





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__      =datasets.utils.logging.get_logger(__name__)





@dataclass
class 			lowerCAmelCase__( datasets.BuilderConfig    ):






       '''simple docstring'''


       __snake_case 			=    1_0_0_0_0
       __snake_case 			=    None
       __snake_case 			=    None





class 			lowerCAmelCase__( datasets.ArrowBasedBuilder    ):






       '''simple docstring'''


       __snake_case 			=    ParquetConfig




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            return datasets.DatasetInfo(features=self.config.features							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Optional[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}"""							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							dl_manager.download_and_extract(self.config.data_files							)
            if isinstance(__lowerCamelCase    ,						(str, list, tuple)							):
                 _SCREAMING_SNAKE_CASE       :	int						    =							data_files
                 if isinstance(__lowerCamelCase    ,						__lowerCamelCase							):
                      _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[dl_manager.iter_files(__lowerCamelCase							) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,						gen_kwargs={"files": files}							)]
            _SCREAMING_SNAKE_CASE       :	Any						    =							[]
            for split_name, files in data_files.items():
                 if isinstance(__lowerCamelCase    ,						__lowerCamelCase							):
                      _SCREAMING_SNAKE_CASE       :	Any						    =							[files]
                 # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							[dl_manager.iter_files(__lowerCamelCase							) 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(__lowerCamelCase							):
                           with open(__lowerCamelCase    ,						"rb"							) as f:
                                _SCREAMING_SNAKE_CASE       :	List[str]						    =							datasets.Features.from_arrow_schema(pq.read_schema(__lowerCamelCase							)							)
                           break
                 splits.append(datasets.SplitGenerator(name=__lowerCamelCase    ,						gen_kwargs={"files": files}							)							)
            return splits




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     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
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							table_cast(__lowerCamelCase    ,						self.info.features.arrow_schema							)
            return pa_table







       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Union[str, 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(__lowerCamelCase							)							):
                 with open(__lowerCamelCase    ,						"rb"							) as f:
                      _SCREAMING_SNAKE_CASE       :	int						    =							pq.ParquetFile(__lowerCamelCase							)
                      try:
                           for batch_idx, record_batch in enumerate(
                               parquet_file.iter_batches(batch_size=self.config.batch_size    ,						columns=self.config.columns							)							):
                                _SCREAMING_SNAKE_CASE       :	str						    =							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(__lowerCamelCase							)
                      except ValueError as e:
                           logger.error(F"""Failed to read file '{file}' with error {type(__lowerCamelCase							)}: {e}"""							)
                           raise






from __future__ import annotations

import requests






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
     return requests.get(__lowerCamelCase	).json()






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							requests.get(__lowerCamelCase	).json()[:max_stories]
     return [get_hackernews_story(__lowerCamelCase	) for story_id in story_ids]






def     lowerCamelCase__			(__lowerCamelCase = 10	):
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							hackernews_top_stories(__lowerCamelCase	)
     return "\n".join("* [{title}]({url})".format(**__lowerCamelCase	) for story in stories	)


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())