

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Tuple=7      )     ->	Any:

					"""simple docstring"""

					_a      :       Union[str, Any]   =						None
					if token is not None:
										_a      :       List[str]   =						{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"""Bearer {token}"""}

					# The id of a workflow (not of a workflow run)
					_a      :       Any   =						'''636036'''

					_a      :       Any   =						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}"""

					_a      :       List[Any]   =						requests.get(__a						,headers=__a      ).json()

					return result["workflow_runs"]



def   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	Tuple:

					"""simple docstring"""

					_a      :       str   =						get_daily_ci_runs(__a      )
					_a      :       List[Any]   =						None
					for workflow_run in workflow_runs:
										if workflow_run["status"] == "completed":
															_a      :       int   =						workflow_run['''id''']
															break

					return workflow_run_id



def   __UpperCAmelCase					(      __a :     Dict						,__a :     str						,__a :     Optional[int]      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       Dict   =						get_last_daily_ci_runs(__a      )
					if workflow_run_id is not None:
										_a      :       Optional[int]   =						get_artifacts_links(worflow_run_id=__a						,token=__a      )
										for artifact_name in artifact_names:
															if artifact_name in artifacts_links:
																				_a      :       List[str]   =						artifacts_links[artifact_name]
																				download_artifact(
																				    artifact_name=__a						,artifact_url=__a						,output_dir=__a						,token=__a      )







def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     str						,__a :     Tuple      )     ->	Union[str, Any]:

					"""simple docstring"""

					get_last_daily_ci_artifacts(__a						,__a						,__a      )

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

					return results






'''simple docstring'''




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:



  class 							lowercase_						:

    '''simple docstring'''





    @staticmethod
    def     snake_case_      (  *a_ ,			**a_  )				->					Tuple:


      """simple docstring"""
      pass


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						hashlib.mda(image.tobytes()    )
  return m.hexdigest()[:10]


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						np.array(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						npimg.shape
  return {"hash": hashimage(SCREAMING_SNAKE_CASE    ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  __lowerCAmelCase       :			Tuple     	= dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						MaskGenerationPipeline(model=a_ ,			image_processor=a_  )
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]
  def     snake_case_      (  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    pass
  @require_tf
  @unittest.skip('Image segmentation not implemented in TF'  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    pass
  @slow
  @require_torch
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model='facebook/sam-vit-huge'  )

    UpperCAmelCase    				=						image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    for i, o in enumerate(outputs['masks']  ):
      new_outupt += [{"mask": mask_to_test_readable(a_  ), "scores": outputs["scores"][i]}]

    # fmt: off
    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
            {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9967},
            {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
            {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9909},
            {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9879},
            {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9834},
            {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9716},
            {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9612},
            {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9599},
            {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9552},
            {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9532},
            {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9516},
            {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9499},
            {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9483},
            {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9464},
            {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9408},
            {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9335},
            {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9326},
            {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9262},
            {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8999},
            {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8986},
            {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8984},
            {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8873},
            {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8871}
        ] ,			)
    # fmt: on





  @require_torch
  @slow
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/sam-vit-huge'
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model=a_  )

    UpperCAmelCase    				=						image_segmenter(
        'http://images.cocodataset.org/val2017/000000039769.jpg' ,			pred_iou_thresh=1 ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    for i, o in enumerate(outputs['masks']  ):
      new_outupt += [{"mask": mask_to_test_readable(a_  ), "scores": outputs["scores"][i]}]

    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0210},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
        ] ,			)


