"""simple docstring"""



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

import black


__lowercase		:		int														=							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.
__lowercase		:		str														=							"    \"\"\"\n    Output class for the scheduler's step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"\"\"\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n"



class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[Any]    )    ->       List[Any]:
         __snake_case				=							tempfile.mkdtemp()
         os.makedirs(os.path.join(self.diffusers_dir	,      '''schedulers/'''    )    )
         __snake_case				=							self.diffusers_dir
         shutil.copy(
             os.path.join(A_	,      '''src/diffusers/schedulers/scheduling_ddpm.py'''    )	,      os.path.join(self.diffusers_dir	,      '''schedulers/scheduling_ddpm.py'''    )	,      )
    def  lowercase					(		self							:     int    )    ->       Union[str, Any]:
         __snake_case				=							'''src/diffusers'''
         shutil.rmtree(self.diffusers_dir    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Optional[Any]	,      A_							:     Tuple	,      A_							:     List[str]	,      A_							:     Any=None    )    ->       List[Any]:
         __snake_case				=							comment + f"\nclass {class_name}(nn.Module):\n" + class_code
         if overwrite_result is not None:
              __snake_case				=							comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
         __snake_case				=							black.Mode(target_versions={black.TargetVersion.PYaa}	,      line_length=119    )
         __snake_case				=							black.format_str(A_	,      mode=A_    )
         __snake_case				=							os.path.join(self.diffusers_dir	,      '''new_code.py'''    )
         with open(A_	,      '''w'''	,      newline='''\n'''    ) as f:
              f.write(A_    )
         if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(A_    )    ) == 0    )
         else:
              check_copies.is_copy_consistent(f.name	,      overwrite=A_    )
              with open(A_	,      '''r'''    ) as f:
                   self.assertTrue(f.read()	,      A_    )
    def  lowercase					(		self							:     str    )    ->       Dict:
         __snake_case				=							check_copies.find_code_in_diffusers('''schedulers.scheduling_ddpm.DDPMSchedulerOutput'''    )
         self.assertEqual(A_	,      A_    )







    def  lowercase					(		self							:     List[str]    )    ->       Any:
         # Base copy consistency
         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'''	,      A_	,      )

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

         # Copy consistency with a really long name
         __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'''	,      A_	,      A_    )	,      )

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



import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowercase		:		int														=							version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case,      snake_case=False,      ):
     output_path.parent.mkdir(parents=snake_case,      exist_ok=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(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      use_external_data_format=snake_case,      enable_onnx_checker=snake_case,      opset_version=snake_case,      )
     else:
          export(
              snake_case,      snake_case,      f=output_path.as_posix(),      input_names=snake_case,      output_names=snake_case,      dynamic_axes=snake_case,      do_constant_folding=snake_case,      opset_version=snake_case,      )





@torch.no_grad()
def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case = False):
     __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(snake_case)

     # 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(
         snake_case,      model_args=(
             torch.randn(1,      snake_case,      25,      25).to(device=snake_case,      dtype=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=snake_case,      )
     del vae_decoder


if __name__ == "__main__":
       __lowercase		:		Union[str, Any]														=							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")

       __lowercase		:		List[Any]														=							parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("SD: Done: ONNX")