


'''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  _snake_case       (					A			,   A			,   A			,   A			,   A			,   A			,   A			,   A=False			,   )						->   Union[str, Any]:
							output_path.parent.mkdir(parents=A			,   exist_ok=A	)
							# 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(
														    A			,   A			,   f=output_path.as_posix()			,   input_names=A			,   output_names=A			,   dynamic_axes=A			,   do_constant_folding=A			,   use_external_data_format=A			,   enable_onnx_checker=A			,   opset_version=A			,   )
							else:
														export(
														    A			,   A			,   f=output_path.as_posix()			,   input_names=A			,   output_names=A			,   dynamic_axes=A			,   do_constant_folding=A			,   opset_version=A			,   )


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A			,   A = False	)						->   int:
							lowerCAmelCase__	       =     torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														lowerCAmelCase__	       =     '''cuda'''
							elif fpaa and not torch.cuda.is_available():
														raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''	)
							else:
														lowerCAmelCase__	       =     '''cpu'''
							lowerCAmelCase__	       =     Path(A	)

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




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_       )	->							None:
														lowerCAmelCase__	       =     order

														# a_{0} ... a_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order
														# b_{0} ... b_{k}
														lowerCAmelCase__	       =     [1.0] + [0.0] * order

														# x[n-1] ... x[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order
														# y[n-1] ... y[n-k]
														lowerCAmelCase__	       =     [0.0] * self.order







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							None:
														if len(lowerCamelCase_       ) < self.order:
																					lowerCAmelCase__	       =     [1.0, *a_coeffs]

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected a_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														if len(lowerCamelCase_       ) != self.order + 1:
																					lowerCAmelCase__	       =     (
																					    F"""Expected b_coeffs to have {self.order + 1} elements """
																					    F"""for {self.order}-order filter, got {len(lowerCamelCase_       )}"""
																					)
																					raise ValueError(lowerCamelCase_       )

														lowerCAmelCase__	       =     a_coeffs
														lowerCAmelCase__	       =     b_coeffs







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							float:
														lowerCAmelCase__	       =     0.0

														# Start at index 1 and do index 0 at the end.
														for i in range(1 ,   self.order + 1       ):
																					result += (
																					    self.b_coeffs[i] * self.input_history[i - 1]
																					    - self.a_coeffs[i] * self.output_history[i - 1]
																					)

														lowerCAmelCase__	       =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

														lowerCAmelCase__	       =     self.input_history[:-1]
														lowerCAmelCase__	       =     self.output_history[:-1]

														lowerCAmelCase__	       =     sample
														lowerCAmelCase__	       =     result

														return result