






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__UpperCAmelCase      (					a_							,					a_=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 						__UpperCAmelCase      (					a_							,					a_=0):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item
														snake_case_           =							new_item.replace('block.'							,					'resnets.')
														snake_case_           =							new_item.replace('conv_shorcut'							,					'conv1')
														snake_case_           =							new_item.replace('in_shortcut'							,					'conv_shortcut')
														snake_case_           =							new_item.replace('temb_proj'							,					'time_emb_proj')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)

														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_=0							,					a_=False):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					snake_case_           =							new_item.replace('attn'							,					'attentions')
														snake_case_           =							new_item.replace('.k.'							,					'.key.')
														snake_case_           =							new_item.replace('.v.'							,					'.value.')
														snake_case_           =							new_item.replace('.q.'							,					'.query.')

														snake_case_           =							new_item.replace('proj_out'							,					'proj_attn')
														snake_case_           =							new_item.replace('norm'							,					'group_norm')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)
														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=None							,					a_=None							,					a_=None):
							assert isinstance(a_							,					a_), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.')

														for path, path_map in attention_paths_to_split.items():
																					snake_case_           =							old_checkpoint[path]
																					snake_case_           =							old_tensor.shape[0] // 3

																					snake_case_           =							(-1, channels) if len(old_tensor.shape) == 3 else (-1)

																					snake_case_           =							old_tensor.shape[0] // config.get('num_head_channels'							,					1) // 3

																					snake_case_           =							old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
																					snake_case_      ,		snake_case_      ,		snake_case_           =							old_tensor.split(channels // num_heads							,					dim=1)

																					snake_case_           =							query.reshape(a_).squeeze()
																					snake_case_           =							key.reshape(a_).squeeze()
																					snake_case_           =							value.reshape(a_).squeeze()

							for path in paths:
														snake_case_           =							path['new']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														snake_case_           =							new_path.replace('down.'							,					'down_blocks.')
														snake_case_           =							new_path.replace('up.'							,					'up_blocks.')

														if additional_replacements is not None:
																					for replacement in additional_replacements:
																												snake_case_           =							new_path.replace(replacement['old']							,					replacement['new'])

														if "attentions" in new_path:
																					snake_case_           =							old_checkpoint[path['old']].squeeze()
														else:
																					snake_case_           =							old_checkpoint[path['old']]





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['temb.dense.0.weight']
							snake_case_           =							checkpoint['temb.dense.0.bias']
							snake_case_           =							checkpoint['temb.dense.1.weight']
							snake_case_           =							checkpoint['temb.dense.1.bias']

							snake_case_           =							checkpoint['norm_out.weight']
							snake_case_           =							checkpoint['norm_out.bias']

							snake_case_           =							checkpoint['conv_in.weight']
							snake_case_           =							checkpoint['conv_in.bias']
							snake_case_           =							checkpoint['conv_out.weight']
							snake_case_           =							checkpoint['conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''down.{i}.downsample.op.weight'''
																					]
																					snake_case_           =							checkpoint[f'''down.{i}.downsample.op.bias''']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[f'''up.{i}.upsample.conv.bias''']

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['encoder.norm_out.weight']
							snake_case_           =							checkpoint['encoder.norm_out.bias']

							snake_case_           =							checkpoint['encoder.conv_in.weight']
							snake_case_           =							checkpoint['encoder.conv_in.bias']
							snake_case_           =							checkpoint['encoder.conv_out.weight']
							snake_case_           =							checkpoint['encoder.conv_out.bias']

							snake_case_           =							checkpoint['decoder.norm_out.weight']
							snake_case_           =							checkpoint['decoder.norm_out.bias']

							snake_case_           =							checkpoint['decoder.conv_in.weight']
							snake_case_           =							checkpoint['decoder.conv_in.bias']
							snake_case_           =							checkpoint['decoder.conv_out.weight']
							snake_case_           =							checkpoint['decoder.conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.bias'''
																					]

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.bias'''
																					]

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							snake_case_           =							checkpoint['quant_conv.weight']
							snake_case_           =							checkpoint['quant_conv.bias']
							if "quantize.embedding.weight" in checkpoint:
														snake_case_           =							checkpoint['quantize.embedding.weight']
							snake_case_           =							checkpoint['post_quant_conv.weight']
							snake_case_           =							checkpoint['post_quant_conv.bias']

							return new_checkpoint


if __name__ == "__main__":
	lowercase  							= argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)

	parser.add_argument(
	    "--config_file",
	    default=None,
	    type=str,
	    required=True,
	    help="The config json file corresponding to the architecture.",
	)

	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

	lowercase  							= parser.parse_args()
	lowercase  							= torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		lowercase  							= json.loads(f.read())

	# unet case
	lowercase  							= {key.split(".")[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		lowercase  							= convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		lowercase  							= convert_ddpm_checkpoint(checkpoint, config)

	if "ddpm" in config:
		del config["ddpm"]

	if config["_class_name"] == "VQModel":
		lowercase  							= VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		lowercase  							= AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		lowercase  							= UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		lowercase  							= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

		lowercase  							= DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




	lowerCAmelCase													=			"halfcheetah-medium-expert-v2"
	lowerCAmelCase													=			"config.offline"






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

							trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
							snake_case_           =							gpt.pos_emb
							trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict())
							trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict())
							trajectory_transformer.head.load_state_dict(gpt.head.state_dict())

							for i, block in enumerate(gpt.blocks):
														trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict())
														trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict())
														trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict())

														trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict())
														trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict())
														trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict())
														trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict())

							torch.save(trajectory_transformer.state_dict()							,					'pytorch_model.bin')


if __name__ == "__main__":
	lowercase  							= Parser().parse_args("plan")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)





