



"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def      a__			(   lowerCAmelCase__   ):
      return ConvertCommand(
          args.model_type			,    args.tf_checkpoint			,    args.pytorch_dump_output			,    args.config			,    args.finetuning_task_name   )


lowerCamelCase      =		"""
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    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @staticmethod
       def   lowercase__       (							_UpperCAmelCase    :						ArgumentParser					)				->		Union[str, Any]:


             '''simple docstring'''




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



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


             '''simple docstring'''




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

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







       def   lowercase__       (							self    :						List[Any]					)				->		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(_UpperCAmelCase					)

                   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(_UpperCAmelCase					)

                   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(_UpperCAmelCase					)

                   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(_UpperCAmelCase					)

                   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(_UpperCAmelCase					)

                   if "ckpt" in self._tf_checkpoint.lower():
                         UpperCAmelCase_    				=							self._tf_checkpoint
                         UpperCAmelCase_    				=							""
                   else:
                         UpperCAmelCase_    				=							self._tf_checkpoint
                         UpperCAmelCase_    				=							""
                   convert_transfo_xl_checkpoint_to_pytorch(
                       _UpperCAmelCase	,       self._config	,       self._pytorch_dump_output	,       _UpperCAmelCase					)
             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(_UpperCAmelCase					)

                   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(_UpperCAmelCase					)

                   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"""





from heapq import heappop, heappush

import numpy as np
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    ):
      UpperCAmelCase_				,					UpperCAmelCase_    				=							grid.shape
      UpperCAmelCase_    				=							[-1, 1, 0, 0]
      UpperCAmelCase_    				=							[0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      UpperCAmelCase_				,					UpperCAmelCase_    				=							[(0, source)], set()
      UpperCAmelCase_    				=							np.full((rows, cols)			,    np.inf   )
      UpperCAmelCase_    				=							0
      UpperCAmelCase_    				=							np.empty((rows, cols)			,    dtype=lowerCAmelCase__   )
      UpperCAmelCase_    				=							None

      while queue:
            ((UpperCAmelCase_)				,					(UpperCAmelCase_))    				=							heappop(lowerCAmelCase__   )
            if (x, y) in visited:
                  continue
            visited.add((x, y)   )

            if (x, y) == destination:
                  UpperCAmelCase_    				=							[]
                  while (x, y) != source:
                        path.append((x, y)   )
                        UpperCAmelCase_				,					UpperCAmelCase_    				=							predecessors[x, y]
                  path.append(lowerCAmelCase__   )  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(lowerCAmelCase__   )   ):
                  UpperCAmelCase_				,					UpperCAmelCase_    				=							x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        UpperCAmelCase_    				=							grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(lowerCAmelCase__			,    (dist + 1, (nx, ny))   )
                              UpperCAmelCase_    				=							dist + 1
                              UpperCAmelCase_    				=							(x, y)

      return np.inf, []


if __name__ == "__main__":
  import doctest

  doctest.testmod()




