


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def     __lowerCamelCase						(			UpperCAmelCase_						:		str = "laptop"						):




      """simple docstring"""





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


if __name__ == "__main__":
     snake_case    :   List[str]	    =					'''headphones'''
     get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")









import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case    :   List[Any]	    =					get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')







class 		_snake_case  (				_snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	BartphoTokenizer
 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	True




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       super().setUp()

       a						:Dict										=	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       a						:Optional[Any]										=	dict(zip(_lowerCamelCase ,  range(len(_lowerCamelCase   )   )   )   )
       a						:Tuple										=	{'''unk_token''': '''<unk>'''}

       a						:Union[str, Any]										=	os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES['''monolingual_vocab_file''']   )
       with open(self.monolingual_vocab_file ,  '''w''' ,  encoding='''utf-8'''   ) as fp:
             for token in vocab_tokens:
                   fp.write(F'''{token} {vocab_tokens[token]}\n'''   )

       a						:Any										=	BartphoTokenizer(_lowerCamelCase ,  self.monolingual_vocab_file ,  **self.special_tokens_map   )
       tokenizer.save_pretrained(self.tmpdirname   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  **_lowerCamelCase   ):
       kwargs.update(self.special_tokens_map   )
       return BartphoTokenizer.from_pretrained(self.tmpdirname ,  **_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:int										=	'''This is a là test'''
       a						:str										=	'''This is a<unk><unk> test'''
       return input_text, output_text




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	BartphoTokenizer(_lowerCamelCase ,  self.monolingual_vocab_file ,  **self.special_tokens_map   )
       a						:Optional[Any]										=	'''This is a là test'''
       a						:Tuple										=	'''▁This ▁is ▁a ▁l à ▁t est'''.split()
       a						:int										=	tokenizer.tokenize(_lowerCamelCase   )
       self.assertListEqual(_lowerCamelCase ,  _lowerCamelCase   )

       a						:Union[str, Any]										=	tokens + [tokenizer.unk_token]
       a						:str										=	[4, 5, 6, 3, 3, 7, 8, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase   ) ,  _lowerCamelCase   )





