




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 		__A   (      unittest.TestCase      ):

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Tuple										=UNetaDModel(
										    block_out_channels=(32, 64)     ,			layers_per_block=2     ,			sample_size=32     ,			in_channels=3     ,			out_channels=3     ,			down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,			up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,			)
										return model


						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.dummy_uncond_unet
										__magic_name__						:						Tuple										=KarrasVeScheduler()

										__magic_name__						:						List[str]										=KarrasVePipeline(unet=__snake_case     ,			scheduler=__snake_case			)
										pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						str										=torch.manual_seed(0			)
										__magic_name__						:						int										=pipe(num_inference_steps=2     ,			generator=__snake_case     ,			output_type="""numpy"""			).images

										__magic_name__						:						Optional[Any]										=torch.manual_seed(0			)
										__magic_name__						:						Union[str, Any]										=pipe(num_inference_steps=2     ,			generator=__snake_case     ,			output_type="""numpy"""     ,			return_dict=__snake_case			)[0]

										__magic_name__						:						Tuple										=image[0, -3:, -3:, -1]
										__magic_name__						:						List[str]										=image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										__magic_name__						:						List[Any]										=np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2




@slow
@require_torch
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Dict										="""google/ncsnpp-celebahq-256"""
										__magic_name__						:						str										=UNetaDModel.from_pretrained(__snake_case			)
										__magic_name__						:						int										=KarrasVeScheduler()

										__magic_name__						:						List[Any]										=KarrasVePipeline(unet=__snake_case     ,			scheduler=__snake_case			)
										pipe.to(__snake_case			)
										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						List[str]										=torch.manual_seed(0			)
										__magic_name__						:						List[str]										=pipe(num_inference_steps=20     ,			generator=__snake_case     ,			output_type="""numpy"""			).images

										__magic_name__						:						Optional[int]										=image[0, -3:, -3:, -1]
										assert image.shape == (1, 2_56, 2_56, 3)
										__magic_name__						:						str										=np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]			)

										assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2











'''simple docstring'''




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


a__					:			Optional[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				)   ->Optional[int]:
	output_path.parent.mkdir(parents=UpperCAmelCase_					,				exist_ok=UpperCAmelCase_							)
	# 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(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				use_external_data_format=UpperCAmelCase_					,				enable_onnx_checker=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)
	else:
		export(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)




@torch.no_grad()
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = False							)   ->Union[str, Any]:
	snake_case__	       =   torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		snake_case__	       =   'cuda'
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'							)
	else:
		snake_case__	       =   'cpu'
	snake_case__	       =   Path(UpperCAmelCase_							)

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


if __name__ == "__main__":
			a__					:			Optional[int]          =     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''')

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



