'''simple docstring'''





import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: List[str]     ):
      assert isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )
      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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: Dict     ):
      snake_case_     :			int       =       tmp_path / "cache"
      snake_case_     :			str       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            snake_case_     :			Union[str, Any]       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_ ,	keep_in_memory=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )






@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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Any     ):
      snake_case_     :			List[str]       =       tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      snake_case_     :			Optional[Any]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			List[str]       =       features.copy() if features else default_expected_features
      snake_case_     :			List[str]       =       (
          Features({feature: Value(lowerCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      snake_case_     :			int       =       CsvDatasetReader(lowerCAmelCase_ ,	features=lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )






@pytest.mark.parametrize("split" ,	[None, NamedSplit("train"     ), "train", "test"]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Tuple ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: Optional[Any]     ):
      snake_case_     :			int       =       tmp_path / "cache"
      snake_case_     :			Tuple       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			Dict       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_ ,	split=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )
      assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type" ,	[str, list]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[Any] ,	lowerCAmelCase_: Dict ,	lowerCAmelCase_: List[Any]     ):
      if issubclass(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            snake_case_     :			Union[str, Any]       =       csv_path
      elif issubclass(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            snake_case_     :			Optional[int]       =       [csv_path]
      snake_case_     :			List[str]       =       tmp_path / "cache"
      snake_case_     :			Optional[int]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			str       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_dataset(lowerCAmelCase_ ,	lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: List[Any] ,	lowerCAmelCase_: List[str] ,	lowerCAmelCase_: int=("train",)     ):
      assert isinstance(lowerCAmelCase_ ,	lowerCAmelCase_     )
      for split in splits:
            snake_case_     :			Optional[int]       =       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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: int     ):
      snake_case_     :			int       =       tmp_path / "cache"
      snake_case_     :			Union[str, Any]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
            snake_case_     :			List[Any]       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_ ,	keep_in_memory=lowerCAmelCase_     ).read()
      _check_csv_datasetdict(lowerCAmelCase_ ,	lowerCAmelCase_     )






@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 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Tuple ,	lowerCAmelCase_: str     ):
      snake_case_     :			List[str]       =       tmp_path / "cache"
      # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
      snake_case_     :			Optional[Any]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			str       =       features.copy() if features else default_expected_features
      snake_case_     :			List[Any]       =       (
          Features({feature: Value(lowerCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
      )
      snake_case_     :			int       =       CsvDatasetReader({"train": csv_path} ,	features=lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_datasetdict(lowerCAmelCase_ ,	lowerCAmelCase_     )






@pytest.mark.parametrize("split" ,	[None, NamedSplit("train"     ), "train", "test"]     )
def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: Optional[int] ,	lowerCAmelCase_: Union[str, Any]     ):
      if split:
            snake_case_     :			Optional[Any]       =       {split: csv_path}
      else:
            snake_case_     :			Tuple       =       {"train": csv_path, "test": csv_path}
      snake_case_     :			Optional[Any]       =       tmp_path / "cache"
      snake_case_     :			List[str]       =       {"col_1": "int64", "col_2": "int64", "col_3": "float64"}
      snake_case_     :			Any       =       CsvDatasetReader(lowerCAmelCase_ ,	cache_dir=lowerCAmelCase_     ).read()
      _check_csv_datasetdict(lowerCAmelCase_ ,	lowerCAmelCase_ ,	splits=list(path.keys()     )     )
      assert all(dataset[split].split == split for split in path.keys()     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Union[str, Any]     ):
      with open(lowerCAmelCase_ ,	encoding="utf-8"     ) as csvfile:
            yield from csv.reader(lowerCAmelCase_     )






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Optional[Any]     ):
      snake_case_     :			Tuple       =       tmp_path / "cache"
      snake_case_     :			Any       =       os.path.join(lowerCAmelCase_ ,	"tmp.csv"     )
      snake_case_     :			Any       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_     ).read()
      CsvDatasetWriter(dataset["train"] ,	lowerCAmelCase_ ,	num_proc=1     ).write()

      snake_case_     :			Optional[int]       =       iter_csv_file(lowerCAmelCase_     )
      snake_case_     :			Union[str, Any]       =       iter_csv_file(lowerCAmelCase_     )

      for rowa, rowa in zip(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            assert rowa == rowa






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Dict ,	lowerCAmelCase_: Dict     ):
      snake_case_     :			str       =       tmp_path / "cache"
      snake_case_     :			List[str]       =       os.path.join(lowerCAmelCase_ ,	"tmp.csv"     )
      snake_case_     :			Optional[int]       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_     ).read()
      CsvDatasetWriter(dataset["train"] ,	lowerCAmelCase_ ,	num_proc=2     ).write()

      snake_case_     :			int       =       iter_csv_file(lowerCAmelCase_     )
      snake_case_     :			Union[str, Any]       =       iter_csv_file(lowerCAmelCase_     )

      for rowa, rowa in zip(lowerCAmelCase_ ,	lowerCAmelCase_     ):
            assert rowa == rowa






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: Any ,	lowerCAmelCase_: Optional[int]     ):
      snake_case_     :			Dict       =       tmp_path / "cache"
      snake_case_     :			int       =       os.path.join(lowerCAmelCase_ ,	"tmp.csv"     )
      snake_case_     :			Tuple       =       CsvDatasetReader({"train": csv_path} ,	cache_dir=lowerCAmelCase_     ).read()
      with pytest.raises(lowerCAmelCase_     ):
            CsvDatasetWriter(dataset["train"] ,	lowerCAmelCase_ ,	num_proc=0     )




import requests
from bsa import BeautifulSoup






def 					SCREAMING_SNAKE_CASE_			(		lowerCAmelCase_: int ,	lowerCAmelCase_: str     ):
      snake_case_     :			Optional[int]       =       (
          "https://www.horoscope.com/us/horoscopes/general/"
          f"horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"
      )
      snake_case_     :			int       =       BeautifulSoup(requests.get(lowerCAmelCase_     ).content ,	"html.parser"     )
      return soup.find("div" ,	class_="main-horoscope"     ).p.text


if __name__ == "__main__":
     print("Daily Horoscope. \n")
     print(
         "enter your Zodiac sign number:\n",
         "1. Aries\n",
         "2. Taurus\n",
         "3. Gemini\n",
         "4. Cancer\n",
         "5. Leo\n",
         "6. Virgo\n",
         "7. Libra\n",
         "8. Scorpio\n",
         "9. Sagittarius\n",
         "10. Capricorn\n",
         "11. Aquarius\n",
         "12. Pisces\n",
     )
     UpperCAmelCase							=       int(input("number> ").strip())
     print("choose some day:\n", "yesterday\n", "today\n", "tomorrow\n")
     UpperCAmelCase							=       input("enter the day> ")
     UpperCAmelCase							=       horoscope(zodiac_sign, day)
     print(horoscope_text)



