"""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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast



@require_vision
class   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			tempfile.mkdtemp()

          snake_case								=			BlipImageProcessor()
          snake_case								=			GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'    )

          snake_case								=			BlipaProcessor(lowerCAmelCase		,    lowerCAmelCase    )

          processor.save_pretrained(self.tmpdirname    )

    def 		snake_case (				self		,    **lowerCAmelCase    ):






          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    ).tokenizer

    def 		snake_case (				self		,    **lowerCAmelCase    ):






          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname		,    **lowerCAmelCase    ).image_processor

    def 		snake_case (				self    ):






          """simple docstring"""

          shutil.rmtree(self.tmpdirname    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			[np.random.randint(2_55		,    size=(3, 30, 4_00)		,    dtype=np.uinta    )]

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

          return image_inputs

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			BlipaProcessor(tokenizer=self.get_tokenizer()		,    image_processor=self.get_image_processor()    )
          processor.save_pretrained(self.tmpdirname    )

          snake_case								=			self.get_tokenizer(bos_token='(BOS)'		,    eos_token='(EOS)'    )
          snake_case								=			self.get_image_processor(do_normalize=lowerCAmelCase		,    padding_value=1.0    )

          snake_case								=			BlipaProcessor.from_pretrained(
              self.tmpdirname		,    bos_token='(BOS)'		,    eos_token='(EOS)'		,    do_normalize=lowerCAmelCase		,    padding_value=1.0    )

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

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

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

          snake_case								=			self.prepare_image_inputs()

          snake_case								=			image_processor(lowerCAmelCase		,    return_tensors='np'    )
          snake_case								=			processor(images=lowerCAmelCase		,    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 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

          snake_case								=			'lower newer'

          snake_case								=			processor(text=lowerCAmelCase    )

          snake_case								=			tokenizer(lowerCAmelCase		,    return_token_type_ids=lowerCAmelCase    )

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

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

          snake_case								=			'lower newer'
          snake_case								=			self.prepare_image_inputs()

          snake_case								=			processor(text=lowerCAmelCase		,    images=lowerCAmelCase    )

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

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

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

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

          snake_case								=			processor.batch_decode(lowerCAmelCase    )
          snake_case								=			tokenizer.batch_decode(lowerCAmelCase    )

          self.assertListEqual(lowerCAmelCase		,    lowerCAmelCase    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.get_image_processor()
          snake_case								=			self.get_tokenizer()

          snake_case								=			BlipaProcessor(tokenizer=lowerCAmelCase		,    image_processor=lowerCAmelCase    )

          snake_case								=			'lower newer'
          snake_case								=			self.prepare_image_inputs()

          snake_case								=			processor(text=lowerCAmelCase		,    images=lowerCAmelCase    )

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



"""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    lowerCAmelCase__    (			_UpperCamelCase       :				Dict[str, torch.Tensor]					)					->				Dict[str, torch.Tensor]:





						"""simple docstring"""






						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]

						for rt in rc.restypes:
												snake_case								=			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								=			{name: i for i, name in enumerate(_UpperCamelCase					)}
												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] * 1_4					)
						restype_atomaa_to_atomaa_list.append([0] * 3_7					)
						restype_atomaa_mask_list.append([0.0] * 1_4					)

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

						snake_case								=			residx_atomaa_mask
						snake_case								=			residx_atomaa_to_atomaa.long()

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

						# create the corresponding mask
						snake_case								=			torch.zeros([2_1, 3_7] ,			dtype=torch.floataa ,			device=protein['aatype'].device					)
						for restype, restype_letter in enumerate(rc.restypes					):
												snake_case								=			rc.restype_atoa[restype_letter]
												snake_case								=			rc.residue_atoms[restype_name]
												for atom_name in atom_names:
																		snake_case								=			rc.atom_order[atom_name]
																		snake_case								=			1

						snake_case								=			restype_atomaa_mask[protein_aatype]
						snake_case								=			residx_atomaa_mask

						return protein





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





						"""simple docstring"""






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


