
'''simple docstring'''




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      lowercase     :							Any       						=     argparse.ArgumentParser(
          description=(
              'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'
              ' Distillation'
          )
      )
      parser.add_argument('--model_type', default='roberta', choices=['roberta', 'gpt2'])
      parser.add_argument('--model_name', default='roberta-large', type=str)
      parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_roberta_048131723.pth', type=str)
      parser.add_argument('--vocab_transform', action='store_true')
      lowercase     :							Dict       						=     parser.parse_args()

      if args.model_type == "roberta":
            lowercase     :							List[str]       						=     RobertaForMaskedLM.from_pretrained(args.model_name)
            lowercase     :							List[str]       						=     'roberta'
      elif args.model_type == "gpt2":
            lowercase     :							Union[str, Any]       						=     GPTaLMHeadModel.from_pretrained(args.model_name)
            lowercase     :							int       						=     'transformer'

      lowercase     :							Tuple       						=     model.state_dict()
      lowercase     :							Any       						=     {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  lowercase     :							Optional[Any]       						=     state_dict[f'''{prefix}.{param_name}''']
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  lowercase     :							List[str]       						=     f'''{prefix}.embeddings.{w}.weight'''
                  lowercase     :							Optional[Any]       						=     state_dict[param_name]
            for w in ["weight", "bias"]:
                  lowercase     :							Tuple       						=     f'''{prefix}.embeddings.LayerNorm.{w}'''
                  lowercase     :							List[str]       						=     state_dict[param_name]

                   # Transformer Blocks #
      lowercase     :							str       						=     0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            if args.model_type == "gpt2":
                  for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                        for w in ["weight", "bias"]:
                              lowercase     :							Tuple       						=     state_dict[
                                  f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                              ]
                  lowercase     :							List[str]       						=     state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
            else:
                  for layer in [
                      "attention.self.query",
                      "attention.self.key",
                      "attention.self.value",
                      "attention.output.dense",
                      "attention.output.LayerNorm",
                      "intermediate.dense",
                      "output.dense",
                      "output.LayerNorm",
                  ]:
                        for w in ["weight", "bias"]:
                              lowercase     :							Union[str, Any]       						=     state_dict[
                                  f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
                              ]
            std_idx += 1

      # Language Modeling Head ###s
      if args.model_type == "roberta":
            for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                  lowercase     :							int       						=     state_dict[f'''{layer}''']
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        lowercase     :							str       						=     state_dict[f'''lm_head.dense.{w}''']
                        lowercase     :							int       						=     state_dict[f'''lm_head.layer_norm.{w}''']
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  lowercase     :							Any       						=     state_dict[f'''{prefix}.ln_f.{w}''']
            lowercase     :							Tuple       						=     state_dict['lm_head.weight']

      print(f'''N layers selected for distillation: {std_idx}''')
      print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

      print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
      torch.save(compressed_sd, args.dump_checkpoint)








"""simple docstring"""
from __future__ import annotations

import queue




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					int   =		data
      _lowerCamelCase	:					List[str]   =		None
      _lowerCamelCase	:					Any   =		None







def 				_snake_case      (   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Any   =		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 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







   if not isinstance(__snake_case						, __snake_case   ) or not node:
      return
   _lowerCamelCase	:					queue.Queue   =		queue.Queue()
   q.put(__snake_case   )
   while not q.empty():
      _lowerCamelCase	:					Optional[Any]   =		[]
      while not q.empty():
         _lowerCamelCase	:					Dict   =		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(__snake_case   )

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      TreeNode   ):




   """simple docstring"""







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

def 				_snake_case      (   __snake_case							:      str = ""						, __snake_case							:      Any=50						, __snake_case							:      List[str]="*"   ):




   """simple docstring"""







   if not s:
      return "\n" + width * char
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		divmod(width - len(__snake_case   ) - 2						, 2   )
   return F'{left * char} {s} {(left + extra) * char}'


if __name__ == "__main__":
 import doctest

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

 UpperCAmelCase				=						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())

