




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     :

        @staticmethod
        def 	__magic_name__(   *__UpperCAmelCase      ,				**__UpperCAmelCase		):
          pass



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	str:
  lowerCAmelCase__				:  List[Any]		       =     hashlib.mda(image.tobytes()      )
  return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  _lowerCAmelCase     (  unittest.TestCase	):
      A__		     =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Union[str, Any]		       =     DepthEstimationPipeline(model=__UpperCAmelCase      ,				image_processor=__UpperCAmelCase		)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
        self.assertEqual({'''predicted_depth''': ANY(torch.Tensor		), '''depth''': ANY(Image.Image		)}      ,				__UpperCAmelCase		)
        import datasets

        lowerCAmelCase__				:  int		       =     datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,				'''image'''      ,				split='''test'''		)
        lowerCAmelCase__				:  Union[str, Any]		       =     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		)},
            ]      ,				__UpperCAmelCase      ,				)

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

      @slow
      @require_torch
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Any		       =     '''Intel/dpt-large'''
        lowerCAmelCase__				:  int		       =     pipeline('''depth-estimation'''      ,				model=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
        lowerCAmelCase__				:  str		       =     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 	__magic_name__(   self		):
        # This is highly irregular to have no small tests.
        self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''		)







import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase_					    = [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def      __lowerCAmelCase			(					)     ->	List[Any]:
  lowerCAmelCase__				:  str		       =     Github(os.environ['''GITHUB_TOKEN''']      )
  lowerCAmelCase__				:  str		       =     g.get_repo('''huggingface/diffusers'''      )
  lowerCAmelCase__				:  List[str]		       =     repo.get_issues(state='''open'''      )

  for issue in open_issues:
    lowerCAmelCase__				:  Optional[Any]		       =     sorted(issue.get_comments()	,      key=lambda UpperCamelCase      : i.created_at	,      reverse=UpperCamelCase      )
    lowerCAmelCase__				:  int		       =     comments[0] if len(UpperCamelCase      ) > 0 else None
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and (dt.utcnow() - issue.updated_at).days > 7
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='''closed'''      )
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='''open'''      )
      issue.remove_from_labels('''stale'''      )
    elif (
        (dt.utcnow() - issue.updated_at).days > 23
        and (dt.utcnow() - issue.created_at).days >= 30
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      issue.create_comment(
          '''This issue has been automatically marked as stale because it has not had '''
          '''recent activity. If you think this still needs to be addressed '''
          '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
          '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
          '''are likely to be ignored.'''      )
      issue.add_to_labels('''stale'''      )


if __name__ == "__main__":
     main()

