






"""simple docstring"""







import torch
from torch import nn


class      UpperCamelCase		(nn.Module     ):






						def __init__(		self  :Tuple			, __magic_name__  :Optional[int]			, __magic_name__  :Tuple			, __magic_name__  :Tuple			, __magic_name__  :Optional[Any]			, __magic_name__  :Union[str, Any]=1			, __magic_name__  :List[str]=False				)      ->List[str]:
										super().__init__()

										lowercase    :      Optional[Any]       = n_token
										lowercase    :      Any       = d_embed
										lowercase    :      Optional[int]       = d_proj

										lowercase    :      List[str]       = cutoffs + [n_token]
										lowercase    :      Union[str, Any]       = [0] + self.cutoffs
										lowercase    :      Dict       = div_val

										lowercase    :      Optional[int]       = self.cutoffs[0]
										lowercase    :      Any       = len(self.cutoffs				) - 1
										lowercase    :      List[str]       = self.shortlist_size + self.n_clusters

										if self.n_clusters > 0:
														lowercase    :      Union[str, Any]       = nn.Parameter(torch.zeros(self.n_clusters			, self.d_embed				)				)
														lowercase    :      int       = nn.Parameter(torch.zeros(self.n_clusters				)				)

										lowercase    :      Optional[Any]       = nn.ModuleList()
										lowercase    :      Dict       = nn.ParameterList()

										if div_val == 1:
														for i in range(len(self.cutoffs				)				):
																		if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(__magic_name__			, __magic_name__				)				)				)
																		else:
																						self.out_projs.append(__magic_name__				)

														self.out_layers.append(nn.Linear(__magic_name__			, __magic_name__				)				)
										else:
														for i in range(len(self.cutoffs				)				):
																		lowercase      ,      lowercase    :      Optional[int]       = self.cutoff_ends[i], self.cutoff_ends[i + 1]
																		lowercase    :      Union[str, Any]       = d_embed // (div_val**i)

																		self.out_projs.append(nn.Parameter(torch.FloatTensor(__magic_name__			, __magic_name__				)				)				)

																		self.out_layers.append(nn.Linear(__magic_name__			, r_idx - l_idx				)				)

										lowercase    :      List[Any]       = keep_order






						def      __snake_case      (		self  :str			, __magic_name__  :List[Any]			, __magic_name__  :Dict			, __magic_name__  :int			, __magic_name__  :str				)      ->Optional[int]:
										if proj is None:
														lowercase    :      List[str]       = nn.functional.linear(__magic_name__			, __magic_name__			, bias=__magic_name__				)
										else:
														# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
														lowercase    :      Any       = nn.functional.linear(__magic_name__			, proj.t().contiguous()				)
														lowercase    :      Union[str, Any]       = nn.functional.linear(__magic_name__			, __magic_name__			, bias=__magic_name__				)
														# else:
														#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
														#     if bias is not None:
														#         logit = logit + bias

										return logit






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :List[str]			, __magic_name__  :List[Any]=None			, __magic_name__  :Optional[Any]=False				)      ->Dict:

										if labels is not None:
														# Shift so that tokens < n predict n
														lowercase    :      int       = hidden[..., :-1, :].contiguous()
														lowercase    :      int       = labels[..., 1:].contiguous()
														lowercase    :      Optional[Any]       = hidden.view(-1			, hidden.size(-1				)				)
														lowercase    :      List[str]       = labels.view(-1				)
														if hidden.size(0				) != labels.size(0				):
																		raise RuntimeError("""Input and labels should have the same size in the batch dimension."""				)
										else:
														lowercase    :      str       = hidden.view(-1			, hidden.size(-1				)				)

										if self.n_clusters == 0:
														lowercase    :      Tuple       = self._compute_logit(__magic_name__			, self.out_layers[0].weight			, self.out_layers[0].bias			, self.out_projs[0]				)
														if labels is not None:
																		lowercase    :      Any       = labels != -100
																		lowercase    :      List[str]       = torch.zeros_like(__magic_name__			, dtype=hidden.dtype			, device=hidden.device				)
																		lowercase    :      Tuple       = (
																		    -nn.functional.log_softmax(__magic_name__			, dim=-1				)[mask].gather(1			, labels[mask].unsqueeze(1				)				).squeeze(1				)
																		)
														else:
																		lowercase    :      str       = nn.functional.log_softmax(__magic_name__			, dim=-1				)
										else:
														# construct weights and biases
														lowercase      ,      lowercase    :      Optional[int]       = [], []
														for i in range(len(self.cutoffs				)				):
																		if self.div_val == 1:
																						lowercase      ,      lowercase    :      Any       = self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						lowercase    :      Tuple       = self.out_layers[0].weight[l_idx:r_idx]
																						lowercase    :      Any       = self.out_layers[0].bias[l_idx:r_idx]
																		else:
																						lowercase    :      List[Any]       = self.out_layers[i].weight
																						lowercase    :      List[Any]       = self.out_layers[i].bias

																		if i == 0:
																						lowercase    :      Optional[int]       = torch.cat([weight_i, self.cluster_weight]			, dim=0				)
																						lowercase    :      List[str]       = torch.cat([bias_i, self.cluster_bias]			, dim=0				)

																		weights.append(__magic_name__				)
																		biases.append(__magic_name__				)

														lowercase      ,      lowercase      ,      lowercase    :      Union[str, Any]       = weights[0], biases[0], self.out_projs[0]

														lowercase    :      Any       = self._compute_logit(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				)
														lowercase    :      List[str]       = nn.functional.log_softmax(__magic_name__			, dim=1				)

														if labels is None:
																		lowercase    :      Optional[int]       = hidden.new_empty((head_logit.size(0				), self.n_token)				)
														else:
																		lowercase    :      List[Any]       = torch.zeros_like(__magic_name__			, dtype=hidden.dtype			, device=hidden.device				)

														lowercase    :      List[str]       = 0
														lowercase    :      List[Any]       = [0] + self.cutoffs
														for i in range(len(__magic_name__				) - 1				):
																		lowercase      ,      lowercase    :      str       = cutoff_values[i], cutoff_values[i + 1]

																		if labels is not None:
																						lowercase    :      List[Any]       = (labels >= l_idx) & (labels < r_idx)
																						lowercase    :      str       = mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																										continue

																						lowercase    :      str       = labels.index_select(0			, __magic_name__				) - l_idx
																						lowercase    :      List[str]       = head_logprob.index_select(0			, __magic_name__				)
																						lowercase    :      Any       = hidden.index_select(0			, __magic_name__				)
																		else:
																						lowercase    :      List[str]       = hidden

																		if i == 0:
																						if labels is not None:
																										lowercase    :      Optional[int]       = head_logprob_i.gather(1			, target_i[:, None]				).squeeze(1				)
																						else:
																										lowercase    :      Optional[int]       = head_logprob[:, : self.cutoffs[0]]
																		else:
																						lowercase      ,      lowercase      ,      lowercase    :      Tuple       = weights[i], biases[i], self.out_projs[i]

																						lowercase    :      Dict       = self._compute_logit(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				)
																						lowercase    :      Optional[int]       = nn.functional.log_softmax(__magic_name__			, dim=1				)
																						lowercase    :      Union[str, Any]       = self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																										lowercase    :      List[Any]       = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																										    1			, target_i[:, None]				).squeeze(1				)
																						else:
																										lowercase    :      str       = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																										lowercase    :      Union[str, Any]       = logprob_i

																		if labels is not None:
																						if (hasattr(self			, """keep_order"""				) and self.keep_order) or keep_order:
																										out.index_copy_(0			, __magic_name__			, -logprob_i				)
																						else:
																										out[offset : offset + logprob_i.size(0				)].copy_(-logprob_i				)
																						offset += logprob_i.size(0				)

										return out






						def      __snake_case      (		self  :Dict			, __magic_name__  :Any				)      ->Tuple:
										if self.n_clusters == 0:
														lowercase    :      Tuple       = self._compute_logit(__magic_name__			, self.out_layers[0].weight			, self.out_layers[0].bias			, self.out_projs[0]				)
														return nn.functional.log_softmax(__magic_name__			, dim=-1				)
										else:
														# construct weights and biases
														lowercase      ,      lowercase    :      Union[str, Any]       = [], []
														for i in range(len(self.cutoffs				)				):
																		if self.div_val == 1:
																						lowercase      ,      lowercase    :      int       = self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						lowercase    :      Optional[int]       = self.out_layers[0].weight[l_idx:r_idx]
																						lowercase    :      List[str]       = self.out_layers[0].bias[l_idx:r_idx]
																		else:
																						lowercase    :      List[Any]       = self.out_layers[i].weight
																						lowercase    :      List[str]       = self.out_layers[i].bias

																		if i == 0:
																						lowercase    :      Tuple       = torch.cat([weight_i, self.cluster_weight]			, dim=0				)
																						lowercase    :      Tuple       = torch.cat([bias_i, self.cluster_bias]			, dim=0				)

																		weights.append(__magic_name__				)
																		biases.append(__magic_name__				)

														lowercase      ,      lowercase      ,      lowercase    :      List[Any]       = weights[0], biases[0], self.out_projs[0]
														lowercase    :      int       = self._compute_logit(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				)

														lowercase    :      Optional[Any]       = hidden.new_empty((head_logit.size(0				), self.n_token)				)
														lowercase    :      Union[str, Any]       = nn.functional.log_softmax(__magic_name__			, dim=1				)

														lowercase    :      List[str]       = [0] + self.cutoffs
														for i in range(len(__magic_name__				) - 1				):
																		lowercase      ,      lowercase    :      Union[str, Any]       = cutoff_values[i], cutoff_values[i + 1]

																		if i == 0:
																						lowercase    :      List[Any]       = head_logprob[:, : self.cutoffs[0]]
																		else:
																						lowercase      ,      lowercase      ,      lowercase    :      str       = weights[i], biases[i], self.out_projs[i]

																						lowercase    :      Optional[Any]       = self._compute_logit(__magic_name__			, __magic_name__			, __magic_name__			, __magic_name__				)
																						lowercase    :      Dict       = nn.functional.log_softmax(__magic_name__			, dim=1				)

																						lowercase    :      int       = head_logprob[:, -i] + tail_logprob_i
																						lowercase    :      int       = logprob_i

														return out








"""simple docstring"""







from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame


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


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