

from collections import deque






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		Union[str, Any]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		int )		->							None:
       SCREAMING_SNAKE_CASE__   =   process_name  # process name
       SCREAMING_SNAKE_CASE__   =   arrival_time  # arrival time of the process
       # completion time of finished process or last interrupted time
       SCREAMING_SNAKE_CASE__   =   arrival_time
       SCREAMING_SNAKE_CASE__   =   burst_time  # remaining burst time
       SCREAMING_SNAKE_CASE__   =   0  # total time of the process wait in ready queue
       SCREAMING_SNAKE_CASE__   =   0  # time from arrival time to completion time






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		int     ,       __lowerCamelCase						:		int     ,       __lowerCamelCase						:		list[int]     ,       __lowerCamelCase						:		deque[Process]     ,       __lowerCamelCase						:		int     ,       )		->							None:
       # total number of mlfq's queues
       SCREAMING_SNAKE_CASE__   =   number_of_queues
       # time slice of queues that round robin algorithm applied
       SCREAMING_SNAKE_CASE__   =   time_slices
       # unfinished process is in this ready_queue
       SCREAMING_SNAKE_CASE__   =   queue
       # current time
       SCREAMING_SNAKE_CASE__   =   current_time
       # finished process is in this sequence queue
       SCREAMING_SNAKE_CASE__   =   deque()






  def 				lowercase_		(							self						:		Dict )		->							list[str]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(self.finish_queue ) ):
            sequence.append(self.finish_queue[i].process_name )
       return sequence






  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		list[Process] )		->							list[int]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(__lowerCamelCase ) ):
            waiting_times.append(queue[i].waiting_time )
       return waiting_times






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		list[Process] )		->							list[int]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(__lowerCamelCase ) ):
            turnaround_times.append(queue[i].turnaround_time )
       return turnaround_times






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		list[Process] )		->							list[int]:
       SCREAMING_SNAKE_CASE__   =   []
       for i in range(len(__lowerCamelCase ) ):
            completion_times.append(queue[i].stop_time )
       return completion_times






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		deque[Process] )		->							list[int]:
       return [q.burst_time for q in queue]






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Process )		->							int:
       process.waiting_time += self.current_time - process.stop_time
       return process.waiting_time






  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		deque[Process] )		->							deque[Process]:
       SCREAMING_SNAKE_CASE__   =   deque()  # sequence deque of finished process
       while len(__lowerCamelCase ) != 0:
            SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase )
            # update current time
            self.current_time += cp.burst_time
            # finish the process and set the process's burst-time 0
            SCREAMING_SNAKE_CASE__   =   0
            # set the process's turnaround time because it is finished
            SCREAMING_SNAKE_CASE__   =   self.current_time - cp.arrival_time
            # set the completion time
            SCREAMING_SNAKE_CASE__   =   self.current_time
            # add the process to queue that has finished queue
            finished.append(__lowerCamelCase )

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






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		deque[Process]     ,       __lowerCamelCase						:		int )		->							tuple[deque[Process], deque[Process]]:
       SCREAMING_SNAKE_CASE__   =   deque()  # sequence deque of terminated process
       # just for 1 cycle and unfinished processes will go back to queue
       for _ in range(len(__lowerCamelCase ) ):
            SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase )
            # 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
                 SCREAMING_SNAKE_CASE__   =   self.current_time
                 # locate the process behind the queue because it is not finished
                 ready_queue.append(__lowerCamelCase )
            else:
                 # use CPU for remaining burst time
                 self.current_time += cp.burst_time
                 # set burst time 0 because the process is finished
                 SCREAMING_SNAKE_CASE__   =   0
                 # set the finish time
                 SCREAMING_SNAKE_CASE__   =   self.current_time
                 # update the process' turnaround time because it is finished
                 SCREAMING_SNAKE_CASE__   =   self.current_time - cp.arrival_time
                 # add the process to queue that has finished queue
                 finished.append(__lowerCamelCase )

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







  def 				lowercase_		(							self						:		int )		->							deque[Process]:

       #  all queues except last one have round_robin algorithm
       for i in range(self.number_of_queues - 1 ):
            SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   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

       _SCREAMING_SNAKE_CASE					:  str             =      Process('''P1''', 0, 53)
       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      Process('''P2''', 0, 17)
       _SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      Process('''P3''', 0, 68)
       _SCREAMING_SNAKE_CASE					:  Any             =      Process('''P4''', 0, 24)
       _SCREAMING_SNAKE_CASE					:  str             =      3
       _SCREAMING_SNAKE_CASE					:  Dict             =      [17, 25]
       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      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])})

       _SCREAMING_SNAKE_CASE					:  List[Any]             =      Process('''P1''', 0, 53)
       _SCREAMING_SNAKE_CASE					:  Dict             =      Process('''P2''', 0, 17)
       _SCREAMING_SNAKE_CASE					:  Optional[int]             =      Process('''P3''', 0, 68)
       _SCREAMING_SNAKE_CASE					:  Dict             =      Process('''P4''', 0, 24)
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      3
       _SCREAMING_SNAKE_CASE					:  Any             =      [17, 25]
       _SCREAMING_SNAKE_CASE					:  Any             =      deque([Pa, Pa, Pa, Pa])
       _SCREAMING_SNAKE_CASE					:  Dict             =      MLFQ(number_of_queues, time_slices, queue, 0)
       _SCREAMING_SNAKE_CASE					:  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()}"
       )





import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


_SCREAMING_SNAKE_CASE					:  Dict             =      '''base_with_context'''
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )
     for lyr_num, lyr in enumerate(model.encoders    ):
          SCREAMING_SNAKE_CASE__   =   weights[F'''layers_{lyr_num}''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )
     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )

     for lyr_num, lyr in enumerate(model.encoders    ):
          SCREAMING_SNAKE_CASE__   =   weights[F'''layers_{lyr_num}''']
          SCREAMING_SNAKE_CASE__   =   ly_weight['''attention''']

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )

     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T    )    )

     for lyr_num, lyr in enumerate(model.decoders    ):
          SCREAMING_SNAKE_CASE__   =   weights[F'''layers_{lyr_num}''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''self_attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''MultiHeadDotProductAttention_0''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T    )    )

     return model
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoints.load_tax_checkpoint(args.checkpoint_path    )
     SCREAMING_SNAKE_CASE__   =   jnp.tree_util.tree_map(onp.array				,		_A    )

     SCREAMING_SNAKE_CASE__   =   [
         '''from __gin__ import dynamic_registration''',
         '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
         '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
         '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
     ]

     SCREAMING_SNAKE_CASE__   =   os.path.join(args.checkpoint_path				,		'''..'''				,		'''config.gin'''    )
     SCREAMING_SNAKE_CASE__   =   inference.parse_training_gin_file(_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   inference.InferenceModel(args.checkpoint_path				,		_A    )

     SCREAMING_SNAKE_CASE__   =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				,		variance_type='''fixed_large'''    )

     SCREAMING_SNAKE_CASE__   =   SpectrogramNotesEncoder(
         max_length=synth_model.sequence_length['''inputs''']				,		vocab_size=synth_model.model.module.config.vocab_size				,		d_model=synth_model.model.module.config.emb_dim				,		dropout_rate=synth_model.model.module.config.dropout_rate				,		num_layers=synth_model.model.module.config.num_encoder_layers				,		num_heads=synth_model.model.module.config.num_heads				,		d_kv=synth_model.model.module.config.head_dim				,		d_ff=synth_model.model.module.config.mlp_dim				,		feed_forward_proj='''gated-gelu'''				,		)

     SCREAMING_SNAKE_CASE__   =   SpectrogramContEncoder(
         input_dims=synth_model.audio_codec.n_dims				,		targets_context_length=synth_model.sequence_length['''targets_context''']				,		d_model=synth_model.model.module.config.emb_dim				,		dropout_rate=synth_model.model.module.config.dropout_rate				,		num_layers=synth_model.model.module.config.num_encoder_layers				,		num_heads=synth_model.model.module.config.num_heads				,		d_kv=synth_model.model.module.config.head_dim				,		d_ff=synth_model.model.module.config.mlp_dim				,		feed_forward_proj='''gated-gelu'''				,		)

     SCREAMING_SNAKE_CASE__   =   TaFilmDecoder(
         input_dims=synth_model.audio_codec.n_dims				,		targets_length=synth_model.sequence_length['''targets_context''']				,		max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time				,		d_model=synth_model.model.module.config.emb_dim				,		num_layers=synth_model.model.module.config.num_decoder_layers				,		num_heads=synth_model.model.module.config.num_heads				,		d_kv=synth_model.model.module.config.head_dim				,		d_ff=synth_model.model.module.config.mlp_dim				,		dropout_rate=synth_model.model.module.config.dropout_rate				,		)

     SCREAMING_SNAKE_CASE__   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_decoder(ta_checkpoint['''target''']['''decoder''']				,		_A    )

     SCREAMING_SNAKE_CASE__   =   OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''    )

     SCREAMING_SNAKE_CASE__   =   SpectrogramDiffusionPipeline(
         notes_encoder=_A				,		continuous_encoder=_A				,		decoder=_A				,		scheduler=_A				,		melgan=_A				,		)
     if args.save:
          pipe.save_pretrained(args.output_path    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Dict             =      argparse.ArgumentParser()

       parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
       parser.add_argument(
           '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
       )
       parser.add_argument(
           '''--checkpoint_path''',
           default=F"{MODEL}/checkpoint_500000",
           type=str,
           required=False,
           help='''Path to the original jax model checkpoint.''',
       )
       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()

       main(args)


