


from statistics import mean

import numpy as np


def    __lowerCamelCase   (      A__						: list						,						A__						: list						,						A__						: list						,						A__						: int					)     ->    list:
   lowerCamelCase_						:   Dict		= 0
   # Number of processes finished
   lowerCamelCase_						:   Tuple		= 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[Any]		= [0] * no_of_process

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

   while no_of_process > finished_process_count:
      lowerCamelCase_						:   Any		= 0
      while finished_process[i] == 1:
         i += 1
      if current_time < arrival_time[i]:
         lowerCamelCase_						:   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_						:   List[str]		= 0
      for i in range(0						,						A__					):
         if finished_process[i] == 0 and arrival_time[i] <= current_time:
            lowerCamelCase_						:   Union[str, Any]		= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
                i
            ]
         if response_ratio < temp:
            lowerCamelCase_						:   List[str]		= temp
            lowerCamelCase_						:   Optional[Any]		= 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_						:   Optional[int]		= 1
      # Increase finished_process_count by 1
      finished_process_count += 1

   return turn_around_time


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


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

      snake_case__				:	Dict					       =					calculate_turn_around_time(
          process_name, arrival_time, burst_time, no_of_process
      )
      snake_case__				:	Tuple					       =					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}')









from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(
           metadata={"help": "The output directory where the model will be written."}						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The encoder model checkpoint for weights initialization."
                   "Don't set if you want to train an encoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The decoder model checkpoint for weights initialization."
                   "Don't set if you want to train a decoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.encoder_config_name					)
   # Use pretrained encoder model's config
   else:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.decoder_config_name					)
   # Use pretrained decoder model's config
   else:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,						decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,						encoder_config=A__						,						decoder_config=A__						,						)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

   model.save_pretrained(model_args.output_dir					)
   image_processor.save_pretrained(model_args.output_dir					)
   tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
      main()





