



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

import requests

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		str=None						):
 UpperCAmelCase	:					Optional[Any]	=	None
 if token is not None:
  UpperCAmelCase	:					str	=	{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

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

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

  for i in range(UpperCamelCase						):
   UpperCAmelCase	:					List[str]	=	requests.get(url + F"&page={i + 2}"			,		headers=UpperCamelCase						).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       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		List[Any]=None						):
 UpperCAmelCase	:					Optional[Any]	=	None
 if token is not None:
  UpperCAmelCase	:					List[str]	=	{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

 UpperCAmelCase	:					Optional[Any]	=	F"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
 UpperCAmelCase	:					Tuple	=	requests.get(UpperCamelCase			,		headers=UpperCamelCase						).json()
 UpperCAmelCase	:					List[str]	=	{}

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

  for i in range(UpperCamelCase						):
   UpperCAmelCase	:					Tuple	=	requests.get(url + F"&page={i + 2}"			,		headers=UpperCamelCase						).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       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		str			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					Any	=	None
 if token is not None:
  UpperCAmelCase	:					Optional[int]	=	{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

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

def       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		str=None						):
 UpperCAmelCase	:					Optional[Any]	=	[]
 UpperCAmelCase	:					Union[str, Any]	=	[]
 UpperCAmelCase	:					List[Any]	=	None

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

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

 UpperCAmelCase	:					Union[str, Any]	=	None
 if job_name and job_links:
  UpperCAmelCase	:					Optional[Any]	=	job_links.get(UpperCamelCase			,		UpperCamelCase						)

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

 return result

def       _snake_case						(						UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		str=None						):
 UpperCAmelCase	:					Tuple	=	[]

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

 return errors

def       _snake_case						(						UpperCamelCase						:		Tuple			,		UpperCamelCase						:		str=None						):
 UpperCAmelCase	:					Optional[int]	=	Counter()
 counter.update([x[1] for x in logs]						)
 UpperCAmelCase	:					str	=	counter.most_common()
 UpperCAmelCase	:					Tuple	=	{}
 for error, count in counts:
  if error_filter is None or error not in error_filter:
   UpperCAmelCase	:					List[Any]	=	{"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

 UpperCAmelCase	:					List[str]	=	dict(sorted(r.items()			,		key=lambda UpperCamelCase						: item[1]["count"]			,		reverse=UpperCamelCase						)						)
 return r

def       _snake_case						(						UpperCamelCase						:		Optional[int]						):
 UpperCAmelCase	:					List[Any]	=	test.split("""::"""						)[0]
 if test.startswith("""tests/models/"""						):
  UpperCAmelCase	:					List[str]	=	test.split("""/"""						)[2]
 else:
  UpperCAmelCase	:					Tuple	=	None

 return test

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		Tuple=None						):
 UpperCAmelCase	:					Union[str, Any]	=	[(x[0], x[1], get_model(x[2]						)) for x in logs]
 UpperCAmelCase	:					Union[str, Any]	=	[x for x in logs if x[2] is not None]
 UpperCAmelCase	:					List[str]	=	{x[2] for x in logs}

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

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

def       _snake_case						(						UpperCamelCase						:		List[Any]						):
 UpperCAmelCase	:					str	=	"""| no. | error | status |"""
 UpperCAmelCase	:					Any	=	"""|-:|:-|:-|"""
 UpperCAmelCase	:					Dict	=	[header, sep]
 for error in reduced_by_error:
  UpperCAmelCase	:					Tuple	=	reduced_by_error[error]["""count"""]
  UpperCAmelCase	:					Optional[int]	=	F"| {count} | {error[:100]} |  |"
  lines.append(UpperCamelCase						)

 return "\n".join(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					str	=	"""| model | no. of errors | major error | count |"""
 UpperCAmelCase	:					Optional[int]	=	"""|-:|-:|-:|-:|"""
 UpperCAmelCase	:					List[str]	=	[header, sep]
 for model in reduced_by_model:
  UpperCAmelCase	:					Union[str, Any]	=	reduced_by_model[model]["""count"""]
  UpperCAmelCase	,		UpperCAmelCase	:					Optional[Any]	=	list(reduced_by_model[model]["""errors"""].items()						)[0]
  UpperCAmelCase	:					Tuple	=	F"| {model} | {count} | {error[:60]} | {_count} |"
  lines.append(UpperCamelCase						)

 return "\n".join(UpperCamelCase						)


if __name__ == "__main__":
      A:   Union[str, 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.")
      A:   Tuple								=		parser.parse_args()

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

      A:   Union[str, Any]								=		get_job_links(args.workflow_run_id, token=args.token)
      A:   Optional[Any]								=		{}
      # 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:
                        A:   str								=		k.find(" / ")
                        A:   Tuple								=		k[index + len(" / ") :]
                  A:   int								=		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)

      A:   Optional[int]								=		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)

      A:   int								=		get_all_errors(args.output_dir, job_links=job_links)

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

      # print the top 30 most common test errors
      A:   Optional[int]								=		counter.most_common(3_0)
      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)

      A:   str								=		reduce_by_error(errors)
      A:   Dict								=		reduce_by_model(errors)

      A:   Union[str, Any]								=		make_github_table(reduced_by_error)
      A:   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 datasets

from .evaluate import evaluate


A:   Optional[Any]								=		"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

A:   Optional[int]								=		"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

A:   int								=		"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,			_KWARGS_DESCRIPTION )
class       SCREAMING_SNAKE_CASE__  (					datasets.Metric ):

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





    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION      ,     citation=_CITATION      ,     inputs_description=_KWARGS_DESCRIPTION      ,     features=datasets.Features(
            {
                """predictions""": {
                    """id""": datasets.Value("""string"""						),
                    """prediction_text""": datasets.features.Sequence(datasets.Value("""string"""						)						),
                },
                """references""": {
                    """id""": datasets.Value("""string"""						),
                    """answers""": datasets.features.Sequence(
                        {
                            """text""": datasets.Value("""string"""						),
                            """answer_start""": datasets.Value("""int32"""						),
                        }						),
                },
            }						)      ,     codebase_urls=["""https://www.atticusprojectai.org/cuad"""]      ,     reference_urls=["""https://www.atticusprojectai.org/cuad"""]      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	{prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
    UpperCAmelCase	:					Tuple	=	[
        {
            """paragraphs""": [
                {
                    """qas""": [
                        {
                            """answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
                            """id""": ref["""id"""],
                        }
                        for ref in references
                    ]
                }
            ]
        }
    ]
    UpperCAmelCase	:					Optional[Any]	=	evaluate(dataset=_SCREAMING_SNAKE_CASE      ,     predictions=_SCREAMING_SNAKE_CASE						)
    return score
