






'''simple docstring'''







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def        __lowercase						(      __SCREAMING_SNAKE_CASE = "laptop"		)				->					Tuple:
     """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(_snake_case				,  headers=_snake_case		).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__":
 SCREAMING_SNAKE_CASE_      =			'headphones'
 get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_      =			{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure)






