




"""simple docstring"""

import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel

from ...utils import logging


__A       						=	logging.get_logger(__name__)
class 	lowerCamelCase__			(     __SCREAMING_SNAKE_CASE	):
			a__       :					str	    =   CLIPConfig

			a__       :					Optional[Any]	    =   ["""CLIPEncoderLayer"""]


			def __init__(   self ,       SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__(_SCREAMING_SNAKE_CASE							)

					snake_case						:  Any    	= CLIPVisionModelWithProjection(config.vision_config							)

					snake_case						:  Union[str, Any]    	= nn.Linear(config.vision_config.projection_dim ,       1							)
					snake_case						:  str    	= nn.Linear(config.vision_config.projection_dim ,       1							)



			@torch.no_grad()
			def 							lowerCamelCase_ (   self ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE ,       SCREAMING_SNAKE_CASE=0.5 ,       SCREAMING_SNAKE_CASE=0.5							):



					"""simple docstring"""







					snake_case						:  Optional[int]    	= self.vision_model(_SCREAMING_SNAKE_CASE							)[0]

					snake_case						:  Dict    	= self.p_head(_SCREAMING_SNAKE_CASE							)
					snake_case						:  Union[str, Any]    	= nsfw_detected.flatten()
					snake_case						:  int    	= nsfw_detected > p_threshold
					snake_case						:  Optional[Any]    	= nsfw_detected.tolist()

					if any(_SCREAMING_SNAKE_CASE							):
							logger.warning(
							    "Potential NSFW content was detected in one or more images. A black image will be returned instead."
							    " Try again with a different prompt and/or seed."							)

					for idx, nsfw_detected_ in enumerate(_SCREAMING_SNAKE_CASE							):
							if nsfw_detected_:
									snake_case						:  int    	= np.zeros(images[idx].shape							)

					snake_case						:  Tuple    	= self.w_head(_SCREAMING_SNAKE_CASE							)
					snake_case						:  int    	= watermark_detected.flatten()
					snake_case						:  Any    	= watermark_detected > w_threshold
					snake_case						:  Tuple    	= watermark_detected.tolist()

					if any(_SCREAMING_SNAKE_CASE							):
							logger.warning(
							    "Potential watermarked content was detected in one or more images. A black image will be returned instead."
							    " Try again with a different prompt and/or seed."							)

					for idx, watermark_detected_ in enumerate(_SCREAMING_SNAKE_CASE							):
							if watermark_detected_:
									snake_case						:  List[str]    	= np.zeros(images[idx].shape							)

					return images, nsfw_detected, watermark_detected






"""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""")