


'''simple docstring'''




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[Any]		=     '''t5'''
     lowerCamelCase				:	Optional[Any]		=     ['''past_key_values''']
     lowerCamelCase				:	List[str]		=     {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__( self       :      str    ,      UpperCAmelCase__       :      int=3_2_1_2_8    ,      UpperCAmelCase__       :      Optional[int]=5_1_2    ,      UpperCAmelCase__       :      Tuple=6_4    ,      UpperCAmelCase__       :      Union[str, Any]=2_0_4_8    ,      UpperCAmelCase__       :      str=6    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      int=8    ,      UpperCAmelCase__       :      Tuple=3_2    ,      UpperCAmelCase__       :      Dict=1_2_8    ,      UpperCAmelCase__       :      List[Any]=0.1    ,      UpperCAmelCase__       :      Optional[Any]=1E-6    ,      UpperCAmelCase__       :      str=1.0    ,      UpperCAmelCase__       :      str="relu"    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      Optional[Any]=True    ,      UpperCAmelCase__       :      Dict=0    ,      UpperCAmelCase__       :      Tuple=1    ,      **UpperCAmelCase__       :      str    ,      )				->  Optional[int]:
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			d_model
        lowerCAmelCase     =			d_kv
        lowerCAmelCase     =			d_ff
        lowerCAmelCase     =			num_layers
        lowerCAmelCase     =			(
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        lowerCAmelCase     =			num_heads
        lowerCAmelCase     =			relative_attention_num_buckets
        lowerCAmelCase     =			relative_attention_max_distance
        lowerCAmelCase     =			dropout_rate
        lowerCAmelCase     =			layer_norm_epsilon
        lowerCAmelCase     =			initializer_factor
        lowerCAmelCase     =			feed_forward_proj
        lowerCAmelCase     =			use_cache

        lowerCAmelCase     =			self.feed_forward_proj.split('-'    )
        lowerCAmelCase     =			act_info[-1]
        lowerCAmelCase     =			act_info[0] == 'gated'

        if len(UpperCAmelCase__    ) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__    ) > 2:
           raise ValueError(
               F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
               '\'gated-gelu\' or \'relu\''    )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
           lowerCAmelCase     =			'gelu_new'

        super().__init__(
            pad_token_id=UpperCAmelCase__    ,      eos_token_id=UpperCAmelCase__    ,      is_encoder_decoder=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )




class 		UpperCAmelCase_      (  __lowercase		):





     @property
     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Mapping[str, Mapping[int, str]]:
        lowerCAmelCase     =			{
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
           lowerCAmelCase     =			'past_encoder_sequence + sequence'
           lowerCAmelCase     =			{0: 'batch'}
           lowerCAmelCase     =			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
           lowerCAmelCase     =			{0: 'batch', 1: 'decoder_sequence'}
           lowerCAmelCase     =			{0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
           self.fill_with_past_key_values_(UpperCAmelCase__    ,      direction='inputs'    )

        return common_inputs





     @property
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  int:
        return 1_3









'''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}''')





