code: 
  from __future__ import annotations
  
  from statistics import mean
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       list[int]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Optional[int]     =	2
  		snake_case     :					int     =	list(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					List[Any]     =	[0] * len(SCREAMING_SNAKE_CASE__ )
  		snake_case     :					Optional[int]     =	0
  		while True:
  				snake_case     :					Union[str, Any]     =	True
  				for i, burst_time in enumerate(SCREAMING_SNAKE_CASE__ ):
  						if rem_burst_times[i] > 0:
  								snake_case     :					List[Any]     =	False
  								if rem_burst_times[i] > quantum:
  										t += quantum
  										rem_burst_times[i] -= quantum
  								else:
  										t += rem_burst_times[i]
  										snake_case     :					Dict     =	t - burst_time
  										snake_case     :					List[str]     =	0
  				if done is True:
  						return waiting_times
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       list[int]:
  
  
  		'''simple docstring'''
  
  
  		return [burst + waiting for burst, waiting in zip(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )]
  
  
  if __name__ == "__main__":
  						lowercase__						=			[3, 5, 7]
  						lowercase__						=			calculate_waiting_times(burst_times)
  						lowercase__						=			calculate_turn_around_times(burst_times, waiting_times)
  						print("Process ID \tBurst Time \tWaiting Time \tTurnaround Time")
  						for i, burst_time in enumerate(burst_times):
  												print(
  												    f"  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  "
  												    f"{turn_around_times[i]}"
  												)
  						print(f"\nAverage waiting time = {mean(waiting_times):.5f}")
  						print(f"Average turn around time = {mean(turn_around_times):.5f}")
  
  
  
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)
  
  
  