





'''simple docstring'''






import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class 		lowerCamelCase	:



						@staticmethod
						def 	_lowerCamelCase     ( *a_		,   **a_						):
								pass
@is_pipeline_test
@require_vision
@require_torch
class 		lowerCamelCase	(   unittest.TestCase							):
	snake_case_   = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_						):
			lowerCAmelCase  :     Tuple       						= pipeline(
			    "zero-shot-object-detection"		,   model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

			lowerCAmelCase  :     Dict       						= [
			    {
			        '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
			        '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
			    }
			]
			return object_detector, examples



	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			lowerCAmelCase  :     Tuple       						= object_detector(examples[0]		,   threshold=0.0						)

			lowerCAmelCase  :     List[str]       						= len(lowerCamelCase_						)
			self.assertGreater(lowerCamelCase_		,   0						)
			self.assertEqual(
			    lowerCamelCase_		,   [
			        {
			            "score": ANY(lowerCamelCase_						),
			            "label": ANY(lowerCamelCase_						),
			            "box": {"xmin": ANY(lowerCamelCase_						), "ymin": ANY(lowerCamelCase_						), "xmax": ANY(lowerCamelCase_						), "ymax": ANY(lowerCamelCase_						)},
			        }
			        for i in range(lowerCamelCase_						)
			    ]		,   )



	@require_tf
	@unittest.skip("Zero Shot Object Detection not implemented in TF"						)
	def 	_lowerCamelCase     ( self						):
			pass



	@require_torch
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[str]       						= pipeline(
			    "zero-shot-object-detection"		,   model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

			lowerCAmelCase  :     Optional[Any]       						= object_detector(
			    "./tests/fixtures/tests_samples/COCO/000000039769.png"		,   candidate_labels=["cat", "remote", "couch"]		,   threshold=0.64		,   )

			self.assertEqual(
			    nested_simplify(lowerCamelCase_		,   decimals=4						)		,   [
			        {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
			        {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
			        {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
			        {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
			        {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
			        {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
			        {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
			        {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
			        {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
			    ]		,   )

			lowerCAmelCase  :     str       						= object_detector(
			    [
			        {
			            "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
			            "candidate_labels": ["cat", "remote", "couch"],
			        }
			    ]		,   threshold=0.64		,   )

			self.assertEqual(
			    nested_simplify(lowerCamelCase_		,   decimals=4						)		,   [
			        [
			            {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
			            {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
			            {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
			            {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
			            {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
			            {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
			            {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
			            {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
			            {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
			        ]
			    ]		,   )



	@require_torch
	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Any       						= pipeline("zero-shot-object-detection"						)

			lowerCAmelCase  :     Any       						= object_detector(
			    "http://images.cocodataset.org/val2017/000000039769.jpg"		,   candidate_labels=["cat", "remote", "couch"]		,   )
			self.assertEqual(
			    nested_simplify(lowerCamelCase_		,   decimals=4						)		,   [
			        {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
			        {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
			        {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
			        {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
			        {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
			    ]		,   )

			lowerCAmelCase  :     str       						= object_detector(
			    [
			        {
			            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
			            "candidate_labels": ["cat", "remote", "couch"],
			        },
			        {
			            "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
			            "candidate_labels": ["cat", "remote", "couch"],
			        },
			    ]		,   )
			self.assertEqual(
			    nested_simplify(lowerCamelCase_		,   decimals=4						)		,   [
			        [
			            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
			            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
			            {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
			            {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
			            {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
			        ],
			        [
			            {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
			            {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
			            {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
			            {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
			            {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
			        ],
			    ]		,   )



	@require_tf
	@unittest.skip("Zero Shot Object Detection not implemented in TF"						)
	def 	_lowerCamelCase     ( self						):
			pass



	@require_torch
	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= 0.2
			lowerCAmelCase  :     List[str]       						= pipeline("zero-shot-object-detection"						)

			lowerCAmelCase  :     Dict       						= object_detector(
			    "http://images.cocodataset.org/val2017/000000039769.jpg"		,   candidate_labels=["cat", "remote", "couch"]		,   threshold=lowerCamelCase_		,   )
			self.assertEqual(
			    nested_simplify(lowerCamelCase_		,   decimals=4						)		,   [
			        {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
			        {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
			        {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
			    ]		,   )





	@require_torch
	@slow
	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= 2
			lowerCAmelCase  :     Optional[Any]       						= pipeline("zero-shot-object-detection"						)

			lowerCAmelCase  :     Any       						= object_detector(
			    "http://images.cocodataset.org/val2017/000000039769.jpg"		,   candidate_labels=["cat", "remote", "couch"]		,   top_k=lowerCamelCase_		,   )
			self.assertEqual(
			    nested_simplify(lowerCamelCase_		,   decimals=4						)		,   [
			        {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
			        {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
			    ]		,   )




'''simple docstring'''

import os
from datetime import datetime as dt

from github import Github


UpperCamelCase__  :						str					=      [
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	Github(os.environ['''GITHUB_TOKEN''']     )
   UpperCAmelCase__				:					Union[str, Any] 					=	g.get_repo('''huggingface/diffusers'''     )
   UpperCAmelCase__				:					Union[str, Any] 					=	repo.get_issues(state='''open'''     )

   for issue in open_issues:
      UpperCAmelCase__				:					int 					=	sorted(issue.get_comments()	,      key=lambda _A     : i.created_at	,      reverse=_A     )
      UpperCAmelCase__				:					int 					=	comments[0] if len(_A     ) > 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()




