


import functools
import operator

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


UpperCAmelCase      =   logging.get_logger(__name__)

UpperCAmelCase      =   {
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  "wavlm"



 def __init__(     self		,				_UpperCAmelCase=32		,				_UpperCAmelCase=7_68		,				_UpperCAmelCase=12		,				_UpperCAmelCase=12		,				_UpperCAmelCase=30_72		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=0.02		,				_UpperCAmelCase=1E-5		,				_UpperCAmelCase="group"		,				_UpperCAmelCase="gelu"		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)		,				_UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,				_UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)		,				_UpperCAmelCase=False		,				_UpperCAmelCase=1_28		,				_UpperCAmelCase=16		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=8_00		,				_UpperCAmelCase=False		,				_UpperCAmelCase=True		,				_UpperCAmelCase=0.05		,				_UpperCAmelCase=10		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=10		,				_UpperCAmelCase=3_20		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase=1_00		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=0.1		,				_UpperCAmelCase="mean"		,				_UpperCAmelCase=False		,				_UpperCAmelCase=False		,				_UpperCAmelCase=2_56		,				_UpperCAmelCase=(5_12, 5_12, 5_12, 5_12, 15_00)		,				_UpperCAmelCase=(5, 3, 3, 1, 1)		,				_UpperCAmelCase=(1, 2, 3, 1, 1)		,				_UpperCAmelCase=5_12		,				_UpperCAmelCase=80		,				_UpperCAmelCase=0		,				_UpperCAmelCase=1		,				_UpperCAmelCase=2		,				_UpperCAmelCase=False		,				_UpperCAmelCase=3		,				_UpperCAmelCase=2		,				_UpperCAmelCase=3		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       super().__init__(**_UpperCAmelCase		,				pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase )
       snake_case_	=				hidden_size
       snake_case_	=				feat_extract_norm
       snake_case_	=				feat_extract_activation
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				conv_bias
       snake_case_	=				num_buckets
       snake_case_	=				max_bucket_distance
       snake_case_	=				num_conv_pos_embeddings
       snake_case_	=				num_conv_pos_embedding_groups
       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_	=				num_ctc_classes
       snake_case_	=				vocab_size
       snake_case_	=				do_stable_layer_norm
       snake_case_	=				use_weighted_layer_sum
       snake_case_	=				classifier_proj_size

       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_	=				apply_spec_augment
       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

       # parameters for pretraining with codevector quantized representations
       snake_case_	=				num_codevectors_per_group
       snake_case_	=				num_codevector_groups
       snake_case_	=				contrastive_logits_temperature
       snake_case_	=				num_negatives
       snake_case_	=				codevector_dim
       snake_case_	=				proj_codevector_dim
       snake_case_	=				diversity_loss_weight

       # 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(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				list(_UpperCAmelCase )
       snake_case_	=				xvector_output_dim





 @property
 def 						UpperCamelCase__					(     self ):
       return functools.reduce(operator.mul		,				self.conv_stride		,				1 )



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 lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 def __init__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return (x_in - self.means[key]) / self.stds[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return x_in * self.stds[key] + self.means[key]



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       for key, val in cond.items():
             snake_case_	=				val.clone()
       return x_in



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       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,)		,				_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
                         snake_case_	=				self.value_function(x.permute(0		,				2		,				1 )		,				_UpperCAmelCase ).sample
                         snake_case_	=				torch.autograd.grad([y.sum()]		,				[x] )[0]

                         snake_case_	=				self.scheduler._get_variance(_UpperCAmelCase )
                         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(_UpperCAmelCase		,				_UpperCAmelCase		,				self.action_dim )

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

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

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





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=64		,				_UpperCAmelCase=32		,				_UpperCAmelCase=2		,				_UpperCAmelCase=0.1 ):
       # normalize the observations and create  batch dimension
       snake_case_	=				self.normalize(_UpperCAmelCase		,				'''observations''' )
       snake_case_	=				obs[None].repeat(_UpperCAmelCase		,				axis=0 )

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

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

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

       snake_case_	=				denorm_actions[selected_index, 0]
       return denorm_actions