import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort





class UpperCAmelCase						( __A ,	unittest.TestCase  ):


				'''simple docstring'''






				pass





@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCAmelCase						( unittest.TestCase  ):


				'''simple docstring'''






				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)
				@property
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    List[str]         =				ort.SessionOptions()
						A_		:    Optional[Any]         =				False
						return options
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png' )
						A_		:    List[Any]         =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
						A_		:    Tuple         =				OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'		,     revision='onnx'		,     safety_checker=lowercase		,     feature_extractor=lowercase		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
						pipe.set_progress_bar_config(disable=lowercase )

						A_		:    List[str]         =				'A red cat sitting on a park bench'

						A_		:    Tuple         =				np.random.RandomState(0 )
						A_		:    Any         =				pipe(
						    prompt=lowercase		,     image=lowercase		,     mask_image=lowercase		,     guidance_scale=7.5		,     num_inference_steps=1_0		,     generator=lowercase		,     output_type='np'		,     )
						A_		:    Optional[int]         =				output.images
						A_		:    Any         =				images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

						assert images.shape == (1, 5_1_2, 5_1_2, 3)
						A_		:    Optional[int]         =				np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3







				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png' )
						A_		:    Optional[Any]         =				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png' )
						A_		:    int         =				LMSDiscreteScheduler.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'		,     subfolder='scheduler'		,     revision='onnx' )
						A_		:    List[Any]         =				OnnxStableDiffusionInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'		,     revision='onnx'		,     scheduler=lowercase		,     safety_checker=lowercase		,     feature_extractor=lowercase		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
						pipe.set_progress_bar_config(disable=lowercase )

						A_		:    List[str]         =				'A red cat sitting on a park bench'

						A_		:    Tuple         =				np.random.RandomState(0 )
						A_		:    str         =				pipe(
						    prompt=lowercase		,     image=lowercase		,     mask_image=lowercase		,     guidance_scale=7.5		,     num_inference_steps=2_0		,     generator=lowercase		,     output_type='np'		,     )
						A_		:    Tuple         =				output.images
						A_		:    Tuple         =				images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

						assert images.shape == (1, 5_1_2, 5_1_2, 3)
						A_		:    List[Any]         =				np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )

						assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3







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


_UpperCAmelCase            =		{
    """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__":
	_UpperCAmelCase            =		"""hopper-medium-v2"""
	_UpperCAmelCase            =		gym.make(env_name)

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

	env.seed(0)
	_UpperCAmelCase            =		env.reset()
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		0
	_UpperCAmelCase            =		1000
	_UpperCAmelCase            =		[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			_UpperCAmelCase            =		pipeline(obs, planning_horizon=32)

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

			_UpperCAmelCase            =		next_observation
	except KeyboardInterrupt:
		pass

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






