






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						str ,					__UpperCAmelCase      :						UNetaDModel ,					__UpperCAmelCase      :						UNetaDModel ,					__UpperCAmelCase      :						DDPMScheduler ,					__UpperCAmelCase      :						Union[str, Any] ,					)    ->int:




       """simple docstring"""


       super().__init__()
       a		  =      value_function
       a		  =      unet
       a		  =      scheduler
       a		  =      env
       a		  =      env.get_dataset()
       a		  =      {}
       for key in self.data.keys():
        try:
         a		  =      self.data[key].mean()
        except:  # noqa: E722
         pass
       a		  =      {}
       for key in self.data.keys():
        try:
         a		  =      self.data[key].std()
        except:  # noqa: E722
         pass
       a		  =      env.observation_space.shape[0]
       a		  =      env.action_space.shape[0]







      def       __lowerCAmelCase					(      self      :						Dict ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Dict  )    ->Optional[Any]:




       """simple docstring"""


       return (x_in - self.means[key]) / self.stds[key]







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						int  )    ->Dict:




       """simple docstring"""


       return x_in * self.stds[key] + self.means[key]







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						Any  )    ->Any:




       """simple docstring"""


       if type(__UpperCAmelCase  ) is dict:
        return {k: self.to_torch(__UpperCAmelCase  ) for k, v in x_in.items()}
       elif torch.is_tensor(__UpperCAmelCase  ):
        return x_in.to(self.unet.device  )
       return torch.tensor(__UpperCAmelCase ,					device=self.unet.device  )







      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Optional[int]  )    ->Tuple:




       """simple docstring"""


       for key, val in cond.items():
        a		  =      val.clone()
       return x_in







      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						Union[str, Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Tuple ,					__UpperCAmelCase      :						Optional[Any]  )    ->List[Any]:




       """simple docstring"""


       a		  =      x.shape[0]
       a		  =      None
       for i in tqdm.tqdm(self.scheduler.timesteps  ):
        # create batch of timesteps to pass into model
        a		  =      torch.full((batch_size,) ,					__UpperCAmelCase ,					device=self.unet.device ,					dtype=torch.long  )
        for _ in range(__UpperCAmelCase  ):
         with torch.enable_grad():
          x.requires_grad_()

          # permute to match dimension for pre-trained models
          a		  =      self.value_function(x.permute(0 ,					2 ,					1  ) ,					__UpperCAmelCase  ).sample
          a		  =      torch.autograd.grad([y.sum()] ,					[x]  )[0]

          a		  =      self.scheduler._get_variance(__UpperCAmelCase  )
          a		  =      torch.exp(0.5 * posterior_variance  )
          a		  =      model_std * grad

         a		  =      0
         a		  =      x.detach()
         a		  =      x + scale * grad
         a		  =      self.reset_xa(__UpperCAmelCase ,					__UpperCAmelCase ,					self.action_dim  )

        a		  =      self.unet(x.permute(0 ,					2 ,					1  ) ,					__UpperCAmelCase  ).sample.permute(0 ,					2 ,					1  )

        # TODO: verify deprecation of this kwarg
        a		  =      self.scheduler.step(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					predict_epsilon=__UpperCAmelCase  )['''prev_sample''']

        # apply conditions to the trajectory (set the initial state)
        a		  =      self.reset_xa(__UpperCAmelCase ,					__UpperCAmelCase ,					self.action_dim  )
        a		  =      self.to_torch(__UpperCAmelCase  )
       return x, y







      def __call__(      self      :						Optional[int] ,					__UpperCAmelCase      :						List[str] ,					__UpperCAmelCase      :						Dict=64 ,					__UpperCAmelCase      :						Union[str, Any]=32 ,					__UpperCAmelCase      :						Union[str, Any]=2 ,					__UpperCAmelCase      :						int=0.1  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.normalize(__UpperCAmelCase ,					'''observations'''  )
       a		  =      obs[None].repeat(__UpperCAmelCase ,					axis=0  )

       a		  =      {0: self.to_torch(__UpperCAmelCase  )}
       a		  =      (batch_size, planning_horizon, self.state_dim + self.action_dim)

       # generate initial noise and apply our conditions (to make the trajectories start at current state)
       a		  =      randn_tensor(__UpperCAmelCase ,					device=self.unet.device  )
       a		  =      self.reset_xa(__UpperCAmelCase ,					__UpperCAmelCase ,					self.action_dim  )
       a		  =      self.to_torch(__UpperCAmelCase  )

       # run the diffusion process
       a  ,     a		  =      self.run_diffusion(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase  )

       # sort output trajectories by value
       a		  =      y.argsort(0 ,					descending=__UpperCAmelCase  ).squeeze()
       a		  =      x[sorted_idx]
       a		  =      sorted_values[:, :, : self.action_dim]
       a		  =      actions.detach().cpu().numpy()
       a		  =      self.de_normalize(__UpperCAmelCase ,					key='''actions'''  )

       # select the action with the highest value
       if y is not None:
        a		  =      0
       else:
        # if we didn't run value guiding, select a random action
        a		  =      np.random.randint(0 ,					__UpperCAmelCase  )

       a		  =      denorm_actions[selected_index, 0]
       return denorm_actions










from __future__ import annotations

UpperCAmelCase__				   =		"Muhammad Umer Farooq"
UpperCAmelCase__				   =		"MIT"
UpperCAmelCase__				   =		"1.0.0"
UpperCAmelCase__				   =		"Muhammad Umer Farooq"
UpperCAmelCase__				   =		"contact@muhammadumerfarooq.me"
UpperCAmelCase__				   =		"Alpha"

import re
from html.parser import HTMLParser
from urllib import parse

import requests






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def __init__(      self      :						Union[str, Any] ,					__UpperCAmelCase      :						str  )    ->None:




       """simple docstring"""


       super().__init__()
       a		  =      []
       a		  =      domain







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						list[tuple[str, str | None]]  )    ->None:




       """simple docstring"""


       if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
         # If href is defined, and not empty nor # print it.
         if name == "href" and value != "#" and value != "":
          # If not already in urls.
          if value not in self.urls:
           a		  =      parse.urljoin(self.domain ,					__UpperCAmelCase  )
           self.urls.append(__UpperCAmelCase  )
def 						_a    (      a :str )							->			str:
 return ".".join(get_sub_domain_name(a ).split('''.''' )[-2:] )
def 						_a    (      a :str )							->			str:
 return parse.urlparse(a ).netloc
def 						_a    (      a :str = "https://github.com" )							->			list[str]:
 a		  =      get_domain_name(a )

 # Initialize the parser
 a		  =      Parser(a )

 try:
  # Open URL
  a		  =      requests.get(a )

  # pass the raw HTML to the parser to get links
  parser.feed(r.text )

  # Get links and loop through
  a		  =      set()
  for link in parser.urls:
   # open URL.
   # read = requests.get(link)
   try:
    a		  =      requests.get(a )
    # Get the valid email.
    a		  =      re.findall('''[a-zA-Z0-9]+@''' + domain	,							read.text )
    # If not in list then append it.
    for email in emails:
     valid_emails.add(a )
   except ValueError:
    pass
 except ValueError:
  raise SystemExit(1 )

 # Finally return a sorted list of email addresses with no duplicates.
 return sorted(a )


if __name__ == "__main__":
     UpperCAmelCase__				   =		emails_from_url("https://github.com")
     print(f"""{len(emails)} emails found:""")
     print("\n".join(sorted(emails)))


