





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def    A_(			A = "laptop"   ):
	UpperCAmelCase_								=     f"""https://www.amazon.in/laptop/s?k={product}"""
	UpperCAmelCase_								=     {
	    """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""",
	}
	UpperCAmelCase_								=     BeautifulSoup(requests.get(A				,      headers=A   ).text   )
	# Initialize a Pandas dataframe with the column titles
	UpperCAmelCase_								=     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:
			UpperCAmelCase_								=     item.ha.text
			UpperCAmelCase_								=     """https://www.amazon.in/""" + item.ha.a["""href"""]
			UpperCAmelCase_								=     item.find("""span"""				,      attrs={"""class""": """a-offscreen"""}   ).text
			try:
				UpperCAmelCase_								=     item.find("""span"""				,      attrs={"""class""": """a-icon-alt"""}   ).text
			except AttributeError:
				UpperCAmelCase_								=     """Not available"""
			try:
				UpperCAmelCase_								=     (
				    """₹"""
				    + item.find(
				        """span"""				,      attrs={"""class""": """a-price a-text-price"""}   ).text.split("""₹"""   )[1]
				)
			except AttributeError:
				UpperCAmelCase_								=     """"""
			try:
				UpperCAmelCase_								=     float(
				    (
				        (
				            float(product_mrp.strip("""₹"""   ).replace(""","""				,      """"""   )   )
				            - float(product_price.strip("""₹"""   ).replace(""","""				,      """"""   )   )
				        )
				        / float(product_mrp.strip("""₹"""   ).replace(""","""				,      """"""   )   )
				    )
				    * 100   )
			except ValueError:
				UpperCAmelCase_								=     float("""nan"""   )
		except AttributeError:
			pass
		UpperCAmelCase_								=     [
		    product_title,
		    product_link,
		    product_price,
		    product_rating,
		    product_mrp,
		    discount,
		]
	UpperCAmelCase_								=     """ """
	UpperCAmelCase_								=     """ """
	data_frame.index += 1
	return data_frame


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









from torch import nn







class   _UpperCamelCase  (       nn.Module  ):







					'''simple docstring'''

					def __init__(						self    :  Dict ,					__lowercase    :  List[str] ,					__lowercase    :  Dict		):






						'''simple docstring'''







						super().__init__()
						UpperCAmelCase_								=     class_size
						UpperCAmelCase_								=     embed_size
						# self.mlp1 = nn.Linear(embed_size, embed_size)
						# self.mlp2 = (nn.Linear(embed_size, class_size))
						UpperCAmelCase_								=     nn.Linear(__lowercase ,					__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.mlp(__lowercase		)
						return logits


