
"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_			      =					logging.get_logger(__name__)


if is_vision_available():
			import PIL
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						['''pixel_values''']




							def __init__(				self  ,							snake_case_ = True  ,							snake_case_ = None  ,							snake_case_ = PILImageResampling.BICUBIC  ,							snake_case_ = True  ,							snake_case_ = None  ,							snake_case_ = True  ,							snake_case_ = 1 / 255  ,							snake_case_ = True  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = True  ,							**snake_case_  ,							)   ->       None:
													super().__init__(**snake_case_							)
													__lowerCAmelCase        =       size if size is not None else {"""shortest_edge""": 224}
													__lowerCAmelCase        =       get_size_dict(snake_case_  ,							default_to_square=snake_case_							)
													__lowerCAmelCase        =       crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
													__lowerCAmelCase        =       get_size_dict(snake_case_							)

													__lowerCAmelCase        =       do_resize
													__lowerCAmelCase        =       size
													__lowerCAmelCase        =       resample
													__lowerCAmelCase        =       do_center_crop
													__lowerCAmelCase        =       crop_size
													__lowerCAmelCase        =       do_rescale
													__lowerCAmelCase        =       rescale_factor
													__lowerCAmelCase        =       do_normalize
													__lowerCAmelCase        =       image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													__lowerCAmelCase        =       image_std if image_std is not None else OPENAI_CLIP_STD
													__lowerCAmelCase        =       do_convert_rgb




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = PILImageResampling.BICUBIC  ,							snake_case_ = None  ,							**snake_case_  ,							)   ->       np.ndarray:
													__lowerCAmelCase        =       get_size_dict(snake_case_  ,							default_to_square=snake_case_							)
													__lowerCAmelCase        =       get_resize_output_image_size(
													    snake_case_  ,							size=(size["""height"""], size["""width"""])  ,							default_to_square=snake_case_							)
													return resize(snake_case_  ,							size=snake_case_  ,							resample=snake_case_  ,							data_format=snake_case_  ,							**snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							**snake_case_  ,							)   ->       np.ndarray:
													__lowerCAmelCase        =       get_size_dict(snake_case_							)
													return center_crop(snake_case_  ,							size=(size["""height"""], size["""width"""])  ,							data_format=snake_case_  ,							**snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							**snake_case_  ,							)   ->       Union[str, Any]:
													return rescale(snake_case_  ,							scale=snake_case_  ,							data_format=snake_case_  ,							**snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_  ,							snake_case_  ,							snake_case_ = None  ,							**snake_case_  ,							)   ->       np.ndarray:
													return normalize(snake_case_  ,							mean=snake_case_  ,							std=snake_case_  ,							data_format=snake_case_  ,							**snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = None  ,							snake_case_ = ChannelDimension.FIRST  ,							**snake_case_  ,							)   ->       PIL.Image.Image:
													__lowerCAmelCase        =       do_resize if do_resize is not None else self.do_resize
													__lowerCAmelCase        =       size if size is not None else self.size
													__lowerCAmelCase        =       get_size_dict(snake_case_  ,							default_to_square=snake_case_							)
													__lowerCAmelCase        =       resample if resample is not None else self.resample
													__lowerCAmelCase        =       do_center_crop if do_center_crop is not None else self.do_center_crop
													__lowerCAmelCase        =       crop_size if crop_size is not None else self.crop_size
													__lowerCAmelCase        =       get_size_dict(snake_case_							)
													__lowerCAmelCase        =       do_rescale if do_rescale is not None else self.do_rescale
													__lowerCAmelCase        =       rescale_factor if rescale_factor is not None else self.rescale_factor
													__lowerCAmelCase        =       do_normalize if do_normalize is not None else self.do_normalize
													__lowerCAmelCase        =       image_mean if image_mean is not None else self.image_mean
													__lowerCAmelCase        =       image_std if image_std is not None else self.image_std
													__lowerCAmelCase        =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													__lowerCAmelCase        =       make_list_of_images(snake_case_							)

													if not valid_images(snake_case_							):
																			raise ValueError(
																			    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																			    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

													if do_resize and size is None:
																			raise ValueError("""Size must be specified if do_resize is True."""							)

													if do_center_crop and crop_size is None:
																			raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

													if do_rescale and rescale_factor is None:
																			raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			__lowerCAmelCase        =       [convert_to_rgb(snake_case_							) for image in images]

													# All transformations expect numpy arrays.
													__lowerCAmelCase        =       [to_numpy_array(snake_case_							) for image in images]

													if do_resize:
																			__lowerCAmelCase        =       [self.resize(image=snake_case_  ,							size=snake_case_  ,							resample=snake_case_							) for image in images]

													if do_center_crop:
																			__lowerCAmelCase        =       [self.center_crop(image=snake_case_  ,							size=snake_case_							) for image in images]

													if do_rescale:
																			__lowerCAmelCase        =       [self.rescale(image=snake_case_  ,							scale=snake_case_							) for image in images]

													if do_normalize:
																			__lowerCAmelCase        =       [self.normalize(image=snake_case_  ,							mean=snake_case_  ,							std=snake_case_							) for image in images]

													__lowerCAmelCase        =       [to_channel_dimension_format(snake_case_  ,							snake_case_							) for image in images]

													__lowerCAmelCase        =       {"""pixel_values""": images}
													return BatchFeature(data=snake_case_  ,							tensor_type=snake_case_							)


"""simple docstring"""
from __future__ import annotations
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       None:
													__lowerCAmelCase        =       data
													__lowerCAmelCase        =       None
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self							)   ->       List[str]:
													__lowerCAmelCase        =       None




							def  A__		(				self  ,							snake_case_							)   ->       int:
													__lowerCAmelCase        =       Node(snake_case_							)
													__lowerCAmelCase        =       self.head
													__lowerCAmelCase        =       new_node
													return self.head.data




							def  A__		(				self							)   ->       int | None:
													__lowerCAmelCase        =       self.head
													__lowerCAmelCase        =       self.head
													if self.head:
																			while fast_pointer and fast_pointer.next:
																									__lowerCAmelCase        =       fast_pointer.next.next
																									__lowerCAmelCase        =       slow_pointer.next
																			return slow_pointer.data
													else:
																			print("""No element found."""							)
																			return None


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