





from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame





def       _UpperCamelCase       (a__				:str = "laptop"    ):






							"""simple docstring"""





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


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











from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map





def       _UpperCamelCase       (a__				:Dict[str, torch.Tensor]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     []
							UpperCamelCase__			       =     []
							UpperCamelCase__			       =     []

							for rt in rc.restypes:
														UpperCamelCase__			       =     rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
														restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]    )
														UpperCamelCase__			       =     {name: i for i, name in enumerate(a__    )}
														restype_atomaa_to_atomaa_list.append(
														    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]    )

														restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]    )

							# Add dummy mapping for restype 'UNK'
							restype_atomaa_to_atomaa_list.append([0] * 14    )
							restype_atomaa_to_atomaa_list.append([0] * 37    )
							restype_atomaa_mask_list.append([0.0] * 14    )

							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.intaa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.intaa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     torch.tensor(
							    a__				,     dtype=torch.floataa				,     device=protein["""aatype"""].device				,     )
							UpperCamelCase__			       =     protein["""aatype"""].to(torch.long    )

							# create the mapping for (residx, atom14) --> atom37, i.e. an array
							# with shape (num_res, 14) containing the atom37 indices for this protein
							UpperCamelCase__			       =     restype_atomaa_to_atomaa[protein_aatype]
							UpperCamelCase__			       =     restype_atomaa_mask[protein_aatype]

							UpperCamelCase__			       =     residx_atomaa_mask
							UpperCamelCase__			       =     residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							UpperCamelCase__			       =     restype_atomaa_to_atomaa[protein_aatype]
							UpperCamelCase__			       =     residx_atomaa_to_atomaa.long()

							# create the corresponding mask
							UpperCamelCase__			       =     torch.zeros([21, 37]				,     dtype=torch.floataa				,     device=protein["""aatype"""].device    )
							for restype, restype_letter in enumerate(rc.restypes    ):
														UpperCamelCase__			       =     rc.restype_atoa[restype_letter]
														UpperCamelCase__			       =     rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					UpperCamelCase__			       =     rc.atom_order[atom_name]
																					UpperCamelCase__			       =     1

							UpperCamelCase__			       =     restype_atomaa_mask[protein_aatype]
							UpperCamelCase__			       =     residx_atomaa_mask

							return protein





def       _UpperCamelCase       (a__				:Dict[str, torch.Tensor]    ):






							"""simple docstring"""





							UpperCamelCase__			       =     tree_map(lambda a__    : torch.tensor(a__				,     device=batch["""aatype"""].device    )				,     a__				,     np.ndarray    )
							UpperCamelCase__			       =     tensor_tree_map(lambda a__    : np.array(a__    )				,     make_atomaa_masks(a__    )    )
							return out




