





from __future__ import annotations

UpperCamelCase__   : str					  = [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]




def    A_(			A				,      A				,      A				,      A				,      A				,      ):
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the reference grid
	UpperCAmelCase_								=     1
	UpperCAmelCase_								=     [
	    [0 for col in range(len(grid[0]   )   )] for row in range(len(A   )   )
	]  # the action grid

	UpperCAmelCase_								=     init[0]
	UpperCAmelCase_								=     init[1]
	UpperCAmelCase_								=     0
	UpperCAmelCase_								=     g + heuristic[x][y]  # cost from starting cell to destination cell
	UpperCAmelCase_								=     [[f, g, x, y]]

	UpperCAmelCase_								=     False  # flag that is set when search is complete
	UpperCAmelCase_								=     False  # flag set if we can't find expand

	while not found and not resign:
		if len(A   ) == 0:
			raise ValueError("""Algorithm is unable to find solution"""   )
		else:  # to choose the least costliest action so as to move closer to the goal
			cell.sort()
			cell.reverse()
			UpperCAmelCase_								=     cell.pop()
			UpperCAmelCase_								=     next_cell[2]
			UpperCAmelCase_								=     next_cell[3]
			UpperCAmelCase_								=     next_cell[1]

			if x == goal[0] and y == goal[1]:
				UpperCAmelCase_								=     True
			else:
				for i in range(len(A   )   ):  # to try out different valid actions
					UpperCAmelCase_								=     x + DIRECTIONS[i][0]
					UpperCAmelCase_								=     y + DIRECTIONS[i][1]
					if xa >= 0 and xa < len(A   ) and ya >= 0 and ya < len(grid[0]   ):
						if closed[xa][ya] == 0 and grid[xa][ya] == 0:
							UpperCAmelCase_								=     g + cost
							UpperCAmelCase_								=     ga + heuristic[xa][ya]
							cell.append([fa, ga, xa, ya]   )
							UpperCAmelCase_								=     1
							UpperCAmelCase_								=     i
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     goal[0]
	UpperCAmelCase_								=     goal[1]
	invpath.append([x, y]   )  # we get the reverse path from here
	while x != init[0] or y != init[1]:
		UpperCAmelCase_								=     x - DIRECTIONS[action[x][y]][0]
		UpperCAmelCase_								=     y - DIRECTIONS[action[x][y]][1]
		UpperCAmelCase_								=     xa
		UpperCAmelCase_								=     ya
		invpath.append([x, y]   )

	UpperCAmelCase_								=     []
	for i in range(len(A   )   ):
		path.append(invpath[len(A   ) - 1 - i]   )
	return path, action


if __name__ == "__main__":
				UpperCamelCase__   : Tuple					  = [
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
				    [0, 1, 0, 0, 0, 0],
				    [0, 1, 0, 0, 1, 0],
				    [0, 0, 0, 0, 1, 0],
				]

				UpperCamelCase__   : Dict					  = [0, 0]
				# all coordinates are given in format [y,x]
				UpperCamelCase__   : Optional[int]					  = [len(grid) - 1, len(grid[0]) - 1]
				UpperCamelCase__   : Optional[Any]					  = 1

				# the cost map which pushes the path closer to the goal
				UpperCamelCase__   : Any					  = [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
				for i in range(len(grid)):
								for j in range(len(grid[0])):
												UpperCamelCase__   : Any					  = abs(i - goal[0]) + abs(j - goal[1])
												if grid[i][j] == 1:
																# added extra penalty in the heuristic map
																UpperCamelCase__   : Dict					  = 99

				UpperCamelCase__   : Dict					  = search(grid, init, goal, cost, heuristic)

				print("""ACTION MAP""")
				for i in range(len(action)):
								print(action[i])

				for i in range(len(path)):
								print(path[i])









from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand




def    A_(			A   ):
	return ConvertCommand(
	    args.model_type				,      args.tf_checkpoint				,      args.pytorch_dump_output				,      args.config				,      args.finetuning_task_name   )


UpperCamelCase__   : Any					  = """
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   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  ArgumentParser		):






						'''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=__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    :  Optional[int] ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					__lowercase    :  str ,					*__lowercase    :  Tuple ,					):






						'''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        SCREAMING_SNAKE_CASE							(						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(__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():
								UpperCAmelCase_								=     self._tf_checkpoint
								UpperCAmelCase_								=     """"""
							else:
								UpperCAmelCase_								=     self._tf_checkpoint
								UpperCAmelCase_								=     """"""
							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]"""		)


