
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)



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     Union[str, Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     Tuple           =   {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""pegasus"""
  a__           =		["""past_key_values"""]
  a__           =		{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  def __init__( self			: Optional[int]			,							UpperCamelCase__			: Optional[int]=5_0265			,							UpperCamelCase__			: Optional[int]=1024			,							UpperCamelCase__			: Any=12			,							UpperCamelCase__			: Union[str, Any]=4096			,							UpperCamelCase__			: Any=16			,							UpperCamelCase__			: Union[str, Any]=12			,							UpperCamelCase__			: List[str]=4096			,							UpperCamelCase__			: Tuple=16			,							UpperCamelCase__			: Optional[int]=0.0			,							UpperCamelCase__			: List[Any]=0.0			,							UpperCamelCase__			: List[str]=True			,							UpperCamelCase__			: List[Any]=True			,							UpperCamelCase__			: List[Any]="gelu"			,							UpperCamelCase__			: List[Any]=1024			,							UpperCamelCase__			: Optional[Any]=0.1			,							UpperCamelCase__			: str=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: Union[str, Any]=0.02			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: int=False			,							UpperCamelCase__			: Any=0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Tuple=1			,							**UpperCamelCase__			: Union[str, Any]			,							)			->	str:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       d_model
    __magic_name__				     =       encoder_ffn_dim
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       encoder_attention_heads
    __magic_name__				     =       decoder_ffn_dim
    __magic_name__				     =       decoder_layers
    __magic_name__				     =       decoder_attention_heads
    __magic_name__				     =       dropout
    __magic_name__				     =       attention_dropout
    __magic_name__				     =       activation_dropout
    __magic_name__				     =       activation_function
    __magic_name__				     =       init_std
    __magic_name__				     =       encoder_layerdrop
    __magic_name__				     =       decoder_layerdrop
    __magic_name__				     =       use_cache
    __magic_name__				     =       encoder_layers
    __magic_name__				     =       scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							is_encoder_decoder=UpperCamelCase__			,							decoder_start_token_id=UpperCamelCase__			,							forced_eos_token_id=UpperCamelCase__			,							**UpperCamelCase__			,							)
  @property
  def 							_lowercase							( self			: List[Any]    )			->	int:







    """simple docstring"""






    return self.encoder_attention_heads





  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return self.d_model

