
from __future__ import annotations

import math
import random
from typing import Any





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					Tuple					):
					__A       =					[]
					__A       =					0
					__A       =					0






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return self.head == self.tail






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					Any					):
					self.data.append(A					)
					__A       =					self.tail + 1






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.data[self.head]
					__A       =					self.head + 1
					return ret






	def 							UpperCamelCase_    (			self   :					str					):
					return self.tail - self.head







	def 							UpperCamelCase_    (			self   :					List[str]					):
					print(self.data					)
					print("**************"					)
					print(self.data[self.head : self.tail]					)





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					str				,A   :					Any					):
					__A       =					data
					__A       =					None
					__A       =					None
					__A       =					1






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					return self.data






	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return self.left






	def 							UpperCamelCase_    (			self   :					int					):
					return self.right






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					return self.height






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Any					):
					__A       =					data






	def 							UpperCamelCase_    (			self   :					Any				,A   :					MyNode | None					):
					__A       =					node






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					MyNode | None					):
					__A       =					node







	def 							UpperCamelCase_    (			self   :					Dict				,A   :					int					):
					__A       =					height





def 		UpperCAmelCase     (      a_		)						->      int:







				"""simple docstring"""



				if node is None:
								return 0
				return node.get_height()


def 		UpperCAmelCase     (      a_						,  a_		)						->      int:







				"""simple docstring"""



				if a > b:
								return a
				return b


def 		UpperCAmelCase     (      a_		)						->      MyNode:







				"""simple docstring"""



				print("left rotation node:"						,  node.get_data()		)
				__A       =					node.get_left()
				assert ret is not None
				node.set_left(ret.get_right()		)
				ret.set_right(a_		)
				__A       =					my_max(get_height(node.get_right()		)						,  get_height(node.get_left()		)		) + 1
				node.set_height(a_		)
				__A       =					my_max(get_height(ret.get_right()		)						,  get_height(ret.get_left()		)		) + 1
				ret.set_height(a_		)
				return ret


def 		UpperCAmelCase     (      a_		)						->      MyNode:







				"""simple docstring"""



				print("right rotation node:"						,  node.get_data()		)
				__A       =					node.get_right()
				assert ret is not None
				node.set_right(ret.get_left()		)
				ret.set_left(a_		)
				__A       =					my_max(get_height(node.get_right()		)						,  get_height(node.get_left()		)		) + 1
				node.set_height(a_		)
				__A       =					my_max(get_height(ret.get_right()		)						,  get_height(ret.get_left()		)		) + 1
				ret.set_height(a_		)
				return ret


def 		UpperCAmelCase     (      a_		)						->      MyNode:







				"""simple docstring"""



				__A       =					node.get_left()
				assert left_child is not None
				node.set_left(left_rotation(a_		)		)
				return right_rotation(a_		)


def 		UpperCAmelCase     (      a_		)						->      MyNode:







				"""simple docstring"""



				__A       =					node.get_right()
				assert right_child is not None
				node.set_right(right_rotation(a_		)		)
				return left_rotation(a_		)


def 		UpperCAmelCase     (      a_						,  a_		)						->      MyNode | None:







				"""simple docstring"""



				if node is None:
								return MyNode(a_		)
				if data < node.get_data():
								node.set_left(insert_node(node.get_left()						,  a_		)		)
								if (
								    get_height(node.get_left()		) - get_height(node.get_right()		) == 2
								):  # an unbalance detected
												__A       =					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
																__A       =					right_rotation(a_		)
												else:
																__A       =					lr_rotation(a_		)
				else:
								node.set_right(insert_node(node.get_right()						,  a_		)		)
								if get_height(node.get_right()		) - get_height(node.get_left()		) == 2:
												__A       =					node.get_right()
												assert right_child is not None
												if data < right_child.get_data():
																__A       =					rl_rotation(a_		)
												else:
																__A       =					left_rotation(a_		)
				__A       =					my_max(get_height(node.get_right()		)						,  get_height(node.get_left()		)		) + 1
				node.set_height(a_		)
				return node


def 		UpperCAmelCase     (      a_		)						->      Any:







				"""simple docstring"""



				while True:
								__A       =					root.get_right()
								if right_child is None:
												break
								__A       =					right_child
				return root.get_data()


def 		UpperCAmelCase     (      a_		)						->      Any:







				"""simple docstring"""



				while True:
								__A       =					root.get_left()
								if left_child is None:
												break
								__A       =					left_child
				return root.get_data()


def 		UpperCAmelCase     (      a_						,  a_		)						->      MyNode | None:







				"""simple docstring"""



				__A       =					root.get_left()
				__A       =					root.get_right()
				if root.get_data() == data:
								if left_child is not None and right_child is not None:
												__A       =					get_left_most(a_		)
												root.set_data(a_		)
												root.set_right(del_node(a_						,  a_		)		)
								elif left_child is not None:
												__A       =					left_child
								elif right_child is not None:
												__A       =					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(a_						,  a_		)		)
				else:  # root.get_data() < data
								if right_child is None:
												return root
								else:
												root.set_right(del_node(a_						,  a_		)		)

				if get_height(a_		) - get_height(a_		) == 2:
								assert right_child is not None
								if get_height(right_child.get_right()		) > get_height(right_child.get_left()		):
												__A       =					left_rotation(a_		)
								else:
												__A       =					rl_rotation(a_		)
				elif get_height(a_		) - get_height(a_		) == -2:
								assert left_child is not None
								if get_height(left_child.get_left()		) > get_height(left_child.get_right()		):
												__A       =					right_rotation(a_		)
								else:
												__A       =					lr_rotation(a_		)
				__A       =					my_max(get_height(root.get_right()		)						,  get_height(root.get_left()		)		) + 1
				root.set_height(a_		)
				return root





class 				UpperCAmelCase :




	'''simple docstring'''





	def __init__(			self   :					List[Any]					):
					__A       =					None






	def 							UpperCamelCase_    (			self   :					List[str]					):
					return get_height(self.root					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Any					):
					print("insert:" + str(A					)					)
					__A       =					insert_node(self.root				,A					)






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					Any					):
					print("delete:" + str(A					)					)
					if self.root is None:
									print("Tree is empty!"					)
									return
					__A       =					del_node(self.root				,A					)







	def __str__(			self   :					Any				,):  # a level traversale, gives a more intuitive look on the tree
					__A       =					""
					__A       =					MyQueue()
					q.push(self.root					)
					__A       =					self.get_height()
					if layer == 0:
									return output
					__A       =					0
					while not q.is_empty():
									__A       =					q.pop()
									__A       =					" " * int(math.pow(2				,layer - 1					)					)
									output += space
									if node is None:
													output += "*"
													q.push(A					)
													q.push(A					)
									else:
													output += str(node.get_data()					)
													q.push(node.get_left()					)
													q.push(node.get_right()					)
									output += space
									__A       =					cnt + 1
									for i in range(1_00					):
													if cnt == math.pow(2				,A					) - 1:
																	__A       =					layer - 1
																	if layer == 0:
																					output += "\n*************************************"
																					return output
																	output += "\n"
																	break
					output += "\n*************************************"
					return output





def 		UpperCAmelCase     (      )						->      None:







				"""simple docstring"""



				import doctest

				doctest.testmod()


if __name__ == "__main__":
					_test()
					SCREAMING_SNAKE_CASE				:Dict									=							AVLtree()
					SCREAMING_SNAKE_CASE				:Optional[int]									=							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)

