'''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     __magic_name__						(    _UpperCAmelCase):







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]				,			lowercase_				:  List[Any]				,			lowercase_				:  List[Any]    ):
										return (x_in - self.means[key]) / self.stds[key]







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]    ):
										return x_in * self.stds[key] + self.means[key]







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







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  str				,			lowercase_				:  Dict				,			lowercase_				:  str    ):
										for key, val in cond.items():
																	lowercase_					:			str										=       val.clone()
										return x_in







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

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

																															lowercase_					:			List[Any]										=       self.scheduler._get_variance(lowercase_    )
																															lowercase_					:			Union[str, Any]										=       torch.exp(0.5 * posterior_variance    )
																															lowercase_					:			List[Any]										=       model_std * grad

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

																	lowercase_					:			List[str]										=       self.unet(x.permute(0				,			2				,			1    )				,			lowercase_    ).sample.permute(0				,			2				,			1    )

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

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







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

										lowercase_					:			List[Any]										=       {0: self.to_torch(lowercase_    )}
										lowercase_					:			Optional[Any]										=       (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)
										lowercase_					:			Dict										=       randn_tensor(lowercase_				,			device=self.unet.device    )
										lowercase_					:			List[Any]										=       self.reset_xa(lowercase_				,			lowercase_				,			self.action_dim    )
										lowercase_					:			Dict										=       self.to_torch(lowercase_    )

										# run the diffusion process
										lowercase_						,						lowercase_					:			int										=       self.run_diffusion(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_    )

										# sort output trajectories by value
										lowercase_					:			Any										=       y.argsort(0				,			descending=lowercase_    ).squeeze()
										lowercase_					:			List[str]										=       x[sorted_idx]
										lowercase_					:			Optional[int]										=       sorted_values[:, :, : self.action_dim]
										lowercase_					:			Dict										=       actions.detach().cpu().numpy()
										lowercase_					:			Optional[int]										=       self.de_normalize(lowercase_				,			key="""actions"""    )

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

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






'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





