

from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  tf.convert_to_tensor(
            [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4     )     )



