


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
   from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


lowerCamelCase__							:						Optional[Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[str]							     =   {
    """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
    """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
    """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
    """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
    """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
    """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Any	   =       'bloom'
     __lowercase				:							str	   =       ['past_key_values']
     __lowercase				:							Optional[Any]	   =       {
         'num_hidden_layers': 'n_layer',
         'num_attention_heads': 'n_head',
     }

     def __init__(					self:Union[str, Any]     ,		_a:int=25_08_80     ,		_a:Dict=64     ,		_a:List[Any]=2     ,		_a:Optional[int]=8     ,		_a:int=1e-5     ,		_a:List[str]=0.02     ,		_a:Union[str, Any]=True     ,		_a:List[str]=1     ,		_a:Tuple=2     ,		_a:Any=False     ,		_a:Optional[Any]=0.0     ,		_a:int=0.0     ,		_a:Any=1     ,		_a:str=False     ,		**_a:Any     ,		):
       snake_case__						= vocab_size
       # Backward compatibility with n_embed kwarg
       snake_case__						= kwargs.pop('''n_embed'''     ,		_a   )
       snake_case__						= hidden_size if n_embed is None else n_embed
       snake_case__						= n_layer
       snake_case__						= n_head
       snake_case__						= layer_norm_epsilon
       snake_case__						= initializer_range
       snake_case__						= use_cache
       snake_case__						= pretraining_tp
       snake_case__						= apply_residual_connection_post_layernorm
       snake_case__						= hidden_dropout
       snake_case__						= attention_dropout

       snake_case__						= bos_token_id
       snake_case__						= eos_token_id
       snake_case__						= slow_but_exact

       super().__init__(bos_token_id=_a     ,		eos_token_id=_a     ,		**_a   )




class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       version.parse('1.12'				)

     def __init__(					self:Union[str, Any]     ,		_a:PretrainedConfig     ,		_a:str = "default"     ,		_a:List[PatchingSpec] = None     ,		_a:bool = False     ,		):
       super().__init__(_a     ,		task=_a     ,		patching_specs=_a     ,		use_past=_a   )
       if not getattr(self._config     ,		'''pad_token_id'''     ,		_a   ):
         # TODO: how to do that better?
         snake_case__						= 0

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}   )
       if self.use_past:
         # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
         self.fill_with_past_key_values_(_a     ,		direction='''inputs'''     ,		inverted_values_shape=_a   )
         snake_case__						= {0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
         snake_case__						= {0: '''batch''', 1: '''sequence'''}

       return common_inputs

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       return self._config.n_layer

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       return self._config.n_head

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       return 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:"PreTrainedTokenizer"     ,		_a:int = -1     ,		_a:int = -1     ,		_a:bool = False     ,		_a:Optional["TensorType"] = None     ,		):
       snake_case__						= super(_a     ,		self   ).generate_dummy_inputs(
           _a     ,		batch_size=_a     ,		seq_length=_a     ,		is_pair=_a     ,		framework=_a   )

       # We need to order the input in the way they appears in the forward()
       snake_case__						= OrderedDict({'''input_ids''': common_inputs['''input_ids''']}   )

       # Need to add the past_keys
       if self.use_past:
         if not is_torch_available():
           raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
         else:
           import torch

           snake_case__				,				snake_case__						= common_inputs['''input_ids'''].shape
           # Not using the same length for past_key_values
           snake_case__						= seqlen + 2
           snake_case__						= self._config.hidden_size // self.num_attention_heads
           snake_case__						= (
               batch * self.num_attention_heads,
               head_dim,
               past_key_values_length,
           )
           snake_case__						= (
               batch * self.num_attention_heads,
               past_key_values_length,
               head_dim,
           )
           snake_case__						= [
               (torch.zeros(_a   ), torch.zeros(_a   )) for _ in range(self.num_layers   )
           ]

       snake_case__						= common_inputs['''attention_mask''']
       if self.use_past:
         snake_case__						= ordered_inputs['''attention_mask'''].dtype
         snake_case__						= torch.cat(
             [ordered_inputs['''attention_mask'''], torch.ones(_a     ,		_a     ,		dtype=_a   )]     ,		dim=1   )

       return ordered_inputs







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return 13





from __future__ import annotations

from statistics import mean







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  snake_case__						= [0] * no_of_processes

  # Initialize remaining_time to waiting_time.

  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i]
  snake_case__						= []

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

  return waiting_time







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)    ->				list[int]:
  snake_case__						= [0] * no_of_processes
  for i in range(__lowerCAmelCase				):
    snake_case__						= burst_time[i] + waiting_time[i]
  return turn_around_time


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

   lowerCamelCase__							:						Tuple							     =   4
   lowerCamelCase__							:						Union[str, Any]							     =   [2, 5, 3, 7]
   lowerCamelCase__							:						Optional[Any]							     =   [0, 0, 0, 0]
   lowerCamelCase__							:						Dict							     =   calculate_waitingtime(arrival_time, burst_time, no_of_processes)
   lowerCamelCase__							:						Union[str, Any]							     =   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}""")

