




'''simple docstring'''



from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def       _UpperCamelCase			(_lowerCamelCase				:  Namespace						)-> List[str]:




     '''simple docstring'''





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


UpperCAmelCase_							:	str	     =		'''
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 (			__lowerCAmelCase):


       @staticmethod
       def    lowerCAmelCase   ( __SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




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


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	*__SCREAMING_SNAKE_CASE     ,	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	logging.get_logger('''transformers-cli/converting'''	)

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




       def    lowerCAmelCase   ( self	)   ->	int:



            '''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(__SCREAMING_SNAKE_CASE	)

                 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(__SCREAMING_SNAKE_CASE	)

                 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(__SCREAMING_SNAKE_CASE	)

                 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(__SCREAMING_SNAKE_CASE	)

                 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(__SCREAMING_SNAKE_CASE	)

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

                 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(__SCREAMING_SNAKE_CASE	)

                 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'''



def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[]
     __snake_case            =	[]
     __snake_case            =	{
         '''^''': 3,
         '''*''': 2,
         '''/''': 2,
         '''%''': 2,
         '''+''': 1,
         '''-''': 1,
     }  # Priority of each operator
     __snake_case            =	len(_lowerCamelCase						) if (len(_lowerCamelCase						) > 7) else 7

     # Print table header for output
     print(
         '''Symbol'''.center(8						) ,						'''Stack'''.center(_lowerCamelCase						) ,						'''Postfix'''.center(_lowerCamelCase						) ,						sep=''' | ''' ,						)
     print('''-''' * (print_width * 3 + 7)						)

     for x in infix:
          if x.isalpha() or x.isdigit():
               post_fix.append(_lowerCamelCase						)  # if x is Alphabet / Digit, add it to Postfix
          elif x == "(":
               stack.append(_lowerCamelCase						)  # if x is "(" push to Stack
          elif x == ")":  # if x is ")" pop stack until "(" is encountered
               while stack[-1] != "(":
                    post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
               stack.pop()
          else:
               if len(_lowerCamelCase						) == 0:
                    stack.append(_lowerCamelCase						)  # If stack is empty, push x to stack
               else:  # while priority of x is not > priority of element in the stack
                    while len(_lowerCamelCase						) > 0 and priority[x] <= priority[stack[-1]]:
                         post_fix.append(stack.pop()						)  # pop stack & add to Postfix
                    stack.append(_lowerCamelCase						)  # push x to stack

          print(
              x.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     while len(_lowerCamelCase						) > 0:  # while stack is not empty
          post_fix.append(stack.pop()						)  # pop stack & add to Postfix
          print(
              ''' '''.center(8						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						(''''''.join(_lowerCamelCase						)).ljust(_lowerCamelCase						) ,						sep=''' | ''' ,						)  # Output in tabular format

     return "".join(_lowerCamelCase						)  # return Postfix as str







def       _UpperCamelCase			(_lowerCamelCase				:  Optional[Any]						)-> str:




     '''simple docstring'''





     __snake_case            =	list(infix[::-1]						)  # reverse the infix equation

     for i in range(len(_lowerCamelCase						)						):
          if infix[i] == "(":
               __snake_case            =	''')'''  # change "(" to ")"
          elif infix[i] == ")":
               __snake_case            =	'''('''  # change ")" to "("

     return (infix_2_postfix(''''''.join(_lowerCamelCase						)						))[
         ::-1
     ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
     UpperCAmelCase_							:	Dict	     =		input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
     UpperCAmelCase_							:	Optional[Any]	     =		''''''.join(Infix.split())  # Remove spaces from the input
     print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')






