
"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 				A__ (			__lowerCAmelCase ,  unittest.TestCase):
		A_		: Tuple            =			DiTPipeline
		A_		: Optional[int]            =			CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		A_		: List[str]            =			PipelineTesterMixin.required_optional_params - {
		    'latents',
		    'num_images_per_prompt',
		    'callback',
		    'callback_steps',
		}
		A_		: Union[str, Any]            =			CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		A_		: int            =			False

		def 							__lowerCamelCase							(   self				):
			torch.manual_seed(0				)
			__lowerCAmelCase    :		Union[str, Any]		    =   TransformeraDModel(
			    sample_size=16 ,	num_layers=2 ,	patch_size=4 ,	attention_head_dim=8 ,	num_attention_heads=2 ,	in_channels=4 ,	out_channels=8 ,	attention_bias=_UpperCamelCase ,	activation_fn='gelu-approximate' ,	num_embeds_ada_norm=10_00 ,	norm_type='ada_norm_zero' ,	norm_elementwise_affine=_UpperCamelCase ,	)
			__lowerCAmelCase    :		Dict		    =   AutoencoderKL()
			__lowerCAmelCase    :		Tuple		    =   DDIMScheduler()
			__lowerCAmelCase    :		Any		    =   {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
			return components

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=0				):


			if str(_UpperCamelCase				).startswith('mps'				):
				__lowerCAmelCase    :		Any		    =   torch.manual_seed(_UpperCamelCase				)
			else:
				__lowerCAmelCase    :		Any		    =   torch.Generator(device=_UpperCamelCase				).manual_seed(_UpperCamelCase				)
			__lowerCAmelCase    :		Optional[int]		    =   {
			    """class_labels""": [1],
			    """generator""": generator,
			    """num_inference_steps""": 2,
			    """output_type""": """numpy""",
			}
			return inputs

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   """cpu"""

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

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

			self.assertEqual(image.shape ,	(1, 16, 16, 3)				)
			__lowerCAmelCase    :		List[Any]		    =   np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]				)
			__lowerCAmelCase    :		Optional[int]		    =   np.abs(image_slice.flatten() - expected_slice				).max()
			self.assertLessEqual(_UpperCamelCase ,	1E-3				)

		def 							__lowerCamelCase							(   self				):
			self._test_inference_batch_single_identical(relax_max_difference=_UpperCamelCase ,	expected_max_diff=1E-3				)







		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available() ,	reason='XFormers attention is only available with CUDA and `xformers` installed' ,	)
		def 							__lowerCamelCase							(   self				):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




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

		def 							__lowerCamelCase							(   self				):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   torch.manual_seed(0				)

			__lowerCAmelCase    :		Dict		    =   DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'				)
			pipe.to('cuda'				)

			__lowerCAmelCase    :		List[str]		    =   ["""vase""", """umbrella""", """white shark""", """white wolf"""]
			__lowerCAmelCase    :		Any		    =   pipe.get_label_ids(_UpperCamelCase				)

			__lowerCAmelCase    :		List[Any]		    =   pipe(_UpperCamelCase ,	generator=_UpperCamelCase ,	num_inference_steps=40 ,	output_type='np'				).images

			for word, image in zip(_UpperCamelCase ,	_UpperCamelCase				):
				__lowerCAmelCase    :		List[str]		    =   load_numpy(
				    f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"				)
				assert np.abs((expected_image - image).max()				) < 1E-2







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'				)
			__lowerCAmelCase    :		List[str]		    =   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
			pipe.to('cuda'				)

			__lowerCAmelCase    :		Tuple		    =   ["""vase""", """umbrella"""]
			__lowerCAmelCase    :		Dict		    =   pipe.get_label_ids(_UpperCamelCase				)

			__lowerCAmelCase    :		List[Any]		    =   torch.manual_seed(0				)
			__lowerCAmelCase    :		List[Any]		    =   pipe(_UpperCamelCase ,	generator=_UpperCamelCase ,	num_inference_steps=25 ,	output_type='np'				).images

			for word, image in zip(_UpperCamelCase ,	_UpperCamelCase				):
				__lowerCAmelCase    :		str		    =   load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    f"/dit/{word}_512.npy"				)

				assert np.abs((expected_image - image).max()				) < 1E-1

"""simple docstring"""







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


lowerCamelCase__           =	{
    """n_samples""": 64,
    """horizon""": 32,
    """num_inference_steps""": 20,
    """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__":
							lowerCamelCase__           =	"""hopper-medium-v2"""
							lowerCamelCase__           =	gym.make(env_name)

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

							env.seed(0)
							lowerCamelCase__           =	env.reset()
							lowerCamelCase__           =	0
							lowerCamelCase__           =	0
							lowerCamelCase__           =	1_000
							lowerCamelCase__           =	[obs.copy()]
							try:
														for t in tqdm.tqdm(range(T)):
																					# call the policy
																					lowerCamelCase__           =	pipeline(obs, planning_horizon=32)

																					# execute action in environment
																					lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__           =	env.step(denorm_actions)
																					lowerCamelCase__           =	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())

																					lowerCamelCase__           =	next_observation
							except KeyboardInterrupt:
														pass

							print(f'Total reward: {total_reward}')