code: 
  
  
  '''simple docstring'''
  
  
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  
  
  
  
  
  
  def 						lowerCamelCase		(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]     ):
  					# we don't care about the past rewards in this model
  
  					__a    : int      					=						states.reshape(1		,   -1		,   model.config.state_dim     )
  					__a    : List[str]      					=						actions.reshape(1		,   -1		,   model.config.act_dim     )
  					__a    : List[str]      					=						returns_to_go.reshape(1		,   -1		,   1     )
  					__a    : Optional[Any]      					=						timesteps.reshape(1		,   -1     )
  
  					if model.config.max_length is not None:
  										__a    : Union[str, Any]      					=						states[:, -model.config.max_length :]
  										__a    : List[Any]      					=						actions[:, -model.config.max_length :]
  										__a    : List[Any]      					=						returns_to_go[:, -model.config.max_length :]
  										__a    : Optional[int]      					=						timesteps[:, -model.config.max_length :]
  
  										# pad all tokens to sequence length
  										__a    : Any      					=						torch.cat(
  										    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
  										__a    : Union[str, Any]      					=						attention_mask.to(dtype=torch.long		,   device=states.device     ).reshape(1		,   -1     )
  										__a    : Dict      					=						torch.cat(
  										    [
  										        torch.zeros(
  										            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim)		,   device=states.device		,   ),
  										        states,
  										    ]		,   dim=1		,   ).to(dtype=torch.floataa     )
  										__a    : Any      					=						torch.cat(
  										    [
  										        torch.zeros(
  										            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim)		,   device=actions.device		,   ),
  										        actions,
  										    ]		,   dim=1		,   ).to(dtype=torch.floataa     )
  										__a    : Optional[Any]      					=						torch.cat(
  										    [
  										        torch.zeros(
  										            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1)		,   device=returns_to_go.device		,   ),
  										        returns_to_go,
  										    ]		,   dim=1		,   ).to(dtype=torch.floataa     )
  										__a    : Optional[int]      					=						torch.cat(
  										    [
  										        torch.zeros(
  										            (timesteps.shape[0], model.config.max_length - timesteps.shape[1])		,   device=timesteps.device     ),
  										        timesteps,
  										    ]		,   dim=1		,   ).to(dtype=torch.long     )
  					else:
  										__a    : Dict      					=						None
  
  					__a    : Union[str, Any]      					=						model(
  					    states=_SCREAMING_SNAKE_CASE		,   actions=_SCREAMING_SNAKE_CASE		,   rewards=_SCREAMING_SNAKE_CASE		,   returns_to_go=_SCREAMING_SNAKE_CASE		,   timesteps=_SCREAMING_SNAKE_CASE		,   attention_mask=_SCREAMING_SNAKE_CASE		,   return_dict=_SCREAMING_SNAKE_CASE		,   )
  
  					return action_preds[0, -1]
  
  
  # build the environment
  
  __lowercase			:		Any =			gym.make('Hopper-v3')
  __lowercase			:		Union[str, Any] =			env.observation_space.shape[0]
  __lowercase			:		List[Any] =			env.action_space.shape[0]
  __lowercase			:		Dict =			10_00
  __lowercase			:		Tuple =			'cuda'
  __lowercase			:		int =			10_00.0  # normalization for rewards/returns
  __lowercase			:		Optional[Any] =			36_00 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __lowercase			:		List[str] =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __lowercase			:		Tuple =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __lowercase			:		Union[str, Any] =			torch.from_numpy(state_mean).to(device=device)
  __lowercase			:		int =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __lowercase			:		List[Any] =			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
  __lowercase			:		Any =			model.to(device)
  model.eval()
  
  for ep in range(10):
  	__lowercase			:		Any =			0, 0
  	__lowercase			:		int =			env.reset()
  	__lowercase			:		List[str] =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
  	__lowercase			:		List[Any] =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
  	__lowercase			:		Tuple =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
  	__lowercase			:		Dict =			torch.zeros(0, device=device, dtype=torch.floataa)
  
  	__lowercase			:		Tuple =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
  	for t in range(max_ep_len):
  		env.render()
  		# add padding
  		__lowercase			:		Tuple =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  		__lowercase			:		Optional[int] =			torch.cat([rewards, torch.zeros(1, device=device)])
  
  		__lowercase			:		Tuple =			get_action(
  		    model,
  		    (states.to(dtype=torch.floataa) - state_mean) / state_std,
  		    actions.to(dtype=torch.floataa),
  		    rewards.to(dtype=torch.floataa),
  		    target_return.to(dtype=torch.floataa),
  		    timesteps.to(dtype=torch.long),
  		)
  		__lowercase			:		List[str] =			action
  		__lowercase			:		Optional[int] =			action.detach().cpu().numpy()
  
  		__lowercase			:		Union[str, Any] =			env.step(action)
  
  		__lowercase			:		int =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  		__lowercase			:		List[str] =			torch.cat([states, cur_state], dim=0)
  		__lowercase			:		int =			reward
  
  		__lowercase			:		Optional[Any] =			target_return[0, -1] - (reward / scale)
  		__lowercase			:		Dict =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  		__lowercase			:		Union[str, Any] =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
  		episode_return += reward
  		episode_length += 1
  
  		if done:
  			break
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from diffusers import UNetaDConditionModel
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     List[Any]=None     ):
  					assert isinstance(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     ), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
  					# Splits the attention layers into three variables.
  					if attention_paths_to_split is not None:
  										for path, path_map in attention_paths_to_split.items():
  															__a    : Union[str, Any]      					=						old_checkpoint[path]
  															__a    : List[str]      					=						old_tensor.shape[0] // 3
  
  															__a    : Dict      					=						(-1, channels) if len(old_tensor.shape     ) == 3 else (-1)
  
  															__a    : List[str]      					=						old_tensor.shape[0] // config['num_head_channels'] // 3
  
  															__a    : str      					=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]     )
  															__a					,       __a					,       __a    : Dict      					=						old_tensor.split(channels // num_heads		,   dim=1     )
  
  															__a    : Optional[int]      					=						query.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : Tuple      					=						key.reshape(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						value.reshape(_SCREAMING_SNAKE_CASE     )
  
  					for path in paths:
  										__a    : Tuple      					=						path['new']
  
  										# These have already been assigned
  										if attention_paths_to_split is not None and new_path in attention_paths_to_split:
  															continue
  
  										if additional_replacements is not None:
  															for replacement in additional_replacements:
  																				__a    : int      					=						new_path.replace(replacement['old']		,   replacement['new']     )
  
          # proj_attn.weight has to be converted from conv 1D to linear
  										__a    : Any      					=						old_checkpoint[path['old']]
  										__a    : Tuple      					=						['proj_attn.weight']
  										__a    : Tuple      					=						['proj_out.weight', 'proj_in.weight']
  										if any(k in new_path for k in names     ):
  															__a    : Tuple      					=						weight[:, :, 0]
  										elif any(k in new_path for k in names_a     ) and len(weight.shape     ) > 2 and ".attentions." not in new_path:
  															__a    : int      					=						weight[:, :, 0]
  										else:
  															__a    : Union[str, Any]      					=						weight
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Dict      					=						old_item
  
  										#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
  										#         new_item = new_item.replace('norm.bias', 'group_norm.bias')
  
  										#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
  										#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
  
  										#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
  
  										mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]=1     ):
  					if n_shave_prefix_segments >= 0:
  										return ".".join(path.split('.'     )[n_shave_prefix_segments:]     )
  					else:
  										return ".".join(path.split('.'     )[:n_shave_prefix_segments]     )
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     str=0     ):
  					__a    : Optional[Any]      					=						[]
  					for old_item in old_list:
  										mapping.append({'old': old_item, 'new': old_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]=0     ):
  					__a    : Tuple      					=						[]
  					for old_item in old_list:
  										__a    : Optional[int]      					=						old_item.replace('in_layers.0'		,   'norm1'     )
  										__a    : Optional[Any]      					=						new_item.replace('in_layers.2'		,   'conv1'     )
  
  										__a    : str      					=						new_item.replace('out_layers.0'		,   'norm2'     )
  										__a    : str      					=						new_item.replace('out_layers.3'		,   'conv2'     )
  
  										__a    : List[Any]      					=						new_item.replace('emb_layers.1'		,   'time_emb_proj'     )
  										__a    : List[Any]      					=						new_item.replace('skip_connection'		,   'conv_shortcut'     )
  
  										__a    : int      					=						shave_segments(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=_SCREAMING_SNAKE_CASE     )
  
  										if "temopral_conv" not in old_item:
  															mapping.append({'old': old_item, 'new': new_item}     )
  
  					return mapping
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     str		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Any=None		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]=False     ):
  					__a    : Dict      					=						{}
  					__a    : Tuple      					=						list(checkpoint.keys()     )
  
  					__a    : Optional[int]      					=						'model.diffusion_model.'
  
  					# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
  					if sum(k.startswith('model_ema'     ) for k in keys     ) > 100 and extract_ema:
  										print(F"""Checkpoint {path} has both EMA and non-EMA weights."""     )
  										print(
  										    'In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'
  										    ' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'     )
  										for key in keys:
  															if key.startswith('model.diffusion_model'     ):
  																				__a    : int      					=						'model_ema.' + ''.join(key.split('.'     )[1:]     )
  																				__a    : Optional[int]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  					else:
  										if sum(k.startswith('model_ema'     ) for k in keys     ) > 100:
  															print(
  															    'In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'
  															    ' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'     )
  
  										for key in keys:
  															__a    : Optional[Any]      					=						checkpoint.pop(_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						{}
  
  					__a    : Optional[Any]      					=						unet_state_dict['time_embed.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['time_embed.0.bias']
  					__a    : Any      					=						unet_state_dict['time_embed.2.weight']
  					__a    : Dict      					=						unet_state_dict['time_embed.2.bias']
  
  					if config["class_embed_type"] is None:
  										# No parameters to port
  										...
  					elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
  										__a    : List[Any]      					=						unet_state_dict['label_emb.0.0.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.0.bias']
  										__a    : Optional[Any]      					=						unet_state_dict['label_emb.0.2.weight']
  										__a    : Union[str, Any]      					=						unet_state_dict['label_emb.0.2.bias']
  					else:
  										raise NotImplementedError(F"""Not implemented `class_embed_type`: {config["class_embed_type"]}"""     )
  
  					__a    : str      					=						unet_state_dict['input_blocks.0.0.weight']
  					__a    : Tuple      					=						unet_state_dict['input_blocks.0.0.bias']
  
  					__a    : Optional[int]      					=						[v for v in unet_state_dict if v.startswith('input_blocks.0.1'     )]
  					__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						{'old': 'input_blocks.0.1', 'new': 'transformer_in'}
  					assign_to_checkpoint(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Any      					=						unet_state_dict['out.0.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.0.bias']
  					__a    : Optional[int]      					=						unet_state_dict['out.2.weight']
  					__a    : Union[str, Any]      					=						unet_state_dict['out.2.bias']
  
  					# Retrieves the keys for the input blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'input_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""input_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the middle blocks only
  					__a    : Dict      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'middle_block' in layer}     )
  					__a    : Optional[Any]      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""middle_block.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					# Retrieves the keys for the output blocks only
  					__a    : Optional[Any]      					=						len({'.'.join(layer.split('.'     )[:2]     ) for layer in unet_state_dict if 'output_blocks' in layer}     )
  					__a    : Dict      					=						{
  					    layer_id: [key for key in unet_state_dict if F"""output_blocks.{layer_id}""" in key]
  					    for layer_id in range(_SCREAMING_SNAKE_CASE     )
  					}
  
  					for i in range(1		,   _SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						(i - 1) // (config['layers_per_block'] + 1)
  										__a    : Tuple      					=						(i - 1) % (config['layers_per_block'] + 1)
  
  										__a    : Optional[Any]      					=						[
  										    key for key in input_blocks[i] if F"""input_blocks.{i}.0""" in key and F"""input_blocks.{i}.0.op""" not in key
  										]
  										__a    : List[str]      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
  										__a    : Tuple      					=						[key for key in input_blocks[i] if F"""input_blocks.{i}.2""" in key]
  
  										if F"""input_blocks.{i}.op.weight""" in unet_state_dict:
  															__a    : Optional[Any]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.weight"""     )
  															__a    : Optional[int]      					=						unet_state_dict.pop(
  															    F"""input_blocks.{i}.op.bias"""     )
  
  										__a    : str      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : Tuple      					=						{'old': F"""input_blocks.{i}.0""", 'new': F"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										__a    : Optional[int]      					=						[key for key in resnets if 'temopral_conv' in key]
  										__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  										__a    : List[Any]      					=						{
  										    'old': F"""input_blocks.{i}.0.temopral_conv""",
  										    'new': F"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  										}
  										assign_to_checkpoint(
  										    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : Optional[Any]      					=						{'old': F"""input_blocks.{i}.1""", 'new': F"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  										if len(_SCREAMING_SNAKE_CASE     ):
  															__a    : List[str]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : str      					=						{
  															    'old': F"""input_blocks.{i}.2""",
  															    'new': F"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : Union[str, Any]      					=						middle_blocks[0]
  					__a    : str      					=						[key for key in resnet_a if 'temopral_conv' in key]
  					__a    : Dict      					=						middle_blocks[1]
  					__a    : str      					=						middle_blocks[2]
  					__a    : List[str]      					=						middle_blocks[3]
  					__a    : Any      					=						[key for key in resnet_a if 'temopral_conv' in key]
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : List[Any]      					=						{'old': 'middle_block.0.temopral_conv', 'new': 'mid_block.temp_convs.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Dict      					=						{'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : Dict      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[int]      					=						{'old': 'middle_block.3.temopral_conv', 'new': 'mid_block.temp_convs.1'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]     )
  
  					__a    : List[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : int      					=						{'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					__a    : str      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  					__a    : Optional[Any]      					=						{'old': 'middle_block.2', 'new': 'mid_block.temp_attentions.0'}
  					assign_to_checkpoint(
  					    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  					for i in range(_SCREAMING_SNAKE_CASE     ):
  										__a    : List[str]      					=						i // (config['layers_per_block'] + 1)
  										__a    : Optional[int]      					=						i % (config['layers_per_block'] + 1)
  										__a    : Any      					=						[shave_segments(_SCREAMING_SNAKE_CASE		,   2     ) for name in output_blocks[i]]
  										__a    : Optional[Any]      					=						{}
  
  										for layer in output_block_layers:
  															__a					,       __a    : Optional[int]      					=						layer.split('.'     )[0], shave_segments(_SCREAMING_SNAKE_CASE		,   1     )
  															if layer_id in output_block_list:
  																				output_block_list[layer_id].append(_SCREAMING_SNAKE_CASE     )
  															else:
  																				__a    : int      					=						[layer_name]
  
  										if len(_SCREAMING_SNAKE_CASE     ) > 1:
  															__a    : Any      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
  															__a    : List[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
  															__a    : Optional[Any]      					=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]
  
  															__a    : Optional[int]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[Any]      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						{'old': F"""output_blocks.{i}.0""", 'new': F"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : Dict      					=						[key for key in resnets if 'temopral_conv' in key]
  															__a    : Tuple      					=						renew_temp_conv_paths(_SCREAMING_SNAKE_CASE     )
  															__a    : List[str]      					=						{
  															    'old': F"""output_blocks.{i}.0.temopral_conv""",
  															    'new': F"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
  															}
  															assign_to_checkpoint(
  															    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															__a    : List[str]      					=						{k: sorted(_SCREAMING_SNAKE_CASE     ) for k, v in output_block_list.items()}
  															if ["conv.bias", "conv.weight"] in output_block_list.values():
  																				__a    : Tuple      					=						list(output_block_list.values()     ).index(['conv.bias', 'conv.weight']     )
  																				__a    : str      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.weight"""
  																				]
  																				__a    : Tuple      					=						unet_state_dict[
  																				    F"""output_blocks.{i}.{index}.conv.bias"""
  																				]
  
  																				# Clear attentions as they have been attributed above.
  																				if len(_SCREAMING_SNAKE_CASE     ) == 2:
  																									__a    : Optional[Any]      					=						[]
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Tuple      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : int      					=						{
  																				    'old': F"""output_blocks.{i}.1""",
  																				    'new': F"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  
  															if len(_SCREAMING_SNAKE_CASE     ):
  																				__a    : Optional[Any]      					=						renew_attention_paths(_SCREAMING_SNAKE_CASE     )
  																				__a    : Optional[Any]      					=						{
  																				    'old': F"""output_blocks.{i}.2""",
  																				    'new': F"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
  																				}
  																				assign_to_checkpoint(
  																				    _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   additional_replacements=[meta_path]		,   config=_SCREAMING_SNAKE_CASE     )
  										else:
  															__a    : int      					=						renew_resnet_paths(_SCREAMING_SNAKE_CASE		,   n_shave_prefix_segments=1     )
  															for path in resnet_0_paths:
  																				__a    : str      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), path['old']]     )
  																				__a    : Dict      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'resnets', str(_SCREAMING_SNAKE_CASE     ), path['new']]     )
  																				__a    : Optional[int]      					=						unet_state_dict[old_path]
  
  															__a    : Union[str, Any]      					=						[l for l in output_block_layers if 'temopral_conv' in l]
  															for path in temopral_conv_paths:
  																				__a    : Any      					=						path.split('temopral_conv.'     )[-1]
  																				__a    : int      					=						'.'.join(['output_blocks', str(_SCREAMING_SNAKE_CASE     ), str(_SCREAMING_SNAKE_CASE     ), 'temopral_conv', pruned_path]     )
  																				__a    : str      					=						'.'.join(['up_blocks', str(_SCREAMING_SNAKE_CASE     ), 'temp_convs', str(_SCREAMING_SNAKE_CASE     ), pruned_path]     )
  																				__a    : Optional[Any]      					=						unet_state_dict[old_path]
  
  					return new_checkpoint
  
  
  if __name__ == "__main__":
  	__lowercase			:		Optional[int] =			argparse.ArgumentParser()
  
  	parser.add_argument(
  	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  	)
  	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  	__lowercase			:		str =			parser.parse_args()
  
  	__lowercase			:		Dict =			torch.load(args.checkpoint_path, map_location='cpu')
  	__lowercase			:		Union[str, Any] =			UNetaDConditionModel()
  
  	__lowercase			:		Optional[Any] =			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)
  
  	__lowercase			:		Optional[int] =			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  	__lowercase			:		List[str] =			set(converted_ckpt.keys()) - set(unet.state_dict().keys())
  
  	assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"
  
  	# load state_dict
  	unet.load_state_dict(converted_ckpt)
  
  	unet.save_pretrained(args.dump_path)
  
  	# -- finish converting the unet --
  
  
  
  