






'''simple docstring'''
import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      Any          =       states.reshape(1  ,						-1  ,						model.config.state_dim							)
   lowercase_		:      List[str]          =       actions.reshape(1  ,						-1  ,						model.config.act_dim							)
   lowercase_		:      str          =       returns_to_go.reshape(1  ,						-1  ,						1							)
   lowercase_		:      int          =       timesteps.reshape(1  ,						-1							)

   if model.config.max_length is not None:
      lowercase_		:      Dict          =       states[:, -model.config.max_length :]
      lowercase_		:      Optional[int]          =       actions[:, -model.config.max_length :]
      lowercase_		:      str          =       returns_to_go[:, -model.config.max_length :]
      lowercase_		:      str          =       timesteps[:, -model.config.max_length :]

      # pad all tokens to sequence length
      lowercase_		:      Union[str, Any]          =       torch.cat(
          [torch.zeros(model.config.max_length - states.shape[1]							), torch.ones(states.shape[1]							)]							)
      lowercase_		:      Tuple          =       attention_mask.to(dtype=torch.long  ,						device=states.device							).reshape(1  ,						-1							)
      lowercase_		:      Tuple          =       torch.cat(
          [
              torch.zeros(
                  (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)  ,						device=states.device  ,						),
              states,
          ]  ,						dim=1  ,						).to(dtype=torch.floataa							)
      lowercase_		:      Dict          =       torch.cat(
          [
              torch.zeros(
                  (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)  ,						device=actions.device  ,						),
              actions,
          ]  ,						dim=1  ,						).to(dtype=torch.floataa							)
      lowercase_		:      int          =       torch.cat(
          [
              torch.zeros(
                  (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)  ,						device=returns_to_go.device  ,						),
              returns_to_go,
          ]  ,						dim=1  ,						).to(dtype=torch.floataa							)
      lowercase_		:      Optional[int]          =       torch.cat(
          [
              torch.zeros(
                  (timesteps.shape[0], model.config.max_length - timesteps.shape[1])  ,						device=timesteps.device							),
              timesteps,
          ]  ,						dim=1  ,						).to(dtype=torch.long							)
   else:
      lowercase_		:      Tuple          =       None

   lowercase_		:      Optional[Any]          =       model(
       states=_UpperCamelCase  ,						actions=_UpperCamelCase  ,						rewards=_UpperCamelCase  ,						returns_to_go=_UpperCamelCase  ,						timesteps=_UpperCamelCase  ,						attention_mask=_UpperCamelCase  ,						return_dict=_UpperCamelCase  ,						)

   return action_preds[0, -1]


# build the environment

UpperCamelCase__	=     gym.make('Hopper-v3')
UpperCamelCase__	=     env.observation_space.shape[0]
UpperCamelCase__	=     env.action_space.shape[0]
UpperCamelCase__	=     1000
UpperCamelCase__	=     'cuda'
UpperCamelCase__	=     1000.0  # normalization for rewards/returns
UpperCamelCase__	=     3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
UpperCamelCase__	=     np.array(
    [
        1.31_12_79,
        -0.08_46_95_21,
        -0.5_38_27_19,
        -0.07_20_15_76,
        0.04_93_23_66,
        2.1_06_68_56,
        -0.15_01_73_54,
        0.00_87_83_45,
        -0.2_84_81_86,
        -0.18_54_00_96,
        -0.28_46_12_86,
    ]
)
UpperCamelCase__	=     np.array(
    [
        0.17_79_07_51,
        0.05_44_46_21,
        0.21_29_71_39,
        0.14_53_04_19,
        0.6_12_44_44,
        0.85_17_44_65,
        1.4_51_52_52,
        0.6_75_16_96,
        1.53_62_39,
        1.6_16_07_46,
        5.6_07_25_36,
    ]
)
UpperCamelCase__	=     torch.from_numpy(state_mean).to(device=device)
UpperCamelCase__	=     torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
UpperCamelCase__	=     DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
UpperCamelCase__	=     model.to(device)
model.eval()

for ep in range(10):
   UpperCamelCase__,       UpperCamelCase__	=     0, 0
   UpperCamelCase__	=     env.reset()
   UpperCamelCase__	=     torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   UpperCamelCase__	=     torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   UpperCamelCase__	=     torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   UpperCamelCase__	=     torch.zeros(0, device=device, dtype=torch.floataa)

   UpperCamelCase__	=     torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
      env.render()
      # add padding
      UpperCamelCase__	=     torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
      UpperCamelCase__	=     torch.cat([rewards, torch.zeros(1, device=device)])

      UpperCamelCase__	=     get_action(
          model,
          (states.to(dtype=torch.floataa) - state_mean) / state_std,
          actions.to(dtype=torch.floataa),
          rewards.to(dtype=torch.floataa),
          target_return.to(dtype=torch.floataa),
          timesteps.to(dtype=torch.long),
      )
      UpperCamelCase__	=     action
      UpperCamelCase__	=     action.detach().cpu().numpy()

      UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__,       UpperCamelCase__	=     env.step(action)

      UpperCamelCase__	=     torch.from_numpy(state).to(device=device).reshape(1, state_dim)
      UpperCamelCase__	=     torch.cat([states, cur_state], dim=0)
      UpperCamelCase__	=     reward

      UpperCamelCase__	=     target_return[0, -1] - (reward / scale)
      UpperCamelCase__	=     torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
      UpperCamelCase__	=     torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

      episode_return += reward
      episode_length += 1

      if done:
         break












'''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 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




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

      lowercase_		:      List[str]          =       BASE_URL.format(_UpperCamelCase  ,						_UpperCamelCase							)
      lowercase_		:      Union[str, Any]          =       get(_UpperCamelCase							)

      # Is the response ok?
      response.raise_for_status()

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

      # This list will store the name and price.
      lowercase_		:      List[str]          =       []

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

            # Get price of the drug.
            lowercase_		:      List[str]          =       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}""")




