



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 			A_      (	__a     :				List[str]		):

				"""simple docstring"""
				a__				      =		tmp_path / """file.csv"""
				a__				      =		textwrap.dedent(
				    """\
        header1,header2
        1,2
        10,20
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """malformed_file.csv"""
				a__				      =		textwrap.dedent(
				    """\
        header1,header2
        1,2
        10,20,
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				Tuple ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """csv_with_image.csv"""
				a__				      =		textwrap.dedent(
				    F'''\
        image
        {image_file}
        '''		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				int		):

				"""simple docstring"""
				a__				      =		tmp_path / """csv_with_label.csv"""
				a__				      =		textwrap.dedent(
				    """\
        label
        good
        bad
        good
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

@pytest.fixture
def 			A_      (	__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		tmp_path / """csv_with_int_list.csv"""
				a__				      =		textwrap.dedent(
				    """\
        int_list
        1 2 3
        4 5 6
        7 8 9
        """		)
				with open(__a ,		"""w"""		) as f:
								f.write(__a		)
				return str(__a		)

def 			A_      (	__a     :				Dict ,		__a     :				Any ,		__a     :				Optional[Any]		):

				"""simple docstring"""
				a__				      =		Csv()
				a__				      =		csv._generate_tables([[csv_file, malformed_csv_file]]		)
				with pytest.raises(__a ,		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(__a		) in record.message
				    for record in caplog.records		)

@require_pil
def 			A_      (	__a     :				int		):

				"""simple docstring"""
				with open(__a ,		encoding="""utf-8"""		) as f:
								a__				      =		f.read().splitlines()[1]
				a__				      =		Csv(encoding="""utf-8""" ,		features=Features({"""image""": Image()}		)		)
				a__				      =		csv._generate_tables([[csv_file_with_image]]		)
				a__				      =		pa.concat_tables([table for _, table in generator]		)
				assert pa_table.schema.field("""image"""		).type == Image()()
				a__				      =		pa_table.to_pydict()["""image"""]
				assert generated_content == [{"path": image_file, "bytes": None}]

def 			A_      (	__a     :				Optional[Any]		):

				"""simple docstring"""
				with open(__a ,		encoding="""utf-8"""		) as f:
								a__				      =		f.read().splitlines()[1:]
				a__				      =		Csv(encoding="""utf-8""" ,		features=Features({"""label""": ClassLabel(names=["""good""", """bad"""]		)}		)		)
				a__				      =		csv._generate_tables([[csv_file_with_label]]		)
				a__				      =		pa.concat_tables([table for _, table in generator]		)
				assert pa_table.schema.field("""label"""		).type == ClassLabel(names=["""good""", """bad"""]		)()
				a__				      =		pa_table.to_pydict()["""label"""]
				assert generated_content == [ClassLabel(names=["""good""", """bad"""]		).straint(__a		) for label in labels]




def 			A_      (	__a     :				str		):

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






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def 			A_      (	__a     :				str = "laptop"		):

				"""simple docstring"""
				a__				      =		F'''https://www.amazon.in/laptop/s?k={product}'''
				a__				      =		{
				    """User-Agent""": """Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36""",
				    """Accept-Language""": """en-US, en;q=0.5""",
				}
				a__				      =		BeautifulSoup(requests.get(__a ,		headers=__a		).text		)
				# Initialize a Pandas dataframe with the column titles
				a__				      =		DataFrame(
				    columns=[
				        """Product Title""",
				        """Product Link""",
				        """Current Price of the product""",
				        """Product Rating""",
				        """MRP of the product""",
				        """Discount""",
				    ]		)
				# Loop through each entry and store them in the dataframe
				for item, _ in zip_longest(
				    soup.find_all(
				        """div""" ,		attrs={"""class""": """s-result-item""", """data-component-type""": """s-search-result"""} ,		) ,		soup.find_all("""div""" ,		attrs={"""class""": """a-row a-size-base a-color-base"""}		) ,		):
								try:
												a__				      =		item.ha.text
												a__				      =		"""https://www.amazon.in/""" + item.ha.a["""href"""]
												a__				      =		item.find("""span""" ,		attrs={"""class""": """a-offscreen"""}		).text
												try:
																a__				      =		item.find("""span""" ,		attrs={"""class""": """a-icon-alt"""}		).text
												except AttributeError:
																a__				      =		"""Not available"""
												try:
																a__				      =		(
																    """₹"""
																    + item.find(
																        """span""" ,		attrs={"""class""": """a-price a-text-price"""}		).text.split("""₹"""		)[1]
																)
												except AttributeError:
																a__				      =		""""""
												try:
																a__				      =		float(
																    (
																        (
																            float(product_mrp.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																            - float(product_price.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																        )
																        / float(product_mrp.strip("""₹"""		).replace(""",""" ,		""""""		)		)
																    )
																    * 100		)
												except ValueError:
																a__				      =		float("""nan"""		)
								except AttributeError:
												pass
								a__				      =		[
								    product_title,
								    product_link,
								    product_price,
								    product_rating,
								    product_mrp,
								    discount,
								]
				a__				      =		""" """
				a__				      =		""" """
				data_frame.index += 1
				return data_frame


if __name__ == "__main__":
	UpperCAmelCase							 =					"""headphones"""
	get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")

