




from statistics import mean

import numpy as np







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    int					=					0
				# Number of processes finished
				_lowerCamelCase  :    str					=					0
				# Displays the finished process.
				# If it is 0, the performance is completed if it is 1, before the performance.
				_lowerCamelCase  :    Dict					=					[0] * no_of_process
				# List to include calculation results
				_lowerCamelCase  :    List[str]					=					[0] * no_of_process

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

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

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

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

				return turn_around_time







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__	):
				_lowerCamelCase  :    Dict					=					[0] * no_of_process
				for i in range(0							,					lowercase__	):
								_lowerCamelCase  :    Dict					=					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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/realm-cc-news-pretrained-embedder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-encoder""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-scorer""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"""
    ),
    """google/realm-cc-news-pretrained-openqa""": (
        """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"""
    ),
    """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json""",
    """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json""",
    """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json""",
    """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json""",
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """realm"""
			def __init__(						self						,   lowercase=30522						,   lowercase=768						,   lowercase=128						,   lowercase=12						,   lowercase=12						,   lowercase=8						,   lowercase=3072						,   lowercase="gelu_new"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=512						,   lowercase=2						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=256						,   lowercase=10						,   lowercase=1E-3						,   lowercase=5						,   lowercase=320						,   lowercase=13353718						,   lowercase=5000						,   lowercase=1						,   lowercase=0						,   lowercase=2						,   **lowercase						,   ):
							super().__init__(pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						,   **lowercase						)

							# Common config
							_lowerCamelCase  :    str					=					vocab_size
							_lowerCamelCase  :    Dict					=					max_position_embeddings
							_lowerCamelCase  :    int					=					hidden_size
							_lowerCamelCase  :    Optional[Any]					=					retriever_proj_size
							_lowerCamelCase  :    Dict					=					num_hidden_layers
							_lowerCamelCase  :    Any					=					num_attention_heads
							_lowerCamelCase  :    int					=					num_candidates
							_lowerCamelCase  :    List[Any]					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Union[str, Any]					=					hidden_dropout_prob
							_lowerCamelCase  :    Dict					=					attention_probs_dropout_prob
							_lowerCamelCase  :    Union[str, Any]					=					initializer_range
							_lowerCamelCase  :    List[Any]					=					type_vocab_size
							_lowerCamelCase  :    int					=					layer_norm_eps

							# Reader config
							_lowerCamelCase  :    Tuple					=					span_hidden_size
							_lowerCamelCase  :    int					=					max_span_width
							_lowerCamelCase  :    Tuple					=					reader_layer_norm_eps
							_lowerCamelCase  :    Union[str, Any]					=					reader_beam_size
							_lowerCamelCase  :    Union[str, Any]					=					reader_seq_len

							# Retrieval config
							_lowerCamelCase  :    Optional[Any]					=					num_block_records
							_lowerCamelCase  :    str					=					searcher_beam_size