





'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import MobileNetVaImageProcessor

class      snake_case     (    unittest.TestCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Union[str, Any]							,	__A       :							Tuple=7							,	__A       :							Tuple=3							,	__A       :							Optional[int]=1_8							,	__A       :							int=3_0							,	__A       :							Any=4_0_0							,	__A       :							Optional[Any]=True							,	__A       :							List[Any]=None							,	__A       :							Union[str, Any]=True							,	__A       :							Any=None							,	):
        __UpperCamelCase       =  size if size is not None else {'shortest_edge': 2_0}
        __UpperCamelCase       =  crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  batch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  min_resolution
        __UpperCamelCase       =  max_resolution
        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        __UpperCamelCase       =  do_center_crop
        __UpperCamelCase       =  crop_size






 def        _lowerCamelCase					(       self       :							Tuple	):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
        }






@require_torch
@require_vision
class      snake_case     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					str					=MobileNetVaImageProcessor if is_vision_available() else None






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  MobileNetVaImageProcessingTester(self	)






 @property
 def        _lowerCamelCase					(       self       :							int	):
        return self.image_processor_tester.prepare_image_processor_dict()






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(__A							,	'do_resize'	)	)
        self.assertTrue(hasattr(__A							,	'size'	)	)
        self.assertTrue(hasattr(__A							,	'do_center_crop'	)	)
        self.assertTrue(hasattr(__A							,	'crop_size'	)	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size							,	{'shortest_edge': 2_0}	)
        self.assertEqual(image_processor.crop_size							,	{'height': 1_8, 'width': 1_8}	)

        __UpperCamelCase       =  self.image_processing_class.from_dict(self.image_processor_dict							,	size=4_2							,	crop_size=8_4	)
        self.assertEqual(image_processor.size							,	{'shortest_edge': 4_2}	)
        self.assertEqual(image_processor.crop_size							,	{'height': 8_4, 'width': 8_4}	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        pass






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # Initialize image_processing
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	Image.Image	)

        # Test not batched input
        __UpperCamelCase       =  image_processing(image_inputs[0]							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)

        # Test batched
        __UpperCamelCase       =  image_processing(__A							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # Initialize image_processing
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	np.ndarray	)

        # Test not batched input
        __UpperCamelCase       =  image_processing(image_inputs[0]							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)

        # Test batched
        __UpperCamelCase       =  image_processing(__A							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        # Initialize image_processing
        __UpperCamelCase       =  self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        __UpperCamelCase       =  prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	torchify=__A	)
        for image in image_inputs:
               self.assertIsInstance(__A							,	torch.Tensor	)

        # Test not batched input
        __UpperCamelCase       =  image_processing(image_inputs[0]							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)

        # Test batched
        __UpperCamelCase       =  image_processing(__A							,	return_tensors='pt'	).pixel_values
        self.assertEqual(
            encoded_images.shape							,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,	)



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


A_       : List[str]         =			{
    '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__":
	A_       : Optional[int]         =			'hopper-medium-v2'
	A_       : List[Any]         =			gym.make(env_name)

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

	env.seed(0)
	A_       : List[Any]         =			env.reset()
	A_       : Optional[int]         =			0
	A_       : str         =			0
	A_       : Optional[Any]         =			1000
	A_       : Union[str, Any]         =			[obs.copy()]
	try:
		for t in tqdm.tqdm(range(T)):
			# call the policy
			A_       : Tuple         =			pipeline(obs, planning_horizon=32)

			# execute action in environment
			A_       ,			A_       ,			A_       ,			A_       : Dict         =			env.step(denorm_actions)
			A_       : List[str]         =			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())

			A_       : int         =			next_observation
	except KeyboardInterrupt:
		pass

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



