


'''simple docstring'''

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class      snake_case    (    lowercase			,						lowercase			):





  """simple docstring"""




  @register_to_config
  def __init__(    self			,  *,
      UpperCamelCase = 4			,  UpperCamelCase = 768			,  UpperCamelCase			,  UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__()

   lowerCamelCase_      =  nn.Parameter(torch.zeros(UpperCamelCase     )     )

   # parameters for additional clip time embeddings
   lowerCamelCase_      =  nn.Linear(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  nn.Linear(UpperCamelCase			,  UpperCamelCase     )

   # parameters for encoder hidden states
   lowerCamelCase_      =  clip_extra_context_tokens
   lowerCamelCase_      =  nn.Linear(
       UpperCamelCase			,  self.clip_extra_context_tokens * cross_attention_dim     )
   lowerCamelCase_      =  nn.Linear(UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  nn.LayerNorm(UpperCamelCase     )







  def   snake_case		(    self			,  *, UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   if do_classifier_free_guidance:
    # Add the classifier free guidance embeddings to the image embeddings
    lowerCamelCase_      =  image_embeddings.shape[0]
    lowerCamelCase_      =  self.learned_classifier_free_guidance_embeddings.unsqueeze(0     )
    lowerCamelCase_      =  classifier_free_guidance_embeddings.expand(
        UpperCamelCase			,  -1     )
    lowerCamelCase_      =  torch.cat([classifier_free_guidance_embeddings, image_embeddings]			,  dim=0     )

   # The image embeddings batch size and the text embeddings batch size are equal
   assert image_embeddings.shape[0] == prompt_embeds.shape[0]

   lowerCamelCase_      =  prompt_embeds.shape[0]

   # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
   # adding CLIP embeddings to the existing timestep embedding, ...
   lowerCamelCase_      =  self.embedding_proj(UpperCamelCase     )
   lowerCamelCase_      =  self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase     )
   lowerCamelCase_      =  time_projected_image_embeddings + time_projected_prompt_embeds

   # ... and by projecting CLIP embeddings into four
   # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
   lowerCamelCase_      =  self.clip_extra_context_tokens_proj(UpperCamelCase     )
   lowerCamelCase_      =  clip_extra_context_tokens.reshape(UpperCamelCase			,  -1			,  self.clip_extra_context_tokens     )
   lowerCamelCase_      =  clip_extra_context_tokens.permute(0			,  2			,  1     )

   lowerCamelCase_      =  self.encoder_hidden_states_proj(UpperCamelCase     )
   lowerCamelCase_      =  self.text_encoder_hidden_states_norm(UpperCamelCase     )
   lowerCamelCase_      =  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]			,  dim=1     )

   return text_encoder_hidden_states, additive_clip_time_embeddings





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")
