




'''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 					_a :





           '''simple docstring'''



           @staticmethod
           def 			snake_case_      (				*__a							,**__a							)      ->				List[str]:
                pass



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      List[str]        =			hashlib.mda(image.tobytes()				)
     return m.hexdigest()







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





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Any:
          snake_case			:      List[str]        =			DepthEstimationPipeline(model=a__							,image_processor=a__							)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]







     def 			snake_case_      (				self							,__a							,__a							)      ->				Union[str, Any]:
          snake_case			:      Any        =			depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
          self.assertEqual({"""predicted_depth""": ANY(torch.Tensor							), """depth""": ANY(Image.Image							)}							,a__							)
          import datasets

          snake_case			:      List[str]        =			datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,"""image"""							,split="""test"""							)
          snake_case			:      List[str]        =			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							)},
              ]							,a__							,)







     @require_tf
     @unittest.skip("""Depth estimation is not implemented in TF"""							)
     def 			snake_case_      (				self							)      ->				int:
          pass







     @slow
     @require_torch
     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      str        =			"Intel/dpt-large"
          snake_case			:      Dict        =			pipeline("""depth-estimation"""							,model=a__							)
          snake_case			:      Any        =			depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""							)
          snake_case			:      Any        =			hashimage(outputs["""depth"""]							)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()							)							,29.304							)
          self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()							)							,2.662							)







     @require_torch
     def 			snake_case_      (				self							)      ->				Any:
          self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""							)





'''simple docstring'''

import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
snake_case									=    """."""


if __name__ == "__main__":
     snake_case									=    os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
     snake_case									=    []
     snake_case									=    []
     with open(doctest_file_path) as fp:
          for line in fp:
               snake_case									=    line.strip()
               snake_case									=    os.path.join(REPO_PATH, line)
               if not (os.path.isfile(path) or os.path.isdir(path)):
                    non_existent_paths.append(line)
               all_paths.append(path)
     if len(non_existent_paths) > 0:
          snake_case									=    """\n""".join(non_existent_paths)
          raise ValueError(f'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
     if all_paths != sorted(all_paths):
          raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")





