"""simple docstring"""
import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image
else:



     class   lowerCAmelCase_    :



         """simple docstring"""

         @staticmethod
         def 		snake_case (				*lowerCAmelCase		,    **lowerCAmelCase    ):






               """simple docstring"""

               pass





def    lowerCAmelCase__    (			_UpperCamelCase       :				Image					)					->				str:





      """simple docstring"""






      snake_case								=			hashlib.mda(image.tobytes()					)
      return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   lowerCAmelCase_    (			unittest.TestCase					):



    """simple docstring"""
    _lowerCAmelCase     :    List[Any]			  =						MODEL_FOR_DEPTH_ESTIMATION_MAPPING

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			DepthEstimationPipeline(model=lowerCAmelCase		,    image_processor=lowerCAmelCase    )
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
          self.assertEqual({'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )}		,    lowerCAmelCase    )
          import datasets

          snake_case								=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,    'image'		,    split='test'    )
          snake_case								=			depth_estimator(
              [
                  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    ),
                  'http://images.cocodataset.org/val2017/000000039769.jpg',
                  # RGBA
                  dataset[0]['file'],
                  # LA
                  dataset[1]['file'],
                  # L
                  dataset[2]['file'],
              ]    )
          self.assertEqual(
              [
                  {'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )},
                  {'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )},
                  {'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )},
                  {'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )},
                  {'predicted_depth': ANY(torch.Tensor    ), 'depth': ANY(Image.Image    )},
              ]		,    lowerCAmelCase		,    )

    @require_tf
    @unittest.skip('Depth estimation is not implemented in TF'    )
    def 		snake_case (				self    ):






          """simple docstring"""

          pass

    @slow
    @require_torch
    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			'Intel/dpt-large'
          snake_case								=			pipeline('depth-estimation'		,    model=lowerCAmelCase    )
          snake_case								=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'    )
          snake_case								=			hashimage(outputs['depth']    )

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()    )		,    29.3_04    )
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()    )		,    2.6_62    )

    @require_torch
    def 		snake_case (				self    ):






          """simple docstring"""

          self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'    )



"""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


