


"""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 	A_ (	__UpperCamelCase ,				unittest.TestCase   ):




      '''simple docstring'''


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




      def 					_snake_case     (    self:	Union[str, Any]    ):
       torch.manual_seed(0    )
       __lowerCamelCase		:							List[str]		 =						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')						,				)
       __lowerCamelCase		:							str		 =						DDIMScheduler()
       __lowerCamelCase		:							Union[str, Any]		 =						{'unet': unet, 'scheduler': scheduler}
       return components




      def 					_snake_case     (    self:	Tuple						,				a:	Optional[Any]						,				a:	str=0    ):
       if str(a    ).startswith('mps'    ):
        __lowerCamelCase		:							Dict		 =						torch.manual_seed(a    )
       else:
        __lowerCamelCase		:							List[Any]		 =						torch.Generator(device=a    ).manual_seed(a    )
       __lowerCamelCase		:							Union[str, Any]		 =						{
           'batch_size': 1,
           'generator': generator,
           'num_inference_steps': 2,
           'output_type': 'numpy',
       }
       return inputs




      def 					_snake_case     (    self:	str    ):
       __lowerCamelCase		:							Dict		 =						'cpu'

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

       __lowerCamelCase		:							Tuple		 =						self.get_dummy_inputs(a    )
       __lowerCamelCase		:							Optional[int]		 =						pipe(**a    ).images
       __lowerCamelCase		:							Any		 =						image[0, -3:, -3:, -1]

       self.assertEqual(image.shape						,				(1, 32, 32, 3)    )
       __lowerCamelCase		:							str		 =						np.array(
           [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]    )
       __lowerCamelCase		:							Optional[int]		 =						np.abs(image_slice.flatten() - expected_slice    ).max()
       self.assertLessEqual(a						,				1e-3    )




      def 					_snake_case     (    self:	List[Any]    ):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )




      def 					_snake_case     (    self:	Any    ):
       super().test_save_load_local(expected_max_difference=3e-3    )




      def 					_snake_case     (    self:	Dict    ):
       super().test_save_load_optional_components(expected_max_difference=3e-3    )




      def 					_snake_case     (    self:	Union[str, Any]    ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3    )



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




      '''simple docstring'''


      def 					_snake_case     (    self:	List[Any]    ):
       __lowerCamelCase		:							int		 =						'google/ddpm-cifar10-32'

       __lowerCamelCase		:							Union[str, Any]		 =						UNetaDModel.from_pretrained(a    )
       __lowerCamelCase		:							Any		 =						DDIMScheduler()

       __lowerCamelCase		:							Optional[int]		 =						DDIMPipeline(unet=a						,				scheduler=a    )
       ddim.to(a    )
       ddim.set_progress_bar_config(disable=a    )

       __lowerCamelCase		:							Any		 =						torch.manual_seed(0    )
       __lowerCamelCase		:							str		 =						ddim(generator=a						,				eta=0.0						,				output_type='numpy'    ).images

       __lowerCamelCase		:							Any		 =						image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __lowerCamelCase		:							Tuple		 =						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 					_snake_case     (    self:	Optional[Any]    ):
       __lowerCamelCase		:							str		 =						'google/ddpm-ema-bedroom-256'

       __lowerCamelCase		:							Any		 =						UNetaDModel.from_pretrained(a    )
       __lowerCamelCase		:							Dict		 =						DDIMScheduler.from_pretrained(a    )

       __lowerCamelCase		:							Any		 =						DDIMPipeline(unet=a						,				scheduler=a    )
       ddpm.to(a    )
       ddpm.set_progress_bar_config(disable=a    )

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

       __lowerCamelCase		:							Any		 =						image[0, -3:, -3:, -1]

       assert image.shape == (1, 256, 256, 3)
       __lowerCamelCase		:							int		 =						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





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


lowercase_	   =     {
    'n_samples': 6_4,
    'horizon': 3_2,
    'num_inference_steps': 2_0,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
       lowercase_	   =     'hopper-medium-v2'
       lowercase_	   =     gym.make(env_name)

       lowercase_	   =     ValueGuidedRLPipeline.from_pretrained(
           'bglick13/hopper-medium-v2-value-function-hor32',
           env=env,
       )

       env.seed(0)
       lowercase_	   =     env.reset()
       lowercase_	   =     0
       lowercase_	   =     0
       lowercase_	   =     1_0_0_0
       lowercase_	   =     [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     lowercase_	   =     pipeline(obs, planning_horizon=3_2)

                     # execute action in environment
                     lowercase_   ,lowercase_   ,lowercase_   ,lowercase_	   =     env.step(denorm_actions)
                     lowercase_	   =     env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         F"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
                         F""" {total_score}"""
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     lowercase_	   =     next_observation
       except KeyboardInterrupt:
              pass

       print(F"""Total reward: {total_reward}""")

