





"""simple docstring"""
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

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


class 							lowerCamelCase_(   A__,				A__,				A__    ):





					'''simple docstring'''



					lowercase__   :       Any										= [r'h\.\d+\.attn\.bias', r'h\.\d+\.attn\.masked_bias']

					@register_to_config
					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 5_0_2_5_7				,       lowerCamelCase__ = 1_0_2_4				,       lowerCamelCase__ = 7_6_8				,       lowerCamelCase__ = 1_2				,       lowerCamelCase__ = 1_2				,       lowerCamelCase__ = None				,       lowerCamelCase__ = "gelu_new"				,       lowerCamelCase__ = 0.1				,       lowerCamelCase__ = 0.1				,       lowerCamelCase__ = 0.1				,       lowerCamelCase__ = 1e-5				,       lowerCamelCase__ = 0.0_2				,       lowerCamelCase__ = True				,       lowerCamelCase__ = True				,       lowerCamelCase__ = False				,       lowerCamelCase__ = False				,       ):
									super().__init__()

									_lowerCamelCase        =				prefix_length

									if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
													raise ValueError(
													    F"""`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"""
													    F""" `n_embd`: {n_embd} are not equal."""   )

									_lowerCamelCase        =				prefix_inner_dim
									_lowerCamelCase        =				prefix_hidden_dim

									_lowerCamelCase        =				(
									    nn.Linear(self.prefix_inner_dim				,       self.prefix_hidden_dim   )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									_lowerCamelCase        =				(
									    nn.Linear(self.prefix_hidden_dim				,       __A   ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									_lowerCamelCase        =				GPTaConfig(
									    vocab_size=__A				,       n_positions=__A				,       n_embd=__A				,       n_layer=__A				,       n_head=__A				,       n_inner=__A				,       activation_function=__A				,       resid_pdrop=__A				,       embd_pdrop=__A				,       attn_pdrop=__A				,       layer_norm_epsilon=__A				,       initializer_range=__A				,       scale_attn_weights=__A				,       use_cache=__A				,       scale_attn_by_inverse_layer_idx=__A				,       reorder_and_upcast_attn=__A				,       )
									_lowerCamelCase        =				GPTaLMHeadModel(__A   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       ):
									_lowerCamelCase        =				self.transformer.transformer.wte(__A   )
									_lowerCamelCase        =				self.encode_prefix(__A   )
									_lowerCamelCase        =				self.decode_prefix(__A   )
									_lowerCamelCase        =				torch.cat((prefix_embeds, embedding_text)				,       dim=1   )

									if labels is not None:
													_lowerCamelCase        =				self.get_dummy_token(input_ids.shape[0]				,       input_ids.device   )
													_lowerCamelCase        =				torch.cat((dummy_token, input_ids)				,       dim=1   )
									_lowerCamelCase        =				self.transformer(inputs_embeds=__A				,       labels=__A				,       attention_mask=__A   )
									if self.prefix_hidden_dim is not None:
													return out, hidden
									else:
													return out

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									return torch.zeros(__A				,       self.prefix_length				,       dtype=torch.intaa				,       device=__A   )

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									return self.encode_prefix(__A   )

					@torch.no_grad()
					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				torch.split(__A				,       1				,       dim=0   )
									_lowerCamelCase        =				[]
									_lowerCamelCase        =				[]
									for feature in features:
													_lowerCamelCase        =				self.decode_prefix(feature.to(__A   )   )  # back to the clip feature
													# Only support beam search for now
													_lowerCamelCase  ,     _lowerCamelCase        =				self.generate_beam(
													    input_embeds=__A				,       device=__A				,       eos_token_id=__A   )
													generated_tokens.append(output_tokens[0]   )
													generated_seq_lengths.append(seq_lengths[0]   )
									_lowerCamelCase        =				torch.stack(__A   )
									_lowerCamelCase        =				torch.stack(__A   )
									return generated_tokens, generated_seq_lengths





					@torch.no_grad()
					def 			snake_case__      (    self				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__=None				,       lowerCamelCase__ = 5				,       lowerCamelCase__ = 6_7				,       lowerCamelCase__ = 1.0				,       lowerCamelCase__ = None				,       ):
									_lowerCamelCase        =				eos_token_id
									_lowerCamelCase        =				None
									_lowerCamelCase        =				None
									_lowerCamelCase        =				torch.ones(__A				,       device=__A				,       dtype=torch.int   )
									_lowerCamelCase        =				torch.zeros(__A				,       device=__A				,       dtype=torch.bool   )

									if input_embeds is not None:
													_lowerCamelCase        =				input_embeds
									else:
													_lowerCamelCase        =				self.transformer.transformer.wte(__A   )

									for i in range(__A   ):
													_lowerCamelCase        =				self.transformer(inputs_embeds=__A   )
													_lowerCamelCase        =				outputs.logits
													_lowerCamelCase        =				logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
													_lowerCamelCase        =				logits.softmax(-1   ).log()

													if scores is None:
																	_lowerCamelCase  ,     _lowerCamelCase        =				logits.topk(__A				,       -1   )
																	_lowerCamelCase        =				generated.expand(__A				,       *generated.shape[1:]   )
																	_lowerCamelCase  ,     _lowerCamelCase        =				next_tokens.permute(1				,       0   ), scores.squeeze(0   )
																	if tokens is None:
																					_lowerCamelCase        =				next_tokens
																	else:
																					_lowerCamelCase        =				tokens.expand(__A				,       *tokens.shape[1:]   )
																					_lowerCamelCase        =				torch.cat((tokens, next_tokens)				,       dim=1   )
													else:
																	_lowerCamelCase        =				-float(np.inf   )
																	_lowerCamelCase        =				0
																	_lowerCamelCase        =				scores[:, None] + logits
																	seq_lengths[~is_stopped] += 1
																	_lowerCamelCase        =				scores_sum / seq_lengths[:, None]
																	_lowerCamelCase  ,     _lowerCamelCase        =				scores_sum_average.view(-1   ).topk(__A				,       -1   )
																	_lowerCamelCase        =				next_tokens // scores_sum.shape[1]
																	_lowerCamelCase        =				seq_lengths[next_tokens_source]
																	_lowerCamelCase        =				next_tokens % scores_sum.shape[1]
																	_lowerCamelCase        =				next_tokens.unsqueeze(1   )
																	_lowerCamelCase        =				tokens[next_tokens_source]
																	_lowerCamelCase        =				torch.cat((tokens, next_tokens)				,       dim=1   )
																	_lowerCamelCase        =				generated[next_tokens_source]
																	_lowerCamelCase        =				scores_sum_average * seq_lengths
																	_lowerCamelCase        =				is_stopped[next_tokens_source]

													_lowerCamelCase        =				self.transformer.transformer.wte(next_tokens.squeeze()   ).view(generated.shape[0]				,       1				,       -1   )
													_lowerCamelCase        =				torch.cat((generated, next_token_embed)				,       dim=1   )
													_lowerCamelCase        =				is_stopped + next_tokens.eq(__A   ).squeeze()
													if is_stopped.all():
																	break

									_lowerCamelCase        =				scores / seq_lengths
									_lowerCamelCase        =				scores.argsort(descending=__A   )
									# tokens tensors are already padded to max_seq_length
									_lowerCamelCase        =				[tokens[i] for i in order]
									_lowerCamelCase        =				torch.stack(__A				,       dim=0   )
									_lowerCamelCase        =				torch.tensor([seq_lengths[i] for i in order]				,       dtype=seq_lengths.dtype   )
									return output_texts, seq_lengths





"""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""")
