

from collections import deque

class    _lowercase				:

   def __init__(				self	:    Any			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    int       ) ->			Optional[Any]:
         """simple docstring"""





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



class    _lowercase				:

   def __init__(				self	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    list[int]			,						lowerCamelCase__	:    deque[Process]			,						lowerCamelCase__	:    int			,						) ->			List[str]:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    int       ) ->			Dict:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    list[Process]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    list[Process]       ) ->			Dict:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Optional[int]			,						lowerCamelCase__	:    list[Process]       ) ->			str:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    deque[Process]       ) ->			str:
         """simple docstring"""





         return [q.burst_time for q in queue]

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Process       ) ->			Any:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[Any]			,						lowerCamelCase__	:    deque[Process]       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		deque()  # sequence deque of finished process
         while len(snake_case__       ) != 0:
               A_							       =		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
               A_							       =		0
               # set the process's turnaround time because it is finished
               A_							       =		self.current_time - cp.arrival_time
               # set the completion time
               A_							       =		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     UpperCamelCase					(				self	:    Optional[Any]			,						lowerCamelCase__	:    deque[Process]			,						lowerCamelCase__	:    int       ) ->			int:
         """simple docstring"""





         A_							       =		deque()  # sequence deque of terminated process
         # just for 1 cycle and unfinished processes will go back to queue
         for _ in range(len(snake_case__       )       ):
               A_							       =		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
                     A_							       =		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
                     A_							       =		0
                     # set the finish time
                     A_							       =		self.current_time
                     # update the process' turnaround time because it is finished
                     A_							       =		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     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





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

      __lowercase				      =						Process("""P1""", 0, 53)
      __lowercase				      =						Process("""P2""", 0, 17)
      __lowercase				      =						Process("""P3""", 0, 68)
      __lowercase				      =						Process("""P4""", 0, 24)
      __lowercase				      =						3
      __lowercase				      =						[17, 25]
      __lowercase				      =						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])})

      __lowercase				      =						Process("""P1""", 0, 53)
      __lowercase				      =						Process("""P2""", 0, 17)
      __lowercase				      =						Process("""P3""", 0, 68)
      __lowercase				      =						Process("""P4""", 0, 24)
      __lowercase				      =						3
      __lowercase				      =						[17, 25]
      __lowercase				      =						deque([Pa, Pa, Pa, Pa])
      __lowercase				      =						MLFQ(number_of_queues, time_slices, queue, 0)
      __lowercase				      =						mlfq.multi_level_feedback_queue()

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








import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_     :    Dict     		=   logging.get_logger(__name__)

a_     :    List[str]     		=   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
a_     :    Union[str, Any]     		=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				Optional[int]:
 '''simple docstring'''


 for attribute in key.split('.'						):
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						)

 if weight_type is not None:
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						).shape
 else:
  SCREAMING_SNAKE_CASE		    =      hf_pointer.shape

 assert hf_shape == value.shape, (
     f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
     f""" {value.shape} for {full_name}"""
 )

 if weight_type == "weight":
  SCREAMING_SNAKE_CASE		    =      value
 elif weight_type == "weight_g":
  SCREAMING_SNAKE_CASE		    =      value
 elif weight_type == "weight_v":
  SCREAMING_SNAKE_CASE		    =      value
 elif weight_type == "bias":
  SCREAMING_SNAKE_CASE		    =      value
 else:
  SCREAMING_SNAKE_CASE		    =      value

 logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE		    =      hf_model.feature_extractor
 SCREAMING_SNAKE_CASE		    =      hf_model.adapter

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE		    =      False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				hf_model.config.feat_extract_norm == 'group'		,				)
   SCREAMING_SNAKE_CASE		    =      True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']						):
   load_adapter(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     SCREAMING_SNAKE_CASE		    =      True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE		    =      name.split(_UpperCamelCase						)[0].split('.'						)[-2]
      SCREAMING_SNAKE_CASE		    =      mapped_key.replace('*'		,				_UpperCamelCase						)
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_g'
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_v'
     elif "bias" in name:
      SCREAMING_SNAKE_CASE		    =      'bias'
     elif "weight" in name:
      SCREAMING_SNAKE_CASE		    =      'weight'
     else:
      SCREAMING_SNAKE_CASE		    =      None
     set_recursively(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
    continue
  if not is_used:
   unused_weights.append(_UpperCamelCase						)

 logger.warning(f"""Unused weights: {unused_weights}"""						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('conv_layers.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)
 SCREAMING_SNAKE_CASE		    =      int(items[0]						)
 SCREAMING_SNAKE_CASE		    =      int(items[1]						)

 if type_id == 0:
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
       f"""{full_name} has size {value.shape}, but"""
       f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
   )
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
       f"""{full_name} has size {value.shape}, but"""
       f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
   )
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
       f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
       " found."
   )
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
  elif "weight" in name:
   assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
       f"""{full_name} has size {value.shape}, but"""
       f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
   )
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Union[str, Any]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('adaptor.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)

 if items[1].isdigit():
  SCREAMING_SNAKE_CASE		    =      int(items[1]						)
 else:
  SCREAMING_SNAKE_CASE		    =      None

 if "adaptor" not in full_name:
  if "proj_ln" in full_name:
   # has to be layer norm
   if "bias" in name:
    assert (
        value.shape == adapter.proj_layer_norm.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj_layer_norm.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
  else:
   # has to be projection layer
   if "bias" in name:
    assert (
        value.shape == adapter.proj.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""						)
 elif isinstance(_UpperCamelCase		,				_UpperCamelCase						):
  if "bias" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.bias.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
  elif "weight" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.weight.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      emb.weight.shape
 SCREAMING_SNAKE_CASE		    =      nn.Linear(_UpperCamelCase		,				_UpperCamelCase		,				bias=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      emb.weight.data
 return lin_layer


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      WavaVecaConfig.from_pretrained(
     _UpperCamelCase		,				add_adapter=_UpperCamelCase		,				adapter_stride=_UpperCamelCase		,				adapter_kernel_size=_UpperCamelCase		,				use_auth_token=_UpperCamelCase		,				output_hidden_size=_UpperCamelCase		,				)
 SCREAMING_SNAKE_CASE		    =      MBartConfig.from_pretrained(_UpperCamelCase						)

 # load model
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]		,				arg_overrides={
         'config_yaml': config_yaml_path,
         'data': '/'.join(dict_path.split('/'						)[:-1]						),
         'w2v_path': checkpoint_path,
         'load_pretrained_decoder_from': None,
     }		,				)
 SCREAMING_SNAKE_CASE		    =      model[0].eval()

 # load feature extractor
 SCREAMING_SNAKE_CASE		    =      WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase		,				use_auth_token=_UpperCamelCase						)

 # set weights for wav2vec2 encoder
 SCREAMING_SNAKE_CASE		    =      WavaVecaModel(_UpperCamelCase						)

 recursively_load_weights_wavaveca(model.encoder		,				_UpperCamelCase						)

 # load decoder weights
 SCREAMING_SNAKE_CASE		    =      MBartForCausalLM(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,				strict=_UpperCamelCase						)
 logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""						)
 logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""						)

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderModel(encoder=_UpperCamelCase		,				decoder=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      False

 SCREAMING_SNAKE_CASE		    =      MBartaaTokenizer(_UpperCamelCase						)
 tokenizer.save_pretrained(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      hf_wavavec.config.to_dict()
 SCREAMING_SNAKE_CASE		    =      tokenizer.pad_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.bos_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      'mbart50'
 SCREAMING_SNAKE_CASE		    =      'wav2vec2'

 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      25_00_04
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase						)

 hf_wavavec.save_pretrained(_UpperCamelCase						)
 feature_extractor.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    List[str]     		=   argparse.ArgumentParser()
      parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
      parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
      parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
      parser.add_argument("--config_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
      parser.add_argument(
          "--encoder_config_path",
          default="facebook/wav2vec2-xls-r-1b",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/mbart-large-50-one-to-many-mmt",
          type=str,
          help="Path to hf decoder checkpoint config",
      )
      parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
      parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
      parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
      parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
      parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

      a_     :    Dict     		=   parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          args.config_yaml_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          add_adapter=args.add_adapter,
          adapter_kernel_size=args.adapter_kernel_size,
          adapter_stride=args.adapter_stride,
          decoder_start_token_id=args.start_token_id,
          encoder_output_dim=args.encoder_output_dim,
      )

