
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     np.inf

							def set_batch_size(_snake_case		) -> None:
														nonlocal batch_size
														if isinstance(_snake_case     ,_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     min(_snake_case     ,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS		)
														elif isinstance(_snake_case     ,_snake_case		):
																					SCREAMING_SNAKE_CASE__		: Tuple     =     min(_snake_case     ,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS		)
														elif isinstance(_snake_case     ,_snake_case		) and feature.dtype == "binary":
																					SCREAMING_SNAKE_CASE__		: Optional[Any]     =     min(_snake_case     ,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS		)

							_visit(_snake_case     ,_snake_case		)

							return None if batch_size is np.inf else batch_size



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = False     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	str:



											"""simple docstring"""




											super().__init__(
											    SCREAMING_SNAKE_CASE__     , split=SCREAMING_SNAKE_CASE__     , features=SCREAMING_SNAKE_CASE__     , cache_dir=SCREAMING_SNAKE_CASE__     , keep_in_memory=SCREAMING_SNAKE_CASE__     , streaming=SCREAMING_SNAKE_CASE__     , num_proc=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )
											SCREAMING_SNAKE_CASE__		: Tuple     =     path_or_paths if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					) else {self.split: path_or_paths}
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     _PACKAGED_DATASETS_MODULES["""parquet"""][1]
											SCREAMING_SNAKE_CASE__		: Any     =     Parquet(
											    cache_dir=SCREAMING_SNAKE_CASE__     , data_files=SCREAMING_SNAKE_CASE__     , features=SCREAMING_SNAKE_CASE__     , hash=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__     , )



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											if self.streaming:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.builder.as_streaming_dataset(split=self.split					)
											# Build regular (map-style) dataset
											else:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     None
																		SCREAMING_SNAKE_CASE__		: Any     =     None
																		SCREAMING_SNAKE_CASE__		: Any     =     None
																		SCREAMING_SNAKE_CASE__		: str     =     None

																		self.builder.download_and_prepare(
																		    download_config=SCREAMING_SNAKE_CASE__     , download_mode=SCREAMING_SNAKE_CASE__     , verification_mode=SCREAMING_SNAKE_CASE__     , base_path=SCREAMING_SNAKE_CASE__     , num_proc=self.num_proc     , )
																		SCREAMING_SNAKE_CASE__		: Any     =     self.builder.as_dataset(
																		    split=self.split     , verification_mode=SCREAMING_SNAKE_CASE__     , in_memory=self.keep_in_memory					)
											return dataset






class 			lowerCAmelCase_		:


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , **SCREAMING_SNAKE_CASE__     , )      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     dataset
											SCREAMING_SNAKE_CASE__		: Any     =     path_or_buf
											SCREAMING_SNAKE_CASE__		: Tuple     =     batch_size or get_writer_batch_size(dataset.features					)
											SCREAMING_SNAKE_CASE__		: int     =     parquet_writer_kwargs



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

											if isinstance(self.path_or_buf     , (str, bytes, os.PathLike)					):
																		with open(self.path_or_buf     , """wb+"""					) as buffer:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     self._write(file_obj=SCREAMING_SNAKE_CASE__     , batch_size=SCREAMING_SNAKE_CASE__     , **self.parquet_writer_kwargs					)
											else:
																		SCREAMING_SNAKE_CASE__		: str     =     self._write(file_obj=self.path_or_buf     , batch_size=SCREAMING_SNAKE_CASE__     , **self.parquet_writer_kwargs					)
											return written



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     0
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     parquet_writer_kwargs.pop("""path_or_buf"""     , SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Any     =     self.dataset.features.arrow_schema

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     pq.ParquetWriter(SCREAMING_SNAKE_CASE__     , schema=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)

											for offset in logging.tqdm(
											    range(0     , len(self.dataset					)     , SCREAMING_SNAKE_CASE__					)     , unit="""ba"""     , disable=not logging.is_progress_bar_enabled()     , desc="""Creating parquet from Arrow format"""     , ):
																		SCREAMING_SNAKE_CASE__		: Any     =     query_table(
																		    table=self.dataset._data     , key=slice(SCREAMING_SNAKE_CASE__     , offset + batch_size					)     , indices=self.dataset._indices if self.dataset._indices is not None else None     , )
																		writer.write_table(SCREAMING_SNAKE_CASE__					)
																		written += batch.nbytes
											writer.close()
											return written






"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def lowercase_						(     _snake_case     ,_snake_case=7		):
							SCREAMING_SNAKE_CASE__		: Dict     =     None
							if token is not None:
														SCREAMING_SNAKE_CASE__		: str     =     {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

							# The id of a workflow (not of a workflow run)
							SCREAMING_SNAKE_CASE__		: List[str]     =     """636036"""

							SCREAMING_SNAKE_CASE__		: Any     =     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}'''

							SCREAMING_SNAKE_CASE__		: str     =     requests.get(_snake_case     ,headers=_snake_case		).json()

							return result["workflow_runs"]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     get_daily_ci_runs(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     workflow_run["""id"""]
																					break

							return workflow_run_id

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_last_daily_ci_runs(_snake_case		)
							if workflow_run_id is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     get_artifacts_links(worflow_run_id=_snake_case     ,token=_snake_case		)
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												SCREAMING_SNAKE_CASE__		: Tuple     =     artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=_snake_case     ,artifact_url=_snake_case     ,output_dir=_snake_case     ,token=_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							get_last_daily_ci_artifacts(_snake_case     ,_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     {}
							for artifact_name in artifact_names:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(_snake_case     ,f'''{artifact_name}.zip'''		)
														if os.path.isfile(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     {}
																					with zipfile.ZipFile(_snake_case		) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_snake_case		):
																																										# read the file
																																										with z.open(_snake_case		) as f:
																																																	SCREAMING_SNAKE_CASE__		: List[Any]     =     f.read().decode("""UTF-8"""		)

							return results




