'''simple docstring'''

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  (						UpperCAmelCase__ ):





       '''simple docstring'''



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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Tuple:
        return (x_in - self.means[key]) / self.stds[key]

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:




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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  str:




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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  int:
        lowercase__    :   Dict						= x.shape[0]
        lowercase__    :   Optional[int]						= None
        for i in tqdm.tqdm(self.scheduler.timesteps			):
         # create batch of timesteps to pass into model
         lowercase__    :   Tuple						= torch.full((batch_size,)     ,       _SCREAMING_SNAKE_CASE     ,       device=self.unet.device     ,       dtype=torch.long			)
         for _ in range(_SCREAMING_SNAKE_CASE			):
          with torch.enable_grad():
           x.requires_grad_()

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

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

          lowercase__    :   str						= 0
          lowercase__    :   Any						= x.detach()
          lowercase__    :   int						= x + scale * grad
          lowercase__    :   Any						= self.reset_xa(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.action_dim			)

         lowercase__    :   Optional[int]						= self.unet(x.permute(0     ,       2     ,       1			)     ,       _SCREAMING_SNAKE_CASE			).sample.permute(0     ,       2     ,       1			)

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

         # apply conditions to the trajectory (set the initial state)
         lowercase__    :   Dict						= self.reset_xa(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.action_dim			)
         lowercase__    :   int						= self.to_torch(_SCREAMING_SNAKE_CASE			)
        return x, y


       def __call__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=64     ,       __lowerCAmelCase=32     ,       __lowerCAmelCase=2     ,       __lowerCAmelCase=0.1			)  ->  Tuple:
        lowercase__    :   int						= self.normalize(_SCREAMING_SNAKE_CASE     ,       '''observations'''			)
        lowercase__    :   int						= obs[None].repeat(_SCREAMING_SNAKE_CASE     ,       axis=0			)

        lowercase__    :   Dict						= {0: self.to_torch(_SCREAMING_SNAKE_CASE			)}
        lowercase__    :   Dict						= (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__    :   str						= randn_tensor(_SCREAMING_SNAKE_CASE     ,       device=self.unet.device			)
        lowercase__    :   Any						= self.reset_xa(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       self.action_dim			)
        lowercase__    :   str						= self.to_torch(_SCREAMING_SNAKE_CASE			)

        # run the diffusion process
        lowercase__    :   Any						= self.run_diffusion(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE			)

        # sort output trajectories by value
        lowercase__    :   List[str]						= y.argsort(0     ,       descending=_SCREAMING_SNAKE_CASE			).squeeze()
        lowercase__    :   Any						= x[sorted_idx]
        lowercase__    :   Dict						= sorted_values[:, :, : self.action_dim]
        lowercase__    :   int						= actions.detach().cpu().numpy()
        lowercase__    :   List[str]						= self.de_normalize(_SCREAMING_SNAKE_CASE     ,       key='''actions'''			)

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

        lowercase__    :   Any						= denorm_actions[selected_index, 0]
        return denorm_actions

'''simple docstring'''

import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   DDIMPipeline
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       SCREAMING_SNAKE_CASE 		=   PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       SCREAMING_SNAKE_CASE 		=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= UNetaDModel(
            block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=3     ,       out_channels=3     ,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')     ,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')     ,       )
        lowercase__    :   Any						= DDIMScheduler()
        lowercase__    :   Union[str, Any]						= {'''unet''': unet, '''scheduler''': scheduler}
        return components

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=0			)  ->  int:
        if str(__lowerCAmelCase			).startswith('''mps'''			):
         lowercase__    :   Optional[Any]						= torch.manual_seed(__lowerCAmelCase			)
        else:
         lowercase__    :   int						= torch.Generator(device=__lowerCAmelCase			).manual_seed(__lowerCAmelCase			)
        lowercase__    :   List[Any]						= {
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   int						= '''cpu'''

        lowercase__    :   Optional[Any]						= self.get_dummy_components()
        lowercase__    :   str						= self.pipeline_class(**__lowerCAmelCase			)
        pipe.to(__lowerCAmelCase			)
        pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   str						= self.get_dummy_inputs(__lowerCAmelCase			)
        lowercase__    :   str						= pipe(**__lowerCAmelCase			).images
        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        self.assertEqual(image.shape     ,       (1, 32, 32, 3)			)
        lowercase__    :   Optional[int]						= np.array(
            [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]			)
        lowercase__    :   Optional[Any]						= np.abs(image_slice.flatten() - expected_slice			).max()
        self.assertLessEqual(__lowerCAmelCase     ,       1E-3			)

       def _lowerCAmelCase(						self			)  ->  str:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        super().test_save_load_local(expected_max_difference=3E-3			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        super().test_save_load_optional_components(expected_max_difference=3E-3			)


       def _lowerCAmelCase(						self			)  ->  int:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3			)



@slow
@require_torch_gpu
class       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   List[str]						= '''google/ddpm-cifar10-32'''

        lowercase__    :   List[Any]						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   str						= DDIMScheduler()

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddim.to(__lowerCAmelCase			)
        ddim.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Dict						= torch.manual_seed(0			)
        lowercase__    :   Optional[int]						= ddim(generator=__lowerCAmelCase     ,       eta=0.0     ,       output_type='''numpy'''			).images

        lowercase__    :   List[str]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        lowercase__    :   Optional[int]						= np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= '''google/ddpm-ema-bedroom-256'''

        lowercase__    :   Tuple						= UNetaDModel.from_pretrained(__lowerCAmelCase			)
        lowercase__    :   Union[str, Any]						= DDIMScheduler.from_pretrained(__lowerCAmelCase			)

        lowercase__    :   List[Any]						= DDIMPipeline(unet=__lowerCAmelCase     ,       scheduler=__lowerCAmelCase			)
        ddpm.to(__lowerCAmelCase			)
        ddpm.set_progress_bar_config(disable=__lowerCAmelCase			)

        lowercase__    :   Union[str, Any]						= torch.manual_seed(0			)
        lowercase__    :   Optional[Any]						= ddpm(generator=__lowerCAmelCase     ,       output_type='''numpy'''			).images

        lowercase__    :   List[Any]						= image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        lowercase__    :   Union[str, Any]						= np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

