






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

import requests




def      __A					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







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

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

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

										for i in range(_lowercase ):
															_A								= requests.get(url + f"""&page={i + 2}"""					,			headers=_lowercase ).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					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







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

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

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

										for i in range(_lowercase ):
															_A								= requests.get(url + f"""&page={i + 2}"""					,			headers=_lowercase ).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					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







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

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




def      __A					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







					_A								= []
					_A								= []
					_A								= None

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

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

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

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

					return result




def      __A					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







					_A								= []

					_A								= [os.path.join(_lowercase					,			_lowercase ) for p in os.listdir(_lowercase ) if p.endswith('''.zip''' )]
					for p in paths:
										errors.extend(get_errors_from_single_artifact(_lowercase					,			job_links=_lowercase ) )

					return errors




def      __A					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







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

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




def      __A					(     _lowercase ):

					'''simple docstring'''







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

					return test




def      __A					(     _lowercase					,			_lowercase=None ):

					'''simple docstring'''







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

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

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




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= '''| no. | error | status |'''
					_A								= '''|-:|:-|:-|'''
					_A								= [header, sep]
					for error in reduced_by_error:
										_A								= reduced_by_error[error]['''count''']
										_A								= f"""| {count} | {error[:1_00]} |  |"""
										lines.append(_lowercase )

					return "\n".join(_lowercase )




def      __A					(     _lowercase ):

					'''simple docstring'''







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

					return "\n".join(_lowercase )


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

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

				__A      =				get_job_links(args.workflow_run_id, token=args.token)
				__A      =				{}
				# 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      =				k.find(' / ')
																__A      =				k[index + len(' / ') :]
												__A      =				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      =				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      =				get_all_errors(args.output_dir, job_links=job_links)

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

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

				__A      =				reduce_by_error(errors)
				__A      =				reduce_by_model(errors)

				__A      =				make_github_table(reduced_by_error)
				__A      =				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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A      =				{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegaForCausalLM',
				    'MegaForMaskedLM',
				    'MegaForMultipleChoice',
				    'MegaForQuestionAnswering',
				    'MegaForSequenceClassification',
				    'MegaForTokenClassification',
				    'MegaModel',
				    'MegaPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mega import (
								    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegaForCausalLM,
								    MegaForMaskedLM,
								    MegaForMultipleChoice,
								    MegaForQuestionAnswering,
								    MegaForSequenceClassification,
								    MegaForTokenClassification,
								    MegaModel,
								    MegaPreTrainedModel,
								)

else:
				import sys

				__A      =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



