





import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=7					)     ->	Dict:
    lowerCamelCase__       : int   					=						None
    if token is not None:
        lowerCamelCase__       : str   					=						{'Accept': 'application/vnd.github+json', 'Authorization': F"""Bearer {token}"""}

    # The id of a workflow (not of a workflow run)
    lowerCamelCase__       : str   					=						'636036'

    lowerCamelCase__       : Dict   					=						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}"""

    lowerCamelCase__       : List[str]   					=						requests.get(_UpperCAmelCase		,      headers=_UpperCAmelCase					).json()

    return result["workflow_runs"]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Any   					=						get_daily_ci_runs(_UpperCAmelCase					)
    lowerCamelCase__       : Union[str, Any]   					=						None
    for workflow_run in workflow_runs:
        if workflow_run["status"] == "completed":
            lowerCamelCase__       : List[str]   					=						workflow_run['id']
            break

    return workflow_run_id


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Union[str, Any]:
    lowerCamelCase__       : str   					=						get_last_daily_ci_runs(_UpperCAmelCase					)
    if workflow_run_id is not None:
        lowerCamelCase__       : Tuple   					=						get_artifacts_links(worflow_run_id=_UpperCAmelCase		,      token=_UpperCAmelCase					)
        for artifact_name in artifact_names:
            if artifact_name in artifacts_links:
                lowerCamelCase__       : Union[str, Any]   					=						artifacts_links[artifact_name]
                download_artifact(
                    artifact_name=_UpperCAmelCase		,      artifact_url=_UpperCAmelCase		,      output_dir=_UpperCAmelCase		,      token=_UpperCAmelCase					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Optional[int]:
    get_last_daily_ci_artifacts(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)

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

    return results












import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


_UpperCAmelCase							:			Optional[Any]        =							datasets.utils.logging.get_logger(__name__)

@dataclass
class   lowerCAmelCase				(   datasets.BuilderConfig     ):
 UpperCAmelCase__						=						1_00_00
 UpperCAmelCase__						=						None
 UpperCAmelCase__						=						None


class   lowerCAmelCase				(   datasets.ArrowBasedBuilder     ):
 UpperCAmelCase__						=						ParquetConfig
 def 	A_      ( self    : int      )     ->      Optional[int]:
     return datasets.DatasetInfo(features=self.config.features      )
 def 	A_      ( self    : int     ,	UpperCAmelCase    : str      )     ->      Any:
     if not self.config.data_files:
         raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""      )
     lowerCamelCase__       : List[Any]   					=						dl_manager.download_and_extract(self.config.data_files      )
     if isinstance(UpperCAmelCase     ,	(str, list, tuple)      ):
         lowerCamelCase__       : int   					=						data_files
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : int   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : str   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         return [datasets.SplitGenerator(name=datasets.Split.TRAIN     ,	gen_kwargs={'files': files}      )]
     lowerCamelCase__       : Optional[Any]   					=						[]
     for split_name, files in data_files.items():
         if isinstance(UpperCAmelCase     ,	UpperCAmelCase      ):
             lowerCamelCase__       : Optional[Any]   					=						[files]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         lowerCamelCase__       : Dict   					=						[dl_manager.iter_files(UpperCAmelCase      ) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
             for file in itertools.chain.from_iterable(UpperCAmelCase      ):
                 with open(UpperCAmelCase     ,	'rb'      ) as f:
                     lowerCamelCase__       : Optional[Any]   					=						datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase      )      )
                 break
         splits.append(datasets.SplitGenerator(name=UpperCAmelCase     ,	gen_kwargs={'files': files}      )      )
     return splits
 def 	A_      ( self    : Optional[int]     ,	UpperCAmelCase    : pa.Table      )     ->      pa.Table:
     if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         lowerCamelCase__       : Any   					=						table_cast(UpperCAmelCase     ,	self.info.features.arrow_schema      )
     return pa_table







 def 	A_      ( self    : Any     ,	UpperCAmelCase    : Tuple      )     ->      List[str]:
     lowerCamelCase__       : Optional[Any]   					=						self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema      ) != sorted(self.config.columns      ):
             raise ValueError(
                 F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""      )
     for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase      )      ):
         with open(UpperCAmelCase     ,	'rb'      ) as f:
             lowerCamelCase__       : List[Any]   					=						pq.ParquetFile(UpperCAmelCase      )
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size     ,	columns=self.config.columns      )      ):
                     lowerCamelCase__       : Tuple   					=						pa.Table.from_batches([record_batch]      )
                     # Uncomment for debugging (will print the Arrow table size and elements)
                     # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                     # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                     yield F"""{file_idx}_{batch_idx}""", self._cast_table(UpperCAmelCase      )
             except ValueError as e:
                 logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase      )}: {e}"""      )
                 raise





