


"""simple docstring"""






import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     TapasConfig.from_json_file(_lowerCamelCase       )
   # set absolute/relative position embeddings parameter
   _lowerCamelCase   :				int							=     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      _lowerCamelCase   :				Tuple							=     TapasForQuestionAnswering(config=_lowerCamelCase       )
   elif task == "WTQ":
      # run_task_main.py hparams
      _lowerCamelCase   :				List[Any]							=     4
      _lowerCamelCase   :				List[str]							=     True
      # hparam_utils.py hparams
      _lowerCamelCase   :				Tuple							=     0.6_6_4_6_9_4
      _lowerCamelCase   :				List[str]							=     0.2_0_7_9_5_1
      _lowerCamelCase   :				Optional[int]							=     0.1_2_1_1_9_4
      _lowerCamelCase   :				int							=     True
      _lowerCamelCase   :				Optional[int]							=     True
      _lowerCamelCase   :				List[str]							=     False
      _lowerCamelCase   :				int							=     0.0_3_5_2_5_1_3

      _lowerCamelCase   :				List[str]							=     TapasForQuestionAnswering(config=_lowerCamelCase       )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      _lowerCamelCase   :				Tuple							=     4
      _lowerCamelCase   :				Union[str, Any]							=     False
      # hparam_utils.py hparams
      _lowerCamelCase   :				Dict							=     3_6.4_5_1_9
      _lowerCamelCase   :				Dict							=     0.9_0_3_4_2_1
      _lowerCamelCase   :				Any							=     2_2_2.0_8_8
      _lowerCamelCase   :				int							=     True
      _lowerCamelCase   :				Optional[int]							=     True
      _lowerCamelCase   :				str							=     True
      _lowerCamelCase   :				Optional[int]							=     0.7_6_3_1_4_1

      _lowerCamelCase   :				Optional[Any]							=     TapasForQuestionAnswering(config=_lowerCamelCase       )
   elif task == "TABFACT":
      _lowerCamelCase   :				Optional[int]							=     TapasForSequenceClassification(config=_lowerCamelCase       )
   elif task == "MLM":
      _lowerCamelCase   :				Optional[int]							=     TapasForMaskedLM(config=_lowerCamelCase       )
   elif task == "INTERMEDIATE_PRETRAINING":
      _lowerCamelCase   :				List[str]							=     TapasModel(config=_lowerCamelCase       )
   else:
      raise ValueError(F"""Task {task} not supported."""       )

   print(F"""Building PyTorch model from configuration: {config}"""       )
   # Load weights from tf checkpoint
   load_tf_weights_in_tapas(_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

   # Save pytorch-model (weights and configuration)
   print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
   model.save_pretrained(_lowerCamelCase       )

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""       )
   _lowerCamelCase   :				Union[str, Any]							=     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"			,		model_max_length=512       )
   tokenizer.save_pretrained(_lowerCamelCase       )

   print("Used relative position embeddings:"			,		model.config.reset_position_index_per_cell       )


if __name__ == "__main__":
  _lowerCAmelCase							:    str					   =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  _lowerCAmelCase							:    int					   =	parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )




import numpy as np


class 	__snake_case   :
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  (0, 0)
						lowerCAmelCase__		  =  None
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
						lowerCAmelCase__		  =  0
		def __eq__(							self    ,a_						):







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



		def 						SCREAMING_SNAKE_CASE_					(							self						):







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



class 	__snake_case   :
		def __init__(							self    ,a_=(5, 5)						):







						"""simple docstring"""
						lowerCAmelCase__		  =  np.zeros(a_						)
						lowerCAmelCase__		  =  world_size[0]
						lowerCAmelCase__		  =  world_size[1]
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  [
						    (-1, -1),
						    (-1, 0),
						    (-1, 1),
						    (0, -1),
						    (0, 1),
						    (1, -1),
						    (1, 0),
						    (1, 1),
						]
						lowerCAmelCase__		  =  cell.position[0]
						lowerCAmelCase__		  =  cell.position[1]
						lowerCAmelCase__		  =  []
						for n in neughbour_cord:
										lowerCAmelCase__		  =  current_x + n[0]
										lowerCAmelCase__		  =  current_y + n[1]
										if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
														lowerCAmelCase__		  =  Cell()
														lowerCAmelCase__		  =  (x, y)
														lowerCAmelCase__		  =  cell
														neighbours.append(a_						)
						return neighbours







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     int:



				"""simple docstring"""





				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  []
				_open.append(snake_case__					)

				while _open:
								lowerCAmelCase__		  =  np.argmin([n.f for n in _open]					)
								lowerCAmelCase__		  =  _open[min_f]
								_closed.append(_open.pop(snake_case__					)					)
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__					):
												for c in _closed:
																if c == n:
																				continue
												lowerCAmelCase__		  =  current.g + 1
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  n.position
												lowerCAmelCase__  ,   lowerCAmelCase__		  =  goal.position
												lowerCAmelCase__		  =  (ya - ya) ** 2 + (xa - xa) ** 2
												lowerCAmelCase__		  =  n.h + n.g

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


if __name__ == "__main__":
	_lowerCAmelCase	:   str    =   Gridworld()
	# Start position and goal
	_lowerCAmelCase	:   List[Any]    =   Cell()
	_lowerCAmelCase	:   List[str]    =   (0, 0)
	_lowerCAmelCase	:   Optional[int]    =   Cell()
	_lowerCAmelCase	:   int    =   (4, 4)
	print(f"""path from {start.position} to {goal.position}""")
	_lowerCAmelCase	:   Optional[int]    =   astar(world, start, goal)
	#   Just for visual reasons.
	for i in s:
		_lowerCAmelCase	:   int    =   1
	print(world.w)
