
from collections import deque


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       Tuple				,       _snake_case		:       str				,       _snake_case		:       int				,       _snake_case		:       int				):
          __lowercase		:			Dict	    =       process_name  # process name
          __lowercase		:			Optional[int]	    =       arrival_time  # arrival time of the process
          # completion time of finished process or last interrupted time
          __lowercase		:			Optional[int]	    =       arrival_time
          __lowercase		:			List[Any]	    =       burst_time  # remaining burst time
          __lowercase		:			List[Any]	    =       0  # total time of the process wait in ready queue
          __lowercase		:			str	    =       0  # time from arrival time to completion time






class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       List[str]				,       _snake_case		:       int				,       _snake_case		:       list[int]				,       _snake_case		:       deque[Process]				,       _snake_case		:       int				,       ):
          # total number of mlfq's queues
          __lowercase		:			Optional[Any]	    =       number_of_queues
          # time slice of queues that round robin algorithm applied
          __lowercase		:			Union[str, Any]	    =       time_slices
          # unfinished process is in this ready_queue
          __lowercase		:			Optional[int]	    =       queue
          # current time
          __lowercase		:			List[Any]	    =       current_time
          # finished process is in this sequence queue
          __lowercase		:			deque[Process]	    =       deque()




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			int	    =       []
          for i in range(len(self.finish_queue				)				):
              sequence.append(self.finish_queue[i].process_name				)
          return sequence




      def 		snake_case_			(      self		:       int				,       _snake_case		:       list[Process]				):
          __lowercase		:			Optional[int]	    =       []
          for i in range(len(_snake_case				)				):
              waiting_times.append(queue[i].waiting_time				)
          return waiting_times




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       list[Process]				):
          __lowercase		:			List[Any]	    =       []
          for i in range(len(_snake_case				)				):
              turnaround_times.append(queue[i].turnaround_time				)
          return turnaround_times




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       list[Process]				):
          __lowercase		:			Any	    =       []
          for i in range(len(_snake_case				)				):
              completion_times.append(queue[i].stop_time				)
          return completion_times




      def 		snake_case_			(      self		:       Tuple				,       _snake_case		:       deque[Process]				):
          return [q.burst_time for q in queue]




      def 		snake_case_			(      self		:       Union[str, Any]				,       _snake_case		:       Process				):
          process.waiting_time += self.current_time - process.stop_time
          return process.waiting_time




      def 		snake_case_			(      self		:       int				,       _snake_case		:       deque[Process]				):
          __lowercase		:			deque[Process]	    =       deque()  # sequence deque of finished process
          while len(_snake_case				) != 0:
              __lowercase		:			str	    =       ready_queue.popleft()  # current process

              # if process's arrival time is later than current time, update current time
              if self.current_time < cp.arrival_time:
                  self.current_time += cp.arrival_time

              # update waiting time of current process
              self.update_waiting_time(_snake_case				)
              # update current time
              self.current_time += cp.burst_time
              # finish the process and set the process's burst-time 0
              __lowercase		:			Tuple	    =       0
              # set the process's turnaround time because it is finished
              __lowercase		:			List[str]	    =       self.current_time - cp.arrival_time
              # set the completion time
              __lowercase		:			List[str]	    =       self.current_time
              # add the process to queue that has finished queue
              finished.append(_snake_case				)

          self.finish_queue.extend(_snake_case				)  # add finished process to finish queue
          # FCFS will finish all remaining processes
          return finished




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       deque[Process]				,       _snake_case		:       int				):
          __lowercase		:			deque[Process]	    =       deque()  # sequence deque of terminated process
          # just for 1 cycle and unfinished processes will go back to queue
          for _ in range(len(_snake_case				)				):
              __lowercase		:			Optional[int]	    =       ready_queue.popleft()  # current process

              # if process's arrival time is later than current time, update current time
              if self.current_time < cp.arrival_time:
                  self.current_time += cp.arrival_time

              # update waiting time of unfinished processes
              self.update_waiting_time(_snake_case				)
              # if the burst time of process is bigger than time-slice
              if cp.burst_time > time_slice:
                  # use CPU for only time-slice
                  self.current_time += time_slice
                  # update remaining burst time
                  cp.burst_time -= time_slice
                  # update end point time
                  __lowercase		:			Tuple	    =       self.current_time
                  # locate the process behind the queue because it is not finished
                  ready_queue.append(_snake_case				)
              else:
                  # use CPU for remaining burst time
                  self.current_time += cp.burst_time
                  # set burst time 0 because the process is finished
                  __lowercase		:			List[Any]	    =       0
                  # set the finish time
                  __lowercase		:			Optional[int]	    =       self.current_time
                  # update the process' turnaround time because it is finished
                  __lowercase		:			str	    =       self.current_time - cp.arrival_time
                  # add the process to queue that has finished queue
                  finished.append(_snake_case				)

          self.finish_queue.extend(_snake_case				)  # add finished process to finish queue
          # return finished processes queue and remaining processes queue
          return finished, ready_queue




      def 		snake_case_			(      self		:       Dict				):

          #  all queues except last one have round_robin algorithm
          for i in range(self.number_of_queues - 1				):
              __lowercase		:			str	    =       self.round_robin(
                  self.ready_queue				,       self.time_slices[i]				)
          #  the last queue has first_come_first_served algorithm
          self.first_come_first_served(self.ready_queue				)

          return self.finish_queue


if __name__ == "__main__":
 import doctest

 __lowerCAmelCase					:			Union[str, Any]			      =		Process("P1", 0, 53)
 __lowerCAmelCase					:			int			      =		Process("P2", 0, 17)
 __lowerCAmelCase					:			List[str]			      =		Process("P3", 0, 68)
 __lowerCAmelCase					:			int			      =		Process("P4", 0, 24)
 __lowerCAmelCase					:			int			      =		3
 __lowerCAmelCase					:			Dict			      =		[17, 25]
 __lowerCAmelCase					:			List[str]			      =		deque([Pa, Pa, Pa, Pa])

 if len(time_slices) != number_of_queues - 1:
  raise SystemExit(0)

 doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})

 __lowerCAmelCase					:			Union[str, Any]			      =		Process("P1", 0, 53)
 __lowerCAmelCase					:			List[Any]			      =		Process("P2", 0, 17)
 __lowerCAmelCase					:			Dict			      =		Process("P3", 0, 68)
 __lowerCAmelCase					:			Optional[Any]			      =		Process("P4", 0, 24)
 __lowerCAmelCase					:			Union[str, Any]			      =		3
 __lowerCAmelCase					:			Any			      =		[17, 25]
 __lowerCAmelCase					:			str			      =		deque([Pa, Pa, Pa, Pa])
 __lowerCAmelCase					:			Union[str, Any]			      =		MLFQ(number_of_queues, time_slices, queue, 0)
 __lowerCAmelCase					:			List[Any]			      =		mlfq.multi_level_feedback_queue()

 # print total waiting times of processes(P1, P2, P3, P4)
 print(
     F'waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'
 )
 # print completion times of processes(P1, P2, P3, P4)
 print(
     F'completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'
 )
 # print total turnaround times of processes(P1, P2, P3, P4)
 print(
     F'turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'
 )
 # print sequence of finished processes
 print(
     F'sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'
 )


from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
 from .deepspeed import (
     DeepSpeedEngineWrapper,
     DeepSpeedOptimizerWrapper,
     DeepSpeedSchedulerWrapper,
     DummyOptim,
     DummyScheduler,
     HfDeepSpeedConfig,
 )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers


