

import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


A_       : int         =			logging.getLogger(__name__)

A_       : Tuple         =			"pytorch_model.bin"



@dataclasses.dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   Union[str, Any]  							=       dataclasses.field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}				)
					UpperCAmelCase__:   Optional[Any]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}		,   )



@dataclasses.dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}				)
					UpperCAmelCase__:   List[str]  							=       dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}				)
					UpperCAmelCase__:   List[Any]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''A csv or a json file containing the validation data.'''}				)
					UpperCAmelCase__:   Optional[int]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''The name of the task to train on.'''}		,   )
					UpperCAmelCase__:   Optional[Any]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''The list of labels for the task.'''}				)



@dataclasses.dataclass
class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   List[str]  							=       dataclasses.field(
					    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}				)
					UpperCAmelCase__:   Optional[int]  							=       dataclasses.field(
					    default='''accuracy'''		,   metadata={'''help''': '''The evaluation metric used for the task.'''}				)
					UpperCAmelCase__:   int  							=       dataclasses.field(
					    default='''no'''		,   metadata={
					        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
					    }		,   )
					UpperCAmelCase__:   Optional[Any]  							=       dataclasses.field(
					    default=10		,   metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,   )
					UpperCAmelCase__:   List[Any]  							=       dataclasses.field(
					    default=0.0		,   metadata={
					        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
					    }		,   )
					UpperCAmelCase__:   Union[str, Any]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}		,   )
					UpperCAmelCase__:   Optional[Any]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}		,   )
					UpperCAmelCase__:   Dict  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}		,   )
					UpperCAmelCase__:   Optional[int]  							=       dataclasses.field(
					    default=0.0		,   metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}		,   )
					UpperCAmelCase__:   int  							=       dataclasses.field(
					    default=1_00		,   metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}		,   )
					UpperCAmelCase__:   List[str]  							=       dataclasses.field(
					    default=UpperCAmelCase__		,   metadata={'''help''': '''Random seed for initialization.'''}		,   )



def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Optional[int]    ,      lowercase_: Any    ,      lowercase_: Union[str, Any]    ,      lowercase_: str    ,      lowercase_: int					)     ->      int:
	A__							:    List[str]        =		datasets.concatenate_datasets([infer_input, infer_output]    ,      axis=1					)

	if args.do_filter_by_confidence:
		A__							:    Any        =		dataset.filter(lambda lowercase_					: example["probability"] > args.confidence_threshold					)

	if args.do_filter_by_val_performance:
		assert eval_result >= 0.0 and eval_result <= 1.0
		A__							:    Optional[Any]        =		int(eval_result * len(_A					)					)
		print(_A					)
		A__							:    Optional[Any]        =		dataset.sort("""probability"""    ,      reverse=_A					)
		A__							:    List[Any]        =		dataset.select(range(_A					)					)

	A__							:    Dict        =		dataset.remove_columns(["""label""", """probability"""]					)
	A__							:    int        =		dataset.rename_column("""prediction"""    ,      """label"""					)
	A__							:    int        =		dataset.map(lambda lowercase_					: {"label": idalabel[example["label"]]}					)
	A__							:    Union[str, Any]        =		dataset.shuffle(seed=args.seed					)

	A__							:    Tuple        =		os.path.join(_A    ,      f"""train_pseudo.{args.data_file_extension}"""					)
	if args.data_file_extension == "csv":
		dataset.to_csv(_A    ,      index=_A					)
	else:
		dataset.to_json(_A					)


def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: int    ,      lowercase_: Optional[int]    ,      lowercase_: Optional[int]    ,      **lowercase_: Dict					)     ->      List[str]:
	A__							:    List[Any]        =		Accelerator()
	# Make one log on every process with the configuration for debugging.
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,      datefmt="""%m/%d/%Y %H:%M:%S"""    ,      level=logging.INFO    ,      )
	logger.info(accelerator.state					)

	# Setup logging, we only want one process per machine to log things on the
	# screen. accelerator.is_local_main_process is only True for one process per
	# machine.
	logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR					)

	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_info()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()

	A__							:    Optional[Any]        =		STModelArguments(model_name_or_path=_A					)
	A__							:    Union[str, Any]        =		STDataArguments(train_file=_A    ,      infer_file=_A					)
	A__							:    int        =		STTrainingArguments(output_dir=_A					)
	A__							:    Union[str, Any]        =		argparse.Namespace()

	for arg_class in (model_args, data_args, training_args):
		for key, value in vars(_A					).items():
			setattr(_A    ,      _A    ,      _A					)

	for key, value in kwargs.items():
		if hasattr(_A    ,      _A					):
			setattr(_A    ,      _A    ,      _A					)

    # Sanity checks
	A__							:    Any        =		{}
	A__							:    Tuple        =		None

	# You need to provide the training data and the data to predict on
	assert args.train_file is not None
	assert args.infer_file is not None
	A__							:    Tuple        =		args.train_file
	A__							:    Tuple        =		args.infer_file

	if args.evaluation_strategy != IntervalStrategy.NO.value:
		assert args.eval_file is not None
		A__							:    List[str]        =		args.eval_file

	for key in data_files:
		A__							:    Any        =		data_files[key].split("""."""					)[-1]
		assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
		if args.data_file_extension is None:
			A__							:    Dict        =		extension
		else:
			assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

	assert (
	    args.eval_metric in datasets.list_metrics()
	), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

	# If passed along, set the training seed now.
	if args.seed is not None:
		set_seed(args.seed					)

	logger.info("""Creating the initial data directory for self-training..."""					)
	A__							:    Optional[Any]        =		f"""{args.output_dir}/self-train_iter-{{}}""".format
	A__							:    Any        =		data_dir_format(0					)

	if accelerator.is_main_process:
		if args.output_dir is not None:
			os.makedirs(args.output_dir    ,      exist_ok=_A					)
			os.makedirs(_A    ,      exist_ok=_A					)
	accelerator.wait_for_everyone()

	A__							:    Optional[Any]        =		None
	A__							:    str        =		None
	A__							:    Union[str, Any]        =		0
	A__							:    int        =		False
	# Show the progress bar
	A__							:    List[Any]        =		tqdm(range(args.max_selftrain_iterations					)    ,      disable=not accelerator.is_local_main_process					)

	# Self-train
	for iteration in range(0    ,      int(args.max_selftrain_iterations					)					):
		A__							:    Optional[Any]        =		data_dir_format(_A					)
		assert os.path.exists(_A					)

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		A__							:    Union[str, Any]        =		os.path.join(_A    ,      """stage-1"""					)
		A__							:    List[Any]        =		{
		    """accelerator""": accelerator,
		    """model_name_or_path""": args.model_name_or_path,
		    """cache_dir""": args.cache_dir,
		    """do_train""": True,
		    """train_file""": data_files["""train"""] if iteration == 0 else data_files["""train_pseudo"""],
		    """do_eval""": True if args.eval_file is not None else False,
		    """eval_file""": data_files["""eval"""],
		    """do_predict""": True,
		    """infer_file""": data_files["""infer"""],
		    """task_name""": args.task_name,
		    """label_list""": args.label_list,
		    """output_dir""": current_output_dir,
		    """eval_metric""": args.eval_metric,
		    """evaluation_strategy""": args.evaluation_strategy,
		    """early_stopping_patience""": args.early_stopping_patience,
		    """early_stopping_threshold""": args.early_stopping_threshold,
		    """seed""": args.seed,
		}
		# Add additional training arguments
		for key, value in kwargs.items():
			if key not in arguments_dict and not hasattr(_A    ,      _A					):
				arguments_dict.update({key: value}					)

		A__							:    List[Any]        =		os.path.join(_A    ,      """best-checkpoint"""    ,      _A					)
		if os.path.exists(_A					):
			logger.info(
			    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."""    ,      _A    ,      _A    ,      )
		else:
			logger.info("""***** Running self-training: iteration: %d, stage: 1 *****"""    ,      _A					)
			finetune(**_A					)
			accelerator.wait_for_everyone()
			assert os.path.exists(_A					)
			logger.info("""Self-training job completed: iteration: %d, stage: 1."""    ,      _A					)

		if iteration > 0 and args.finetune_on_labeled_data:
			# Stage 2 (optional): fine-tuning on the original labeled data
			A__							:    Tuple        =		os.path.join(_A    ,      """best-checkpoint"""					)
			A__							:    List[str]        =		os.path.join(_A    ,      """stage-2"""					)
			# Update arguments_dict
			A__							:    str        =		model_path
			A__							:    Optional[int]        =		data_files["""train"""]
			A__							:    List[Any]        =		current_output_dir

			A__							:    str        =		os.path.join(_A    ,      """best-checkpoint"""    ,      _A					)
			if os.path.exists(_A					):
				logger.info(
				    """Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."""    ,      _A    ,      _A    ,      )
			else:
				logger.info("""***** Running self-training: iteration: %d, stage: 2 *****"""    ,      _A					)
				finetune(**_A					)
				accelerator.wait_for_everyone()
				assert os.path.exists(_A					)
				logger.info("""Self-training job completed: iteration: %d, stage: 2."""    ,      _A					)

		A__							:    Any        =		iteration
		A__							:    Optional[int]        =		data_dir_format(iteration + 1					)

		A__							:    List[Any]        =		AutoConfig.from_pretrained(os.path.join(_A    ,      """best-checkpoint"""					)					)
		A__							:    Optional[int]        =		config.idalabel
		A__							:    List[str]        =		os.path.join(_A    ,      """eval_results_best-checkpoint.json"""					)
		A__							:    str        =		os.path.join(_A    ,      """test_results_best-checkpoint.json"""					)
		assert os.path.exists(_A					)

		with open(_A    ,      """r"""					) as f:
			A__							:    List[str]        =		float(json.load(_A					)[args.eval_metric]					)
		A__							:    Any        =		os.path.join(_A    ,      """infer_output_best-checkpoint.csv"""					)
		assert os.path.exists(_A					)
		# Loading the dataset from local csv or json files.
		A__							:    Optional[Any]        =		load_dataset(args.data_file_extension    ,      data_files={"""data""": data_files["""infer"""]}					)["""data"""]
		A__							:    Optional[Any]        =		load_dataset("""csv"""    ,      data_files={"""data""": infer_output_file}					)["""data"""]

		if accelerator.is_main_process:
			os.makedirs(_A    ,      exist_ok=_A					)
			shutil.copy(_A    ,      os.path.join(_A    ,      f"""eval_results_iter-{iteration}.json"""					)					)
			if os.path.exists(_A					):
				shutil.copy(_A    ,      os.path.join(_A    ,      f"""test_results_iter-{iteration}.json"""					)					)
			create_pseudo_labeled_data(_A    ,      _A    ,      _A    ,      _A    ,      _A    ,      _A					)
		accelerator.wait_for_everyone()

		A__							:    Union[str, Any]        =		os.path.join(_A    ,      f"""train_pseudo.{args.data_file_extension}"""					)

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			A__							:    Any        =		eval_result

			if best_iteration is None:
				A__							:    int        =		new_iteration
				A__							:    List[Any]        =		new_eval_result
			else:
				if new_eval_result - best_eval_result > args.early_stopping_threshold:
					A__							:    Optional[int]        =		new_iteration
					A__							:    Dict        =		new_eval_result
					A__							:    Any        =		0
				else:
					if new_eval_result == best_eval_result:
						A__							:    List[Any]        =		new_iteration
						A__							:    Optional[Any]        =		new_eval_result
					early_stopping_patience_counter += 1

				if early_stopping_patience_counter >= args.early_stopping_patience:
					A__							:    Dict        =		True

		progress_bar.update(1					)

		if should_training_stop:
			break

	if best_iteration is not None:
		# Save the best iteration
		logger.info("""Best iteration: %d"""    ,      _A					)
		logger.info("""Best evaluation result: %s = %f"""    ,      args.eval_metric    ,      _A					)
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(_A    ,      f"""eval_results_iter-{iteration}.json"""					)    ,      os.path.join(_A    ,      """eval_results_best-iteration.json"""					)    ,      )
	else:
		# Assume that the last iteration is the best
		logger.info("""Best iteration: %d"""    ,      args.max_selftrain_iterations - 1					)
		logger.info("""Best evaluation result: %s = %f"""    ,      args.eval_metric    ,      _A					)
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(_A    ,      f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""					)    ,      os.path.join(_A    ,      """eval_results_best-iteration.json"""					)    ,      )









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)
