


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				(							__A):


       """simple docstring"""




       def __init__(     self							:    List[Any]      ,   __lowerCamelCase							:    UNetaDModel      ,   __lowerCamelCase							:    UNetaDModel      ,   __lowerCamelCase							:    DDPMScheduler      ,   __lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             super().__init__()
             lowerCamelCase__	:       str					=						value_function
             lowerCamelCase__	:       Union[str, Any]					=						unet
             lowerCamelCase__	:       List[str]					=						scheduler
             lowerCamelCase__	:       Optional[Any]					=						env
             lowerCamelCase__	:       Any					=						env.get_dataset()
             lowerCamelCase__	:       Tuple					=						{}
             for key in self.data.keys():
                   try:
                         lowerCamelCase__	:       Any					=						self.data[key].mean()
                   except:  # noqa: E722
                         pass
             lowerCamelCase__	:       Tuple					=						{}
             for key in self.data.keys():
                   try:
                         lowerCamelCase__	:       Tuple					=						self.data[key].std()
                   except:  # noqa: E722
                         pass
             lowerCamelCase__	:       Optional[int]					=						env.observation_space.shape[0]
             lowerCamelCase__	:       Dict					=						env.action_space.shape[0]





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    Optional[int]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



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

                               # permute to match dimension for pre-trained models
                               lowerCamelCase__	:       Dict					=						self.value_function(x.permute(0      ,   2      ,   1 )      ,   __lowerCamelCase ).sample
                               lowerCamelCase__	:       List[Any]					=						torch.autograd.grad([y.sum()]      ,   [x] )[0]

                               lowerCamelCase__	:       Union[str, Any]					=						self.scheduler._get_variance(__lowerCamelCase )
                               lowerCamelCase__	:       Any					=						torch.exp(0.5 * posterior_variance )
                               lowerCamelCase__	:       str					=						model_std * grad

                         lowerCamelCase__	:       Any					=						0
                         lowerCamelCase__	:       List[Any]					=						x.detach()
                         lowerCamelCase__	:       Optional[Any]					=						x + scale * grad
                         lowerCamelCase__	:       Any					=						self.reset_xa(__lowerCamelCase      ,   __lowerCamelCase      ,   self.action_dim )

                   lowerCamelCase__	:       Dict					=						self.unet(x.permute(0      ,   2      ,   1 )      ,   __lowerCamelCase ).sample.permute(0      ,   2      ,   1 )

                   # TODO: verify deprecation of this kwarg
                   lowerCamelCase__	:       Optional[int]					=						self.scheduler.step(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   predict_epsilon=__lowerCamelCase )['''prev_sample''']

                   # apply conditions to the trajectory (set the initial state)
                   lowerCamelCase__	:       Tuple					=						self.reset_xa(__lowerCamelCase      ,   __lowerCamelCase      ,   self.action_dim )
                   lowerCamelCase__	:       Any					=						self.to_torch(__lowerCamelCase )
             return x, y





       def __call__(     self							:    Optional[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[str]=64      ,   __lowerCamelCase							:    Dict=32      ,   __lowerCamelCase							:    List[Any]=2      ,   __lowerCamelCase							:    List[str]=0.1 ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.normalize(__lowerCamelCase      ,   "observations" )
             lowerCamelCase__	:       Dict					=						obs[None].repeat(__lowerCamelCase      ,   axis=0 )

             lowerCamelCase__	:       str					=						{0: self.to_torch(__lowerCamelCase )}
             lowerCamelCase__	:       Any					=						(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)
             lowerCamelCase__	:       Tuple					=						randn_tensor(__lowerCamelCase      ,   device=self.unet.device )
             lowerCamelCase__	:       Optional[Any]					=						self.reset_xa(__lowerCamelCase      ,   __lowerCamelCase      ,   self.action_dim )
             lowerCamelCase__	:       Dict					=						self.to_torch(__lowerCamelCase )

             # run the diffusion process
             lowerCamelCase__	:       Tuple					=						self.run_diffusion(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )

             # sort output trajectories by value
             lowerCamelCase__	:       int					=						y.argsort(0      ,   descending=__lowerCamelCase ).squeeze()
             lowerCamelCase__	:       Union[str, Any]					=						x[sorted_idx]
             lowerCamelCase__	:       Optional[int]					=						sorted_values[:, :, : self.action_dim]
             lowerCamelCase__	:       str					=						actions.detach().cpu().numpy()
             lowerCamelCase__	:       List[Any]					=						self.de_normalize(__lowerCamelCase      ,   key="actions" )

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

             lowerCamelCase__	:       List[Any]					=						denorm_actions[selected_index, 0]
             return denorm_actions






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


