






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


__snake_case												=  logging.get_logger(__name__)  # pylint: disable=invalid-name







class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )				->      int:
          super().__init__()
          self.register_modules(unet=__lowerCAmelCase    ,				scheduler=__lowerCAmelCase )




      @torch.no_grad()
      def __call__(				self    ,				SCREAMING_SNAKE_CASE_ = 1    ,				SCREAMING_SNAKE_CASE_ = 100    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = True    ,				)				->      Optional[Any]:



          if audio_length_in_s is None:
              UpperCamelCase       :List[Any]								=   self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCamelCase       :List[str]								=   audio_length_in_s * self.unet.config.sample_rate

          UpperCamelCase       :str								=   2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
                  F''' {3 * down_scale_factor / self.unet.config.sample_rate}.''' )

          UpperCamelCase       :Optional[Any]								=   int(__lowerCAmelCase )
          if sample_size % down_scale_factor != 0:
              UpperCamelCase       :Any								=   (
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
                  F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
                  ''' process.''' )
          UpperCamelCase       :Tuple								=   int(__lowerCAmelCase )

          UpperCamelCase       :Optional[Any]								=   next(iter(self.unet.parameters() ) ).dtype
          UpperCamelCase       :Any								=   (batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(__lowerCAmelCase    ,				__lowerCAmelCase ) and len(__lowerCAmelCase ) != batch_size:
              raise ValueError(
                  F'''You have passed a list of generators of length {len(__lowerCAmelCase )}, but requested an effective batch'''
                  F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

          UpperCamelCase       :int								=   randn_tensor(__lowerCAmelCase    ,				generator=__lowerCAmelCase    ,				device=self.device    ,				dtype=__lowerCAmelCase )

          # set step values
          self.scheduler.set_timesteps(__lowerCAmelCase    ,				device=audio.device )
          UpperCamelCase       :List[str]								=   self.scheduler.timesteps.to(__lowerCAmelCase )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCamelCase       :Dict								=   self.unet(__lowerCAmelCase    ,				__lowerCAmelCase ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCamelCase       :Dict								=   self.scheduler.step(__lowerCAmelCase    ,				__lowerCAmelCase    ,				__lowerCAmelCase ).prev_sample

          UpperCamelCase       :str								=   audio.clamp(-1    ,				1 ).float().cpu().numpy()

          UpperCamelCase       :Dict								=   audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=__lowerCAmelCase )






"""simple docstring"""






import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


__lowerCAmelCase    :       Dict					     =version.parse(version.parse(torch.__version__).base_version) < version.parse("""1.11""")


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :tuple					,		lowerCAmelCase__     :Path					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :int					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :List[str]=False					,		) ->    Union[str, Any]:
      '''simple docstring'''


      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		) ->    str:
      '''simple docstring'''


      lowercase														=	torch.floataa if fpaa else torch.floataa
      if fpaa and torch.cuda.is_available():
            lowercase														=	"""cuda"""
      elif fpaa and not torch.cuda.is_available():
            raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""		)
      else:
            lowercase														=	"""cpu"""
      lowercase														=	Path(lowerCAmelCase__		)

      # VAE DECODER
      lowercase														=	AutoencoderKL.from_pretrained(model_path + """/vae"""		)
      lowercase														=	vae_decoder.config.latent_channels
      # forward only through the decoder part
      lowercase														=	vae_decoder.decode
      onnx_export(
          lowerCAmelCase__					,		model_args=(
              torch.randn(1					,		lowerCAmelCase__					,		2_5					,		2_5		).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    :       Tuple					     =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=1_4,
           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    :       Dict					     =parser.parse_args()
       print(args.output_path)
       convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
       print("""SD: Done: ONNX""")



