'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DeformableDetrImageProcessor


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def __init__(							self :       int  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Union[str, Any]=7  ,     __magic_name__ :       Any=3  ,     __magic_name__ :       Optional[Any]=30  ,     __magic_name__ :       List[str]=400  ,     __magic_name__ :       str=True  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Optional[int]=True  ,     __magic_name__ :       int=[0.5, 0.5, 0.5]  ,     __magic_name__ :       Dict=[0.5, 0.5, 0.5]  ,     __magic_name__ :       Optional[int]=True  ,     __magic_name__ :       int=1 / 255  ,     __magic_name__ :       List[str]=True  ,     )  ->	Tuple:
        # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
        SCREAMING_SNAKE_CASE_          =  size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  min_resolution
        SCREAMING_SNAKE_CASE_          =  max_resolution
        SCREAMING_SNAKE_CASE_          =  do_resize
        SCREAMING_SNAKE_CASE_          =  size
        SCREAMING_SNAKE_CASE_          =  do_normalize
        SCREAMING_SNAKE_CASE_          =  image_mean
        SCREAMING_SNAKE_CASE_          =  image_std
        SCREAMING_SNAKE_CASE_          =  do_rescale
        SCREAMING_SNAKE_CASE_          =  rescale_factor
        SCREAMING_SNAKE_CASE_          =  do_pad







  def    __A     (							self :       str   )  ->	Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       List[Any]=False   )  ->	Dict:
        if not batched:
              SCREAMING_SNAKE_CASE_          =  image_inputs[0]
              if isinstance(__A  ,     Image.Image   ):
                    SCREAMING_SNAKE_CASE_          =  image.size
              else:
                    SCREAMING_SNAKE_CASE_          =  image.shape[1], image.shape[2]
              if w < h:
                    SCREAMING_SNAKE_CASE_          =  int(self.size["shortest_edge"] * h / w   )
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]
              elif w > h:
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]
                    SCREAMING_SNAKE_CASE_          =  int(self.size["shortest_edge"] * w / h   )
              else:
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]
                    SCREAMING_SNAKE_CASE_          =  self.size["shortest_edge"]

        else:
              SCREAMING_SNAKE_CASE_          =  []
              for image in image_inputs:
                    SCREAMING_SNAKE_CASE_          =  self.get_expected_values([image]   )
                    expected_values.append((expected_height, expected_width)   )
              SCREAMING_SNAKE_CASE_          =  max(__A  ,     key=lambda __magic_name__   : item[0]   )[0]
              SCREAMING_SNAKE_CASE_          =  max(__A  ,     key=lambda __magic_name__   : item[1]   )[1]

        return expected_height, expected_width



@require_torch
@require_vision
class   lowerCamelCase					(UpperCamelCase_  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						DeformableDetrImageProcessor if is_vision_available() else None







  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  DeformableDetrImageProcessingTester(self   )







  @property
  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        return self.image_processor_tester.prepare_image_processor_dict()







  def    __A     (							self :       Tuple   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(__A  ,     "image_mean"   )   )
        self.assertTrue(hasattr(__A  ,     "image_std"   )   )
        self.assertTrue(hasattr(__A  ,     "do_normalize"   )   )
        self.assertTrue(hasattr(__A  ,     "do_resize"   )   )
        self.assertTrue(hasattr(__A  ,     "do_rescale"   )   )
        self.assertTrue(hasattr(__A  ,     "do_pad"   )   )
        self.assertTrue(hasattr(__A  ,     "size"   )   )







  def    __A     (							self :       Any   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size  ,     {"shortest_edge": 18, "longest_edge": 1_333}   )
        self.assertEqual(image_processor.do_pad  ,     __A   )

        SCREAMING_SNAKE_CASE_          =  self.image_processing_class.from_dict(
            self.image_processor_dict  ,     size=42  ,     max_size=84  ,     pad_and_return_pixel_mask=__A   )
        self.assertEqual(image_processor.size  ,     {"shortest_edge": 42, "longest_edge": 84}   )
        self.assertEqual(image_processor.do_pad  ,     __A   )







  def    __A     (							self :       str   )  ->	str:
        pass







  def    __A     (							self :       List[str]   )  ->	Any:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__A   )
        for image in image_inputs:
              self.assertIsInstance(__A  ,     Image.Image   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  image_processing(image_inputs[0]  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__A   )

        self.assertEqual(
            encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

        # Test batched
        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__A  ,     batched=__A   )

        SCREAMING_SNAKE_CASE_          =  image_processing(__A  ,     return_tensors="pt"   ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,     )







  def    __A     (							self :       int   )  ->	int:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__A  ,     numpify=__A   )
        for image in image_inputs:
              self.assertIsInstance(__A  ,     np.ndarray   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  image_processing(image_inputs[0]  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__A   )

        self.assertEqual(
            encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

        # Test batched
        SCREAMING_SNAKE_CASE_          =  image_processing(__A  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__A  ,     batched=__A   )

        self.assertEqual(
            encoded_images.shape  ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,     )







  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        # Initialize image_processing
        SCREAMING_SNAKE_CASE_          =  self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE_          =  prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__A  ,     torchify=__A   )
        for image in image_inputs:
              self.assertIsInstance(__A  ,     torch.Tensor   )

        # Test not batched input
        SCREAMING_SNAKE_CASE_          =  image_processing(image_inputs[0]  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__A   )

        self.assertEqual(
            encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

        # Test batched
        SCREAMING_SNAKE_CASE_          =  image_processing(__A  ,     return_tensors="pt"   ).pixel_values

        SCREAMING_SNAKE_CASE_          =  self.image_processor_tester.get_expected_values(__A  ,     batched=__A   )

        self.assertEqual(
            encoded_images.shape  ,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,     )







  @slow
  def    __A     (							self :       Optional[Any]   )  ->	List[Any]:
        # prepare image and target
        SCREAMING_SNAKE_CASE_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
        with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"  ,     "r"   ) as f:
              SCREAMING_SNAKE_CASE_          =  json.loads(f.read()   )

        SCREAMING_SNAKE_CASE_          =  {"image_id": 39_769, "annotations": target}

        # encode them
        SCREAMING_SNAKE_CASE_          =  DeformableDetrImageProcessor()
        SCREAMING_SNAKE_CASE_          =  image_processing(images=__A  ,     annotations=__A  ,     return_tensors="pt"   )

        # verify pixel values
        SCREAMING_SNAKE_CASE_          =  torch.Size([1, 3, 800, 1_066]   )
        self.assertEqual(encoding["pixel_values"].shape  ,     __A   )

        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.2796, 0.3138, 0.3481]   )
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,     __A  ,     atol=1e-4   )   )

        # verify area
        SCREAMING_SNAKE_CASE_          =  torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,     __A   )   )
        # verify boxes
        SCREAMING_SNAKE_CASE_          =  torch.Size([6, 4]   )
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,     __A   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,     __A  ,     atol=1e-3   )   )
        # verify image_id
        SCREAMING_SNAKE_CASE_          =  torch.tensor([39_769]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,     __A   )   )
        # verify is_crowd
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,     __A   )   )
        # verify class_labels
        SCREAMING_SNAKE_CASE_          =  torch.tensor([75, 75, 63, 65, 17, 17]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,     __A   )   )
        # verify orig_size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,     __A   )   )
        # verify size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([800, 1_066]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,     __A   )   )







  @slow
  def    __A     (							self :       Optional[int]   )  ->	str:
        # prepare image, target and masks_path
        SCREAMING_SNAKE_CASE_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
        with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"  ,     "r"   ) as f:
              SCREAMING_SNAKE_CASE_          =  json.loads(f.read()   )

        SCREAMING_SNAKE_CASE_          =  {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

        SCREAMING_SNAKE_CASE_          =  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"   )

        # encode them
        SCREAMING_SNAKE_CASE_          =  DeformableDetrImageProcessor(format="coco_panoptic"   )
        SCREAMING_SNAKE_CASE_          =  image_processing(images=__A  ,     annotations=__A  ,     masks_path=__A  ,     return_tensors="pt"   )

        # verify pixel values
        SCREAMING_SNAKE_CASE_          =  torch.Size([1, 3, 800, 1_066]   )
        self.assertEqual(encoding["pixel_values"].shape  ,     __A   )

        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.2796, 0.3138, 0.3481]   )
        self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]  ,     __A  ,     atol=1e-4   )   )

        # verify area
        SCREAMING_SNAKE_CASE_          =  torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["area"]  ,     __A   )   )
        # verify boxes
        SCREAMING_SNAKE_CASE_          =  torch.Size([6, 4]   )
        self.assertEqual(encoding["labels"][0]["boxes"].shape  ,     __A   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]  ,     __A  ,     atol=1e-3   )   )
        # verify image_id
        SCREAMING_SNAKE_CASE_          =  torch.tensor([39_769]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]  ,     __A   )   )
        # verify is_crowd
        SCREAMING_SNAKE_CASE_          =  torch.tensor([0, 0, 0, 0, 0, 0]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]  ,     __A   )   )
        # verify class_labels
        SCREAMING_SNAKE_CASE_          =  torch.tensor([17, 17, 63, 75, 75, 93]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]  ,     __A   )   )
        # verify masks
        SCREAMING_SNAKE_CASE_          =  822_873
        self.assertEqual(encoding["labels"][0]["masks"].sum().item()  ,     __A   )
        # verify orig_size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([480, 640]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]  ,     __A   )   )
        # verify size
        SCREAMING_SNAKE_CASE_          =  torch.tensor([800, 1_066]   )
        self.assertTrue(torch.allclose(encoding["labels"][0]["size"]  ,     __A   )   )



import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple

import numpy as np

from . import residue_constants


A     :  int					     =       Mapping[str, np.ndarray]
A     :  Any					     =       Mapping[str, Any]  # Is a nested dict.
A     :  Dict					     =       0.01


@dataclasses.dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class   lowerCamelCase					:
  """simple docstring"""







  lowerCamelCase__     =						42  # [num_res, num_atom_type, 3]

  # Amino-acid type for each residue represented as an integer between 0 and
  # 20, where 20 is 'X'.
  lowerCamelCase__     =						42  # [num_res]

  # Binary float mask to indicate presence of a particular atom. 1.0 if an atom
  # is present and 0.0 if not. This should be used for loss masking.
  lowerCamelCase__     =						42  # [num_res, num_atom_type]

  # Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
  lowerCamelCase__     =						42  # [num_res]

  # B-factors, or temperature factors, of each residue (in sq. angstroms units),
  # representing the displacement of the residue from its ground truth mean
  # value.
  lowerCamelCase__     =						42  # [num_res, num_atom_type]

  # Chain indices for multi-chain predictions
  lowerCamelCase__     =						None

  # Optional remark about the protein. Included as a comment in output PDB
  # files
  lowerCamelCase__     =						None

  # Templates used to generate this protein (prediction-only)
  lowerCamelCase__     =						None

  # Chain corresponding to each parent
  lowerCamelCase__     =						None



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  r"(\[[A-Z]+\]\n)"
      SCREAMING_SNAKE_CASE_          =  [tag.strip() for tag in re.split(__UpperCamelCase     ,      __UpperCamelCase				) if len(__UpperCamelCase				) > 0]
      SCREAMING_SNAKE_CASE_          =  zip(tags[0::2]     ,      [l.split("\n"				) for l in tags[1::2]]				)

      SCREAMING_SNAKE_CASE_          =  ["N", "CA", "C"]
      SCREAMING_SNAKE_CASE_          =  None
      SCREAMING_SNAKE_CASE_          =  None
      SCREAMING_SNAKE_CASE_          =  None
      for g in groups:
            if "[PRIMARY]" == g[0]:
                  SCREAMING_SNAKE_CASE_          =  g[1][0].strip()
                  for i in range(len(__UpperCamelCase				)				):
                        if seq[i] not in residue_constants.restypes:
                              SCREAMING_SNAKE_CASE_          =  "X"  # FIXME: strings are immutable
                  SCREAMING_SNAKE_CASE_          =  np.array(
                      [residue_constants.restype_order.get(__UpperCamelCase     ,      residue_constants.restype_num				) for res_symbol in seq]				)
            elif "[TERTIARY]" == g[0]:
                  SCREAMING_SNAKE_CASE_          =  []
                  for axis in range(3				):
                        tertiary.append(list(map(__UpperCamelCase     ,      g[1][axis].split()				)				)				)
                  SCREAMING_SNAKE_CASE_          =  np.array(__UpperCamelCase				)
                  SCREAMING_SNAKE_CASE_          =  np.zeros((len(tertiary[0]				) // 3, residue_constants.atom_type_num, 3)				).astype(np.floataa				)
                  for i, atom in enumerate(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  np.transpose(tertiary_np[:, i::3]				)
                  atom_positions *= PICO_TO_ANGSTROM
            elif "[MASK]" == g[0]:
                  SCREAMING_SNAKE_CASE_          =  np.array(list(map({"-": 0, "+": 1}.get     ,      g[1][0].strip()				)				)				)
                  SCREAMING_SNAKE_CASE_          =  np.zeros(
                      (
                          len(__UpperCamelCase				),
                          residue_constants.atom_type_num,
                      )				).astype(np.floataa				)
                  for i, atom in enumerate(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  1
                  atom_mask *= mask[..., None]

      assert aatype is not None

      return Protein(
          atom_positions=__UpperCamelCase     ,      atom_mask=__UpperCamelCase     ,      aatype=__UpperCamelCase     ,      residue_index=np.arange(len(__UpperCamelCase				)				)     ,      b_factors=__UpperCamelCase     ,      )


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase = 0				):
      SCREAMING_SNAKE_CASE_          =  []

      SCREAMING_SNAKE_CASE_          =  prot.remark
      if remark is not None:
            pdb_headers.append(F'''REMARK {remark}'''				)

      SCREAMING_SNAKE_CASE_          =  prot.parents
      SCREAMING_SNAKE_CASE_          =  prot.parents_chain_index
      if parents is not None and parents_chain_index is not None:
            SCREAMING_SNAKE_CASE_          =  [p for i, p in zip(__UpperCamelCase     ,      __UpperCamelCase				) if i == chain_id]

      if parents is None or len(__UpperCamelCase				) == 0:
            SCREAMING_SNAKE_CASE_          =  ["N/A"]

      pdb_headers.append(F'''PARENT {" ".join(__UpperCamelCase				)}'''				)

      return pdb_headers


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  pdb_str.split("\n"				)

      SCREAMING_SNAKE_CASE_          =  prot.remark
      if remark is not None:
            out_pdb_lines.append(F'''REMARK {remark}'''				)

      SCREAMING_SNAKE_CASE_          =  42
      if prot.parents is not None and len(prot.parents				) > 0:
            SCREAMING_SNAKE_CASE_          =  []
            if prot.parents_chain_index is not None:
                  SCREAMING_SNAKE_CASE_          =  {}
                  for p, i in zip(prot.parents     ,      prot.parents_chain_index				):
                        parent_dict.setdefault(str(__UpperCamelCase				)     ,      []				)
                        parent_dict[str(__UpperCamelCase				)].append(__UpperCamelCase				)

                  SCREAMING_SNAKE_CASE_          =  max([int(__UpperCamelCase				) for chain_idx in parent_dict]				)
                  for i in range(max_idx + 1				):
                        SCREAMING_SNAKE_CASE_          =  parent_dict.get(str(__UpperCamelCase				)     ,      ["N/A"]				)
                        parents_per_chain.append(__UpperCamelCase				)
            else:
                  parents_per_chain.append(list(prot.parents				)				)
      else:
            SCREAMING_SNAKE_CASE_          =  [["N/A"]]

      def make_parent_line(__UpperCamelCase				) -> str:
            return F'''PARENT {" ".join(__UpperCamelCase				)}'''

      out_pdb_lines.append(make_parent_line(parents_per_chain[0]				)				)

      SCREAMING_SNAKE_CASE_          =  0
      for i, l in enumerate(__UpperCamelCase				):
            if "PARENT" not in l and "REMARK" not in l:
                  out_pdb_lines.append(__UpperCamelCase				)
            if "TER" in l and "END" not in lines[i + 1]:
                  chain_counter += 1
                  if not chain_counter >= len(__UpperCamelCase				):
                        SCREAMING_SNAKE_CASE_          =  parents_per_chain[chain_counter]
                  else:
                        SCREAMING_SNAKE_CASE_          =  ["N/A"]

                  out_pdb_lines.append(make_parent_line(__UpperCamelCase				)				)

      return "\n".join(__UpperCamelCase				)


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  residue_constants.restypes + ["X"]

      def res_atoa(__UpperCamelCase				) -> str:
            return residue_constants.restype_atoa.get(restypes[r]     ,      "UNK"				)

      SCREAMING_SNAKE_CASE_          =  residue_constants.atom_types

      SCREAMING_SNAKE_CASE_          =  []

      SCREAMING_SNAKE_CASE_          =  prot.atom_mask
      SCREAMING_SNAKE_CASE_          =  prot.aatype
      SCREAMING_SNAKE_CASE_          =  prot.atom_positions
      SCREAMING_SNAKE_CASE_          =  prot.residue_index.astype(np.intaa				)
      SCREAMING_SNAKE_CASE_          =  prot.b_factors
      SCREAMING_SNAKE_CASE_          =  prot.chain_index

      if np.any(aatype > residue_constants.restype_num				):
            raise ValueError("Invalid aatypes."				)

      SCREAMING_SNAKE_CASE_          =  get_pdb_headers(__UpperCamelCase				)
      if len(__UpperCamelCase				) > 0:
            pdb_lines.extend(__UpperCamelCase				)

      SCREAMING_SNAKE_CASE_          =  aatype.shape[0]
      SCREAMING_SNAKE_CASE_          =  1
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  string.ascii_uppercase
      SCREAMING_SNAKE_CASE_          =  None
      # Add all atom sites.
      for i in range(__UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  res_atoa(aatype[i]				)
            for atom_name, pos, mask, b_factor in zip(__UpperCamelCase     ,      atom_positions[i]     ,      atom_mask[i]     ,      b_factors[i]				):
                  if mask < 0.5:
                        continue

                  SCREAMING_SNAKE_CASE_          =  "ATOM"
                  SCREAMING_SNAKE_CASE_          =  atom_name if len(__UpperCamelCase				) == 4 else F''' {atom_name}'''
                  SCREAMING_SNAKE_CASE_          =  ""
                  SCREAMING_SNAKE_CASE_          =  ""
                  SCREAMING_SNAKE_CASE_          =  1.00
                  SCREAMING_SNAKE_CASE_          =  atom_name[0]  # Protein supports only C, N, O, S, this works.
                  SCREAMING_SNAKE_CASE_          =  ""

                  SCREAMING_SNAKE_CASE_          =  "A"
                  if chain_index is not None:
                        SCREAMING_SNAKE_CASE_          =  chain_tags[chain_index[i]]

                  # PDB is a columnar format, every space matters here!
                  SCREAMING_SNAKE_CASE_          =  (
                      F'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
                      F'''{res_name_a:>3} {chain_tag:>1}'''
                      F'''{residue_index[i]:>4}{insertion_code:>1}   '''
                      F'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
                      F'''{occupancy:>6.2f}{b_factor:>6.2f}          '''
                      F'''{element:>2}{charge:>2}'''
                  )
                  pdb_lines.append(__UpperCamelCase				)
                  atom_index += 1

            SCREAMING_SNAKE_CASE_          =  i == n - 1
            if chain_index is not None:
                  if i != n - 1 and chain_index[i + 1] != prev_chain_index:
                        SCREAMING_SNAKE_CASE_          =  True
                        SCREAMING_SNAKE_CASE_          =  chain_index[i + 1]

            if should_terminate:
                  # Close the chain.
                  SCREAMING_SNAKE_CASE_          =  "TER"
                  SCREAMING_SNAKE_CASE_          =  (
                      F'''{chain_end:<6}{atom_index:>5}      {res_atoa(aatype[i]				):>3} {chain_tag:>1}{residue_index[i]:>4}'''
                  )
                  pdb_lines.append(__UpperCamelCase				)
                  atom_index += 1

                  if i != n - 1:
                        # "prev" is a misnomer here. This happens at the beginning of
                        # each new chain.
                        pdb_lines.extend(get_pdb_headers(__UpperCamelCase     ,      __UpperCamelCase				)				)

      pdb_lines.append("END"				)
      pdb_lines.append(""				)
      return "\n".join(__UpperCamelCase				)


def      a__ (		__UpperCamelCase				):
      return residue_constants.STANDARD_ATOM_MASK[prot.aatype]





def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = None     ,      __UpperCamelCase = None     ,      __UpperCamelCase = None     ,      __UpperCamelCase = None     ,      __UpperCamelCase = None     ,      ):
      return Protein(
          aatype=features["aatype"]     ,      atom_positions=result["final_atom_positions"]     ,      atom_mask=result["final_atom_mask"]     ,      residue_index=features["residue_index"] + 1     ,      b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"]				)     ,      chain_index=__UpperCamelCase     ,      remark=__UpperCamelCase     ,      parents=__UpperCamelCase     ,      parents_chain_index=__UpperCamelCase     ,      )


