



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 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					List[str]			,						__lowerCAmelCase					:					UNetaDModel			,						__lowerCAmelCase					:					UNetaDModel			,						__lowerCAmelCase					:					DDPMScheduler			,						__lowerCAmelCase					:					Any			,						)   ->	Any:
    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     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					int      )   ->	Any:
    return (x_in - self.means[key]) / self.stds[key]

 def     snake_case_      (    self					:					Union[str, Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Any      )   ->	Any:
    return x_in * self.stds[key] + self.means[key]

 def     snake_case_      (    self					:					List[str]			,						__lowerCAmelCase					:					Optional[Any]      )   ->	List[str]:
    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     snake_case_      (    self					:					Tuple			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					int      )   ->	List[Any]:
    for key, val in cond.items():
       _A      =       val.clone()
    return x_in

 def     snake_case_      (    self					:					Any			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Optional[int]      )   ->	Tuple:
    _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,)			,						__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
             _A      =       self.value_function(x.permute(0			,						2			,						1      )			,						__lowerCAmelCase      ).sample
             _A      =       torch.autograd.grad([y.sum()]			,						[x]      )[0]

             _A      =       self.scheduler._get_variance(__lowerCAmelCase      )
             _A      =       torch.exp(0.5 * posterior_variance      )
             _A      =       model_std * grad

          _A      =       0
          _A      =       x.detach()
          _A      =       x + scale * grad
          _A      =       self.reset_xa(__lowerCAmelCase			,						__lowerCAmelCase			,						self.action_dim      )

       _A      =       self.unet(x.permute(0			,						2			,						1      )			,						__lowerCAmelCase      ).sample.permute(0			,						2			,						1      )

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

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

 def __call__(    self					:					Dict			,						__lowerCAmelCase					:					Tuple			,						__lowerCAmelCase					:					Optional[Any]=64			,						__lowerCAmelCase					:					Union[str, Any]=32			,						__lowerCAmelCase					:					Optional[int]=2			,						__lowerCAmelCase					:					Any=0.1      )   ->	List[str]:
    # normalize the observations and create  batch dimension
    _A      =       self.normalize(__lowerCAmelCase			,						'''observations'''      )
    _A      =       obs[None].repeat(__lowerCAmelCase			,						axis=0      )

    _A      =       {0: self.to_torch(__lowerCAmelCase      )}
    _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(__lowerCAmelCase			,						device=self.unet.device      )
    _A      =       self.reset_xa(__lowerCAmelCase			,						__lowerCAmelCase			,						self.action_dim      )
    _A      =       self.to_torch(__lowerCAmelCase      )

    # run the diffusion process
    _A  ,    _A      =       self.run_diffusion(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

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

    _A      =       denorm_actions[selected_index, 0]
    return denorm_actions





# Imports
import numpy as np





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:List[str]=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Optional[int]=None		,    lowerCAmelCase__							:List[str]=None     )  ->int:
										self.set_matricies(red=lowerCAmelCase__		,    green=lowerCAmelCase__		,    blue=lowerCAmelCase__		,    red_edge=lowerCAmelCase__		,    nir=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[Any]=None		,    lowerCAmelCase__							:Any=None		,    lowerCAmelCase__							:Tuple=None		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Optional[Any]=None     )  ->List[Any]:
										if red is not None:
														lowercase         =      red
										if green is not None:
														lowercase         =      green
										if blue is not None:
														lowercase         =      blue
										if red_edge is not None:
														lowercase         =      red_edge
										if nir is not None:
														lowercase         =      nir
										return True





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:Dict=""		,    lowerCAmelCase__							:Optional[Any]=None		,    lowerCAmelCase__							:Union[str, Any]=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:int=None		,    lowerCAmelCase__							:Union[str, Any]=None     )  ->Optional[Any]:
										self.set_matricies(red=lowerCAmelCase__		,    green=lowerCAmelCase__		,    blue=lowerCAmelCase__		,    red_edge=lowerCAmelCase__		,    nir=lowerCAmelCase__     )
										lowercase         =      {
										    "ARVI2": self.arvaa,
										    "CCCI": self.ccci,
										    "CVI": self.cvi,
										    "GLI": self.gli,
										    "NDVI": self.ndvi,
										    "BNDVI": self.bndvi,
										    "redEdgeNDVI": self.red_edge_ndvi,
										    "GNDVI": self.gndvi,
										    "GBNDVI": self.gbndvi,
										    "GRNDVI": self.grndvi,
										    "RBNDVI": self.rbndvi,
										    "PNDVI": self.pndvi,
										    "ATSAVI": self.atsavi,
										    "BWDRVI": self.bwdrvi,
										    "CIgreen": self.ci_green,
										    "CIrededge": self.ci_rededge,
										    "CI": self.ci,
										    "CTVI": self.ctvi,
										    "GDVI": self.gdvi,
										    "EVI": self.evi,
										    "GEMI": self.gemi,
										    "GOSAVI": self.gosavi,
										    "GSAVI": self.gsavi,
										    "Hue": self.hue,
										    "IVI": self.ivi,
										    "IPVI": self.ipvi,
										    "I": self.i,
										    "RVI": self.rvi,
										    "MRVI": self.mrvi,
										    "MSAVI": self.m_savi,
										    "NormG": self.norm_g,
										    "NormNIR": self.norm_nir,
										    "NormR": self.norm_r,
										    "NGRDI": self.ngrdi,
										    "RI": self.ri,
										    "S": self.s,
										    "IF": self._if,
										    "DVI": self.dvi,
										    "TVI": self.tvi,
										    "NDRE": self.ndre,
										}

										try:
														return funcs[index]()
										except KeyError:
														print("Index not in the list!"     )
														return False





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->int:
										return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
										    (self.nir - self.red) / (self.nir + self.red)
										)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										return self.nir * (self.red / (self.green**2))





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Dict:
										return (2 * self.green - self.red - self.blue) / (
										    2 * self.green + self.red + self.blue
										)





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Optional[int]:
										return (self.nir - self.red) / (self.nir + self.red)





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										return (self.nir - self.blue) / (self.nir + self.blue)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										return (self.redEdge - self.red) / (self.redEdge + self.red)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->str:
										return (self.nir - self.green) / (self.nir + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Any:
										return (self.nir - (self.green + self.blue)) / (
										    self.nir + (self.green + self.blue)
										)





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[Any]:
										return (self.nir - (self.green + self.red)) / (
										    self.nir + (self.green + self.red)
										)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Union[str, Any]:
										return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Union[str, Any]:
										return (self.nir - (self.green + self.red + self.blue)) / (
										    self.nir + (self.green + self.red + self.blue)
										)





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:Union[str, Any]=0.08		,    lowerCAmelCase__							:Dict=1.22		,    lowerCAmelCase__							:Tuple=0.03     )  ->Tuple:
										return a * (
										    (self.nir - a * self.red - b)
										    / (a * self.nir + self.red - a * b + x * (1 + a**2))
										)





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->Any:
										return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->int:
										return (self.nir / self.green) - 1





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										return (self.nir / self.redEdge) - 1





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[Any]:
										return (self.red - self.blue) / self.red





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->int:
										lowercase         =      self.ndvi()
										return ((ndvi + 0.5) / (abs(ndvi + 0.5     ))) * (abs(ndvi + 0.5     ) ** (1 / 2))





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:
										return self.nir - self.green





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										return 2.5 * (
										    (self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
										)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:
										lowercase         =      (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
										    self.nir + self.red + 0.5
										)
										return n * (1 - 0.25 * n) - (self.red - 0.1_25) / (1 - self.red)





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple=0.16     )  ->Any:
										return (self.nir - self.green) / (self.nir + self.green + y)





						def        SCREAMING_SNAKE_CASE(			self							:Dict		,    lowerCAmelCase__							:Any=0.5     )  ->str:
										return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[str]:
										return np.arctan(
										    ((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue)     )





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:str=None		,    lowerCAmelCase__							:Tuple=None     )  ->Dict:
										return (self.nir - b) / (a * self.red)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->List[Any]:
										return (self.red + self.green + self.blue) / 30.5





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->Optional[Any]:
										return self.nir / self.red





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										return (self.rvi() - 1) / (self.rvi() + 1)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Dict:
										return (
										    (2 * self.nir + 1)
										    - ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
										) / 2





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										return self.green / (self.nir + self.red + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Any:
										return self.nir / (self.nir + self.red + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Tuple:
										return self.red / (self.nir + self.red + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[Any]:
										return (self.green - self.red) / (self.green + self.red)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->str:
										return (self.red - self.green) / (self.red + self.green)





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->List[str]:
										lowercase         =      np.max([np.max(self.red     ), np.max(self.green     ), np.max(self.blue     )]     )
										lowercase         =      np.min([np.min(self.red     ), np.min(self.green     ), np.min(self.blue     )]     )
										return (max_value - min_value) / max_value





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Any:
										return (2 * self.red - self.green - self.blue) / (self.green - self.blue)





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[Any]:
										return self.nir / self.red





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->List[str]:
										return (self.ndvi() + 0.5) ** (1 / 2)





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Union[str, Any]:
										return (self.nir - self.redEdge) / (self.nir + self.redEdge)
