






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
class 			UpperCAmelCase_						(					lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					torch.FloatTensor
      UpperCamelCase_				:					torch.FloatTensor
      UpperCamelCase_				:					Optional[torch.FloatTensor]           =None







class 			UpperCAmelCase_						(					lowercase,	lowercase ):



      """simple docstring"""
      UpperCamelCase_				:					Union[str, Any]           =2
      @register_to_config
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_ = 0.02    ,				SCREAMING_SNAKE_CASE_ = 100    ,				SCREAMING_SNAKE_CASE_ = 1.007    ,				SCREAMING_SNAKE_CASE_ = 80    ,				SCREAMING_SNAKE_CASE_ = 0.05    ,				SCREAMING_SNAKE_CASE_ = 50    ,				)				->      Optional[Any]:
          UpperCamelCase       :List[str]								=   sigma_max

          # setable values
          UpperCamelCase       :Dict								=   None
          UpperCamelCase       :Union[str, Any]								=   None
          UpperCamelCase       :List[str]								=   None  # sigma(t_i)
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      Optional[Any]:
          return sample
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      Optional[Any]:
          UpperCamelCase       :str								=   num_inference_steps
          UpperCamelCase       :Union[str, Any]								=   np.arange(0    ,				self.num_inference_steps )[::-1].copy()
          UpperCamelCase       :Optional[int]								=   torch.from_numpy(__lowerCAmelCase ).to(__lowerCAmelCase )
          UpperCamelCase       :Optional[Any]								=   [
              (
                  self.config.sigma_max**2
                  * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
              )
              for i in self.timesteps
          ]
          UpperCamelCase       :Union[str, Any]								=   torch.tensor(__lowerCAmelCase    ,				dtype=torch.floataa    ,				device=__lowerCAmelCase )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None )				->      int:



          if self.config.s_min <= sigma <= self.config.s_max:
              UpperCamelCase       :int								=   min(self.config.s_churn / self.num_inference_steps    ,				2**0.5 - 1 )
          else:
              UpperCamelCase       :Any								=   0

          # sample eps ~ N(0, S_noise^2 * I)
          UpperCamelCase       :List[Any]								=   self.config.s_noise * randn_tensor(sample.shape    ,				generator=__lowerCAmelCase ).to(sample.device )
          UpperCamelCase       :Tuple								=   sigma + gamma * sigma
          UpperCamelCase       :Union[str, Any]								=   sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

          return sample_hat, sigma_hat
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = True    ,				)				->      int:
          UpperCamelCase       :Tuple								=   sample_hat + sigma_hat * model_output
          UpperCamelCase       :Optional[int]								=   (sample_hat - pred_original_sample) / sigma_hat
          UpperCamelCase       :Union[str, Any]								=   sample_hat + (sigma_prev - sigma_hat) * derivative

          if not return_dict:
              return (sample_prev, derivative)

          return KarrasVeOutput(
              prev_sample=__lowerCAmelCase    ,				derivative=__lowerCAmelCase    ,				pred_original_sample=__lowerCAmelCase )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = True    ,				)				->      Optional[int]:
          UpperCamelCase       :Any								=   sample_prev + sigma_prev * model_output
          UpperCamelCase       :List[str]								=   (sample_prev - pred_original_sample) / sigma_prev
          UpperCamelCase       :Optional[Any]								=   sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

          if not return_dict:
              return (sample_prev, derivative)

          return KarrasVeOutput(
              prev_sample=__lowerCAmelCase    ,				derivative=__lowerCAmelCase    ,				pred_original_sample=__lowerCAmelCase )




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )				->      Tuple:
          raise NotImplementedError()






"""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""")



