






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_A			:    str					  ={
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """albert"""




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Optional[int]=30_000    ,							UpperCamelCase__:       Union[str, Any]=128    ,							UpperCamelCase__:       List[Any]=4_096    ,							UpperCamelCase__:       Union[str, Any]=12    ,							UpperCamelCase__:       Tuple=1    ,							UpperCamelCase__:       Dict=64    ,							UpperCamelCase__:       Any=16_384    ,							UpperCamelCase__:       int=1    ,							UpperCamelCase__:       Any="gelu_new"    ,							UpperCamelCase__:       List[Any]=0    ,							UpperCamelCase__:       Optional[int]=0    ,							UpperCamelCase__:       Optional[int]=512    ,							UpperCamelCase__:       List[Any]=2    ,							UpperCamelCase__:       List[Any]=0.02    ,							UpperCamelCase__:       List[str]=1e-12    ,							UpperCamelCase__:       Dict=0.1    ,							UpperCamelCase__:       Any="absolute"    ,							UpperCamelCase__:       Union[str, Any]=0    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       List[str]=3    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

														lowerCamelCase__      :			Dict 						= vocab_size
														lowerCamelCase__      :			List[Any] 						= embedding_size
														lowerCamelCase__      :			List[str] 						= hidden_size
														lowerCamelCase__      :			str 						= num_hidden_layers
														lowerCamelCase__      :			Tuple 						= num_hidden_groups
														lowerCamelCase__      :			Optional[int] 						= num_attention_heads
														lowerCamelCase__      :			Optional[Any] 						= inner_group_num
														lowerCamelCase__      :			Tuple 						= hidden_act
														lowerCamelCase__      :			int 						= intermediate_size
														lowerCamelCase__      :			List[str] 						= hidden_dropout_prob
														lowerCamelCase__      :			Optional[Any] 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= max_position_embeddings
														lowerCamelCase__      :			Union[str, Any] 						= type_vocab_size
														lowerCamelCase__      :			Optional[Any] 						= initializer_range
														lowerCamelCase__      :			Tuple 						= layer_norm_eps
														lowerCamelCase__      :			Optional[int] 						= classifier_dropout_prob
														lowerCamelCase__      :			Dict 						= position_embedding_type



class _lowercase			(       _lowercase	):




							@property
							def 	lowerCamelCase_       (							self:       List[str]  ):
														if self.task == "multiple-choice":
																					lowerCamelCase__      :			List[Any] 						= {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					lowerCamelCase__      :			Dict 						= {0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""input_ids""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														        ("""token_type_ids""", dynamic_axis),
														    ]  )









'''simple docstring'''







from statistics import mean

import numpy as np





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			Optional[int] 						= 0
							# Number of processes finished
							lowerCamelCase__      :			Union[str, Any] 						= 0
							# Displays the finished process.
							# If it is 0, the performance is completed if it is 1, before the performance.
							lowerCamelCase__      :			Tuple 						= [0] * no_of_process
							# List to include calculation results
							lowerCamelCase__      :			List[str] 						= [0] * no_of_process

							# Sort by arrival time.
							lowerCamelCase__      :			Union[str, Any] 						= [burst_time[i] for i in np.argsort(UpperCamelCase   )]
							lowerCamelCase__      :			List[Any] 						= [process_name[i] for i in np.argsort(UpperCamelCase   )]
							arrival_time.sort()

							while no_of_process > finished_process_count:
														lowerCamelCase__      :			str 						= 0
														while finished_process[i] == 1:
																					i += 1
														if current_time < arrival_time[i]:
																					lowerCamelCase__      :			Union[str, Any] 						= arrival_time[i]

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

        # Calculate the turn around time
														lowerCamelCase__      :			Optional[int] 						= current_time + burst_time[loc] - arrival_time[loc]
														current_time += burst_time[loc]
														# Indicates that the process has been performed.
														lowerCamelCase__      :			List[str] 						= 1
														# Increase finished_process_count by 1
														finished_process_count += 1

							return turn_around_time





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> list:
							lowerCamelCase__      :			int 						= [0] * no_of_process
							for i in range(0       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[Any] 						= turn_around_time[i] - burst_time[i]
							return waiting_time


if __name__ == "__main__":
			_A			:    List[str]					  =5
			_A			:    Optional[Any]					  =['''A''', '''B''', '''C''', '''D''', '''E''']
			_A			:    Optional[int]					  =[1, 2, 3, 4, 5]
			_A			:    Dict					  =[1, 2, 3, 4, 5]

			_A			:    Any					  =calculate_turn_around_time(
			    process_name, arrival_time, burst_time, no_of_process
			)
			_A			:    Optional[int]					  =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}')

