





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

import requests


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

			__lowercase 					=   F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
			__lowercase 					=   requests.get(_UpperCamelCase       ,			headers=_UpperCamelCase  ).json()
			__lowercase 					=   {}

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

						for i in range(_UpperCamelCase  ):
									__lowercase 					=   requests.get(url + F'&page={i + 2}'       ,			headers=_UpperCamelCase  ).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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

			__lowercase 					=   F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
			__lowercase 					=   requests.get(_UpperCamelCase       ,			headers=_UpperCamelCase  ).json()
			__lowercase 					=   {}

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

						for i in range(_UpperCamelCase  ):
									__lowercase 					=   requests.get(url + F'&page={i + 2}'       ,			headers=_UpperCamelCase  ).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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







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

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   None

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

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

			__lowercase 					=   None
			if job_name and job_links:
						__lowercase 					=   job_links.get(_UpperCamelCase       ,			_UpperCamelCase  )

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

			return result


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







			__lowercase 					=   []

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

			return errors


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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

			return test


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=None  ):


			'''simple docstring'''







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

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

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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   '''| no. | error | status |'''
			__lowercase 					=   '''|-:|:-|:-|'''
			__lowercase 					=   [header, sep]
			for error in reduced_by_error:
						__lowercase 					=   reduced_by_error[error]['''count''']
						__lowercase 					=   F'| {count} | {error[:1_00]} |  |'
						lines.append(_UpperCamelCase  )

			return "\n".join(_UpperCamelCase  )


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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

			return "\n".join(_UpperCamelCase  )


if __name__ == "__main__":
	a : List[Any]		=				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 : Tuple		=				parser.parse_args()

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

	a : str		=				get_job_links(args.workflow_run_id, token=args.token)
	a : Dict		=				{}
	# 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 : Dict		=				k.find(''' / ''')
				a : Optional[int]		=				k[index + len(''' / ''') :]
			a : Dict		=				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 : Optional[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)

	a : Optional[Any]		=				get_all_errors(args.output_dir, job_links=job_links)

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

	# print the top 30 most common test errors
	a : Optional[int]		=				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 : Union[str, Any]		=				reduce_by_error(errors)
	a : Any		=				reduce_by_model(errors)

	a : Optional[int]		=				make_github_table(reduced_by_error)
	a : List[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)






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								return {"x": self.x[i], "y": self.y[i]}




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a[0] + self.b[0]




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

			__lowercase 					=   AutoTokenizer.from_pretrained('''bert-base-cased'''  )
			__lowercase 					=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			__lowercase 					=   load_dataset('''csv'''       ,			data_files=_UpperCamelCase  )
			__lowercase 					=   datasets['''train'''].unique('''label'''  )

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

			def tokenize_function(_UpperCamelCase  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase 					=   tokenizer(
						    examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase       ,			padding='''max_length'''  )
						if "label" in examples:
									__lowercase 					=   [label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowercase 					=   datasets.map(
			    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,			)

			def collate_fn(_UpperCamelCase  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


