"""simple docstring"""





import math

import flax.linen as nn
import jax.numpy as jnp





def 				a_     (      _lowerCAmelCase  : jnp.ndarray   , _lowerCAmelCase  : int   , _lowerCAmelCase  : float = 1   , _lowerCAmelCase  : float = 1   , _lowerCAmelCase  : float = 1.0E4   , _lowerCAmelCase  : bool = False   , _lowerCAmelCase  : float = 1.0   , ):





       '''simple docstring'''

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
       lowercase__			:		Optional[Any]       =	float(embedding_dim // 2     )
       lowercase__			:		Optional[Any]       =	math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
       lowercase__			:		Any       =	min_timescale * jnp.exp(jnp.arange(_lowerCAmelCase   , dtype=jnp.floataa     ) * -log_timescale_increment     )
       lowercase__			:		Dict       =	jnp.expand_dims(_lowerCAmelCase   , 1     ) * jnp.expand_dims(_lowerCAmelCase   , 0     )

       # scale embeddings
       lowercase__			:		List[str]       =	scale * emb

       if flip_sin_to_cos:
              lowercase__			:		Dict       =	jnp.concatenate([jnp.cos(_lowerCAmelCase     ), jnp.sin(_lowerCAmelCase     )]   , axis=1     )
       else:
              lowercase__			:		Optional[int]       =	jnp.concatenate([jnp.sin(_lowerCAmelCase     ), jnp.cos(_lowerCAmelCase     )]   , axis=1     )
       lowercase__			:		List[Any]       =	jnp.reshape(_lowerCAmelCase   , [jnp.shape(_lowerCAmelCase     )[0], embedding_dim]     )
       return signal


class      UpperCAmelCase_	(    nn.Module):
      lowerCamelCase__		:    int   =  3_2
      lowerCamelCase__		:    jnp.dtype   =  jnp.floataa


      @nn.compact
      def __call__(   self		,			a							)       ->					Any:
             lowercase__			:		str       =	nn.Dense(self.time_embed_dim		,			dtype=self.dtype		,			name='linear_1'							)(a							)
             lowercase__			:		Union[str, Any]       =	nn.silu(a							)
             lowercase__			:		Optional[Any]       =	nn.Dense(self.time_embed_dim		,			dtype=self.dtype		,			name='linear_2'							)(a							)
             return temb





class      UpperCAmelCase_	(    nn.Module):
      lowerCamelCase__		:    int   =  3_2
      lowerCamelCase__		:    bool   =  False
      lowerCamelCase__		:    float   =  1


      @nn.compact
      def __call__(   self		,			a							)       ->					str:
             return get_sinusoidal_embeddings(
                 a		,			embedding_dim=self.dim		,			flip_sin_to_cos=self.flip_sin_to_cos		,			freq_shift=self.freq_shift							)



from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def   lowerCAmelCase_						(					_snake_case      :  str = "laptop"   )				->		DataFrame:







	'''simple docstring'''


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