
from __future__ import annotations

import math
import random
from typing import Any





class 							UpperCAmelCase				:




	'''simple docstring'''





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






	def     UpperCamelCase_  (	self :			Optional[int]			):
					return self.head == self.tail






	def     UpperCamelCase_  (	self :			Tuple							,A :			Any			):
					self.data.append(A			)
					__A             =			self.tail + 1






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






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







	def     UpperCamelCase_  (	self :			Optional[int]			):
					print(self.data			)
					print("**************"			)
					print(self.data[self.head : self.tail]			)





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			Optional[Any]							,A :			Any			):
					__A             =			data
					__A             =			None
					__A             =			None
					__A             =			1






	def     UpperCamelCase_  (	self :			Dict			):
					return self.data






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






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






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






	def     UpperCamelCase_  (	self :			List[str]							,A :			Any			):
					__A             =			data






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			MyNode | None			):
					__A             =			node






	def     UpperCamelCase_  (	self :			Optional[Any]							,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 :			Dict			):
					__A             =			None






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






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






	def     UpperCamelCase_  (	self :			List[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 :			str							,):  # 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							:List[Any]										=    AVLtree()
					SCREAMING_SNAKE_CASE							:str										=    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))


from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






	@register_to_config
	def __init__(	self :			Tuple							,A :			int							,A :			int							,A :			Optional[int] = None							,A :			int = 5_02_57							,A :			int = 10_24							,A :			int = 7_68							,A :			int = 12							,A :			int = 12							,A :			Optional[int] = None							,A :			str = "gelu_new"							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 1E-5							,A :			float = 0.02							,A :			bool = True							,A :			bool = True							,A :			bool = False							,A :			bool = False							,):
					super().__init__()

					__A             =			prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
									    f''' `n_embd`: {n_embd} are not equal.'''			)

					__A             =			prefix_inner_dim
					__A             =			prefix_hidden_dim

					__A             =			(
					    nn.Linear(self.prefix_inner_dim							,self.prefix_hidden_dim			)
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					__A             =			(
					    nn.Linear(self.prefix_hidden_dim							,A			) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					__A             =			GPTaConfig(
					    vocab_size=A							,n_positions=A							,n_embd=A							,n_layer=A							,n_head=A							,n_inner=A							,activation_function=A							,resid_pdrop=A							,embd_pdrop=A							,attn_pdrop=A							,layer_norm_epsilon=A							,initializer_range=A							,scale_attn_weights=A							,use_cache=A							,scale_attn_by_inverse_layer_idx=A							,reorder_and_upcast_attn=A							,)
					__A             =			GPTaLMHeadModel(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			torch.Tensor							,A :			torch.Tensor							,A :			Optional[torch.Tensor] = None							,A :			Optional[torch.Tensor] = None							,):
					__A             =			self.transformer.transformer.wte(A			)
					__A             =			self.encode_prefix(A			)
					__A             =			self.decode_prefix(A			)
					__A             =			torch.cat((prefix_embeds, embedding_text)							,dim=1			)

					if labels is not None:
									__A             =			self.get_dummy_token(input_ids.shape[0]							,input_ids.device			)
									__A             =			torch.cat((dummy_token, input_ids)							,dim=1			)
					__A             =			self.transformer(inputs_embeds=A							,labels=A							,attention_mask=A			)
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out






	def     UpperCamelCase_  (	self :			int							,A :			int							,A :			torch.device			):
					return torch.zeros(A							,self.prefix_length							,dtype=torch.intaa							,device=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
					return self.encode_prefix(A			)






	@torch.no_grad()
	def     UpperCamelCase_  (	self :			str							,A :			List[str]							,A :			List[Any]							,A :			List[Any]			):
					__A             =			torch.split(A							,1							,dim=0			)
					__A             =			[]
					__A             =			[]
					for feature in features:
									__A             =			self.decode_prefix(feature.to(A			)			)  # back to the clip feature
									# Only support beam search for now
									__A      ,							__A             =			self.generate_beam(
									    input_embeds=A							,device=A							,eos_token_id=A			)
									generated_tokens.append(output_tokens[0]			)
									generated_seq_lengths.append(seq_lengths[0]			)
					__A             =			torch.stack(A			)
					__A             =			torch.stack(A			)
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def     UpperCamelCase_  (	self :			Tuple							,A :			List[str]=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			int = 5							,A :			int = 67							,A :			float = 1.0							,A :			Optional[int] = None							,):
					__A             =			eos_token_id
					__A             =			None
					__A             =			None
					__A             =			torch.ones(A							,device=A							,dtype=torch.int			)
					__A             =			torch.zeros(A							,device=A							,dtype=torch.bool			)

					if input_embeds is not None:
									__A             =			input_embeds
					else:
									__A             =			self.transformer.transformer.wte(A			)

					for i in range(A			):
									__A             =			self.transformer(inputs_embeds=A			)
									__A             =			outputs.logits
									__A             =			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									__A             =			logits.softmax(-1			).log()

									if scores is None:
													__A      ,							__A             =			logits.topk(A							,-1			)
													__A             =			generated.expand(A							,*generated.shape[1:]			)
													__A      ,							__A             =			next_tokens.permute(1							,0			), scores.squeeze(0			)
													if tokens is None:
																	__A             =			next_tokens
													else:
																	__A             =			tokens.expand(A							,*tokens.shape[1:]			)
																	__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
									else:
													__A             =			-float(np.inf			)
													__A             =			0
													__A             =			scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													__A             =			scores_sum / seq_lengths[:, None]
													__A      ,							__A             =			scores_sum_average.view(-1			).topk(A							,-1			)
													__A             =			next_tokens // scores_sum.shape[1]
													__A             =			seq_lengths[next_tokens_source]
													__A             =			next_tokens % scores_sum.shape[1]
													__A             =			next_tokens.unsqueeze(1			)
													__A             =			tokens[next_tokens_source]
													__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
													__A             =			generated[next_tokens_source]
													__A             =			scores_sum_average * seq_lengths
													__A             =			is_stopped[next_tokens_source]

									__A             =			self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]							,1							,-1			)
									__A             =			torch.cat((generated, next_token_embed)							,dim=1			)
									__A             =			is_stopped + next_tokens.eq(A			).squeeze()
									if is_stopped.all():
													break

					__A             =			scores / seq_lengths
					__A             =			scores.argsort(descending=A			)
					# tokens tensors are already padded to max_seq_length
					__A             =			[tokens[i] for i in order]
					__A             =			torch.stack(A							,dim=0			)
					__A             =			torch.tensor([seq_lengths[i] for i in order]							,dtype=seq_lengths.dtype			)
					return output_texts, seq_lengths
