

"""simple docstring"""




import sys

from transformers import AutoTokenizer


UpperCAmelCase__	:       Dict			      =      sys.argv[1]
UpperCAmelCase__	:       str			      =      sys.argv[2]
UpperCAmelCase__	:       Dict			      =      int(sys.argv[3])

UpperCAmelCase__	:       str			      =      0

UpperCAmelCase__	:       List[str]			      =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
  for line in f_p:
    UpperCAmelCase__	:       Dict			      =      line.rstrip()

    if not line:
      print(line)
      UpperCAmelCase__	:       List[str]			      =      0
      continue

    UpperCAmelCase__	:       Optional[Any]			      =      line.split()[0]

    UpperCAmelCase__	:       Dict			      =      len(tokenizer.tokenize(token))

    # Token contains strange control characters like \x96 or \x95
    # Just filter out the complete line
    if current_subwords_len == 0:
      continue

    if (subword_len_counter + current_subwords_len) > max_len:
      print("""""")
      print(line)
      UpperCAmelCase__	:       List[Any]			      =      current_subwords_len
      continue

    subword_len_counter += current_subwords_len

    print(line)







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()




