




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any] = "laptop"  ):

					'''simple docstring'''





					__lowerCAmelCase            = F"""https://www.amazon.in/laptop/s?k={product}"""
					__lowerCAmelCase            = {
					    "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            = BeautifulSoup(requests.get(_lowerCAmelCase   ,   headers=_lowerCAmelCase  ).text  )
					# Initialize a Pandas dataframe with the column titles
					__lowerCAmelCase            = 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            = item.ha.text
															__lowerCAmelCase            = "https://www.amazon.in/" + item.ha.a["href"]
															__lowerCAmelCase            = item.find("span"   ,   attrs={"class": "a-offscreen"}  ).text
															try:
																				__lowerCAmelCase            = item.find("span"   ,   attrs={"class": "a-icon-alt"}  ).text
															except AttributeError:
																				__lowerCAmelCase            = "Not available"
															try:
																				__lowerCAmelCase            = (
																				    "₹"
																				    + item.find(
																				        "span"   ,   attrs={"class": "a-price a-text-price"}  ).text.split("₹"  )[1]
																				)
															except AttributeError:
																				__lowerCAmelCase            = ""
															try:
																				__lowerCAmelCase            = float(
																				    (
																				        (
																				            float(product_mrp.strip("₹"  ).replace(","   ,   ""  )  )
																				            - float(product_price.strip("₹"  ).replace(","   ,   ""  )  )
																				        )
																				        / float(product_mrp.strip("₹"  ).replace(","   ,   ""  )  )
																				    )
																				    * 1_00  )
															except ValueError:
																				__lowerCAmelCase            = float("nan"  )
										except AttributeError:
															pass
										__lowerCAmelCase            = [
										    product_title,
										    product_link,
										    product_price,
										    product_rating,
										    product_mrp,
										    discount,
										]
					__lowerCAmelCase            = " "
					__lowerCAmelCase            = " "
					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 MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class     a__					:






											@staticmethod
											def        __SCREAMING_SNAKE_CASE(     *_A	,    **_A    ):




																"""simple docstring"""



																pass
@is_pipeline_test
@require_torch
@require_vision
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = [
											    {
											        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
											        "question": "How many cats are there?",
											    },
											    {
											        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
											        "question": "How many cats are there?",
											    },
											]
											return vqa_pipeline, examples






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = vqa_pipeline(_A	,    top_k=1    )
											self.assertEqual(
											    _A	,    [
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											    ]	,    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question="How many cats are there?"	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )






						@slow
						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="dandelin/vilt-b32-finetuned-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question=_A	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline(
											    [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [[{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]] * 2	,    )






						@require_tf
						@unittest.skip("Visual question answering not implemented in TF"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass




