




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    BartForSequenceClassification,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    TapexTokenizer,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

__UpperCamelCase   :			List[str]          = logging.getLogger(__name__)

@dataclass
class 						__lowerCAmelCase      :
							UpperCamelCase__   	=    field(
							    default='''tab_fact''' ,			metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}					)
							UpperCamelCase__   	=    field(
							    default='''tab_fact''' ,			metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} ,			)
							UpperCamelCase__   	=    field(
							    default=1024 ,			metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}					)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={
							        '''help''': (
							            '''Whether to pad all samples to `max_seq_length`. '''
							            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
							            '''value if set.'''
							        )
							    } ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''A csv or a json file containing the training data.'''}					)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''A csv or a json file containing the validation data.'''}					)
							UpperCamelCase__   	=    field(default=__magic_name__ ,			metadata={'''help''': '''A csv or a json file containing the test data.'''}					)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								if self.dataset_name is not None:
									pass
								elif self.train_file is None or self.validation_file is None:
									raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name."""							)
								else:
									a									=				self.train_file.split("""."""							)[-1]
									assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
									a									=				self.validation_file.split("""."""							)[-1]
									assert (
									    validation_extension == train_extension
									), "`validation_file` should have the same extension (csv or json) as `train_file`."







@dataclass
class 						__lowerCAmelCase      :
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} ,			)
							UpperCamelCase__   	=    field(
							    default='''main''' ,			metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} ,			)
							UpperCamelCase__   	=    field(
							    default=__magic_name__ ,			metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    } ,			)







def 						__A						(      ) ->			List[str]:
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	a									=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
	if len(sys.argv       ) == 2 and sys.argv[1].endswith(""".json"""       ):
		# If we pass only one argument to the script and it's the path to a json file,
		# let's parse it to get our arguments.
		a							,					a							,					a									=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
	else:
		a							,					a							,					a									=				parser.parse_args_into_dataclasses()

	# Setup logging
	logging.basicConfig(
	    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""				,     datefmt="""%m/%d/%Y %H:%M:%S"""				,     handlers=[logging.StreamHandler(sys.stdout       )]				,     )

	a									=				training_args.get_process_log_level()
	logger.setLevel(__lowerCamelCase       )
	datasets.utils.logging.set_verbosity(__lowerCamelCase       )
	transformers.utils.logging.set_verbosity(__lowerCamelCase       )
	transformers.utils.logging.enable_default_handler()
	transformers.utils.logging.enable_explicit_format()

	# Log on each process the small summary:
	logger.warning(
	    f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
	    + f'distributed training: {bool(training_args.local_rank != -1       )}, 16-bits training: {training_args.fpaa}'       )
	logger.info(f'Training/evaluation parameters {training_args}'       )

	# Detecting last checkpoint.
	a									=				None
	if os.path.isdir(training_args.output_dir       ) and training_args.do_train and not training_args.overwrite_output_dir:
		a									=				get_last_checkpoint(training_args.output_dir       )
		if last_checkpoint is None and len(os.listdir(training_args.output_dir       )       ) > 0:
			raise ValueError(
			    f'Output directory ({training_args.output_dir}) already exists and is not empty. '
			    """Use --overwrite_output_dir to overcome."""       )
		elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
			logger.info(
			    f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
			    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""       )

    # Set seed before initializing model.
	set_seed(training_args.seed       )

	# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
	# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
	#
	# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
	#
	# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
	# single column. You can easily tweak this behavior (see below)
	#
	# In distributed training, the load_dataset function guarantee that only one local process can concurrently
	# download the dataset.
	if data_args.dataset_name is not None:
		# Downloading and loading a dataset from the hub.
		a									=				load_dataset(
		    data_args.dataset_name				,     data_args.dataset_config_name				,     cache_dir=model_args.cache_dir       )
	else:
		# Loading a dataset from your local files.
		# CSV/JSON training and evaluation files are needed.
		a									=				{"""train""": data_args.train_file, """validation""": data_args.validation_file}

		# Get the test dataset: you can provide your own CSV/JSON test file (see below)
		# when you use `do_predict` without specifying a GLUE benchmark task.
		if training_args.do_predict:
			if data_args.test_file is not None:
				a									=				data_args.train_file.split("""."""       )[-1]
				a									=				data_args.test_file.split("""."""       )[-1]
				assert (
				    test_extension == train_extension
				), "`test_file` should have the same extension (csv or json) as `train_file`."
				a									=				data_args.test_file
			else:
				raise ValueError("""Need either a GLUE task or a test file for `do_predict`."""       )

		for key in data_files.keys():
			logger.info(f'load a local file for {key}: {data_files[key]}'       )

		if data_args.train_file.endswith(""".csv"""       ):
			# Loading a dataset from local csv files
			a									=				load_dataset("""csv"""				,     data_files=__lowerCamelCase				,     cache_dir=model_args.cache_dir       )
		else:
			# Loading a dataset from local json files
			a									=				load_dataset("""json"""				,     data_files=__lowerCamelCase				,     cache_dir=model_args.cache_dir       )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
	a									=				raw_datasets["""train"""].features["""label"""].names
	a									=				len(__lowerCamelCase       )

	# Load pretrained model and tokenizer
	#
	# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	a									=				AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,     num_labels=__lowerCamelCase				,     cache_dir=model_args.cache_dir				,     revision=model_args.model_revision				,     use_auth_token=True if model_args.use_auth_token else None				,     )
	# load tapex tokenizer
	a									=				TapexTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,     cache_dir=model_args.cache_dir				,     use_fast=model_args.use_fast_tokenizer				,     revision=model_args.model_revision				,     use_auth_token=True if model_args.use_auth_token else None				,     add_prefix_space=__lowerCamelCase				,     )
	a									=				BartForSequenceClassification.from_pretrained(
	    model_args.model_name_or_path				,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path       )				,     config=__lowerCamelCase				,     cache_dir=model_args.cache_dir				,     revision=model_args.model_revision				,     use_auth_token=True if model_args.use_auth_token else None				,     )

	# Padding strategy
	if data_args.pad_to_max_length:
		a									=				"""max_length"""
	else:
		# We will pad later, dynamically at batch creation, to the max sequence length in each batch
		a									=				False

	# Some models have set the order of the labels to use, so let's make sure we do use it.
	a									=				{"""Refused""": 0, """Entailed""": 1}
	a									=				{0: """Refused""", 1: """Entailed"""}

	if data_args.max_seq_length > tokenizer.model_max_length:
		logger.warning(
		    f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
		    f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'       )
	a									=				min(data_args.max_seq_length				,     tokenizer.model_max_length       )

	def preprocess_tabfact_function(__lowerCamelCase       ):
		# Tokenize the texts
		def _convert_table_text_to_pandas(__lowerCamelCase       ):
			a									=				[_table_row.split("""#"""       ) for _table_row in _table_text.strip("""\n"""       ).split("""\n"""       )]
			a									=				pd.DataFrame.from_records(_table_content[1:]				,     columns=_table_content[0]       )
			return _table_pd

		a									=				examples["""statement"""]
		a									=				list(map(_convert_table_text_to_pandas				,     examples["""table_text"""]       )       )
		a									=				tokenizer(__lowerCamelCase				,     __lowerCamelCase				,     padding=__lowerCamelCase				,     max_length=__lowerCamelCase				,     truncation=__lowerCamelCase       )

		a									=				examples["""label"""]
		return result

	with training_args.main_process_first(desc="""dataset map pre-processing"""       ):
		a									=				raw_datasets.map(
		    __lowerCamelCase				,     batched=__lowerCamelCase				,     load_from_cache_file=not data_args.overwrite_cache				,     desc="""Running tokenizer on dataset"""				,     )
	if training_args.do_train:
		if "train" not in raw_datasets:
			raise ValueError("""--do_train requires a train dataset"""       )
		a									=				raw_datasets["""train"""]
		if data_args.max_train_samples is not None:
			a									=				train_dataset.select(range(data_args.max_train_samples       )       )

	if training_args.do_eval:
		if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
			raise ValueError("""--do_eval requires a validation dataset"""       )
		a									=				raw_datasets["""validation"""]
		if data_args.max_eval_samples is not None:
			a									=				eval_dataset.select(range(data_args.max_eval_samples       )       )

	if training_args.do_predict or data_args.test_file is not None:
		if "test" not in raw_datasets and "test_matched" not in raw_datasets:
			raise ValueError("""--do_predict requires a test dataset"""       )
		a									=				raw_datasets["""test"""]
		if data_args.max_predict_samples is not None:
			a									=				predict_dataset.select(range(data_args.max_predict_samples       )       )

    # Log a few random samples from the training set:
	if training_args.do_train:
		for index in random.sample(range(len(__lowerCamelCase       )       )				,     3       ):
			logger.info(f'Sample {index} of the training set: {train_dataset[index]}.'       )

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
	def compute_metrics(__lowerCamelCase       ):
		a									=				p.predictions[0] if isinstance(p.predictions				,     __lowerCamelCase       ) else p.predictions
		a									=				np.argmax(__lowerCamelCase				,     axis=1       )
		return {"accuracy": (preds == p.label_ids).astype(np.floataa       ).mean().item()}

	# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
	if data_args.pad_to_max_length:
		a									=				default_data_collator
	elif training_args.fpaa:
		a									=				DataCollatorWithPadding(__lowerCamelCase				,     pad_to_multiple_of=8       )
	else:
		a									=				None

	# Initialize our Trainer
	a									=				Trainer(
	    model=__lowerCamelCase				,     args=__lowerCamelCase				,     train_dataset=train_dataset if training_args.do_train else None				,     eval_dataset=eval_dataset if training_args.do_eval else None				,     compute_metrics=__lowerCamelCase				,     tokenizer=__lowerCamelCase				,     data_collator=__lowerCamelCase				,     )

	# Training
	if training_args.do_train:
		a									=				None
		if training_args.resume_from_checkpoint is not None:
			a									=				training_args.resume_from_checkpoint
		elif last_checkpoint is not None:
			a									=				last_checkpoint
		a									=				trainer.train(resume_from_checkpoint=__lowerCamelCase       )
		a									=				train_result.metrics
		a									=				(
		    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCamelCase       )
		)
		a									=				min(__lowerCamelCase				,     len(__lowerCamelCase       )       )

		trainer.save_model()  # Saves the tokenizer too for easy upload

		trainer.log_metrics("""train"""				,     __lowerCamelCase       )
		trainer.save_metrics("""train"""				,     __lowerCamelCase       )
		trainer.save_state()

	# Evaluation
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""       )

		a									=				trainer.evaluate(eval_dataset=__lowerCamelCase       )
		a									=				data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCamelCase       )
		a									=				min(__lowerCamelCase				,     len(__lowerCamelCase       )       )

		trainer.log_metrics("""eval"""				,     __lowerCamelCase       )
		trainer.save_metrics("""eval"""				,     __lowerCamelCase       )

	if training_args.do_predict:
		logger.info("""*** Predict ***"""       )

		# Removing the `label` columns because it contains -1 and Trainer won't like that.
		a									=				predict_dataset.remove_columns("""label"""       )
		a									=				trainer.predict(__lowerCamelCase				,     metric_key_prefix="""predict"""       ).predictions
		a									=				np.argmax(__lowerCamelCase				,     axis=1       )

		a									=				os.path.join(training_args.output_dir				,     """predict_results_tabfact.txt"""       )
		if trainer.is_world_process_zero():
			with open(__lowerCamelCase				,     """w"""       ) as writer:
				logger.info("""***** Predict Results *****"""       )
				writer.write("""index\tprediction\n"""       )
				for index, item in enumerate(__lowerCamelCase       ):
					a									=				label_list[item]
					writer.write(f'{index}\t{item}\n'       )

	a									=				{"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}

	if training_args.push_to_hub:
		trainer.push_to_hub(**__lowerCamelCase       )
	else:
		trainer.create_model_card(**__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()







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

import requests





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	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(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	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(__lowerCamelCase				,     headers=__lowerCamelCase       ).json()
	a									=				{}

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

		for i in range(__lowerCamelCase       ):
			a									=				requests.get(url + f'&page={i + 2}'				,     headers=__lowerCamelCase       ).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						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				None
	if token is not None:
		a									=				{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	a									=				[]
	a									=				[]
	a									=				None

	with zipfile.ZipFile(__lowerCamelCase       ) as z:
		for filename in z.namelist():
			if not os.path.isdir(__lowerCamelCase       ):
				# read the file
				if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
					with z.open(__lowerCamelCase       ) 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(__lowerCamelCase       )
							elif filename == "job_name.txt":
								a									=				line

	if len(__lowerCamelCase       ) != len(__lowerCamelCase       ):
		raise ValueError(
		    f'`errors` and `failed_tests` should have the same number of elements. Got {len(__lowerCamelCase       )} for `errors` '
		    f'and {len(__lowerCamelCase       )} 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(__lowerCamelCase				,     __lowerCamelCase       )

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

	return result





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Dict:
	a									=				[]

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

	return errors





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Tuple:
	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 __lowerCamelCase       : item[1]["count"]				,     reverse=__lowerCamelCase       )       )
	return r





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				test.split("""::"""       )[0]
	if test.startswith("""tests/models/"""       ):
		a									=				test.split("""/"""       )[2]
	else:
		a									=				None

	return test





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=None       ) ->			Any:
	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 __lowerCamelCase       : item[1]["count"]				,     reverse=__lowerCamelCase       )       )
	return r





def 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	a									=				"""| no. | error | status |"""
	a									=				"""|-:|:-|:-|"""
	a									=				[header, sep]
	for error in reduced_by_error:
		a									=				reduced_by_error[error]["""count"""]
		a									=				f'| {count} | {error[:100]} |  |'
		lines.append(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	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(__lowerCamelCase       )

	return "\n".join(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Dict          = 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.")
		__UpperCamelCase   :			Any          = parser.parse_args()

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

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

		__UpperCamelCase   :			Tuple          = 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)

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

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

		# print the top 30 most common test errors
		__UpperCamelCase   :			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)

		__UpperCamelCase   :			Union[str, Any]          = reduce_by_error(errors)
		__UpperCamelCase   :			Dict          = reduce_by_model(errors)

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

