



'''simple docstring'''




import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def        lowerCAmelCase_     (			_lowerCamelCase:     Dict      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.exp(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.sum(_lowerCamelCase ,   dim=1      )  # sum of exp(x_i)
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.sum(x * exp_x ,   dim=1      )  # sum of x_i * exp(x_i)
		return torch.log(_lowerCamelCase      ) - B / A






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				List[str]							,  lowerCAmelCase__  :				Dict		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	config.output_attentions
					__SCREAMING_SNAKE_CASE :	Dict					 =	config.output_hidden_states
					__SCREAMING_SNAKE_CASE :	Tuple					 =	nn.ModuleList([BertLayer(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)
					__SCREAMING_SNAKE_CASE :	Any					 =	nn.ModuleList([BertHighway(lowerCAmelCase__		) for _ in range(config.num_hidden_layers		)]		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	[-1 for _ in range(config.num_hidden_layers		)]

			def UpperCamelCase__      (							self  :				Any							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					if (type(lowerCAmelCase__		) is float) or (type(lowerCAmelCase__		) is int):
							for i in range(len(self.early_exit_entropy		)		):
									__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	x
					else:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	x

			def UpperCamelCase__      (							self  :				Optional[Any]							,  lowerCAmelCase__  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	pooler.state_dict()
					for highway in self.highway:
							for name, param in highway.pooler.state_dict().items():
									param.copy_(loaded_model[name]		)



			def UpperCamelCase__      (							self  :				Tuple							,  lowerCAmelCase__  :				str							,  lowerCAmelCase__  :				Any=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				Dict=None							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	()
					__SCREAMING_SNAKE_CASE :	Dict					 =	()
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	()
					for i, layer_module in enumerate(self.layer		):
							if self.output_hidden_states:
									__SCREAMING_SNAKE_CASE :	Tuple					 =	all_hidden_states + (hidden_states,)

							__SCREAMING_SNAKE_CASE :	List[Any]					 =	layer_module(
							    lowerCAmelCase__							,  lowerCAmelCase__							,  head_mask[i]							,  lowerCAmelCase__							,  lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	layer_outputs[0]

							if self.output_attentions:
									__SCREAMING_SNAKE_CASE :	str					 =	all_attentions + (layer_outputs[1],)

							__SCREAMING_SNAKE_CASE :	int					 =	(hidden_states,)
							if self.output_hidden_states:
									__SCREAMING_SNAKE_CASE :	Tuple					 =	current_outputs + (all_hidden_states,)
							if self.output_attentions:
									__SCREAMING_SNAKE_CASE :	str					 =	current_outputs + (all_attentions,)

							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.highway[i](lowerCAmelCase__		)
							# logits, pooled_output

							if not self.training:
									__SCREAMING_SNAKE_CASE :	str					 =	highway_exit[0]
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	entropy(lowerCAmelCase__		)
									__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
									__SCREAMING_SNAKE_CASE :	Dict					 =	all_highway_exits + (highway_exit,)

									if highway_entropy < self.early_exit_entropy[i]:
											__SCREAMING_SNAKE_CASE :	int					 =	(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
											raise HighwayException(lowerCAmelCase__							,  i + 1		)
							else:
									__SCREAMING_SNAKE_CASE :	List[str]					 =	all_highway_exits + (highway_exit,)

        # Add last layer
					if self.output_hidden_states:
							__SCREAMING_SNAKE_CASE :	Dict					 =	all_hidden_states + (hidden_states,)

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(hidden_states,)
					if self.output_hidden_states:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	outputs + (all_hidden_states,)
					if self.output_attentions:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	outputs + (all_attentions,)

					__SCREAMING_SNAKE_CASE :	str					 =	outputs + (all_highway_exits,)
					return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits






@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''					,      lowerCamelCase__					,      )
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Dict							,  lowerCAmelCase__  :				Optional[Any]		):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	config

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BertEmbeddings(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	DeeBertEncoder(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BertPooler(lowerCAmelCase__		)

					self.init_weights()

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					self.encoder.init_highway_pooler(self.pooler		)

			def UpperCamelCase__      (							self  :				int		):





					"""simple docstring"""






					return self.embeddings.word_embeddings

			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	value

			def UpperCamelCase__      (							self  :				Tuple							,  lowerCAmelCase__  :				List[Any]		):





					"""simple docstring"""






					for layer, heads in heads_to_prune.items():
							self.encoder.layer[layer].attention.prune_heads(lowerCAmelCase__		)



			@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
			def UpperCamelCase__      (							self  :				Union[str, Any]							,  lowerCAmelCase__  :				Union[str, Any]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Optional[int]=None							,  lowerCAmelCase__  :				str=None							,  lowerCAmelCase__  :				Optional[Any]=None							,  lowerCAmelCase__  :				str=None							,  ):





					"""simple docstring"""






					if input_ids is not None and inputs_embeds is not None:
							raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""		)
					elif input_ids is not None:
							__SCREAMING_SNAKE_CASE :	Tuple					 =	input_ids.size()
					elif inputs_embeds is not None:
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	inputs_embeds.size()[:-1]
					else:
							raise ValueError("""You have to specify either input_ids or inputs_embeds"""		)

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
							__SCREAMING_SNAKE_CASE :	str					 =	torch.ones(lowerCAmelCase__							,  device=lowerCAmelCase__		)
					if encoder_attention_mask is None:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.ones(lowerCAmelCase__							,  device=lowerCAmelCase__		)
					if token_type_ids is None:
							__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.zeros(lowerCAmelCase__							,  dtype=torch.long							,  device=lowerCAmelCase__		)

					# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
					# ourselves in which case we just need to make it broadcastable to all heads.
					__SCREAMING_SNAKE_CASE :	torch.Tensor					 =	self.get_extended_attention_mask(lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__		)

					# If a 2D ou 3D attention mask is provided for the cross-attention
					# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
					if encoder_attention_mask.dim() == 3:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.dim() == 2:
							__SCREAMING_SNAKE_CASE :	Any					 =	encoder_attention_mask[:, None, None, :]

					__SCREAMING_SNAKE_CASE :	int					 =	encoder_extended_attention_mask.to(
					    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(1.0 - encoder_extended_attention_mask) * -1_00_00.0

					# Prepare head mask if needed
					# 1.0 in head_mask indicate we keep the head
					# attention_probs has shape bsz x n_heads x N x N
					# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
					# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.get_head_mask(lowerCAmelCase__							,  self.config.num_hidden_layers		)

					__SCREAMING_SNAKE_CASE :	str					 =	self.embeddings(
					    input_ids=lowerCAmelCase__							,  position_ids=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  inputs_embeds=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.encoder(
					    lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  head_mask=lowerCAmelCase__							,  encoder_hidden_states=lowerCAmelCase__							,  encoder_attention_mask=lowerCAmelCase__							,  )
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	encoder_outputs[0]
					__SCREAMING_SNAKE_CASE :	Tuple					 =	self.pooler(lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
					    sequence_output,
					    pooled_output,
					) + encoder_outputs[
					    1:
					]  # add hidden_states and attentions if they are here
					return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits






class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				Union[str, Any]							,  lowerCAmelCase__  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	message
					__SCREAMING_SNAKE_CASE :	List[str]					 =	exit_layer  # start from 1!






class 		_UpperCamelCase						(							nn.Module					):


			'''simple docstring'''

			def __init__(							self  :				Tuple							,  lowerCAmelCase__  :				Optional[int]		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE :	Any					 =	BertPooler(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	nn.Dropout(config.hidden_dropout_prob		)
					__SCREAMING_SNAKE_CASE :	Dict					 =	nn.Linear(config.hidden_size							,  config.num_labels		)



			def UpperCamelCase__      (							self  :				List[str]							,  lowerCAmelCase__  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	encoder_outputs[0]
					__SCREAMING_SNAKE_CASE :	str					 =	self.pooler(lowerCAmelCase__		)
					# "return" pooler_output

					# BertModel
					__SCREAMING_SNAKE_CASE :	str					 =	(pooler_input, pooler_output) + encoder_outputs[1:]
					# "return" bmodel_output

					# Dropout and classification
					__SCREAMING_SNAKE_CASE :	Tuple					 =	bmodel_output[1]

					__SCREAMING_SNAKE_CASE :	Dict					 =	self.dropout(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	int					 =	self.classifier(lowerCAmelCase__		)

					return logits, pooled_output






@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''					,      lowerCamelCase__					,      )
class 		_UpperCamelCase						(							lowerCamelCase__					):


			'''simple docstring'''

			def __init__(							self  :				Optional[Any]							,  lowerCAmelCase__  :				int		):





					"""simple docstring"""






					super().__init__(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	List[str]					 =	config.num_labels
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	config.num_hidden_layers

					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	DeeBertModel(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	str					 =	nn.Dropout(config.hidden_dropout_prob		)
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	nn.Linear(config.hidden_size							,  self.config.num_labels		)

					self.init_weights()



			@add_start_docstrings_to_model_forward(lowerCAmelCase__		)
			def UpperCamelCase__      (							self  :				List[Any]							,  lowerCAmelCase__  :				str=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Dict=None							,  lowerCAmelCase__  :				Tuple=None							,  lowerCAmelCase__  :				int=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				List[str]=None							,  lowerCAmelCase__  :				Tuple=-1							,  lowerCAmelCase__  :				Union[str, Any]=False							,  ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	self.num_layers
					try:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.bert(
							    lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  position_ids=lowerCAmelCase__							,  head_mask=lowerCAmelCase__							,  inputs_embeds=lowerCAmelCase__							,  )
							# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

							__SCREAMING_SNAKE_CASE :	List[Any]					 =	outputs[1]

							__SCREAMING_SNAKE_CASE :	str					 =	self.dropout(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	self.classifier(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	List[Any]					 =	(logits,) + outputs[2:]  # add hidden states and attention if they are here
					except HighwayException as e:
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	e.message
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	e.exit_layer
							__SCREAMING_SNAKE_CASE :	List[str]					 =	outputs[0]

					if not self.training:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	entropy(lowerCAmelCase__		)
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[]
							__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[]
					if labels is not None:
							if self.num_labels == 1:
									#  We are doing regression
									__SCREAMING_SNAKE_CASE :	Tuple					 =	MSELoss()
									__SCREAMING_SNAKE_CASE :	Dict					 =	loss_fct(logits.view(-1		)							,  labels.view(-1		)		)
							else:
									__SCREAMING_SNAKE_CASE :	Optional[int]					 =	CrossEntropyLoss()
									__SCREAMING_SNAKE_CASE :	str					 =	loss_fct(logits.view(-1							,  self.num_labels		)							,  labels.view(-1		)		)

							# work with highway exits
							__SCREAMING_SNAKE_CASE :	List[str]					 =	[]
							for highway_exit in outputs[-1]:
									__SCREAMING_SNAKE_CASE :	str					 =	highway_exit[0]
									if not self.training:
											highway_logits_all.append(lowerCAmelCase__		)
											highway_entropy.append(highway_exit[2]		)
									if self.num_labels == 1:
											#  We are doing regression
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	MSELoss()
											__SCREAMING_SNAKE_CASE :	List[Any]					 =	loss_fct(highway_logits.view(-1		)							,  labels.view(-1		)		)
									else:
											__SCREAMING_SNAKE_CASE :	Any					 =	CrossEntropyLoss()
											__SCREAMING_SNAKE_CASE :	Dict					 =	loss_fct(highway_logits.view(-1							,  self.num_labels		)							,  labels.view(-1		)		)
									highway_losses.append(lowerCAmelCase__		)

							if train_highway:
									__SCREAMING_SNAKE_CASE :	int					 =	(sum(highway_losses[:-1]		),) + outputs
									# exclude the final highway, of course
							else:
									__SCREAMING_SNAKE_CASE :	Dict					 =	(loss,) + outputs
					if not self.training:
							__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	outputs + ((original_entropy, highway_entropy), exit_layer)
							if output_layer >= 0:
									__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
									    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
									)  # use the highway of the last layer

					return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)




'''simple docstring'''




import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

		elif hor == 32:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	str					 =	(32, 64, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	Tuple					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	model.state_dict()
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{
		    """down_block_types""": down_block_types,
		    """block_out_channels""": block_out_channels,
		    """up_block_types""": up_block_types,
		    """layers_per_block""": 1,
		    """use_timestep_embedding""": True,
		    """out_block_type""": """OutConv1DBlock""",
		    """norm_num_groups""": 8,
		    """downsample_each_block""": False,
		    """in_channels""": 14,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """sample_size""": 6_55_36,
		    """mid_block_type""": """MidResTemporalBlock1D""",
		    """act_fn""": """mish""",
		}
		__SCREAMING_SNAKE_CASE :	int					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	state_dict.pop(_lowerCamelCase      )
		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   F"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"      )
		with open(F"hub/hopper-medium-v2/unet/hor{hor}/config.json" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """in_channels""": 14,
		    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
		    """up_block_types""": (),
		    """out_block_type""": """ValueFunction""",
		    """mid_block_type""": """ValueFunctionMidBlock1D""",
		    """block_out_channels""": (32, 64, 1_28, 2_56),
		    """layers_per_block""": 1,
		    """downsample_each_block""": True,
		    """sample_size""": 6_55_36,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """use_timestep_embedding""": True,
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """norm_num_groups""": 8,
		    """act_fn""": """mish""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )

		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	state_dict.pop(_lowerCamelCase      )

		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""      )
		with open("""hub/hopper-medium-v2/value_function/config.json""" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()