



'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__:     Tuple										=    logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCamelCase__:     Tuple										=    []
for i in range(6):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
						)
						rename_keys.append(
						    (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
						)
						rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
						rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
						rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
						rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
						rename_keys.append(
						    (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
						)
						rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
						rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
						rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
						# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
						)
						rename_keys.append(
						    (
						        F"transformer.decoder.layers.{i}.cross_attn.out_proj.weight",
						        F"decoder.layers.{i}.encoder_attn.out_proj.weight",
						    )
						)
						rename_keys.append(
						    (
						        F"transformer.decoder.layers.{i}.cross_attn.out_proj.bias",
						        F"decoder.layers.{i}.encoder_attn.out_proj.bias",
						    )
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
						rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
						rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
						rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
						rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

						# q, k, v projections in self/cross-attention in decoder for conditional DETR
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", F"decoder.layers.{i}.sa_qcontent_proj.weight")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", F"decoder.layers.{i}.sa_kcontent_proj.weight")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.sa_qpos_proj.weight", F"decoder.layers.{i}.sa_qpos_proj.weight")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.sa_kpos_proj.weight", F"decoder.layers.{i}.sa_kpos_proj.weight")
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.weight", F"decoder.layers.{i}.sa_v_proj.weight"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", F"decoder.layers.{i}.ca_qcontent_proj.weight")
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", F"decoder.layers.{i}.ca_kcontent_proj.weight")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_kpos_proj.weight", F"decoder.layers.{i}.ca_kpos_proj.weight")
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.weight", F"decoder.layers.{i}.ca_v_proj.weight"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", F"decoder.layers.{i}.ca_qpos_sine_proj.weight")
						)

						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", F"decoder.layers.{i}.sa_qcontent_proj.bias")
						)
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", F"decoder.layers.{i}.sa_kcontent_proj.bias")
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.sa_qpos_proj.bias", F"decoder.layers.{i}.sa_qpos_proj.bias"))
						rename_keys.append((F"transformer.decoder.layers.{i}.sa_kpos_proj.bias", F"decoder.layers.{i}.sa_kpos_proj.bias"))
						rename_keys.append((F"transformer.decoder.layers.{i}.sa_v_proj.bias", F"decoder.layers.{i}.sa_v_proj.bias"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", F"decoder.layers.{i}.ca_qcontent_proj.bias")
						)
						# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", F"decoder.layers.{i}.ca_kcontent_proj.bias")
						)
						rename_keys.append((F"transformer.decoder.layers.{i}.ca_kpos_proj.bias", F"decoder.layers.{i}.ca_kpos_proj.bias"))
						rename_keys.append((F"transformer.decoder.layers.{i}.ca_v_proj.bias", F"decoder.layers.{i}.ca_v_proj.bias"))
						rename_keys.append(
						    (F"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", F"decoder.layers.{i}.ca_qpos_sine_proj.bias")
						)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)


def 				snake_case_  (      _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Tuple     ,   _lowerCAmelCase						:		Tuple						)   ->     Union[str, Any]:
							UpperCAmelCase				: Optional[int]											=	state_dict.pop(_lowerCAmelCase						)
							UpperCAmelCase				: List[Any]											=	val


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     Union[str, Any]:
							UpperCAmelCase				: str											=	OrderedDict()
							for key, value in state_dict.items():
														if "backbone.0.body" in key:
																					UpperCAmelCase				: int											=	key.replace('''backbone.0.body'''     ,   '''backbone.conv_encoder.model'''						)
																					UpperCAmelCase				: str											=	value
														else:
																					UpperCAmelCase				: List[str]											=	value

							return new_state_dict


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[Any]=False						)   ->     List[Any]:
							UpperCAmelCase				: Dict											=	''''''
							if is_panoptic:
														UpperCAmelCase				: List[str]											=	'''conditional_detr.'''

							# first: transformer encoder
							for i in range(6						):
														# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
														UpperCAmelCase				: Optional[Any]											=	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
														UpperCAmelCase				: List[Any]											=	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""						)
														# next, add query, keys and values (in that order) to the state dict
														UpperCAmelCase				: Optional[Any]											=	in_proj_weight[:256, :]
														UpperCAmelCase				: int											=	in_proj_bias[:256]
														UpperCAmelCase				: Union[str, Any]											=	in_proj_weight[256:512, :]
														UpperCAmelCase				: Optional[Any]											=	in_proj_bias[256:512]
														UpperCAmelCase				: Optional[int]											=	in_proj_weight[-256:, :]
														UpperCAmelCase				: List[Any]											=	in_proj_bias[-256:]


def 				snake_case_  (      )   ->     List[str]:
							UpperCAmelCase				: List[Any]											=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase				: Tuple											=	Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase						).raw						)

							return im


@torch.no_grad()
def 				snake_case_  (      _lowerCAmelCase						:		Dict     ,   _lowerCAmelCase						:		List[Any]						)   ->     str:
							UpperCAmelCase				: Dict											=	ConditionalDetrConfig()
							# set backbone and dilation attributes
							if "resnet101" in model_name:
														UpperCAmelCase				: Any											=	'''resnet101'''
							if "dc5" in model_name:
														UpperCAmelCase				: int											=	True
							UpperCAmelCase				: str											=	'''panoptic''' in model_name
							if is_panoptic:
														UpperCAmelCase				: List[str]											=	250
							else:
														UpperCAmelCase				: Union[str, Any]											=	91
														UpperCAmelCase				: List[str]											=	'''huggingface/label-files'''
														UpperCAmelCase				: Union[str, Any]											=	'''coco-detection-id2label.json'''
														UpperCAmelCase				: List[Any]											=	json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type='''dataset'''						)     ,   '''r'''						)						)
														UpperCAmelCase				: str											=	{int(_lowerCAmelCase						): v for k, v in idalabel.items()}
														UpperCAmelCase				: List[Any]											=	idalabel
														UpperCAmelCase				: Any											=	{v: k for k, v in idalabel.items()}

							# load image processor
							UpperCAmelCase				: List[Any]											=	'''coco_panoptic''' if is_panoptic else '''coco_detection'''
							UpperCAmelCase				: Union[str, Any]											=	ConditionalDetrImageProcessor(format=_lowerCAmelCase						)

							# prepare image
							UpperCAmelCase				: int											=	prepare_img()
							UpperCAmelCase				: Union[str, Any]											=	image_processor(images=_lowerCAmelCase     ,   return_tensors='''pt'''						)
							UpperCAmelCase				: Tuple											=	encoding['''pixel_values''']

							logger.info(f"""Converting model {model_name}..."""						)

							# load original model from torch hub
							UpperCAmelCase				: List[Any]											=	torch.hub.load('''DeppMeng/ConditionalDETR'''     ,   _lowerCAmelCase     ,   pretrained=_lowerCAmelCase						).eval()
							UpperCAmelCase				: Optional[int]											=	conditional_detr.state_dict()
							# rename keys
							for src, dest in rename_keys:
														if is_panoptic:
																					UpperCAmelCase				: Tuple											=	'''conditional_detr.''' + src
														rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)
							UpperCAmelCase				: Optional[Any]											=	rename_backbone_keys(_lowerCAmelCase						)
							# query, key and value matrices need special treatment
							read_in_q_k_v(_lowerCAmelCase     ,   is_panoptic=_lowerCAmelCase						)
							# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
							UpperCAmelCase				: List[str]											=	'''conditional_detr.model.''' if is_panoptic else '''model.'''
							for key in state_dict.copy().keys():
														if is_panoptic:
																					if (
																					    key.startswith('''conditional_detr'''						)
																					    and not key.startswith('''class_labels_classifier'''						)
																					    and not key.startswith('''bbox_predictor'''						)
																					):
																												UpperCAmelCase				: str											=	state_dict.pop(_lowerCAmelCase						)
																												UpperCAmelCase				: Optional[Any]											=	val
																					elif "class_labels_classifier" in key or "bbox_predictor" in key:
																												UpperCAmelCase				: int											=	state_dict.pop(_lowerCAmelCase						)
																												UpperCAmelCase				: Tuple											=	val
																					elif key.startswith('''bbox_attention'''						) or key.startswith('''mask_head'''						):
																												continue
																					else:
																												UpperCAmelCase				: Dict											=	state_dict.pop(_lowerCAmelCase						)
																												UpperCAmelCase				: Dict											=	val
														else:
																					if not key.startswith('''class_labels_classifier'''						) and not key.startswith('''bbox_predictor'''						):
																												UpperCAmelCase				: int											=	state_dict.pop(_lowerCAmelCase						)
																												UpperCAmelCase				: Union[str, Any]											=	val
    # finally, create HuggingFace model and load state dict
							UpperCAmelCase				: List[Any]											=	ConditionalDetrForSegmentation(_lowerCAmelCase						) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCAmelCase						)
							model.load_state_dict(_lowerCAmelCase						)
							model.eval()
							model.push_to_hub(repo_id=_lowerCAmelCase     ,   organization='''DepuMeng'''     ,   commit_message='''Add model'''						)
							# verify our conversion
							UpperCAmelCase				: str											=	conditional_detr(_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	model(_lowerCAmelCase						)
							assert torch.allclose(outputs.logits     ,   original_outputs['''pred_logits''']     ,   atol=1e-4						)
							assert torch.allclose(outputs.pred_boxes     ,   original_outputs['''pred_boxes''']     ,   atol=1e-4						)
							if is_panoptic:
														assert torch.allclose(outputs.pred_masks     ,   original_outputs['''pred_masks''']     ,   atol=1e-4						)

							# Save model and image processor
							logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""						)
							Path(_lowerCAmelCase						).mkdir(exist_ok=_lowerCAmelCase						)
							model.save_pretrained(_lowerCAmelCase						)
							image_processor.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
						UpperCamelCase__:     List[str]										=    argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name",
						    default="conditional_detr_resnet50",
						    type=str,
						    help="Name of the CONDITIONAL_DETR model you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
						)
						UpperCamelCase__:     List[str]										=    parser.parse_args()
						convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''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





