

'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_	= logging.get_logger(__name__)

A_	= {
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					'data2vec-text'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=30522   ,			SCREAMING_SNAKE_CASE_=768   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=3072   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=512   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=1E-12   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=0   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_="absolute"   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=None   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     str:






							'''simple docstring'''



							super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,			bos_token_id=SCREAMING_SNAKE_CASE_   ,			eos_token_id=SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      vocab_size
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      max_position_embeddings
							lowerCamelCase_  				=      type_vocab_size
							lowerCamelCase_  				=      initializer_range
							lowerCamelCase_  				=      layer_norm_eps
							lowerCamelCase_  				=      position_embedding_type
							lowerCamelCase_  				=      use_cache
							lowerCamelCase_  				=      classifier_dropout


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		@property
		def 			UpperCamelCase(					self		)  ->     Mapping[str, Mapping[int, str]]:






							'''simple docstring'''



							if self.task == "multiple-choice":
												lowerCamelCase_  				=      {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
												lowerCamelCase_  				=      {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]		)








'''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""")



