

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"xlnet"
						_lowercase    						=			["mems"]
						_lowercase    						=			{
						    "n_token": "vocab_size",  # Backward compatibility
						    "hidden_size": "d_model",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=32000						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=True						,					A_="bi"						,					A_=0.02						,					A_=1e-12						,					A_=0.1						,					A_=512						,					A_=None						,					A_=True						,					A_=False						,					A_=False						,					A_=-1						,					A_=False						,					A_="last"						,					A_=True						,					A_="tanh"						,					A_=0.1						,					A_=5						,					A_=5						,					A_=5						,					A_=1						,					A_=2						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    vocab_size
										_UpperCAmelCase       :		str 		=    d_model
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		int 		=    n_head
										if d_model % n_head != 0:
														raise ValueError(f'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'	)
										if "d_head" in kwargs:
														if kwargs["d_head"] != d_model // n_head:
																		raise ValueError(
																		    f'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'	)
										_UpperCAmelCase       :		Union[str, Any] 		=    d_model // n_head
										_UpperCAmelCase       :		Union[str, Any] 		=    ff_activation
										_UpperCAmelCase       :		Optional[Any] 		=    d_inner
										_UpperCAmelCase       :		Any 		=    untie_r
										_UpperCAmelCase       :		Dict 		=    attn_type

										_UpperCAmelCase       :		List[Any] 		=    initializer_range
										_UpperCAmelCase       :		Optional[Any] 		=    layer_norm_eps

										_UpperCAmelCase       :		List[str] 		=    dropout
										_UpperCAmelCase       :		List[Any] 		=    mem_len
										_UpperCAmelCase       :		Any 		=    reuse_len
										_UpperCAmelCase       :		Union[str, Any] 		=    bi_data
										_UpperCAmelCase       :		Optional[int] 		=    clamp_len
										_UpperCAmelCase       :		str 		=    same_length

										_UpperCAmelCase       :		Optional[int] 		=    summary_type
										_UpperCAmelCase       :		Any 		=    summary_use_proj
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_activation
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_last_dropout
										_UpperCAmelCase       :		List[str] 		=    start_n_top
										_UpperCAmelCase       :		List[str] 		=    end_n_top

										_UpperCAmelCase       :		Optional[int] 		=    bos_token_id
										_UpperCAmelCase       :		Optional[Any] 		=    pad_token_id
										_UpperCAmelCase       :		Any 		=    eos_token_id

										if "use_cache" in kwargs:
														warnings.warn(
														    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
														    " instead."						,					A_						,					)
														_UpperCAmelCase       :		Optional[Any] 		=    kwargs["use_cache"]

										_UpperCAmelCase       :		List[Any] 		=    use_mems_eval
										_UpperCAmelCase       :		Tuple 		=    use_mems_train
										super().__init__(pad_token_id=A_						,					bos_token_id=A_						,					eos_token_id=A_						,					**A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.'	)
										return -1





						@max_position_embeddings.setter
						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										raise NotImplementedError(
										    f'The model {self.model_type} is one of the few models that has no sequence length limit.'	)





from __future__ import annotations

import pandas as pd

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       list[int]:
				_UpperCAmelCase       :		List[Any] 		=    [0] * no_of_processes
				_UpperCAmelCase       :		Dict 		=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    burst_time[i]

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		Any 		=    False

				# Process until all processes are completed
				while complete != no_of_processes:
								for j in range(lowerCAmelCase ):
												if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																if remaining_time[j] < minm:
																				_UpperCAmelCase       :		Optional[int] 		=    remaining_time[j]
																				_UpperCAmelCase       :		int 		=    j
																				_UpperCAmelCase       :		Tuple 		=    True

								if not check:
												increment_time += 1
												continue
								remaining_time[short] -= 1

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

								if remaining_time[short] == 0:
												complete += 1
												_UpperCAmelCase       :		int 		=    False

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

												# Calculate waiting time
												_UpperCAmelCase       :		str 		=    finish_time - arrival_time[short]
												_UpperCAmelCase       :		Union[str, Any] 		=    finar - burst_time[short]

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    total_turn_around_time + turn_around_time[i]
				print(F'Average waiting time = {total_waiting_time / no_of_processes:.5f}' )
				print("Average turn around time ="  ,      total_turn_around_time / no_of_processes )


if __name__ == "__main__":
					print('Enter how many process you want to analyze')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print('Enter the arrival time and burst time for process:--' + str(i + 1))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      burst_time
					SCREAMING_SNAKE_CASE_        =      no_of_processes
					SCREAMING_SNAKE_CASE_        =      waiting_time
					SCREAMING_SNAKE_CASE_        =      calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        'Process',
					        'BurstTime',
					        'ArrivalTime',
					        'WaitingTime',
					        'TurnAroundTime',
					    ],
					)

					# Printing the dataFrame
					pd.set_option('display.max_rows', fcfs.shape[0] + 1)
					print(fcfs)


