



'''simple docstring'''




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


UpperCamelCase__ :			Dict												=				version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')


def        lowerCAmelCase_     (			_lowerCamelCase:     Optional[int] ,   _lowerCamelCase:     tuple ,   _lowerCamelCase:     Path ,   _lowerCamelCase:     Union[str, Any] ,   _lowerCamelCase:     Tuple ,   _lowerCamelCase:     Any ,   _lowerCamelCase:     Dict ,   _lowerCamelCase:     Optional[int]=False ,   ):
		output_path.parent.mkdir(parents=_lowerCamelCase ,   exist_ok=_lowerCamelCase      )
		# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
		# so we check the torch version for backwards compatibility
		if is_torch_less_than_1_11:
				export(
				    _lowerCamelCase ,   _lowerCamelCase ,   f=output_path.as_posix() ,   input_names=_lowerCamelCase ,   output_names=_lowerCamelCase ,   dynamic_axes=_lowerCamelCase ,   do_constant_folding=_lowerCamelCase ,   use_external_data_format=_lowerCamelCase ,   enable_onnx_checker=_lowerCamelCase ,   opset_version=_lowerCamelCase ,   )
		else:
				export(
				    _lowerCamelCase ,   _lowerCamelCase ,   f=output_path.as_posix() ,   input_names=_lowerCamelCase ,   output_names=_lowerCamelCase ,   dynamic_axes=_lowerCamelCase ,   do_constant_folding=_lowerCamelCase ,   opset_version=_lowerCamelCase ,   )


@torch.no_grad()
def        lowerCAmelCase_     (			_lowerCamelCase:     str ,   _lowerCamelCase:     str ,   _lowerCamelCase:     int ,   _lowerCamelCase:     bool = False      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.floataa if fpaa else torch.floataa
		if fpaa and torch.cuda.is_available():
				__SCREAMING_SNAKE_CASE :	int					 =	"""cuda"""
		elif fpaa and not torch.cuda.is_available():
				raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""      )
		else:
				__SCREAMING_SNAKE_CASE :	int					 =	"""cpu"""
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	Path(_lowerCamelCase      )

		# VAE DECODER
		__SCREAMING_SNAKE_CASE :	Dict					 =	AutoencoderKL.from_pretrained(model_path + """/vae"""      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	vae_decoder.config.latent_channels
		# forward only through the decoder part
		__SCREAMING_SNAKE_CASE :	int					 =	vae_decoder.decode
		onnx_export(
		    _lowerCamelCase ,   model_args=(
		        torch.randn(1 ,   _lowerCamelCase ,   25 ,   25      ).to(device=_lowerCamelCase ,   dtype=_lowerCamelCase      ),
		        False,
		    ) ,   output_path=output_path / """vae_decoder""" / """model.onnx""" ,   ordered_input_names=["""latent_sample""", """return_dict"""] ,   output_names=["""sample"""] ,   dynamic_axes={
		        """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
		    } ,   opset=_lowerCamelCase ,   )
		del vae_decoder


if __name__ == "__main__":
						UpperCamelCase__ :			Tuple												=				argparse.ArgumentParser()

						parser.add_argument(
						    '''--model_path''',
						    type=str,
						    required=True,
						    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
						)

						parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
						parser.add_argument(
						    '''--opset''',
						    default=14,
						    type=int,
						    help='''The version of the ONNX operator set to use.''',
						)
						parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

						UpperCamelCase__ :			str												=				parser.parse_args()
						print(args.output_path)
						convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
						print('''SD: Done: ONNX''')




'''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()