


"""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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

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

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      36.4_519
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      222.088
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[Any]											=      TapasModel(config=A_	)
  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(A_  ,       A_  ,       A_	)

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

  # Save tokenizer files
  print(F'''Save tokenizer files to {pytorch_dump_path}'''	)
  a						:		Optional[int]											=      TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"  ,       model_max_length=512	)
  tokenizer.save_pretrained(A_	)

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


if __name__ == "__main__":
 __lowercase    =				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."""
 )
 __lowercase    =				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,
 )










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







  print("\n********Press N to stop entering at any point of time********\n"	)
  a						:		int											=      input("Enter the value of the root node: "	).strip().lower()
  a						:		queue.Queue											=      queue.Queue()
  a						:		Tuple											=      TreeNode(int(A_	)	)
  q.put(A_	)
  while not q.empty():
    a						:		Union[str, Any]											=      q.get()
    a						:		Optional[int]											=      F'''Enter the left node of {node_found.data}: '''
    a						:		Union[str, Any]											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		List[str]											=      TreeNode(int(A_	)	)
    a						:		Any											=      left_node
    q.put(A_	)
    a						:		Dict											=      F'''Enter the right node of {node_found.data}: '''
    a						:		str											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		Any											=      TreeNode(int(A_	)	)
    a						:		str											=      right_node
    q.put(A_	)
  raise


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  print(node.data  ,       end=","	)
  pre_order(node.left	)
  pre_order(node.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  in_order(node.left	)
  print(node.data  ,       end=","	)
  in_order(node.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  post_order(node.left	)
  post_order(node.right	)
  print(node.data  ,       end=","	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      q.get()
    print(node_dequeued.data  ,       end=","	)
    if node_dequeued.left:
      q.put(node_dequeued.left	)
    if node_dequeued.right:
      q.put(node_dequeued.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		Any											=      []
    while not q.empty():
      a						:		Any											=      q.get()
      print(node_dequeued.data  ,       end=","	)
      if node_dequeued.left:
        list_.append(node_dequeued.left	)
      if node_dequeued.right:
        list_.append(node_dequeued.right	)
    print()
    for node in list_:
      q.put(A_	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		int											=      node
  while n or stack:
    while n:  # start from root node, find its left child
      print(n.data  ,       end=","	)
      stack.append(A_	)
      a						:		Tuple											=      n.left
    # end of while means current node doesn't have left child
    a						:		Optional[Any]											=      stack.pop()
    # start to traverse its right child
    a						:		str											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		Union[str, Any]											=      node
  while n or stack:
    while n:
      stack.append(A_	)
      a						:		int											=      n.left
    a						:		str											=      stack.pop()
    print(n.data  ,       end=","	)
    a						:		Optional[int]											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a							,		a						:		Tuple											=      [], []
  a						:		Tuple											=      node
  stacka.append(A_	)
  while stacka:  # to find the reversed order of post order, store it in stack2
    a						:		Optional[Any]											=      stacka.pop()
    if n.left:
      stacka.append(n.left	)
    if n.right:
      stacka.append(n.right	)
    stacka.append(A_	)
  while stacka:  # pop up from stack2 will be the post order
    print(stacka.pop().data  ,       end=","	)


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







  if not s:
    return "\n" + width * char
  a							,		a						:		Dict											=      divmod(width - len(A_	) - 2  ,       2	)
  return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(prompt("""Binary Tree Traversals"""))

 __lowercase    =				build_tree()
 print(prompt("""Pre Order Traversal"""))
 pre_order(node)
 print(prompt() + """\n""")

 print(prompt("""In Order Traversal"""))
 in_order(node)
 print(prompt() + """\n""")

 print(prompt("""Post Order Traversal"""))
 post_order(node)
 print(prompt() + """\n""")

 print(prompt("""Level Order Traversal"""))
 level_order(node)
 print(prompt() + """\n""")

 print(prompt("""Actual Level Order Traversal"""))
 level_order_actual(node)
 print("""*""" * 50 + """\n""")

 print(prompt("""Pre Order Traversal - Iteration Version"""))
 pre_order_iter(node)
 print(prompt() + """\n""")

 print(prompt("""In Order Traversal - Iteration Version"""))
 in_order_iter(node)
 print(prompt() + """\n""")

 print(prompt("""Post Order Traversal - Iteration Version"""))
 post_order_iter(node)
 print(prompt())






