





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   lowerCamelCase__       (       UpperCamelCase__     :    List[Any]    ,      UpperCamelCase__     :    tuple    ,      UpperCamelCase__     :    Path    ,      UpperCamelCase__     :    Optional[Any]    ,      UpperCamelCase__     :    str    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Tuple=False    ,      )     ->	Optional[int]:



     '''simple docstring'''




     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   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    bool = False  )     ->	Any:



     '''simple docstring'''




     _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(__A  )

     # 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(
         __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""")










import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class      UpperCamelCase_						:




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return self.get_dummy_input()




					@property
					def 		lowerCAmelCase     (						self					)						->							Optional[Any]:
										if self.block_type == "down":
															return (4, 32, 16, 16)
										elif self.block_type == "mid":
															return (4, 32, 32, 32)
										elif self.block_type == "up":
															return (4, 32, 64, 64)

										raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.'''					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=True			,  lowerCAmelCase_=False			,  lowerCAmelCase_=False			,  lowerCAmelCase_=False			,  )						->							List[str]:
										_snake_case													=			4
										_snake_case													=			32
										_snake_case													=			(32, 32)

										_snake_case													=			torch.manual_seed(0					)
										_snake_case													=			torch.device(lowerCAmelCase_					)
										_snake_case													=			(batch_size, num_channels) + sizes
										_snake_case													=			randn_tensor(lowerCAmelCase_			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					)
										_snake_case													=			{'hidden_states': hidden_states}

										if include_temb:
															_snake_case													=			128
															_snake_case													=			randn_tensor((batch_size, temb_channels)			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					)

										if include_res_hidden_states_tuple:
															_snake_case													=			torch.manual_seed(1					)
															_snake_case													=			(randn_tensor(lowerCAmelCase_			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					),)

										if include_encoder_hidden_states:
															_snake_case													=			floats_tensor((batch_size, 32, 32)					).to(lowerCAmelCase_					)

										if include_skip_sample:
															_snake_case													=			randn_tensor(((batch_size, 3) + sizes)			,  generator=lowerCAmelCase_			,  device=lowerCAmelCase_					)

										return dummy_input




					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case													=			{
										    'in_channels': 32,
										    'out_channels': 32,
										    'temb_channels': 128,
										}
										if self.block_type == "up":
															_snake_case													=			32

										if self.block_type == "mid":
															init_dict.pop('out_channels'					)

										_snake_case													=			self.dummy_input
										return init_dict, inputs_dict




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Optional[int]:
										_snake_case							,       _snake_case													=			self.prepare_init_args_and_inputs_for_common()
										_snake_case													=			self.block_class(**lowerCAmelCase_					)
										unet_block.to(lowerCAmelCase_					)
										unet_block.eval()

										with torch.no_grad():
															_snake_case													=			unet_block(**lowerCAmelCase_					)

										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			output[0]

										self.assertEqual(output.shape			,  self.output_shape					)

										_snake_case													=			output[0, -1, -3:, -3:]
										_snake_case													=			torch.tensor(lowerCAmelCase_					).to(lowerCAmelCase_					)
										assert torch_all_close(output_slice.flatten()			,  lowerCAmelCase_			,  atol=5E-3					)






					@unittest.skipIf(torch_device == 'mps'			,  'Training is not supported in mps'					)
					def 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case							,       _snake_case													=			self.prepare_init_args_and_inputs_for_common()
										_snake_case													=			self.block_class(**lowerCAmelCase_					)
										model.to(lowerCAmelCase_					)
										model.train()
										_snake_case													=			model(**lowerCAmelCase_					)

										if isinstance(lowerCAmelCase_			,  lowerCAmelCase_					):
															_snake_case													=			output[0]

										_snake_case													=			torch.device(lowerCAmelCase_					)
										_snake_case													=			randn_tensor(output.shape			,  device=lowerCAmelCase_					)
										_snake_case													=			torch.nn.functional.mse_loss(lowerCAmelCase_			,  lowerCAmelCase_					)
										loss.backward()



