






"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCamelCase     			=  logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 					lowerCamelCase__					(       snake_case ):
    SCREAMING_SNAKE_CASE		=			42
    SCREAMING_SNAKE_CASE		=			None







def _a (		_snake_case      ,       _snake_case=0.999      ,       _snake_case="cosine"      ,       ):







      """simple docstring"""






      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_snake_case							):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_snake_case							):
                  return math.exp(t * -12.0							)

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

      UpperCAmelCase							=       []
      for i in range(_snake_case							):
            UpperCAmelCase							=       i / num_diffusion_timesteps
            UpperCAmelCase							=       (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_snake_case							) / alpha_bar_fn(_snake_case							)      ,       _snake_case							)							)
      return torch.tensor(_snake_case      ,       dtype=torch.floataa							)







class 					lowerCamelCase__					(       snake_case    ,     snake_case ):
    SCREAMING_SNAKE_CASE		=			[e.name for e in KarrasDiffusionSchedulers]
    SCREAMING_SNAKE_CASE		=			1






    @register_to_config
    def __init__( self	,A = 1_000	,A = 0.0001	,A = 0.02	,A = "linear"	,A = None	,A = "epsilon"	,A = "linspace"	,A = 0	,):
          if trained_betas is not None:
                UpperCAmelCase							=       torch.tensor(A	,dtype=torch.floataa					)
          elif beta_schedule == "linear":
                UpperCAmelCase							=       torch.linspace(A	,A	,A	,dtype=torch.floataa					)
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                UpperCAmelCase							=       (
                    torch.linspace(beta_start**0.5	,beta_end**0.5	,A	,dtype=torch.floataa					) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                UpperCAmelCase							=       betas_for_alpha_bar(A					)
          else:
                raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

          UpperCAmelCase							=       1.0 - self.betas
          UpperCAmelCase							=       torch.cumprod(self.alphas	,dim=0					)

          UpperCAmelCase							=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
          UpperCAmelCase							=       np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
          UpperCAmelCase							=       torch.from_numpy(A					)

          # setable values
          UpperCAmelCase							=       None
          UpperCAmelCase							=       np.linspace(0	,num_train_timesteps - 1	,A	,dtype=A					)[::-1].copy()
          UpperCAmelCase							=       torch.from_numpy(A					)
          UpperCAmelCase							=       False






    @property
    def 				_UpperCamelCase							( self					):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
                return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5






    def 				_UpperCamelCase							( self	,A	,A					):
          if isinstance(A	,torch.Tensor					):
                UpperCAmelCase							=       timestep.to(self.timesteps.device					)
          UpperCAmelCase							=       (self.timesteps == timestep).nonzero().item()
          UpperCAmelCase							=       self.sigmas[step_index]
          UpperCAmelCase							=       sample / ((sigma**2 + 1) ** 0.5)
          UpperCAmelCase							=       True
          return sample






    def 				_UpperCamelCase							( self	,A	,A = None					):
          UpperCAmelCase							=       num_inference_steps

          # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
          if self.config.timestep_spacing == "linspace":
                UpperCAmelCase							=       np.linspace(0	,self.config.num_train_timesteps - 1	,A	,dtype=A					)[
                    ::-1
                ].copy()
          elif self.config.timestep_spacing == "leading":
                UpperCAmelCase							=       self.config.num_train_timesteps // self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase							=       (np.arange(0	,A					) * step_ratio).round()[::-1].copy().astype(A					)
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                UpperCAmelCase							=       self.config.num_train_timesteps / self.num_inference_steps
                # creates integer timesteps by multiplying by ratio
                # casting to int to avoid issues when num_inference_step is power of 3
                UpperCAmelCase							=       (np.arange(self.config.num_train_timesteps	,0	,-step_ratio					)).round().copy().astype(A					)
                timesteps -= 1
          else:
                raise ValueError(
                    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

          UpperCAmelCase							=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
          UpperCAmelCase							=       np.interp(A	,np.arange(0	,len(A					)					)	,A					)
          UpperCAmelCase							=       np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
          UpperCAmelCase							=       torch.from_numpy(A					).to(device=A					)
          if str(A					).startswith("""mps"""					):
                # mps does not support float64
                UpperCAmelCase							=       torch.from_numpy(A					).to(A	,dtype=torch.floataa					)
          else:
                UpperCAmelCase							=       torch.from_numpy(A					).to(device=A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,A = None	,A = True	,):

          if (
              isinstance(A	,A					)
              or isinstance(A	,torch.IntTensor					)
              or isinstance(A	,torch.LongTensor					)
          ):
                raise ValueError(
                    (
                        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
                        """ one of the `scheduler.timesteps` as a timestep."""
                    )	,)

          if not self.is_scale_input_called:
                logger.warning(
                    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                    """See `StableDiffusionPipeline` for a usage example."""					)

          if isinstance(A	,torch.Tensor					):
                UpperCAmelCase							=       timestep.to(self.timesteps.device					)

          UpperCAmelCase							=       (self.timesteps == timestep).nonzero().item()
          UpperCAmelCase							=       self.sigmas[step_index]

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
                UpperCAmelCase							=       sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
                # * c_out + input * c_skip
                UpperCAmelCase							=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
                raise NotImplementedError("""prediction_type not implemented yet: sample"""					)
          else:
                raise ValueError(
                    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

          UpperCAmelCase							=       self.sigmas[step_index]
          UpperCAmelCase							=       self.sigmas[step_index + 1]
          UpperCAmelCase							=       (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          UpperCAmelCase							=       (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          UpperCAmelCase							=       (sample - pred_original_sample) / sigma

          UpperCAmelCase							=       sigma_down - sigma

          UpperCAmelCase							=       sample + derivative * dt

          UpperCAmelCase							=       model_output.device
          UpperCAmelCase							=       randn_tensor(model_output.shape	,dtype=model_output.dtype	,device=A	,generator=A					)

          UpperCAmelCase							=       prev_sample + noise * sigma_up

          if not return_dict:
                return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=A	,pred_original_sample=A					)






    def 				_UpperCamelCase							( self	,A	,A	,A	,):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          UpperCAmelCase							=       self.sigmas.to(device=original_samples.device	,dtype=original_samples.dtype					)
          if original_samples.device.type == "mps" and torch.is_floating_point(A					):
                # mps does not support float64
                UpperCAmelCase							=       self.timesteps.to(original_samples.device	,dtype=torch.floataa					)
                UpperCAmelCase							=       timesteps.to(original_samples.device	,dtype=torch.floataa					)
          else:
                UpperCAmelCase							=       self.timesteps.to(original_samples.device					)
                UpperCAmelCase							=       timesteps.to(original_samples.device					)

          UpperCAmelCase							=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

          UpperCAmelCase							=       sigmas[step_indices].flatten()
          while len(sigma.shape					) < len(original_samples.shape					):
                UpperCAmelCase							=       sigma.unsqueeze(-1					)

          UpperCAmelCase							=       original_samples + noise * sigma
          return noisy_samples






    def __len__( self					):
          return self.config.num_train_timesteps











"""simple docstring"""







from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

_UpperCamelCase     			=  """https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true"""

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






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

            UpperCAmelCase							=       BASE_URL.format(_snake_case      ,       _snake_case							)
            UpperCAmelCase							=       get(_snake_case							)

            # Is the response ok?
            response.raise_for_status()

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

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

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

                        # Get price of the drug.
                        UpperCAmelCase							=       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
  _UpperCamelCase     			=  input("""Enter drug name: """).strip()
  _UpperCamelCase     			=  input("""Enter zip code: """).strip()

  _UpperCamelCase     			=  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:
      _UpperCamelCase     			=  pharmacy_price["""pharmacy_name"""]
      _UpperCamelCase     			=  pharmacy_price["""price"""]

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



