapt apt'''
								return input_text, output_text




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			CTRLTokenizer(self.vocab_file,  self.merges_file,  **self.special_tokens_map  )
								lowercase__    =			'''adapt react readapt apt'''
								lowercase__    =			'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
								lowercase__    =			tokenizer.tokenize(lowerCamelCase  )
								self.assertListEqual(lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			tokens + [tokenizer.unk_token]

								lowercase__    =			[0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase  ),  lowerCamelCase  )




