code: 
  from __future__ import annotations
  
  
  
  
  
  
  class   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: int			)	-> None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	data
  				snake_case     :					Union[str, Any]     =	None
  
  
  
  
  
  
  
  class   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: List[Any]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	None
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: int			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	Node(UpperCamelCase__			)
  				snake_case     :					Tuple     =	self.head
  				snake_case     :					Tuple     =	new_node
  				return self.head.data
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]			)	-> int | None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	self.head
  				snake_case     :					int     =	self.head
  				if self.head:
  						while fast_pointer and fast_pointer.next:
  								snake_case     :					Any     =	fast_pointer.next.next
  								snake_case     :					Optional[int]     =	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())
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  						)
  						parser.add_argument(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
  						        "This specifies the model architecture."
  						    ),
  						)
  						parser.add_argument(
  						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  