


"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


__lowercase    					=      logging.get_logger(__name__)

__lowercase    					=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase    					=      {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

__lowercase    					=      {
    '''gpt-neox-20b''': 2048,
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       VOCAB_FILES_NAMES
    UpperCAmelCase       :							Dict	=       PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase       :							Union[str, Any]	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase       :							Union[str, Any]	=       ["input_ids", "attention_mask"]






    def __init__(					self				:    Union[str, Any]				, __UpperCAmelCase				:    int=None				, __UpperCAmelCase				:    List[str]=None				, __UpperCAmelCase				:    str=None				, __UpperCAmelCase				:    Union[str, Any]="<|endoftext|>"				, __UpperCAmelCase				:    Optional[Any]="<|endoftext|>"				, __UpperCAmelCase				:    List[str]="<|endoftext|>"				, __UpperCAmelCase				:    List[Any]=False				, **__UpperCAmelCase				:    Optional[int]				, ):
      super().__init__(
          UpperCAmelCase__				, UpperCAmelCase__				, tokenizer_file=UpperCAmelCase__				, unk_token=UpperCAmelCase__				, bos_token=UpperCAmelCase__				, eos_token=UpperCAmelCase__				, add_prefix_space=UpperCAmelCase__				, **UpperCAmelCase__				, )

      a						:		Union[str, Any]      							=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
      if pre_tok_state.get("add_prefix_space"				, UpperCAmelCase__) != add_prefix_space:
        a						:		List[str]      							=			getattr(UpperCAmelCase__				, pre_tok_state.pop("type"))
        a						:		List[str]      							=			add_prefix_space
        a						:		Optional[int]      							=			pre_tok_class(**UpperCAmelCase__)

      a						:		List[Any]      							=			add_prefix_space






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    Optional[str] = None):
      a						:		Union[str, Any]      							=			self._tokenizer.model.save(UpperCAmelCase__				, name=UpperCAmelCase__)
      return tuple(UpperCAmelCase__)






    def 	__snake_case							(					self				:    Optional[int]				, __UpperCAmelCase				:    "Conversation"):
      a						:		Dict      							=			[]
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(UpperCAmelCase__				, add_special_tokens=UpperCAmelCase__) + [self.eos_token_id])

      if len(UpperCAmelCase__) > self.model_max_length:
        a						:		Tuple      							=			input_ids[-self.model_max_length :]
      return input_ids










"""simple docstring"""







from __future__ import annotations

import pandas as pd




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					# Copy the burst time into remaining_time[]
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i]

					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				9_9999_9999
					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				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:
																									__SCREAMING_SNAKE_CASE						   =				remaining_time[j]
																									__SCREAMING_SNAKE_CASE						   =				j
																									__SCREAMING_SNAKE_CASE						   =				True

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

										__SCREAMING_SNAKE_CASE						   =				remaining_time[short]
										if minm == 0:
															__SCREAMING_SNAKE_CASE						   =				9_9999_9999

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

															# Find finish time of current process
															__SCREAMING_SNAKE_CASE						   =				increment_time + 1

															# Calculate waiting time
															__SCREAMING_SNAKE_CASE						   =				finish_time - arrival_time[short]
															__SCREAMING_SNAKE_CASE						   =				finar - burst_time[short]

															if waiting_time[short] < 0:
																				__SCREAMING_SNAKE_CASE						   =				0

        # Increment time
										increment_time += 1
					return waiting_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				[0] * no_of_processes
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				burst_time[i] + waiting_time[i]
					return turn_around_time




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				0
					__SCREAMING_SNAKE_CASE						   =				0
					for i in range(lowerCAmelCase_   ):
										__SCREAMING_SNAKE_CASE						   =				total_waiting_time + waiting_time[i]
										__SCREAMING_SNAKE_CASE						   =				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''')
		a__				:							Optional[Any]						=     int(input())
		a__				:							Optional[int]						=     [0] * no_of_processes
		a__				:							int						=     [0] * no_of_processes
		a__				:							List[Any]						=     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))
				a__			,     a__				:							Tuple						=     map(int, input().split())

		a__				:							int						=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

		a__				:							Dict						=     burst_time
		a__				:							Any						=     no_of_processes
		a__				:							Optional[int]						=     waiting_time
		a__				:							Union[str, Any]						=     calculate_turnaroundtime(bt, n, wt)

		calculate_average_times(waiting_time, turn_around_time, no_of_processes)

		a__				:							str						=     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)
