



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  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[Any]    )->       Any:





						"""simple docstring"""





						assert isinstance(_A			,							_A    )
						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  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				Dict			,							_A			:				List[Any]    )->       List[str]:





						"""simple docstring"""





						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												A__							=       ParquetDatasetReader(_A			,							cache_dir=_A			,							keep_in_memory=_A    ).read()
						_check_parquet_dataset(_A			,							_A    )
@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  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				Union[str, Any]    )->       List[str]:





						"""simple docstring"""





						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						A__							=       features.copy() if features else default_expected_features
						A__							=       (
						    Features({feature: Value(_A    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						A__							=       ParquetDatasetReader(_A			,							features=_A			,							cache_dir=_A    ).read()
						_check_parquet_dataset(_A			,							_A    )
@pytest.mark.parametrize("split"			,							[None, NamedSplit("train"    ), "train", "test"]    )
def  UpperCamelCase						(       _A			:				str			,							_A			:				Tuple			,							_A			:				Union[str, Any]    )->       List[str]:





						"""simple docstring"""





						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						A__							=       ParquetDatasetReader(_A			,							cache_dir=_A			,							split=_A    ).read()
						_check_parquet_dataset(_A			,							_A    )
						assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type"			,							[str, list]    )
def  UpperCamelCase						(       _A			:				int			,							_A			:				Tuple			,							_A			:				int    )->       Optional[int]:





						"""simple docstring"""





						if issubclass(_A			,							_A    ):
												A__							=       parquet_path
						elif issubclass(_A			,							_A    ):
												A__							=       [parquet_path]
						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						A__							=       ParquetDatasetReader(_A			,							cache_dir=_A    ).read()
						_check_parquet_dataset(_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optional[int]			,							_A			:				List[str]			,							_A			:				Tuple=("train",)    )->       List[str]:





						"""simple docstring"""





						assert isinstance(_A			,							_A    )
						for split in splits:
												A__							=       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  UpperCamelCase						(       _A			:				int			,							_A			:				int			,							_A			:				Tuple    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
												A__							=       ParquetDatasetReader(
												    {"train": parquet_path}			,							cache_dir=_A			,							keep_in_memory=_A    ).read()
						_check_parquet_datasetdict(_A			,							_A    )
@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  UpperCamelCase						(       _A			:				Dict			,							_A			:				Any			,							_A			:				Union[str, Any]    )->       int:





						"""simple docstring"""





						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						A__							=       features.copy() if features else default_expected_features
						A__							=       (
						    Features({feature: Value(_A    ) for feature, dtype in features.items()}    ) if features is not None else None
						)
						A__							=       ParquetDatasetReader({"train": parquet_path}			,							features=_A			,							cache_dir=_A    ).read()
						_check_parquet_datasetdict(_A			,							_A    )
@pytest.mark.parametrize("split"			,							[None, NamedSplit("train"    ), "train", "test"]    )
def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				Tuple			,							_A			:				int    )->       Union[str, Any]:





						"""simple docstring"""





						if split:
												A__							=       {split: parquet_path}
						else:
												A__							=       "train"
												A__							=       {"train": parquet_path, "test": parquet_path}
						A__							=       tmp_path / "cache"
						A__							=       {"col_1": "string", "col_2": "int64", "col_3": "float64"}
						A__							=       ParquetDatasetReader(_A			,							cache_dir=_A    ).read()
						_check_parquet_datasetdict(_A			,							_A			,							splits=list(path.keys()    )    )
						assert all(dataset[split].split == split for split in path.keys()    )
def  UpperCamelCase						(       _A			:				List[str]			,							_A			:				Tuple    )->       Any:





						"""simple docstring"""





						A__							=       ParquetDatasetWriter(_A			,							tmp_path / "foo.parquet"    )
						assert writer.write() > 0
						A__							=       pq.ParquetFile(tmp_path / "foo.parquet"    )
						A__							=       pf.read()
						assert dataset.data.table == output_table
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Optional[Any]    )->       Dict:





						"""simple docstring"""





						A__							=       str(shared_datadir / "test_image_rgb.jpg"    )
						A__							=       {"image": [image_path]}
						A__							=       Features({"image": Image()}    )
						A__							=       Dataset.from_dict(_A			,							features=_A    )
						A__							=       ParquetDatasetWriter(_A			,							tmp_path / "foo.parquet"    )
						assert writer.write() > 0

						A__							=       Dataset.from_parquet(str(tmp_path / "foo.parquet"    )    )
						assert dataset.features == reloaded_dataset.features

						A__							=       ParquetDatasetReader(str(tmp_path / "foo.parquet"    )			,							streaming=_A    ).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  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				str    )->       Tuple:





						"""simple docstring"""





						assert get_writer_batch_size(_A    ) == expected










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None
						if token is not None:
												A__							=       {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

						A__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		with zipfile.ZipFile(_A    ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(_A    ):
																																				# read the file
																																				with z.open(_A    ) as f:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





