





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      (       _UpperCamelCase	):





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      )					->	Any:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	List[Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case   )					->	int:
      """simple docstring"""





      for key, val in cond.items():
         a__						:    Optional[int]   		=	val.clone()
      return x_in





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )					->	Dict:
      """simple docstring"""





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

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

               a__						:    Tuple   		=	self.scheduler._get_variance(snake_case   )
               a__						:    List[str]   		=	torch.exp(0.5 * posterior_variance   )
               a__						:    Any   		=	model_std * grad

            a__						:    Optional[int]   		=	0
            a__						:    Any   		=	x.detach()
            a__						:    str   		=	x + scale * grad
            a__						:    List[str]   		=	self.reset_xa(snake_case       ,      snake_case       ,      self.action_dim   )

         a__						:    str   		=	self.unet(x.permute(0       ,      2       ,      1   )       ,      snake_case   ).sample.permute(0       ,      2       ,      1   )

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

         # apply conditions to the trajectory (set the initial state)
         a__						:    Optional[int]   		=	self.reset_xa(snake_case       ,      snake_case       ,      self.action_dim   )
         a__						:    int   		=	self.to_torch(snake_case   )
      return x, y





   def __call__(			self       ,      snake_case       ,      snake_case=64       ,      snake_case=32       ,      snake_case=2       ,      snake_case=0.1   )					->	Dict:
      """simple docstring"""





      a__						:    Optional[Any]   		=	self.normalize(snake_case       ,      "observations"   )
      a__						:    List[Any]   		=	obs[None].repeat(snake_case       ,      axis=0   )

      a__						:    List[str]   		=	{0: self.to_torch(snake_case   )}
      a__						:    List[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)
      a__						:    str   		=	randn_tensor(snake_case       ,      device=self.unet.device   )
      a__						:    List[Any]   		=	self.reset_xa(snake_case       ,      snake_case       ,      self.action_dim   )
      a__						:    List[Any]   		=	self.to_torch(snake_case   )

      # run the diffusion process
      a__		,  a__						:    Dict   		=	self.run_diffusion(snake_case       ,      snake_case       ,      snake_case       ,      snake_case   )

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

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

      a__						:    Union[str, Any]   		=	denorm_actions[selected_index, 0]
      return denorm_actions







import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
		import jax
		import jax.numpy as jnp

@slow
@require_flax
class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:int						, __magic_name__		:Optional[Any]							):







								'''simple docstring'''







								return F'gaussian_noise_s={seed}_shape={"_".join([str(__magic_name__							) for s in shape]							)}.npy'
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								super().tearDown()
								gc.collect()
							def  lowerCamelCase__				(		self		:int						, __magic_name__		:List[str]=0						, __magic_name__		:List[Any]=(4, 4, 64, 64)						, __magic_name__		:Optional[int]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return image
							def  lowerCamelCase__				(		self		:Any						, __magic_name__		:int=False						, __magic_name__		:List[str]="CompVis/stable-diffusion-v1-4"							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				"""bf16""" if fpaa else None

								a							,					a									=				FlaxUNetaDConditionModel.from_pretrained(
								    __magic_name__						, subfolder="""unet"""						, dtype=__magic_name__						, revision=__magic_name__							)
								return model, params
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:Union[str, Any]=0						, __magic_name__		:Any=(4, 77, 768)						, __magic_name__		:Optional[Any]=False							):







								'''simple docstring'''







								a									=				jnp.bfloataa if fpaa else jnp.floataa
								a									=				jnp.array(load_hf_numpy(self.get_file_format(__magic_name__						, __magic_name__							)							)						, dtype=__magic_name__							)
								return hidden_states
							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
							        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
							        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
							        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
							        # fmt: on
							    ]							)
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:str						, __magic_name__		:Optional[Any]						, __magic_name__		:Optional[int]							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, atol=1E-2							)





							@parameterized.expand(
							    [
							        # fmt: off
							        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
							        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
							        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
							        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
							        # fmt: on
							    ]							)
							def  lowerCamelCase__				(		self		:Optional[Any]						, __magic_name__		:List[Any]						, __magic_name__		:int						, __magic_name__		:str							):







								'''simple docstring'''







								a							,					a									=				self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""						, fpaa=__magic_name__							)
								a									=				self.get_latents(__magic_name__						, shape=(4, 4, 96, 96)						, fpaa=__magic_name__							)
								a									=				self.get_encoder_hidden_states(__magic_name__						, shape=(4, 77, 1024)						, fpaa=__magic_name__							)

								a									=				model.apply(
								    {"""params""": params}						, __magic_name__						, jnp.array(__magic_name__						, dtype=jnp.intaa							)						, encoder_hidden_states=__magic_name__						, ).sample

								assert sample.shape == latents.shape

								a									=				jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())							)						, dtype=jnp.floataa							)
								a									=				jnp.array(__magic_name__						, dtype=jnp.floataa							)

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(__magic_name__						, __magic_name__						, atol=1E-2							)

