

import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

UpperCAmelCase__	:       Dict			      =      logging.get_logger(__name__)

UpperCAmelCase__	:       int			      =      {"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase__	:       Optional[Any]			      =      {
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
UpperCAmelCase__	:       str			      =      {
    """hf-internal-testing/llama-tokenizer""": 2_048,
}
UpperCAmelCase__	:       str			      =      """▁"""

UpperCAmelCase__      ,				UpperCAmelCase__	:       Dict			      =      """[INST]""", """[/INST]"""
UpperCAmelCase__      ,				UpperCAmelCase__	:       int			      =      """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase__	:       Optional[int]			      =      """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""







class     a__    (				UpperCAmelCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	Optional[int]         =VOCAB_FILES_NAMES
  UpperCAmelCase__ :	Any         =PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__ :	Union[str, Any]         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__ :	Any         =["""input_ids""", """attention_mask"""]


  def __init__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	int="<unk>"			,			UpperCAmelCase__      :	Optional[Any]="<s>"			,			UpperCAmelCase__      :	str="</s>"			,			UpperCAmelCase__      :	List[Any]=None			,			UpperCAmelCase__      :	Optional[Dict[str, Any]] = None			,			UpperCAmelCase__      :	Optional[Any]=True			,			UpperCAmelCase__      :	Any=False			,			UpperCAmelCase__      :	Union[str, Any]=False			,			UpperCAmelCase__      :	str=True			,			**UpperCAmelCase__      :	List[Any]			,			)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[int]					       =				{} if sp_model_kwargs is None else sp_model_kwargs
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else bos_token
        SCREAMING_SNAKE_CASE	: Any					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else eos_token
        SCREAMING_SNAKE_CASE	: List[Any]					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else unk_token
        SCREAMING_SNAKE_CASE	: Tuple					       =				AddedToken(UpperCAmelCase__			,			lstrip=UpperCAmelCase__			,			rstrip=UpperCAmelCase__						) if isinstance(UpperCAmelCase__			,			UpperCAmelCase__						) else pad_token
        super().__init__(
            bos_token=UpperCAmelCase__			,			eos_token=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			pad_token=UpperCAmelCase__			,			add_bos_token=UpperCAmelCase__			,			add_eos_token=UpperCAmelCase__			,			sp_model_kwargs=self.sp_model_kwargs			,			clean_up_tokenization_spaces=UpperCAmelCase__			,			legacy=UpperCAmelCase__			,			**UpperCAmelCase__			,			)
        if legacy:
              logger.warning_once(
                  f"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
                  """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565"""						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				legacy
        SCREAMING_SNAKE_CASE	: List[str]					       =				vocab_file
        SCREAMING_SNAKE_CASE	: List[str]					       =				add_bos_token
        SCREAMING_SNAKE_CASE	: Any					       =				add_eos_token
        SCREAMING_SNAKE_CASE	: str					       =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.Load(UpperCAmelCase__						)


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







        SCREAMING_SNAKE_CASE	: Tuple					       =				self.__dict__.copy()
        SCREAMING_SNAKE_CASE	: Tuple					       =				None
        SCREAMING_SNAKE_CASE	: Tuple					       =				self.sp_model.serialized_model_proto()
        return state


  def __setstate__(     self      :	Union[str, Any]			,			UpperCAmelCase__      :	List[str]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Tuple					       =				d
        SCREAMING_SNAKE_CASE	: Any					       =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)


  @property
  def 	_lowercase (     self      :	Tuple						)   ->List[Any]:
        """simple docstring"""







        return self.sp_model.get_piece_size()


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[str]					       =				{self.convert_ids_to_tokens(UpperCAmelCase__						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab


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







        if not self.legacy:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				SPIECE_UNDERLINE + text.replace(UpperCAmelCase__			,			""" """						)
        return super().tokenize(UpperCAmelCase__			,			**UpperCAmelCase__						)


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







        if not self.legacy:
              SCREAMING_SNAKE_CASE	: Any					       =				text.startswith(UpperCAmelCase__						)
              if is_first:
                    SCREAMING_SNAKE_CASE	: int					       =				text[1:]

        SCREAMING_SNAKE_CASE	: Dict					       =				self.sp_model.encode(UpperCAmelCase__			,			out_type=UpperCAmelCase__						)

        if not self.legacy and not is_first and not text.startswith(""" """						) and tokens[0].startswith(UpperCAmelCase__						):
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				([tokens[0][1:]] if len(tokens[0]						) > 1 else []) + tokens[1:]
        return tokens


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







        return self.sp_model.piece_to_id(UpperCAmelCase__						)


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







        SCREAMING_SNAKE_CASE	: Dict					       =				self.sp_model.IdToPiece(UpperCAmelCase__						)
        return token


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







        SCREAMING_SNAKE_CASE	: Any					       =				[]
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				""""""
        SCREAMING_SNAKE_CASE	: List[Any]					       =				False
        for i, token in enumerate(UpperCAmelCase__						):
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    if not prev_is_special and i != 0:
                          out_string += " "
                    out_string += self.sp_model.decode(UpperCAmelCase__						) + token
                    SCREAMING_SNAKE_CASE	: Optional[Any]					       =				True
                    SCREAMING_SNAKE_CASE	: Optional[int]					       =				[]
              else:
                    current_sub_tokens.append(UpperCAmelCase__						)
                    SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				False
        out_string += self.sp_model.decode(UpperCAmelCase__						)
        return out_string


  def 	_lowercase (     self      :	Union[str, Any]			,			UpperCAmelCase__      :	List[Any]			,			UpperCAmelCase__      :	Optional[str] = None						)   ->Tuple[str]:
        """simple docstring"""







        if not os.path.isdir(UpperCAmelCase__						):
              logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
              return
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				os.path.join(
            UpperCAmelCase__			,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

        if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCAmelCase__						) and os.path.isfile(self.vocab_file						):
              copyfile(self.vocab_file			,			UpperCAmelCase__						)
        elif not os.path.isfile(self.vocab_file						):
              with open(UpperCAmelCase__			,			"""wb"""						) as fi:
                    SCREAMING_SNAKE_CASE	: Tuple					       =				self.sp_model.serialized_model_proto()
                    fi.write(UpperCAmelCase__						)

        return (out_vocab_file,)


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	Dict			,			UpperCAmelCase__      :	str=None						)   ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Any					       =				[self.bos_token_id] if self.add_bos_token else []
        SCREAMING_SNAKE_CASE	: Dict					       =				[self.eos_token_id] if self.add_eos_token else []

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				bos_token_id + token_ids_a + eos_token_id

        if token_ids_a is not None:
              SCREAMING_SNAKE_CASE	: Optional[int]					       =				output + bos_token_id + token_ids_a + eos_token_id

        return output


  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	List[int]			,			UpperCAmelCase__      :	Optional[List[int]] = None			,			UpperCAmelCase__      :	bool = False						)   ->List[int]:
        """simple docstring"""







        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCAmelCase__			,			token_ids_a=UpperCAmelCase__			,			already_has_special_tokens=UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: List[Any]					       =				[1] if self.add_bos_token else []
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				[1] if self.add_eos_token else []

        if token_ids_a is None:
              return bos_token_id + ([0] * len(UpperCAmelCase__						)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(UpperCAmelCase__						))
            + eos_token_id
            + bos_token_id
            + ([0] * len(UpperCAmelCase__						))
            + eos_token_id
        )


  def 	_lowercase (     self      :	Optional[Any]			,			UpperCAmelCase__      :	List[int]			,			UpperCAmelCase__      :	Optional[List[int]] = None						)   ->List[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				[self.bos_token_id] if self.add_bos_token else []
        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				[self.eos_token_id] if self.add_eos_token else []

        SCREAMING_SNAKE_CASE	: Tuple					       =				[0] * len(bos_token_id + token_ids_a + eos_token_id						)

        if token_ids_a is not None:
              output += [1] * len(bos_token_id + token_ids_a + eos_token_id						)

        return output




  def 	_lowercase (     self      :	Dict			,			UpperCAmelCase__      :	"Conversation"						)   ->List[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Dict					       =				list(conversation.iter_texts()						)
        if not all([is_user for is_user, msg in dialogue[::2]]						) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]						):
              raise ValueError(
                  """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)"""						)

        SCREAMING_SNAKE_CASE	: List[int]					       =				[]
        if len(conversation.past_user_inputs						) > 0:
              if not conversation.past_user_inputs[0].startswith(UpperCAmelCase__						) or E_SYS not in conversation.past_user_inputs[0]:
                    SCREAMING_SNAKE_CASE	: Tuple					       =				(
                        B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                    )
        elif not dialogue[0][1].startswith(UpperCAmelCase__						) or E_SYS not in dialogue[0][1]:
              SCREAMING_SNAKE_CASE	: List[Any]					       =				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "			,			add_special_tokens=UpperCAmelCase__						)
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2]			,			dialogue[1::2]						)
            ]			,			[]			,			)
        if not (dialogue[-1][0]):
              raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"						)
        dialog_tokens += [self.bos_token_id] + self.encode(
            f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"			,			add_special_tokens=UpperCAmelCase__						)
        return dialog_tokens







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()




