

"""simple docstring"""




import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

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







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	torch.FloatTensor
  UpperCAmelCase__ :	Optional[torch.FloatTensor]         =None






def    __lowercase (       _A  ,      _A=0.999  ,      _A="cosine"  ,      )       ->  str:
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_A     ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(_A     ):
                  return math.exp(t * -12.0     )

      else:
            raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"     )

      SCREAMING_SNAKE_CASE	: List[Any]					       =				[]
      for i in range(_A     ):
            SCREAMING_SNAKE_CASE	: int					       =				i / num_diffusion_timesteps
            SCREAMING_SNAKE_CASE	: Any					       =				(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(_A     ) / alpha_bar_fn(_A     )  ,      _A     )     )
      return torch.tensor(_A  ,      dtype=torch.floataa     )







class     a__    (				UpperCAmelCase		,     UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	str         =[e.name for e in KarrasDiffusionSchedulers]
  UpperCAmelCase__ :	List[Any]         =1


  @register_to_config
  def __init__(     self      :	Optional[int]			,			UpperCAmelCase__      :	int = 1_0_0_0			,			UpperCAmelCase__      :	float = 0.00_01			,			UpperCAmelCase__      :	float = 0.02			,			UpperCAmelCase__      :	str = "linear"			,			UpperCAmelCase__      :	Optional[Union[np.ndarray, List[float]]] = None			,			UpperCAmelCase__      :	Optional[bool] = False			,			UpperCAmelCase__      :	str = "epsilon"			,			UpperCAmelCase__      :	str = "linspace"			,			UpperCAmelCase__      :	int = 0			,			)   ->List[str]:
        """simple docstring"""







        if trained_betas is not None:
              SCREAMING_SNAKE_CASE	: Dict					       =				torch.tensor(UpperCAmelCase__			,			dtype=torch.floataa						)
        elif beta_schedule == "linear":
              SCREAMING_SNAKE_CASE	: List[str]					       =				torch.linspace(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			dtype=torch.floataa						)
        elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              SCREAMING_SNAKE_CASE	: Tuple					       =				(
                  torch.linspace(beta_start**0.5			,			beta_end**0.5			,			UpperCAmelCase__			,			dtype=torch.floataa						) ** 2
              )
        elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              SCREAMING_SNAKE_CASE	: Any					       =				betas_for_alpha_bar(UpperCAmelCase__						)
        else:
              raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"						)

        SCREAMING_SNAKE_CASE	: Any					       =				1.0 - self.betas
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				torch.cumprod(self.alphas			,			dim=0						)

        SCREAMING_SNAKE_CASE	: str					       =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				np.concatenate([sigmas[::-1], [0.0]]						).astype(np.floataa						)
        SCREAMING_SNAKE_CASE	: str					       =				torch.from_numpy(UpperCAmelCase__						)

        # setable values
        SCREAMING_SNAKE_CASE	: str					       =				None
        SCREAMING_SNAKE_CASE	: Any					       =				use_karras_sigmas
        self.set_timesteps(UpperCAmelCase__			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				[]
        SCREAMING_SNAKE_CASE	: List[Any]					       =				False


  @property
  def 	_lowercase (     self      :	Union[str, Any]						)   ->str:
        """simple docstring"""







        if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

        return (self.sigmas.max() ** 2 + 1) ** 0.5


  def 	_lowercase (     self      :	List[Any]			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	Union[float, torch.FloatTensor]						)   ->torch.FloatTensor:
        """simple docstring"""







        if isinstance(UpperCAmelCase__			,			torch.Tensor						):
              SCREAMING_SNAKE_CASE	: List[Any]					       =				timestep.to(self.timesteps.device						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				(self.timesteps == timestep).nonzero().item()
        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.sigmas[step_index]
        SCREAMING_SNAKE_CASE	: str					       =				sample / ((sigma**2 + 1) ** 0.5)
        SCREAMING_SNAKE_CASE	: Any					       =				True
        return sample


  def 	_lowercase (     self      :	str			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	Tuple						)   ->List[Any]:
        """simple docstring"""







        def lms_derivative(UpperCAmelCase__      :	Tuple						):
              SCREAMING_SNAKE_CASE	: str					       =				1.0
              for k in range(UpperCAmelCase__						):
                    if current_order == k:
                          continue
                    prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
              return prod

        SCREAMING_SNAKE_CASE	: int					       =				integrate.quad(UpperCAmelCase__			,			self.sigmas[t]			,			self.sigmas[t + 1]			,			epsrel=1e-4						)[0]

        return integrated_coeff


  def 	_lowercase (     self      :	int			,			UpperCAmelCase__      :	int			,			UpperCAmelCase__      :	Union[str, torch.device] = None						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				num_inference_steps

        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
        if self.config.timestep_spacing == "linspace":
              SCREAMING_SNAKE_CASE	: List[Any]					       =				np.linspace(0			,			self.config.num_train_timesteps - 1			,			UpperCAmelCase__			,			dtype=UpperCAmelCase__						)[
                  ::-1
              ].copy()
        elif self.config.timestep_spacing == "leading":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.config.num_train_timesteps // self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              SCREAMING_SNAKE_CASE	: Optional[Any]					       =				(np.arange(0			,			UpperCAmelCase__						) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase__						)
              timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
              SCREAMING_SNAKE_CASE	: Tuple					       =				self.config.num_train_timesteps / self.num_inference_steps
              # creates integer timesteps by multiplying by ratio
              # casting to int to avoid issues when num_inference_step is power of 3
              SCREAMING_SNAKE_CASE	: Dict					       =				(np.arange(self.config.num_train_timesteps			,			0			,			-step_ratio						)).round().copy().astype(UpperCAmelCase__						)
              timesteps -= 1
        else:
              raise ValueError(
                  f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."						)

        SCREAMING_SNAKE_CASE	: str					       =				np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				np.log(UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: Any					       =				np.interp(UpperCAmelCase__			,			np.arange(0			,			len(UpperCAmelCase__						)						)			,			UpperCAmelCase__						)

        if self.use_karras_sigmas:
              SCREAMING_SNAKE_CASE	: int					       =				self._convert_to_karras(in_sigmas=UpperCAmelCase__						)
              SCREAMING_SNAKE_CASE	: str					       =				np.array([self._sigma_to_t(UpperCAmelCase__			,			UpperCAmelCase__						) for sigma in sigmas]						)

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)

        SCREAMING_SNAKE_CASE	: Tuple					       =				torch.from_numpy(UpperCAmelCase__						).to(device=UpperCAmelCase__						)
        if str(UpperCAmelCase__						).startswith("""mps"""						):
              # mps does not support float64
              SCREAMING_SNAKE_CASE	: Dict					       =				torch.from_numpy(UpperCAmelCase__						).to(UpperCAmelCase__			,			dtype=torch.floataa						)
        else:
              SCREAMING_SNAKE_CASE	: Dict					       =				torch.from_numpy(UpperCAmelCase__						).to(device=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				[]


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	Optional[int]			,			UpperCAmelCase__      :	Tuple						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				np.log(UpperCAmelCase__						)

        # get distribution
        SCREAMING_SNAKE_CASE	: List[Any]					       =				log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        SCREAMING_SNAKE_CASE	: List[str]					       =				np.cumsum((dists >= 0)			,			axis=0						).argmax(axis=0						).clip(max=log_sigmas.shape[0] - 2						)
        SCREAMING_SNAKE_CASE	: int					       =				low_idx + 1

        SCREAMING_SNAKE_CASE	: Tuple					       =				log_sigmas[low_idx]
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				log_sigmas[high_idx]

        # interpolate sigmas
        SCREAMING_SNAKE_CASE	: Any					       =				(low - log_sigma) / (low - high)
        SCREAMING_SNAKE_CASE	: str					       =				np.clip(UpperCAmelCase__			,			0			,			1						)

        # transform interpolation to time range
        SCREAMING_SNAKE_CASE	: List[Any]					       =				(1 - w) * low_idx + w * high_idx
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				t.reshape(sigma.shape						)
        return t


  def 	_lowercase (     self      :	List[str]			,			UpperCAmelCase__      :	torch.FloatTensor						)   ->torch.FloatTensor:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: float					       =				in_sigmas[-1].item()
        SCREAMING_SNAKE_CASE	: float					       =				in_sigmas[0].item()

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				7.0  # 7.0 is the value used in the paper
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				np.linspace(0			,			1			,			self.num_inference_steps						)
        SCREAMING_SNAKE_CASE	: List[Any]					       =				sigma_min ** (1 / rho)
        SCREAMING_SNAKE_CASE	: str					       =				sigma_max ** (1 / rho)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	Union[float, torch.FloatTensor]			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	int = 4			,			UpperCAmelCase__      :	bool = True			,			)   ->Union[LMSDiscreteSchedulerOutput, Tuple]:
        """simple docstring"""







        if not self.is_scale_input_called:
              warnings.warn(
                  """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                  """See `StableDiffusionPipeline` for a usage example."""						)

        if isinstance(UpperCAmelCase__			,			torch.Tensor						):
              SCREAMING_SNAKE_CASE	: str					       =				timestep.to(self.timesteps.device						)
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				(self.timesteps == timestep).nonzero().item()
        SCREAMING_SNAKE_CASE	: int					       =				self.sigmas[step_index]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              SCREAMING_SNAKE_CASE	: List[Any]					       =				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              SCREAMING_SNAKE_CASE	: int					       =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        elif self.config.prediction_type == "sample":
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				model_output
        else:
              raise ValueError(
                  f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

        # 2. Convert to an ODE derivative
        SCREAMING_SNAKE_CASE	: str					       =				(sample - pred_original_sample) / sigma
        self.derivatives.append(UpperCAmelCase__						)
        if len(self.derivatives						) > order:
              self.derivatives.pop(0						)

        # 3. Compute linear multistep coefficients
        SCREAMING_SNAKE_CASE	: List[Any]					       =				min(step_index + 1			,			UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				[self.get_lms_coefficient(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__						) for curr_order in range(UpperCAmelCase__						)]

        # 4. Compute previous sample based on the derivatives path
        SCREAMING_SNAKE_CASE	: str					       =				sample + sum(
            coeff * derivative for coeff, derivative in zip(UpperCAmelCase__			,			reversed(self.derivatives						)						)						)

        if not return_dict:
              return (prev_sample,)

        return LMSDiscreteSchedulerOutput(prev_sample=UpperCAmelCase__			,			pred_original_sample=UpperCAmelCase__						)


  def 	_lowercase (     self      :	Any			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	torch.FloatTensor			,			UpperCAmelCase__      :	torch.FloatTensor			,			)   ->torch.FloatTensor:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				self.sigmas.to(device=original_samples.device			,			dtype=original_samples.dtype						)
        if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase__						):
              # mps does not support float64
              SCREAMING_SNAKE_CASE	: Dict					       =				self.timesteps.to(original_samples.device			,			dtype=torch.floataa						)
              SCREAMING_SNAKE_CASE	: List[Any]					       =				timesteps.to(original_samples.device			,			dtype=torch.floataa						)
        else:
              SCREAMING_SNAKE_CASE	: Dict					       =				self.timesteps.to(original_samples.device						)
              SCREAMING_SNAKE_CASE	: str					       =				timesteps.to(original_samples.device						)

        SCREAMING_SNAKE_CASE	: List[str]					       =				[(schedule_timesteps == t).nonzero().item() for t in timesteps]

        SCREAMING_SNAKE_CASE	: Optional[int]					       =				sigmas[step_indices].flatten()
        while len(sigma.shape						) < len(original_samples.shape						):
              SCREAMING_SNAKE_CASE	: int					       =				sigma.unsqueeze(-1						)

        SCREAMING_SNAKE_CASE	: Any					       =				original_samples + noise * sigma
        return noisy_samples




  def __len__(     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        return self.config.num_train_timesteps







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


UpperCAmelCase__	:       int			      =      """1.5.0"""

UpperCAmelCase__	:       Any			      =      ["""vision-encoder-decoder"""]






def    __lowercase (       _A     )       ->  Any:
      if is_optimum_available():
            from optimum.version import __version__ as optimum_version

            SCREAMING_SNAKE_CASE	: int					       =				version.parse(_A     )
            if parsed_optimum_version < version.parse(_A     ):
                  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]"""     )
      SCREAMING_SNAKE_CASE	: List[str]					       =				[
          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}",
      ]
      SCREAMING_SNAKE_CASE	: Dict					       =				subprocess.Popen(""" """.join(_A     )  ,      stdout=subprocess.PIPE  ,      shell=_A     )
      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    __lowercase (       _A     )       ->  Any:
      SCREAMING_SNAKE_CASE	: List[str]					       =				args.output if args.output.is_file() else args.output.joinpath("""model.onnx"""     )
      if not args.output.parent.exists():
            args.output.parent.mkdir(parents=_A     )

      # Allocate the model
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				FeaturesManager.get_model_from_feature(
          args.feature  ,      args.model  ,      framework=args.framework  ,      cache_dir=args.cache_dir     )

      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Any					       =				FeaturesManager.check_supported_model_or_raise(_A  ,      feature=args.feature     )
      SCREAMING_SNAKE_CASE	: str					       =				model_onnx_config(model.config     )

      if model_kind in ENCODER_DECODER_MODELS:
            SCREAMING_SNAKE_CASE	: str					       =				model.get_encoder()
            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				model.get_decoder()

            SCREAMING_SNAKE_CASE	: Optional[int]					       =				onnx_config.get_encoder_config(encoder_model.config     )
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				onnx_config.get_decoder_config(
                encoder_model.config  ,      decoder_model.config  ,      feature=args.feature     )

            if args.opset is None:
                  SCREAMING_SNAKE_CASE	: Optional[int]					       =				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."     )

            SCREAMING_SNAKE_CASE	: List[str]					       =				AutoFeatureExtractor.from_pretrained(args.model     )

            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[int]					       =				export(
                _A  ,      _A  ,      _A  ,      args.opset  ,      args.output.parent.joinpath("""encoder_model.onnx"""     )  ,      )

            validate_model_outputs(
                _A  ,      _A  ,      _A  ,      args.output.parent.joinpath("""encoder_model.onnx"""     )  ,      _A  ,      args.atol if args.atol else encoder_onnx_config.atol_for_validation  ,      )

            SCREAMING_SNAKE_CASE	: Optional[int]					       =				AutoTokenizer.from_pretrained(args.model     )

            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: List[Any]					       =				export(
                _A  ,      _A  ,      _A  ,      args.opset  ,      args.output.parent.joinpath("""decoder_model.onnx"""     )  ,      )

            validate_model_outputs(
                _A  ,      _A  ,      _A  ,      args.output.parent.joinpath("""decoder_model.onnx"""     )  ,      _A  ,      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":
                  SCREAMING_SNAKE_CASE	: List[Any]					       =				get_preprocessor(args.model     )
            elif args.preprocessor == "tokenizer":
                  SCREAMING_SNAKE_CASE	: Optional[Any]					       =				AutoTokenizer.from_pretrained(args.model     )
            elif args.preprocessor == "image_processor":
                  SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				AutoImageProcessor.from_pretrained(args.model     )
            elif args.preprocessor == "feature_extractor":
                  SCREAMING_SNAKE_CASE	: int					       =				AutoFeatureExtractor.from_pretrained(args.model     )
            elif args.preprocessor == "processor":
                  SCREAMING_SNAKE_CASE	: List[Any]					       =				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:
                  SCREAMING_SNAKE_CASE	: Dict					       =				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."     )

            SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: Optional[Any]					       =				export(
                _A  ,      _A  ,      _A  ,      args.opset  ,      args.output  ,      )

            if args.atol is None:
                  SCREAMING_SNAKE_CASE	: List[Any]					       =				onnx_config.atol_for_validation

            validate_model_outputs(_A  ,      _A  ,      _A  ,      args.output  ,      _A  ,      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."""  ,      _A  ,      )






def    __lowercase (       )       ->  List[str]:
      SCREAMING_SNAKE_CASE	: List[Any]					       =				ArgumentParser("""Hugging Face Transformers ONNX exporter"""     )
      parser.add_argument(
          """-m"""  ,      """--model"""  ,      type=_A  ,      required=_A  ,      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=_A  ,      default=_A  ,      help="""ONNX opset version to export the model with."""     )
      parser.add_argument(
          """--atol"""  ,      type=_A  ,      default=_A  ,      help="""Absolute difference tolerance when validating the model."""     )
      parser.add_argument(
          """--framework"""  ,      type=_A  ,      choices=["""pt""", """tf"""]  ,      default=_A  ,      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=_A  ,      help="""Path indicating where to store generated ONNX model."""     )
      parser.add_argument("""--cache_dir"""  ,      type=_A  ,      default=_A  ,      help="""Path indicating where to store cache."""     )
      parser.add_argument(
          """--preprocessor"""  ,      type=_A  ,      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."""
          )  ,      )

      SCREAMING_SNAKE_CASE	: Tuple					       =				parser.parse_args()
      if args.export_with_transformers or not is_optimum_available():
            export_with_transformers(_A     )
      else:
            export_with_optimum(_A     )


if __name__ == "__main__":
  UpperCAmelCase__	:       Optional[Any]			      =      logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
  logger.setLevel(logging.INFO)
  main()




