

import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


A_ :				int						 =    logging.get_logger(__name__)

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				str      )  ->   str:
	A__							:					List[str]       =	set()
	A__							:					List[str]       =	[]

	def parse_line(lowercase_:				Optional[int]      ):
		for line in fp:
			if isinstance(lowercase_					,   lowercase_      ):
				A__							:					Optional[Any]       =	line.decode("""UTF-8"""      )
			if "warnings summary (final)" in line:
				continue
			# This means we are outside the body of a warning
			elif not line.startswith(""" """      ):
				# process a single warning and move it to `selected_warnings`.
				if len(lowercase_      ) > 0:
					A__							:					Union[str, Any]       =	"""\n""".join(lowercase_      )
					# Only keep the warnings specified in `targets`
					if any(f""": {x}: """ in warning for x in targets      ):
						selected_warnings.add(lowercase_      )
					buffer.clear()
				continue
			else:
				A__							:					Any       =	line.strip()
				buffer.append(lowercase_      )

	if from_gh:
		for filename in os.listdir(lowercase_      ):
			A__							:					Union[str, Any]       =	os.path.join(lowercase_					,   lowercase_      )
			if not os.path.isdir(lowercase_      ):
				# read the file
				if filename != "warnings.txt":
					continue
				with open(lowercase_      ) as fp:
					parse_line(lowercase_      )
	else:
		try:
			with zipfile.ZipFile(lowercase_      ) as z:
				for filename in z.namelist():
					if not os.path.isdir(lowercase_      ):
						# read the file
						if filename != "warnings.txt":
							continue
						with z.open(lowercase_      ) as fp:
							parse_line(lowercase_      )
		except Exception:
			logger.warning(
			    f"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""      )

	return selected_warnings

def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				Optional[Any]      )  ->   int:
	A__							:					Dict       =	set()

	A__							:					Tuple       =	[os.path.join(lowercase_					,   lowercase_      ) for p in os.listdir(lowercase_      ) if (p.endswith(""".zip"""      ) or from_gh)]
	for p in paths:
		selected_warnings.update(extract_warnings_from_single_artifact(lowercase_					,   lowercase_      )      )

	return selected_warnings


if __name__ == "__main__":

	def 		UpperCamelCase      (lowercase_:				Tuple      )  ->   Dict:
		return values.split(""","""      )

	A_ :				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.')
	# optional parameters
	parser.add_argument(
	    '--targets',
	    default='DeprecationWarning,UserWarning,FutureWarning',
	    type=list_str,
	    help='Comma-separated list of target warning(s) which we want to extract.',
	)
	parser.add_argument(
	    '--from_gh',
	    action='store_true',
	    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
	)

	A_ :				str						 =    parser.parse_args()

	A_ :				Tuple						 =    args.from_gh
	if from_gh:
		# The artifacts have to be downloaded using `actions/download-artifact@v3`
		pass
	else:
		os.makedirs(args.output_dir, exist_ok=True)

		# get download links
		A_ :				List[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)

		# download artifacts
		for idx, (name, url) in enumerate(artifacts.items()):
			print(name)
			print(url)
			print('=' * 80)
			download_artifact(name, url, args.output_dir, args.token)
			# Be gentle to GitHub
			time.sleep(1)

    # extract warnings from artifacts
	A_ :				int						 =    extract_warnings(args.output_dir, args.targets)
	A_ :				str						 =    sorted(selected_warnings)
	with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
		json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import datasets
import datasets.config

from .utils import require_beam



class 					_a		(datasets.BeamBasedBuilder       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						return datasets.DatasetInfo(
						    features=datasets.Features({"""content""": datasets.Value("""string"""    )}    )					, supervised_keys=A__					, )






					def 						__A    (				self					, A__					, A__    ):
						return [datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""examples""": get_test_dummy_examples()}    )]






					def 						__A    (				self					, A__					, A__    ):
						import apache_beam as beam

						return pipeline | "Load Examples" >> beam.Create(A__    )



class 					_a		(datasets.BeamBasedBuilder       ):


					'''simple docstring'''






					def 						__A    (				self    ):
						return datasets.DatasetInfo(
						    features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""    )}    )}    )					, supervised_keys=A__					, )






					def 						__A    (				self					, A__					, A__    ):
						return [
						    datasets.SplitGenerator(name=datasets.Split.TRAIN					, gen_kwargs={"""examples""": get_test_nested_examples()}    )
						]






					def 						__A    (				self					, A__					, A__    ):
						import apache_beam as beam

						return pipeline | "Load Examples" >> beam.Create(A__    )



def 		UpperCamelCase      ()  ->   Dict:
	return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]      )]

def 		UpperCamelCase      ()  ->   Tuple:
	return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""]      )]



class 					_a		(__magic_name__       ):


					'''simple docstring'''






					@require_beam
					def 						__A    (				self    ):
						A__							:					Dict       =	len(get_test_dummy_examples()    )
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					int       =	DummyBeamDataset(cache_dir=A__					, beam_runner="""DirectRunner"""    )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train.arrow"""    )    )    )
							self.assertDictEqual(builder.info.features					, datasets.Features({"""content""": datasets.Value("""string"""    )}    )    )
							A__							:					int       =	builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows					, A__    )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					, A__    )
							self.assertDictEqual(dset["""train"""][0]					, get_test_dummy_examples()[0][1]    )
							self.assertDictEqual(
							    dset["""train"""][expected_num_examples - 1]					, get_test_dummy_examples()[expected_num_examples - 1][1]    )
							self.assertTrue(
							    os.path.exists(os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, """dataset_info.json"""    )    )    )
							del dset






					@require_beam
					def 						__A    (				self    ):
						import apache_beam as beam

						A__							:					int       =	beam.io.parquetio.WriteToParquet

						A__							:					List[str]       =	len(get_test_dummy_examples()    )
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					str       =	DummyBeamDataset(cache_dir=A__					, beam_runner="""DirectRunner"""    )
							with patch("""apache_beam.io.parquetio.WriteToParquet"""    ) as write_parquet_mock:
								A__							:					Optional[Any]       =	partial(A__					, num_shards=2    )
								builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train-00000-of-00002.arrow"""    )    )    )
							self.assertTrue(
							    os.path.exists(
							        os.path.join(
							            A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train-00000-of-00002.arrow"""    )    )    )
							self.assertDictEqual(builder.info.features					, datasets.Features({"""content""": datasets.Value("""string"""    )}    )    )
							A__							:					Optional[int]       =	builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows					, A__    )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					, A__    )
							# Order is not preserved when sharding, so we just check that all the elements are there
							self.assertListEqual(sorted(dset["""train"""]["""content"""]    )					, sorted(["""foo""", """bar""", """foobar"""]    )    )
							self.assertTrue(
							    os.path.exists(os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, """dataset_info.json"""    )    )    )
							del dset






					@require_beam
					def 						__A    (				self    ):
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					int       =	DummyBeamDataset(cache_dir=A__    )
							self.assertRaises(datasets.builder.MissingBeamOptions					, builder.download_and_prepare    )






					@require_beam
					def 						__A    (				self    ):
						A__							:					List[Any]       =	len(get_test_nested_examples()    )
						with tempfile.TemporaryDirectory() as tmp_cache_dir:
							A__							:					Optional[int]       =	NestedBeamDataset(cache_dir=A__					, beam_runner="""DirectRunner"""    )
							builder.download_and_prepare()
							self.assertTrue(
							    os.path.exists(
							        os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, F"""{builder.name}-train.arrow"""    )    )    )
							self.assertDictEqual(
							    builder.info.features					, datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string"""    )}    )}    )    )
							A__							:					Optional[int]       =	builder.as_dataset()
							self.assertEqual(dset["""train"""].num_rows					, A__    )
							self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples					, A__    )
							self.assertDictEqual(dset["""train"""][0]					, get_test_nested_examples()[0][1]    )
							self.assertDictEqual(
							    dset["""train"""][expected_num_examples - 1]					, get_test_nested_examples()[expected_num_examples - 1][1]    )
							self.assertTrue(
							    os.path.exists(os.path.join(A__					, builder.name					, """default"""					, """0.0.0"""					, """dataset_info.json"""    )    )    )
							del dset



