





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def     a_       (					lowerCAmelCase_ :     Optional[int],      lowerCAmelCase_ :     List[Any]=0.999,      lowerCAmelCase_ :     List[str]="cosine",      ):
   if alpha_transform_type == "cosine":

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

   elif alpha_transform_type == "exp":

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

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

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




class 							_UpperCAmelCase       (       _UpperCamelCase						,       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					[e.name for e in KarrasDiffusionSchedulers]
  a_              =					2






  @register_to_config
  def __init__(							self					:						int   ,    lowerCAmelCase_					:						int = 1_0_0_0   ,    lowerCAmelCase_					:						float = 0.0_00_85   ,    lowerCAmelCase_					:						float = 0.0_12   ,    lowerCAmelCase_					:						str = "linear"   ,    lowerCAmelCase_					:						Optional[Union[np.ndarray, List[float]]] = None   ,    lowerCAmelCase_					:						str = "epsilon"   ,    lowerCAmelCase_					:						Optional[bool] = False   ,    lowerCAmelCase_					:						Optional[bool] = False   ,    lowerCAmelCase_					:						float = 1.0   ,    lowerCAmelCase_					:						str = "linspace"   ,    lowerCAmelCase_					:						int = 0   ,    )				->		List[Any]:
     if trained_betas is not None:
        __lowerCAmelCase				      =      torch.tensor(lowerCAmelCase_   ,    dtype=torch.floataa	)
     elif beta_schedule == "linear":
        __lowerCAmelCase				      =      torch.linspace(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_   ,    dtype=torch.floataa	)
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        __lowerCAmelCase				      =      (
            torch.linspace(beta_start**0.5   ,    beta_end**0.5   ,    lowerCAmelCase_   ,    dtype=torch.floataa	) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        __lowerCAmelCase				      =      betas_for_alpha_bar(lowerCAmelCase_   ,    alpha_transform_type='cosine'	)
     elif beta_schedule == "exp":
        __lowerCAmelCase				      =      betas_for_alpha_bar(lowerCAmelCase_   ,    alpha_transform_type='exp'	)
     else:
        raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""	)

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

     #  set all values
     self.set_timesteps(lowerCAmelCase_   ,    lowerCAmelCase_   ,    lowerCAmelCase_	)
     __lowerCAmelCase				      =      use_karras_sigmas






  def 							lowercase						(							self					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Optional[Any]=None	)				->		str:
     if schedule_timesteps is None:
        __lowerCAmelCase				      =      self.timesteps

     __lowerCAmelCase				      =      (schedule_timesteps == timestep).nonzero()

     # The sigma index that is taken for the **very** first `step`
     # is always the second index (or the last index if there is only 1)
     # This way we can ensure we don't accidentally skip a sigma in
     # case we start in the middle of the denoising schedule (e.g. for image-to-image)
     if len(self._index_counter	) == 0:
        __lowerCAmelCase				      =      1 if len(lowerCAmelCase_	) > 1 else 0
     else:
        __lowerCAmelCase				      =      timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_	) else timestep
        __lowerCAmelCase				      =      self._index_counter[timestep_int]

     return indices[pos].item()






  @property
  def 							lowercase						(							self					:						Any	)				->		List[Any]:
     # 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 							lowercase						(							self					:						Optional[Any]   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						Union[float, torch.FloatTensor]   ,    )				->		torch.FloatTensor:
     __lowerCAmelCase				      =      self.index_for_timestep(lowerCAmelCase_	)

     __lowerCAmelCase				      =      self.sigmas[step_index]
     __lowerCAmelCase				      =      sample / ((sigma**2 + 1) ** 0.5)
     return sample






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						int   ,    lowerCAmelCase_					:						Union[str, torch.device] = None   ,    lowerCAmelCase_					:						Optional[int] = None   ,    )				->		Union[str, Any]:
     __lowerCAmelCase				      =      num_inference_steps

     __lowerCAmelCase				      =      num_train_timesteps or self.config.num_train_timesteps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
        __lowerCAmelCase				      =      np.linspace(0   ,    num_train_timesteps - 1   ,    lowerCAmelCase_   ,    dtype=lowerCAmelCase_	)[::-1].copy()
     elif self.config.timestep_spacing == "leading":
        __lowerCAmelCase				      =      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
        __lowerCAmelCase				      =      (np.arange(0   ,    lowerCAmelCase_	) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase_	)
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        __lowerCAmelCase				      =      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
        __lowerCAmelCase				      =      (np.arange(lowerCAmelCase_   ,    0   ,    -step_ratio	)).round().copy().astype(lowerCAmelCase_	)
        timesteps -= 1
     else:
        raise ValueError(
            f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

     __lowerCAmelCase				      =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
     __lowerCAmelCase				      =      np.log(lowerCAmelCase_	)
     __lowerCAmelCase				      =      np.interp(lowerCAmelCase_   ,    np.arange(0   ,    len(lowerCAmelCase_	)	)   ,    lowerCAmelCase_	)

     if self.config.use_karras_sigmas:
        __lowerCAmelCase				      =      self._convert_to_karras(in_sigmas=lowerCAmelCase_   ,    num_inference_steps=self.num_inference_steps	)
        __lowerCAmelCase				      =      np.array([self._sigma_to_t(lowerCAmelCase_   ,    lowerCAmelCase_	) for sigma in sigmas]	)

     __lowerCAmelCase				      =      np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
     __lowerCAmelCase				      =      torch.from_numpy(lowerCAmelCase_	).to(device=lowerCAmelCase_	)
     __lowerCAmelCase				      =      torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2	), sigmas[-1:]]	)

     __lowerCAmelCase				      =      torch.from_numpy(lowerCAmelCase_	)
     __lowerCAmelCase				      =      torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2	)]	)

     if str(lowerCAmelCase_	).startswith('mps'	):
        # mps does not support float64
        __lowerCAmelCase				      =      timesteps.to(lowerCAmelCase_   ,    dtype=torch.floataa	)
     else:
        __lowerCAmelCase				      =      timesteps.to(device=lowerCAmelCase_	)

     # empty dt and derivative
     __lowerCAmelCase				      =      None
     __lowerCAmelCase				      =      None

     # for exp beta schedules, such as the one for `pipeline_shap_e.py`
     # we need an index counter
     __lowerCAmelCase				      =      defaultdict(lowerCAmelCase_	)






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						List[Any]	)				->		int:
     # get log sigma
     __lowerCAmelCase				      =      np.log(lowerCAmelCase_	)

     # get distribution
     __lowerCAmelCase				      =      log_sigma - log_sigmas[:, np.newaxis]

     # get sigmas range
     __lowerCAmelCase				      =      np.cumsum((dists >= 0)   ,    axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
     __lowerCAmelCase				      =      low_idx + 1

     __lowerCAmelCase				      =      log_sigmas[low_idx]
     __lowerCAmelCase				      =      log_sigmas[high_idx]

     # interpolate sigmas
     __lowerCAmelCase				      =      (low - log_sigma) / (low - high)
     __lowerCAmelCase				      =      np.clip(lowerCAmelCase_   ,    0   ,    1	)

     # transform interpolation to time range
     __lowerCAmelCase				      =      (1 - w) * low_idx + w * high_idx
     __lowerCAmelCase				      =      t.reshape(sigma.shape	)
     return t






  def 							lowercase						(							self					:						Tuple   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						Optional[Any]	)				->		torch.FloatTensor:
     __lowerCAmelCase				      =      in_sigmas[-1].item()
     __lowerCAmelCase				      =      in_sigmas[0].item()

     __lowerCAmelCase				      =      7.0  # 7.0 is the value used in the paper
     __lowerCAmelCase				      =      np.linspace(0   ,    1   ,    lowerCAmelCase_	)
     __lowerCAmelCase				      =      sigma_min ** (1 / rho)
     __lowerCAmelCase				      =      sigma_max ** (1 / rho)
     __lowerCAmelCase				      =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas






  @property
  def 							lowercase						(							self					:						List[str]	)				->		Any:
     return self.dt is None






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Union[torch.FloatTensor, np.ndarray]   ,    lowerCAmelCase_					:						Union[float, torch.FloatTensor]   ,    lowerCAmelCase_					:						Union[torch.FloatTensor, np.ndarray]   ,    lowerCAmelCase_					:						bool = True   ,    )				->		Union[SchedulerOutput, Tuple]:
     __lowerCAmelCase				      =      self.index_for_timestep(lowerCAmelCase_	)

     # advance index counter by 1
     __lowerCAmelCase				      =      timestep.cpu().item() if torch.is_tensor(lowerCAmelCase_	) else timestep
     self._index_counter[timestep_int] += 1

     if self.state_in_first_order:
        __lowerCAmelCase				      =      self.sigmas[step_index]
        __lowerCAmelCase				      =      self.sigmas[step_index + 1]
     else:
        # 2nd order / Heun's method
        __lowerCAmelCase				      =      self.sigmas[step_index - 1]
        __lowerCAmelCase				      =      self.sigmas[step_index]

     # currently only gamma=0 is supported. This usually works best anyways.
     # We can support gamma in the future but then need to scale the timestep before
     # passing it to the model which requires a change in API
     __lowerCAmelCase				      =      0
     __lowerCAmelCase				      =      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        __lowerCAmelCase				      =      sigma_hat if self.state_in_first_order else sigma_next
        __lowerCAmelCase				      =      sample - sigma_input * model_output
     elif self.config.prediction_type == "v_prediction":
        __lowerCAmelCase				      =      sigma_hat if self.state_in_first_order else sigma_next
        __lowerCAmelCase				      =      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
            sample / (sigma_input**2 + 1)
        )
     elif self.config.prediction_type == "sample":
        __lowerCAmelCase				      =      model_output
     else:
        raise ValueError(
            f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

     if self.config.clip_sample:
        __lowerCAmelCase				      =      pred_original_sample.clamp(
            -self.config.clip_sample_range   ,    self.config.clip_sample_range	)

     if self.state_in_first_order:
        # 2. Convert to an ODE derivative for 1st order
        __lowerCAmelCase				      =      (sample - pred_original_sample) / sigma_hat
        # 3. delta timestep
        __lowerCAmelCase				      =      sigma_next - sigma_hat

        # store for 2nd order step
        __lowerCAmelCase				      =      derivative
        __lowerCAmelCase				      =      dt
        __lowerCAmelCase				      =      sample
     else:
        # 2. 2nd order / Heun's method
        __lowerCAmelCase				      =      (sample - pred_original_sample) / sigma_next
        __lowerCAmelCase				      =      (self.prev_derivative + derivative) / 2

        # 3. take prev timestep & sample
        __lowerCAmelCase				      =      self.dt
        __lowerCAmelCase				      =      self.sample

        # free dt and derivative
        # Note, this puts the scheduler in "first order mode"
        __lowerCAmelCase				      =      None
        __lowerCAmelCase				      =      None
        __lowerCAmelCase				      =      None

     __lowerCAmelCase				      =      sample + derivative * dt

     if not return_dict:
        return (prev_sample,)

     return SchedulerOutput(prev_sample=lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    lowerCAmelCase_					:						torch.FloatTensor   ,    )				->		torch.FloatTensor:
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     __lowerCAmelCase				      =      self.sigmas.to(device=original_samples.device   ,    dtype=original_samples.dtype	)
     if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase_	):
        # mps does not support float64
        __lowerCAmelCase				      =      self.timesteps.to(original_samples.device   ,    dtype=torch.floataa	)
        __lowerCAmelCase				      =      timesteps.to(original_samples.device   ,    dtype=torch.floataa	)
     else:
        __lowerCAmelCase				      =      self.timesteps.to(original_samples.device	)
        __lowerCAmelCase				      =      timesteps.to(original_samples.device	)

     __lowerCAmelCase				      =      [self.index_for_timestep(lowerCAmelCase_   ,    lowerCAmelCase_	) for t in timesteps]

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

     __lowerCAmelCase				      =      original_samples + noise * sigma
     return noisy_samples






  def __len__(							self					:						Union[str, Any]	)				->		Union[str, Any]:
     return self.config.num_train_timesteps



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   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						def __init__(			self							:str		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:UNetaDModel		,    lowerCAmelCase__							:DDPMScheduler		,    lowerCAmelCase__							:Optional[Any]		,    )  ->Optional[int]:
										super().__init__()
										lowercase         =      value_function
										lowercase         =      unet
										lowercase         =      scheduler
										lowercase         =      env
										lowercase         =      env.get_dataset()
										lowercase         =      {}
										for key in self.data.keys():
														try:
																		lowercase         =      self.data[key].mean()
														except:  # noqa: E722
																		pass
										lowercase         =      {}
										for key in self.data.keys():
														try:
																		lowercase         =      self.data[key].std()
														except:  # noqa: E722
																		pass
										lowercase         =      env.observation_space.shape[0]
										lowercase         =      env.action_space.shape[0]





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str     )  ->Any:
										return (x_in - self.means[key]) / self.stds[key]





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										return x_in * self.stds[key] + self.means[key]





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Tuple:
										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        SCREAMING_SNAKE_CASE(			self							:Optional[int]		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:List[str]     )  ->Any:
										for key, val in cond.items():
														lowercase         =      val.clone()
										return x_in





						def        SCREAMING_SNAKE_CASE(			self							:str		,    lowerCAmelCase__							:Union[str, Any]		,    lowerCAmelCase__							:str		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										lowercase         =      x.shape[0]
										lowercase         =      None
										for i in tqdm.tqdm(self.scheduler.timesteps     ):
														# create batch of timesteps to pass into model
														lowercase         =      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
																						lowercase         =      self.value_function(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample
																						lowercase         =      torch.autograd.grad([y.sum()]		,    [x]     )[0]

																						lowercase         =      self.scheduler._get_variance(lowerCAmelCase__     )
																						lowercase         =      torch.exp(0.5 * posterior_variance     )
																						lowercase         =      model_std * grad

																		lowercase         =      0
																		lowercase         =      x.detach()
																		lowercase         =      x + scale * grad
																		lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )

														lowercase         =      self.unet(x.permute(0		,    2		,    1     )		,    lowerCAmelCase__     ).sample.permute(0		,    2		,    1     )

														# TODO: verify deprecation of this kwarg
														lowercase         =      self.scheduler.step(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    predict_epsilon=lowerCAmelCase__     )["prev_sample"]

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





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

										lowercase         =      {0: self.to_torch(lowerCAmelCase__     )}
										lowercase         =      (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)
										lowercase         =      randn_tensor(lowerCAmelCase__		,    device=self.unet.device     )
										lowercase         =      self.reset_xa(lowerCAmelCase__		,    lowerCAmelCase__		,    self.action_dim     )
										lowercase         =      self.to_torch(lowerCAmelCase__     )

										# run the diffusion process
										lowercase     ,	lowercase         =      self.run_diffusion(lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__		,    lowerCAmelCase__     )

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

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

										lowercase         =      denorm_actions[selected_index, 0]
										return denorm_actions
