

"""simple docstring"""


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      return ConvertCommand(
          args.model_type  ,					args.tf_checkpoint  ,					args.pytorch_dump_output  ,					args.config  ,					args.finetuning_task_name							)


a					: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      __a				(UpperCamelCase_):






     '''simple docstring'''

     @staticmethod
     def 					_a			(						_a  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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	,    _a	,    _a	,    _a	,    _a	,    _a	,    *_a	,    )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	logging.get_logger("""transformers-cli/converting"""  )

           self._logger.info(f'''Loading model {model_type}'''  )
           SCREAMING_SNAKE_CASE__   :      int      		=	model_type
           SCREAMING_SNAKE_CASE__   :      int      		=	tf_checkpoint
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pytorch_dump_output
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	config
           SCREAMING_SNAKE_CASE__   :      Any      		=	finetuning_task_name





     def 					_a			(						self  )  ->      Dict:


           """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():
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	self._tf_checkpoint
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	""""""
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	self._tf_checkpoint
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	""""""
                 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  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				bool:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(__lowerCAmelCase							) + 1
      SCREAMING_SNAKE_CASE__   :      int      		=	len(__lowerCAmelCase							) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      SCREAMING_SNAKE_CASE__   :      Dict      		=	[[0 for i in range(__lowerCAmelCase							)] for j in range(__lowerCAmelCase							)]

      # since string of zero length match pattern of zero length
      SCREAMING_SNAKE_CASE__   :      Dict      		=	1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1  ,					__lowerCAmelCase							):
            SCREAMING_SNAKE_CASE__   :      int      		=	dp[0][j - 2] if pattern[j - 1] == """*""" else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1  ,					__lowerCAmelCase							):
            for j in range(1  ,					__lowerCAmelCase							):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        SCREAMING_SNAKE_CASE__   :      Any      		=	dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              SCREAMING_SNAKE_CASE__   :      List[str]      		=	1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              SCREAMING_SNAKE_CASE__   :      List[Any]      		=	dp[i - 1][j]
                        else:
                              SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0
                  else:
                        SCREAMING_SNAKE_CASE__   :      Dict      		=	0

      return bool(dp[-1][-1]							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  # inputing the strings
  # input_string = input("input a string :")
  # pattern = input("input a pattern :")

  a					:Any    = "aab"
  a					:Optional[Any]    = "c*a*b"

  # using function to check whether given string matches the given pattern
  if match_pattern(input_string, pattern):
    print(f'{input_string} matches the given pattern {pattern}')
  else:
    print(f'{input_string} does not match with the given pattern {pattern}')




