
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer




@dataclass
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		torch.FloatTensor





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase ):
      @register_to_config
      def __init__(			self		,	lowerCamelCase = 3		,	lowerCamelCase = 3		,	lowerCamelCase = ("DownEncoderBlock2D",)		,	lowerCamelCase = ("UpDecoderBlock2D",)		,	lowerCamelCase = (64,)		,	lowerCamelCase = 1		,	lowerCamelCase = "silu"		,	lowerCamelCase = 3		,	lowerCamelCase = 32		,	lowerCamelCase = 2_56		,	lowerCamelCase = 32		,	lowerCamelCase = None		,	lowerCamelCase = 0.1_8_2_1_5		,	lowerCamelCase = "group"		,	):
          super().__init__()

          # pass init params to Encoder
          snake_case__						=				Encoder(
              in_channels=lowerCamelCase		,	out_channels=lowerCamelCase		,	down_block_types=lowerCamelCase		,	block_out_channels=lowerCamelCase		,	layers_per_block=lowerCamelCase		,	act_fn=lowerCamelCase		,	norm_num_groups=lowerCamelCase		,	double_z=lowerCamelCase		,	)

          snake_case__						=				vq_embed_dim if vq_embed_dim is not None else latent_channels

          snake_case__						=				nn.Convad(lowerCamelCase		,	lowerCamelCase		,	1 )
          snake_case__						=				VectorQuantizer(lowerCamelCase		,	lowerCamelCase		,	beta=0.2_5		,	remap=lowerCamelCase		,	sane_index_shape=lowerCamelCase )
          snake_case__						=				nn.Convad(lowerCamelCase		,	lowerCamelCase		,	1 )

          # pass init params to Decoder
          snake_case__						=				Decoder(
              in_channels=lowerCamelCase		,	out_channels=lowerCamelCase		,	up_block_types=lowerCamelCase		,	block_out_channels=lowerCamelCase		,	layers_per_block=lowerCamelCase		,	act_fn=lowerCamelCase		,	norm_num_groups=lowerCamelCase		,	norm_type=lowerCamelCase		,	)
      @apply_forward_hook
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = True ):
          snake_case__						=				self.encoder(lowerCamelCase )
          snake_case__						=				self.quant_conv(lowerCamelCase )

          if not return_dict:
              return (h,)

          return VQEncoderOutput(latents=lowerCamelCase )
      @apply_forward_hook
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = False		,	lowerCamelCase = True ):
          # also go through quantization layer
          if not force_not_quantize:
              snake_case__						,					snake_case__						,					snake_case__						=				self.quantize(lowerCamelCase )
          else:
              snake_case__						=				h
          snake_case__						=				self.post_quant_conv(lowerCamelCase )
          snake_case__						=				self.decoder(lowerCamelCase		,	quant if self.config.norm_type == "spatial" else None )

          if not return_dict:
              return (dec,)

          return DecoderOutput(sample=lowerCamelCase )



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = True ):
          snake_case__						=				sample
          snake_case__						=				self.encode(lowerCamelCase ).latents
          snake_case__						=				self.decode(lowerCamelCase ).sample

          if not return_dict:
              return (dec,)

          return DecoderOutput(sample=lowerCamelCase )





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = "laptop"							):
    snake_case__						=				F"""https://www.amazon.in/laptop/s?k={product}"""
    snake_case__						=				{
        "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",
    }
    snake_case__						=				BeautifulSoup(requests.get(__lowerCAmelCase    ,		headers=__lowerCAmelCase							).text							)
    # Initialize a Pandas dataframe with the column titles
    snake_case__						=				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:
            snake_case__						=				item.ha.text
            snake_case__						=				"https://www.amazon.in/" + item.ha.a["href"]
            snake_case__						=				item.find("span"    ,		attrs={"class": "a-offscreen"}							).text
            try:
                snake_case__						=				item.find("span"    ,		attrs={"class": "a-icon-alt"}							).text
            except AttributeError:
                snake_case__						=				"Not available"
            try:
                snake_case__						=				(
                    "₹"
                    + item.find(
                        "span"    ,		attrs={"class": "a-price a-text-price"}							).text.split("₹"							)[1]
                )
            except AttributeError:
                snake_case__						=				""
            try:
                snake_case__						=				float(
                    (
                        (
                            float(product_mrp.strip("₹"							).replace(","    ,		""							)							)
                            - float(product_price.strip("₹"							).replace(","    ,		""							)							)
                        )
                        / float(product_mrp.strip("₹"							).replace(","    ,		""							)							)
                    )
                    * 100							)
            except ValueError:
                snake_case__						=				float("nan"							)
        except AttributeError:
            pass
        snake_case__						=				[
            product_title,
            product_link,
            product_price,
            product_rating,
            product_mrp,
            discount,
        ]
    snake_case__						=				" "
    snake_case__						=				" "
    data_frame.index += 1
    return data_frame


if __name__ == "__main__":
     __magic_name__            =  '''headphones'''
     get_amazon_product_data(product).to_csv(F'''Amazon Product Data for {product}.csv''')



