




import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__    :							Any       		=     logging.get_logger(__name__)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		int:





  """simple docstring"""





  _lowercase:			Optional[int]      =     MobileNetVaConfig(layer_norm_eps=0.001				)

  if "quant" in model_name:
    raise ValueError('Quantized models are not supported.'				)

  _lowercase:			Any      =     re.match(r'^.*mobilenet_v2_([^_]*)_([^_]*)$'       ,  UpperCamelCase				)
  if matches:
    _lowercase:			int      =     float(matches[1]				)
    _lowercase:			str      =     int(matches[2]				)

  if model_name.startswith('deeplabv3_'				):
    _lowercase:			Dict      =     8
    _lowercase:			Union[str, Any]      =     21
    _lowercase:			Tuple      =     'pascal-voc-id2label.json'
  else:
    # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
    # of the usual 1000. The first class (index 0) is "background".
    _lowercase:			List[str]      =     1_001
    _lowercase:			Any      =     'imagenet-1k-id2label.json'

  _lowercase:			Any      =     'huggingface/label-files'
  _lowercase:			Dict      =     json.load(open(hf_hub_download(UpperCamelCase       ,  UpperCamelCase       ,  repo_type='dataset'				)       ,  'r'				)				)

  if config.num_labels == 1_001:
    _lowercase:			List[Any]      =     {int(UpperCamelCase				) + 1: v for k, v in idalabel.items()}
    _lowercase:			Optional[int]      =     'background'
  else:
    _lowercase:			Optional[int]      =     {int(UpperCamelCase				): v for k, v in idalabel.items()}

  _lowercase:			str      =     idalabel
  _lowercase:			List[Any]      =     {v: k for k, v in idalabel.items()}

  return config


def 		_lowerCamelCase					(       )      ->		Dict:





  """simple docstring"""





  _lowercase:			Tuple      =     'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase:			Optional[Any]      =     Image.open(requests.get(UpperCamelCase       ,  stream=UpperCamelCase				).raw				)
  return im


@torch.no_grad()
def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase=False				)      ->		int:





  """simple docstring"""





  _lowercase:			Any      =     get_mobilenet_va_config(UpperCamelCase				)

  # Load 🤗 model
  if model_name.startswith('deeplabv3_'				):
    _lowercase:			Dict      =     MobileNetVaForSemanticSegmentation(UpperCamelCase				).eval()
  else:
    _lowercase:			List[str]      =     MobileNetVaForImageClassification(UpperCamelCase				).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase				)

  # Check outputs on an image, prepared by MobileNetV2ImageProcessor
  _lowercase:			str      =     MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}       ,  size={'shortest_edge': config.image_size + 32}       ,  )
  _lowercase:			Tuple      =     image_processor(images=prepare_img()       ,  return_tensors='pt'				)
  _lowercase:			Any      =     model(**UpperCamelCase				)
  _lowercase:			int      =     outputs.logits

  if model_name.startswith('deeplabv3_'				):
    assert logits.shape == (1, 21, 65, 65)

    if model_name == "deeplabv3_mobilenet_v2_1.0_513":
      _lowercase:			Optional[Any]      =     torch.tensor(
          [
              [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
              [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
              [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
          ]				)

    else:
      raise ValueError(F'Unknown model name: {model_name}'				)

    assert torch.allclose(logits[0, :3, :3, :3]       ,  UpperCamelCase       ,  atol=1E-4				)
  else:
    assert logits.shape == (1, 1_001)

    if model_name == "mobilenet_v2_1.4_224":
      _lowercase:			List[Any]      =     torch.tensor([0.0_181, -1.0_015, 0.4_688]				)
    elif model_name == "mobilenet_v2_1.0_224":
      _lowercase:			List[Any]      =     torch.tensor([0.2_445, -1.1_993, 0.1_905]				)
    elif model_name == "mobilenet_v2_0.75_160":
      _lowercase:			str      =     torch.tensor([0.2_482, 0.4_136, 0.6_669]				)
    elif model_name == "mobilenet_v2_0.35_96":
      _lowercase:			int      =     torch.tensor([0.1_451, -0.4_624, 0.7_192]				)
    else:
      _lowercase:			Tuple      =     None

    if expected_logits is not None:
      assert torch.allclose(logits[0, :3]       ,  UpperCamelCase       ,  atol=1E-4				)

  Path(UpperCamelCase				).mkdir(exist_ok=UpperCamelCase				)
  print(F'Saving model {model_name} to {pytorch_dump_folder_path}'				)
  model.save_pretrained(UpperCamelCase				)
  print(F'Saving image processor to {pytorch_dump_folder_path}'				)
  image_processor.save_pretrained(UpperCamelCase				)

  if push_to_hub:
    print('Pushing to the hub...'				)
    _lowercase:			Dict      =     'google/' + model_name
    image_processor.push_to_hub(UpperCamelCase				)
    model.push_to_hub(UpperCamelCase				)


if __name__ == "__main__":
   UpperCAmelCase__    :							List[str]       		=     argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='mobilenet_v2_1.0_224',
       type=str,
       help='Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.',
   )
   parser.add_argument(
       '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   UpperCAmelCase__    :							int       		=     parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )






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())
