





'''simple docstring'''

from __future__ import annotations

import queue
class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			data
            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None







def    a__							(							)						->						TreeNode:





     """simple docstring"""

     print('''\n********Press N to stop entering at any point of time********\n''' )
     _UpperCamelCase							       =			input('''Enter the value of the root node: ''' ).strip().lower()
     _UpperCamelCase							       =			queue.Queue()
     _UpperCamelCase							       =			TreeNode(int(lowercase ) )
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			q.get()
          _UpperCamelCase							       =			F"""Enter the left node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			left_node
          q.put(lowercase )
          _UpperCamelCase							       =			F"""Enter the right node of {node_found.data}: """
          _UpperCamelCase							       =			input(lowercase ).strip().lower() or '''n'''
          if check == "n":
               return tree_node
          _UpperCamelCase							       =			TreeNode(int(lowercase ) )
          _UpperCamelCase							       =			right_node
          q.put(lowercase )
     raise







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

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







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

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







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

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







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			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    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			queue.Queue()
     q.put(lowercase )
     while not q.empty():
          _UpperCamelCase							       =			[]
          while not q.empty():
               _UpperCamelCase							       =			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(lowercase )







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data,   end=''',''' )
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          # end of while means current node doesn't have left child
          _UpperCamelCase							       =			stack.pop()
          # start to traverse its right child
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ) or not node:
          return
     _UpperCamelCase							       =			[]
     _UpperCamelCase							       =			node
     while n or stack:
          while n:
               stack.append(lowercase )
               _UpperCamelCase							       =			n.left
          _UpperCamelCase							       =			stack.pop()
          print(n.data,   end=''',''' )
          _UpperCamelCase							       =			n.right







def    a__							(							lowercase   :	TreeNode )						->						None:





     """simple docstring"""

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







def    a__							(							lowercase   :	str = "",   lowercase   :	List[str]=50,   lowercase   :	List[str]="*" )						->						str:





     """simple docstring"""

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


if __name__ == "__main__":
   import doctest

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

   lowercase__		:     TreeNode			=							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())










'''simple docstring'''

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowercase__		:     Dict			=							[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def    a__							(							lowercase   :	List[str] )						->						List[Any]:





     """simple docstring"""

     for pegasus_name, hf_name in PATTERNS:
          _UpperCamelCase							       =			k.replace(lowercase,   lowercase )
     return k







def    a__							(							lowercase   :	dict,   lowercase   :	dict )						->						PegasusForConditionalGeneration:





     """simple docstring"""

     _UpperCamelCase							       =			DEFAULTS.copy()
     cfg_kwargs.update(lowercase )
     _UpperCamelCase							       =			PegasusConfig(**lowercase )
     _UpperCamelCase							       =			PegasusForConditionalGeneration(lowercase )
     _UpperCamelCase							       =			torch_model.model.state_dict()
     _UpperCamelCase							       =			{}
     for k, v in tf_weights.items():
          _UpperCamelCase							       =			rename_state_dict_key(lowercase )
          if new_k not in sd:
               raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""" )

          if "dense" in k or "proj" in new_k:
               _UpperCamelCase							       =			v.T
          _UpperCamelCase							       =			torch.tensor(lowercase,   dtype=sd[new_k].dtype )
          assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
     # make sure embedding.padding_idx is respected
     _UpperCamelCase							       =			torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1] )
     _UpperCamelCase							       =			mapping['''shared.weight''']
     _UpperCamelCase							       =			mapping['''shared.weight''']
     _UpperCamelCase							       =			{k: torch.zeros_like(lowercase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
     mapping.update(**lowercase )
     _UpperCamelCase       ,							_UpperCamelCase							       =			torch_model.model.load_state_dict(lowercase,   strict=lowercase )
     _UpperCamelCase							       =			[
         k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
     ]
     assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
     assert extra == [], F"""no matches found for the following tf keys {extra}"""
     return torch_model







def    a__							(							lowercase   :	List[Any]="./ckpt/aeslc/model.ckpt-32000" )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			tf.train.list_variables(lowercase )
     _UpperCamelCase							       =			{}
     _UpperCamelCase							       =			['''Adafactor''', '''global_step''']
     for name, shape in tqdm(lowercase,   desc='''converting tf checkpoint to dict''' ):
          _UpperCamelCase							       =			any(pat in name for pat in ignore_name )
          if skip_key:
               continue
          _UpperCamelCase							       =			tf.train.load_variable(lowercase,   lowercase )
          _UpperCamelCase							       =			array
     return tf_weights







def    a__							(							lowercase   :	str,   lowercase   :	str )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			Path(lowercase ).parent.name
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
     _UpperCamelCase							       =			PegasusTokenizer.from_pretrained('''sshleifer/pegasus''',   model_max_length=lowercase )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(lowercase )

     # convert model
     _UpperCamelCase							       =			get_tf_weights_as_numpy(lowercase )
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]
     if dataset == "large":
          _UpperCamelCase							       =			task_specific_params
     _UpperCamelCase							       =			convert_pegasus(lowercase,   lowercase )
     torch_model.save_pretrained(lowercase )
     _UpperCamelCase							       =			torch_model.state_dict()
     sd.pop('''model.decoder.embed_positions.weight''' )
     sd.pop('''model.encoder.embed_positions.weight''' )
     torch.save(lowercase,   Path(lowercase ) / '''pytorch_model.bin''' )


if __name__ == "__main__":
   lowercase__		:     List[Any]			=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   lowercase__		:     Optional[int]			=							parser.parse_args()
   if args.save_dir is None:
      lowercase__		:     Tuple			=							Path(args.tf_ckpt_path).parent.name
      lowercase__		:     Optional[Any]			=							os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)



