
"""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__     (		lowercase__   ):





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





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							str							,						snake_case_   :							Any    ):
												return (x_in - self.means[key]) / self.stds[key]





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]    ):
												return x_in * self.stds[key] + self.means[key]





					def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Dict    ):
												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 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							Tuple							,						snake_case_   :							Union[str, Any]							,						snake_case_   :							Union[str, Any]    ):
												for key, val in cond.items():
																			__snake_case     			=       val.clone()
												return x_in





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Tuple							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Any							,						snake_case_   :							Optional[Any]    ):
												__snake_case     			=       x.shape[0]
												__snake_case     			=       None
												for i in tqdm.tqdm(self.scheduler.timesteps    ):
																			# create batch of timesteps to pass into model
																			__snake_case     			=       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
																																	__snake_case     			=       self.value_function(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample
																																	__snake_case     			=       torch.autograd.grad([y.sum()]							,						[x]    )[0]

																																	__snake_case     			=       self.scheduler._get_variance(snake_case_    )
																																	__snake_case     			=       torch.exp(0.5 * posterior_variance    )
																																	__snake_case     			=       model_std * grad

																										__snake_case     			=       0
																										__snake_case     			=       x.detach()
																										__snake_case     			=       x + scale * grad
																										__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )

																			__snake_case     			=       self.unet(x.permute(0							,						2							,						1    )							,						snake_case_    ).sample.permute(0							,						2							,						1    )

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

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





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

												__snake_case     			=       {0: self.to_torch(snake_case_    )}
												__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)
												__snake_case     			=       randn_tensor(snake_case_							,						device=self.unet.device    )
												__snake_case     			=       self.reset_xa(snake_case_							,						snake_case_							,						self.action_dim    )
												__snake_case     			=       self.to_torch(snake_case_    )

												# run the diffusion process
												__snake_case			,   __snake_case     			=       self.run_diffusion(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

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

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

												__snake_case     			=       denorm_actions[selected_index, 0]
												return denorm_actions


"""simple docstring"""







import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   'data2vec-audio'





					def __init__(				self   :							Any							,						snake_case_   :							Optional[Any]=32							,						snake_case_   :							Tuple=768							,						snake_case_   :							Optional[int]=12							,						snake_case_   :							str=12							,						snake_case_   :							int=3072							,						snake_case_   :							Tuple="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							str=0.1							,						snake_case_   :							int=0.1							,						snake_case_   :							int=0.0							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Dict=0.1							,						snake_case_   :							Dict=0.02							,						snake_case_   :							Optional[int]=1e-5							,						snake_case_   :							Optional[Any]="gelu"							,						snake_case_   :							Tuple=(512, 512, 512, 512, 512, 512, 512)							,						snake_case_   :							int=(5, 2, 2, 2, 2, 2, 2)							,						snake_case_   :							Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							int=16							,						snake_case_   :							List[Any]=19							,						snake_case_   :							str=5							,						snake_case_   :							List[Any]=0.05							,						snake_case_   :							Dict=10							,						snake_case_   :							Dict=2							,						snake_case_   :							Dict=0.0							,						snake_case_   :							Union[str, Any]=10							,						snake_case_   :							Optional[Any]=0							,						snake_case_   :							Optional[Any]="sum"							,						snake_case_   :							List[str]=False							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Optional[int]=256							,						snake_case_   :							int=(512, 512, 512, 512, 1500)							,						snake_case_   :							List[Any]=(5, 3, 3, 1, 1)							,						snake_case_   :							Any=(1, 2, 3, 1, 1)							,						snake_case_   :							List[Any]=512							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							Dict=1							,						snake_case_   :							List[str]=2							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							Optional[Any]=3							,						snake_case_   :							Tuple=2							,						snake_case_   :							Any=3							,						snake_case_   :							Union[str, Any]=None							,						**snake_case_   :							int							,						):
												super().__init__(**snake_case_							,						pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						eos_token_id=snake_case_    )
												__snake_case     			=       hidden_size
												__snake_case     			=       feat_extract_activation
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       conv_bias
												__snake_case     			=       num_conv_pos_embeddings
												__snake_case     			=       num_conv_pos_embedding_groups
												__snake_case     			=       conv_pos_kernel_size
												__snake_case     			=       len(self.conv_dim    )
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       feat_proj_dropout
												__snake_case     			=       final_dropout
												__snake_case     			=       layerdrop
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												__snake_case     			=       vocab_size
												__snake_case     			=       use_weighted_layer_sum

												if (
												    (len(self.conv_stride    ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																			    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																			    F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
																			    F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__snake_case     			=       mask_time_prob
												__snake_case     			=       mask_time_length
												__snake_case     			=       mask_time_min_masks
												__snake_case     			=       mask_feature_prob
												__snake_case     			=       mask_feature_length
												__snake_case     			=       mask_feature_min_masks

												# ctc loss
												__snake_case     			=       ctc_loss_reduction
												__snake_case     			=       ctc_zero_infinity

												# adapter
												__snake_case     			=       add_adapter
												__snake_case     			=       adapter_kernel_size
												__snake_case     			=       adapter_stride
												__snake_case     			=       num_adapter_layers
												__snake_case     			=       output_hidden_size or hidden_size

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												__snake_case     			=       classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       xvector_output_dim





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return math.prod(self.conv_stride    )
