
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests

def a__    (       A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       None
  if token is not None:
    __magic_name__				     =       {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

  __magic_name__				     =       f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
  __magic_name__				     =       requests.get(A_,     headers=A_					).json()
  __magic_name__				     =       {}

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

    for i in range(A_					):
      __magic_name__				     =       requests.get(url + f'''&page={i + 2}''',     headers=A_					).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__    (       A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       None
  if token is not None:
    __magic_name__				     =       {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

  __magic_name__				     =       f'''https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'''
  __magic_name__				     =       requests.get(A_,     headers=A_					).json()
  __magic_name__				     =       {}

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

    for i in range(A_					):
      __magic_name__				     =       requests.get(url + f'''&page={i + 2}''',     headers=A_					).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__    (       A_,     A_,     A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       None
  if token is not None:
    __magic_name__				     =       {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

  __magic_name__				     =       requests.get(A_,     headers=A_,     allow_redirects=A_					)
  __magic_name__				     =       result.headers["""Location"""]
  __magic_name__				     =       requests.get(A_,     allow_redirects=A_					)
  __magic_name__				     =       os.path.join(A_,     f'''{artifact_name}.zip'''					)
  with open(A_,     """wb"""					) as fp:
    fp.write(response.content					)

def a__    (       A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       []
  __magic_name__				     =       []
  __magic_name__				     =       None

  with zipfile.ZipFile(A_					) as z:
    for filename in z.namelist():
      if not os.path.isdir(A_					):
        # read the file
        if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
          with z.open(A_					) as f:
            for line in f:
              __magic_name__				     =       line.decode("""UTF-8"""					).strip()
              if filename == "failures_line.txt":
                try:
                  # `error_line` is the place where `error` occurs
                  __magic_name__				     =       line[: line.index(""": """					)]
                  __magic_name__				     =       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
                __magic_name__				     =       line[len("""FAILED """					) :]
                failed_tests.append(A_					)
              elif filename == "job_name.txt":
                __magic_name__				     =       line

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

  __magic_name__				     =       None
  if job_name and job_links:
    __magic_name__				     =       job_links.get(A_,     A_					)

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

  return result

def a__    (       A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       []

  __magic_name__				     =       [os.path.join(A_,     A_					) for p in os.listdir(A_					) if p.endswith(""".zip"""					)]
  for p in paths:
    errors.extend(get_errors_from_single_artifact(A_,     job_links=A_					)					)

  return errors

def a__    (       A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       Counter()
  counter.update([x[1] for x in logs]					)
  __magic_name__				     =       counter.most_common()
  __magic_name__				     =       {}
  for error, count in counts:
    if error_filter is None or error not in error_filter:
      __magic_name__				     =       {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

  __magic_name__				     =       dict(sorted(r.items(),     key=lambda A_					: item[1]["count"],     reverse=A_					)					)
  return r

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       test.split("""::"""					)[0]
  if test.startswith("""tests/models/"""					):
    __magic_name__				     =       test.split("""/"""					)[2]
  else:
    __magic_name__				     =       None

  return test

def a__    (       A_,     A_=None					):






  '''simple docstring'''





  __magic_name__				     =       [(x[0], x[1], get_model(x[2]					)) for x in logs]
  __magic_name__				     =       [x for x in logs if x[2] is not None]
  __magic_name__				     =       {x[2] for x in logs}

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

  __magic_name__				     =       dict(sorted(r.items(),     key=lambda A_					: item[1]["count"],     reverse=A_					)					)
  return r

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       """| no. | error | status |"""
  __magic_name__				     =       """|-:|:-|:-|"""
  __magic_name__				     =       [header, sep]
  for error in reduced_by_error:
    __magic_name__				     =       reduced_by_error[error]["""count"""]
    __magic_name__				     =       f'''| {count} | {error[:100]} |  |'''
    lines.append(A_					)

  return "\n".join(A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       """| model | no. of errors | major error | count |"""
  __magic_name__				     =       """|-:|-:|-:|-:|"""
  __magic_name__				     =       [header, sep]
  for model in reduced_by_model:
    __magic_name__				     =       reduced_by_model[model]["""count"""]
    __magic_name__     ,    __magic_name__				     =       list(reduced_by_model[model]["""errors"""].items()					)[0]
    __magic_name__				     =       f'''| {model} | {count} | {error[:60]} | {_count} |'''
    lines.append(A_					)

  return "\n".join(A_					)


if __name__ == "__main__":
    __lowerCAmelCase			:     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.')
    __lowerCAmelCase			:     List[Any]           =   parser.parse_args()

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

    __lowerCAmelCase			:     List[Any]           =   get_job_links(args.workflow_run_id, token=args.token)
    __lowerCAmelCase			:     Tuple           =   {}
    # 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:
                __lowerCAmelCase			:     List[Any]           =   k.find(' / ')
                __lowerCAmelCase			:     Optional[Any]           =   k[index + len(' / ') :]
            __lowerCAmelCase			:     Optional[Any]           =   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)

    __lowerCAmelCase			:     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)

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

    __lowerCAmelCase			:     str           =   get_all_errors(args.output_dir, job_links=job_links)

    # `e[1]` is the error
    __lowerCAmelCase			:     List[str]           =   Counter()
    counter.update([e[1] for e in errors])

    # print the top 30 most common test errors
    __lowerCAmelCase			:     List[Any]           =   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)

    __lowerCAmelCase			:     int           =   reduce_by_error(errors)
    __lowerCAmelCase			:     Union[str, Any]           =   reduce_by_model(errors)

    __lowerCAmelCase			:     Dict           =   make_github_table(reduced_by_error)
    __lowerCAmelCase			:     Any           =   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)





'''simple docstring'''



import unittest

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


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





@require_torch
@require_vision
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         __lowerCamelCase   :	Tuple								=		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(SCREAMING_SNAKE_CASE_	)

         from datasets import load_dataset

         __lowerCamelCase   :	str								=		load_dataset('nielsr/rvlcdip-demo'	)

         __lowerCamelCase   :	List[Any]								=		dataset['train'][0]['image'].convert('RGB'	)

         __lowerCamelCase   :	str								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	str								=		model(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		outputs.logits

         __lowerCamelCase   :	List[Any]								=		torch.Size((1, 16)	)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]			,     device=SCREAMING_SNAKE_CASE_			,     dtype=torch.float			,     )
         self.assertTrue(torch.allclose(logits[0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)






