



'''simple docstring'''






import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


UpperCamelCase__:     int										=    logging.get_logger(__name__)

# General docstring
UpperCamelCase__:     Optional[int]										=    "PoolFormerConfig"

# Base docstring
UpperCamelCase__:     Union[str, Any]										=    "sail/poolformer_s12"
UpperCamelCase__:     Union[str, Any]										=    [1, 512, 7, 7]

# Image classification docstring
UpperCamelCase__:     Union[str, Any]										=    "sail/poolformer_s12"
UpperCamelCase__:     List[Any]										=    "tabby, tabby cat"

UpperCamelCase__:     int										=    [
    "sail/poolformer_s12",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Any = 0.0     ,   _lowerCAmelCase						:		Optional[int] = False						)   ->     List[str]:



							if drop_prob == 0.0 or not training:
														return input
							UpperCAmelCase				: List[str]											=	1 - drop_prob
							UpperCAmelCase				: List[str]											=	(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
							UpperCAmelCase				: str											=	keep_prob + torch.rand(_lowerCamelCase     ,   dtype=input.dtype     ,   device=input.device						)
							random_tensor.floor_()  # binarize
							UpperCAmelCase				: Any											=	input.div(_lowerCamelCase						) * random_tensor
							return output

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  List[str]	,    __snake_case    :  str = None	)    ->	Optional[int]:
								super().__init__()
								UpperCAmelCase				: int											=	drop_prob







	def   A			(		self    :  Dict	,    __snake_case    :  Optional[Any]	)    ->	List[Any]:
								return drop_path(_A	,    self.drop_prob	,    self.training	)







	def   A			(		self    :  int	)    ->	Optional[int]:
								return "p={}".format(self.drop_prob	)

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  List[str]	,    __snake_case    :  Any	,    __snake_case    :  str	,    __snake_case    :  Dict	,    __snake_case    :  str=None	)    ->	Tuple:
								super().__init__()
								UpperCAmelCase				: Optional[Any]											=	patch_size if isinstance(_A	,    collections.abc.Iterable	) else (patch_size, patch_size)
								UpperCAmelCase				: Union[str, Any]											=	stride if isinstance(_A	,    collections.abc.Iterable	) else (stride, stride)
								UpperCAmelCase				: Optional[Any]											=	padding if isinstance(_A	,    collections.abc.Iterable	) else (padding, padding)

								UpperCAmelCase				: List[Any]											=	nn.Convad(_A	,    _A	,    kernel_size=_A	,    stride=_A	,    padding=_A	)
								UpperCAmelCase				: Any											=	norm_layer(_A	) if norm_layer else nn.Identity()







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Dict	)    ->	str:
								UpperCAmelCase				: str											=	self.projection(_A	)
								UpperCAmelCase				: Union[str, Any]											=	self.norm(_A	)
								return embeddings

class        SCREAMING_SNAKE_CASE(							nn.GroupNorm    ):







	"""simple docstring"""






	def __init__(		self    :  Tuple	,    __snake_case    :  int	,    **__snake_case    :  Any	)    ->	Any:
								super().__init__(1	,    _A	,    **_A	)

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  List[str]	)    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Union[str, Any]											=	nn.AvgPoolad(_A	,    stride=1	,    padding=pool_size // 2	,    count_include_pad=_A	)







	def   A			(		self    :  Tuple	,    __snake_case    :  Dict	)    ->	Optional[Any]:
								return self.pool(_A	) - hidden_states

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  int	,    __snake_case    :  Tuple	,    __snake_case    :  Any	,    __snake_case    :  Tuple	,    __snake_case    :  Any	)    ->	int:
								super().__init__()
								UpperCAmelCase				: str											=	nn.Convad(_A	,    _A	,    1	)
								UpperCAmelCase				: Optional[Any]											=	nn.Convad(_A	,    _A	,    1	)
								UpperCAmelCase				: Union[str, Any]											=	PoolFormerDropPath(_A	)
								if isinstance(config.hidden_act	,    _A	):
															UpperCAmelCase				: Optional[int]											=	ACTaFN[config.hidden_act]
								else:
															UpperCAmelCase				: str											=	config.hidden_act







	def   A			(		self    :  Optional[int]	,    __snake_case    :  List[Any]	)    ->	Optional[int]:
								UpperCAmelCase				: Tuple											=	self.conva(_A	)
								UpperCAmelCase				: Optional[Any]											=	self.act_fn(_A	)
								UpperCAmelCase				: List[str]											=	self.drop(_A	)
								UpperCAmelCase				: Union[str, Any]											=	self.conva(_A	)
								UpperCAmelCase				: Any											=	self.drop(_A	)

								return hidden_states

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[int]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  List[Any]	,    __snake_case    :  Tuple	,    __snake_case    :  List[Any]	,    __snake_case    :  Any	)    ->	List[Any]:
								super().__init__()
								UpperCAmelCase				: int											=	PoolFormerPooling(_A	)
								UpperCAmelCase				: int											=	PoolFormerOutput(_A	,    _A	,    _A	,    _A	)
								UpperCAmelCase				: List[Any]											=	PoolFormerGroupNorm(_A	)
								UpperCAmelCase				: Dict											=	PoolFormerGroupNorm(_A	)

								# Useful for training neural nets
								UpperCAmelCase				: Optional[Any]											=	PoolFormerDropPath(_A	) if drop_path > 0.0 else nn.Identity()
								UpperCAmelCase				: Any											=	config.use_layer_scale
								if config.use_layer_scale:
															UpperCAmelCase				: List[str]											=	nn.Parameter(
															    config.layer_scale_init_value * torch.ones((_A)	)	,    requires_grad=_A	)
															UpperCAmelCase				: Optional[Any]											=	nn.Parameter(
															    config.layer_scale_init_value * torch.ones((_A)	)	,    requires_grad=_A	)







	def   A			(		self    :  Dict	,    __snake_case    :  int	)    ->	Dict:

								if self.use_layer_scale:
															UpperCAmelCase				: Optional[int]											=	self.pooling(self.before_norm(_A	)	)
															UpperCAmelCase				: List[str]											=	self.layer_scale_a.unsqueeze(-1	).unsqueeze(-1	) * pooling_output
															# First residual connection
															UpperCAmelCase				: Union[str, Any]											=	hidden_states + self.drop_path(_A	)
															UpperCAmelCase				: Union[str, Any]											=	()

															UpperCAmelCase				: Optional[int]											=	self.output(self.after_norm(_A	)	)
															UpperCAmelCase				: Optional[Any]											=	self.layer_scale_a.unsqueeze(-1	).unsqueeze(-1	) * layer_output
															# Second residual connection
															UpperCAmelCase				: int											=	hidden_states + self.drop_path(_A	)

															UpperCAmelCase				: int											=	(output,) + outputs
															return outputs

								else:
															UpperCAmelCase				: List[Any]											=	self.drop_path(self.pooling(self.before_norm(_A	)	)	)
															# First residual connection
															UpperCAmelCase				: int											=	pooling_output + hidden_states
															UpperCAmelCase				: List[str]											=	()

															# Second residual connection inside the PoolFormerOutput block
															UpperCAmelCase				: Tuple											=	self.drop_path(self.output(self.after_norm(_A	)	)	)
															UpperCAmelCase				: str											=	hidden_states + layer_output

															UpperCAmelCase				: Union[str, Any]											=	(output,) + outputs
															return outputs

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  Optional[int]	)    ->	List[Any]:
								super().__init__()
								UpperCAmelCase				: Optional[int]											=	config
								# stochastic depth decay rule
								UpperCAmelCase				: str											=	[x.item() for x in torch.linspace(0	,    config.drop_path_rate	,    sum(config.depths	)	)]

								# patch embeddings
								UpperCAmelCase				: Optional[Any]											=	[]
								for i in range(config.num_encoder_blocks	):
															embeddings.append(
															    PoolFormerEmbeddings(
															        patch_size=config.patch_sizes[i]	,    stride=config.strides[i]	,    padding=config.padding[i]	,    num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]	,    hidden_size=config.hidden_sizes[i]	,    )	)
								UpperCAmelCase				: Dict											=	nn.ModuleList(_A	)

								# Transformer blocks
								UpperCAmelCase				: List[Any]											=	[]
								UpperCAmelCase				: Tuple											=	0
								for i in range(config.num_encoder_blocks	):
															# each block consists of layers
															UpperCAmelCase				: int											=	[]
															if i != 0:
																						cur += config.depths[i - 1]
															for j in range(config.depths[i]	):
																						layers.append(
																						    PoolFormerLayer(
																						        _A	,    num_channels=config.hidden_sizes[i]	,    pool_size=config.pool_size	,    hidden_size=config.hidden_sizes[i]	,    intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio	)	,    drop_path=dpr[cur + j]	,    )	)
															blocks.append(nn.ModuleList(_A	)	)

								UpperCAmelCase				: Tuple											=	nn.ModuleList(_A	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Any	,    __snake_case    :  Optional[Any]=False	,    __snake_case    :  Optional[Any]=True	)    ->	str:
								UpperCAmelCase				: Dict											=	() if output_hidden_states else None

								UpperCAmelCase				: str											=	pixel_values
								for idx, layers in enumerate(zip(self.patch_embeddings	,    self.block	)	):
															UpperCAmelCase				: Optional[int]											=	layers
															# Get patch embeddings from hidden_states
															UpperCAmelCase				: Dict											=	embedding_layer(_A	)
															# Send the embeddings through the blocks
															for _, blk in enumerate(_A	):
																						UpperCAmelCase				: Optional[int]											=	blk(_A	)
																						UpperCAmelCase				: int											=	layer_outputs[0]

															if output_hidden_states:
																						UpperCAmelCase				: List[str]											=	all_hidden_states + (hidden_states,)

								if not return_dict:
															return tuple(v for v in [hidden_states, all_hidden_states] if v is not None	)

								return BaseModelOutputWithNoAttention(last_hidden_state=_A	,    hidden_states=_A	)

class        SCREAMING_SNAKE_CASE(							a__    ):







	"""simple docstring"""






	lowerCamelCase__    				= PoolFormerConfig
	lowerCamelCase__    				= """poolformer"""
	lowerCamelCase__    				= """pixel_values"""
	lowerCamelCase__    				= True







	def   A			(		self    :  Tuple	,    __snake_case    :  List[Any]	)    ->	List[Any]:

								if isinstance(_A	,    (nn.Linear, nn.Convad)	):
															module.weight.data.normal_(mean=0.0	,    std=self.config.initializer_range	)
															if module.bias is not None:
																						module.bias.data.zero_()
								elif isinstance(_A	,    nn.LayerNorm	):
															module.bias.data.zero_()
															module.weight.data.fill_(1.0	)







	def   A			(		self    :  List[str]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Dict=False	)    ->	List[Any]:

								if isinstance(_A	,    _A	):
															UpperCAmelCase				: Any											=	value

UpperCamelCase__:     List[Any]										=    r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCamelCase__:     List[str]										=    r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`PoolFormerImageProcessor.__call__`] for details.\n"

@add_start_docstrings(
    """The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."""							,	a__							,	)
class        SCREAMING_SNAKE_CASE(							a__    ):







	"""simple docstring"""






	def __init__(		self    :  Tuple	,    __snake_case    :  Union[str, Any]	)    ->	Tuple:
								super().__init__(_A	)
								UpperCAmelCase				: List[Any]											=	config

								UpperCAmelCase				: int											=	PoolFormerEncoder(_A	)

								# Initialize weights and apply final processing
								self.post_init()







	def   A			(		self    :  Any	)    ->	int:
								return self.embeddings.patch_embeddings







	@add_start_docstrings_to_model_forward(_A	)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC	,    output_type=_A	,    config_class=_CONFIG_FOR_DOC	,    modality='''vision'''	,    expected_output=_EXPECTED_OUTPUT_SHAPE	,    )
	def   A			(		self    :  Tuple	,    __snake_case    :  Tuple = None	,    __snake_case    :  List[Any] = None	,    __snake_case    :  List[str] = None	,    )    ->	List[Any]:
								UpperCAmelCase				: List[str]											=	(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase				: List[str]											=	return_dict if return_dict is not None else self.config.use_return_dict

								if pixel_values is None:
															raise ValueError('''You have to specify pixel_values'''	)

								UpperCAmelCase				: List[Any]											=	self.encoder(
								    _A	,    output_hidden_states=_A	,    return_dict=_A	,    )
								UpperCAmelCase				: Optional[int]											=	encoder_outputs[0]

								if not return_dict:
															return (sequence_output, None) + encoder_outputs[1:]

								return BaseModelOutputWithNoAttention(
								    last_hidden_state=_A	,    hidden_states=encoder_outputs.hidden_states	,    )

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






	def __init__(		self    :  Optional[Any]	,    __snake_case    :  Dict	)    ->	List[str]:
								super().__init__()
								UpperCAmelCase				: Dict											=	nn.Linear(config.hidden_size	,    config.hidden_size	)







	def   A			(		self    :  Dict	,    __snake_case    :  int	)    ->	Optional[int]:
								UpperCAmelCase				: int											=	self.dense(_A	)
								return output

@add_start_docstrings(
    """\n    PoolFormer Model transformer with an image classification head on top\n    """							,	a__							,	)
class        SCREAMING_SNAKE_CASE(							a__    ):







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  Union[str, Any]	)    ->	int:
								super().__init__(_A	)
								UpperCAmelCase				: Optional[int]											=	config.num_labels
								UpperCAmelCase				: Optional[int]											=	PoolFormerModel(_A	)

								# Final norm
								UpperCAmelCase				: Tuple											=	PoolFormerGroupNorm(config.hidden_sizes[-1]	)
								# Classifier head
								UpperCAmelCase				: Tuple											=	(
								    nn.Linear(config.hidden_sizes[-1]	,    config.num_labels	) if config.num_labels > 0 else nn.Identity()
								)

								# Initialize weights and apply final processing
								self.post_init()







	@add_start_docstrings_to_model_forward(_A	)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT	,    output_type=_A	,    config_class=_CONFIG_FOR_DOC	,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,    )
	def   A			(		self    :  Any	,    __snake_case    :  Any = None	,    __snake_case    :  List[str] = None	,    __snake_case    :  str = None	,    __snake_case    :  List[str] = None	,    )    ->	int:
								UpperCAmelCase				: Any											=	return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase				: Dict											=	self.poolformer(
								    _A	,    output_hidden_states=_A	,    return_dict=_A	,    )

								UpperCAmelCase				: Tuple											=	outputs[0]

								UpperCAmelCase				: Any											=	self.classifier(self.norm(_A	).mean([-2, -1]	)	)

								UpperCAmelCase				: List[Any]											=	None
								if labels is not None:
															if self.config.problem_type is None:
																						if self.num_labels == 1:
																													UpperCAmelCase				: int											=	'regression'
																						elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																													UpperCAmelCase				: str											=	'single_label_classification'
																						else:
																													UpperCAmelCase				: Optional[int]											=	'multi_label_classification'

															if self.config.problem_type == "regression":
																						UpperCAmelCase				: Tuple											=	MSELoss()
																						if self.num_labels == 1:
																													UpperCAmelCase				: Union[str, Any]											=	loss_fct(logits.squeeze()	,    labels.squeeze()	)
																						else:
																													UpperCAmelCase				: List[str]											=	loss_fct(_A	,    _A	)
															elif self.config.problem_type == "single_label_classification":
																						UpperCAmelCase				: Any											=	CrossEntropyLoss()
																						UpperCAmelCase				: List[Any]											=	loss_fct(logits.view(-1	,    self.num_labels	)	,    labels.view(-1	)	)
															elif self.config.problem_type == "multi_label_classification":
																						UpperCAmelCase				: List[str]											=	BCEWithLogitsLoss()
																						UpperCAmelCase				: Any											=	loss_fct(_A	,    _A	)

								if not return_dict:
															UpperCAmelCase				: Any											=	(logits,) + outputs[2:]
															return ((loss,) + output) if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=_A	,    logits=_A	,    hidden_states=outputs.hidden_states	)










'''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        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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







	def   A			(		self    :  Any	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Union[str, Any]	)    ->	Optional[int]:
								return (x_in - self.means[key]) / self.stds[key]







	def   A			(		self    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  Dict	)    ->	Any:
								return x_in * self.stds[key] + self.means[key]







	def   A			(		self    :  Any	,    __snake_case    :  int	)    ->	List[Any]:
								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   A			(		self    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  str	,    __snake_case    :  Union[str, Any]	)    ->	Dict:
								for key, val in cond.items():
															UpperCAmelCase				: List[Any]											=	val.clone()
								return x_in







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

																													UpperCAmelCase				: Tuple											=	self.scheduler._get_variance(__snake_case	)
																													UpperCAmelCase				: Optional[int]											=	torch.exp(0.5 * posterior_variance	)
																													UpperCAmelCase				: str											=	model_std * grad

																						UpperCAmelCase				: Dict											=	0
																						UpperCAmelCase				: Optional[int]											=	x.detach()
																						UpperCAmelCase				: List[str]											=	x + scale * grad
																						UpperCAmelCase				: List[str]											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)

															UpperCAmelCase				: str											=	self.unet(x.permute(0	,    2	,    1	)	,    __snake_case	).sample.permute(0	,    2	,    1	)

															# TODO: verify deprecation of this kwarg
															UpperCAmelCase				: Optional[int]											=	self.scheduler.step(__snake_case	,    __snake_case	,    __snake_case	,    predict_epsilon=__snake_case	)['''prev_sample''']

															# apply conditions to the trajectory (set the initial state)
															UpperCAmelCase				: Any											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
															UpperCAmelCase				: List[str]											=	self.to_torch(__snake_case	)
								return x, y







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

								UpperCAmelCase				: Tuple											=	{0: self.to_torch(__snake_case	)}
								UpperCAmelCase				: Tuple											=	(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				: Union[str, Any]											=	randn_tensor(__snake_case	,    device=self.unet.device	)
								UpperCAmelCase				: int											=	self.reset_xa(__snake_case	,    __snake_case	,    self.action_dim	)
								UpperCAmelCase				: List[Any]											=	self.to_torch(__snake_case	)

								# run the diffusion process
								UpperCAmelCase							,	UpperCAmelCase				: Optional[int]											=	self.run_diffusion(__snake_case	,    __snake_case	,    __snake_case	,    __snake_case	)

								# sort output trajectories by value
								UpperCAmelCase				: str											=	y.argsort(0	,    descending=__snake_case	).squeeze()
								UpperCAmelCase				: Tuple											=	x[sorted_idx]
								UpperCAmelCase				: List[Any]											=	sorted_values[:, :, : self.action_dim]
								UpperCAmelCase				: str											=	actions.detach().cpu().numpy()
								UpperCAmelCase				: List[str]											=	self.de_normalize(__snake_case	,    key='''actions'''	)

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

								UpperCAmelCase				: Optional[int]											=	denorm_actions[selected_index, 0]
								return denorm_actions





