





import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE 		=						logging.getLogger()


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     List[str]     =		{}
	snake_case_						:     Optional[Any]     =		os.path.join(__a				,	'all_results.json'				)
	if os.path.exists(__a				):
		with open(__a				,	'r'				) as f:
			snake_case_						:     Tuple     =		json.load(__a				)
	else:
		raise ValueError(f"""can't find {path}"""				)
	return results


_SCREAMING_SNAKE_CASE 		=						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


@require_torch_tpu
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):






		def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







			"""simple docstring"""


			import xla_spawn

			snake_case_						:     Tuple     =		self.get_auto_remove_tmp_dir()
			snake_case_						:     Optional[Any]     =		F"""
            ./examples/pytorch/text-classification/run_glue.py
            --num_cores=8
            ./examples/pytorch/text-classification/run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --debug tpu_metrics_debug
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

			with patch.object(_A						,				'argv'						,				_A		):
				snake_case_						:     str     =		time()
				xla_spawn.main()
				snake_case_						:     Tuple     =		time()

				snake_case_						:     Any     =		get_results(_A		)
				self.assertGreaterEqual(result['eval_accuracy']						,				0.7_5		)

				# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
				self.assertLess(end - start						,				500		)






		def UpperCAmelCase_			( self : Optional[int]		)							->      str:







			"""simple docstring"""


			import xla_spawn

			snake_case_						:     Optional[Any]     =		'\n            ./tests/test_trainer_tpu.py\n            --num_cores=8\n            ./tests/test_trainer_tpu.py\n            '.split()
			with patch.object(_A						,				'argv'						,				_A		):
				xla_spawn.main()








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

import requests


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		None
	if token is not None:
		snake_case_						:     List[str]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

	snake_case_						:     Union[str, Any]     =		f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
	snake_case_						:     Optional[int]     =		requests.get(__a				,	headers=__a				).json()
	snake_case_						:     List[str]     =		{}

	try:
		job_links.update({job['name']: job['html_url'] for job in result['jobs']}				)
		snake_case_						:     Dict     =		math.ceil((result['total_count'] - 1_00) / 1_00				)

		for i in range(__a				):
			snake_case_						:     Optional[Any]     =		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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Union[str, Any]     =		None
	if token is not None:
		snake_case_						:     List[Any]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

	snake_case_						:     Optional[Any]     =		f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
	snake_case_						:     Union[str, Any]     =		requests.get(__a				,	headers=__a				).json()
	snake_case_						:     Any     =		{}

	try:
		artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}				)
		snake_case_						:     str     =		math.ceil((result['total_count'] - 1_00) / 1_00				)

		for i in range(__a				):
			snake_case_						:     int     =		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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				):
	snake_case_						:     Dict     =		None
	if token is not None:
		snake_case_						:     List[Any]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Any     =		[]
	snake_case_						:     Any     =		[]
	snake_case_						:     Tuple     =		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_						:     Tuple     =		line.decode('UTF-8'				).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									snake_case_						:     Tuple     =		line[: line.index(': '				)]
									snake_case_						:     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
								snake_case_						:     Any     =		line[len('FAILED '				) :]
								failed_tests.append(__a				)
							elif filename == "job_name.txt":
								snake_case_						:     Union[str, Any]     =		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_						:     List[str]     =		None
	if job_name and job_links:
		snake_case_						:     Union[str, Any]     =		job_links.get(__a				,	__a				)

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

	return result


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Any     =		[]

	snake_case_						:     Any     =		[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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		Counter()
	counter.update([x[1] for x in logs]				)
	snake_case_						:     str     =		counter.most_common()
	snake_case_						:     Tuple     =		{}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			snake_case_						:     int     =		{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

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


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		test.split('::'				)[0]
	if test.startswith('tests/models/'				):
		snake_case_						:     List[str]     =		test.split('/'				)[2]
	else:
		snake_case_						:     Union[str, Any]     =		None

	return test


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		[(x[0], x[1], get_model(x[2]				)) for x in logs]
	snake_case_						:     str     =		[x for x in logs if x[2] is not None]
	snake_case_						:     int     =		{x[2] for x in logs}

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

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


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		'| no. | error | status |'
	snake_case_						:     str     =		'|-:|:-|:-|'
	snake_case_						:     Tuple     =		[header, sep]
	for error in reduced_by_error:
		snake_case_						:     Dict     =		reduced_by_error[error]['count']
		snake_case_						:     List[str]     =		f"""| {count} | {error[:1_00]} |  |"""
		lines.append(__a				)

	return "\n".join(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		'| model | no. of errors | major error | count |'
	snake_case_						:     Union[str, Any]     =		'|-:|-:|-:|-:|'
	snake_case_						:     Optional[int]     =		[header, sep]
	for model in reduced_by_model:
		snake_case_						:     Any     =		reduced_by_model[model]['count']
		snake_case_  ,snake_case_						:     Dict     =		list(reduced_by_model[model]['errors'].items()				)[0]
		snake_case_						:     Any     =		f"""| {model} | {count} | {error[:60]} | {_count} |"""
		lines.append(__a				)

	return "\n".join(__a				)


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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.""")
						_SCREAMING_SNAKE_CASE 		=						parser.parse_args()

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

						_SCREAMING_SNAKE_CASE 		=						get_job_links(args.workflow_run_id, token=args.token)
						_SCREAMING_SNAKE_CASE 		=						{}
						# 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:
																								_SCREAMING_SNAKE_CASE 		=						k.find(""" / """)
																								_SCREAMING_SNAKE_CASE 		=						k[index + len(""" / """) :]
																		_SCREAMING_SNAKE_CASE 		=						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)

						_SCREAMING_SNAKE_CASE 		=						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)

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

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

						# print the top 30 most common test errors
						_SCREAMING_SNAKE_CASE 		=						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)

						_SCREAMING_SNAKE_CASE 		=						reduce_by_error(errors)
						_SCREAMING_SNAKE_CASE 		=						reduce_by_model(errors)

						_SCREAMING_SNAKE_CASE 		=						make_github_table(reduced_by_error)
						_SCREAMING_SNAKE_CASE 		=						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)

