



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     _A      =      ['image_processor', 'tokenizer']
     _A      =      'AutoImageProcessor'
     _A      =      'AutoTokenizer'







     def __init__(self					,      __a=None					,      __a=None					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		None
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                     " instead."					,      __a					,      )
                 lowerCamelCase             =		kwargs.pop("feature_extractor"     )

           lowerCamelCase             =		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__(__a					,      __a     )
           lowerCamelCase             =		self.image_processor
           lowerCamelCase             =		False







     def __call__(self					,      *__a					,      **__a     ):






           '''simple docstring'''



           if self._in_target_context_manager:
                 return self.current_processor(*__a					,      **__a     )

           lowerCamelCase             =		kwargs.pop("images"					,      __a     )
           lowerCamelCase             =		kwargs.pop("text"					,      __a     )
           if len(__a     ) > 0:
                 lowerCamelCase             =		args[0]
                 lowerCamelCase             =		args[1:]

           if images is None and text is None:
                 raise ValueError("You need to specify either an `images` or `text` input to process."     )

           if images is not None:
                 lowerCamelCase             =		self.image_processor(__a					,      *__a					,      **__a     )
           if text is not None:
                 lowerCamelCase             =		self.tokenizer(__a					,      **__a     )

           if text is None:
                 return inputs
           elif images is None:
                 return encodings
           else:
                 lowerCamelCase             =		encodings["input_ids"]
                 return inputs







     def     _a   (self					,      *__a					,      **__a     ):






           '''simple docstring'''



           return self.tokenizer.batch_decode(*__a					,      **__a     )







     def     _a   (self					,      *__a					,      **__a     ):






           '''simple docstring'''



           return self.tokenizer.decode(*__a					,      **__a     )







     @contextmanager
     def     _a   (self     ):






           '''simple docstring'''



           warnings.warn(
               "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
               "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
               "your images inputs, or in a separate call."     )
           lowerCamelCase             =		True
           lowerCamelCase             =		self.tokenizer
           yield
           lowerCamelCase             =		self.image_processor
           lowerCamelCase             =		False







     def     _a   (self					,      __a					,      __a=False					,      __a=None     ):






           '''simple docstring'''



           if added_vocab is None:
                 lowerCamelCase             =		self.tokenizer.get_added_vocab()

           lowerCamelCase             =		{}

           while tokens:
                 lowerCamelCase             =		re.search(R"<s_(.*?)>"					,      __a					,      re.IGNORECASE     )
                 if start_token is None:
                       break
                 lowerCamelCase             =		start_token.group(1     )
                 lowerCamelCase             =		re.search(RF"""</s_{key}>"""					,      __a					,      re.IGNORECASE     )
                 lowerCamelCase             =		start_token.group()
                 if end_token is None:
                       lowerCamelCase             =		tokens.replace(__a					,      ""     )
                 else:
                       lowerCamelCase             =		end_token.group()
                       lowerCamelCase             =		re.escape(__a     )
                       lowerCamelCase             =		re.escape(__a     )
                       lowerCamelCase             =		re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""					,      __a					,      re.IGNORECASE     )
                       if content is not None:
                             lowerCamelCase             =		content.group(1     ).strip()
                             if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                   lowerCamelCase             =		self.tokenajson(__a					,      is_inner_value=__a					,      added_vocab=__a     )
                                   if value:
                                         if len(__a     ) == 1:
                                               lowerCamelCase             =		value[0]
                                         lowerCamelCase             =		value
                             else:  # leaf nodes
                                   lowerCamelCase             =		[]
                                   for leaf in content.split(R"<sep/>"     ):
                                         lowerCamelCase             =		leaf.strip()
                                         if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                               lowerCamelCase             =		leaf[1:-2]  # for categorical special tokens
                                         output[key].append(__a     )
                                   if len(output[key]     ) == 1:
                                         lowerCamelCase             =		output[key][0]

                       lowerCamelCase             =		tokens[tokens.find(__a     ) + len(__a     ) :].strip()
                       if tokens[:6] == r"<sep/>":  # non-leaf nodes
                             return [output] + self.tokenajson(tokens[6:]					,      is_inner_value=__a					,      added_vocab=__a     )

           if len(__a     ):
                 return [output] if is_inner_value else output
           else:
                 return [] if is_inner_value else {"text_sequence": tokens}







     @property
     def     _a   (self     ):






           '''simple docstring'''



           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,      __a					,      )
           return self.image_processor_class







     @property
     def     _a   (self     ):






           '''simple docstring'''



           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,      __a					,      )
           return self.image_processor




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def      __lowercase(						UpperCAmelCase__ = "laptop"			):


      """simple docstring"""



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


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