
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 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case			):
		return (x_in - self.means[key]) / self.stds[key]





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





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		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       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case						,       snake_case			):
		for key, val in cond.items():
			lowercase	    =       val.clone()
		return x_in





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

					lowercase	    =       self.scheduler._get_variance(snake_case			)
					lowercase	    =       torch.exp(0.5 * posterior_variance			)
					lowercase	    =       model_std * grad

				lowercase	    =       0
				lowercase	    =       x.detach()
				lowercase	    =       x + scale * grad
				lowercase	    =       self.reset_xa(snake_case						,       snake_case						,       self.action_dim			)

			lowercase	    =       self.unet(x.permute(0						,       2						,       1			)						,       snake_case			).sample.permute(0						,       2						,       1			)

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

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





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

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

		# run the diffusion process
		lowercase    ,   lowercase	    =       self.run_diffusion(snake_case						,       snake_case						,       snake_case						,       snake_case			)

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

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

		lowercase	    =       denorm_actions[selected_index, 0]
		return denorm_actions




import functools
import operator

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


UpperCAmelCase_							:					List[str]		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''unispeech'''




	def __init__(     self						:     str					,      __A						:     Tuple=32					,      __A						:     List[str]=768					,      __A						:     Dict=12					,      __A						:     Union[str, Any]=12					,      __A						:     Tuple=3072					,      __A						:     Any="gelu"					,      __A						:     int=0.1					,      __A						:     Optional[int]=0.1					,      __A						:     List[Any]=0.1					,      __A						:     Any=0.0					,      __A						:     List[str]=0.0					,      __A						:     int=0.1					,      __A						:     List[Any]=0.1					,      __A						:     List[str]=0.0_2					,      __A						:     List[str]=1e-5					,      __A						:     List[Any]="group"					,      __A						:     int="gelu"					,      __A						:     Any=(512, 512, 512, 512, 512, 512, 512)					,      __A						:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)					,      __A						:     Tuple=(10, 3, 3, 3, 3, 2, 2)					,      __A						:     Optional[int]=False					,      __A						:     Any=128					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[Any]=False					,      __A						:     str=True					,      __A						:     Dict=0.0_5					,      __A						:     Optional[Any]=10					,      __A						:     Dict=2					,      __A						:     int=0.0					,      __A						:     List[str]=10					,      __A						:     str=0					,      __A						:     List[str]=320					,      __A						:     List[Any]=2					,      __A						:     Tuple=0.1					,      __A						:     Optional[int]=100					,      __A						:     Any=256					,      __A						:     Dict=256					,      __A						:     Tuple=0.1					,      __A						:     List[str]="mean"					,      __A						:     int=False					,      __A						:     List[str]=False					,      __A						:     List[Any]=256					,      __A						:     str=80					,      __A						:     Tuple=0					,      __A						:     Tuple=1					,      __A						:     int=2					,      __A						:     Dict=0.5					,      **__A						:     List[Any]					,      ):
		super().__init__(**__A					,      pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A							)
		__A :			Dict		    =			hidden_size
		__A :			Optional[Any]		    =			feat_extract_norm
		__A :			List[Any]		    =			feat_extract_activation
		__A :			str		    =			list(__A							)
		__A :			Optional[Any]		    =			list(__A							)
		__A :			Optional[int]		    =			list(__A							)
		__A :			List[Any]		    =			conv_bias
		__A :			Optional[int]		    =			num_conv_pos_embeddings
		__A :			List[Any]		    =			num_conv_pos_embedding_groups
		__A :			int		    =			len(self.conv_dim							)
		__A :			Optional[Any]		    =			num_hidden_layers
		__A :			List[str]		    =			intermediate_size
		__A :			Union[str, Any]		    =			hidden_act
		__A :			Optional[int]		    =			num_attention_heads
		__A :			Tuple		    =			hidden_dropout
		__A :			Optional[Any]		    =			attention_dropout
		__A :			Union[str, Any]		    =			activation_dropout
		__A :			Dict		    =			feat_proj_dropout
		__A :			Optional[int]		    =			final_dropout
		__A :			Dict		    =			layerdrop
		__A :			Optional[int]		    =			layer_norm_eps
		__A :			Optional[Any]		    =			initializer_range
		__A :			Optional[int]		    =			num_ctc_classes
		__A :			Dict		    =			vocab_size
		__A :			List[str]		    =			do_stable_layer_norm
		__A :			Tuple		    =			use_weighted_layer_sum
		__A :			Any		    =			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
		__A :			Tuple		    =			apply_spec_augment
		__A :			Union[str, Any]		    =			mask_time_prob
		__A :			Optional[Any]		    =			mask_time_length
		__A :			List[Any]		    =			mask_time_min_masks
		__A :			List[Any]		    =			mask_feature_prob
		__A :			Any		    =			mask_feature_length
		__A :			List[Any]		    =			mask_feature_min_masks

		# parameters for pretraining with codevector quantized representations
		__A :			Any		    =			num_codevectors_per_group
		__A :			Tuple		    =			num_codevector_groups
		__A :			List[str]		    =			contrastive_logits_temperature
		__A :			Optional[int]		    =			feat_quantizer_dropout
		__A :			int		    =			num_negatives
		__A :			List[str]		    =			codevector_dim
		__A :			int		    =			proj_codevector_dim
		__A :			Union[str, Any]		    =			diversity_loss_weight

		# ctc loss
		__A :			List[str]		    =			ctc_loss_reduction
		__A :			Any		    =			ctc_zero_infinity

		# pretraining loss
		__A :			Union[str, Any]		    =			replace_prob




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





