



"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






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


       '''simple docstring'''





       _lowerCamelCase						:    str         =			F'''https://www.amazon.in/laptop/s?k={product}'''
       _lowerCamelCase						:    List[str]         =			{
           '''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''',
       }
       _lowerCamelCase						:    Optional[Any]         =			BeautifulSoup(requests.get(A_,    headers=A_						).text						)
       # Initialize a Pandas dataframe with the column titles
       _lowerCamelCase						:    int         =			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:
                     _lowerCamelCase						:    Dict         =			item.ha.text
                     _lowerCamelCase						:    Union[str, Any]         =			'''https://www.amazon.in/''' + item.ha.a['''href''']
                     _lowerCamelCase						:    Union[str, Any]         =			item.find('''span''',    attrs={'''class''': '''a-offscreen'''}						).text
                     try:
                            _lowerCamelCase						:    int         =			item.find('''span''',    attrs={'''class''': '''a-icon-alt'''}						).text
                     except AttributeError:
                            _lowerCamelCase						:    Tuple         =			'''Not available'''
                     try:
                            _lowerCamelCase						:    List[Any]         =			(
                                '''₹'''
                                + item.find(
                                    '''span''',    attrs={'''class''': '''a-price a-text-price'''}						).text.split('''₹'''						)[1]
                            )
                     except AttributeError:
                            _lowerCamelCase						:    Any         =			''''''
                     try:
                            _lowerCamelCase						:    Optional[int]         =			float(
                                (
                                    (
                                        float(product_mrp.strip('''₹'''						).replace(''',''',    ''''''						)						)
                                        - float(product_price.strip('''₹'''						).replace(''',''',    ''''''						)						)
                                    )
                                    / float(product_mrp.strip('''₹'''						).replace(''',''',    ''''''						)						)
                                )
                                * 1_00						)
                     except ValueError:
                            _lowerCamelCase						:    List[Any]         =			float('''nan'''						)
              except AttributeError:
                     pass
              _lowerCamelCase						:    str         =			[
                  product_title,
                  product_link,
                  product_price,
                  product_rating,
                  product_mrp,
                  discount,
              ]
       _lowerCamelCase						:    List[Any]         =			''' '''
       _lowerCamelCase						:    Tuple         =			''' '''
       data_frame.index += 1
       return data_frame


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







"""simple docstring"""






from torch import nn






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:    List[str]         =			class_size
         _lowerCamelCase						:    List[str]         =			embed_size
         # self.mlp1 = nn.Linear(embed_size, embed_size)
         # self.mlp2 = (nn.Linear(embed_size, class_size))
         _lowerCamelCase						:    Optional[int]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self.mlp(__lowerCAmelCase		)
         return logits


