



from statistics import mean

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		list:







						"""simple docstring"""

						A__	:							str						    = 0
						# Number of processes finished
						A__	:							Optional[Any]						    = 0
						# Displays the finished process.
						# If it is 0, the performance is completed if it is 1, before the performance.
						A__	:							List[str]						    = [0] * no_of_process
						# List to include calculation results
						A__	:							Optional[int]						    = [0] * no_of_process

						# Sort by arrival time.
						A__	:							Tuple						    = [burst_time[i] for i in np.argsort(__lowercase       )]
						A__	:							Optional[int]						    = [process_name[i] for i in np.argsort(__lowercase       )]
						arrival_time.sort()

						while no_of_process > finished_process_count:
												A__	:							Dict						    = 0
												while finished_process[i] == 1:
																		i += 1
												if current_time < arrival_time[i]:
																		A__	:							List[str]						    = arrival_time[i]

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

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

						return turn_around_time
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		list:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = [0] * no_of_process
						for i in range(0							,     __lowercase       ):
												A__	:							int						    = turn_around_time[i] - burst_time[i]
						return waiting_time


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

		snake_case    :						Dict  =						calculate_turn_around_time(
		    process_name, arrival_time, burst_time, no_of_process
		)
		snake_case    :						List[Any]  =						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}""")










import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


snake_case    :						Any  =						logging.getLogger(__name__)




class 							lowerCAmelCase__  :







					def __init__(       self      :     Union[str, Any]):
											A__	:							Dict						    = False







					def  _lowercase			(       self      :     Optional[int]			,		_A      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if not self.initialized:
																	A__	:							List[Any]						    = RagRetriever(
																	    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
																	A__	:							Dict						    = True







					def  _lowercase			(       self      :     Union[str, Any]):
											self.retriever.index.init_index()







					def  _lowercase			(       self      :     List[str]			,		_A      :     Optional[int]			,		_A      :     List[str]):
											A__    ,       A__	:							str						    = self.retriever._main_retrieve(_A			,		_A)
											return doc_ids, retrieved_doc_embeds




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def __init__(       self      :     Optional[Any]			,		_A      :     Union[str, Any]			,		_A      :     Tuple			,		_A      :     int			,		_A      :     Tuple			,		_A      :     Union[str, Any]=None):
											if index is not None and index.is_initialized() and len(_A) > 0:
																	raise ValueError(
																	    "When using Ray for distributed fine-tuning, "
																	    "you'll need to provide the paths instead, "
																	    "as the dataset and the index are loaded "
																	    "separately. More info in examples/rag/use_own_knowledge_dataset.py ")
											super().__init__(
											    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		index=_A			,		init_retrieval=_A			,		)
											A__	:							str						    = retrieval_workers
											if len(self.retrieval_workers) > 0:
																	ray.get(
																	    [
																	        worker.create_rag_retriever.remote(_A			,		_A			,		_A			,		_A)
																	        for worker in self.retrieval_workers
																	    ])







					def  _lowercase			(       self      :     Optional[Any]):
											logger.info("initializing retrieval")

											if len(self.retrieval_workers) > 0:
																	ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
											else:
																	# Non-distributed training. Load index into this same process.
																	self.index.init_index()







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     List[str]			,		_A      :     Optional[Any]):
											if len(self.retrieval_workers) > 0:
																	# Select a random retrieval actor.
																	A__	:							Optional[Any]						    = self.retrieval_workers[random.randint(0			,		len(self.retrieval_workers) - 1)]
																	A__    ,       A__	:							List[Any]						    = ray.get(random_worker.retrieve.remote(_A			,		_A))
											else:
																	A__    ,       A__	:							Union[str, Any]						    = self._main_retrieve(_A			,		_A)
											return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_A)







					@classmethod
					def  _lowercase			(       cls      :     int			,		_A      :     Optional[int]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											return super(_A			,		cls).get_tokenizers(_A			,		_A			,		**_A)







					@classmethod
					def  _lowercase			(       cls      :     Any			,		_A      :     Union[str, Any]			,		_A      :     Optional[Any]			,		_A      :     List[str]=None			,		**_A      :     Optional[Any]):
											A__	:							str						    = kwargs.pop("config"			,		_A) or RagConfig.from_pretrained(_A			,		**_A)
											A__	:							int						    = RagTokenizer.from_pretrained(_A			,		config=_A)
											A__	:							List[str]						    = rag_tokenizer.question_encoder
											A__	:							str						    = rag_tokenizer.generator
											if indexed_dataset is not None:
																	A__	:							Tuple						    = "custom"
																	A__	:							Tuple						    = CustomHFIndex(config.retrieval_vector_size			,		_A)
											else:
																	A__	:							Union[str, Any]						    = cls._build_index(_A)
											return cls(
											    _A			,		question_encoder_tokenizer=_A			,		generator_tokenizer=_A			,		retrieval_workers=_A			,		index=_A			,		)