






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

import math
import random
from typing import Any


class _lowerCAmelCase						:







						def __init__(    self							)		->							None:
										_SCREAMING_SNAKE_CASE   :		list[Any]       		=    []
										_SCREAMING_SNAKE_CASE   :		int       		=    0
										_SCREAMING_SNAKE_CASE   :		int       		=    0







						def 			A      (    self							)		->							bool:
										return self.head == self.tail







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										self.data.append(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    self.tail + 1







						def 			A      (    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		int       		=    self.data[self.head]
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.head + 1
										return ret







						def 			A      (    self							)		->							int:
										return self.tail - self.head







						def 			A      (    self							)		->							None:
										print(self.data							)
										print('**************'							)
										print(self.data[self.head : self.tail]							)







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    data
										_SCREAMING_SNAKE_CASE   :		MyNode | None       		=    None
										_SCREAMING_SNAKE_CASE   :		MyNode | None       		=    None
										_SCREAMING_SNAKE_CASE   :		int       		=    1







						def 			A      (    self							)		->							Any:
										return self.data







						def 			A      (    self							)		->							MyNode | None:
										return self.left







						def 			A      (    self							)		->							MyNode | None:
										return self.right







						def 			A      (    self							)		->							int:
										return self.height







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    data







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Any       		=    node







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Any       		=    node







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		int       		=    height







def 				lowercase__		(					lowerCamelCase       ):
				if node is None:
								return 0
				return node.get_height()





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				if a > b:
								return a
				return b





def 				lowercase__		(					lowerCamelCase       ):
				print('left rotation node:',						node.get_data()       )
				_SCREAMING_SNAKE_CASE   :		int       		=    node.get_left()
				assert ret is not None
				node.set_left(ret.get_right()       )
				ret.set_right(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    my_max(get_height(node.get_right()       ),						get_height(node.get_left()       )       ) + 1
				node.set_height(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    my_max(get_height(ret.get_right()       ),						get_height(ret.get_left()       )       ) + 1
				ret.set_height(lowerCamelCase       )
				return ret





def 				lowercase__		(					lowerCamelCase       ):
				print('right rotation node:',						node.get_data()       )
				_SCREAMING_SNAKE_CASE   :		int       		=    node.get_right()
				assert ret is not None
				node.set_right(ret.get_left()       )
				ret.set_left(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    my_max(get_height(node.get_right()       ),						get_height(node.get_left()       )       ) + 1
				node.set_height(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    my_max(get_height(ret.get_right()       ),						get_height(ret.get_left()       )       ) + 1
				ret.set_height(lowerCamelCase       )
				return ret





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    node.get_left()
				assert left_child is not None
				node.set_left(left_rotation(lowerCamelCase       )       )
				return right_rotation(lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    node.get_right()
				assert right_child is not None
				node.set_right(right_rotation(lowerCamelCase       )       )
				return left_rotation(lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				if node is None:
								return MyNode(lowerCamelCase       )
				if data < node.get_data():
								node.set_left(insert_node(node.get_left(),						lowerCamelCase       )       )
								if (
								    get_height(node.get_left()       ) - get_height(node.get_right()       ) == 2
								):  # an unbalance detected
												_SCREAMING_SNAKE_CASE   :		str       		=    node.get_left()
												assert left_child is not None
												if (
												    data < left_child.get_data()
												):  # new node is the left child of the left child
																_SCREAMING_SNAKE_CASE   :		Any       		=    right_rotation(lowerCamelCase       )
												else:
																_SCREAMING_SNAKE_CASE   :		str       		=    lr_rotation(lowerCamelCase       )
				else:
								node.set_right(insert_node(node.get_right(),						lowerCamelCase       )       )
								if get_height(node.get_right()       ) - get_height(node.get_left()       ) == 2:
												_SCREAMING_SNAKE_CASE   :		Dict       		=    node.get_right()
												assert right_child is not None
												if data < right_child.get_data():
																_SCREAMING_SNAKE_CASE   :		Dict       		=    rl_rotation(lowerCamelCase       )
												else:
																_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    left_rotation(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    my_max(get_height(node.get_right()       ),						get_height(node.get_left()       )       ) + 1
				node.set_height(lowerCamelCase       )
				return node





def 				lowercase__		(					lowerCamelCase       ):
				while True:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    root.get_right()
								if right_child is None:
												break
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    right_child
				return root.get_data()





def 				lowercase__		(					lowerCamelCase       ):
				while True:
								_SCREAMING_SNAKE_CASE   :		Dict       		=    root.get_left()
								if left_child is None:
												break
								_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    left_child
				return root.get_data()





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Any       		=    root.get_left()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    root.get_right()
				if root.get_data() == data:
								if left_child is not None and right_child is not None:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    get_left_most(lowerCamelCase       )
												root.set_data(lowerCamelCase       )
												root.set_right(del_node(lowerCamelCase,						lowerCamelCase       )       )
								elif left_child is not None:
												_SCREAMING_SNAKE_CASE   :		int       		=    left_child
								elif right_child is not None:
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    right_child
								else:
												return None
				elif root.get_data() > data:
								if left_child is None:
												print('No such data'       )
												return root
								else:
												root.set_left(del_node(lowerCamelCase,						lowerCamelCase       )       )
				else:  # root.get_data() < data
								if right_child is None:
												return root
								else:
												root.set_right(del_node(lowerCamelCase,						lowerCamelCase       )       )

				if get_height(lowerCamelCase       ) - get_height(lowerCamelCase       ) == 2:
								assert right_child is not None
								if get_height(right_child.get_right()       ) > get_height(right_child.get_left()       ):
												_SCREAMING_SNAKE_CASE   :		Dict       		=    left_rotation(lowerCamelCase       )
								else:
												_SCREAMING_SNAKE_CASE   :		str       		=    rl_rotation(lowerCamelCase       )
				elif get_height(lowerCamelCase       ) - get_height(lowerCamelCase       ) == -2:
								assert left_child is not None
								if get_height(left_child.get_left()       ) > get_height(left_child.get_right()       ):
												_SCREAMING_SNAKE_CASE   :		str       		=    right_rotation(lowerCamelCase       )
								else:
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    lr_rotation(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		str       		=    my_max(get_height(root.get_right()       ),						get_height(root.get_left()       )       ) + 1
				root.set_height(lowerCamelCase       )
				return root


class _lowerCAmelCase						:







						def __init__(    self							)		->							None:
										_SCREAMING_SNAKE_CASE   :		MyNode | None       		=    None







						def 			A      (    self							)		->							int:
										return get_height(self.root							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										print('insert:' + str(lowerCAmelCase_							)							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    insert_node(self.root   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										print('delete:' + str(lowerCAmelCase_							)							)
										if self.root is None:
														print('Tree is empty!'							)
														return
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    del_node(self.root   ,				lowerCAmelCase_							)







						def __str__(    self   ,				)		->							str:  # a level traversale, gives a more intuitive look on the tree
										_SCREAMING_SNAKE_CASE   :		Any       		=    ''
										_SCREAMING_SNAKE_CASE   :		int       		=    MyQueue()
										q.push(self.root							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.get_height()
										if layer == 0:
														return output
										_SCREAMING_SNAKE_CASE   :		str       		=    0
										while not q.is_empty():
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    q.pop()
														_SCREAMING_SNAKE_CASE   :		Any       		=    ' ' * int(math.pow(2   ,				layer - 1							)							)
														output += space
														if node is None:
																		output += "*"
																		q.push(lowerCAmelCase_							)
																		q.push(lowerCAmelCase_							)
														else:
																		output += str(node.get_data()							)
																		q.push(node.get_left()							)
																		q.push(node.get_right()							)
														output += space
														_SCREAMING_SNAKE_CASE   :		Any       		=    cnt + 1
														for i in range(1_0_0							):
																		if cnt == math.pow(2   ,				lowerCAmelCase_							) - 1:
																						_SCREAMING_SNAKE_CASE   :		Any       		=    layer - 1
																						if layer == 0:
																										output += "\n*************************************"
																										return output
																						output += "\n"
																						break
										output += "\n*************************************"
										return output







def 				lowercase__		(					):
				import doctest

				doctest.testmod()


if __name__ == "__main__":
			_test()
			lowerCAmelCase__    						=			AVLtree()
			lowerCAmelCase__    						=			list(range(10))
			random.shuffle(lst)
			for i in lst:
						t.insert(i)
						print(str(t))
			random.shuffle(lst)
			for i in lst:
						t.del_node(i)
						print(str(t))









"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			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')
			lowerCAmelCase__    						=			parser.parse_args()

			if args.model_type == "roberta":
						lowerCAmelCase__    						=			RobertaForMaskedLM.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'roberta'
			elif args.model_type == "gpt2":
						lowerCAmelCase__    						=			GPTaLMHeadModel.from_pretrained(args.model_name)
						lowerCAmelCase__    						=			'transformer'

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

			# Embeddings #
			if args.model_type == "gpt2":
						for param_name in ["wte.weight", "wpe.weight"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.{param_name}']
			else:
						for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.{w}.weight'
									lowerCAmelCase__    						=			state_dict[param_name]
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			F'{prefix}.embeddings.LayerNorm.{w}'
									lowerCAmelCase__    						=			state_dict[param_name]

    # Transformer Blocks #
			lowerCAmelCase__    						=			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"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			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"]:
															lowerCAmelCase__    						=			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"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			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)

