



import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
		# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
		# setup instructions, if using on-demand hardware

		# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
		# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
		# Throw an error if user passes both BYO and on-demand cluster args
		# Otherwise, use default values
		snake_case    :						List[str]  =						argparse.ArgumentParser()
		parser.add_argument('--user', type=str, default='ubuntu')
		parser.add_argument('--host', type=str, default='localhost')
		parser.add_argument('--key_path', type=str, default=None)
		parser.add_argument('--instance', type=str, default='V100:1')
		parser.add_argument('--provider', type=str, default='cheapest')
		parser.add_argument('--use_spot', type=bool, default=False)
		parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
		snake_case    :						List[str]  =						parser.parse_known_args()
		if args.host != "localhost":
				if args.instance != "V100:1" or args.provider != "cheapest":
						raise ValueError('Cannot specify both BYO and on-demand cluster args')
				snake_case    :						int  =						rh.cluster(
				    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
				)
		else:
				snake_case    :						Dict  =						rh.cluster(
				    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
				)
		snake_case    :						int  =						args.example.rsplit('/', 1)[0]

		# Set up remote environment
		cluster.install_packages(['pip:./'])  # Installs transformers from local source
		# Note transformers is copied into the home directory on the remote machine, so we can install from there
		cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
		cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

		# Run example. You can bypass the CLI wrapper and paste your own code here.
		cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

		# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
		# from my_script... import train
		# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
		# launch_train_gpu = rh.function(fn=train,
		#                                system=gpu,
		#                                reqs=reqs,
		#                                name='train_bert_glue')
		#
		# We can pass in arguments just like we would to a function:
		# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
		#                  stream_logs=True)




import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							List[str]						    = checkpoint

						A__	:							Dict						    = {}

						A__	:							Union[str, Any]						    = vae_state_dict["encoder.conv_in.weight"]
						A__	:							int						    = vae_state_dict["encoder.conv_in.bias"]
						A__	:							List[Any]						    = vae_state_dict["encoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.conv_out.bias"]
						A__	:							int						    = vae_state_dict["encoder.norm_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.norm_out.bias"]

						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_in.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.conv_in.bias"]
						A__	:							str						    = vae_state_dict["decoder.conv_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["decoder.conv_out.bias"]
						A__	:							Union[str, Any]						    = vae_state_dict["decoder.norm_out.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["decoder.norm_out.bias"]

						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.bias"]
						A__	:							Dict						    = vae_state_dict["post_quant_conv.weight"]
						A__	:							Union[str, Any]						    = vae_state_dict["post_quant_conv.bias"]

						# Retrieves the keys for the encoder down blocks only
						A__	:							str						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "encoder.down" in layer}       )
						A__	:							Optional[int]						    = {
						    layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						# Retrieves the keys for the decoder up blocks only
						A__	:							Any						    = len({".".join(layer.split("."       )[:3]       ) for layer in vae_state_dict if "decoder.up" in layer}       )
						A__	:							Any						    = {
						    layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(__lowercase       )
						}

						for i in range(__lowercase       ):
												A__	:							Any						    = [key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]

												if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
																		A__	:							Any						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.weight'       )
																		A__	:							Optional[int]						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.bias'       )

												A__	:							Any						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "encoder.mid.block" in key]
						A__	:							Any						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Optional[int]						    = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

												A__	:							Optional[Any]						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							str						    = [key for key in vae_state_dict if "encoder.mid.attn" in key]
						A__	:							List[Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							Optional[int]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )

						for i in range(__lowercase       ):
												A__	:							Dict						    = num_up_blocks - 1 - i
												A__	:							int						    = [
												    key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
												]

												if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
																		A__	:							Tuple						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.weight'
																		]
																		A__	:							List[Any]						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.bias'
																		]

												A__	:							str						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							List[str]						    = {"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							int						    = [key for key in vae_state_dict if "decoder.mid.block" in key]
						A__	:							Optional[int]						    = 2
						for i in range(1							,     num_mid_res_blocks + 1       ):
												A__	:							Union[str, Any]						    = [key for key in mid_resnets if F'decoder.mid.block_{i}' in key]

												A__	:							Dict						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							Optional[Any]						    = [key for key in vae_state_dict if "decoder.mid.attn" in key]
						A__	:							Union[str, Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							List[str]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )
						return new_checkpoint
def 	snake_case__       (					__lowercase							,     __lowercase							,     )		->		Tuple:







						"""simple docstring"""

						A__	:							List[str]						    = requests.get(
						    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"       )
						A__	:							Dict						    = io.BytesIO(r.content       )

						A__	:							str						    = OmegaConf.load(__lowercase       )
						A__	:							Any						    = 5_1_2
						A__	:							Optional[Any]						    = "cuda" if torch.cuda.is_available() else "cpu"
						if checkpoint_path.endswith("safetensors"       ):
												from safetensors import safe_open

												A__	:							str						    = {}
												with safe_open(__lowercase							,     framework="pt"							,     device="cpu"       ) as f:
																		for key in f.keys():
																								A__	:							List[str]						    = f.get_tensor(__lowercase       )
						else:
												A__	:							str						    = torch.load(__lowercase							,     map_location=__lowercase       )["state_dict"]

						# Convert the VAE model.
						A__	:							List[str]						    = create_vae_diffusers_config(__lowercase							,     image_size=__lowercase       )
						A__	:							List[Any]						    = custom_convert_ldm_vae_checkpoint(__lowercase							,     __lowercase       )

						A__	:							Dict						    = AutoencoderKL(**__lowercase       )
						vae.load_state_dict(__lowercase       )
						vae.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						List[Any]  =						argparse.ArgumentParser()

		parser.add_argument('--vae_pt_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')
		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the VAE.pt to convert.')

		snake_case    :						int  =						parser.parse_args()

		vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)