


'''simple docstring'''





import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


UpperCAmelCase__  			=				version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: tuple,_SCREAMING_SNAKE_CASE					: Path,_SCREAMING_SNAKE_CASE					: Tuple,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: List[Any],_SCREAMING_SNAKE_CASE					: List[Any]=False,):






			"""simple docstring"""




			output_path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE,exist_ok=_SCREAMING_SNAKE_CASE )
			# 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(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,f=output_path.as_posix(),input_names=_SCREAMING_SNAKE_CASE,output_names=_SCREAMING_SNAKE_CASE,dynamic_axes=_SCREAMING_SNAKE_CASE,do_constant_folding=_SCREAMING_SNAKE_CASE,use_external_data_format=_SCREAMING_SNAKE_CASE,enable_onnx_checker=_SCREAMING_SNAKE_CASE,opset_version=_SCREAMING_SNAKE_CASE,)
			else:
						export(
						    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,f=output_path.as_posix(),input_names=_SCREAMING_SNAKE_CASE,output_names=_SCREAMING_SNAKE_CASE,dynamic_axes=_SCREAMING_SNAKE_CASE,do_constant_folding=_SCREAMING_SNAKE_CASE,opset_version=_SCREAMING_SNAKE_CASE,)






@torch.no_grad()
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: bool = False ):






			"""simple docstring"""




			__A=	torch.floataa if fpaa else torch.floataa
			if fpaa and torch.cuda.is_available():
						__A=	'cuda'
			elif fpaa and not torch.cuda.is_available():
						raise ValueError('`float16` model export is only supported on GPUs with CUDA' )
			else:
						__A=	'cpu'
			__A=	Path(_SCREAMING_SNAKE_CASE )

			# VAE DECODER
			__A=	AutoencoderKL.from_pretrained(model_path + '/vae' )
			__A=	vae_decoder.config.latent_channels
			# forward only through the decoder part
			__A=	vae_decoder.decode
			onnx_export(
			    _SCREAMING_SNAKE_CASE,model_args=(
			        torch.randn(1,_SCREAMING_SNAKE_CASE,25,25 ).to(device=_SCREAMING_SNAKE_CASE,dtype=_SCREAMING_SNAKE_CASE ),
			        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=_SCREAMING_SNAKE_CASE,)
			del vae_decoder


if __name__ == "__main__":
					UpperCAmelCase__  			=				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=1_4,
					    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__  			=				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'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


if __name__ == "__main__":
					print(F"""{solution() = }""")



