






from collections import deque


class 							__UpperCamelCase     :







	def __init__(	self   :				Union[str, Any]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int      )		->			None:
		"""simple docstring"""







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


class 							__UpperCamelCase     :







	def __init__(	self   :				List[str]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				list[int]							,					_lowerCAmelCase   :				deque[Process]							,					_lowerCAmelCase   :				int							,					)		->			None:
		"""simple docstring"""







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







	def 			_a (	self   :				List[Any]      )		->			list[str]:
		"""simple docstring"""







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







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				list[Process]      )		->			list[int]:
		"""simple docstring"""







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







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				list[Process]      )		->			list[int]:
		"""simple docstring"""







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







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				list[Process]      )		->			list[int]:
		"""simple docstring"""







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







	def 			_a (	self   :				Optional[int]							,					_lowerCAmelCase   :				deque[Process]      )		->			list[int]:
		"""simple docstring"""







		return [q.burst_time for q in queue]







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				Process      )		->			int:
		"""simple docstring"""







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







	def 			_a (	self   :				Tuple							,					_lowerCAmelCase   :				deque[Process]      )		->			deque[Process]:
		"""simple docstring"""







		__lowercase   							=	deque()  # sequence deque of finished process
		while len(_lowerCAmelCase      ) != 0:
			__lowercase   							=	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
			__lowercase   							=	0
			# set the process's turnaround time because it is finished
			__lowercase   							=	self.current_time - cp.arrival_time
			# set the completion time
			__lowercase   							=	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 			_a (	self   :				int							,					_lowerCAmelCase   :				deque[Process]							,					_lowerCAmelCase   :				int      )		->			tuple[deque[Process], deque[Process]]:
		"""simple docstring"""







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







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

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

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


"""simple docstring"""




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()
__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)

__lowerCAmelCase     :				Dict    				=    {
    "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",
}
__lowerCAmelCase     :				Optional[Any]    				=    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		for attribute in key.split("""."""  ):
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  )

		if weight_type is not None:
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  ).shape
		else:
				lowerCAmelCase__   = 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":
				lowerCAmelCase__   = value
		elif weight_type == "weight_g":
				lowerCAmelCase__   = value
		elif weight_type == "weight_v":
				lowerCAmelCase__   = value
		elif weight_type == "bias":
				lowerCAmelCase__   = value
		else:
				lowerCAmelCase__   = value

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = []
		lowerCAmelCase__   = fairseq_model.state_dict()

		lowerCAmelCase__   = hf_model.feature_extractor
		lowerCAmelCase__   = hf_model.adapter

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

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""conv_layers."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )
		lowerCAmelCase__   = int(items[0]  )
		lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = 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."
						)
						lowerCAmelCase__   = 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."""
						)
						lowerCAmelCase__   = value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""adaptor."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )

		if items[1].isdigit():
				lowerCAmelCase__   = int(items[1]  )
		else:
				lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
		elif isinstance(lowerCamelCase__      ,	lowerCamelCase__  ):
				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."""
						lowerCAmelCase__   = 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."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = emb.weight.shape
		lowerCAmelCase__   = nn.Linear(lowerCamelCase__      ,	lowerCamelCase__      ,	bias=lowerCamelCase__  )
		lowerCAmelCase__   = emb.weight.data
		return lin_layer







@torch.no_grad()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = WavaVecaConfig.from_pretrained(
		    lowerCamelCase__      ,	add_adapter=lowerCamelCase__      ,	adapter_stride=lowerCamelCase__      ,	adapter_kernel_size=lowerCamelCase__      ,	use_auth_token=lowerCamelCase__      ,	output_hidden_size=lowerCamelCase__      ,	)
		lowerCAmelCase__   = MBartConfig.from_pretrained(lowerCamelCase__  )

		# load model
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = 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,
		    }      ,	)
		lowerCAmelCase__   = model[0].eval()

		# load feature extractor
		lowerCAmelCase__   = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__      ,	use_auth_token=lowerCamelCase__  )

		# set weights for wav2vec2 encoder
		lowerCAmelCase__   = WavaVecaModel(lowerCamelCase__  )

		recursively_load_weights_wavaveca(model.encoder      ,	lowerCamelCase__  )

		# load decoder weights
		lowerCAmelCase__   = MBartForCausalLM(lowerCamelCase__  )
		lowerCAmelCase__ ,       lowerCAmelCase__   = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,	strict=lowerCamelCase__  )
		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}"""  )

		lowerCAmelCase__   = SpeechEncoderDecoderModel(encoder=lowerCamelCase__      ,	decoder=lowerCamelCase__  )
		lowerCAmelCase__   = False

		lowerCAmelCase__   = MBartaaTokenizer(lowerCamelCase__  )
		tokenizer.save_pretrained(lowerCamelCase__  )

		lowerCAmelCase__   = hf_wavavec.config.to_dict()
		lowerCAmelCase__   = tokenizer.pad_token_id
		lowerCAmelCase__   = tokenizer.bos_token_id
		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = """mbart50"""
		lowerCAmelCase__   = """wav2vec2"""

		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = 25_0004
		lowerCAmelCase__   = tokenizer.eos_token_id

		lowerCAmelCase__   = SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__  )

		hf_wavavec.save_pretrained(lowerCamelCase__  )
		feature_extractor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				int    				=    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=10_24, type=int, help="encoder output dim")
			parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

			__lowerCAmelCase     :				Union[str, Any]    				=    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,
			)




