

'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
				import torch


a       :      Any  	=			logging.get_logger(__name__)







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case   ,      snake_case=2_1   ,      snake_case=[]   ,      snake_case=None      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       model
							UpperCAmelCase		:							str        =       trace_batch_nums
							UpperCAmelCase		:							List[Any]        =       abort_after_batch_num

							# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
							UpperCAmelCase		:							str        =       collections.deque([]   ,      snake_case      )
							UpperCAmelCase		:							str        =       []
							UpperCAmelCase		:							Dict        =       0
							UpperCAmelCase		:							List[Any]        =       0
							UpperCAmelCase		:							List[Any]        =       False
							UpperCAmelCase		:							Optional[Any]        =       "                 "

							self.analyse_model()

							self.register_forward_hook()







		def 				A_	(			self   ,      snake_case=None      ):


							'''simple docstring'''

							if frame is not None:
												self.expand_frame(snake_case      )
							self.frames.append("\n".join(self.frame      )      )
							UpperCAmelCase		:							Optional[Any]        =       []  # start a new frame







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							self.frame.append(snake_case      )







		def 				A_	(			self      ):


							'''simple docstring'''

							print("\n".join(self.frames      )      )
							UpperCAmelCase		:							List[str]        =       []







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							List[str]        =       []







		def 				A_	(			self      ):


							'''simple docstring'''

							print(f"\nDetected inf/nan during batch_number={self.batch_number}"      )
							print(f"Last {len(self.frames      )} forward frames:"      )
							print(f"{'abs min':8} {'abs max':8} metadata"      )
							print("\n".join(self.frames      )      )
							print("\n\n"      )
							UpperCAmelCase		:							Dict        =       []







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[int]        =       {m: name for name, m in self.model.named_modules()}
							# self.longest_module_name = max(len(v) for v in self.module_names.values())







		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							if torch.is_tensor(snake_case      ):
												self.expand_frame(get_abs_min_max(snake_case   ,      snake_case      )      )
												if detect_overflow(snake_case   ,      snake_case      ):
																	UpperCAmelCase		:							Any        =       True
							elif var is None:
												self.expand_frame(f"{'None':>17} {ctx}"      )
							else:
												self.expand_frame(f"{'not a tensor':>17} {ctx}"      )







		def 				A_	(			self      ):


							'''simple docstring'''

							self.expand_frame(f"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"      )
							self.expand_frame(f"{'abs min':8} {'abs max':8} metadata"      )







		def 				A_	(			self      ):


							'''simple docstring'''

							self.expand_frame(f"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							self.expand_frame(f"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"      )

							# params
							for name, p in module.named_parameters(recurse=snake_case      ):
												self.analyse_variable(snake_case   ,      snake_case      )

							# inputs
							if isinstance(snake_case   ,      snake_case      ):
												for i, x in enumerate(snake_case      ):
																	self.analyse_variable(snake_case   ,      f"input[{i}]"      )
							else:
												self.analyse_variable(snake_case   ,      "input"      )

							# outputs
							if isinstance(snake_case   ,      snake_case      ):
												for i, x in enumerate(snake_case      ):
																	# possibly a tuple of tuples
																	if isinstance(snake_case   ,      snake_case      ):
																						for j, y in enumerate(snake_case      ):
																											self.analyse_variable(snake_case   ,      f"output[{i}][{j}]"      )
																	else:
																						self.analyse_variable(snake_case   ,      f"output[{i}]"      )
							else:
												self.analyse_variable(snake_case   ,      "output"      )

							self.save_frame()







		def 				A_	(			self      ):


							'''simple docstring'''

							self.model.apply(self._register_forward_hook      )







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							module.register_forward_hook(self.forward_hook      )







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       False

							UpperCAmelCase		:							Any        =       True if self.batch_number in self.trace_batch_nums else False
							if trace_mode:
												self.reset_saved_frames()

							if self.total_calls == 0:
												self.batch_start_frame()
							self.total_calls += 1

							# count batch numbers - the very first forward hook of the batch will be called when the
							# batch completes - i.e. it gets called very last - we know this batch has finished
							if module == self.model:
												self.batch_number += 1
												UpperCAmelCase		:							Optional[int]        =       True

							self.create_frame(snake_case   ,      snake_case   ,      snake_case      )

							# if last_frame_of_batch:
							#     self.batch_end_frame()

							if trace_mode:
												self.trace_frames()

							if last_frame_of_batch:
												self.batch_start_frame()

							if self.detected_overflow and not trace_mode:
												self.dump_saved_frames()

												# now we can abort, as it's pointless to continue running
												raise ValueError(
												    "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
												    "Please scroll up above this traceback to see the activation values prior to this event."      )

							# abort after certain batch if requested to do so
							if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
												raise ValueError(
												    f"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
												    f" `abort_after_batch_num={self.abort_after_batch_num}` arg"      )





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any        =       var.abs()
					return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[Any]        =       False
					if torch.isnan(__magic_name__							).any().item():
										UpperCAmelCase		:							Optional[int]        =       True
										print(F"{ctx} has nans"							)
					if torch.isinf(__magic_name__							).any().item():
										UpperCAmelCase		:							List[str]        =       True
										print(F"{ctx} has infs"							)

					# if needed to monitor large elements can enable the following
					if 0:  # and detected:
										UpperCAmelCase		:							str        =       var[torch.ge(var.abs()			,   100							)]
										if naaa.numel() > 0:
															print(F"{ctx}:  n100={naaa.numel()}"							)
										UpperCAmelCase		:							Tuple        =       var[torch.ge(var.abs()			,   1000							)]
										if naaaa.numel() > 0:
															print(F"{ctx}: n1000={naaaa.numel()}"							)
										UpperCAmelCase		:							List[Any]        =       var[torch.ge(var.abs()			,   1_0000							)]
										if naaaaa.numel() > 0:
															print(F"{ctx}: n10000={naaaaa.numel()}"							)

					if 0:
										print(F"min={var.min():9.2e} max={var.max():9.2e}"							)

					if 0:
										print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"							)

					return detected







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Union[str, Any]       =							"underflow_overflow"
		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							"tpu_metrics_debug"



'''simple docstring'''

from __future__ import annotations







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       data
							UpperCAmelCase		:							Dict        =       None







class   UpperCamelCase__   :


		"""simple docstring"""

		def __init__(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							str        =       None







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							int        =       Node(snake_case      )
							UpperCAmelCase		:							Optional[int]        =       self.head
							UpperCAmelCase		:							Union[str, Any]        =       new_node
							return self.head.data







		def 				A_	(			self      ):


							'''simple docstring'''

							UpperCAmelCase		:							Dict        =       self.head
							UpperCAmelCase		:							str        =       self.head
							if self.head:
												while fast_pointer and fast_pointer.next:
																	UpperCAmelCase		:							List[str]        =       fast_pointer.next.next
																	UpperCAmelCase		:							Any        =       slow_pointer.next
												return slow_pointer.data
							else:
												print("No element found."      )
												return None


if __name__ == "__main__":
				a       :      List[str]  	=			LinkedList()
				for _ in range(int(input().strip())):
								a       :      str  	=			int(input().strip())
								link.push(data)
				print(link.middle_element())
