
'''simple docstring'''






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







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



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   F'https://www.amazon.in/laptop/s?k={product}'
      _lowerCamelCase				:   Tuple								=   {
          "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
          "Accept-Language": "en-US, en;q=0.5",
      }
      _lowerCamelCase				:   int								=   BeautifulSoup(requests.get(_lowerCAmelCase				,	headers=_lowerCAmelCase							).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				:   Optional[int]								=   "https://www.amazon.in/" + item.ha.a["href"]
                  _lowerCamelCase				:   Optional[int]								=   item.find("span"				,	attrs={"class": "a-offscreen"}							).text
                  try:
                        _lowerCamelCase				:   Union[str, Any]								=   item.find("span"				,	attrs={"class": "a-icon-alt"}							).text
                  except AttributeError:
                        _lowerCamelCase				:   Optional[Any]								=   "Not available"
                  try:
                        _lowerCamelCase				:   Dict								=   (
                            "₹"
                            + item.find(
                                "span"				,	attrs={"class": "a-price a-text-price"}							).text.split("₹"							)[1]
                        )
                  except AttributeError:
                        _lowerCamelCase				:   Union[str, Any]								=   ""
                  try:
                        _lowerCamelCase				:   Union[str, Any]								=   float(
                            (
                                (
                                    float(product_mrp.strip("₹"							).replace(","				,	""							)							)
                                    - float(product_price.strip("₹"							).replace(","				,	""							)							)
                                )
                                / float(product_mrp.strip("₹"							).replace(","				,	""							)							)
                            )
                            * 100							)
                  except ValueError:
                        _lowerCamelCase				:   List[str]								=   float("nan"							)
            except AttributeError:
                  pass
            _lowerCamelCase				:   Dict								=   [
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      _lowerCamelCase				:   List[str]								=   " "
      _lowerCamelCase				:   Tuple								=   " "
      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''')

'''simple docstring'''






import warnings

from ..trainer import Trainer
from ..utils import logging


UpperCAmelCase_       :   Union[str, Any]   =       logging.get_logger(__name__)

class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       int,__A      :       Any=None,**__A      :       Optional[Any]     ):
          warnings.warn(
              "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
              "instead.",__A,)
          super().__init__(args=__A,**__A     )