code: 
  
  
  
  import subprocess
  import sys
  import warnings
  from argparse import ArgumentParser
  from pathlib import Path
  
  from packaging import version
  
  from .. import AutoFeatureExtractor, AutoImageProcessor, AutoProcessor, AutoTokenizer
  from ..utils import logging
  from ..utils.import_utils import is_optimum_available
  from .convert import export, validate_model_outputs
  from .features import FeaturesManager
  from .utils import get_preprocessor
  
  
  __A       					=		'''1.5.0'''
  
  __A       					=		['''vision-encoder-decoder''']
  
  
  
  def  __a  (					lowerCAmelCase_     :      Any       )   ->				Any:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						if is_optimum_available():
  												from optimum.version import __version__ as optimum_version
  
  												UpperCAmelCase_=     version.parse(lowerCAmelCase_       )
  												if parsed_optimum_version < version.parse(lowerCAmelCase_       ):
  																		raise RuntimeError(
  																		    F"""transformers.onnx requires optimum >= {MIN_OPTIMUM_VERSION} but {optimum_version} is installed. You """
  																		    """can upgrade optimum by running: pip install -U optimum[exporters]"""       )
  						else:
  												raise RuntimeError(
  												    """transformers.onnx requires optimum to run, you can install the library by running: pip install """
  												    """optimum[exporters]"""       )
  						UpperCAmelCase_=     [
  						    sys.executable,
  						    """-m""",
  						    """optimum.exporters.onnx""",
  						    F"""--model {args.model}""",
  						    F"""--task {args.feature}""",
  						    F"""--framework {args.framework}""" if args.framework is not None else """""",
  						    F"""{args.output}""",
  						]
  						UpperCAmelCase_=     subprocess.Popen(""" """.join(lowerCAmelCase_       )    ,stdout=subprocess.PIPE    ,shell=lowerCAmelCase_       )
  						proc.wait()
  
  						logger.info(
  						    """The export was done by optimum.exporters.onnx. We recommend using to use this package directly in future, as """
  						    """transformers.onnx is deprecated, and will be removed in v5. You can find more information here: """
  						    """https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""       )
  
  
  
  def  __a  (					lowerCAmelCase_     :      Any       )   ->				Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""       )
  						if not args.output.parent.exists():
  												args.output.parent.mkdir(parents=lowerCAmelCase_       )
  
  						# Allocate the model
  						UpperCAmelCase_=     FeaturesManager.get_model_from_feature(
  						    args.feature    ,args.model    ,framework=args.framework    ,cache_dir=args.cache_dir       )
  
  						UpperCAmelCase_,     UpperCAmelCase_=     FeaturesManager.check_supported_model_or_raise(lowerCAmelCase_    ,feature=args.feature       )
  						UpperCAmelCase_=     model_onnx_config(model.config       )
  
  						if model_kind in ENCODER_DECODER_MODELS:
  												UpperCAmelCase_=     model.get_encoder()
  												UpperCAmelCase_=     model.get_decoder()
  
  												UpperCAmelCase_=     onnx_config.get_encoder_config(encoder_model.config       )
  												UpperCAmelCase_=     onnx_config.get_decoder_config(
  												    encoder_model.config    ,decoder_model.config    ,feature=args.feature       )
  
  												if args.opset is None:
  																		UpperCAmelCase_=     max(encoder_onnx_config.default_onnx_opset    ,decoder_onnx_config.default_onnx_opset       )
  
  												if args.opset < min(encoder_onnx_config.default_onnx_opset    ,decoder_onnx_config.default_onnx_opset       ):
  																		raise ValueError(
  																		    F"""Opset {args.opset} is not sufficient to export {model_kind}. At least """
  																		    F""" {min(encoder_onnx_config.default_onnx_opset    ,decoder_onnx_config.default_onnx_opset       )} is required."""       )
  
  												UpperCAmelCase_=     AutoFeatureExtractor.from_pretrained(args.model       )
  
  												UpperCAmelCase_,     UpperCAmelCase_=     export(
  												    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,args.opset    ,args.output.parent.joinpath("""encoder_model.onnx"""       )    ,)
  
  												validate_model_outputs(
  												    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,args.output.parent.joinpath("""encoder_model.onnx"""       )    ,lowerCAmelCase_    ,args.atol if args.atol else encoder_onnx_config.atol_for_validation    ,)
  
  												UpperCAmelCase_=     AutoTokenizer.from_pretrained(args.model       )
  
  												UpperCAmelCase_,     UpperCAmelCase_=     export(
  												    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,args.opset    ,args.output.parent.joinpath("""decoder_model.onnx"""       )    ,)
  
  												validate_model_outputs(
  												    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,args.output.parent.joinpath("""decoder_model.onnx"""       )    ,lowerCAmelCase_    ,args.atol if args.atol else decoder_onnx_config.atol_for_validation    ,)
  												logger.info(
  												    F"""All good, model saved at: {args.output.parent.joinpath("encoder_model.onnx"       ).as_posix()},"""
  												    F""" {args.output.parent.joinpath("decoder_model.onnx"       ).as_posix()}"""       )
  
  						else:
  												# Instantiate the appropriate preprocessor
  												if args.preprocessor == "auto":
  																		UpperCAmelCase_=     get_preprocessor(args.model       )
  												elif args.preprocessor == "tokenizer":
  																		UpperCAmelCase_=     AutoTokenizer.from_pretrained(args.model       )
  												elif args.preprocessor == "image_processor":
  																		UpperCAmelCase_=     AutoImageProcessor.from_pretrained(args.model       )
  												elif args.preprocessor == "feature_extractor":
  																		UpperCAmelCase_=     AutoFeatureExtractor.from_pretrained(args.model       )
  												elif args.preprocessor == "processor":
  																		UpperCAmelCase_=     AutoProcessor.from_pretrained(args.model       )
  												else:
  																		raise ValueError(F"""Unknown preprocessor type '{args.preprocessor}'"""       )
  
  												# Ensure the requested opset is sufficient
  												if args.opset is None:
  																		UpperCAmelCase_=     onnx_config.default_onnx_opset
  
  												if args.opset < onnx_config.default_onnx_opset:
  																		raise ValueError(
  																		    F"""Opset {args.opset} is not sufficient to export {model_kind}. """
  																		    F"""At least  {onnx_config.default_onnx_opset} is required."""       )
  
  												UpperCAmelCase_,     UpperCAmelCase_=     export(
  												    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,args.opset    ,args.output    ,)
  
  												if args.atol is None:
  																		UpperCAmelCase_=     onnx_config.atol_for_validation
  
  												validate_model_outputs(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,args.output    ,lowerCAmelCase_    ,args.atol       )
  												logger.info(F"""All good, model saved at: {args.output.as_posix()}"""       )
  												warnings.warn(
  												    """The export was done by transformers.onnx which is deprecated and will be removed in v5. We recommend"""
  												    """ using optimum.exporters.onnx in future. You can find more information here:"""
  												    """ https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model."""    ,lowerCAmelCase_    ,)
  
  
  
  def  __a  (					)   ->				List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     ArgumentParser("""Hugging Face Transformers ONNX exporter"""       )
  						parser.add_argument(
  						    """-m"""    ,"""--model"""    ,type=lowerCAmelCase_    ,required=lowerCAmelCase_    ,help="""Model ID on huggingface.co or path on disk to load model from."""       )
  						parser.add_argument(
  						    """--feature"""    ,default="""default"""    ,help="""The type of features to export the model with."""    ,)
  						parser.add_argument("""--opset"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""ONNX opset version to export the model with."""       )
  						parser.add_argument(
  						    """--atol"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""Absolute difference tolerance when validating the model."""       )
  						parser.add_argument(
  						    """--framework"""    ,type=lowerCAmelCase_    ,choices=["""pt""", """tf"""]    ,default=lowerCAmelCase_    ,help=(
  						        """The framework to use for the ONNX export."""
  						        """ If not provided, will attempt to use the local checkpoint's original framework"""
  						        """ or what is available in the environment."""
  						    )    ,)
  						parser.add_argument("""output"""    ,type=lowerCAmelCase_    ,help="""Path indicating where to store generated ONNX model."""       )
  						parser.add_argument("""--cache_dir"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""Path indicating where to store cache."""       )
  						parser.add_argument(
  						    """--preprocessor"""    ,type=lowerCAmelCase_    ,choices=["""auto""", """tokenizer""", """feature_extractor""", """image_processor""", """processor"""]    ,default="""auto"""    ,help="""Which type of preprocessor to use. 'auto' tries to automatically detect it."""    ,)
  						parser.add_argument(
  						    """--export_with_transformers"""    ,action="""store_true"""    ,help=(
  						        """Whether to use transformers.onnx instead of optimum.exporters.onnx to perform the ONNX export. It can be """
  						        """useful when exporting a model supported in transformers but not in optimum, otherwise it is not """
  						        """recommended."""
  						    )    ,)
  
  						UpperCAmelCase_=     parser.parse_args()
  						if args.export_with_transformers or not is_optimum_available():
  												export_with_transformers(lowerCAmelCase_       )
  						else:
  												export_with_optimum(lowerCAmelCase_       )
  
  
  if __name__ == "__main__":
  	__A       					=		logging.get_logger('''transformers.onnx''')  # pylint: disable=invalid-name
  	logger.setLevel(logging.INFO)
  	main()
  
  
  
  
  
style_context: 
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  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 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      CommonSchedulerState
  	a__   :      jnp.ndarray
  
  	# setable values
  	a__   :      jnp.ndarray
  	a__   :      jnp.ndarray
  	a__   :      Optional[int]	     =							None
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
  							return cls(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      DDIMSchedulerState
  
  
  
  
  
  
  class 	lowercase		(				snake_case__				,					snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	a__   :      jnp.dtype
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return True
  
  
  
  
  	@register_to_config
  	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     dtype
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
  							if common is None:
  													UpperCAmelCase_=     CommonSchedulerState.create(self  )
  
  							# At every step in ddim, we are looking into the previous alphas_cumprod
  							# For the final step, there is no previous alphas_cumprod because we are already at 0
  							# `set_alpha_to_one` decides whether we set this parameter simply to one or
  							# whether we use the final alpha of the "non-previous" one.
  							UpperCAmelCase_=     (
  							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  							)
  
  							# standard deviation of the initial noise distribution
  							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )
  
  							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]
  
  							return DDIMSchedulerState.create(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
  							return sample
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
  							UpperCAmelCase_=     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
  							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  							return state.replace(
  							    num_inference_steps=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
  							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(
  							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
  							UpperCAmelCase_=     1 - alpha_prod_t
  							UpperCAmelCase_=     1 - alpha_prod_t_prev
  
  							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  							return variance
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
  							if state.num_inference_steps is None:
  													raise ValueError(
  													    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )
  
  							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  							# Ideally, read DDIM paper in-detail understanding
  
  							# Notation (<variable name> -> <name in paper>
  							# - pred_noise_t -> e_theta(x_t, t)
  							# - pred_original_sample -> f_theta(x_t, t) or x_0
  							# - std_dev_t -> sigma_t
  							# - eta -> η
  							# - pred_sample_direction -> "direction pointing to x_t"
  							# - pred_prev_sample -> "x_t-1"
  
  							# 1. get previous step value (=t-1)
  							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  							UpperCAmelCase_=     state.common.alphas_cumprod
  							UpperCAmelCase_=     state.final_alpha_cumprod
  
  							# 2. compute alphas, betas
  							UpperCAmelCase_=     alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     1 - alpha_prod_t
  
  							# 3. compute predicted original sample from predicted noise also called
  							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							if self.config.prediction_type == "epsilon":
  													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  													UpperCAmelCase_=     model_output
  							elif self.config.prediction_type == "sample":
  													UpperCAmelCase_=     model_output
  													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  							elif self.config.prediction_type == "v_prediction":
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  							else:
  													raise ValueError(
  													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
  													    """ `v_prediction`"""  )
  
  							# 4. compute variance: "sigma_t(η)" -> see formula (16)
  							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     eta * variance ** 0.5
  
  							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  							if not return_dict:
  													return (prev_sample, state)
  
  							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
  							return self.config.num_train_timesteps
  
  
  
  
  