






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__    (	A       ):
   if is_optimum_available():
      from optimum.version import __version__ as optimum_version

      lowercase__    :						Optional[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]"""       )
   lowercase__    :						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}''',
   ]
   lowercase__    :						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    a__    (	A       ):
   lowercase__    :						Any       =			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
   lowercase__    :						int       =			FeaturesManager.get_model_from_feature(
       args.feature		,					args.model		,					framework=args.framework		,					cache_dir=args.cache_dir       )

   lowercase__  ,       lowercase__    :						str       =			FeaturesManager.check_supported_model_or_raise(A		,					feature=args.feature       )
   lowercase__    :						Tuple       =			model_onnx_config(model.config       )

   if model_kind in ENCODER_DECODER_MODELS:
      lowercase__    :						Any       =			model.get_encoder()
      lowercase__    :						Optional[int]       =			model.get_decoder()

      lowercase__    :						Dict       =			onnx_config.get_encoder_config(encoder_model.config       )
      lowercase__    :						Union[str, Any]       =			onnx_config.get_decoder_config(
          encoder_model.config		,					decoder_model.config		,					feature=args.feature       )

      if args.opset is None:
         lowercase__    :						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.'''       )

      lowercase__    :						Tuple       =			AutoFeatureExtractor.from_pretrained(args.model       )

      lowercase__  ,       lowercase__    :						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		,					)

      lowercase__    :						int       =			AutoTokenizer.from_pretrained(args.model       )

      lowercase__  ,       lowercase__    :						Union[str, 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":
         lowercase__    :						List[str]       =			get_preprocessor(args.model       )
      elif args.preprocessor == "tokenizer":
         lowercase__    :						str       =			AutoTokenizer.from_pretrained(args.model       )
      elif args.preprocessor == "image_processor":
         lowercase__    :						Optional[Any]       =			AutoImageProcessor.from_pretrained(args.model       )
      elif args.preprocessor == "feature_extractor":
         lowercase__    :						List[str]       =			AutoFeatureExtractor.from_pretrained(args.model       )
      elif args.preprocessor == "processor":
         lowercase__    :						int       =			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:
         lowercase__    :						Tuple       =			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.'''       )

      lowercase__  ,       lowercase__    :						Union[str, Any]       =			export(
          A		,					A		,					A		,					args.opset		,					args.output		,					)

      if args.atol is None:
         lowercase__    :						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    a__    (	):
   lowercase__    :						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."""
       )		,					)

   lowercase__    :						Optional[int]       =			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__":
 a 			=							logging.get_logger('transformers.onnx')  # pylint: disable=invalid-name
 logger.setLevel(logging.INFO)
 main()









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

import numpy as np
import torch

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


lowercase     :			Optional[int]     						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class 			__A(    __UpperCAmelCase			):
     __A							=							42






class 			__A(    __UpperCAmelCase    ,      __UpperCAmelCase			):
     __A							=							1

     @register_to_config
     def __init__(   self,							A = 40,							A = 0.002,							A = 80.0,							A = 0.5,							A = 1.0,							A = 7.0,							A = True,							):





            """simple docstring"""

            _UpperCamelCase														=    sigma_max

            _UpperCamelCase														=    np.linspace(0,							1,							A   )
            _UpperCamelCase														=    self._convert_to_karras(A   )
            _UpperCamelCase														=    self.sigma_to_t(A   )

            # setable values
            _UpperCamelCase														=    None
            _UpperCamelCase														=    torch.from_numpy(A   )
            _UpperCamelCase														=    torch.from_numpy(A   )
            _UpperCamelCase														=    False
            _UpperCamelCase														=    False

     def 						_UpperCamelCase  (   self,							A,							A=None   ):





            """simple docstring"""

            if schedule_timesteps is None:
                   _UpperCamelCase														=    self.timesteps

            _UpperCamelCase														=    (schedule_timesteps == timestep).nonzero()
            return indices.item()

     def 						_UpperCamelCase  (   self,							A,							A   ):





            """simple docstring"""

            if isinstance(A,							torch.Tensor   ):
                   _UpperCamelCase														=    timestep.to(self.timesteps.device   )
            _UpperCamelCase														=    self.index_for_timestep(A   )
            _UpperCamelCase														=    self.sigmas[step_idx]

            _UpperCamelCase														=    sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

            _UpperCamelCase														=    True
            return sample

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            if not isinstance(A,							np.ndarray   ):
                   _UpperCamelCase														=    np.array(A,							dtype=np.floataa   )

            _UpperCamelCase														=    1000 * 0.25 * np.log(sigmas + 1E-4_4   )

            return timesteps

     def 						_UpperCamelCase  (   self,							A = None,							A = None,							A = None,							):





            """simple docstring"""

            if num_inference_steps is None and timesteps is None:
                   raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''   )

            if num_inference_steps is not None and timesteps is not None:
                   raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''   )

            # Follow DDPMScheduler custom timesteps logic
            if timesteps is not None:
                   for i in range(1,							len(A   )   ):
                          if timesteps[i] >= timesteps[i - 1]:
                                 raise ValueError('''`timesteps` must be in descending order.'''   )

                   if timesteps[0] >= self.config.num_train_timesteps:
                          raise ValueError(
                              F'''`timesteps` must start before `self.config.train_timesteps`:'''
                              F''' {self.config.num_train_timesteps}.'''   )

                   _UpperCamelCase														=    np.array(A,							dtype=np.intaa   )
                   _UpperCamelCase														=    True
            else:
                   if num_inference_steps > self.config.num_train_timesteps:
                          raise ValueError(
                              F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                              F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                              F''' maximal {self.config.num_train_timesteps} timesteps.'''   )

                   _UpperCamelCase														=    num_inference_steps

                   _UpperCamelCase														=    self.config.num_train_timesteps // self.num_inference_steps
                   _UpperCamelCase														=    (np.arange(0,							A   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
                   _UpperCamelCase														=    False

            # Map timesteps to Karras sigmas directly for multistep sampling
            # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
            _UpperCamelCase														=    self.config.num_train_timesteps
            _UpperCamelCase														=    timesteps[::-1].copy()
            _UpperCamelCase														=    ramp / (num_train_timesteps - 1)
            _UpperCamelCase														=    self._convert_to_karras(A   )
            _UpperCamelCase														=    self.sigma_to_t(A   )

            _UpperCamelCase														=    np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
            _UpperCamelCase														=    torch.from_numpy(A   ).to(device=A   )

            if str(A   ).startswith('''mps'''   ):
                   # mps does not support float64
                   _UpperCamelCase														=    torch.from_numpy(A   ).to(A,							dtype=torch.floataa   )
            else:
                   _UpperCamelCase														=    torch.from_numpy(A   ).to(device=A   )

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config.sigma_min
            _UpperCamelCase														=    self.config.sigma_max

            _UpperCamelCase														=    self.config.rho
            _UpperCamelCase														=    sigma_min ** (1 / rho)
            _UpperCamelCase														=    sigma_max ** (1 / rho)
            _UpperCamelCase														=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
            return sigmas

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config.sigma_data

            _UpperCamelCase														=    sigma_data**2 / (sigma**2 + sigma_data**2)
            _UpperCamelCase														=    sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
            return c_skip, c_out

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config.sigma_min
            _UpperCamelCase														=    self.config.sigma_data

            _UpperCamelCase														=    sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
            _UpperCamelCase														=    (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
            return c_skip, c_out

     def 						_UpperCamelCase  (   self,							A,							A,							A,							A = None,							A = True,							):





            """simple docstring"""

            if (
                isinstance(A,							A   )
                or isinstance(A,							torch.IntTensor   )
                or isinstance(A,							torch.LongTensor   )
            ):
                   raise ValueError(
                       (
                           '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                           F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                           ''' one of the `scheduler.timesteps` as a timestep.'''
                       ),							)

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

            if isinstance(A,							torch.Tensor   ):
                   _UpperCamelCase														=    timestep.to(self.timesteps.device   )

            _UpperCamelCase														=    self.config.sigma_min
            _UpperCamelCase														=    self.config.sigma_max

            _UpperCamelCase														=    self.index_for_timestep(A   )

            # sigma_next corresponds to next_t in original implementation
            _UpperCamelCase														=    self.sigmas[step_index]
            if step_index + 1 < self.config.num_train_timesteps:
                   _UpperCamelCase														=    self.sigmas[step_index + 1]
            else:
                   # Set sigma_next to sigma_min
                   _UpperCamelCase														=    self.sigmas[-1]

            # Get scalings for boundary conditions
            _UpperCamelCase							,		_UpperCamelCase														=    self.get_scalings_for_boundary_condition(A   )

            # 1. Denoise model output using boundary conditions
            _UpperCamelCase														=    c_out * model_output + c_skip * sample
            if self.config.clip_denoised:
                   _UpperCamelCase														=    denoised.clamp(-1,							1   )

            # 2. Sample z ~ N(0, s_noise^2 * I)
            # Noise is not used for onestep sampling.
            if len(self.timesteps   ) > 1:
                   _UpperCamelCase														=    randn_tensor(
                       model_output.shape,							dtype=model_output.dtype,							device=model_output.device,							generator=A   )
            else:
                   _UpperCamelCase														=    torch.zeros_like(A   )
            _UpperCamelCase														=    noise * self.config.s_noise

            _UpperCamelCase														=    sigma_next.clamp(min=A,							max=A   )

            # 3. Return noisy sample
            # tau = sigma_hat, eps = sigma_min
            _UpperCamelCase														=    denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

            if not return_dict:
                   return (prev_sample,)

            return CMStochasticIterativeSchedulerOutput(prev_sample=A   )

     def 						_UpperCamelCase  (   self,							A,							A,							A,							):





            """simple docstring"""

            _UpperCamelCase														=    self.sigmas.to(device=original_samples.device,							dtype=original_samples.dtype   )
            if original_samples.device.type == "mps" and torch.is_floating_point(A   ):
                   # mps does not support float64
                   _UpperCamelCase														=    self.timesteps.to(original_samples.device,							dtype=torch.floataa   )
                   _UpperCamelCase														=    timesteps.to(original_samples.device,							dtype=torch.floataa   )
            else:
                   _UpperCamelCase														=    self.timesteps.to(original_samples.device   )
                   _UpperCamelCase														=    timesteps.to(original_samples.device   )

            _UpperCamelCase														=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

            _UpperCamelCase														=    original_samples + noise * sigma
            return noisy_samples



     def __len__(   self   ):





            """simple docstring"""

            return self.config.num_train_timesteps






