


'''simple docstring'''







import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil


@pytest.fixture
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[str]  )  ->			Dict:
       _a    : Dict     					=tmp_path / """file.csv"""
       _a    : Union[str, Any]     					=textwrap.dedent(
           """\
        header1,header2
        1,2
        10,20
        """  )
       with open(_UpperCAmelCase    ,"""w"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


@pytest.fixture
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Optional[Any]  )  ->			Tuple:
       _a    : List[Any]     					=tmp_path / """malformed_file.csv"""
       _a    : Union[str, Any]     					=textwrap.dedent(
           """\
        header1,header2
        1,2
        10,20,
        """  )
       with open(_UpperCAmelCase    ,"""w"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


@pytest.fixture
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[Any]    ,_UpperCAmelCase	:	Union[str, Any]  )  ->			Any:
       _a    : int     					=tmp_path / """csv_with_image.csv"""
       _a    : Tuple     					=textwrap.dedent(
           F"\\n        image\n        {image_file}\n        "  )
       with open(_UpperCAmelCase    ,"""w"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


@pytest.fixture
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			str:
       _a    : int     					=tmp_path / """csv_with_label.csv"""
       _a    : Union[str, Any]     					=textwrap.dedent(
           """\
        label
        good
        bad
        good
        """  )
       with open(_UpperCAmelCase    ,"""w"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


@pytest.fixture
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			Tuple:
       _a    : Optional[int]     					=tmp_path / """csv_with_int_list.csv"""
       _a    : Any     					=textwrap.dedent(
           """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """  )
       with open(_UpperCAmelCase    ,"""w"""  ) as f:
              f.write(_UpperCAmelCase  )
       return str(_UpperCAmelCase  )


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	Any    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	List[Any]  )  ->			Union[str, Any]:
       _a    : Union[str, Any]     					=Csv()
       _a    : List[str]     					=csv._generate_tables([[csv_file, malformed_csv_file]]  )
       with pytest.raises(_UpperCAmelCase    ,match="""Error tokenizing data"""  ):
              for _ in generator:
                     pass
       assert any(
           record.levelname == """ERROR"""
           and """Failed to read file""" in record.message
           and os.path.basename(_UpperCAmelCase  ) in record.message
           for record in caplog.records  )


@require_pil
def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			Any:
       with open(_UpperCAmelCase    ,encoding="""utf-8"""  ) as f:
              _a    : Optional[int]     					=f.read().splitlines()[1]
       _a    : Dict     					=Csv(encoding="""utf-8"""    ,features=Features({"""image""": Image()}  )  )
       _a    : Any     					=csv._generate_tables([[csv_file_with_image]]  )
       _a    : List[str]     					=pa.concat_tables([table for _, table in generator]  )
       assert pa_table.schema.field("""image"""  ).type == Image()()
       _a    : Tuple     					=pa_table.to_pydict()["""image"""]
       assert generated_content == [{"path": image_file, "bytes": None}]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int  )  ->			str:
       with open(_UpperCAmelCase    ,encoding="""utf-8"""  ) as f:
              _a    : str     					=f.read().splitlines()[1:]
       _a    : Any     					=Csv(encoding="""utf-8"""    ,features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]  )}  )  )
       _a    : Dict     					=csv._generate_tables([[csv_file_with_label]]  )
       _a    : Union[str, Any]     					=pa.concat_tables([table for _, table in generator]  )
       assert pa_table.schema.field("""label"""  ).type == ClassLabel(names=["""good""", """bad"""]  )()
       _a    : Any     					=pa_table.to_pydict()["""label"""]
       assert generated_content == [ClassLabel(names=["""good""", """bad"""]  ).straint(_UpperCAmelCase  ) for label in labels]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	List[str]  )  ->			Tuple:
       _a    : List[str]     					=Csv(encoding="""utf-8"""    ,sep=""","""    ,converters={"""int_list""": lambda _UpperCAmelCase  : [int(_UpperCAmelCase  ) for i in x.split()]}  )
       _a    : int     					=csv._generate_tables([[csv_file_with_int_list]]  )
       _a    : int     					=pa.concat_tables([table for _, table in generator]  )
       assert pa.types.is_list(pa_table.schema.field("""int_list"""  ).type  )
       _a    : str     					=pa_table.to_pydict()["""int_list"""]
       assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]






'''simple docstring'''







from __future__ import annotations

import requests


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			dict:
       _a    : Any     					=F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
       return requests.get(_UpperCAmelCase  ).json()


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10  )  ->			list[dict]:
       _a    : Union[str, Any]     					="""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
       _a    : int     					=requests.get(_UpperCAmelCase  ).json()[:max_stories]
       return [get_hackernews_story(_UpperCAmelCase  ) for story_id in story_ids]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	int = 10  )  ->			str:
       _a    : Union[str, Any]     					=hackernews_top_stories(_UpperCAmelCase  )
       return "\n".join("""* [{title}]({url})""".format(**_UpperCAmelCase  ) for story in stories  )


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


