






"""simple docstring"""





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_						:					Any      				=			"""1.5.0"""

UpperCamelCase_						:					List[str]      				=			["""vision-encoder-decoder"""]



def      __lowercase       (     a			:					int			)       ->       Optional[int]:
      if is_optimum_available():
            from optimum.version import __version__ as optimum_version

            __snake_case   :     Union[str, Any]	      =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]'''			)
      __snake_case   :     int	      =[
          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}''',
      ]
      __snake_case   :     Optional[Any]	      =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			:					Optional[int]			)       ->       Optional[Any]:
      __snake_case   :     Optional[int]	      =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
      __snake_case   :     Optional[int]	      =FeaturesManager.get_model_from_feature(
          args.feature							,		args.model							,		framework=args.framework							,		cache_dir=args.cache_dir			)

      __snake_case      ,					__snake_case   :     Optional[Any]	      =FeaturesManager.check_supported_model_or_raise(a							,		feature=args.feature			)
      __snake_case   :     List[Any]	      =model_onnx_config(model.config			)

      if model_kind in ENCODER_DECODER_MODELS:
            __snake_case   :     List[str]	      =model.get_encoder()
            __snake_case   :     str	      =model.get_decoder()

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

            if args.opset is None:
                  __snake_case   :     Dict	      =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.'''			)

            __snake_case   :     Union[str, Any]	      =AutoFeatureExtractor.from_pretrained(args.model			)

            __snake_case      ,					__snake_case   :     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							,		)

            __snake_case   :     List[str]	      =AutoTokenizer.from_pretrained(args.model			)

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

            __snake_case      ,					__snake_case   :     int	      =export(
                a							,		a							,		a							,		args.opset							,		args.output							,		)

            if args.atol is None:
                  __snake_case   :     int	      =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       (     )       ->       int:
      __snake_case   :     Tuple	      =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.'''
          )							,		)

      __snake_case   :     Any	      =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_						:					int      				=			logging.get_logger("""transformers.onnx""")  # pylint: disable=invalid-name
    logger.setLevel(logging.INFO)
    main()










"""simple docstring"""





class   _lowercase			:


      def __init__(     self					:						Optional[Any]				, a					:						Union[str, Any]    ):





            """simple docstring"""







            __snake_case   :     Union[str, Any]	      =data
            __snake_case   :     Optional[Any]	      =None
            __snake_case   :     Any	      =None







      def __str__(     self					:						Any    ):





            """simple docstring"""







            return f'''{self.data}'''



class   _lowercase			:


      def __init__(     self					:						Optional[int]    ):





            """simple docstring"""







            __snake_case   :     Any	      =None
            __snake_case   :     int	      =None


      def __iter__(     self					:						int    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =self.head
            while node:
                  yield node.data
                  __snake_case   :     str	      =node.next


      def __str__(     self					:						Optional[int]    ):





            """simple docstring"""







            return "->".join([str(a    ) for item in self]    )


      def __len__(     self					:						str    ):





            """simple docstring"""







            return sum(1 for _ in self    )


      def _UpperCamelCase    (     self					:						int				, a					:						List[Any]    ):





            """simple docstring"""







            self.insert_at_nth(0				, a    )


      def _UpperCamelCase    (     self					:						str				, a					:						Optional[int]    ):





            """simple docstring"""







            self.insert_at_nth(len(self    )				, a    )


      def _UpperCamelCase    (     self					:						Optional[Any]				, a					:						int				, a					:						Tuple    ):





            """simple docstring"""







            __snake_case   :     List[str]	      =len(self    )

            if not 0 <= index <= length:
                  raise IndexError('''list index out of range'''    )
            __snake_case   :     List[Any]	      =Node(a    )
            if self.head is None:
                  __snake_case   :     Dict	      =new_node
            elif index == 0:
                  __snake_case   :     Union[str, Any]	      =new_node
                  __snake_case   :     Any	      =self.head
                  __snake_case   :     List[Any]	      =new_node
            elif index == length:
                  __snake_case   :     int	      =new_node
                  __snake_case   :     Optional[Any]	      =self.tail
                  __snake_case   :     Optional[Any]	      =new_node
            else:
                  __snake_case   :     Any	      =self.head
                  for _ in range(0				, a    ):
                        __snake_case   :     Optional[Any]	      =temp.next
                  __snake_case   :     Optional[Any]	      =new_node
                  __snake_case   :     Union[str, Any]	      =temp.previous
                  __snake_case   :     List[Any]	      =temp
                  __snake_case   :     str	      =new_node


      def _UpperCamelCase    (     self					:						List[str]    ):





            """simple docstring"""







            return self.delete_at_nth(0    )


      def _UpperCamelCase    (     self					:						List[Any]    ):





            """simple docstring"""







            return self.delete_at_nth(len(self    ) - 1    )


      def _UpperCamelCase    (     self					:						Optional[int]				, a					:						int    ):





            """simple docstring"""







            __snake_case   :     Tuple	      =len(self    )

            if not 0 <= index <= length - 1:
                  raise IndexError('''list index out of range'''    )
            __snake_case   :     str	      =self.head  # default first node
            if length == 1:
                  __snake_case   :     List[Any]	      =None
            elif index == 0:
                  __snake_case   :     List[Any]	      =self.head.next
                  __snake_case   :     Optional[int]	      =None
            elif index == length - 1:
                  __snake_case   :     str	      =self.tail
                  __snake_case   :     Union[str, Any]	      =self.tail.previous
                  __snake_case   :     Optional[Any]	      =None
            else:
                  __snake_case   :     Optional[int]	      =self.head
                  for _ in range(0				, a    ):
                        __snake_case   :     List[Any]	      =temp.next
                  __snake_case   :     Union[str, Any]	      =temp
                  __snake_case   :     Optional[int]	      =temp.previous
                  __snake_case   :     int	      =temp.next
            return delete_node.data


      def _UpperCamelCase    (     self					:						List[Any]				, a					:						Any    ):





            """simple docstring"""







            __snake_case   :     Optional[int]	      =self.head

            while current.data != data:  # Find the position to delete
                  if current.next:
                        __snake_case   :     Dict	      =current.next
                  else:  # We have reached the end an no value matches
                        raise ValueError('''No data matching given value'''    )

            if current == self.head:
                  self.delete_head()

            elif current == self.tail:
                  self.delete_tail()

            else:  # Before: 1 <--> 2(current) <--> 3
                  __snake_case   :     Tuple	      =current.next  # 1 --> 3
                  __snake_case   :     Optional[Any]	      =current.previous  # 1 <--> 3
            return data







      def _UpperCamelCase    (     self					:						int    ):





            """simple docstring"""







            return len(self    ) == 0



def      __lowercase       (     )       ->       None:
      __snake_case   :     List[str]	      =DoublyLinkedList()
      assert linked_list.is_empty() is True
      assert str(a			) == ""

      try:
            linked_list.delete_head()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      try:
            linked_list.delete_tail()
            raise AssertionError  # This should not happen.
      except IndexError:
            assert True  # This should happen.

      for i in range(10			):
            assert len(a			) == i
            linked_list.insert_at_nth(a							,		i + 1			)
      assert str(a			) == "->".join(str(a			) for i in range(1							,		11			)			)

      linked_list.insert_at_head(0			)
      linked_list.insert_at_tail(11			)
      assert str(a			) == "->".join(str(a			) for i in range(0							,		12			)			)

      assert linked_list.delete_head() == 0
      assert linked_list.delete_at_nth(9			) == 10
      assert linked_list.delete_tail() == 11
      assert len(a			) == 9
      assert str(a			) == "->".join(str(a			) for i in range(1							,		10			)			)


if __name__ == "__main__":
    from doctest import testmod

    testmod()


