


'''simple docstring'''






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def lowerCamelCase__      (						__lowerCamelCase	:							Namespace							):







   '''simple docstring'''




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


lowercase							='''
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  __magic_name__							(		SCREAMING_SNAKE_CASE_    ):






       @staticmethod
       def       lowerCAmelCase		(      snake_case)						->  str:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =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  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							*snake_case  ,							)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =logging.get_logger('transformers-cli/converting')

          self._logger.info(f"Loading model {model_type}")
          _UpperCAmelCase       :       Dict							   =model_type
          _UpperCAmelCase       :       Optional[Any]							   =tf_checkpoint
          _UpperCAmelCase       :       str							   =pytorch_dump_output
          _UpperCAmelCase       :       Dict							   =config
          _UpperCAmelCase       :       str							   =finetuning_task_name






       def       lowerCAmelCase		(      self)						->  List[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(__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():
                _UpperCAmelCase       :       Optional[int]							   =self._tf_checkpoint
                _UpperCAmelCase       :       List[str]							   =''
             else:
                _UpperCAmelCase       :       int							   =self._tf_checkpoint
                _UpperCAmelCase       :       Tuple							   =''
             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]')



'''simple docstring'''






import numpy as np






class  __magic_name__							:






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




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =(0, 0)
          _UpperCAmelCase       :       Any							   =None
          _UpperCAmelCase       :       List[str]							   =0
          _UpperCAmelCase       :       Dict							   =0
          _UpperCAmelCase       :       Dict							   =0






       def __eq__(      self  ,							snake_case)						->  Dict:




          '''simple docstring'''
          return self.position == cell.position






       def       lowerCAmelCase		(      self)						->  Optional[int]:




          '''simple docstring'''
          print(self.position)






class  __magic_name__							:






       def __init__(      self  ,							snake_case=(5, 5))						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       List[str]							   =np.zeros(snake_case)
          _UpperCAmelCase       :       Optional[Any]							   =world_size[0]
          _UpperCAmelCase       :       Any							   =world_size[1]






       def       lowerCAmelCase		(      self)						->  Tuple:




          '''simple docstring'''
          print(self.w)






       def       lowerCAmelCase		(      self  ,							snake_case)						->  Any:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =[
              (-1, -1),
              (-1, 0),
              (-1, 1),
              (0, -1),
              (0, 1),
              (1, -1),
              (1, 0),
              (1, 1),
          ]
          _UpperCAmelCase       :       int							   =cell.position[0]
          _UpperCAmelCase       :       Optional[Any]							   =cell.position[1]
          _UpperCAmelCase       :       Dict							   =[]
          for n in neughbour_cord:
             _UpperCAmelCase       :       List[str]							   =current_x + n[0]
             _UpperCAmelCase       :       int							   =current_y + n[1]
             if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                _UpperCAmelCase       :       List[Any]							   =Cell()
                _UpperCAmelCase       :       Dict							   =(x, y)
                _UpperCAmelCase       :       Any							   =cell
                neighbours.append(snake_case)
          return neighbours






def lowerCamelCase__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[Any]							   =[]
   _UpperCAmelCase       :       Union[str, Any]							   =[]
   _open.append(__lowerCamelCase							)

   while _open:
      _UpperCAmelCase       :       List[str]							   =np.argmin([n.f for n in _open]							)
      _UpperCAmelCase       :       Optional[int]							   =_open[min_f]
      _closed.append(_open.pop(__lowerCamelCase							)							)
      if current == goal:
         break
      for n in world.get_neigbours(__lowerCamelCase							):
         for c in _closed:
            if c == n:
               continue
         _UpperCAmelCase       :       Tuple							   =current.g + 1
         _UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =n.position
         _UpperCAmelCase   ,	_UpperCAmelCase       :       List[str]							   =goal.position
         _UpperCAmelCase       :       Optional[int]							   =(ya - ya) ** 2 + (xa - xa) ** 2
         _UpperCAmelCase       :       Optional[Any]							   =n.h + n.g

         for c in _open:
            if c == n and c.f < n.f:
               continue
         _open.append(__lowerCamelCase							)
   _UpperCAmelCase       :       str							   =[]
   while current.parent is not None:
      path.append(current.position							)
      _UpperCAmelCase       :       Any							   =current.parent
   path.append(current.position							)
   return path[::-1]


if __name__ == "__main__":
 lowercase							=Gridworld()
 # Start position and goal
 lowercase							=Cell()
 lowercase							=(0, 0)
 lowercase							=Cell()
 lowercase							=(4, 4)
 print(F"""path from {start.position} to {goal.position}""")
 lowercase							=astar(world, start, goal)
 #   Just for visual reasons.
 for i in s:
  lowercase							=1
 print(world.w)

