





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     A     :
								'''simple docstring'''




								@staticmethod
								def   a_			(     *__lowerCAmelCase  :       List[str]				,      **__lowerCAmelCase  :       Optional[int]  )      ->      Tuple:







										"""simple docstring"""






										pass







def   __lowerCamelCase   (				__a  :Any       )						->							Tuple:






		"""simple docstring"""

		A__     =  hashlib.mda(image.tobytes()       )
		return m.hexdigest()[:1_0]







def   __lowerCamelCase   (				__a  :Tuple       )						->							str:






		"""simple docstring"""

		A__     =  np.array(__lowerCAmelCase       )
		A__     =  npimg.shape
		return {"hash": hashimage(__lowerCAmelCase       ), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			Any							     =       dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])       )
			__lowerCamelCase      :			str							     =       dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()       ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])       )






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       int  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  MaskGenerationPipeline(model=__lowerCamelCase				,      image_processor=__lowerCamelCase  )
					return image_segmenter, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       Optional[int]  )      ->      int:







					"""simple docstring"""






					pass






			@require_tf
			@unittest.skip("""Image segmentation not implemented in TF"""  )
			def   a_			(     self  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					pass






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







					"""simple docstring"""






					A__     =  pipeline("""mask-generation"""				,      model="""facebook/sam-vit-huge"""  )

					A__     =  image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""				,      points_per_batch=2_56  )

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

					# fmt: off
					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,      decimals=4  )				,      [
					        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
					        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1},
					        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
					        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
					        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
					        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_6_7},
					        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_3},
					        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (4_80, 6_40)}, """scores""": 0.9_9_0_9},
					        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_7_9},
					        {"""mask""": {"""hash""": """801064ff79""", """shape""": (4_80, 6_40)}, """scores""": 0.9_8_3_4},
					        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (4_80, 6_40)}, """scores""": 0.9_7_1_6},
					        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (4_80, 6_40)}, """scores""": 0.9_6_1_2},
					        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_9_9},
					        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_5_2},
					        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_3_2},
					        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_5_1_6},
					        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_9_9},
					        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_8_3},
					        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_6_4},
					        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
					        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_3},
					        {"""mask""": {"""hash""": """c749b25868""", """shape""": (4_80, 6_40)}, """scores""": 0.9_4_0_8},
					        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_3_5},
					        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (4_80, 6_40)}, """scores""": 0.9_3_2_6},
					        {"""mask""": {"""hash""": """788b798e24""", """shape""": (4_80, 6_40)}, """scores""": 0.9_2_6_2},
					        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_9_9},
					        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_6},
					        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (4_80, 6_40)}, """scores""": 0.8_9_8_4},
					        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_3},
					        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (4_80, 6_40)}, """scores""": 0.8_8_7_1}
					    ]				,      )
					# fmt: on







			@require_torch
			@slow
			def   a_			(     self  :       Optional[int]  )      ->      List[str]:







					"""simple docstring"""






					A__     =  """facebook/sam-vit-huge"""
					A__     =  pipeline("""mask-generation"""				,      model=__lowerCamelCase  )

					A__     =  image_segmenter(
					    """http://images.cocodataset.org/val2017/000000039769.jpg"""				,      pred_iou_thresh=1				,      points_per_batch=2_56  )

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

					self.assertEqual(
					    nested_simplify(__lowerCamelCase				,      decimals=4  )				,      [
					        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (4_80, 6_40)}, """scores""": 1.0_4_4_4},
					        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (4_80, 6_40)}, """scores""": 1.0_2_1_0},
					        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_6_7},
					        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (4_80, 6_40)}, """scores""": 1.0_1_3_2},
					        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (4_80, 6_40)}, """scores""": 1.0_0_5_3},
					    ]				,      )









import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__magic_name__  :  List[Any]					=       logging.get_logger(__name__)
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()
  lowerCAmelCase__										=      []

  def parse_line(__lowerCAmelCase				):
    for line in fp:
      if isinstance(__lowerCAmelCase   , __lowerCAmelCase				):
        lowerCAmelCase__										=      line.decode('''UTF-8'''				)
      if "warnings summary (final)" in line:
        continue
      # This means we are outside the body of a warning
      elif not line.startswith(''' '''				):
        # process a single warning and move it to `selected_warnings`.
        if len(__lowerCAmelCase				) > 0:
          lowerCAmelCase__										=      '''\n'''.join(__lowerCAmelCase				)
          # Only keep the warnings specified in `targets`
          if any(F""": {x}: """ in warning for x in targets				):
            selected_warnings.add(__lowerCAmelCase				)
          buffer.clear()
        continue
      else:
        lowerCAmelCase__										=      line.strip()
        buffer.append(__lowerCAmelCase				)

  if from_gh:
    for filename in os.listdir(__lowerCAmelCase				):
      lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
      if not os.path.isdir(__lowerCAmelCase				):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(__lowerCAmelCase				) as fp:
          parse_line(__lowerCAmelCase				)
  else:
    try:
      with zipfile.ZipFile(__lowerCAmelCase				) as z:
        for filename in z.namelist():
          if not os.path.isdir(__lowerCAmelCase				):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(__lowerCAmelCase				) as fp:
              parse_line(__lowerCAmelCase				)
    except Exception:
      logger.warning(
          F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""				)

  return selected_warnings
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase				):
  lowerCAmelCase__										=      set()

  lowerCAmelCase__										=      [os.path.join(__lowerCAmelCase   , __lowerCAmelCase				) for p in os.listdir(__lowerCAmelCase				) if (p.endswith('''.zip'''				) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(__lowerCAmelCase   , __lowerCAmelCase				)				)

  return selected_warnings


if __name__ == "__main__":
      def 		a_							( __lowerCAmelCase				):
        return values.split(''','''				)

      __magic_name__  :  Optional[int]					=       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
      parser.add_argument(
          """--output_dir""",
          type=str,
          required=True,
          help="""Where to store the downloaded artifacts and other result files.""",
      )
      parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
      # optional parameters
      parser.add_argument(
          """--targets""",
          default="""DeprecationWarning,UserWarning,FutureWarning""",
          type=list_str,
          help="""Comma-separated list of target warning(s) which we want to extract.""",
      )
      parser.add_argument(
          """--from_gh""",
          action="""store_true""",
          help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
      )

      __magic_name__  :  Optional[Any]					=       parser.parse_args()

      __magic_name__  :  Optional[int]					=       args.from_gh
      if from_gh:
            # The artifacts have to be downloaded using `actions/download-artifact@v3`
            pass
      else:
            os.makedirs(args.output_dir, exist_ok=True)

            # get download links
            __magic_name__  :  Optional[Any]					=       get_artifacts_links(args.workflow_run_id, token=args.token)
            with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
                  json.dump(artifacts, fp, ensure_ascii=False, indent=4)

            # download artifacts
            for idx, (name, url) in enumerate(artifacts.items()):
                  print(name)
                  print(url)
                  print("""=""" * 80)
                  download_artifact(name, url, args.output_dir, args.token)
                  # Be gentle to GitHub
                  time.sleep(1)

    # extract warnings from artifacts
      __magic_name__  :  Optional[Any]					=       extract_warnings(args.output_dir, args.targets)
      __magic_name__  :  Any					=       sorted(selected_warnings)
      with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
            json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

