
'''simple docstring'''







import argparse
import os
import shutil
from pathlib import Path

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

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline


_lowercase :  List[Any]    							=       version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: tuple      ,      __SCREAMING_SNAKE_CASE					: Path      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=False      ,      ):



						"""simple docstring"""





						output_path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE      ,      exist_ok=__SCREAMING_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(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      f=output_path.as_posix()      ,      input_names=__SCREAMING_SNAKE_CASE      ,      output_names=__SCREAMING_SNAKE_CASE      ,      dynamic_axes=__SCREAMING_SNAKE_CASE      ,      do_constant_folding=__SCREAMING_SNAKE_CASE      ,      use_external_data_format=__SCREAMING_SNAKE_CASE      ,      enable_onnx_checker=__SCREAMING_SNAKE_CASE      ,      opset_version=__SCREAMING_SNAKE_CASE      ,      )
						else:
												export(
												    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      f=output_path.as_posix()      ,      input_names=__SCREAMING_SNAKE_CASE      ,      output_names=__SCREAMING_SNAKE_CASE      ,      dynamic_axes=__SCREAMING_SNAKE_CASE      ,      do_constant_folding=__SCREAMING_SNAKE_CASE      ,      opset_version=__SCREAMING_SNAKE_CASE      ,      )
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: bool = False							):



						"""simple docstring"""





						lowercase_	:     List[Any]													=       torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												lowercase_	:     Tuple													=       '''cuda'''
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('''`float16` model export is only supported on GPUs with CUDA'''							)
						else:
												lowercase_	:     Any													=       '''cpu'''
						lowercase_	:     List[Any]													=       StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      ,      torch_dtype=__SCREAMING_SNAKE_CASE							).to(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     int													=       Path(__SCREAMING_SNAKE_CASE							)

						# TEXT ENCODER
						lowercase_	:     Any													=       pipeline.text_encoder.config.max_position_embeddings
						lowercase_	:     Union[str, Any]													=       pipeline.text_encoder.config.hidden_size
						lowercase_	:     Any													=       pipeline.tokenizer(
						    '''A sample prompt'''      ,      padding='''max_length'''      ,      max_length=pipeline.tokenizer.model_max_length      ,      truncation=__SCREAMING_SNAKE_CASE      ,      return_tensors='''pt'''      ,      )
						onnx_export(
						    pipeline.text_encoder      ,      model_args=(text_input.input_ids.to(device=__SCREAMING_SNAKE_CASE      ,      dtype=torch.intaa							))      ,      output_path=output_path / '''text_encoder''' / '''model.onnx'''      ,      ordered_input_names=['''input_ids''']      ,      output_names=['''last_hidden_state''', '''pooler_output''']      ,      dynamic_axes={
						        '''input_ids''': {0: '''batch''', 1: '''sequence'''},
						    }      ,      opset=__SCREAMING_SNAKE_CASE      ,      )
						del pipeline.text_encoder

						# UNET
						lowercase_	:     str													=       pipeline.unet.config.in_channels
						lowercase_	:     str													=       pipeline.unet.config.sample_size
						lowercase_	:     List[Any]													=       output_path / '''unet''' / '''model.onnx'''
						onnx_export(
						    pipeline.unet      ,      model_args=(
						        torch.randn(2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        torch.randn(2							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        torch.randn(2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        False,
						    )      ,      output_path=__SCREAMING_SNAKE_CASE      ,      ordered_input_names=['''sample''', '''timestep''', '''encoder_hidden_states''', '''return_dict''']      ,      output_names=['''out_sample''']      ,      dynamic_axes={
						        '''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
						        '''timestep''': {0: '''batch'''},
						        '''encoder_hidden_states''': {0: '''batch''', 1: '''sequence'''},
						    }      ,      opset=__SCREAMING_SNAKE_CASE      ,      use_external_data_format=__SCREAMING_SNAKE_CASE      ,      )
						lowercase_	:     str													=       str(unet_path.absolute().as_posix()							)
						lowercase_	:     Optional[Any]													=       os.path.dirname(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     List[str]													=       onnx.load(__SCREAMING_SNAKE_CASE							)
						# clean up existing tensor files
						shutil.rmtree(__SCREAMING_SNAKE_CASE							)
						os.mkdir(__SCREAMING_SNAKE_CASE							)
						# collate external tensor files into one
						onnx.save_model(
						    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      save_as_external_data=__SCREAMING_SNAKE_CASE      ,      all_tensors_to_one_file=__SCREAMING_SNAKE_CASE      ,      location='''weights.pb'''      ,      convert_attribute=__SCREAMING_SNAKE_CASE      ,      )
						del pipeline.unet

						# VAE ENCODER
						lowercase_	:     Optional[int]													=       pipeline.vae
						lowercase_	:     Optional[Any]													=       vae_encoder.config.in_channels
						lowercase_	:     int													=       vae_encoder.config.sample_size
						# need to get the raw tensor output (sample) from the encoder
						lowercase_	:     Union[str, Any]													=       lambda __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							: vae_encoder.encode(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)[0].sample()
						onnx_export(
						    __SCREAMING_SNAKE_CASE      ,      model_args=(
						        torch.randn(1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
						        False,
						    )      ,      output_path=output_path / '''vae_encoder''' / '''model.onnx'''      ,      ordered_input_names=['''sample''', '''return_dict''']      ,      output_names=['''latent_sample''']      ,      dynamic_axes={
						        '''sample''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
						    }      ,      opset=__SCREAMING_SNAKE_CASE      ,      )

						# VAE DECODER
						lowercase_	:     List[Any]													=       pipeline.vae
						lowercase_	:     Any													=       vae_decoder.config.latent_channels
						lowercase_	:     str													=       vae_decoder.config.out_channels
						# forward only through the decoder part
						lowercase_	:     Dict													=       vae_encoder.decode
						onnx_export(
						    __SCREAMING_SNAKE_CASE      ,      model_args=(
						        torch.randn(1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_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=__SCREAMING_SNAKE_CASE      ,      )
						del pipeline.vae

						# SAFETY CHECKER
						if pipeline.safety_checker is not None:
												lowercase_	:     Dict													=       pipeline.safety_checker
												lowercase_	:     List[Any]													=       safety_checker.config.vision_config.num_channels
												lowercase_	:     Tuple													=       safety_checker.config.vision_config.image_size
												lowercase_	:     Tuple													=       safety_checker.forward_onnx
												onnx_export(
												    pipeline.safety_checker      ,      model_args=(
												        torch.randn(
												            1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      ).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
												        torch.randn(1      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE      ,      dtype=__SCREAMING_SNAKE_CASE							),
												    )      ,      output_path=output_path / '''safety_checker''' / '''model.onnx'''      ,      ordered_input_names=['''clip_input''', '''images''']      ,      output_names=['''out_images''', '''has_nsfw_concepts''']      ,      dynamic_axes={
												        '''clip_input''': {0: '''batch''', 1: '''channels''', 2: '''height''', 3: '''width'''},
												        '''images''': {0: '''batch''', 1: '''height''', 2: '''width''', 3: '''channels'''},
												    }      ,      opset=__SCREAMING_SNAKE_CASE      ,      )
												del pipeline.safety_checker
												lowercase_	:     List[str]													=       OnnxRuntimeModel.from_pretrained(output_path / '''safety_checker'''							)
												lowercase_	:     Optional[int]													=       pipeline.feature_extractor
						else:
												lowercase_	:     int													=       None
												lowercase_	:     str													=       None

						lowercase_	:     Optional[int]													=       OnnxStableDiffusionPipeline(
						    vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_encoder'''							)      ,      vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / '''vae_decoder'''							)      ,      text_encoder=OnnxRuntimeModel.from_pretrained(output_path / '''text_encoder'''							)      ,      tokenizer=pipeline.tokenizer      ,      unet=OnnxRuntimeModel.from_pretrained(output_path / '''unet'''							)      ,      scheduler=pipeline.scheduler      ,      safety_checker=__SCREAMING_SNAKE_CASE      ,      feature_extractor=__SCREAMING_SNAKE_CASE      ,      requires_safety_checker=safety_checker is not None      ,      )

						onnx_pipeline.save_pretrained(__SCREAMING_SNAKE_CASE							)
						print('''ONNX pipeline saved to'''      ,      __SCREAMING_SNAKE_CASE							)

						del pipeline
						del onnx_pipeline
						lowercase_	:     Tuple													=       OnnxStableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE      ,      provider='''CPUExecutionProvider'''							)
						print('''ONNX pipeline is loadable'''							)


if __name__ == "__main__":
							_lowercase :  Optional[int]    							=       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")

							_lowercase :  Union[str, Any]    							=       parser.parse_args()

							convert_models(args.model_path, args.output_path, args.opset, args.fpaa)





'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Tuple													=       CommonSchedulerState.create(self		)

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, Any]													=       jnp.array(1.0				,							dtype=self.dtype		)

											lowercase_	:     List[Any]													=       jnp.arange(0				,							self.config.num_train_timesteps		).round()[::-1]

											return DDPMSchedulerState.create(
											    common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self.config.num_train_timesteps // num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# rounding to avoid issues when num_inference_step is power of 3
											lowercase_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

											return state.replace(
											    num_inference_steps=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     str													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											lowercase_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowercase_						, lowercase_	:     Optional[Any]													=       jnp.split(__SCREAMING_SNAKE_CASE				,							sample.shape[1]				,							axis=1		)
											else:
																	lowercase_	:     int													=       None

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[int]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     int													=       1 - alpha_prod_t
											lowercase_	:     str													=       1 - alpha_prod_t_prev

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																	lowercase_	:     Tuple													=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											else:
																	raise ValueError(
																	    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																	    ''' for the FlaxDDPMScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	lowercase_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-1				,							1		)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[Any]													=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     Optional[Any]													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																	lowercase_	:     str													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							num=1		)
																	lowercase_	:     List[Any]													=       jax.random.normal(__SCREAMING_SNAKE_CASE				,							shape=model_output.shape				,							dtype=self.dtype		)
																	return (self._get_variance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							predicted_variance=__SCREAMING_SNAKE_CASE		) ** 0.5) * noise

											lowercase_	:     Optional[Any]													=       jnp.where(t > 0				,							random_variance()				,							jnp.zeros(model_output.shape				,							dtype=self.dtype		)		)

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return add_noise_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return get_velocity_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



