





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def _A       (   __magic_name__	):
   return ConvertCommand(
       args.model_type  , args.tf_checkpoint  , args.pytorch_dump_output  , args.config  , args.finetuning_task_name	)


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







class  lowerCAmelCase							(       SCREAMING_SNAKE_CASE_ ):







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :ArgumentParser ):






     '''simple docstring'''





     lowercase__			       =  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=__a   ,							required=__a   ,							help="Model\'s type." )
     train_parser.add_argument(
         "--tf_checkpoint"   ,							type=__a   ,							required=__a   ,							help="TensorFlow checkpoint path or folder." )
     train_parser.add_argument(
         "--pytorch_dump_output"   ,							type=__a   ,							required=__a   ,							help="Path to the PyTorch saved model output." )
     train_parser.add_argument("--config"   ,							type=__a   ,							default=""   ,							help="Configuration file path or folder." )
     train_parser.add_argument(
         "--finetuning_task_name"   ,							type=__a   ,							default=__a   ,							help="Optional fine-tuning task name if the TF model was a finetuned model."   ,							)
     train_parser.set_defaults(func=__a )







  def __init__(      self  :Tuple   ,							_lowercase  :str   ,							_lowercase  :str   ,							_lowercase  :str   ,							_lowercase  :str   ,							_lowercase  :str   ,							*_lowercase  :int   ,							):






     '''simple docstring'''





     lowercase__			       =  logging.get_logger("transformers-cli/converting" )

     self._logger.info(f'''Loading model {model_type}''' )
     lowercase__			       =  model_type
     lowercase__			       =  tf_checkpoint
     lowercase__			       =  pytorch_dump_output
     lowercase__			       =  config
     lowercase__			       =  finetuning_task_name







  def   UpperCAmelCase					(      self  :List[str] ):






     '''simple docstring'''





     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(__a )

        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(__a )

        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(__a )

        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(__a )

        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(__a )

        if "ckpt" in self._tf_checkpoint.lower():
           lowercase__			       =  self._tf_checkpoint
           lowercase__			       =  ''
        else:
           lowercase__			       =  self._tf_checkpoint
           lowercase__			       =  ''
        convert_transfo_xl_checkpoint_to_pytorch(
            __a   ,							self._config   ,							self._pytorch_dump_output   ,							__a )
     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(__a )

        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(__a )

        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]" )











import math


def _A       (   __magic_name__	):
   lowercase__			       =  math.loga(math.sqrt(4 * positive_integer + 1	) / 2 + 1 / 2	)

   return exponent == int(__magic_name__	)


def _A       (   __magic_name__ = 1 / 1_2345	):
   lowercase__			       =  0
   lowercase__			       =  0

   lowercase__			       =  3
   while True:
      lowercase__			       =  (integer**2 - 1) / 4
      # if candidate is an integer, then there is a partition for k
      if partition_candidate == int(__magic_name__	):
         lowercase__			       =  int(__magic_name__	)
         total_partitions += 1
         if check_partition_perfect(__magic_name__	):
            perfect_partitions += 1
      if perfect_partitions > 0:
         if perfect_partitions / total_partitions < max_proportion:
            return int(__magic_name__	)
      integer += 1


if __name__ == "__main__":
  print(F"""{solution() = }""")






