

'''simple docstring'''

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					if "model" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("model."			,   ""							)
					if "norm1" in orig_key:
										UpperCAmelCase		:							List[Any]        =       orig_key.replace("norm1"			,   "attention.output.LayerNorm"							)
					if "norm2" in orig_key:
										UpperCAmelCase		:							Dict        =       orig_key.replace("norm2"			,   "output.LayerNorm"							)
					if "norm" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("norm"			,   "LayerNorm"							)
					if "transformer" in orig_key:
										UpperCAmelCase		:							Dict        =       orig_key.split("."							)[0].split("_"							)[-1]
										UpperCAmelCase		:							str        =       orig_key.replace(F"transformer_{layer_num}"			,   F"encoder.layer.{layer_num}"							)
					if "mha.attn" in orig_key:
										UpperCAmelCase		:							Dict        =       orig_key.replace("mha.attn"			,   "attention.self"							)
					if "mha" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("mha"			,   "attention"							)
					if "W_q" in orig_key:
										UpperCAmelCase		:							List[Any]        =       orig_key.replace("W_q"			,   "self.query"							)
					if "W_k" in orig_key:
										UpperCAmelCase		:							Tuple        =       orig_key.replace("W_k"			,   "self.key"							)
					if "W_v" in orig_key:
										UpperCAmelCase		:							List[Any]        =       orig_key.replace("W_v"			,   "self.value"							)
					if "ff1" in orig_key:
										UpperCAmelCase		:							str        =       orig_key.replace("ff1"			,   "intermediate.dense"							)
					if "ff2" in orig_key:
										UpperCAmelCase		:							Optional[Any]        =       orig_key.replace("ff2"			,   "output.dense"							)
					if "ff" in orig_key:
										UpperCAmelCase		:							Optional[Any]        =       orig_key.replace("ff"			,   "output.dense"							)
					if "mlm_class" in orig_key:
										UpperCAmelCase		:							List[str]        =       orig_key.replace("mlm.mlm_class"			,   "cls.predictions.decoder"							)
					if "mlm" in orig_key:
										UpperCAmelCase		:							Union[str, Any]        =       orig_key.replace("mlm"			,   "cls.predictions.transform"							)
					if "cls" not in orig_key:
										UpperCAmelCase		:							str        =       "nystromformer." + orig_key

					return orig_key





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					for key in orig_state_dict.copy().keys():
										UpperCAmelCase		:							Optional[Any]        =       orig_state_dict.pop(__magic_name__							)

										if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
															continue
										else:
															UpperCAmelCase		:							str        =       val

					UpperCAmelCase		:							int        =       orig_state_dict["cls.predictions.decoder.bias"]
					UpperCAmelCase		:							List[str]        =       (
					    torch.arange(config.max_position_embeddings							).expand((1, -1)							) + 2
					)

					return orig_state_dict





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							List[str]        =       torch.load(__magic_name__			,   map_location="cpu"							)["model_state_dict"]
					UpperCAmelCase		:							Dict        =       NystromformerConfig.from_json_file(__magic_name__							)
					UpperCAmelCase		:							Dict        =       NystromformerForMaskedLM(__magic_name__							)

					UpperCAmelCase		:							Union[str, Any]        =       convert_checkpoint_helper(__magic_name__			,   __magic_name__							)

					model.load_state_dict(__magic_name__							)
					model.eval()
					model.save_pretrained(__magic_name__							)

					print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)


if __name__ == "__main__":
				a       :      List[Any]  	=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The json file for Nystromformer model config.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				a       :      Tuple  	=			parser.parse_args()
				convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



'''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())
