



'''simple docstring'''







import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
							import torch


SCREAMING_SNAKE_CASE__     : Optional[int]				=   logging.get_logger(__name__)




class 				__lowerCAmelCase:






			def __init__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=21      ,       SCREAMING_SNAKE_CASE  :							str=[]      ,       SCREAMING_SNAKE_CASE  :							Dict=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      model
					SCREAMING_SNAKE_CASE_						:List[Any]										=      trace_batch_nums
					SCREAMING_SNAKE_CASE_						:str										=      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
					SCREAMING_SNAKE_CASE_						:Any										=      collections.deque([]      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      []
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      0
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      0
					SCREAMING_SNAKE_CASE_						:List[Any]										=      False
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      '                 '

					self.analyse_model()

					self.register_forward_hook()






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Any=None	):




					"""simple docstring"""







					if frame is not None:
							self.expand_frame(SCREAMING_SNAKE_CASE	)
					self.frames.append('\n'.join(self.frame	)	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      []  # start a new frame






			def 				_lowercase	(   self  :							Dict      ,       SCREAMING_SNAKE_CASE  :							Tuple	):




					"""simple docstring"""







					self.frame.append(SCREAMING_SNAKE_CASE	)






			def 				_lowercase	(   self  :							List[Any]	):




					"""simple docstring"""







					print('\n'.join(self.frames	)	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      []






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      []






			def 				_lowercase	(   self  :							Tuple	):




					"""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'	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      []






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      {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 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					if torch.is_tensor(SCREAMING_SNAKE_CASE	):
							self.expand_frame(get_abs_min_max(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)	)
							if detect_overflow(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	):
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      True
					elif var is None:
							self.expand_frame(f'{"None":>17} {ctx}'	)
					else:
							self.expand_frame(f'{"not a tensor":>17} {ctx}'	)






			def 				_lowercase	(   self  :							Tuple	):




					"""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 				_lowercase	(   self  :							int	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							List[str]	):




					"""simple docstring"""







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

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

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

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

					self.save_frame()






			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







					self.model.apply(self._register_forward_hook	)






			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							List[Any]	):




					"""simple docstring"""







					module.register_forward_hook(self.forward_hook	)






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							Any      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[int]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      False

					SCREAMING_SNAKE_CASE_						:Tuple										=      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
							SCREAMING_SNAKE_CASE_						:List[str]										=      True

					self.create_frame(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      var.abs()
		return F'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      False
		if torch.isnan(SCREAMING_SNAKE_CASE     ).any().item():
				SCREAMING_SNAKE_CASE_						:Tuple										=      True
				print(F'{ctx} has nans'     )
		if torch.isinf(SCREAMING_SNAKE_CASE     ).any().item():
				SCREAMING_SNAKE_CASE_						:List[str]										=      True
				print(F'{ctx} has infs'     )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_						:Dict										=      var[torch.ge(var.abs() ,		100     )]
				if naaa.numel() > 0:
						print(F'{ctx}:  n100={naaa.numel()}'     )
				SCREAMING_SNAKE_CASE_						:Dict										=      var[torch.ge(var.abs() ,		1000     )]
				if naaaa.numel() > 0:
						print(F'{ctx}: n1000={naaaa.numel()}'     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      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 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[Any]		=      'underflow_overflow'
			__snake_case  :		Optional[int]		=      'tpu_metrics_debug'






from __future__ import annotations

class   UpperCAmelCase    :






   def __init__(						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      None:
     _lowercase:			Optional[Any]      =     data
     _lowercase:			Optional[int]      =     None

class   UpperCAmelCase    :






   def __init__(						self :							Optional[Any]  )->      Union[str, Any]:
     _lowercase:			List[str]      =     None






   def 			_a    (						self :							List[Any]    ,       UpperCamelCase_ :							int  )->      int:
     _lowercase:			Any      =     Node(UpperCamelCase_  )
     _lowercase:			List[Any]      =     self.head
     _lowercase:			List[str]      =     new_node
     return self.head.data






   def 			_a    (						self :							Tuple  )->      int | None:
     _lowercase:			List[str]      =     self.head
     _lowercase:			str      =     self.head
     if self.head:
       while fast_pointer and fast_pointer.next:
         _lowercase:			Optional[int]      =     fast_pointer.next.next
         _lowercase:			Tuple      =     slow_pointer.next
       return slow_pointer.data
     else:
       print('No element found.'  )
       return None


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