




"""simple docstring"""

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 	lowerCamelCase__			(     __SCREAMING_SNAKE_CASE ,     unittest.TestCase	):
			a__       :					Dict	    =   FunnelTokenizer
			a__       :					List[str]	    =   FunnelTokenizerFast
			a__       :					Optional[Any]	    =   True
			a__       :					int	    =   True


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					super().setUp()

					snake_case						:  str    	= [
					    "<unk>",
					    "<cls>",
					    "<sep>",
					    "want",
					    "##want",
					    "##ed",
					    "wa",
					    "un",
					    "runn",
					    "##ing",
					    ",",
					    "low",
					    "lowest",
					]
					snake_case						:  int    	= os.path.join(self.tmpdirname ,       VOCAB_FILES_NAMES["vocab_file"]							)
					with open(self.vocab_file ,       "w" ,       encoding="utf-8"							) as vocab_writer:
							vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)


			def 							lowerCamelCase_ (   self ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return FunnelTokenizer.from_pretrained(self.tmpdirname ,       **_SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_ (   self ,       **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return FunnelTokenizerFast.from_pretrained(self.tmpdirname ,       **_SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]    	= "UNwant\u00E9d,running"
					snake_case						:  str    	= "unwanted, running"
					return input_text, output_text


			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Any    	= self.tokenizer_class(self.vocab_file							)

					snake_case						:  Any    	= tokenizer.tokenize("UNwant\u00E9d,running"							)
					self.assertListEqual(_SCREAMING_SNAKE_CASE ,       ["un", "##want", "##ed", ",", "runn", "##ing"]							)
					self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE							) ,       [7, 4, 5, 10, 8, 9]							)



			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					snake_case						:  Optional[Any]    	= self.get_tokenizers(do_lower_case=_SCREAMING_SNAKE_CASE							)
					for tokenizer in tokenizers:
							snake_case						:  List[str]    	= tokenizer("UNwant\u00E9d,running"							)
							snake_case						:  Optional[int]    	= len(inputs["input_ids"]							) - 1
							self.assertListEqual(inputs["token_type_ids"] ,       [2] + [0] * sentence_len							)

							snake_case						:  List[str]    	= tokenizer("UNwant\u00E9d,running" ,       "UNwant\u00E9d,running"							)
							self.assertListEqual(inputs["token_type_ids"] ,       [2] + [0] * sentence_len + [1] * sentence_len							)






"""simple docstring"""






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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