





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DPTImageProcessor







class     __lowerCAmelCase	(   unittest.TestCase):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=7		,				lowerCAmelCase__=3		,				lowerCAmelCase__=1_8		,				lowerCAmelCase__=3_0		,				lowerCAmelCase__=4_0_0		,				lowerCAmelCase__=True		,				lowerCAmelCase__=None		,				lowerCAmelCase__=True		,				lowerCAmelCase__=[0.5, 0.5, 0.5]		,				lowerCAmelCase__=[0.5, 0.5, 0.5]		,				)       ->						List[str]:



        '''simple docstring'''




        a__     : str  =size if size is not None else {"height": 1_8, "width": 1_8}
        a__     : List[Any]  =parent
        a__     : int  =batch_size
        a__     : List[Any]  =num_channels
        a__     : str  =image_size
        a__     : Union[str, Any]  =min_resolution
        a__     : Any  =max_resolution
        a__     : Tuple  =do_resize
        a__     : Any  =size
        a__     : Union[str, Any]  =do_normalize
        a__     : List[str]  =image_mean
        a__     : Tuple  =image_std







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
        }







@require_torch
@require_vision
class     __lowerCAmelCase	(   UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : List[str]            =      DPTImageProcessor if is_vision_available() else None







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__     : Union[str, Any]  =DPTImageProcessingTester(self							)







      @property
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"image_mean"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"image_std"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"do_normalize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"size"							)							)







      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"height": 1_8, "width": 1_8}							)

        a__     : Optional[int]  =self.image_processing_class.from_dict(self.image_processor_dict		,				size=4_2							)
        self.assertEqual(image_processor.size		,				{"height": 4_2, "width": 4_2}							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[Any]  =self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__     : str  =prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,				Image.Image							)

        # Test not batched input
        a__     : Tuple  =image_processing(image_inputs[0]		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )		,				)

        # Test batched
        a__     : Any  =image_processing(lowerCAmelCase__		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )		,				)







      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        a__     : Optional[int]  =self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__     : Dict  =prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase__		,				numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,				np.ndarray							)

        # Test not batched input
        a__     : str  =image_processing(image_inputs[0]		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )		,				)

        # Test batched
        a__     : Tuple  =image_processing(lowerCAmelCase__		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )		,				)







      def        _lowercase  (   self							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__     : Optional[Any]  =prepare_image_inputs(self.image_processor_tester		,				equal_resolution=lowerCAmelCase__		,				torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,				torch.Tensor							)

        # Test not batched input
        a__     : int  =image_processing(image_inputs[0]		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )		,				)

        # Test batched
        a__     : Union[str, Any]  =image_processing(lowerCAmelCase__		,				return_tensors="pt"							).pixel_values
        self.assertEqual(
            encoded_images.shape		,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size["height"],
                self.image_processor_tester.size["width"],
            )		,				)













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


UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : Tuple  =set()
  a__     : Optional[Any]  =[]

  def parse_line(SCREAMING_SNAKE_CASE					:       Optional[int]						):
    for line in fp:
      if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
        a__     : str  =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(SCREAMING_SNAKE_CASE						) > 0:
          a__     : Union[str, Any]  ="\n".join(SCREAMING_SNAKE_CASE						)
          # Only keep the warnings specified in `targets`
          if any(f''': {x}: ''' in warning for x in targets						):
            selected_warnings.add(SCREAMING_SNAKE_CASE						)
          buffer.clear()
        continue
      else:
        a__     : Optional[Any]  =line.strip()
        buffer.append(SCREAMING_SNAKE_CASE						)

  if from_gh:
    for filename in os.listdir(SCREAMING_SNAKE_CASE						):
      a__     : str  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE						):
        # read the file
        if filename != "warnings.txt":
          continue
        with open(SCREAMING_SNAKE_CASE						) as fp:
          parse_line(SCREAMING_SNAKE_CASE						)
  else:
    try:
      with zipfile.ZipFile(SCREAMING_SNAKE_CASE						) as z:
        for filename in z.namelist():
          if not os.path.isdir(SCREAMING_SNAKE_CASE						):
            # read the file
            if filename != "warnings.txt":
              continue
            with z.open(SCREAMING_SNAKE_CASE						) as fp:
              parse_line(SCREAMING_SNAKE_CASE						)
    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	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[Any]						):





  """simple docstring"""



  a__     : Optional[int]  =set()

  a__     : Any  =[os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						) for p in os.listdir(SCREAMING_SNAKE_CASE						) if (p.endswith(".zip"						) or from_gh)]
  for p in paths:
    selected_warnings.update(extract_warnings_from_single_artifact(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)						)

  return selected_warnings


if __name__ == "__main__":
     def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





       """simple docstring"""



       return values.split(","						)

     UpperCAmelCase     :     Any							=	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.""",
     )

     UpperCAmelCase     :     List[Any]							=	parser.parse_args()

     UpperCAmelCase     :     str							=	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
          UpperCAmelCase     :     Dict							=	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
     UpperCAmelCase     :     Tuple							=	extract_warnings(args.output_dir, args.targets)
     UpperCAmelCase     :     Optional[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)






