

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 GLPNImageProcessor





class A__    ( unittest.TestCase ):



 """simple docstring"""







 def __init__(		self :	List[Any]     ,						A_ :	int     ,						A_ :	int=7     ,						A_ :	int=3     ,						A_ :	List[str]=1_8     ,						A_ :	Optional[Any]=3_0     ,						A_ :	str=4_0_0     ,						A_ :	Tuple=True     ,						A_ :	List[str]=3_2     ,						A_ :	List[str]=True     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    parent
      _lowerCAmelCase   :					Union[str, Any]        =    batch_size
      _lowerCAmelCase   :					Tuple        =    num_channels
      _lowerCAmelCase   :					Optional[Any]        =    image_size
      _lowerCAmelCase   :					Any        =    min_resolution
      _lowerCAmelCase   :					str        =    max_resolution
      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					Any        =    size_divisor
      _lowerCAmelCase   :					Union[str, Any]        =    do_rescale


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






      '''simple docstring'''




      return {
          "do_resize": self.do_resize,
          "size_divisor": self.size_divisor,
          "do_rescale": self.do_rescale,
      }





@require_torch
@require_vision
class A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					int      =						GLPNImageProcessor if is_vision_available() else None

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    GLPNImageProcessingTester(self       )

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(A_     ,						"do_resize"       )       )
      self.assertTrue(hasattr(A_     ,						"size_divisor"       )       )
      self.assertTrue(hasattr(A_     ,						"resample"       )       )
      self.assertTrue(hasattr(A_     ,						"do_rescale"       )       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      pass

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      _lowerCAmelCase   :					List[Any]        =    prepare_image_inputs(self.image_processor_tester     ,						equal_resolution=A_       )
      for image in image_inputs:
           self.assertIsInstance(A_     ,						Image.Image       )

      # Test not batched input (GLPNImageProcessor doesn't support batching)
      _lowerCAmelCase   :					List[str]        =    image_processing(image_inputs[0]     ,						return_tensors="pt"       ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      _lowerCAmelCase   :					Dict        =    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 (GLPNImageProcessor doesn't support batching)
      _lowerCAmelCase   :					Optional[int]        =    image_processing(image_inputs[0]     ,						return_tensors="pt"       ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      _lowerCAmelCase   :					Union[str, Any]        =    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 (GLPNImageProcessor doesn't support batching)
      _lowerCAmelCase   :					Optional[int]        =    image_processing(image_inputs[0]     ,						return_tensors="pt"       ).pixel_values
      self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0       )
      self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0       )









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






