
"""simple docstring"""


from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def 	lowerCamelCase__			(							UpperCAmelCase_ = "laptop"				)->							DataFrame:







       """simple docstring"""
       UpperCamelCase					 =		F"https://www.amazon.in/laptop/s?k={product}"
       UpperCamelCase					 =		{
           "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",
       }
       UpperCamelCase					 =		BeautifulSoup(requests.get(UpperCAmelCase_		,      headers=UpperCAmelCase_				).text				)
       # Initialize a Pandas dataframe with the column titles
       UpperCamelCase					 =		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:
                     UpperCamelCase					 =		item.ha.text
                     UpperCamelCase					 =		"https://www.amazon.in/" + item.ha.a["href"]
                     UpperCamelCase					 =		item.find("span"		,      attrs={"class": "a-offscreen"}				).text
                     try:
                            UpperCamelCase					 =		item.find("span"		,      attrs={"class": "a-icon-alt"}				).text
                     except AttributeError:
                            UpperCamelCase					 =		"Not available"
                     try:
                            UpperCamelCase					 =		(
                                "₹"
                                + item.find(
                                    "span"		,      attrs={"class": "a-price a-text-price"}				).text.split("₹"				)[1]
                            )
                     except AttributeError:
                            UpperCamelCase					 =		""
                     try:
                            UpperCamelCase					 =		float(
                                (
                                    (
                                        float(product_mrp.strip("₹"				).replace(","		,      ""				)				)
                                        - float(product_price.strip("₹"				).replace(","		,      ""				)				)
                                    )
                                    / float(product_mrp.strip("₹"				).replace(","		,      ""				)				)
                                )
                                * 1_00				)
                     except ValueError:
                            UpperCamelCase					 =		float("nan"				)
              except AttributeError:
                     pass
              UpperCamelCase					 =		[
                  product_title,
                  product_link,
                  product_price,
                  product_rating,
                  product_mrp,
                  discount,
              ]
       UpperCamelCase					 =		" "
       UpperCamelCase					 =		" "
       data_frame.index += 1
       return data_frame


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







"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_torch
class   __a  (      unittest.TestCase	):






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-base"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 768)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		torch.tensor(
                  [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]			)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-large"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 1_024)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		torch.tensor(
                  [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]			)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)





