"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

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 DetrImageProcessor




class    __snake_case       (     unittest.TestCase				):



      def __init__(						self    ,			lowercase    ,			lowercase=7    ,			lowercase=3    ,			lowercase=30    ,			lowercase=4_00    ,			lowercase=True    ,			lowercase=None    ,			lowercase=True    ,			lowercase=1 / 2_55    ,			lowercase=True    ,			lowercase=[0.5, 0.5, 0.5]    ,			lowercase=[0.5, 0.5, 0.5]    ,			lowercase=True    ,			)	->			Optional[int]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
        a__:     Dict										=	parent
        a__:     str										=	batch_size
        a__:     str										=	num_channels
        a__:     int										=	min_resolution
        a__:     Union[str, Any]										=	max_resolution
        a__:     Optional[Any]										=	do_resize
        a__:     Union[str, Any]										=	size
        a__:     int										=	do_rescale
        a__:     List[Any]										=	rescale_factor
        a__:     Dict										=	do_normalize
        a__:     Tuple										=	image_mean
        a__:     List[Any]										=	image_std
        a__:     Union[str, Any]										=	do_pad



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=False)	->			Optional[Any]:





        '''simple docstring'''





        if not batched:
          a__:     List[Any]										=	image_inputs[0]
          if isinstance(lowercase    ,			Image.Image):
            a__							,    a__:     str										=	image.size
          else:
            a__							,    a__:     Any										=	image.shape[1], image.shape[2]
          if w < h:
            a__:     Dict										=	int(self.size['shortest_edge'] * h / w)
            a__:     str										=	self.size['shortest_edge']
          elif w > h:
            a__:     Union[str, Any]										=	self.size['shortest_edge']
            a__:     Dict										=	int(self.size['shortest_edge'] * w / h)
          else:
            a__:     Tuple										=	self.size['shortest_edge']
            a__:     Optional[Any]										=	self.size['shortest_edge']

        else:
          a__:     List[str]										=	[]
          for image in image_inputs:
            a__							,    a__:     Optional[Any]										=	self.get_expected_values([image])
            expected_values.append((expected_height, expected_width))
          a__:     List[str]										=	max(lowercase    ,			key=lambda lowercase: item[0])[0]
          a__:     Tuple										=	max(lowercase    ,			key=lambda lowercase: item[1])[1]

        return expected_height, expected_width







@require_torch
@require_vision
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		DetrImageProcessor if is_vision_available() else None



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





        '''simple docstring'''





        a__:     str										=	DetrImageProcessingTester(self)



      @property
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.image_processor_tester.prepare_image_processor_dict()



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     int										=	self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(lowercase    ,			'image_mean'))
        self.assertTrue(hasattr(lowercase    ,			'image_std'))
        self.assertTrue(hasattr(lowercase    ,			'do_normalize'))
        self.assertTrue(hasattr(lowercase    ,			'do_rescale'))
        self.assertTrue(hasattr(lowercase    ,			'rescale_factor'))
        self.assertTrue(hasattr(lowercase    ,			'do_resize'))
        self.assertTrue(hasattr(lowercase    ,			'size'))
        self.assertTrue(hasattr(lowercase    ,			'do_pad'))



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 18, 'longest_edge': 13_33})
        self.assertEqual(image_processor.do_pad    ,			lowercase)

        a__:     Union[str, Any]										=	self.image_processing_class.from_dict(
            self.image_processor_dict    ,			size=42    ,			max_size=84    ,			pad_and_return_pixel_mask=lowercase)
        self.assertEqual(image_processor.size    ,			{'shortest_edge': 42, 'longest_edge': 84})
        self.assertEqual(image_processor.do_pad    ,			lowercase)



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





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__:     int										=	self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        a__:     Tuple										=	prepare_image_inputs(self.image_processor_tester    ,			equal_resolution=lowercase)
        for image in image_inputs:
          self.assertIsInstance(lowercase    ,			Image.Image)

        # Test not batched input
        a__:     str										=	image_processing(image_inputs[0]    ,			return_tensors='pt').pixel_values

        a__							,    a__:     str										=	self.image_processor_tester.get_expected_values(lowercase)

        self.assertEqual(
            encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

        # Test batched
        a__							,    a__:     Tuple										=	self.image_processor_tester.get_expected_values(lowercase    ,			batched=lowercase)

        a__:     Optional[Any]										=	image_processing(lowercase    ,			return_tensors='pt').pixel_values
        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





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

        # Test not batched input
        a__:     int										=	image_processing(image_inputs[0]    ,			return_tensors='pt').pixel_values

        a__							,    a__:     str										=	self.image_processor_tester.get_expected_values(lowercase)

        self.assertEqual(
            encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

        # Test batched
        a__:     Dict										=	image_processing(lowercase    ,			return_tensors='pt').pixel_values

        a__							,    a__:     Any										=	self.image_processor_tester.get_expected_values(lowercase    ,			batched=lowercase)

        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





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

        # Test not batched input
        a__:     Optional[int]										=	image_processing(image_inputs[0]    ,			return_tensors='pt').pixel_values

        a__							,    a__:     Optional[int]										=	self.image_processor_tester.get_expected_values(lowercase)

        self.assertEqual(
            encoded_images.shape    ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,			)

        # Test batched
        a__:     int										=	image_processing(lowercase    ,			return_tensors='pt').pixel_values

        a__							,    a__:     Tuple										=	self.image_processor_tester.get_expected_values(lowercase    ,			batched=lowercase)

        self.assertEqual(
            encoded_images.shape    ,			(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )    ,			)



      @slow
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
        with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,			'r') as f:
          a__:     Dict										=	json.loads(f.read())

        a__:     Union[str, Any]										=	{'image_id': 3_97_69, 'annotations': target}

        # encode them
        a__:     List[str]										=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50')
        a__:     Dict										=	image_processing(images=lowercase    ,			annotations=lowercase    ,			return_tensors='pt')

        # verify pixel values
        a__:     str										=	torch.Size([1, 3, 8_00, 10_66])
        self.assertEqual(encoding['pixel_values'].shape    ,			lowercase)

        a__:     List[str]										=	torch.tensor([0.2796, 0.3138, 0.3481])
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowercase    ,			atol=1e-4))

        # verify area
        a__:     str										=	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438])
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowercase))
        # verify boxes
        a__:     Optional[Any]										=	torch.Size([6, 4])
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowercase)
        a__:     Optional[Any]										=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowercase    ,			atol=1e-3))
        # verify image_id
        a__:     Optional[Any]										=	torch.tensor([3_97_69])
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowercase))
        # verify is_crowd
        a__:     int										=	torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowercase))
        # verify class_labels
        a__:     Dict										=	torch.tensor([75, 75, 63, 65, 17, 17])
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowercase))
        # verify orig_size
        a__:     Optional[Any]										=	torch.tensor([4_80, 6_40])
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowercase))
        # verify size
        a__:     Tuple										=	torch.tensor([8_00, 10_66])
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowercase))



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     List[Any]										=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
        with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,			'r') as f:
          a__:     List[str]										=	json.loads(f.read())

        a__:     Optional[Any]										=	{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

        a__:     Optional[int]										=	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

        # encode them
        a__:     Dict										=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic')
        a__:     Optional[int]										=	image_processing(images=lowercase    ,			annotations=lowercase    ,			masks_path=lowercase    ,			return_tensors='pt')

        # verify pixel values
        a__:     str										=	torch.Size([1, 3, 8_00, 10_66])
        self.assertEqual(encoding['pixel_values'].shape    ,			lowercase)

        a__:     Any										=	torch.tensor([0.2796, 0.3138, 0.3481])
        self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,			lowercase    ,			atol=1e-4))

        # verify area
        a__:     Any										=	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147])
        self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,			lowercase))
        # verify boxes
        a__:     Optional[Any]										=	torch.Size([6, 4])
        self.assertEqual(encoding['labels'][0]['boxes'].shape    ,			lowercase)
        a__:     List[str]										=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
        self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,			lowercase    ,			atol=1e-3))
        # verify image_id
        a__:     Union[str, Any]										=	torch.tensor([3_97_69])
        self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,			lowercase))
        # verify is_crowd
        a__:     Union[str, Any]										=	torch.tensor([0, 0, 0, 0, 0, 0])
        self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,			lowercase))
        # verify class_labels
        a__:     List[Any]										=	torch.tensor([17, 17, 63, 75, 75, 93])
        self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,			lowercase))
        # verify masks
        a__:     int										=	82_28_73
        self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,			lowercase)
        # verify orig_size
        a__:     Optional[Any]										=	torch.tensor([4_80, 6_40])
        self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,			lowercase))
        # verify size
        a__:     Any										=	torch.tensor([8_00, 10_66])
        self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,			lowercase))



"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->Optional[int]:
  a__:     int										=	None
  if token is not None:
    a__:     Tuple										=	{'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:     Optional[Any]										=	F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
  a__:     str										=	requests.get(_SCREAMING_SNAKE_CASE		,     headers=_SCREAMING_SNAKE_CASE   ).json()
  a__:     str										=	{}

  try:
    job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )
    a__:     int										=	math.ceil((result['total_count'] - 100) / 100   )

    for i in range(_SCREAMING_SNAKE_CASE   ):
      a__:     Dict										=	requests.get(url + F'&page={i + 2}'		,     headers=_SCREAMING_SNAKE_CASE   ).json()
      job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )

    return job_links
  except Exception:
    print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'   )

  return {}







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->Dict:
  a__:     Dict										=	None
  if token is not None:
    a__:     List[str]										=	{'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:     Dict										=	F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
  a__:     Union[str, Any]										=	requests.get(_SCREAMING_SNAKE_CASE		,     headers=_SCREAMING_SNAKE_CASE   ).json()
  a__:     List[Any]										=	{}

  try:
    artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )
    a__:     Dict										=	math.ceil((result['total_count'] - 100) / 100   )

    for i in range(_SCREAMING_SNAKE_CASE   ):
      a__:     Optional[int]										=	requests.get(url + F'&page={i + 2}'		,     headers=_SCREAMING_SNAKE_CASE   ).json()
      artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )

    return artifacts
  except Exception:
    print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'   )

  return {}







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->str:
  a__:     List[Any]										=	None
  if token is not None:
    a__:     Optional[int]										=	{'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:     Union[str, Any]										=	requests.get(_SCREAMING_SNAKE_CASE		,     headers=_SCREAMING_SNAKE_CASE		,     allow_redirects=_SCREAMING_SNAKE_CASE   )
  a__:     Optional[Any]										=	result.headers['Location']
  a__:     Optional[int]										=	requests.get(_SCREAMING_SNAKE_CASE		,     allow_redirects=_SCREAMING_SNAKE_CASE   )
  a__:     int										=	os.path.join(_SCREAMING_SNAKE_CASE		,     F'{artifact_name}.zip'   )
  with open(_SCREAMING_SNAKE_CASE		,     'wb'   ) as fp:
    fp.write(response.content   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[Any]:
  a__:     List[Any]										=	[]
  a__:     Optional[Any]										=	[]
  a__:     List[Any]										=	None

  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 in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
          with z.open(_SCREAMING_SNAKE_CASE   ) as f:
            for line in f:
              a__:     Optional[int]										=	line.decode('UTF-8'   ).strip()
              if filename == "failures_line.txt":
                try:
                  # `error_line` is the place where `error` occurs
                  a__:     Union[str, Any]										=	line[: line.index(': '   )]
                  a__:     Union[str, Any]										=	line[line.index(': '   ) + len(': '   ) :]
                  errors.append([error_line, error]   )
                except Exception:
                  # skip un-related lines
                  pass
              elif filename == "summary_short.txt" and line.startswith('FAILED '   ):
                # `test` is the test method that failed
                a__:     Optional[int]										=	line[len('FAILED '   ) :]
                failed_tests.append(_SCREAMING_SNAKE_CASE   )
              elif filename == "job_name.txt":
                a__:     Union[str, Any]										=	line

  if len(_SCREAMING_SNAKE_CASE   ) != len(_SCREAMING_SNAKE_CASE   ):
    raise ValueError(
        F'`errors` and `failed_tests` should have the same number of elements. Got {len(_SCREAMING_SNAKE_CASE   )} for `errors` '
        F'and {len(_SCREAMING_SNAKE_CASE   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
        ' problem.'   )

  a__:     Tuple										=	None
  if job_name and job_links:
    a__:     Dict										=	job_links.get(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

  # A list with elements of the form (line of error, error, failed test)
  a__:     int										=	[x + [y] + [job_link] for x, y in zip(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )]

  return result







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->str:
  a__:     int										=	[]

  a__:     Optional[int]										=	[os.path.join(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) for p in os.listdir(_SCREAMING_SNAKE_CASE   ) if p.endswith('.zip'   )]
  for p in paths:
    errors.extend(get_errors_from_single_artifact(_SCREAMING_SNAKE_CASE		,     job_links=_SCREAMING_SNAKE_CASE   )   )

  return errors







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->Any:
  a__:     str										=	Counter()
  counter.update([x[1] for x in logs]   )
  a__:     int										=	counter.most_common()
  a__:     Any										=	{}
  for error, count in counts:
    if error_filter is None or error not in error_filter:
      a__:     List[str]										=	{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

  a__:     Optional[Any]										=	dict(sorted(r.items()		,     key=lambda _SCREAMING_SNAKE_CASE   : item[1]["count"]		,     reverse=_SCREAMING_SNAKE_CASE   )   )
  return r







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  a__:     List[str]										=	test.split('::'   )[0]
  if test.startswith('tests/models/'   ):
    a__:     Dict										=	test.split('/'   )[2]
  else:
    a__:     Any										=	None

  return test







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[str]:
  a__:     int										=	[(x[0], x[1], get_model(x[2]   )) for x in logs]
  a__:     List[Any]										=	[x for x in logs if x[2] is not None]
  a__:     Optional[Any]										=	{x[2] for x in logs}

  a__:     Dict										=	{}
  for test in tests:
    a__:     Union[str, Any]										=	Counter()
    # count by errors in `test`
    counter.update([x[1] for x in logs if x[2] == test]   )
    a__:     Union[str, Any]										=	counter.most_common()
    a__:     List[str]										=	{error: count for error, count in counts if (error_filter is None or error not in error_filter)}
    a__:     List[Any]										=	sum(error_counts.values()   )
    if n_errors > 0:
      a__:     Any										=	{'count': n_errors, 'errors': error_counts}

  a__:     Optional[int]										=	dict(sorted(r.items()		,     key=lambda _SCREAMING_SNAKE_CASE   : item[1]["count"]		,     reverse=_SCREAMING_SNAKE_CASE   )   )
  return r







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  a__:     Any										=	'| no. | error | status |'
  a__:     Any										=	'|-:|:-|:-|'
  a__:     str										=	[header, sep]
  for error in reduced_by_error:
    a__:     int										=	reduced_by_error[error]['count']
    a__:     Tuple										=	F'| {count} | {error[:100]} |  |'
    lines.append(_SCREAMING_SNAKE_CASE   )

  return "\n".join(_SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->str:
  a__:     List[str]										=	'| model | no. of errors | major error | count |'
  a__:     str										=	'|-:|-:|-:|-:|'
  a__:     int										=	[header, sep]
  for model in reduced_by_model:
    a__:     Tuple										=	reduced_by_model[model]['count']
    a__							,    a__:     Dict										=	list(reduced_by_model[model]['errors'].items()   )[0]
    a__:     Dict										=	F'| {model} | {count} | {error[:60]} | {_count} |'
    lines.append(_SCREAMING_SNAKE_CASE   )

  return "\n".join(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	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.')
 lowercase__					       =	parser.parse_args()

 os.makedirs(args.output_dir, exist_ok=True)

 lowercase__					       =	get_job_links(args.workflow_run_id, token=args.token)
 lowercase__					       =	{}
 # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
 # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
 if _job_links:
  for k, v in _job_links.items():
   # This is how GitHub actions combine job names.
   if " / " in k:
    lowercase__					       =	k.find(' / ')
    lowercase__					       =	k[index + len(' / ') :]
   lowercase__					       =	v
 with open(os.path.join(args.output_dir, 'job_links.json'), 'w', encoding='UTF-8') as fp:
  json.dump(job_links, fp, ensure_ascii=False, indent=4)

 lowercase__					       =	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)

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

 lowercase__					       =	get_all_errors(args.output_dir, job_links=job_links)

 # `e[1]` is the error
 lowercase__					       =	Counter()
 counter.update([e[1] for e in errors])

 # print the top 30 most common test errors
 lowercase__					       =	counter.most_common(30)
 for item in most_common:
  print(item)

 with open(os.path.join(args.output_dir, 'errors.json'), 'w', encoding='UTF-8') as fp:
  json.dump(errors, fp, ensure_ascii=False, indent=4)

 lowercase__					       =	reduce_by_error(errors)
 lowercase__					       =	reduce_by_model(errors)

 lowercase__					       =	make_github_table(reduced_by_error)
 lowercase__					       =	make_github_table_per_model(reduced_by_model)

 with open(os.path.join(args.output_dir, 'reduced_by_error.txt'), 'w', encoding='UTF-8') as fp:
  fp.write(sa)
 with open(os.path.join(args.output_dir, 'reduced_by_model.txt'), 'w', encoding='UTF-8') as fp:
  fp.write(sa)


