





'''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 	UpperCAmelCase_							(					__lowercase							:      Dict[str, torch.Tensor]       )		->       Dict[str, torch.Tensor]:


     '''simple docstring'''



     _UpperCAmelCase     							=   []
     _UpperCAmelCase     							=   []
     _UpperCAmelCase     							=   []

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

     _UpperCAmelCase     							=   torch.tensor(
         __lowercase			,     dtype=torch.intaa			,     device=protein["aatype"].device			,     )
     _UpperCAmelCase     							=   torch.tensor(
         __lowercase			,     dtype=torch.intaa			,     device=protein["aatype"].device			,     )
     _UpperCAmelCase     							=   torch.tensor(
         __lowercase			,     dtype=torch.floataa			,     device=protein["aatype"].device			,     )
     _UpperCAmelCase     							=   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
     _UpperCAmelCase     							=   restype_atomaa_to_atomaa[protein_aatype]
     _UpperCAmelCase     							=   restype_atomaa_mask[protein_aatype]

     _UpperCAmelCase     							=   residx_atomaa_mask
     _UpperCAmelCase     							=   residx_atomaa_to_atomaa.long()

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

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

     _UpperCAmelCase     							=   restype_atomaa_mask[protein_aatype]
     _UpperCAmelCase     							=   residx_atomaa_mask

     return protein






def 	UpperCAmelCase_							(					__lowercase							:      Dict[str, torch.Tensor]       )		->       Dict[str, np.ndarray]:


     '''simple docstring'''



     _UpperCAmelCase     							=   tree_map(lambda __lowercase       : torch.tensor(__lowercase			,     device=batch["aatype"].device       )			,     __lowercase			,     np.ndarray       )
     _UpperCAmelCase     							=   tensor_tree_map(lambda __lowercase       : np.array(__lowercase       )			,     make_atomaa_masks(__lowercase       )       )
     return out











'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class   A_	(  unittest.TestCase			):




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   tempfile.mkdtemp()

      _UpperCAmelCase     							=   BlipImageProcessor()
      _UpperCAmelCase     							=   BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"    )

      _UpperCAmelCase     							=   BlipProcessor(snake_case_	,			snake_case_    )

      processor.save_pretrained(self.tmpdirname    )




 def      lowercase	(					self       :    Tuple	,			**snake_case_       :    int    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).tokenizer




 def      lowercase	(					self       :    Dict	,			**snake_case_       :    Any    ):
      return AutoProcessor.from_pretrained(self.tmpdirname	,			**snake_case_    ).image_processor




 def      lowercase	(					self       :    int    ):
      shutil.rmtree(self.tmpdirname    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   [np.random.randint(2_5_5	,			size=(3, 3_0, 4_0_0)	,			dtype=np.uinta    )]

      _UpperCAmelCase     							=   [Image.fromarray(np.moveaxis(snake_case_	,			0	,			-1    )    ) for x in image_inputs]

      return image_inputs




 def      lowercase	(					self       :    int    ):
      _UpperCAmelCase     							=   BlipProcessor(tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()    )
      processor.save_pretrained(self.tmpdirname    )

      _UpperCAmelCase     							=   self.get_tokenizer(bos_token="(BOS)"	,			eos_token="(EOS)"    )
      _UpperCAmelCase     							=   self.get_image_processor(do_normalize=snake_case_	,			padding_value=1.0    )

      _UpperCAmelCase     							=   BlipProcessor.from_pretrained(
          self.tmpdirname	,			bos_token="(BOS)"	,			eos_token="(EOS)"	,			do_normalize=snake_case_	,			padding_value=1.0    )

      self.assertEqual(processor.tokenizer.get_vocab()	,			tokenizer_add_kwargs.get_vocab()    )
      self.assertIsInstance(processor.tokenizer	,			snake_case_    )

      self.assertEqual(processor.image_processor.to_json_string()	,			image_processor_add_kwargs.to_json_string()    )
      self.assertIsInstance(processor.image_processor	,			snake_case_    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   image_processor(snake_case_	,			return_tensors="np"    )
      _UpperCAmelCase     							=   processor(images=snake_case_	,			return_tensors="np"    )

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()	,			input_processor[key].sum()	,			delta=1e-2    )




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"

      _UpperCAmelCase     							=   processor(text=snake_case_    )

      _UpperCAmelCase     							=   tokenizer(snake_case_	,			return_token_type_ids=snake_case_    )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]	,			encoded_processor[key]    )




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

      self.assertListEqual(list(inputs.keys()    )	,			["pixel_values", "input_ids", "attention_mask"]    )

      # test if it raises when no input is passed
      with pytest.raises(snake_case_    ):
           processor()




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _UpperCAmelCase     							=   processor.batch_decode(snake_case_    )
      _UpperCAmelCase     							=   tokenizer.batch_decode(snake_case_    )

      self.assertListEqual(snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.get_image_processor()
      _UpperCAmelCase     							=   self.get_tokenizer()

      _UpperCAmelCase     							=   BlipProcessor(tokenizer=snake_case_	,			image_processor=snake_case_    )

      _UpperCAmelCase     							=   "lower newer"
      _UpperCAmelCase     							=   self.prepare_image_inputs()

      _UpperCAmelCase     							=   processor(text=snake_case_	,			images=snake_case_    )

      # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
      self.assertListEqual(list(inputs.keys()    )	,			["pixel_values", "input_ids", "attention_mask"]    )




