

from collections.abc import Iterable
from typing import Any



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__ = None    ):
						A__							:					Tuple       =	value
						A__							:					str       =	None  # Added in order to delete a node easier
						A__							:					Optional[Any]       =	None
						A__							:					Dict       =	None






					def __repr__(				self    ):
						from pprint import pformat

						if self.left is None and self.right is None:
							return str(self.value    )
						return pformat({F"""{self.value}""": (self.left, self.right)}					, indent=1    )



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__ = None    ):
						A__							:					Union[str, Any]       =	root






					def __str__(				self    ):
						return str(self.root    )






					def 						__A    (				self					, A__					, A__    ):
						if new_children is not None:  # reset its kids
							A__							:					Union[str, Any]       =	node.parent
						if node.parent is not None:  # reset its parent
							if self.is_right(snake_case_    ):  # If it is the right children
								A__							:					Union[str, Any]       =	new_children
							else:
								A__							:					Dict       =	new_children
						else:
							A__							:					List[Any]       =	new_children






					def 						__A    (				self					, A__    ):
						if node.parent and node.parent.right:
							return node == node.parent.right
						return False






					def 						__A    (				self    ):
						return self.root is None






					def 						__A    (				self					, A__    ):
						A__							:					Dict       =	Node(snake_case_    )  # create a new Node
						if self.empty():  # if Tree is empty
							A__							:					Tuple       =	new_node  # set its root
						else:  # Tree is not empty
							A__							:					str       =	self.root  # from root
							if parent_node is None:
								return
							while True:  # While we don't get to a leaf
								if value < parent_node.value:  # We go left
									if parent_node.left is None:
										A__							:					List[str]       =	new_node  # We insert the new node in a leaf
										break
									else:
										A__							:					Dict       =	parent_node.left
								else:
									if parent_node.right is None:
										A__							:					str       =	new_node
										break
									else:
										A__							:					str       =	parent_node.right
							A__							:					List[str]       =	parent_node






					def 						__A    (				self					, *A__    ):
						for value in values:
							self.__insert(snake_case_    )






					def 						__A    (				self					, A__    ):
						if self.empty():
							raise IndexError("""Warning: Tree is empty! please use another."""    )
						else:
							A__							:					int       =	self.root
							# use lazy evaluation here to avoid NoneType Attribute error
							while node is not None and node.value is not value:
								A__							:					Optional[int]       =	node.left if value < node.value else node.right
							return node






					def 						__A    (				self					, A__ = None    ):
						if node is None:
							if self.root is None:
								return None
							A__							:					str       =	self.root

						if not self.empty():
							while node.right is not None:
								A__							:					Union[str, Any]       =	node.right
						return node






					def 						__A    (				self					, A__ = None    ):
						if node is None:
							A__							:					Tuple       =	self.root
						if self.root is None:
							return None
						if not self.empty():
							A__							:					int       =	self.root
							while node.left is not None:
								A__							:					Any       =	node.left
						return node






					def 						__A    (				self					, A__    ):
						A__							:					List[str]       =	self.search(snake_case_    )  # Look for the node with that label
						if node is not None:
							if node.left is None and node.right is None:  # If it has no children
								self.__reassign_nodes(snake_case_					, snake_case_    )
							elif node.left is None:  # Has only right children
								self.__reassign_nodes(snake_case_					, node.right    )
							elif node.right is None:  # Has only left children
								self.__reassign_nodes(snake_case_					, node.left    )
							else:
								A__							:					Optional[int]       =	self.get_max(
								    node.left    )  # Gets the max value of the left branch
								self.remove(tmp_node.value    )  # type: ignore
								A__							:					List[str]       =	(
								    tmp_node.value  # type: ignore
								)  # Assigns the value to the node to delete and keep tree structure






					def 						__A    (				self					, A__    ):
						if node is not None:
							yield node  # Preorder Traversal
							yield from self.preorder_traverse(node.left    )
							yield from self.preorder_traverse(node.right    )






					def 						__A    (				self					, A__=None    ):
						if traversal_function is None:
							return self.preorder_traverse(self.root    )
						else:
							return traversal_function(self.root    )






					def 						__A    (				self					, A__					, A__    ):
						if node:
							self.inorder(snake_case_					, node.left    )
							arr.append(node.value    )
							self.inorder(snake_case_					, node.right    )






					def 						__A    (				self					, A__					, A__    ):
						A__							:					Optional[int]       =	[]
						self.inorder(snake_case_					, snake_case_    )  # append all values to list using inorder traversal
						return arr[k - 1]



def 		UpperCamelCase      (lowercase_:				List[Any]      )  ->   list[Node]:
	A__							:					Optional[Any]       =	[]
	if curr_node is not None:
		A__							:					Tuple       =	postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
	return node_list

def 		UpperCamelCase      ()  ->   None:
	A__							:					Any       =	(8, 3, 6, 1, 10, 14, 13, 4, 7)
	A__							:					Dict       =	BinarySearchTree()
	for i in testlist:
		t.insert(lowercase_      )

	# Prints all the elements of the list in order traversal
	print(lowercase_      )

	if t.search(6      ) is not None:
		print("""The value 6 exists"""      )
	else:
		print("""The value 6 doesn\'t exist"""      )

	if t.search(-1      ) is not None:
		print("""The value -1 exists"""      )
	else:
		print("""The value -1 doesn\'t exist"""      )

	if not t.empty():
		print("""Max Value: """					,   t.get_max().value      )  # type: ignore
		print("""Min Value: """					,   t.get_min().value      )  # type: ignore

	for i in testlist:
		t.remove(lowercase_      )
		print(lowercase_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)






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 					_a		(__magic_name__	,     __magic_name__	,     __magic_name__       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']






					@register_to_config
					def __init__(				self					, A__					, A__					, A__ = None					, A__ = 5_0257					, A__ = 1024					, A__ = 768					, A__ = 12					, A__ = 12					, A__ = None					, A__ = "gelu_new"					, A__ = 0.1					, A__ = 0.1					, A__ = 0.1					, A__ = 1e-5					, A__ = 0.0_2					, A__ = True					, A__ = True					, A__ = False					, A__ = False					, ):
						super().__init__()

						A__							:					Union[str, Any]       =	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__							:					str       =	prefix_inner_dim
						A__							:					Optional[Any]       =	prefix_hidden_dim

						A__							:					Tuple       =	(
						    nn.Linear(self.prefix_inner_dim					, self.prefix_hidden_dim    )
						    if self.prefix_hidden_dim is not None
						    else nn.Identity()
						)
						A__							:					int       =	(
						    nn.Linear(self.prefix_hidden_dim					, A__    ) if self.prefix_hidden_dim is not None else nn.Identity()
						)

						A__							:					Tuple       =	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__							:					int       =	GPTaLMHeadModel(A__    )






					def 						__A    (				self					, A__					, A__					, A__ = None					, A__ = None					, ):
						A__							:					List[str]       =	self.transformer.transformer.wte(A__    )
						A__							:					int       =	self.encode_prefix(A__    )
						A__							:					int       =	self.decode_prefix(A__    )
						A__							:					Optional[Any]       =	torch.cat((prefix_embeds, embedding_text)					, dim=1    )

						if labels is not None:
							A__							:					Any       =	self.get_dummy_token(input_ids.shape[0]					, input_ids.device    )
							A__							:					List[Any]       =	torch.cat((dummy_token, input_ids)					, dim=1    )
						A__							:					List[str]       =	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 						__A    (				self					, A__					, A__    ):
						return torch.zeros(A__					, self.prefix_length					, dtype=torch.intaa					, device=A__    )






					def 						__A    (				self					, A__    ):
						return self.encode_prefix(A__    )






					@torch.no_grad()
					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					List[Any]       =	torch.split(A__					, 1					, dim=0    )
						A__							:					Optional[int]       =	[]
						A__							:					str       =	[]
						for feature in features:
							A__							:					Dict       =	self.decode_prefix(feature.to(A__    )    )  # back to the clip feature
							# Only support beam search for now
							A__     ,     A__							:					Union[str, Any]       =	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__							:					int       =	torch.stack(A__    )
						A__							:					List[Any]       =	torch.stack(A__    )
						return generated_tokens, generated_seq_lengths






					@torch.no_grad()
					def 						__A    (				self					, A__=None					, A__=None					, A__=None					, A__ = 5					, A__ = 67					, A__ = 1.0					, A__ = None					, ):
						A__							:					Any       =	eos_token_id
						A__							:					Any       =	None
						A__							:					Optional[int]       =	None
						A__							:					Optional[Any]       =	torch.ones(A__					, device=A__					, dtype=torch.int    )
						A__							:					Any       =	torch.zeros(A__					, device=A__					, dtype=torch.bool    )

						if input_embeds is not None:
							A__							:					Dict       =	input_embeds
						else:
							A__							:					str       =	self.transformer.transformer.wte(A__    )

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

							if scores is None:
								A__     ,     A__							:					Optional[int]       =	logits.topk(A__					, -1    )
								A__							:					List[Any]       =	generated.expand(A__					, *generated.shape[1:]    )
								A__     ,     A__							:					List[Any]       =	next_tokens.permute(1					, 0    ), scores.squeeze(0    )
								if tokens is None:
									A__							:					Optional[Any]       =	next_tokens
								else:
									A__							:					List[Any]       =	tokens.expand(A__					, *tokens.shape[1:]    )
									A__							:					int       =	torch.cat((tokens, next_tokens)					, dim=1    )
							else:
								A__							:					Optional[int]       =	-float(np.inf    )
								A__							:					List[Any]       =	0
								A__							:					str       =	scores[:, None] + logits
								seq_lengths[~is_stopped] += 1
								A__							:					Dict       =	scores_sum / seq_lengths[:, None]
								A__     ,     A__							:					List[Any]       =	scores_sum_average.view(-1    ).topk(A__					, -1    )
								A__							:					Tuple       =	next_tokens // scores_sum.shape[1]
								A__							:					Optional[Any]       =	seq_lengths[next_tokens_source]
								A__							:					List[str]       =	next_tokens % scores_sum.shape[1]
								A__							:					Optional[int]       =	next_tokens.unsqueeze(1    )
								A__							:					int       =	tokens[next_tokens_source]
								A__							:					List[Any]       =	torch.cat((tokens, next_tokens)					, dim=1    )
								A__							:					str       =	generated[next_tokens_source]
								A__							:					Optional[Any]       =	scores_sum_average * seq_lengths
								A__							:					Union[str, Any]       =	is_stopped[next_tokens_source]

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

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



