


'''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   (       UpperCAmelCase__		):




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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase) ->    Tuple:
								return (x_in - self.means[key]) / self.stds[key]




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase) ->    Union[str, Any]:
								return x_in * self.stds[key] + self.means[key]




					def 			snake_case__  ( self							,							_lowerCamelCase) ->    Optional[Any]:




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




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase) ->    str:




								for key, val in cond.items():
											UpperCAmelCase__					:   Optional[Any]       				=       val.clone()
								return x_in




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

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

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

														UpperCAmelCase__					:   str       				=       0
														UpperCAmelCase__					:   Any       				=       x.detach()
														UpperCAmelCase__					:   int       				=       x + scale * grad
														UpperCAmelCase__					:   Any       				=       self.reset_xa(_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							self.action_dim)

											UpperCAmelCase__					:   Optional[int]       				=       self.unet(x.permute(0							,							2							,							1)							,							_SCREAMING_SNAKE_CASE).sample.permute(0							,							2							,							1)

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

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




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase=64							,							_lowerCamelCase=32							,							_lowerCamelCase=2							,							_lowerCamelCase=0.1) ->    Tuple:
								UpperCAmelCase__					:   int       				=       self.normalize(_SCREAMING_SNAKE_CASE							,							"""observations""")
								UpperCAmelCase__					:   int       				=       obs[None].repeat(_SCREAMING_SNAKE_CASE							,							axis=0)

								UpperCAmelCase__					:   Dict       				=       {0: self.to_torch(_SCREAMING_SNAKE_CASE)}
								UpperCAmelCase__					:   Dict       				=       (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__					:   str       				=       randn_tensor(_SCREAMING_SNAKE_CASE							,							device=self.unet.device)
								UpperCAmelCase__					:   Any       				=       self.reset_xa(_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							self.action_dim)
								UpperCAmelCase__					:   str       				=       self.to_torch(_SCREAMING_SNAKE_CASE)

								# run the diffusion process
								UpperCAmelCase__					:   Any       				=       self.run_diffusion(_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE)

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

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

								UpperCAmelCase__					:   Any       				=       denorm_actions[selected_index, 0]
								return denorm_actions



'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import tensorflow as tf

		from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM


@require_tf
@require_sentencepiece
@require_tokenizers
class 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

								UpperCAmelCase__					:   Optional[Any]       				=       tokenizer("""Hello there"""							,							return_tensors="""tf""").input_ids
								UpperCAmelCase__					:   Tuple       				=       tokenizer("""Hi I am"""							,							return_tensors="""tf""").input_ids

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)