


'''simple docstring'''







import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available


class 			lowerCAmelCase_    (							snake_case__   ):





  """simple docstring"""



  a_							:Dict				       =['image_processor', 'tokenizer']
  a_							:Union[str, Any]				       ='OwlViTImageProcessor'
  a_							:Optional[int]				       =('CLIPTokenizer', 'CLIPTokenizerFast')

  def __init__(       self  :				Union[str, Any]					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]=None					,				SCREAMING_SNAKE_CASE__  :				List[str]=None					,				**SCREAMING_SNAKE_CASE__  :				Tuple    ):





       '''simple docstring'''

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

       __a   			= 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__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    )

  def __call__(       self  :				str					,				SCREAMING_SNAKE_CASE__  :				str=None					,				SCREAMING_SNAKE_CASE__  :				Any=None					,				SCREAMING_SNAKE_CASE__  :				List[str]=None					,				SCREAMING_SNAKE_CASE__  :				int="max_length"					,				SCREAMING_SNAKE_CASE__  :				Optional[Any]="np"					,				**SCREAMING_SNAKE_CASE__  :				List[str]    ):





       '''simple docstring'''

       if text is None and query_images is None and images is None:
            raise ValueError(
                """You have to specify at least one text or query image or image. All three cannot be none."""    )

       if text is not None:
            if isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    ) or (isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    ) and not isinstance(text[0]					,				SCREAMING_SNAKE_CASE__    )):
                 __a   			= [self.tokenizer(SCREAMING_SNAKE_CASE__					,				padding=SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )]

            elif isinstance(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__    ) and isinstance(text[0]					,				SCREAMING_SNAKE_CASE__    ):
                 __a   			= []

                 # Maximum number of queries across batch
                 __a   			= max([len(SCREAMING_SNAKE_CASE__    ) for t in text]    )

                 # Pad all batch samples to max number of text queries
                 for t in text:
                      if len(SCREAMING_SNAKE_CASE__    ) != max_num_queries:
                           __a   			= t + [""" """] * (max_num_queries - len(SCREAMING_SNAKE_CASE__    ))

                      __a   			= self.tokenizer(SCREAMING_SNAKE_CASE__					,				padding=SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )
                      encodings.append(SCREAMING_SNAKE_CASE__    )
            else:
                 raise TypeError("""Input text should be a string, a list of strings or a nested list of strings"""    )

            if return_tensors == "np":
                 __a   			= np.concatenate([encoding["""input_ids"""] for encoding in encodings]					,				axis=0    )
                 __a   			= np.concatenate([encoding["""attention_mask"""] for encoding in encodings]					,				axis=0    )

            elif return_tensors == "jax" and is_flax_available():
                 import jax.numpy as jnp

                 __a   			= jnp.concatenate([encoding["""input_ids"""] for encoding in encodings]					,				axis=0    )
                 __a   			= jnp.concatenate([encoding["""attention_mask"""] for encoding in encodings]					,				axis=0    )

            elif return_tensors == "pt" and is_torch_available():
                 import torch

                 __a   			= torch.cat([encoding["""input_ids"""] for encoding in encodings]					,				dim=0    )
                 __a   			= torch.cat([encoding["""attention_mask"""] for encoding in encodings]					,				dim=0    )

            elif return_tensors == "tf" and is_tf_available():
                 import tensorflow as tf

                 __a   			= tf.stack([encoding["""input_ids"""] for encoding in encodings]					,				axis=0    )
                 __a   			= tf.stack([encoding["""attention_mask"""] for encoding in encodings]					,				axis=0    )

            else:
                 raise ValueError("""Target return tensor type could not be returned"""    )

            __a   			= BatchEncoding()
            __a   			= input_ids
            __a   			= attention_mask

       if query_images is not None:
            __a   			= BatchEncoding()
            __a   			= self.image_processor(
                SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    ).pixel_values
            __a   			= query_pixel_values

       if images is not None:
            __a   			= self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )

       if text is not None and images is not None:
            __a   			= image_features.pixel_values
            return encoding
       elif query_images is not None and images is not None:
            __a   			= image_features.pixel_values
            return encoding
       elif text is not None or query_images is not None:
            return encoding
       else:
            return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__    )					,				tensor_type=SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				Any					,				*SCREAMING_SNAKE_CASE__  :				Optional[Any]					,				**SCREAMING_SNAKE_CASE__  :				str    ):





       '''simple docstring'''

       return self.image_processor.post_process(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				List[Any]					,				*SCREAMING_SNAKE_CASE__  :				List[Any]					,				**SCREAMING_SNAKE_CASE__  :				Optional[int]    ):





       '''simple docstring'''

       return self.image_processor.post_process_object_detection(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				List[str]					,				*SCREAMING_SNAKE_CASE__  :				List[str]					,				**SCREAMING_SNAKE_CASE__  :				Optional[int]    ):





       '''simple docstring'''

       return self.image_processor.post_process_image_guided_detection(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__    )

  def __a    (       self  :				str					,				*SCREAMING_SNAKE_CASE__  :				int					,				**SCREAMING_SNAKE_CASE__  :				Any    ):





       '''simple docstring'''

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

  def __a    (       self  :				Any					,				*SCREAMING_SNAKE_CASE__  :				str					,				**SCREAMING_SNAKE_CASE__  :				int    ):





       '''simple docstring'''

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

  @property
  def __a    (       self  :				Tuple    ):





       '''simple docstring'''

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


  @property
  def __a    (       self  :				Optional[Any]    ):





       '''simple docstring'''

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











'''simple docstring'''





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame




def lowerCAmelCase    (__A = "laptop"):



     """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(__A    ,      headers=__A).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__":
  lowercase_									=    "headphones"
  get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")



