




import datasets

from .evaluate import evaluate


lowercase__				:     Tuple 		=						"\\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"

lowercase__				:     Optional[Any] 		=						"\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"

lowercase__				:     Dict 		=						"\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 		UpperCAmelCase   (				datasets.Metric			):







						'''simple docstring'''





						def        snake_case__						(						self							:     Union[str, 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        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     int       ,	__lowercase							:     List[Any]						):






							"""simple docstring"""






							snake_case_		    =     {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							snake_case_		    =     [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							snake_case_		    =     evaluate(dataset=__lowercase       ,	predictions=__lowercase						)
							return score






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

import requests


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





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

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

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

		for i in range(_A							):
			snake_case_		    =     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 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





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

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

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

		for i in range(_A							):
			snake_case_		    =     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 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





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

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


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     []
	snake_case_		    =     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:
							snake_case_		    =     line.decode("UTF-8"							).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									snake_case_		    =     line[: line.index(": "							)]
									snake_case_		    =     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
								snake_case_		    =     line[len("FAILED "							) :]
								failed_tests.append(_A							)
							elif filename == "job_name.txt":
								snake_case_		    =     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."							)

	snake_case_		    =     None
	if job_name and job_links:
		snake_case_		    =     job_links.get(_A     ,	_A							)

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

	return result


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     []

	snake_case_		    =     [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 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





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

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	return test


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





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

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

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


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	return "\n".join(_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





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

	return "\n".join(_A							)


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

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

		lowercase__				:     Optional[Any] 		=						get_job_links(args.workflow_run_id, token=args.token)
		lowercase__				:     List[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:
								lowercase__				:     Tuple 		=						k.find(" / ")
								lowercase__				:     Union[str, Any] 		=						k[index + len(" / ") :]
						lowercase__				:     Union[str, 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)

		lowercase__				:     Any 		=						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__				:     List[Any] 		=						get_all_errors(args.output_dir, job_links=job_links)

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

		# print the top 30 most common test errors
		lowercase__				:     Union[str, 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)

		lowercase__				:     int 		=						reduce_by_error(errors)
		lowercase__				:     List[str] 		=						reduce_by_model(errors)

		lowercase__				:     Tuple 		=						make_github_table(reduced_by_error)
		lowercase__				:     Optional[int] 		=						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)
