




'''simple docstring'''




from statistics import mean

import numpy as np
def      __snake_case       (   lowercase   :							Tuple			, lowercase   :							Union[str, Any]			, lowercase   :							Optional[int]			, lowercase   :							Union[str, Any]  ):
					snake_case_	=      0
					# Number of processes finished
					snake_case_	=      0
					# Displays the finished process.
					# If it is 0, the performance is completed if it is 1, before the performance.
					snake_case_	=      [0] * no_of_process
					# List to include calculation results
					snake_case_	=      [0] * no_of_process

					# Sort by arrival time.
					snake_case_	=      [burst_time[i] for i in np.argsort(SCREAMING_SNAKE_CASE_  )]
					snake_case_	=      [process_name[i] for i in np.argsort(SCREAMING_SNAKE_CASE_  )]
					arrival_time.sort()

					while no_of_process > finished_process_count:
										snake_case_	=      0
										while finished_process[i] == 1:
															i += 1
										if current_time < arrival_time[i]:
															snake_case_	=      arrival_time[i]

										snake_case_	=      0
										# Index showing the location of the process being performed
										snake_case_	=      0
										# Saves the current response ratio.
										snake_case_	=      0
										for i in range(0			, SCREAMING_SNAKE_CASE_  ):
															if finished_process[i] == 0 and arrival_time[i] <= current_time:
																				snake_case_	=      (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
																				    i
																				]
															if response_ratio < temp:
																				snake_case_	=      temp
																				snake_case_	=      i

        # Calculate the turn around time
										snake_case_	=      current_time + burst_time[loc] - arrival_time[loc]
										current_time += burst_time[loc]
										# Indicates that the process has been performed.
										snake_case_	=      1
										# Increase finished_process_count by 1
										finished_process_count += 1

					return turn_around_time
def      __snake_case       (   lowercase   :							str			, lowercase   :							int			, lowercase   :							List[str]			, lowercase   :							Tuple  ):
					snake_case_	=      [0] * no_of_process
					for i in range(0			, SCREAMING_SNAKE_CASE_  ):
										snake_case_	=      turn_around_time[i] - burst_time[i]
					return waiting_time


if __name__ == "__main__":
					lowercase__  							=					5
					lowercase__  							=					['''A''', '''B''', '''C''', '''D''', '''E''']
					lowercase__  							=					[1, 2, 3, 4, 5]
					lowercase__  							=					[1, 2, 3, 4, 5]

					lowercase__  							=					calculate_turn_around_time(
					    process_name, arrival_time, burst_time, no_of_process
					)
					lowercase__  							=					calculate_waiting_time(
					    process_name, turn_around_time, burst_time, no_of_process
					)

					print('''Process name \tArrival time \tBurst time \tTurn around time \tWaiting time''')
					for i in range(0, no_of_process):
										print(
										    f"""{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t"""
										    f"""{turn_around_time[i]}\t\t\t{waiting_time[i]}"""
										)

					print(f"""average waiting time : {mean(waiting_time):.5f}""")
					print(f"""average turn around time : {mean(turn_around_time):.5f}""")











'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""openai-gpt"""
					_UpperCamelCase							=		{
					    """max_position_embeddings""": """n_positions""",
					    """hidden_size""": """n_embd""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}



					def __init__(	self     :						Any      ,       _lowercase     :						List[Any]=40_478      ,       _lowercase     :						Tuple=512      ,       _lowercase     :						Dict=768      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Optional[int]=12      ,       _lowercase     :						Tuple="gelu"      ,       _lowercase     :						int=0.1      ,       _lowercase     :						Tuple=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						List[str]=1E-5      ,       _lowercase     :						str=0.02      ,       _lowercase     :						Optional[Any]="cls_index"      ,       _lowercase     :						int=True      ,       _lowercase     :						str=None      ,       _lowercase     :						Tuple=True      ,       _lowercase     :						List[Any]=0.1      ,       **_lowercase     :						int      ,       )    ->							Optional[int]:
								A_									= vocab_size
								A_									= n_positions
								A_									= n_embd
								A_									= n_layer
								A_									= n_head
								A_									= afn
								A_									= resid_pdrop
								A_									= embd_pdrop
								A_									= attn_pdrop
								A_									= layer_norm_epsilon
								A_									= initializer_range
								A_									= summary_type
								A_									= summary_use_proj
								A_									= summary_activation
								A_									= summary_first_dropout
								A_									= summary_proj_to_labels
								super().__init__(**_lowercase)




