


import socket

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    List[str]          =   socket.socket(socket.AF_INET    , socket.SOCK_STREAM     )
 UpperCamelCase__					:    List[str]          =   socket.gethostname()
 UpperCamelCase__					:    Dict          =   12312

 sock.connect((host, port)     )
 sock.send(B'''Hello server!'''     )

 with open('''Received_file'''    , '''wb'''     ) as out_file:
  print('''File opened'''     )
  print('''Receiving data...'''     )
  while True:
   UpperCamelCase__					:    int          =   sock.recv(1024     )
   if not data:
    break
   out_file.write(SCREAMING_SNAKE_CASE     )

 print('''Successfully received the file'''     )
 sock.close()
 print('''Connection closed'''     )


if __name__ == "__main__":
       main()






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 			_a  (				SCREAMING_SNAKE_CASE	:				Namespace     ):







 """simple docstring"""



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


__UpperCamelCase       :       List[Any]						       =				"\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       __magic_name__	(     __lowerCAmelCase):


     @staticmethod
     def      UpperCAmelCase__	(      lowerCamelCase__							:     ArgumentParser			)  ->      Optional[int]:





      '''simple docstring'''






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


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     str							,	*lowerCamelCase__							:     Optional[int]							,	)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   logging.get_logger('''transformers-cli/converting'''			)

      self._logger.info(F"Loading model {model_type}"			)
      UpperCamelCase__					:    List[str]          =   model_type
      UpperCamelCase__					:    Optional[int]          =   tf_checkpoint
      UpperCamelCase__					:    List[Any]          =   pytorch_dump_output
      UpperCamelCase__					:    List[Any]          =   config
      UpperCamelCase__					:    Any          =   finetuning_task_name


     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Optional[Any]:





      '''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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       if "ckpt" in self._tf_checkpoint.lower():
        UpperCamelCase__					:    str          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       else:
        UpperCamelCase__					:    Any          =   self._tf_checkpoint
        UpperCamelCase__					:    List[Any]          =   ''''''
       convert_transfo_xl_checkpoint_to_pytorch(
           lowerCamelCase__							,	self._config							,	self._pytorch_dump_output							,	lowerCamelCase__			)
      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(lowerCamelCase__			)

       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(lowerCamelCase__			)

       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]'''			)


