
"""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  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_						 = value_function
									SCREAMING_SNAKE_CASE_						 = unet
									SCREAMING_SNAKE_CASE_						 = scheduler
									SCREAMING_SNAKE_CASE_						 = env
									SCREAMING_SNAKE_CASE_						 = env.get_dataset()
									SCREAMING_SNAKE_CASE_						 = {}
									for key in self.data.keys():
											try:
													SCREAMING_SNAKE_CASE_						 = self.data[key].mean()
											except:  # noqa: E722
													pass
									SCREAMING_SNAKE_CASE_						 = {}
									for key in self.data.keys():
											try:
													SCREAMING_SNAKE_CASE_						 = self.data[key].std()
											except:  # noqa: E722
													pass
									SCREAMING_SNAKE_CASE_						 = env.observation_space.shape[0]
									SCREAMING_SNAKE_CASE_						 = env.action_space.shape[0]

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return (x_in - self.means[key]) / self.stds[key]

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return x_in * self.stds[key] + self.means[key]

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if type(UpperCAmelCase__    ) is dict:
											return {k: self.to_torch(UpperCAmelCase__    ) for k, v in x_in.items()}
									elif torch.is_tensor(UpperCAmelCase__    ):
											return x_in.to(self.unet.device    )
									return torch.tensor(UpperCAmelCase__       ,       device=self.unet.device    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									for key, val in cond.items():
											SCREAMING_SNAKE_CASE_						 = val.clone()
									return x_in

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

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

															SCREAMING_SNAKE_CASE_						 = self.scheduler._get_variance(UpperCAmelCase__    )
															SCREAMING_SNAKE_CASE_						 = torch.exp(0.5 * posterior_variance    )
															SCREAMING_SNAKE_CASE_						 = model_std * grad

													SCREAMING_SNAKE_CASE_						 = 0
													SCREAMING_SNAKE_CASE_						 = x.detach()
													SCREAMING_SNAKE_CASE_						 = x + scale * grad
													SCREAMING_SNAKE_CASE_						 = self.reset_xa(UpperCAmelCase__       ,       UpperCAmelCase__       ,       self.action_dim    )

											SCREAMING_SNAKE_CASE_						 = self.unet(x.permute(0       ,       2       ,       1    )       ,       UpperCAmelCase__    ).sample.permute(0       ,       2       ,       1    )

											# TODO: verify deprecation of this kwarg
											SCREAMING_SNAKE_CASE_						 = self.scheduler.step(UpperCAmelCase__       ,       UpperCAmelCase__       ,       UpperCAmelCase__       ,       predict_epsilon=UpperCAmelCase__    )['''prev_sample''']

											# apply conditions to the trajectory (set the initial state)
											SCREAMING_SNAKE_CASE_						 = self.reset_xa(UpperCAmelCase__       ,       UpperCAmelCase__       ,       self.action_dim    )
											SCREAMING_SNAKE_CASE_						 = self.to_torch(UpperCAmelCase__    )
									return x, y







							def __call__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=32       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=0.1    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.normalize(UpperCAmelCase__       ,       '''observations'''    )
									SCREAMING_SNAKE_CASE_						 = obs[None].repeat(UpperCAmelCase__       ,       axis=0    )

									SCREAMING_SNAKE_CASE_						 = {0: self.to_torch(UpperCAmelCase__    )}
									SCREAMING_SNAKE_CASE_						 = (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)
									SCREAMING_SNAKE_CASE_						 = randn_tensor(UpperCAmelCase__       ,       device=self.unet.device    )
									SCREAMING_SNAKE_CASE_						 = self.reset_xa(UpperCAmelCase__       ,       UpperCAmelCase__       ,       self.action_dim    )
									SCREAMING_SNAKE_CASE_						 = self.to_torch(UpperCAmelCase__    )

									# run the diffusion process
									SCREAMING_SNAKE_CASE_						 = self.run_diffusion(UpperCAmelCase__       ,       UpperCAmelCase__       ,       UpperCAmelCase__       ,       UpperCAmelCase__    )

									# sort output trajectories by value
									SCREAMING_SNAKE_CASE_						 = y.argsort(0       ,       descending=UpperCAmelCase__    ).squeeze()
									SCREAMING_SNAKE_CASE_						 = x[sorted_idx]
									SCREAMING_SNAKE_CASE_						 = sorted_values[:, :, : self.action_dim]
									SCREAMING_SNAKE_CASE_						 = actions.detach().cpu().numpy()
									SCREAMING_SNAKE_CASE_						 = self.de_normalize(UpperCAmelCase__       ,       key='''actions'''    )

									# select the action with the highest value
									if y is not None:
											SCREAMING_SNAKE_CASE_						 = 0
									else:
											# if we didn't run value guiding, select a random action
											SCREAMING_SNAKE_CASE_						 = np.random.randint(0       ,       UpperCAmelCase__    )

									SCREAMING_SNAKE_CASE_						 = denorm_actions[selected_index, 0]
									return denorm_actions

"""simple docstring"""




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      CTRLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE_						 = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return CTRLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									return input_text, output_text







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = CTRLTokenizer(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map    )
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokens + [tokenizer.unk_token]

									SCREAMING_SNAKE_CASE_						 = [0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )