






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def     a_   (__A = "laptop"      )				->						DataFrame:



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


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













import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
			import optax
			from flax.training.common_utils import onehot

			from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
			from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



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




											'''simple docstring'''



											__a					:						Optional[Any]      =					FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"   )
											__a					:						Dict      =					AutoTokenizer.from_pretrained("google/mt5-small"   )

											__a					:						Any      =					tokenizer("Hello there" ,					return_tensors="np"   ).input_ids
											__a					:						Union[str, Any]      =					tokenizer("Hi I am" ,					return_tensors="np"   ).input_ids

											__a					:						Union[str, Any]      =					shift_tokens_right(__UpperCAmelCase ,					model.config.pad_token_id ,					model.config.decoder_start_token_id   )

											__a					:						Tuple      =					model(__UpperCAmelCase ,					decoder_input_ids=__UpperCAmelCase   ).logits
											__a					:						str      =					optax.softmax_cross_entropy(__UpperCAmelCase ,					onehot(__UpperCAmelCase ,					logits.shape[-1]   )   ).mean()

											__a					:						List[str]      =					-(labels.shape[-1] * loss.item())

											__a					:						Optional[int]      =					-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE   ) < 1E-4   )





