





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class   A							(						__lowercase    ):
      _snake_case				  =42

class   A							(						__lowercase		,   __lowercase    ):



      @register_to_config
      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    int = 6_5536		,	_lowerCAmelCase:    Optional[int] = None		,	_lowerCAmelCase:    int = 2		,	_lowerCAmelCase:    int = 2		,	_lowerCAmelCase:    int = 0		,	_lowerCAmelCase:    str = "fourier"		,	_lowerCAmelCase:    bool = True		,	_lowerCAmelCase:    bool = False		,	_lowerCAmelCase:    float = 0.0		,	_lowerCAmelCase:    Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")		,	_lowerCAmelCase:    Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")		,	_lowerCAmelCase:    Tuple[str] = "UNetMidBlock1D"		,	_lowerCAmelCase:    str = None		,	_lowerCAmelCase:    Tuple[int] = (32, 32, 64)		,	_lowerCAmelCase:    str = None		,	_lowerCAmelCase:    int = 8		,	_lowerCAmelCase:    int = 1		,	_lowerCAmelCase:    bool = False		,	)    ->     int:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =sample_size

         # time
         if time_embedding_type == "fourier":
            UpperCAmelCase_           =GaussianFourierProjection(
                embedding_size=8		,	set_W_to_weight=_lowerCAmelCase		,	log=_lowerCAmelCase		,	flip_sin_to_cos=_lowerCAmelCase							)
            UpperCAmelCase_           =2 * block_out_channels[0]
         elif time_embedding_type == "positional":
            UpperCAmelCase_           =Timesteps(
                block_out_channels[0]		,	flip_sin_to_cos=_lowerCAmelCase		,	downscale_freq_shift=_lowerCAmelCase							)
            UpperCAmelCase_           =block_out_channels[0]

         if use_timestep_embedding:
            UpperCAmelCase_           =block_out_channels[0] * 4
            UpperCAmelCase_           =TimestepEmbedding(
                in_channels=_lowerCAmelCase		,	time_embed_dim=_lowerCAmelCase		,	act_fn=_lowerCAmelCase		,	out_dim=block_out_channels[0]		,	)

         UpperCAmelCase_           =nn.ModuleList([]							)
         UpperCAmelCase_           =None
         UpperCAmelCase_           =nn.ModuleList([]							)
         UpperCAmelCase_           =None

         # down
         UpperCAmelCase_           =in_channels
         for i, down_block_type in enumerate(_lowerCAmelCase							):
            UpperCAmelCase_           =output_channel
            UpperCAmelCase_           =block_out_channels[i]

            if i == 0:
               input_channel += extra_in_channels

            UpperCAmelCase_           =i == len(_lowerCAmelCase							) - 1

            UpperCAmelCase_           =get_down_block(
                _lowerCAmelCase		,	num_layers=_lowerCAmelCase		,	in_channels=_lowerCAmelCase		,	out_channels=_lowerCAmelCase		,	temb_channels=block_out_channels[0]		,	add_downsample=not is_final_block or downsample_each_block		,	)
            self.down_blocks.append(_lowerCAmelCase							)

         # mid
         UpperCAmelCase_           =get_mid_block(
             _lowerCAmelCase		,	in_channels=block_out_channels[-1]		,	mid_channels=block_out_channels[-1]		,	out_channels=block_out_channels[-1]		,	embed_dim=block_out_channels[0]		,	num_layers=_lowerCAmelCase		,	add_downsample=_lowerCAmelCase		,	)

         # up
         UpperCAmelCase_           =list(reversed(_lowerCAmelCase							)							)
         UpperCAmelCase_           =reversed_block_out_channels[0]
         if out_block_type is None:
            UpperCAmelCase_           =out_channels
         else:
            UpperCAmelCase_           =block_out_channels[0]

         for i, up_block_type in enumerate(_lowerCAmelCase							):
            UpperCAmelCase_           =output_channel
            UpperCAmelCase_           =(
                reversed_block_out_channels[i + 1] if i < len(_lowerCAmelCase							) - 1 else final_upsample_channels
            )

            UpperCAmelCase_           =i == len(_lowerCAmelCase							) - 1

            UpperCAmelCase_           =get_up_block(
                _lowerCAmelCase		,	num_layers=_lowerCAmelCase		,	in_channels=_lowerCAmelCase		,	out_channels=_lowerCAmelCase		,	temb_channels=block_out_channels[0]		,	add_upsample=not is_final_block		,	)
            self.up_blocks.append(_lowerCAmelCase							)
            UpperCAmelCase_           =output_channel

         # out
         UpperCAmelCase_           =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,	32							)
         UpperCAmelCase_           =get_out_block(
             out_block_type=_lowerCAmelCase		,	num_groups_out=_lowerCAmelCase		,	embed_dim=block_out_channels[0]		,	out_channels=_lowerCAmelCase		,	act_fn=_lowerCAmelCase		,	fc_dim=block_out_channels[-1] // 4		,	)






      def 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    Union[torch.Tensor, float, int]		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[UNetaDOutput, Tuple]:
         '''simple docstring'''

         UpperCAmelCase_           =timestep
         if not torch.is_tensor(_lowerCAmelCase							):
            UpperCAmelCase_           =torch.tensor([timesteps]		,	dtype=torch.long		,	device=sample.device							)
         elif torch.is_tensor(_lowerCAmelCase							) and len(timesteps.shape							) == 0:
            UpperCAmelCase_           =timesteps[None].to(sample.device							)

         UpperCAmelCase_           =self.time_proj(_lowerCAmelCase							)
         if self.config.use_timestep_embedding:
            UpperCAmelCase_           =self.time_mlp(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =timestep_embed[..., None]
            UpperCAmelCase_           =timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
            UpperCAmelCase_           =timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

         # 2. down
         UpperCAmelCase_           =()
         for downsample_block in self.down_blocks:
            UpperCAmelCase_    ,							UpperCAmelCase_           =downsample_block(hidden_states=_lowerCAmelCase		,	temb=_lowerCAmelCase							)
            down_block_res_samples += res_samples

         # 3. mid
         if self.mid_block:
            UpperCAmelCase_           =self.mid_block(_lowerCAmelCase		,	_lowerCAmelCase							)

         # 4. up
         for i, upsample_block in enumerate(self.up_blocks							):
            UpperCAmelCase_           =down_block_res_samples[-1:]
            UpperCAmelCase_           =down_block_res_samples[:-1]
            UpperCAmelCase_           =upsample_block(_lowerCAmelCase		,	res_hidden_states_tuple=_lowerCAmelCase		,	temb=_lowerCAmelCase							)

         # 5. post-process
         if self.out_block:
            UpperCAmelCase_           =self.out_block(_lowerCAmelCase		,	_lowerCAmelCase							)

         if not return_dict:
            return (sample,)

         return UNetaDOutput(sample=_lowerCAmelCase							)












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 		_a       ( a					:Union[str, Any]      ,				a					:tuple      ,				a					:Path      ,				a					:List[Any]      ,				a					:Union[str, Any]      ,				a					:Optional[int]      ,				a					:List[Any]      ,				a					:Union[str, Any]=False      ,				)				->     Any:
 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 		_a       ( a					:str      ,				a					:str      ,				a					:int      ,				a					:bool = False					)				->     Union[str, Any]:
 a						    = torch.floataa if fpaa else torch.floataa
 if fpaa and torch.cuda.is_available():
  a						    = '''cuda'''
 elif fpaa and not torch.cuda.is_available():
  raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''					)
 else:
  a						    = '''cpu'''
 a						    = Path(a					)

 # VAE DECODER
 a						    = AutoencoderKL.from_pretrained(model_path + '''/vae'''					)
 a						    = vae_decoder.config.latent_channels
 # forward only through the decoder part
 a						    = 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")


