
from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather




@overload
def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  None					)     ->    torch.Tensor:
						...




@overload
def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor					)     ->    Tuple[torch.Tensor, torch.Tensor]:
						...




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  int		,      SCREAMING_SNAKE_CASE_  :  Dict		,      SCREAMING_SNAKE_CASE_  :  Union[str, Any]					)     ->    Optional[Any]:
						_lowercase									=				aatype == rc.restype_order["""G"""]
						_lowercase									=				rc.atom_order["""CA"""]
						_lowercase									=				rc.atom_order["""CB"""]
						_lowercase									=				torch.where(
						    is_gly[..., None].expand(*((-1,) * len(is_gly.shape					))		,      3					)		,      all_atom_positions[..., ca_idx, :]		,      all_atom_positions[..., cb_idx, :]		,      )

						if all_atom_masks is not None:
												_lowercase									=				torch.where(
												    SCREAMING_SNAKE_CASE_		,      all_atom_masks[..., ca_idx]		,      all_atom_masks[..., cb_idx]		,      )
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  Dict[str, torch.Tensor]					)     ->    torch.Tensor:
						_lowercase									=				batched_gather(
						    SCREAMING_SNAKE_CASE_		,      batch["""residx_atom37_to_atom14"""]		,      dim=-2		,      no_batch_dims=len(atomaa.shape[:-2]					)		,      )

						_lowercase									=				atomaa_data * batch["""atom37_atom_exists"""][..., None]

						return atomaa_data




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict[str, torch.Tensor]					)     ->    torch.Tensor:
						_lowercase									=				template_feats["""template_aatype"""]
						_lowercase									=				template_feats["""template_torsion_angles_sin_cos"""]
						_lowercase									=				template_feats["""template_alt_torsion_angles_sin_cos"""]
						_lowercase									=				template_feats["""template_torsion_angles_mask"""]
						_lowercase									=				torch.cat(
						    [
						        nn.functional.one_hot(SCREAMING_SNAKE_CASE_		,      22					),
						        torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2]		,      14					),
						        alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2]		,      14					),
						        torsion_angles_mask,
						    ]		,      dim=-1		,      )

						return template_angle_feat




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict[str, torch.Tensor]		,      SCREAMING_SNAKE_CASE_  :  torch.types.Number		,      SCREAMING_SNAKE_CASE_  :  torch.types.Number		,      SCREAMING_SNAKE_CASE_  :  int		,      SCREAMING_SNAKE_CASE_  :  bool = False		,      SCREAMING_SNAKE_CASE_  :  float = 1E-20		,      SCREAMING_SNAKE_CASE_  :  float = 1E8		,      )     ->    torch.Tensor:
						_lowercase									=				batch["""template_pseudo_beta_mask"""]
						_lowercase									=				template_mask[..., None] * template_mask[..., None, :]

						# Compute distogram (this seems to differ slightly from Alg. 5)
						_lowercase									=				batch["""template_pseudo_beta"""]
						_lowercase									=				torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2		,      dim=-1		,      keepdim=SCREAMING_SNAKE_CASE_					)
						_lowercase									=				torch.linspace(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		,      device=tpb.device					) ** 2
						_lowercase									=				torch.cat([lower[1:], lower.new_tensor([inf]					)]		,      dim=-1					)
						_lowercase									=				((dgram > lower) * (dgram < upper)).type(dgram.dtype					)

						_lowercase									=				[dgram, template_mask_ad[..., None]]

						_lowercase									=				nn.functional.one_hot(
						    batch["""template_aatype"""]		,      rc.restype_num + 2		,      )

						_lowercase									=				batch["""template_aatype"""].shape[-1]
						to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]		,      SCREAMING_SNAKE_CASE_		,      -1		,      -1					)					)
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]		,      -1		,      SCREAMING_SNAKE_CASE_		,      -1					)					)

						_lowercase					,	_lowercase					,	_lowercase									=				[rc.atom_order[a] for a in ["""N""", """CA""", """C"""]]
						_lowercase									=				Rigid.make_transform_from_reference(
						    n_xyz=batch["""template_all_atom_positions"""][..., n, :]		,      ca_xyz=batch["""template_all_atom_positions"""][..., ca, :]		,      c_xyz=batch["""template_all_atom_positions"""][..., c, :]		,      eps=SCREAMING_SNAKE_CASE_		,      )
						_lowercase									=				rigids.get_trans()[..., None, :, :]
						_lowercase									=				rigids[..., None].invert_apply(SCREAMING_SNAKE_CASE_					)

						_lowercase									=				torch.rsqrt(eps + torch.sum(rigid_vec**2		,      dim=-1					)					)

						_lowercase									=				batch["""template_all_atom_mask"""]
						_lowercase									=				t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
						_lowercase									=				template_mask[..., None] * template_mask[..., None, :]

						_lowercase									=				inv_distance_scalar * template_mask_ad
						_lowercase									=				rigid_vec * inv_distance_scalar[..., None]

						if not use_unit_vector:
												_lowercase									=				unit_vector * 0.0

						to_concat.extend(torch.unbind(unit_vector[..., None, :]		,      dim=-1					)					)
						to_concat.append(template_mask_ad[..., None]					)

						_lowercase									=				torch.cat(SCREAMING_SNAKE_CASE_		,      dim=-1					)
						_lowercase									=				act * template_mask_ad[..., None]

						return act




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Dict[str, torch.Tensor]					)     ->    torch.Tensor:
						_lowercase									=				nn.functional.one_hot(batch["""extra_msa"""]		,      23					)
						_lowercase									=				[
						    msa_ahot,
						    batch["""extra_has_deletion"""].unsqueeze(-1					),
						    batch["""extra_deletion_value"""].unsqueeze(-1					),
						]
						return torch.cat(SCREAMING_SNAKE_CASE_		,      dim=-1					)




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Rigid		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      )     ->    Rigid:
						# [*, N, 8, 4, 4]
						_lowercase									=				rrgdf[aatype, ...]

						# [*, N, 8] transformations, i.e.
						#   One [*, N, 8, 3, 3] rotation matrix and
						#   One [*, N, 8, 3]    translation matrix
						_lowercase									=				r.from_tensor_axa(SCREAMING_SNAKE_CASE_					)

						_lowercase									=				alpha.new_zeros((*((1,) * len(alpha.shape[:-1]					)), 2)					)
						_lowercase									=				1

						# [*, N, 8, 2]
						_lowercase									=				torch.cat([bb_rot.expand(*alpha.shape[:-2]		,      -1		,      -1					), alpha]		,      dim=-2					)

						# [*, N, 8, 3, 3]
						# Produces rotation matrices of the form:
						# [
						#   [1, 0  , 0  ],
						#   [0, a_2,-a_1],
						#   [0, a_1, a_2]
						# ]
						# This follows the original code rather than the supplement, which uses
						# different indices.

						_lowercase									=				alpha.new_zeros(default_r.get_rots().get_rot_mats().shape					)
						_lowercase									=				1
						_lowercase									=				alpha[..., 1]
						_lowercase									=				-alpha[..., 0]
						_lowercase									=				alpha

						_lowercase									=				default_r.compose(Rigid(Rotation(rot_mats=SCREAMING_SNAKE_CASE_					)		,      SCREAMING_SNAKE_CASE_					)					)

						_lowercase									=				all_frames[..., 5]
						_lowercase									=				all_frames[..., 6]
						_lowercase									=				all_frames[..., 7]

						_lowercase									=				all_frames[..., 4]
						_lowercase									=				chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				chia_frame_to_bb.compose(SCREAMING_SNAKE_CASE_					)

						_lowercase									=				Rigid.cat(
						    [
						        all_frames[..., :5],
						        chia_frame_to_bb.unsqueeze(-1					),
						        chia_frame_to_bb.unsqueeze(-1					),
						        chia_frame_to_bb.unsqueeze(-1					),
						    ]		,      dim=-1		,      )

						_lowercase									=				r[..., None].compose(SCREAMING_SNAKE_CASE_					)

						return all_frames_to_global




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Rigid		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      SCREAMING_SNAKE_CASE_  :  torch.Tensor		,      )     ->    torch.Tensor:
						# [*, N, 14]
						_lowercase									=				group_idx[aatype, ...]

						# [*, N, 14, 8]
						_lowercase									=				nn.functional.one_hot(
						    SCREAMING_SNAKE_CASE_		,      num_classes=default_frames.shape[-3]		,      )

						# [*, N, 14, 8]
						_lowercase									=				r[..., None, :] * group_mask_one_hot

						# [*, N, 14]
						_lowercase									=				t_atoms_to_global.map_tensor_fn(lambda SCREAMING_SNAKE_CASE_					: torch.sum(SCREAMING_SNAKE_CASE_		,      dim=-1					)					)

						# [*, N, 14, 1]
						_lowercase									=				atom_mask[aatype, ...].unsqueeze(-1					)

						# [*, N, 14, 3]
						_lowercase									=				lit_positions[aatype, ...]
						_lowercase									=				t_atoms_to_global.apply(SCREAMING_SNAKE_CASE_					)
						_lowercase									=				pred_positions * atom_mask

						return pred_positions

from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

A		:   Union[str, Any]         =    '''https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'''




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  str		,      SCREAMING_SNAKE_CASE_  :  str					)     ->    list | None:

						try:
												# Has user provided both inputs?
												if not drug_name or not zip_code:
																		return None

												_lowercase									=				BASE_URL.format(SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_					)
												_lowercase									=				get(SCREAMING_SNAKE_CASE_					)

												# Is the response ok?
												response.raise_for_status()

												# Scrape the data using bs4
												_lowercase									=				BeautifulSoup(response.text		,      """html.parser"""					)

												# This list will store the name and price.
												_lowercase									=				[]

												# Fetch all the grids that contains the items.
												_lowercase									=				soup.find_all("""div"""		,      {"""class""": """grid-x pharmCard"""}					)
												if grid_list and len(SCREAMING_SNAKE_CASE_					) > 0:
																		for grid in grid_list:
																								# Get the pharmacy price.
																								_lowercase									=				grid.find("""p"""		,      {"""class""": """list-title"""}					).text

																								# Get price of the drug.
																								_lowercase									=				grid.find("""span"""		,      {"""p""", """price price-large"""}					).text

																								pharmacy_price_list.append(
																								    {
																								        """pharmacy_name""": pharmacy_name,
																								        """price""": price,
																								    }					)

												return pharmacy_price_list

						except (HTTPError, exceptions.RequestException, ValueError):
												return None


if __name__ == "__main__":
		# Enter a drug name and a zip code
		A		:   Union[str, Any]         =    input('''Enter drug name: ''').strip()
		A		:   Dict         =    input('''Enter zip code: ''').strip()

		A		:   list | None         =    fetch_pharmacy_and_price_list(
		    drug_name, zip_code
		)

		if pharmacy_price_list:
				print(F'\nSearch results for {drug_name} at location {zip_code}:')
				for pharmacy_price in pharmacy_price_list:
						A		:   List[str]         =    pharmacy_price['''pharmacy_name''']
						A		:   Dict         =    pharmacy_price['''price''']

						print(F'Pharmacy: {name} Price: {price}')
		else:
				print(F'No results found for {drug_name}')