

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
			import datasets






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			@slow
			@require_torch
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"					,					"prajjwal1/bert-tiny"		)
				SCREAMING_SNAKE_CASE      :					Any    =				BertTokenizer.from_pretrained("bert-base-uncased"		)

				SCREAMING_SNAKE_CASE      :					int    =				bertabert.config.encoder.vocab_size
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				tokenizer.sep_token_id
				SCREAMING_SNAKE_CASE      :					Tuple    =				tokenizer.cls_token_id
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				128

				SCREAMING_SNAKE_CASE      :					str    =				datasets.load_dataset("cnn_dailymail"					,					"3.0.0"					,					split="train[:1%]"		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				datasets.load_dataset("cnn_dailymail"					,					"3.0.0"					,					split="validation[:1%]"		)

				SCREAMING_SNAKE_CASE      :					List[str]    =				train_dataset.select(range(32		)		)
				SCREAMING_SNAKE_CASE      :					Dict    =				val_dataset.select(range(16		)		)

				SCREAMING_SNAKE_CASE      :					str    =				4

				def _map_to_encoder_decoder_inputs(a							:       List[str]		):
					# Tokenizer will automatically set [BOS] <text> [EOS]
					SCREAMING_SNAKE_CASE      :					Any    =				tokenizer(batch["article"]					,					padding="max_length"					,					truncation=a					,					max_length=512		)
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tokenizer(batch["highlights"]					,					padding="max_length"					,					truncation=a					,					max_length=128		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				inputs.input_ids
					SCREAMING_SNAKE_CASE      :					List[str]    =				inputs.attention_mask

					SCREAMING_SNAKE_CASE      :					str    =				outputs.input_ids
					SCREAMING_SNAKE_CASE      :					List[str]    =				outputs.input_ids.copy()
					SCREAMING_SNAKE_CASE      :					List[str]    =				[
					    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
					]
					SCREAMING_SNAKE_CASE      :					Any    =				outputs.attention_mask

					assert all(len(a		) == 512 for x in inputs.input_ids		)
					assert all(len(a		) == 128 for x in outputs.input_ids		)

					return batch

				def _compute_metrics(a							:       int		):
					SCREAMING_SNAKE_CASE      :					Dict    =				pred.label_ids
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				pred.predictions

					# all unnecessary tokens are removed
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				tokenizer.batch_decode(a					,					skip_special_tokens=a		)
					SCREAMING_SNAKE_CASE      :					int    =				tokenizer.batch_decode(a					,					skip_special_tokens=a		)

					SCREAMING_SNAKE_CASE      :					int    =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(a		)		)]		) / len(a		)

					return {"accuracy": accuracy}

				# map train dataset
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				train_dataset.map(
				    _map_to_encoder_decoder_inputs					,					batched=a					,					batch_size=a					,					remove_columns=["article", "highlights"]					,					)
				train_dataset.set_format(
				    type="torch"					,					columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]					,					)

				# same for validation dataset
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				val_dataset.map(
				    _map_to_encoder_decoder_inputs					,					batched=a					,					batch_size=a					,					remove_columns=["article", "highlights"]					,					)
				val_dataset.set_format(
				    type="torch"					,					columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]					,					)

				SCREAMING_SNAKE_CASE      :					int    =				self.get_auto_remove_tmp_dir()

				SCREAMING_SNAKE_CASE      :					Any    =				SeqaSeqTrainingArguments(
				    output_dir=a					,					per_device_train_batch_size=a					,					per_device_eval_batch_size=a					,					predict_with_generate=a					,					evaluation_strategy="steps"					,					do_train=a					,					do_eval=a					,					warmup_steps=0					,					eval_steps=2					,					logging_steps=2					,					)

				# instantiate trainer
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				SeqaSeqTrainer(
				    model=a					,					args=a					,					compute_metrics=_compute_metrics					,					train_dataset=a					,					eval_dataset=a					,					tokenizer=a					,					)

				# start training
				trainer.train()



'''simple docstring'''




from __future__ import annotations

from statistics import mean





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     list[int]	, lowerCamelCase	:     int				):
   lowerCAmelCase     =			[0] * no_of_processes
   lowerCAmelCase     =			[0] * no_of_processes

   # Initialize remaining_time to waiting_time.

   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i]
   lowerCAmelCase     =			[]

   lowerCAmelCase     =			0
   lowerCAmelCase     =			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     =			[]
      lowerCAmelCase     =			-1

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

      if len(lowerCamelCase				) > 0:
         lowerCAmelCase     =			ready_process[0]
         for i in ready_process:
            if remaining_time[i] < remaining_time[target_process]:
               lowerCAmelCase     =			i
         total_time += burst_time[target_process]
         completed += 1
         lowerCAmelCase     =			0
         lowerCAmelCase     =			(
             total_time - arrival_time[target_process] - burst_time[target_process]
         )
      else:
         total_time += 1

   return waiting_time





def 	a_					( lowerCamelCase	:     list[int]	, lowerCamelCase	:     int	, lowerCamelCase	:     list[int]				):
   lowerCAmelCase     =			[0] * no_of_processes
   for i in range(lowerCamelCase				):
      lowerCAmelCase     =			burst_time[i] + waiting_time[i]
   return turn_around_time


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

       __snake_case    						=4
       __snake_case    						=[2, 5, 3, 7]
       __snake_case    						=[0, 0, 0, 0]
       __snake_case    						=calculate_waitingtime(arrival_time, burst_time, no_of_processes)
       __snake_case    						=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}''')





