




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def 					__SCREAMING_SNAKE_CASE   (  lowercase_ = "laptop"      )       ->	DataFrame:

    """simple docstring"""


    __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(lowercase_					,     headers=lowercase_      ).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(''','''					,     ''''''      )      )
                    )
                    * 1_00      )
            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__":
 a_   		=     "headphones"
 get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["image_processor", "tokenizer"]
      lowerCAmelCase__			: List[Any]    =					"ViTImageProcessor"
      lowerCAmelCase__			: Optional[int]    =					("CLIPTokenizer", "CLIPTokenizerFast")







      def __init__(     self					: Any ,       snake_case					: Any=None ,       snake_case					: Dict=None ,       **snake_case					: Tuple				):
          __UpperCamelCase  		=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                  ''' instead.''' ,       snake_case ,       )
              __UpperCamelCase  		=						kwargs.pop('''feature_extractor'''				)

          __UpperCamelCase  		=						image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
              raise ValueError('''You need to specify an `image_processor`.'''				)
          if tokenizer is None:
              raise ValueError('''You need to specify a `tokenizer`.'''				)

          super().__init__(snake_case ,       snake_case				)







      def __call__(     self					: Dict ,       snake_case					: Union[str, Any]=None ,       snake_case					: Tuple=None ,       snake_case					: Any=None ,       snake_case					: Any=None ,       **snake_case					: Optional[int]				):
          if text is None and visual_prompt is None and images is None:
              raise ValueError('''You have to specify either text, visual prompt or images.'''				)

          if text is not None and visual_prompt is not None:
              raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''				)

          if text is not None:
              __UpperCamelCase  		=						self.tokenizer(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if images is not None:
              __UpperCamelCase  		=						self.image_processor(snake_case ,       return_tensors=snake_case ,       **snake_case				)

          if visual_prompt is not None and images is not None:
              __UpperCamelCase  		=						{
                  '''pixel_values''': image_features.pixel_values,
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          elif text is not None and images is not None:
              __UpperCamelCase  		=						image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          elif visual_prompt is not None:
              __UpperCamelCase  		=						{
                  '''conditional_pixel_values''': prompt_features.pixel_values,
              }
              return encoding
          else:
              return BatchEncoding(data=dict(**snake_case				) ,       tensor_type=snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Tuple ,       **snake_case					: Any				):
          return self.tokenizer.batch_decode(*snake_case ,       **snake_case				)







      def       snake_case	(     self					: Optional[int] ,       *snake_case					: Dict ,       **snake_case					: List[str]				):
          return self.tokenizer.decode(*snake_case ,       **snake_case				)







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,       snake_case ,       )
          return self.image_processor_class







      @property
      def       snake_case	(     self					: Optional[Any]				):
          warnings.warn(
              '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,       snake_case ,       )
          return self.image_processor






