


"""simple docstring"""




import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCamelCase		= os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCamelCase		= '''    \"\"\"
    Output class for the scheduler\'s step function output.

    Args:
        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
            denoising loop.
        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
            The predicted denoised sample (x_{0}) based on the model output from the current timestep.
            `pred_original_sample` can be used to preview progress or for guidance.
    \"\"\"

    prev_sample: torch.FloatTensor
    pred_original_sample: Optional[torch.FloatTensor] = None
'''
class    lowerCAmelCase      (   unittest.TestCase       ):




		'''simple docstring'''







		def 				__A		(      self							)	->				int:
								SCREAMING_SNAKE_CASE						=   tempfile.mkdtemp()
								os.makedirs(os.path.join(self.diffusers_dir		,			'schedulers/'							)							)
								SCREAMING_SNAKE_CASE						=   self.diffusers_dir
								shutil.copy(
								    os.path.join(snake_case__		,			'src/diffusers/schedulers/scheduling_ddpm.py'							)		,			os.path.join(self.diffusers_dir		,			'schedulers/scheduling_ddpm.py'							)		,			)







		def 				__A		(      self							)	->				Optional[Any]:
								SCREAMING_SNAKE_CASE						=   "src/diffusers"
								shutil.rmtree(self.diffusers_dir							)







		def 				__A		(      self		,			lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__=None							)	->				Union[str, Any]:
								SCREAMING_SNAKE_CASE						=   comment + F'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
														SCREAMING_SNAKE_CASE						=   comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
								SCREAMING_SNAKE_CASE						=   black.Mode(target_versions={black.TargetVersion.PYaa}		,			line_length=119							)
								SCREAMING_SNAKE_CASE						=   black.format_str(snake_case__		,			mode=snake_case__							)
								SCREAMING_SNAKE_CASE						=   os.path.join(self.diffusers_dir		,			'new_code.py'							)
								with open(snake_case__		,			'w'		,			newline='\n'							) as f:
														f.write(snake_case__							)
								if overwrite_result is None:
														self.assertTrue(len(check_copies.is_copy_consistent(snake_case__							)							) == 0							)
								else:
														check_copies.is_copy_consistent(f.name		,			overwrite=snake_case__							)
														with open(snake_case__		,			'r'							) as f:
																				self.assertTrue(f.read()		,			snake_case__							)







		def 				__A		(      self							)	->				List[str]:
								SCREAMING_SNAKE_CASE						=   check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'							)
								self.assertEqual(snake_case__		,			snake_case__							)







		def 				__A		(      self							)	->				Tuple:
								self.check_copy_consistency(
								    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,			'DDPMSchedulerOutput'		,			REFERENCE_CODE + '\n'		,			)

								# With no empty line at the end
								self.check_copy_consistency(
								    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'		,			'DDPMSchedulerOutput'		,			snake_case__		,			)

								# Copy consistency with rename
								self.check_copy_consistency(
								    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,			'TestSchedulerOutput'		,			re.sub('DDPM'		,			'Test'		,			snake_case__							)		,			)

								# Copy consistency with a really long name
								SCREAMING_SNAKE_CASE						=   "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
								self.check_copy_consistency(
								    F'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'		,			F'{long_class_name}SchedulerOutput'		,			re.sub('Bert'		,			snake_case__		,			snake_case__							)		,			)

								# Copy consistency with overwrite
								self.check_copy_consistency(
								    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'		,			'TestSchedulerOutput'		,			snake_case__		,			overwrite_result=re.sub('DDPM'		,			'Test'		,			snake_case__							)		,			)




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCamelCase				=			version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					tuple							,   __lowerCamelCase	:					Path							,   __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					Any							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]=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      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					str							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					bool = False       ):
       snake_case			:  Optional[int]     							= torch.floataa if fpaa else torch.floataa
       if fpaa and torch.cuda.is_available():
              snake_case			:  str     							= "cuda"
       elif fpaa and not torch.cuda.is_available():
              raise ValueError("`float16` model export is only supported on GPUs with CUDA"       )
       else:
              snake_case			:  Tuple     							= "cpu"
       snake_case			:  Union[str, Any]     							= Path(__lowerCamelCase       )

       # VAE DECODER
       snake_case			:  Any     							= AutoencoderKL.from_pretrained(model_path + "/vae"       )
       snake_case			:  Tuple     							= vae_decoder.config.latent_channels
       # forward only through the decoder part
       snake_case			:  Tuple     							= 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__":
   __lowerCamelCase				=			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""")

   __lowerCamelCase				=			parser.parse_args()
   print(args.output_path)
   convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
   print("""SD: Done: ONNX""")





