
def 				a__ (				snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""

  def update_area_of_max_square(snake_case    ,       snake_case				) -> int:
    # BASE CASE
    if row >= rows or col >= cols:
      return 0

    __SCREAMING_SNAKE_CASE	:     List[Any] =						update_area_of_max_square(lowerCAmelCase__    ,       col + 1				)
    __SCREAMING_SNAKE_CASE	:     Optional[int] =						update_area_of_max_square(row + 1    ,       col + 1				)
    __SCREAMING_SNAKE_CASE	:     Dict =						update_area_of_max_square(row + 1    ,       lowerCAmelCase__				)

    if mat[row][col]:
      __SCREAMING_SNAKE_CASE	:     Any =						1 + min([right, diagonal, down]				)
      __SCREAMING_SNAKE_CASE	:     List[Any] =						max(largest_square_area[0]    ,       lowerCAmelCase__				)
      return sub_problem_sol
    else:
      return 0

  __SCREAMING_SNAKE_CASE	:     str =						[0]
  update_area_of_max_square(0    ,       0				)
  return largest_square_area[0]


def 				a__ (				snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""

  def update_area_of_max_square_using_dp_array(
      snake_case    ,       snake_case    ,       snake_case				) -> int:
    if row >= rows or col >= cols:
      return 0
    if dp_array[row][col] != -1:
      return dp_array[row][col]

    __SCREAMING_SNAKE_CASE	:     Optional[Any] =						update_area_of_max_square_using_dp_array(lowerCAmelCase__    ,       col + 1    ,       lowerCAmelCase__				)
    __SCREAMING_SNAKE_CASE	:     Any =						update_area_of_max_square_using_dp_array(row + 1    ,       col + 1    ,       lowerCAmelCase__				)
    __SCREAMING_SNAKE_CASE	:     Any =						update_area_of_max_square_using_dp_array(row + 1    ,       lowerCAmelCase__    ,       lowerCAmelCase__				)

    if mat[row][col]:
      __SCREAMING_SNAKE_CASE	:     int =						1 + min([right, diagonal, down]				)
      __SCREAMING_SNAKE_CASE	:     Optional[int] =						max(largest_square_area[0]    ,       lowerCAmelCase__				)
      __SCREAMING_SNAKE_CASE	:     Optional[int] =						sub_problem_sol
      return sub_problem_sol
    else:
      return 0

  __SCREAMING_SNAKE_CASE	:     Any =						[0]
  __SCREAMING_SNAKE_CASE	:     Optional[Any] =						[[-1] * cols for _ in range(lowerCAmelCase__				)]
  update_area_of_max_square_using_dp_array(0    ,       0    ,       lowerCAmelCase__				)

  return largest_square_area[0]


def 				a__ (				snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Optional[int] =						[[0] * (cols + 1) for _ in range(rows + 1				)]
  __SCREAMING_SNAKE_CASE	:     str =						0
  for row in range(rows - 1    ,       -1    ,       -1				):
    for col in range(cols - 1    ,       -1    ,       -1				):
      __SCREAMING_SNAKE_CASE	:     Any =						dp_array[row][col + 1]
      __SCREAMING_SNAKE_CASE	:     List[str] =						dp_array[row + 1][col + 1]
      __SCREAMING_SNAKE_CASE	:     List[str] =						dp_array[row + 1][col]

      if mat[row][col] == 1:
        __SCREAMING_SNAKE_CASE	:     Dict =						1 + min(lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__				)
        __SCREAMING_SNAKE_CASE	:     int =						max(dp_array[row][col]    ,       lowerCAmelCase__				)
      else:
        __SCREAMING_SNAKE_CASE	:     List[Any] =						0

  return largest_square_area


def 				a__ (				snake_case    ,       snake_case    ,       snake_case				):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE	:     Optional[int] =						[0] * (cols + 1)
  __SCREAMING_SNAKE_CASE	:     Tuple =						[0] * (cols + 1)
  __SCREAMING_SNAKE_CASE	:     Optional[int] =						0
  for row in range(rows - 1    ,       -1    ,       -1				):
    for col in range(cols - 1    ,       -1    ,       -1				):
      __SCREAMING_SNAKE_CASE	:     Optional[Any] =						current_row[col + 1]
      __SCREAMING_SNAKE_CASE	:     Optional[int] =						next_row[col + 1]
      __SCREAMING_SNAKE_CASE	:     List[str] =						next_row[col]

      if mat[row][col] == 1:
        __SCREAMING_SNAKE_CASE	:     Union[str, Any] =						1 + min(lowerCAmelCase__    ,       lowerCAmelCase__    ,       lowerCAmelCase__				)
        __SCREAMING_SNAKE_CASE	:     List[str] =						max(current_row[col]    ,       lowerCAmelCase__				)
      else:
        __SCREAMING_SNAKE_CASE	:     Any =						0
    __SCREAMING_SNAKE_CASE	:     str =						current_row

  return largest_square_area


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))




"""simple docstring"""




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def        lowercase							(		lowerCAmelCase__ :     Namespace			) ->      Tuple:
		return ConvertCommand(
		    args.model_type    ,					args.tf_checkpoint    ,					args.pytorch_dump_output    ,					args.config    ,					args.finetuning_task_name			)


lowercase_			     =  "\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					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@staticmethod
		def 			__UpperCAmelCase       (  _a      ):
				__a           =			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							, _a							, _a							, _a							, _a							, _a							, *_a							, ):
				__a           =			logging.get_logger('''transformers-cli/converting'''      )

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





		def 			__UpperCAmelCase       (  self      ):
				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():
								__a           =			self._tf_checkpoint
								__a           =			''''''
						else:
								__a           =			self._tf_checkpoint
								__a           =			''''''
						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]'''      )






