

import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def        lowerCamelCase__       (    _a							,	_a):
	assert isinstance(snake_case_							,	snake_case_)
	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        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		SCREAMING_SNAKE_CASE      :					Dict    =				ParquetDatasetReader(snake_case_							,	cache_dir=snake_case_							,	keep_in_memory=snake_case_).read()
	_check_parquet_dataset(snake_case_							,	snake_case_)

@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        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				features.copy() if features else default_expected_features
	SCREAMING_SNAKE_CASE      :					Tuple    =				(
	    Features({feature: Value(snake_case_) for feature, dtype in features.items()}) if features is not None else None
	)
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				ParquetDatasetReader(snake_case_							,	features=snake_case_							,	cache_dir=snake_case_).read()
	_check_parquet_dataset(snake_case_							,	snake_case_)

@pytest.mark.parametrize("split"							,	[None, NamedSplit("train"), "train", "test"])
def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Dict    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					int    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				ParquetDatasetReader(snake_case_							,	cache_dir=snake_case_							,	split=snake_case_).read()
	_check_parquet_dataset(snake_case_							,	snake_case_)
	assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"							,	[str, list])
def        lowerCamelCase__       (    _a							,	_a							,	_a):
	if issubclass(snake_case_							,	snake_case_):
		SCREAMING_SNAKE_CASE      :					str    =				parquet_path
	elif issubclass(snake_case_							,	snake_case_):
		SCREAMING_SNAKE_CASE      :					List[Any]    =				[parquet_path]
	SCREAMING_SNAKE_CASE      :					Tuple    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					List[str]    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	SCREAMING_SNAKE_CASE      :					str    =				ParquetDatasetReader(snake_case_							,	cache_dir=snake_case_).read()
	_check_parquet_dataset(snake_case_							,	snake_case_)

def        lowerCamelCase__       (    _a							,	_a							,	_a=("train",)):
	assert isinstance(snake_case_							,	snake_case_)
	for split in splits:
		SCREAMING_SNAKE_CASE      :					str    =				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        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					Any    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
		SCREAMING_SNAKE_CASE      :					List[str]    =				ParquetDatasetReader(
		    {"train": parquet_path}							,	cache_dir=snake_case_							,	keep_in_memory=snake_case_).read()
	_check_parquet_datasetdict(snake_case_							,	snake_case_)

@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        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					Any    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	SCREAMING_SNAKE_CASE      :					str    =				features.copy() if features else default_expected_features
	SCREAMING_SNAKE_CASE      :					List[Any]    =				(
	    Features({feature: Value(snake_case_) for feature, dtype in features.items()}) if features is not None else None
	)
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				ParquetDatasetReader({"train": parquet_path}							,	features=snake_case_							,	cache_dir=snake_case_).read()
	_check_parquet_datasetdict(snake_case_							,	snake_case_)

@pytest.mark.parametrize("split"							,	[None, NamedSplit("train"), "train", "test"])
def        lowerCamelCase__       (    _a							,	_a							,	_a):
	if split:
		SCREAMING_SNAKE_CASE      :					List[str]    =				{split: parquet_path}
	else:
		SCREAMING_SNAKE_CASE      :					List[Any]    =				"""train"""
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{"""train""": parquet_path, """test""": parquet_path}
	SCREAMING_SNAKE_CASE      :					str    =				tmp_path / """cache"""
	SCREAMING_SNAKE_CASE      :					Dict    =				{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				ParquetDatasetReader(snake_case_							,	cache_dir=snake_case_).read()
	_check_parquet_datasetdict(snake_case_							,	snake_case_							,	splits=list(path.keys()))
	assert all(dataset[split].split == split for split in path.keys())

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					Dict    =				ParquetDatasetWriter(snake_case_							,	tmp_path / "foo.parquet")
	assert writer.write() > 0
	SCREAMING_SNAKE_CASE      :					List[str]    =				pq.ParquetFile(tmp_path / "foo.parquet")
	SCREAMING_SNAKE_CASE      :					Dict    =				pf.read()
	assert dataset.data.table == output_table

def        lowerCamelCase__       (    _a							,	_a):
	SCREAMING_SNAKE_CASE      :					int    =				str(shared_datadir / "test_image_rgb.jpg")
	SCREAMING_SNAKE_CASE      :					Any    =				{"""image""": [image_path]}
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				Features({"image": Image()})
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				Dataset.from_dict(snake_case_							,	features=snake_case_)
	SCREAMING_SNAKE_CASE      :					str    =				ParquetDatasetWriter(snake_case_							,	tmp_path / "foo.parquet")
	assert writer.write() > 0

	SCREAMING_SNAKE_CASE      :					int    =				Dataset.from_parquet(str(tmp_path / "foo.parquet"))
	assert dataset.features == reloaded_dataset.features

	SCREAMING_SNAKE_CASE      :					int    =				ParquetDatasetReader(str(tmp_path / "foo.parquet")							,	streaming=snake_case_).read()
	assert dataset.features == reloaded_iterable_dataset.features


@pytest.mark.parametrize(
    "feature, expected"							,	[
        (Features({"foo": Value("int32")}), None),
        (Features({"image": Image(), "foo": Value("int32")}), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio())}), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ]							,	)
def        lowerCamelCase__       (    _a							,	_a):
	assert get_writer_batch_size(snake_case_) == expected





import requests
from bsa import BeautifulSoup







def 				lowerCAmelCase_	(     snake_case_ = "AAPL"     ):
      _A							:     str			=						f'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
      _A							:     List[Any]			=						BeautifulSoup(requests.get(snake_case_     ).text,"""html.parser"""     )
      _A							:     Union[str, Any]			=						"""My(6px) Pos(r) smartphone_Mt(6px)"""
      return soup.find("""div""",class_=class_     ).find("""span"""     ).text


if __name__ == "__main__":
  for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
    print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")

