






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def  _lowerCamelCase						(						__lowerCamelCase = "laptop"       ) ->			DataFrame:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			F"https://www.amazon.in/laptop/s?k={product}"
     UpperCAmelCase__      :  Tuple				      =			{
         """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__      :  Optional[int]				      =			BeautifulSoup(requests.get(__lowerCamelCase ,							headers=__lowerCamelCase       ).text       )
     # Initialize a Pandas dataframe with the column titles
     UpperCAmelCase__      :  str				      =			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__      :  str				      =			item.ha.text
               UpperCAmelCase__      :  Union[str, Any]				      =			"""https://www.amazon.in/""" + item.ha.a["""href"""]
               UpperCAmelCase__      :  Union[str, Any]				      =			item.find("""span""" ,							attrs={"""class""": """a-offscreen"""}       ).text
               try:
                    UpperCAmelCase__      :  Optional[Any]				      =			item.find("""span""" ,							attrs={"""class""": """a-icon-alt"""}       ).text
               except AttributeError:
                    UpperCAmelCase__      :  Dict				      =			"""Not available"""
               try:
                    UpperCAmelCase__      :  List[str]				      =			(
                        """₹"""
                        + item.find(
                            """span""" ,							attrs={"""class""": """a-price a-text-price"""}       ).text.split("""₹"""       )[1]
                    )
               except AttributeError:
                    UpperCAmelCase__      :  int				      =			""""""
               try:
                    UpperCAmelCase__      :  int				      =			float(
                        (
                            (
                                float(product_mrp.strip("""₹"""       ).replace(""",""" ,							""""""       )       )
                                - float(product_price.strip("""₹"""       ).replace(""",""" ,							""""""       )       )
                            )
                            / float(product_mrp.strip("""₹"""       ).replace(""",""" ,							""""""       )       )
                        )
                        * 100       )
               except ValueError:
                    UpperCAmelCase__      :  Any				      =			float("""nan"""       )
          except AttributeError:
               pass
          UpperCAmelCase__      :  int				      =			[
              product_title,
              product_link,
              product_price,
              product_rating,
              product_mrp,
              discount,
          ]
     UpperCAmelCase__      :  Any				      =			""" """
     UpperCAmelCase__      :  Union[str, Any]				      =			""" """
     data_frame.index += 1
     return data_frame


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				"""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,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_							:	Any	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






