

"""simple docstring"""





from collections import deque







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







class 							SCREAMING_SNAKE_CASE   :






					"""simple docstring"""



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







					def 			__lowerCAmelCase					(			self     :							List[Any]						):
						lowerCAmelCase__      :		Optional[int]    							=      []
						for i in range(len(self.finish_queue						)						):
							sequence.append(self.finish_queue[i].process_name						)
						return sequence







					def 			__lowerCAmelCase					(			self     :							List[Any]  ,lowercase_     :							Union[str, Any]						):
						lowerCAmelCase__      :		Union[str, Any]    							=      []
						for i in range(len(__UpperCamelCase						)						):
							waiting_times.append(queue[i].waiting_time						)
						return waiting_times







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							List[str]						):
						lowerCAmelCase__      :		int    							=      []
						for i in range(len(__UpperCamelCase						)						):
							turnaround_times.append(queue[i].turnaround_time						)
						return turnaround_times







					def 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							Tuple						):
						lowerCAmelCase__      :		Any    							=      []
						for i in range(len(__UpperCamelCase						)						):
							completion_times.append(queue[i].stop_time						)
						return completion_times







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Tuple						):
						return [q.burst_time for q in queue]







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							Optional[Any]						):
						process.waiting_time += self.current_time - process.stop_time
						return process.waiting_time







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

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







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

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







					def 			__lowerCAmelCase					(			self     :							Any						):

						#  all queues except last one have round_robin algorithm
						for i in range(self.number_of_queues - 1						):
							lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      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    : Tuple				 =						Process('''P1''', 0, 5_3)
							__UpperCamelCase    : Dict				 =						Process('''P2''', 0, 1_7)
							__UpperCamelCase    : Dict				 =						Process('''P3''', 0, 6_8)
							__UpperCamelCase    : Optional[int]				 =						Process('''P4''', 0, 2_4)
							__UpperCamelCase    : str				 =						3
							__UpperCamelCase    : Union[str, Any]				 =						[1_7, 2_5]
							__UpperCamelCase    : 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])})

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

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


A		: int								=     ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
A		: Any								=     {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
A		: Optional[int]								=     logging.get_logger(__name__)

A		: int								=     ''' Hello world! cécé herlolip'''

A		: List[Any]								=     [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     Tuple:
						_lowercase					       =					[
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """_float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     List[str]:
						_lowercase					       =					dct.pop(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					val




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any       )  ->     Any:
						_lowercase					       =					torch.load(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )
						_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   """bart.large.cnn"""       ).eval()
						hub_interface.model.load_state_dict(sd["""model"""]       )
						return hub_interface




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[int]:
						_lowercase       ,						_lowercase					       =					emb.weight.shape
						_lowercase					       =					nn.Linear(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   bias=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					emb.weight.data
						return lin_layer




@torch.no_grad()
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Tuple		,   SCREAMING_SNAKE_CASE_	:	int=None       )  ->     int:
						if not os.path.exists(SCREAMING_SNAKE_CASE_       ):
												_lowercase					       =					torch.hub.load("""pytorch/fairseq"""		,   SCREAMING_SNAKE_CASE_       ).eval()
						else:
												_lowercase					       =					load_xsum_checkpoint(SCREAMING_SNAKE_CASE_       )

						bart.model.upgrade_state_dict(bart.model.state_dict()       )
						if hf_checkpoint_name is None:
												_lowercase					       =					checkpoint_path.replace("""."""		,   """-"""       )
						_lowercase					       =					BartConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					bart.encode(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       )
						_lowercase					       =					BartTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       ).encode(SCREAMING_SNAKE_CASE_		,   return_tensors="""pt"""       ).unsqueeze(0       )
						if not torch.eq(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ).all():
												raise ValueError(
												    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""       )

						if checkpoint_path == "bart.large.mnli":
												_lowercase					       =					bart.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""model.decoder.embed_tokens.weight"""]
												for src, dest in mnli_rename_keys:
																		rename_key(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					BartForSequenceClassification(SCREAMING_SNAKE_CASE_       ).eval()
												model.load_state_dict(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					bart.predict("""mnli"""		,   SCREAMING_SNAKE_CASE_		,   return_logits=SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )[0]  # logits
						else:  # no classification heads to worry about
												_lowercase					       =					bart.model.state_dict()
												remove_ignore_keys_(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					state_dict["""decoder.embed_tokens.weight"""]
												_lowercase					       =					bart.extract_features(SCREAMING_SNAKE_CASE_       )
												if hf_checkpoint_name == "facebook/bart-large":
																		_lowercase					       =					BartModel(SCREAMING_SNAKE_CASE_       ).eval()
																		model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       ).model[0]
												else:
																		_lowercase					       =					BartForConditionalGeneration(SCREAMING_SNAKE_CASE_       ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(SCREAMING_SNAKE_CASE_       )
																		if hasattr(SCREAMING_SNAKE_CASE_		,   """lm_head"""       ):
																								_lowercase					       =					make_linear_from_emb(model.model.shared       )
																		_lowercase					       =					model.model(SCREAMING_SNAKE_CASE_       )[0]

    # Check results
						if fairseq_output.shape != new_model_outputs.shape:
												raise ValueError(
												    f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""       )
						if (fairseq_output != new_model_outputs).any().item():
												raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
						Path(SCREAMING_SNAKE_CASE_       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_       )
						model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		A		: List[Any]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
		)
		parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument(
		    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
		)
		A		: Any								=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)