


'''simple docstring'''
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def 			_lowercase       (				UpperCamelCase__       :					Namespace			):
 return ConvertCommand(
     args.model_type,				args.tf_checkpoint,				args.pytorch_dump_output,				args.config,				args.finetuning_task_name			)


UpperCAmelCase_ :     Union[str, 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 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 @staticmethod
 def  snake_case__   (      __lowercase						):

  """simple docstring"""







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


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      *__lowercase      ,      ):

  """simple docstring"""







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

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







 def  snake_case__   (      self						):

  """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(__lowercase						)

   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(__lowercase						)

   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(__lowercase						)

   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(__lowercase						)

   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(__lowercase						)

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

   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(__lowercase						)

   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 			_lowercase       (				UpperCamelCase__       :					np.ndarray,				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					tuple[int, int],				UpperCamelCase__       :					bool,				):
 __A     ,__A  :		Optional[Any]       =			grid.shape
 __A  :		List[Any]       =			[-1, 1, 0, 0]
 __A  :		Optional[int]       =			[0, 0, -1, 1]
 if allow_diagonal:
  dx += [-1, -1, 1, 1]
  dy += [-1, 1, -1, 1]

 __A     ,__A  :		int       =			[(0, source)], set()
 __A  :		Any       =			np.full((rows, cols),				np.inf			)
 __A  :		int       =			0
 __A  :		Any       =			np.empty((rows, cols),				dtype=UpperCamelCase__			)
 __A  :		List[Any]       =			None

 while queue:
  ((__A)     ,(__A))  :		List[Any]       =			heappop(UpperCamelCase__			)
  if (x, y) in visited:
   continue
  visited.add((x, y)			)

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

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

 return np.inf, []


if __name__ == "__main__":
 import doctest

 doctest.testmod()






