



"""simple docstring"""






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 			A__   (  __SCREAMING_SNAKE_CASE  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	LongformerTokenizer
	SCREAMING_SNAKE_CASE          =	True
	SCREAMING_SNAKE_CASE          =	LongformerTokenizerFast
	SCREAMING_SNAKE_CASE          =	True

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       str:

				"""simple docstring"""

				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				__lowerCAmelCase      :  List[Any]												=       [
				    "l",
				    "o",
				    "w",
				    "e",
				    "r",
				    "s",
				    "t",
				    "i",
				    "d",
				    "n",
				    "\u0120",
				    "\u0120l",
				    "\u0120n",
				    "\u0120lo",
				    "\u0120low",
				    "er",
				    "\u0120lowest",
				    "\u0120newer",
				    "\u0120wider",
				    "<unk>",
				]
				__lowerCAmelCase      :  Tuple												=       dict(zip(_SCREAMING_SNAKE_CASE       ,			range(len(_SCREAMING_SNAKE_CASE))))
				__lowerCAmelCase      :  Optional[int]												=       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
				__lowerCAmelCase      :  List[Any]												=       {"unk_token": "<unk>"}

				__lowerCAmelCase      :  List[Any]												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["vocab_file"])
				__lowerCAmelCase      :  List[str]												=       os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES["merges_file"])
				with open(self.vocab_file       ,			"w"       ,			encoding="utf-8") as fp:
							fp.write(json.dumps(_SCREAMING_SNAKE_CASE) + "\n")
				with open(self.merges_file       ,			"w"       ,			encoding="utf-8") as fp:
							fp.write("\n".join(_SCREAMING_SNAKE_CASE))

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			**_SCREAMING_SNAKE_CASE:		int)				->       List[Any]:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map)
				return self.tokenizer_class.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			**_SCREAMING_SNAKE_CASE:		Optional[int])				->       Tuple:

				"""simple docstring"""

				kwargs.update(self.special_tokens_map)
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,			**_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       List[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       "lower newer"
				__lowerCAmelCase      :  Dict												=       "lower newer"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.tokenizer_class(self.vocab_file       ,			self.merges_file       ,			**self.special_tokens_map)
				__lowerCAmelCase      :  List[str]												=       "lower newer"
				__lowerCAmelCase      :  Dict												=       ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
				__lowerCAmelCase      :  Any												=       tokenizer.tokenize(_SCREAMING_SNAKE_CASE)  # , add_prefix_space=True)
				self.assertListEqual(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Union[str, Any]												=       tokens + [tokenizer.unk_token]
				__lowerCAmelCase      :  Any												=       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)       ,			_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.get_tokenizer()

				self.assertListEqual(tokenizer.encode("Hello world!"       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)       ,			[0, 3_1414, 232, 328, 2])
				self.assertListEqual(
				    tokenizer.encode("Hello world! cécé herlolip 418"       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)       ,			[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]       ,			)

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

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.tokenizer_class.from_pretrained("allenai/longformer-base-4096")

				__lowerCAmelCase      :  Any												=       tokenizer.encode("sequence builders"       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Union[str, Any]												=       tokenizer.encode("multi-sequence build"       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  List[str]												=       tokenizer.encode(
				    "sequence builders"       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Dict												=       tokenizer.encode(
				    "sequence builders"       ,			"multi-sequence build"       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Any												=       tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[Any]												=       tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				assert encoded_sentence == encoded_text_from_decode
				assert encoded_pair == encoded_pair_from_decode

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_tokenizer()

				__lowerCAmelCase      :  str												=       "Encode this sequence."
				__lowerCAmelCase      :  Dict												=       tokenizer.byte_encoder[" ".encode("utf-8")[0]]

				# Testing encoder arguments
				__lowerCAmelCase      :  Dict												=       tokenizer.encode(_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Tuple												=       tokenizer.convert_ids_to_tokens(encoded[0])[0]
				self.assertNotEqual(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       tokenizer.encode(_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       tokenizer.convert_ids_to_tokens(encoded[0])[0]
				self.assertEqual(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				tokenizer.add_special_tokens({"bos_token": "<s>"})
				__lowerCAmelCase      :  List[Any]												=       tokenizer.encode(_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       tokenizer.convert_ids_to_tokens(encoded[1])[0]
				self.assertNotEqual(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				# Testing spaces after special tokens
				__lowerCAmelCase      :  str												=       "<mask>"
				tokenizer.add_special_tokens(
				    {"mask_token": AddedToken(_SCREAMING_SNAKE_CASE       ,			lstrip=_SCREAMING_SNAKE_CASE       ,			rstrip=_SCREAMING_SNAKE_CASE)})  # mask token has a left space
				__lowerCAmelCase      :  str												=       tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Optional[int]												=       "Encode <mask> sequence"
				__lowerCAmelCase      :  Optional[int]												=       "Encode <mask>sequence"

				__lowerCAmelCase      :  Tuple												=       tokenizer.encode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       encoded.index(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
				self.assertEqual(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase      :  Tuple												=       tokenizer.encode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  Optional[int]												=       encoded.index(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
				self.assertNotEqual(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase      :  Any												=       self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Optional[Any]												=       self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Optional[int]												=       "A, <mask> AllenNLP sentence."
										__lowerCAmelCase      :  str												=       tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			return_token_type_ids=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Dict												=       tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			return_token_type_ids=_SCREAMING_SNAKE_CASE)

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r["token_type_ids"])       ,			sum(tokens_p["token_type_ids"]))

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"])       ,			sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"])       ,			)

										__lowerCAmelCase      :  str												=       tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
										__lowerCAmelCase      :  Optional[int]												=       tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p["input_ids"]       ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
										self.assertSequenceEqual(tokens_r["input_ids"]       ,			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

										self.assertSequenceEqual(
										    _SCREAMING_SNAKE_CASE       ,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
										self.assertSequenceEqual(
										    _SCREAMING_SNAKE_CASE       ,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       List[Any]:

				"""simple docstring"""

				for trim_offsets, add_prefix_space in itertools.product([True, False]       ,			repeat=2):
							__lowerCAmelCase      :  Optional[Any]												=       self.rust_tokenizer_class.from_pretrained(
							    self.tmpdirname       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)

							__lowerCAmelCase      :  Optional[Any]												=       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
							__lowerCAmelCase      :  Optional[int]												=       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

							self.assertEqual(pre_tokenizer_state["add_prefix_space"]       ,			_SCREAMING_SNAKE_CASE)

							self.assertEqual(post_processor_state["add_prefix_space"]       ,			_SCREAMING_SNAKE_CASE)
							self.assertEqual(post_processor_state["trim_offsets"]       ,			_SCREAMING_SNAKE_CASE)







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

				"""simple docstring"""

				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
										__lowerCAmelCase      :  Any												=       "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
										__lowerCAmelCase      :  Any												=       F"""{text_of_1_token} {text_of_1_token}"""

										__lowerCAmelCase      :  List[Any]												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  int												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(0, len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(len(_SCREAMING_SNAKE_CASE) + 1, len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)

										__lowerCAmelCase      :  Any												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Optional[int]												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(0, len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(len(_SCREAMING_SNAKE_CASE) + 1, len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)

										__lowerCAmelCase      :  int												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Optional[Any]												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(0, len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(len(_SCREAMING_SNAKE_CASE), len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)

										__lowerCAmelCase      :  Optional[int]												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Any												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(0, len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(len(_SCREAMING_SNAKE_CASE), len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)

										__lowerCAmelCase      :  Tuple												=       F""" {text}"""

										# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
										#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
										# )
										# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
										# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
										# self.assertEqual(
										#     encoding.offset_mapping[1],
										#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
										# )

										__lowerCAmelCase      :  str												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  str												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(1, 1 + len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(1 + len(_SCREAMING_SNAKE_CASE) + 1, 1 + len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)

										__lowerCAmelCase      :  List[str]												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Optional[Any]												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(0, 1 + len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(1 + len(_SCREAMING_SNAKE_CASE), 1 + len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)

										__lowerCAmelCase      :  List[Any]												=       self.rust_tokenizer_class.from_pretrained(
										    _SCREAMING_SNAKE_CASE       ,			use_fast=_SCREAMING_SNAKE_CASE       ,			add_prefix_space=_SCREAMING_SNAKE_CASE       ,			trim_offsets=_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase      :  Tuple												=       tokenizer_r(_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE)
										self.assertEqual(encoding.offset_mapping[0]       ,			(0, 1 + len(_SCREAMING_SNAKE_CASE)))
										self.assertEqual(
										    encoding.offset_mapping[1]       ,			(1 + len(_SCREAMING_SNAKE_CASE), 1 + len(_SCREAMING_SNAKE_CASE) + 1 + len(_SCREAMING_SNAKE_CASE))       ,			)




"""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""")