



"""simple docstring"""






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
   assert isinstance(__snake_case      ,__snake_case			)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"      ,[False, True]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
   __lowerCAmelCase   :						Dict				= tmp_path / "cache"
   __lowerCAmelCase   :						Union[str, Any]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase   :						Optional[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
   _check_parquet_dataset(__snake_case      ,__snake_case			)


@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
   __lowerCAmelCase   :						Tuple				= tmp_path / "cache"
   __lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   __lowerCAmelCase   :						List[Any]				= features.copy() if features else default_expected_features
   __lowerCAmelCase   :						Dict				= (
       Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
   )
   __lowerCAmelCase   :						Optional[int]				= ParquetDatasetReader(__snake_case      ,features=__snake_case      ,cache_dir=__snake_case			).read()
   _check_parquet_dataset(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Any:
   __lowerCAmelCase   :						List[str]				= tmp_path / "cache"
   __lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   __lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case      ,split=__snake_case			).read()
   _check_parquet_dataset(__snake_case      ,__snake_case			)
   assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"      ,[str, list]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
   if issubclass(__snake_case      ,__snake_case			):
      __lowerCAmelCase   :						List[Any]				= parquet_path
   elif issubclass(__snake_case      ,__snake_case			):
      __lowerCAmelCase   :						List[Any]				= [parquet_path]
   __lowerCAmelCase   :						str				= tmp_path / "cache"
   __lowerCAmelCase   :						List[str]				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   __lowerCAmelCase   :						Tuple				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
   _check_parquet_dataset(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=("train",)			)		->     int:
   assert isinstance(__snake_case      ,__snake_case			)
   for split in splits:
      __lowerCAmelCase   :						str				= dataset_dict[split]
      assert dataset.num_rows == 4
      assert dataset.num_columns == 3
      assert dataset.column_names == ["col_1", "col_2", "col_3"]
      for feature, expected_dtype in expected_features.items():
         assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"      ,[False, True]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
   __lowerCAmelCase   :						Any				= tmp_path / "cache"
   __lowerCAmelCase   :						int				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      __lowerCAmelCase   :						str				= ParquetDatasetReader(
          {"train": parquet_path}      ,cache_dir=__snake_case      ,keep_in_memory=__snake_case			).read()
   _check_parquet_datasetdict(__snake_case      ,__snake_case			)


@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Dict:
   __lowerCAmelCase   :						List[Any]				= tmp_path / "cache"
   __lowerCAmelCase   :						Dict				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   __lowerCAmelCase   :						str				= features.copy() if features else default_expected_features
   __lowerCAmelCase   :						str				= (
       Features({feature: Value(__snake_case			) for feature, dtype in features.items()}			) if features is not None else None
   )
   __lowerCAmelCase   :						str				= ParquetDatasetReader({"train": parquet_path}      ,features=__snake_case      ,cache_dir=__snake_case			).read()
   _check_parquet_datasetdict(__snake_case      ,__snake_case			)


@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"			), "train", "test"]			)
def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     str:
   if split:
      __lowerCAmelCase   :						Optional[int]				= {split: parquet_path}
   else:
      __lowerCAmelCase   :						str				= "train"
      __lowerCAmelCase   :						Optional[Any]				= {"train": parquet_path, "test": parquet_path}
   __lowerCAmelCase   :						Tuple				= tmp_path / "cache"
   __lowerCAmelCase   :						int				= {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   __lowerCAmelCase   :						Union[str, Any]				= ParquetDatasetReader(__snake_case      ,cache_dir=__snake_case			).read()
   _check_parquet_datasetdict(__snake_case      ,__snake_case      ,splits=list(path.keys()			)			)
   assert all(dataset[split].split == split for split in path.keys()			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Union[str, Any]:
   __lowerCAmelCase   :						int				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
   assert writer.write() > 0
   __lowerCAmelCase   :						str				= pq.ParquetFile(tmp_path / "foo.parquet"			)
   __lowerCAmelCase   :						int				= pf.read()
   assert dataset.data.table == output_table


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
   __lowerCAmelCase   :						int				= str(shared_datadir / "test_image_rgb.jpg"			)
   __lowerCAmelCase   :						Any				= {"image": [image_path]}
   __lowerCAmelCase   :						List[Any]				= Features({"image": Image()}			)
   __lowerCAmelCase   :						Tuple				= Dataset.from_dict(__snake_case      ,features=__snake_case			)
   __lowerCAmelCase   :						Dict				= ParquetDatasetWriter(__snake_case      ,tmp_path / "foo.parquet"			)
   assert writer.write() > 0

   __lowerCAmelCase   :						List[Any]				= Dataset.from_parquet(str(tmp_path / "foo.parquet"			)			)
   assert dataset.features == reloaded_dataset.features

   __lowerCAmelCase   :						List[Any]				= ParquetDatasetReader(str(tmp_path / "foo.parquet"			)      ,streaming=__snake_case			).read()
   assert dataset.features == reloaded_iterable_dataset.features





@pytest.mark.parametrize(
    "feature, expected"      ,[
        (Features({"foo": Value("int32"			)}			), None),
        (Features({"image": Image(), "foo": Value("int32"			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()			)}			), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]      ,)
def     _lowercase       (						__snake_case      ,__snake_case			)		->     List[Any]:
   assert get_writer_batch_size(__snake_case			) == expected




"""simple docstring"""






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


__snake_case							:     Tuple   =					logging.get_logger(__name__)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Dict:
			__lowerCAmelCase   :						Optional[Any]				= set()
			__lowerCAmelCase   :						List[Any]				= []

			def parse_line(__snake_case			):
						for line in fp:
									if isinstance(__snake_case      ,__snake_case			):
												__lowerCAmelCase   :						Tuple				= 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(__snake_case			) > 0:
															__lowerCAmelCase   :						List[Any]				= "\n".join(__snake_case			)
															# Only keep the warnings specified in `targets`
															if any(F""": {x}: """ in warning for x in targets			):
																		selected_warnings.add(__snake_case			)
															buffer.clear()
												continue
									else:
												__lowerCAmelCase   :						List[str]				= line.strip()
												buffer.append(__snake_case			)

			if from_gh:
						for filename in os.listdir(__snake_case			):
									__lowerCAmelCase   :						List[str]				= os.path.join(__snake_case      ,__snake_case			)
									if not os.path.isdir(__snake_case			):
												# read the file
												if filename != "warnings.txt":
															continue
												with open(__snake_case			) as fp:
															parse_line(__snake_case			)
			else:
						try:
									with zipfile.ZipFile(__snake_case			) as z:
												for filename in z.namelist():
															if not os.path.isdir(__snake_case			):
																		# read the file
																		if filename != "warnings.txt":
																					continue
																		with z.open(__snake_case			) as fp:
																					parse_line(__snake_case			)
						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     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
			__lowerCAmelCase   :						Any				= set()

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

			return selected_warnings


if __name__ == "__main__":


	def     _lowercase       (						__snake_case			)		->     Optional[Any]:
				return values.split(","			)

	__snake_case							:     Optional[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.',
	)

	__snake_case							:     Tuple   =					parser.parse_args()

	__snake_case							:     Any   =					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
		__snake_case							:     str   =					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
	__snake_case							:     Tuple   =					extract_warnings(args.output_dir, args.targets)
	__snake_case							:     List[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)