


from __future__ import annotations

from statistics import mean


def    __lowerCamelCase   (      A__						: list[int]						,						A__						: list[int]						,						A__						: int					)     ->    list[int]:
   lowerCamelCase_						:   int		= [0] * no_of_processes
   lowerCamelCase_						:   Optional[Any]		= [0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(A__					):
      lowerCamelCase_						:   Optional[int]		= burst_time[i]
   lowerCamelCase_						:   list[int]		= []

   lowerCamelCase_						:   Optional[int]		= 0
   lowerCamelCase_						:   List[str]		= 0

   # When processes are not completed,
   # A process whose arrival time has passed \
   # and has remaining execution time is put into the ready_process.
   # The shortest process in the ready_process, target_process is executed.

   while completed != no_of_processes:
      lowerCamelCase_						:   Optional[int]		= []
      lowerCamelCase_						:   List[str]		= -1

      for i in range(A__					):
         if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
            ready_process.append(A__					)

      if len(A__					) > 0:
         lowerCamelCase_						:   Union[str, Any]		= ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               lowerCamelCase_						:   Union[str, Any]		= i
         total_time += burst_time[target_process]
         completed += 1
         lowerCamelCase_						:   Optional[int]		= 0
         lowerCamelCase_						:   Tuple		= (
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time


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


if __name__ == "__main__":
      print('[TEST CASE 01]')

      snake_case__				:	Optional[int]					       =					4
      snake_case__				:	Optional[int]					       =					[2, 5, 3, 7]
      snake_case__				:	Optional[Any]					       =					[0, 0, 0, 0]
      snake_case__				:	Optional[Any]					       =					calculate_waitingtime(arrival_time, burst_time, no_of_processes)
      snake_case__				:	str					       =					calculate_turnaroundtime(
          burst_time, no_of_processes, waiting_time
      )

      # Printing the Result
      print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
      for i, process_id in enumerate(list(range(1, 5))):
            print(
                F'{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'
                F'{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'
            )
      print(F'\nAverage waiting time = {mean(waiting_time):.5f}')
      print(F'Average turnaround 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()





