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   a_					(_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   None       )->				torch.Tensor:
    ...

@overload
def   a_					(_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor       )->				Tuple[torch.Tensor, torch.Tensor]:
    ...

def   a_					(_lowerCAmelCase  :   Dict			,	_lowerCAmelCase  :   Any			,	_lowerCAmelCase  :   Optional[Any]       )->				Optional[int]:
    snake_case:						List[str]         =   aatype == rc.restype_order["""G"""]
    snake_case:						Optional[Any]         =   rc.atom_order["""CA"""]
    snake_case:						Any         =   rc.atom_order["""CB"""]
    snake_case:						Dict         =   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:
        snake_case:						str         =   torch.where(
            _lowerCAmelCase			,	all_atom_masks[..., ca_idx]			,	all_atom_masks[..., cb_idx]			,	)
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta

def   a_					(_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   Dict[str, torch.Tensor]       )->				torch.Tensor:
    snake_case:						Any         =   batched_gather(
        _lowerCAmelCase			,	batch["""residx_atom37_to_atom14"""]			,	dim=-2			,	no_batch_dims=len(atomaa.shape[:-2]       )			,	)

    snake_case:						Tuple         =   atomaa_data * batch["""atom37_atom_exists"""][..., None]

    return atomaa_data

def   a_					(_lowerCAmelCase  :   Dict[str, torch.Tensor]       )->				torch.Tensor:
    snake_case:						Union[str, Any]         =   template_feats["""template_aatype"""]
    snake_case:						Tuple         =   template_feats["""template_torsion_angles_sin_cos"""]
    snake_case:						Tuple         =   template_feats["""template_alt_torsion_angles_sin_cos"""]
    snake_case:						Optional[Any]         =   template_feats["""template_torsion_angles_mask"""]
    snake_case:						Optional[Any]         =   torch.cat(
        [
            nn.functional.one_hot(_lowerCAmelCase			,	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   a_					(_lowerCAmelCase  :   Dict[str, torch.Tensor]			,	_lowerCAmelCase  :   torch.types.Number			,	_lowerCAmelCase  :   torch.types.Number			,	_lowerCAmelCase  :   int			,	_lowerCAmelCase  :   bool = False			,	_lowerCAmelCase  :   float = 1e-20			,	_lowerCAmelCase  :   float = 1e8			,	)->				torch.Tensor:
    snake_case:						Dict         =   batch["""template_pseudo_beta_mask"""]
    snake_case:						Any         =   template_mask[..., None] * template_mask[..., None, :]

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

    snake_case:						Tuple         =   [dgram, template_mask_ad[..., None]]

    snake_case:						torch.LongTensor         =   nn.functional.one_hot(
        batch["""template_aatype"""]			,	rc.restype_num + 2			,	)

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

    snake_case    , snake_case    , snake_case:						List[str]         =   [rc.atom_order[a] for a in ["""N""", """CA""", """C"""]]
    snake_case:						Optional[Any]         =   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=_lowerCAmelCase			,	)
    snake_case:						Union[str, Any]         =   rigids.get_trans()[..., None, :, :]
    snake_case:						Optional[Any]         =   rigids[..., None].invert_apply(_lowerCAmelCase       )

    snake_case:						List[str]         =   torch.rsqrt(eps + torch.sum(rigid_vec**2			,	dim=-1       )       )

    snake_case:						Optional[int]         =   batch["""template_all_atom_mask"""]
    snake_case:						Tuple         =   t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    snake_case:						Any         =   template_mask[..., None] * template_mask[..., None, :]

    snake_case:						Dict         =   inv_distance_scalar * template_mask_ad
    snake_case:						Union[str, Any]         =   rigid_vec * inv_distance_scalar[..., None]

    if not use_unit_vector:
        snake_case:						Any         =   unit_vector * 0.0

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

    snake_case:						str         =   torch.cat(_lowerCAmelCase			,	dim=-1       )
    snake_case:						Dict         =   act * template_mask_ad[..., None]

    return act

def   a_					(_lowerCAmelCase  :   Dict[str, torch.Tensor]       )->				torch.Tensor:
    snake_case:						torch.LongTensor         =   nn.functional.one_hot(batch["""extra_msa"""]			,	23       )
    snake_case:						Any         =   [
        msa_ahot,
        batch["""extra_has_deletion"""].unsqueeze(-1       ),
        batch["""extra_deletion_value"""].unsqueeze(-1       ),
    ]
    return torch.cat(_lowerCAmelCase			,	dim=-1       )

def   a_					(_lowerCAmelCase  :   Rigid			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	)->				Rigid:
    # [*, N, 8, 4, 4]
    snake_case:						Union[str, Any]         =   rrgdf[aatype, ...]

    # [*, N, 8] transformations, i.e.
    #   One [*, N, 8, 3, 3] rotation matrix and
    #   One [*, N, 8, 3]    translation matrix
    snake_case:						str         =   r.from_tensor_axa(_lowerCAmelCase       )

    snake_case:						Union[str, Any]         =   alpha.new_zeros((*((1,) * len(alpha.shape[:-1]       )), 2)       )
    snake_case:						int         =   1

    # [*, N, 8, 2]
    snake_case:						Tuple         =   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.

    snake_case:						List[str]         =   alpha.new_zeros(default_r.get_rots().get_rot_mats().shape       )
    snake_case:						Dict         =   1
    snake_case:						Any         =   alpha[..., 1]
    snake_case:						Tuple         =   -alpha[..., 0]
    snake_case:						int         =   alpha

    snake_case:						List[Any]         =   default_r.compose(Rigid(Rotation(rot_mats=_lowerCAmelCase       )			,	_lowerCAmelCase       )       )

    snake_case:						Dict         =   all_frames[..., 5]
    snake_case:						Any         =   all_frames[..., 6]
    snake_case:						int         =   all_frames[..., 7]

    snake_case:						List[str]         =   all_frames[..., 4]
    snake_case:						List[str]         =   chia_frame_to_bb.compose(_lowerCAmelCase       )
    snake_case:						int         =   chia_frame_to_bb.compose(_lowerCAmelCase       )
    snake_case:						Optional[Any]         =   chia_frame_to_bb.compose(_lowerCAmelCase       )

    snake_case:						Optional[Any]         =   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			,	)

    snake_case:						str         =   r[..., None].compose(_lowerCAmelCase       )

    return all_frames_to_global




def   a_					(_lowerCAmelCase  :   Rigid			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	_lowerCAmelCase  :   torch.Tensor			,	)->				torch.Tensor:
    # [*, N, 14]
    snake_case:						List[Any]         =   group_idx[aatype, ...]

    # [*, N, 14, 8]
    snake_case:						torch.LongTensor         =   nn.functional.one_hot(
        _lowerCAmelCase			,	num_classes=default_frames.shape[-3]			,	)

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

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

    # [*, N, 14, 1]
    snake_case:						Optional[Any]         =   atom_mask[aatype, ...].unsqueeze(-1       )

    # [*, N, 14, 3]
    snake_case:						Dict         =   lit_positions[aatype, ...]
    snake_case:						Tuple         =   t_atoms_to_global.apply(_lowerCAmelCase       )
    snake_case:						Optional[Any]         =   pred_positions * atom_mask

    return pred_positions



from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

__lowerCAmelCase    :  Optional[int]										= 'https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   str       )->				list | None:

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

        snake_case:						List[str]         =   BASE_URL.format(_lowerCAmelCase			,	_lowerCAmelCase       )
        snake_case:						Optional[Any]         =   get(_lowerCAmelCase       )

        # Is the response ok?
        response.raise_for_status()

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

        # This list will store the name and price.
        snake_case:						List[Any]         =   []

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

                # Get price of the drug.
                snake_case:						Optional[Any]         =   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
    __lowerCAmelCase    :  Union[str, Any]										= input('Enter drug name: ').strip()
    __lowerCAmelCase    :  Tuple										= input('Enter zip code: ').strip()

    __lowerCAmelCase    :  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:
            __lowerCAmelCase    :  int										= pharmacy_price['pharmacy_name']
            __lowerCAmelCase    :  List[str]										= pharmacy_price['price']

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


