
"""simple docstring"""



from collections import deque




class 		a_						:







    def __init__(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   process_name  # process name
        _UpperCAmelCase          =   arrival_time  # arrival time of the process
        # completion time of finished process or last interrupted time
        _UpperCAmelCase          =   arrival_time
        _UpperCAmelCase          =   burst_time  # remaining burst time
        _UpperCAmelCase          =   0  # total time of the process wait in ready queue
        _UpperCAmelCase          =   0  # time from arrival time to completion time







class 		a_						:







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       list[int]     ,					__UpperCamelCase			:       deque[Process]     ,					__UpperCamelCase			:       int     ,					)		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   number_of_queues
        # time slice of queues that round robin algorithm applied
        _UpperCAmelCase          =   time_slices
        # unfinished process is in this ready_queue
        _UpperCAmelCase          =   queue
        # current time
        _UpperCAmelCase          =   current_time
        # finished process is in this sequence queue
        _UpperCAmelCase          =   deque()







    def      _snake_case					(			self			:       str     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for i in range(len(self.finish_queue     )     ):
            sequence.append(self.finish_queue[i].process_name     )
        return sequence







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       list[Process]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for i in range(len(__snake_case     )     ):
            waiting_times.append(queue[i].waiting_time     )
        return waiting_times







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       list[Process]     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for i in range(len(__snake_case     )     ):
            turnaround_times.append(queue[i].turnaround_time     )
        return turnaround_times







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       list[Process]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for i in range(len(__snake_case     )     ):
            completion_times.append(queue[i].stop_time     )
        return completion_times







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       deque[Process]     )		->Any:






        '''simple docstring'''






        return [q.burst_time for q in queue]







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Process     )		->Any:






        '''simple docstring'''






        process.waiting_time += self.current_time - process.stop_time
        return process.waiting_time







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       deque[Process]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   deque()  # sequence deque of finished process
        while len(__snake_case     ) != 0:
            _UpperCAmelCase          =   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
            _UpperCAmelCase          =   0
            # set the process's turnaround time because it is finished
            _UpperCAmelCase          =   self.current_time - cp.arrival_time
            # set the completion time
            _UpperCAmelCase          =   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			:       Tuple     ,					__UpperCamelCase			:       deque[Process]     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   deque()  # sequence deque of terminated process
        # just for 1 cycle and unfinished processes will go back to queue
        for _ in range(len(__snake_case     )     ):
            _UpperCAmelCase          =   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
                _UpperCAmelCase          =   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
                _UpperCAmelCase          =   0
                # set the finish time
                _UpperCAmelCase          =   self.current_time
                # update the process' turnaround time because it is finished
                _UpperCAmelCase          =   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			:       str     )		->Any:






        '''simple docstring'''






        for i in range(self.number_of_queues - 1     ):
            _UpperCAmelCase          =   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

      a					:	Dict							=		Process('''P1''', 0, 5_3)
      a					:	Any							=		Process('''P2''', 0, 1_7)
      a					:	Any							=		Process('''P3''', 0, 6_8)
      a					:	Union[str, Any]							=		Process('''P4''', 0, 2_4)
      a					:	Optional[int]							=		3
      a					:	Dict							=		[1_7, 2_5]
      a					:	Optional[Any]							=		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])})

      a					:	str							=		Process('''P1''', 0, 5_3)
      a					:	Optional[Any]							=		Process('''P2''', 0, 1_7)
      a					:	List[str]							=		Process('''P3''', 0, 6_8)
      a					:	Optional[int]							=		Process('''P4''', 0, 2_4)
      a					:	Dict							=		3
      a					:	List[Any]							=		[1_7, 2_5]
      a					:	Union[str, Any]							=		deque([Pa, Pa, Pa, Pa])
      a					:	Optional[int]							=		MLFQ(number_of_queues, time_slices, queue, 0)
      a					:	List[str]							=		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()}"
      )

"""simple docstring"""



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 		a_						( nn.Module	):







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       int = 16     ,					__UpperCamelCase			:       int = 88     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       int = 1     ,					__UpperCamelCase			:       float = 0.0     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       bool = False     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       str = "geglu"     ,					__UpperCamelCase			:       Optional[int] = None     ,					)		->Dict:






        '''simple docstring'''






        super().__init__()
        _UpperCAmelCase          =   nn.ModuleList(
            [
                TransformeraDModel(
                    num_attention_heads=__UpperCamelCase     ,					attention_head_dim=__UpperCamelCase     ,					in_channels=__UpperCamelCase     ,					num_layers=__UpperCamelCase     ,					dropout=__UpperCamelCase     ,					norm_num_groups=__UpperCamelCase     ,					cross_attention_dim=__UpperCamelCase     ,					attention_bias=__UpperCamelCase     ,					sample_size=__UpperCamelCase     ,					num_vector_embeds=__UpperCamelCase     ,					activation_fn=__UpperCamelCase     ,					num_embeds_ada_norm=__UpperCamelCase     ,					)
                for _ in range(2     )
            ]     )

        # Variables that can be set by a pipeline:

        # The ratio of transformer1 to transformer2's output states to be combined during inference
        _UpperCAmelCase          =   0.5

        # The shape of `encoder_hidden_states` is expected to be
        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
        _UpperCAmelCase          =   [77, 2_57]

        # Which transformer to use to encode which condition.
        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
        _UpperCAmelCase          =   [1, 0]







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       bool = True     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   hidden_states

        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   0
        # attention_mask is not used yet
        for i in range(2     ):
            # for each of the two transformers, pass the corresponding condition tokens
            _UpperCAmelCase          =   encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            _UpperCAmelCase          =   self.transformer_index_for_condition[i]
            _UpperCAmelCase          =   self.transformers[transformer_index](
                __UpperCamelCase     ,					encoder_hidden_states=__UpperCamelCase     ,					timestep=__UpperCamelCase     ,					cross_attention_kwargs=__UpperCamelCase     ,					return_dict=__UpperCamelCase     ,					)[0]
            encoded_states.append(encoded_state - input_states     )
            tokens_start += self.condition_lengths[i]

        _UpperCAmelCase          =   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        _UpperCAmelCase          =   output_states + input_states

        if not return_dict:
            return (output_states,)

        return TransformeraDModelOutput(sample=__UpperCamelCase     )