

'''simple docstring'''
import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image
else:







     class 							A__    :





       @staticmethod
       def 						A  (		*_a      :	Tuple							,					**_a      :	Optional[Any] )	->    Optional[Any]:





            '''simple docstring'''






            pass







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





  def 						A  (		self      :	str							,					_a      :	Union[str, Any]							,					_a      :	Optional[Any]							,					_a      :	Tuple )	->    Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =ObjectDetectionPipeline(model=_a							,					image_processor=_a )
       return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]





  def 						A  (		self      :	List[Any]							,					_a      :	int							,					_a      :	List[Any] )	->    List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'							,					threshold=0.0 )

       self.assertGreater(len(_a )							,					0 )
       for detected_object in outputs:
            self.assertEqual(
                _a							,					{
                    'score': ANY(_a ),
                    'label': ANY(_a ),
                    'box': {'xmin': ANY(_a ), 'ymin': ANY(_a ), 'xmax': ANY(_a ), 'ymax': ANY(_a )},
                }							,					)

       import datasets

       _SCREAMING_SNAKE_CASE							    =datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,					'image'							,					split='test' )

       _SCREAMING_SNAKE_CASE							    =[
           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'],
       ]
       _SCREAMING_SNAKE_CASE							    =object_detector(_a							,					threshold=0.0 )

       self.assertEqual(len(_a )							,					len(_a ) )
       for outputs in batch_outputs:
            self.assertGreater(len(_a )							,					0 )
            for detected_object in outputs:
                 self.assertEqual(
                     _a							,					{
                         'score': ANY(_a ),
                         'label': ANY(_a ),
                         'box': {'xmin': ANY(_a ), 'ymin': ANY(_a ), 'xmax': ANY(_a ), 'ymax': ANY(_a )},
                     }							,					)





  @require_tf
  @unittest.skip('Object detection not implemented in TF' )
  def 						A  (		self      :	Tuple )	->    int:





       '''simple docstring'''






       pass





  @require_torch
  def 						A  (		self      :	Optional[int] )	->    Any:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='hf-internal-testing/tiny-detr-mobilenetsv3'

       _SCREAMING_SNAKE_CASE							    =AutoModelForObjectDetection.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =AutoFeatureExtractor.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =ObjectDetectionPipeline(model=_a							,					feature_extractor=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'							,					threshold=0.0 )

       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
               {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
           ]							,					)

       _SCREAMING_SNAKE_CASE							    =object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]							,					threshold=0.0							,					)

       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               [
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
               ],
               [
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                   {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
               ],
           ]							,					)





  @require_torch
  @slow
  def 						A  (		self      :	List[str] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='facebook/detr-resnet-50'

       _SCREAMING_SNAKE_CASE							    =AutoModelForObjectDetection.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =AutoFeatureExtractor.from_pretrained(_a )
       _SCREAMING_SNAKE_CASE							    =ObjectDetectionPipeline(model=_a							,					feature_extractor=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
               {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
               {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
               {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
               {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
           ]							,					)

       _SCREAMING_SNAKE_CASE							    =object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ] )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
           ]							,					)





  @require_torch
  @slow
  def 						A  (		self      :	List[Any] )	->    List[str]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='facebook/detr-resnet-50'

       _SCREAMING_SNAKE_CASE							    =pipeline('object-detection'							,					model=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
               {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
               {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
               {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
               {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
           ]							,					)

       _SCREAMING_SNAKE_CASE							    =object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ] )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
               [
                   {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                   {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                   {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                   {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                   {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
               ],
           ]							,					)





  @require_torch
  @slow
  def 						A  (		self      :	int )	->    Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =0.99_85
       _SCREAMING_SNAKE_CASE							    ='facebook/detr-resnet-50'

       _SCREAMING_SNAKE_CASE							    =pipeline('object-detection'							,					model=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'							,					threshold=_a )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
               {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
           ]							,					)





  @require_torch
  @require_pytesseract
  @slow
  def 						A  (		self      :	int )	->    Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    ='Narsil/layoutlmv3-finetuned-funsd'
       _SCREAMING_SNAKE_CASE							    =0.99_93

       _SCREAMING_SNAKE_CASE							    =pipeline('object-detection'							,					model=_a							,					threshold=_a )

       _SCREAMING_SNAKE_CASE							    =object_detector(
           'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png' )
       self.assertEqual(
           nested_simplify(_a							,					decimals=4 )							,					[
               {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
               {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
           ]							,					)


"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def   __magic_name__	(      __snake_case				:       Union[str, Any]     , __snake_case				:       List[str]=7  ) ->				str:
       lowercase    :				int       		=							None
       if token is not None:
              lowercase    :				Any       		=							{"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

       # The id of a workflow (not of a workflow run)
       lowercase    :				int       		=							"636036"

       lowercase    :				Dict       		=							f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
       # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
       url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

       lowercase    :				int       		=							requests.get(__snake_case     , headers=__snake_case  ).json()

       return result["workflow_runs"]


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Tuple:
       lowercase    :				Tuple       		=							get_daily_ci_runs(__snake_case  )
       lowercase    :				Union[str, Any]       		=							None
       for workflow_run in workflow_runs:
              if workflow_run["status"] == "completed":
                     lowercase    :				List[Any]       		=							workflow_run["id"]
                     break

       return workflow_run_id


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Optional[Any]     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase    :				Dict       		=							get_last_daily_ci_runs(__snake_case  )
       if workflow_run_id is not None:
              lowercase    :				Dict       		=							get_artifacts_links(worflow_run_id=__snake_case     , token=__snake_case  )
              for artifact_name in artifact_names:
                     if artifact_name in artifacts_links:
                            lowercase    :				Optional[int]       		=							artifacts_links[artifact_name]
                            download_artifact(
                                artifact_name=__snake_case     , artifact_url=__snake_case     , output_dir=__snake_case     , token=__snake_case  )


def   __magic_name__	(      __snake_case				:       List[Any]     , __snake_case				:       Optional[int]     , __snake_case				:       Tuple  ) ->				Optional[int]:
       get_last_daily_ci_artifacts(__snake_case     , __snake_case     , __snake_case  )

       lowercase    :				str       		=							{}
       for artifact_name in artifact_names:
              lowercase    :				Optional[Any]       		=							os.path.join(__snake_case     , f"""{artifact_name}.zip"""  )
              if os.path.isfile(__snake_case  ):
                     lowercase    :				List[Any]       		=							{}
                     with zipfile.ZipFile(__snake_case  ) as z:
                            for filename in z.namelist():
                                   if not os.path.isdir(__snake_case  ):
                                          # read the file
                                          with z.open(__snake_case  ) as f:
                                                 lowercase    :				str       		=							f.read().decode("UTF-8"  )

       return results


