



"""simple docstring"""
import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__SCREAMING_SNAKE_CASE  :						str    							=      {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__()
									_lowerCamelCase        =				torchvision.models.resnetaaa(pretrained=lowerCamelCase__   )
									_lowerCamelCase        =				list(model.children()   )[:-2]
									_lowerCamelCase        =				nn.Sequential(*lowerCamelCase__   )
									_lowerCamelCase        =				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
									_lowerCamelCase        =				self.pool(self.model(lowerCamelCase__   )   )
									_lowerCamelCase        =				torch.flatten(lowerCamelCase__				,       start_dim=2   )
									_lowerCamelCase        =				out.transpose(1				,       2   ).contiguous()
									return out  # BxNx2048


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				[json.loads(lowerCamelCase__   ) for l in open(lowerCamelCase__   )]
									_lowerCamelCase        =				os.path.dirname(lowerCamelCase__   )
									_lowerCamelCase        =				tokenizer
									_lowerCamelCase        =				labels
									_lowerCamelCase        =				len(lowerCamelCase__   )
									_lowerCamelCase        =				max_seq_length

									_lowerCamelCase        =				transforms

					def __len__(    self   ):
									return len(self.data   )

					def __getitem__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,       add_special_tokens=lowerCamelCase__   )   )
									_lowerCamelCase  ,     _lowerCamelCase  ,     _lowerCamelCase        =				sentence[0], sentence[1:-1], sentence[-1]
									_lowerCamelCase        =				sentence[: self.max_seq_length]

									_lowerCamelCase        =				torch.zeros(self.n_classes   )
									_lowerCamelCase        =				1

									_lowerCamelCase        =				Image.open(os.path.join(self.data_dir				,       self.data[index]['''img''']   )   ).convert('''RGB'''   )
									_lowerCamelCase        =				self.transforms(lowerCamelCase__   )

									return {
									    "image_start_token": start_token,
									    "image_end_token": end_token,
									    "sentence": sentence,
									    "image": image,
									    "label": label,
									}





					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				Counter()
									for row in self.data:
													label_freqs.update(row['''label''']   )
									return label_freqs



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       Any:
				_lowerCamelCase        =				[len(row['''sentence''']							) for row in batch]
				_lowerCamelCase  ,     _lowerCamelCase        =				len(lowercase_							), max(lowercase_							)

				_lowerCamelCase        =				torch.zeros(lowercase_						,   lowercase_						,   dtype=torch.long							)
				_lowerCamelCase        =				torch.zeros(lowercase_						,   lowercase_						,   dtype=torch.long							)

				for i_batch, (input_row, length) in enumerate(zip(lowercase_						,   lowercase_							)							):
								_lowerCamelCase        =				input_row['''sentence''']
								_lowerCamelCase        =				1

				_lowerCamelCase        =				torch.stack([row['''image'''] for row in batch]							)
				_lowerCamelCase        =				torch.stack([row['''label'''] for row in batch]							)
				_lowerCamelCase        =				torch.stack([row['''image_start_token'''] for row in batch]							)
				_lowerCamelCase        =				torch.stack([row['''image_end_token'''] for row in batch]							)

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor



def        lowerCAmelCase_(   )	->       Union[str, Any]:
				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]




def        lowerCAmelCase_(   )	->       List[str]:
				return transforms.Compose(
				    [
				        transforms.Resize(2_56							),
				        transforms.CenterCrop(2_24							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]						,   std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]						,   ),
				    ]							)





"""simple docstring"""
from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame



def        lowerCAmelCase_(   lowercase_     :							str = "laptop"							)	->       DataFrame:
				_lowerCamelCase        =				F"""https://www.amazon.in/laptop/s?k={product}"""
				_lowerCamelCase        =				{
				    '''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
				    '''Accept-Language''': '''en-US, en;q=0.5''',
				}
				_lowerCamelCase        =				BeautifulSoup(requests.get(lowercase_						,   headers=lowercase_							).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__":
			__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''headphones'''
			get_amazon_product_data(product).to_csv(F"""Amazon Product Data for {product}.csv""")
