



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 						__a (     lowerCamelCase__				):



	def __init__(						self							:    Any				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[Any]=None				,	snake_case_							:    str=None				,	snake_case_							:    int=0)->							List[str]:
						__lowerCAmelCase	   =1.0 if scale is None else scale
						__lowerCAmelCase	   =0.0 if loc is None else loc

						super().__init__(__lowerCamelCase				,	[AffineTransform(loc=self.loc				,	scale=self.scale				,	event_dim=__lowerCamelCase)])



	@property
	def 		UpperCamelCase							(						self							:    Tuple)->							List[str]:
						return self.base_dist.mean * self.scale + self.loc



	@property
	def 		UpperCamelCase							(						self							:    List[Any])->							List[Any]:
						return self.base_dist.variance * self.scale**2



	@property
	def 		UpperCamelCase							(						self							:    str)->							Optional[Any]:
						return self.variance.sqrt()






class 						__a (     nn.Module				):



	def __init__(						self							:    Tuple				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[str]				,	**snake_case_							:    Optional[Any])->							None:
						super().__init__(**__lowerCamelCase)
						__lowerCAmelCase	   =args_dim
						__lowerCAmelCase	   =nn.ModuleList([nn.Linear(__lowerCamelCase				,	__lowerCamelCase) for dim in args_dim.values()])
						__lowerCAmelCase	   =domain_map



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Optional[int])->							Tuple[torch.Tensor]:
						__lowerCAmelCase	   =[proj(__lowerCamelCase) for proj in self.proj]

						return self.domain_map(*__lowerCamelCase)






class 						__a (     nn.Module				):



	def __init__(						self							:    str				,	snake_case_							:    int)->							List[str]:
						super().__init__()
						__lowerCAmelCase	   =function



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    int				,	*snake_case_							:    Union[str, Any])->							Union[str, Any]:
						return self.function(__lowerCamelCase				,	*__lowerCamelCase)






class 						__a :
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42
	SCREAMING_SNAKE_CASE       =       42



	def __init__(						self							:    str				,	snake_case_							:    str = 1)->							None:
						__lowerCAmelCase	   =dim
						__lowerCAmelCase	   ={k: dim * self.args_dim[k] for k in self.args_dim}



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    Any)->							int:






						if self.dim == 1:
											return self.distribution_class(*__lowerCamelCase)
						else:
											return Independent(self.distribution_class(*__lowerCamelCase)				,	1)



	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[Any] = None				,	snake_case_							:    Optional[int] = None				,	)->							Distribution:
						__lowerCAmelCase	   =self._base_distribution(__lowerCamelCase)
						if loc is None and scale is None:
											return distr
						else:
											return AffineTransformed(__lowerCamelCase				,	loc=__lowerCamelCase				,	scale=__lowerCamelCase				,	event_dim=self.event_dim)



	@property
	def 		UpperCamelCase							(						self							:    List[str])->							Tuple:
						return () if self.dim == 1 else (self.dim,)



	@property
	def 		UpperCamelCase							(						self							:    Any)->							int:
						return len(self.event_shape)



	@property
	def 		UpperCamelCase							(						self							:    List[str])->							float:
						return 0.0



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple)->							nn.Module:
						return ParameterProjection(
						    in_features=__lowerCamelCase				,	args_dim=self.args_dim				,	domain_map=LambdaLayer(self.domain_map)				,	)



	def 		UpperCamelCase							(						self							:    int				,	*snake_case_							:    Dict)->							Tuple:
						raise NotImplementedError()



	@staticmethod
	def 		UpperCamelCase							(						snake_case_							:    List[str])->							torch.Tensor:
						return (x + torch.sqrt(torch.square(__lowerCamelCase) + 4.0)) / 2.0






class 						__a (     lowerCamelCase__				):
	SCREAMING_SNAKE_CASE       =       {"df": 1, "loc": 1, "scale": 1}
	SCREAMING_SNAKE_CASE       =       StudentT



	@classmethod
	def 		UpperCamelCase							(						cls							:    Optional[Any]				,	snake_case_							:    int				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Optional[Any])->							int:
						__lowerCAmelCase	   =cls.squareplus(__lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
						__lowerCAmelCase	   =2.0 + cls.squareplus(__lowerCamelCase)
						return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)






class 						__a (     lowerCamelCase__				):
	SCREAMING_SNAKE_CASE       =       {"loc": 1, "scale": 1}
	SCREAMING_SNAKE_CASE       =       Normal



	@classmethod
	def 		UpperCamelCase							(						cls							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    List[Any])->							Optional[Any]:
						__lowerCAmelCase	   =cls.squareplus(__lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
						return loc.squeeze(-1), scale.squeeze(-1)






class 						__a (     lowerCamelCase__				):
	SCREAMING_SNAKE_CASE       =       {"total_count": 1, "logits": 1}
	SCREAMING_SNAKE_CASE       =       NegativeBinomial



	@classmethod
	def 		UpperCamelCase							(						cls							:    Optional[Any]				,	snake_case_							:    List[str]				,	snake_case_							:    List[str])->							Optional[Any]:
						__lowerCAmelCase	   =cls.squareplus(__lowerCamelCase)
						return total_count.squeeze(-1), logits.squeeze(-1)



	def 		UpperCamelCase							(						self							:    Optional[int]				,	snake_case_							:    Optional[int])->							Distribution:
						__lowerCAmelCase	   =distr_args
						if self.dim == 1:
											return self.distribution_class(total_count=__lowerCamelCase				,	logits=__lowerCamelCase)
						else:
											return Independent(self.distribution_class(total_count=__lowerCamelCase				,	logits=__lowerCamelCase)				,	1)



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Tuple = None				,	snake_case_							:    Any = None)->							Distribution:
						__lowerCAmelCase	   =distr_args

						if scale is not None:
											# See scaling property of Gamma.
											logits += scale.log()

						return self._base_distribution((total_count, logits))






from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame






def 					__lowerCAmelCase		(  __lowerCamelCase	:    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(__lowerCamelCase				,   headers=__lowerCamelCase	).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(""","""				,   """"""	)	)
																				    )
																				    * 100	)
															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__":
				lowercase_	       =			'''headphones'''
				get_amazon_product_data(product).to_csv(F"Amazon Product Data for {product}.csv")




