



'''simple docstring'''






import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")


def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     Any:
							UpperCAmelCase				: Any											=	test_file.split(os.path.sep						)
							if components[0:2] != ["tests", "models"]:
														raise ValueError(
														    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
														    f"""{test_file} instead."""						)
							UpperCAmelCase				: int											=	components[-1]
							if not test_fn.endswith('''py'''						):
														raise ValueError(f"""`test_file` should be a python file. Got {test_fn} instead."""						)
							if not test_fn.startswith('''test_modeling_'''						):
														raise ValueError(
														    f"""`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead."""						)

							UpperCAmelCase				: List[str]											=	components[:-1] + [test_fn.replace('''.py'''     ,   ''''''						)]
							UpperCAmelCase				: Optional[int]											=	".".join(_lowerCAmelCase						)

							return test_module_path


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     Any:
							UpperCAmelCase				: Optional[Any]											=	get_module_path(_lowerCAmelCase						)
							UpperCAmelCase				: Any											=	importlib.import_module(_lowerCAmelCase						)

							return test_module


def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     Union[str, Any]:
							UpperCAmelCase				: int											=	[]
							UpperCAmelCase				: Optional[Any]											=	get_test_module(_lowerCAmelCase						)
							for attr in dir(_lowerCAmelCase						):
														if attr.endswith('''ModelTester'''						):
																					tester_classes.append(getattr(_lowerCAmelCase     ,   _lowerCAmelCase						)						)

																					 # sort with class names
							return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase						: x.__name__						)


def 				snake_case_  (      _lowerCAmelCase						:		Dict						)   ->     Optional[int]:
							UpperCAmelCase				: Dict											=	[]
							UpperCAmelCase				: Union[str, Any]											=	get_test_module(_lowerCAmelCase						)
							for attr in dir(_lowerCAmelCase						):
														UpperCAmelCase				: Any											=	getattr(_lowerCAmelCase     ,   _lowerCAmelCase						)
														# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
														# `all_model_classes` is not empty (which also excludes other special classes).
														UpperCAmelCase				: List[Any]											=	getattr(_lowerCAmelCase     ,   '''all_model_classes'''     ,   []						)
														if len(_lowerCAmelCase						) > 0:
																					test_classes.append(_lowerCAmelCase						)

																					 # sort with class names
							return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase						: x.__name__						)


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]						)   ->     Any:
							UpperCAmelCase				: Tuple											=	get_test_classes(_lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	set()
							for test_class in test_classes:
														model_classes.update(test_class.all_model_classes						)

							# sort with class names
							return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase						: x.__name__						)


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Optional[int]:
							UpperCAmelCase				: Dict											=	test_class()
							if hasattr(_lowerCAmelCase     ,   '''setUp'''						):
														test.setUp()

							UpperCAmelCase				: List[str]											=	None
							if hasattr(_lowerCAmelCase     ,   '''model_tester'''						):
														# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
														if test.model_tester is not None:
																					UpperCAmelCase				: List[Any]											=	test.model_tester.__class__

							return model_tester


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     Dict:
							UpperCAmelCase				: int											=	get_test_classes(_lowerCAmelCase						)

							UpperCAmelCase				: Union[str, Any]											=	[]
							for test_class in test_classes:
														if model_class in test_class.all_model_classes:
																					target_test_classes.append(_lowerCAmelCase						)

																					 # sort with class names
							return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase						: x.__name__						)


def 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Any						)   ->     Tuple:
							UpperCAmelCase				: str											=	get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase						)

							UpperCAmelCase				: str											=	[]
							for test_class in test_classes:
														UpperCAmelCase				: List[str]											=	get_model_tester_from_test_class(_lowerCAmelCase						)
														if tester_class is not None:
																					tester_classes.append(_lowerCAmelCase						)

																					 # sort with class names
							return sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase						: x.__name__						)


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Union[str, Any]:
							UpperCAmelCase				: str											=	get_test_classes(_lowerCAmelCase						)
							UpperCAmelCase				: str											=	{test_class: get_model_tester_from_test_class(_lowerCAmelCase						) for test_class in test_classes}
							return test_tester_mapping


def 				snake_case_  (      _lowerCAmelCase						:		List[str]						)   ->     Optional[Any]:
							UpperCAmelCase				: str											=	get_model_classes(_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	{
							    model_class: get_test_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase						) for model_class in model_classes
							}
							return model_test_mapping


def 				snake_case_  (      _lowerCAmelCase						:		Any						)   ->     List[str]:
							UpperCAmelCase				: int											=	get_model_classes(_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	{
							    model_class: get_tester_classes_for_model(_lowerCAmelCase     ,   _lowerCAmelCase						) for model_class in model_classes
							}
							return model_to_tester_mapping






def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     str:
							if isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														return o
							elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														return o.__name__
							elif isinstance(_lowerCAmelCase     ,   (list, tuple)						):
														return [to_json(_lowerCAmelCase						) for x in o]
							elif isinstance(_lowerCAmelCase     ,   _lowerCAmelCase						):
														return {to_json(_lowerCAmelCase						): to_json(_lowerCAmelCase						) for k, v in o.items()}
							else:
														return o










'''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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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







	def   A			(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	)    ->	Optional[int]:
								return (x_in - self.means[key]) / self.stds[key]







	def   A			(		self    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								return x_in * self.stds[key] + self.means[key]







	def   A			(		self    :  Any	,    __snake_case    :  int	)    ->	List[Any]:
								if type(__snake_case	) is dict:
															return {k: self.to_torch(__snake_case	) for k, v in x_in.items()}
								elif torch.is_tensor(__snake_case	):
															return x_in.to(self.unet.device	)
								return torch.tensor(__snake_case	,    device=self.unet.device	)







	def   A			(		self    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Dict:
								for key, val in cond.items():
															UpperCAmelCase				: List[Any]											=	val.clone()
								return x_in







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

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

																													UpperCAmelCase				: Tuple											=	self.scheduler._get_variance(__snake_case	)
																													UpperCAmelCase				: Optional[int]											=	torch.exp(0.5 * posterior_variance	)
																													UpperCAmelCase				: str											=	model_std * grad

																						UpperCAmelCase				: Dict											=	0
																						UpperCAmelCase				: Optional[int]											=	x.detach()
																						UpperCAmelCase				: List[str]											=	x + scale * grad
																						UpperCAmelCase				: List[str]											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(__snake_case	,    __snake_case	,    __snake_case	,    predict_epsilon=__snake_case	)['''prev_sample''']

															# apply conditions to the trajectory (set the initial state)
															UpperCAmelCase				: Any											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
															UpperCAmelCase				: List[str]											=	self.to_torch(__snake_case	)
								return x, y







	def __call__(		self    :  Any	,    __snake_case    :  List[str]	,    __snake_case    :  Optional[Any]=64	,    __snake_case    :  Optional[int]=32	,    __snake_case    :  Union[str, Any]=2	,    __snake_case    :  Optional[int]=0.1	)    ->	Tuple:
								# normalize the observations and create  batch dimension
								UpperCAmelCase				: Optional[Any]											=	self.normalize(__snake_case	,    '''observations'''	)
								UpperCAmelCase				: Optional[int]											=	obs[None].repeat(__snake_case	,    axis=0	)

								UpperCAmelCase				: Tuple											=	{0: self.to_torch(__snake_case	)}
								UpperCAmelCase				: Tuple											=	(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)
								UpperCAmelCase				: Union[str, Any]											=	randn_tensor(__snake_case	,    device=self.unet.device	)
								UpperCAmelCase				: int											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
								UpperCAmelCase				: List[Any]											=	self.to_torch(__snake_case	)

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

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

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

								UpperCAmelCase				: Optional[int]											=	denorm_actions[selected_index, 0]
								return denorm_actions





