


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      __snake_case					       =   (DPMSolverMultistepScheduler,)
      __snake_case					       =   (("""num_inference_steps""", 25),)




      def      _snake_case	(				self:       Optional[Any]						,      **a:       Optional[int]				):
       __lowerCamelCase		:							Tuple							=						{
           'num_train_timesteps': 1000,
           'beta_start': 0.0_0_0_1,
           'beta_end': 0.0_2,
           'beta_schedule': 'linear',
           'solver_order': 2,
           'prediction_type': 'epsilon',
           'thresholding': False,
           'sample_max_value': 1.0,
           'algorithm_type': 'dpmsolver++',
           'solver_type': 'midpoint',
           'lower_order_final': False,
           'lambda_min_clipped': -float('inf'				),
           'variance_type': None,
       }

       config.update(**a				)
       return config




      def      _snake_case	(				self:       Optional[int]						,      a:       Optional[Any]=0						,      **a:       Tuple				):
       __lowerCamelCase		:							Optional[int]							=						dict(self.forward_default_kwargs				)
       __lowerCamelCase		:							Dict							=						kwargs.pop('num_inference_steps'						,      a				)
       __lowerCamelCase		:							Any							=						self.dummy_sample
       __lowerCamelCase		:							Dict							=						0.1 * sample
       __lowerCamelCase		:							Dict							=						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

       for scheduler_class in self.scheduler_classes:
        __lowerCamelCase		:							int							=						self.get_scheduler_config(**a				)
        __lowerCamelCase		:							List[str]							=						scheduler_class(**a				)
        scheduler.set_timesteps(a				)
        # copy over dummy past residuals
        __lowerCamelCase		:							Tuple							=						dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(a				)
         __lowerCamelCase		:							Tuple							=						scheduler_class.from_pretrained(a				)
         new_scheduler.set_timesteps(a				)
         # copy over dummy past residuals
         __lowerCamelCase		:							Union[str, Any]							=						dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCamelCase					,     __lowerCamelCase		:							str							=						sample, sample
        for t in range(a						,      time_step + scheduler.config.solver_order + 1				):
         __lowerCamelCase		:							Union[str, Any]							=						scheduler.step(a						,      a						,      a						,      **a				).prev_sample
         __lowerCamelCase		:							List[Any]							=						new_scheduler.step(a						,      a						,      a						,      **a				).prev_sample

         assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"




      def      _snake_case	(				self:       int				):
       pass




      def      _snake_case	(				self:       Dict						,      a:       Union[str, Any]=0						,      **a:       Optional[Any]				):
       __lowerCamelCase		:							int							=						dict(self.forward_default_kwargs				)
       __lowerCamelCase		:							int							=						kwargs.pop('num_inference_steps'						,      a				)
       __lowerCamelCase		:							Optional[Any]							=						self.dummy_sample
       __lowerCamelCase		:							Tuple							=						0.1 * sample
       __lowerCamelCase		:							Optional[int]							=						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

       for scheduler_class in self.scheduler_classes:
        __lowerCamelCase		:							List[str]							=						self.get_scheduler_config()
        __lowerCamelCase		:							Union[str, Any]							=						scheduler_class(**a				)
        scheduler.set_timesteps(a				)

        # copy over dummy past residuals (must be after setting timesteps)
        __lowerCamelCase		:							str							=						dummy_past_residuals[: scheduler.config.solver_order]

        with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(a				)
         __lowerCamelCase		:							Tuple							=						scheduler_class.from_pretrained(a				)
         # copy over dummy past residuals
         new_scheduler.set_timesteps(a				)

         # copy over dummy past residual (must be after setting timesteps)
         __lowerCamelCase		:							Tuple							=						dummy_past_residuals[: new_scheduler.config.solver_order]

        __lowerCamelCase		:							Optional[Any]							=						scheduler.step(a						,      a						,      a						,      **a				).prev_sample
        __lowerCamelCase		:							int							=						new_scheduler.step(a						,      a						,      a						,      **a				).prev_sample

        assert torch.sum(torch.abs(output - new_output				)				) < 1e-5, "Scheduler outputs are not identical"




      def      _snake_case	(				self:       Tuple						,      a:       Dict=None						,      **a:       str				):
       if scheduler is None:
        __lowerCamelCase		:							Optional[Any]							=						self.scheduler_classes[0]
        __lowerCamelCase		:							List[str]							=						self.get_scheduler_config(**a				)
        __lowerCamelCase		:							Optional[Any]							=						scheduler_class(**a				)

       __lowerCamelCase		:							str							=						10
       __lowerCamelCase		:							str							=						self.dummy_model()
       __lowerCamelCase		:							Union[str, Any]							=						self.dummy_sample_deter
       scheduler.set_timesteps(a				)

       for i, t in enumerate(scheduler.timesteps				):
        __lowerCamelCase		:							Union[str, Any]							=						model(a						,      a				)
        __lowerCamelCase		:							Optional[Any]							=						scheduler.step(a						,      a						,      a				).prev_sample

       return sample




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							int							=						dict(self.forward_default_kwargs				)

       __lowerCamelCase		:							Optional[int]							=						kwargs.pop('num_inference_steps'						,      a				)

       for scheduler_class in self.scheduler_classes:
        __lowerCamelCase		:							List[str]							=						self.get_scheduler_config()
        __lowerCamelCase		:							Union[str, Any]							=						scheduler_class(**a				)

        __lowerCamelCase		:							str							=						self.dummy_sample
        __lowerCamelCase		:							List[str]							=						0.1 * sample

        if num_inference_steps is not None and hasattr(a						,      'set_timesteps'				):
         scheduler.set_timesteps(a				)
        elif num_inference_steps is not None and not hasattr(a						,      'set_timesteps'				):
         __lowerCamelCase		:							List[Any]							=						num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        __lowerCamelCase		:							Union[str, Any]							=						[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
        __lowerCamelCase		:							Tuple							=						dummy_past_residuals[: scheduler.config.solver_order]

        __lowerCamelCase		:							List[str]							=						scheduler.timesteps[5]
        __lowerCamelCase		:							str							=						scheduler.timesteps[6]

        __lowerCamelCase		:							Union[str, Any]							=						scheduler.step(a						,      a						,      a						,      **a				).prev_sample
        __lowerCamelCase		:							List[str]							=						scheduler.step(a						,      a						,      a						,      **a				).prev_sample

        self.assertEqual(output_a.shape						,      sample.shape				)
        self.assertEqual(output_a.shape						,      output_a.shape				)




      def      _snake_case	(				self:       Tuple				):
       for timesteps in [25, 50, 100, 999, 1000]:
        self.check_over_configs(num_train_timesteps=a				)




      def      _snake_case	(				self:       Any				):
       self.check_over_configs(thresholding=a				)
       for order in [1, 2, 3]:
        for solver_type in ["midpoint", "heun"]:
         for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(
               thresholding=a						,      prediction_type=a						,      sample_max_value=a						,      algorithm_type='dpmsolver++'						,      solver_order=a						,      solver_type=a						,      )




      def      _snake_case	(				self:       str				):
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=a				)




      def      _snake_case	(				self:       List[Any]				):
       for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
        for solver_type in ["midpoint", "heun"]:
         for order in [1, 2, 3]:
          for prediction_type in ["epsilon", "sample"]:
           if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
            if order == 3:
             continue
           else:
            self.check_over_configs(
                solver_order=a						,      solver_type=a						,      prediction_type=a						,      algorithm_type=a						,      )
           __lowerCamelCase		:							List[str]							=						self.full_loop(
               solver_order=a						,      solver_type=a						,      prediction_type=a						,      algorithm_type=a						,      )
           assert not torch.isnan(a				).any(), "Samples have nan numbers"




      def      _snake_case	(				self:       Tuple				):
       self.check_over_configs(lower_order_final=a				)
       self.check_over_configs(lower_order_final=a				)




      def      _snake_case	(				self:       Dict				):
       self.check_over_configs(lambda_min_clipped=-float('inf'				)				)
       self.check_over_configs(lambda_min_clipped=-5.1				)




      def      _snake_case	(				self:       List[Any]				):
       self.check_over_configs(variance_type=a				)
       self.check_over_configs(variance_type='learned_range'				)




      def      _snake_case	(				self:       List[str]				):
       for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
        self.check_over_forward(num_inference_steps=a						,      time_step=0				)




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							List[str]							=						self.full_loop()
       __lowerCamelCase		:							Dict							=						torch.mean(torch.abs(a				)				)

       assert abs(result_mean.item() - 0.3_3_0_1				) < 1e-3




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[Any]							=						self.full_loop(thresholding=a						,      dynamic_thresholding_ratio=0.8_7						,      sample_max_value=0.5				)
       __lowerCamelCase		:							Dict							=						torch.mean(torch.abs(a				)				)

       assert abs(result_mean.item() - 1.1_3_6_4				) < 1e-3




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Dict							=						self.full_loop(prediction_type='v_prediction'				)
       __lowerCamelCase		:							int							=						torch.mean(torch.abs(a				)				)

       assert abs(result_mean.item() - 0.2_2_5_1				) < 1e-3




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							Optional[int]							=						self.full_loop(prediction_type='v_prediction'						,      use_karras_sigmas=a				)
       __lowerCamelCase		:							List[Any]							=						torch.mean(torch.abs(a				)				)

       assert abs(result_mean.item() - 0.2_0_9_6				) < 1e-3




      def      _snake_case	(				self:       Optional[Any]				):
       # make sure that iterating over schedulers with same config names gives same results
       # for defaults
       __lowerCamelCase		:							Optional[int]							=						DPMSolverMultistepScheduler(**self.get_scheduler_config()				)
       __lowerCamelCase		:							Optional[Any]							=						self.full_loop(scheduler=a				)
       __lowerCamelCase		:							int							=						torch.mean(torch.abs(a				)				)

       assert abs(result_mean.item() - 0.3_3_0_1				) < 1e-3

       __lowerCamelCase		:							Tuple							=						DPMSolverSinglestepScheduler.from_config(scheduler.config				)
       __lowerCamelCase		:							Union[str, Any]							=						UniPCMultistepScheduler.from_config(scheduler.config				)
       __lowerCamelCase		:							int							=						DEISMultistepScheduler.from_config(scheduler.config				)
       __lowerCamelCase		:							Any							=						DPMSolverMultistepScheduler.from_config(scheduler.config				)

       __lowerCamelCase		:							int							=						self.full_loop(scheduler=a				)
       __lowerCamelCase		:							Tuple							=						torch.mean(torch.abs(a				)				)

       assert abs(result_mean.item() - 0.3_3_0_1				) < 1e-3




      def      _snake_case	(				self:       Dict				):
       __lowerCamelCase		:							Dict							=						self.scheduler_classes[0]
       __lowerCamelCase		:							str							=						self.get_scheduler_config(thresholding=a						,      dynamic_thresholding_ratio=0				)
       __lowerCamelCase		:							Optional[int]							=						scheduler_class(**a				)

       __lowerCamelCase		:							List[str]							=						10
       __lowerCamelCase		:							Dict							=						self.dummy_model()
       __lowerCamelCase		:							int							=						self.dummy_sample_deter.half()
       scheduler.set_timesteps(a				)

       for i, t in enumerate(scheduler.timesteps				):
        __lowerCamelCase		:							Optional[int]							=						model(a						,      a				)
        __lowerCamelCase		:							Union[str, Any]							=						scheduler.step(a						,      a						,      a				).prev_sample

       assert sample.dtype == torch.floataa




      def      _snake_case	(				self:       str						,      **a:       str				):
       for scheduler_class in self.scheduler_classes:
        __lowerCamelCase		:							str							=						self.get_scheduler_config(**a				)
        __lowerCamelCase		:							Tuple							=						scheduler_class(**a				)

        scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
        assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps





from __future__ import annotations


class        A_					:




      '''simple docstring'''


      def __init__(				self:       Union[str, Any]						,      a:       int				):
       __lowerCamelCase		:							List[Any]							=						data
       __lowerCamelCase		:							Optional[Any]							=						None



class        A_					:




      '''simple docstring'''


      def __init__(				self:       Dict				):
       __lowerCamelCase		:							Dict							=						None




      def      _snake_case	(				self:       Dict						,      a:       int				):
       __lowerCamelCase		:							List[Any]							=						Node(a				)
       __lowerCamelCase		:							Dict							=						self.head
       __lowerCamelCase		:							Optional[int]							=						new_node
       return self.head.data




      def      _snake_case	(				self:       Optional[Any]				):
       __lowerCamelCase		:							Union[str, Any]							=						self.head
       __lowerCamelCase		:							List[Any]							=						self.head
       if self.head:
        while fast_pointer and fast_pointer.next:
         __lowerCamelCase		:							Any							=						fast_pointer.next.next
         __lowerCamelCase		:							str							=						slow_pointer.next
        return slow_pointer.data
       else:
        print('No element found.'				)
        return None


if __name__ == "__main__":
       lowercase_           =	LinkedList()
       for _ in range(int(input().strip())):
              lowercase_           =	int(input().strip())
              link.push(data)
       print(link.middle_element())

