






'''simple docstring'''
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def 	_a(					UpperCamelCase__			:  Namespace							):




      '''simple docstring'''






      return ConvertCommand(
          args.model_type,		args.tf_checkpoint,		args.pytorch_dump_output,		args.config,		args.finetuning_task_name							)


a_					=	'\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'







class        __SCREAMING_SNAKE_CASE (     lowerCamelCase			):




 @staticmethod
 def        __magic_name__		( __lowercase		:							ArgumentParser )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Tuple													=parser.add_parser(
           '''convert'''   , help='''CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'''   , )
       train_parser.add_argument('''--model_type'''   , type=__lowercase   , required=__lowercase   , help='''Model\'s type.''' )
       train_parser.add_argument(
           '''--tf_checkpoint'''   , type=__lowercase   , required=__lowercase   , help='''TensorFlow checkpoint path or folder.''' )
       train_parser.add_argument(
           '''--pytorch_dump_output'''   , type=__lowercase   , required=__lowercase   , help='''Path to the PyTorch saved model output.''' )
       train_parser.add_argument('''--config'''   , type=__lowercase   , default=''''''   , help='''Configuration file path or folder.''' )
       train_parser.add_argument(
           '''--finetuning_task_name'''   , type=__lowercase   , default=__lowercase   , help='''Optional fine-tuning task name if the TF model was a finetuned model.'''   , )
       train_parser.set_defaults(func=__lowercase )




 def __init__( self		:							Dict   , __lowercase		:							str   , __lowercase		:							str   , __lowercase		:							str   , __lowercase		:							str   , __lowercase		:							str   , *__lowercase		:							Optional[int]   , )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=logging.get_logger('''transformers-cli/converting''' )

       self._logger.info(F"Loading model {model_type}" )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=model_type
       SCREAMING_SNAKE_CASE__       :			Tuple													=tf_checkpoint
       SCREAMING_SNAKE_CASE__       :			List[str]													=pytorch_dump_output
       SCREAMING_SNAKE_CASE__       :			str													=config
       SCREAMING_SNAKE_CASE__       :			Any													=finetuning_task_name




 def        __magic_name__		( self		:							Tuple )		->  int:
       if self._model_type == "albert":
             try:
                   from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                       convert_tf_checkpoint_to_pytorch,
                   )
             except ImportError:
                   raise ImportError(__lowercase )

             convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       elif self._model_type == "bert":
             try:
                   from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                       convert_tf_checkpoint_to_pytorch,
                   )
             except ImportError:
                   raise ImportError(__lowercase )

             convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       elif self._model_type == "funnel":
             try:
                   from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                       convert_tf_checkpoint_to_pytorch,
                   )
             except ImportError:
                   raise ImportError(__lowercase )

             convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       elif self._model_type == "t5":
             try:
                   from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
             except ImportError:
                   raise ImportError(__lowercase )

             convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       elif self._model_type == "gpt":
             from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                 convert_openai_checkpoint_to_pytorch,
             )

             convert_openai_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       elif self._model_type == "transfo_xl":
             try:
                   from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                       convert_transfo_xl_checkpoint_to_pytorch,
                   )
             except ImportError:
                   raise ImportError(__lowercase )

             if "ckpt" in self._tf_checkpoint.lower():
                   SCREAMING_SNAKE_CASE__       :			str													=self._tf_checkpoint
                   SCREAMING_SNAKE_CASE__       :			str													=''''''
             else:
                   SCREAMING_SNAKE_CASE__       :			Any													=self._tf_checkpoint
                   SCREAMING_SNAKE_CASE__       :			List[Any]													=''''''
             convert_transfo_xl_checkpoint_to_pytorch(
                 __lowercase   , self._config   , self._pytorch_dump_output   , __lowercase )
       elif self._model_type == "gpt2":
             try:
                   from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                       convert_gpta_checkpoint_to_pytorch,
                   )
             except ImportError:
                   raise ImportError(__lowercase )

             convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       elif self._model_type == "xlnet":
             try:
                   from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                       convert_xlnet_checkpoint_to_pytorch,
                   )
             except ImportError:
                   raise ImportError(__lowercase )

             convert_xlnet_checkpoint_to_pytorch(
                 self._tf_checkpoint   , self._config   , self._pytorch_dump_output   , self._finetuning_task_name )
       elif self._model_type == "xlm":
             from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                 convert_xlm_checkpoint_to_pytorch,
             )

             convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint   , self._pytorch_dump_output )
       elif self._model_type == "lxmert":
             from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                 convert_lxmert_checkpoint_to_pytorch,
             )

             convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint   , self._pytorch_dump_output )
       elif self._model_type == "rembert":
             from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                 convert_rembert_tf_checkpoint_to_pytorch,
             )

             convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint   , self._config   , self._pytorch_dump_output )
       else:
             raise ValueError(
                 '''--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]''' )







'''simple docstring'''
from __future__ import annotations





def 	_a(					UpperCamelCase__			:  List[str],		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  int,		UpperCamelCase__			:  List[str]							):  # noqa: E741




      '''simple docstring'''






      while r - l > 1:
            SCREAMING_SNAKE_CASE__       :			List[str]													=(l + r) // 2
            if v[m] >= key:
                  SCREAMING_SNAKE_CASE__       :			Dict													=m
            else:
                  SCREAMING_SNAKE_CASE__       :			Any													=m  # noqa: E741
      return r





def 	_a(					UpperCamelCase__			:  list[int]							):




      '''simple docstring'''






      if len(UpperCamelCase__							) == 0:
            return 0

      SCREAMING_SNAKE_CASE__       :			List[Any]													=[0] * len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Dict													=1

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v[0]

      for i in range(1,		len(UpperCamelCase__							)							):
            if v[i] < tail[0]:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=v[i]
            elif v[i] > tail[length - 1]:
                  SCREAMING_SNAKE_CASE__       :			int													=v[i]
                  length += 1
            else:
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=v[i]

      return length


if __name__ == "__main__":
  import doctest

  doctest.testmod()