"""simple docstring"""







from collections import deque

class 		UpperCamelCase      :






				"""simple docstring"""




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







class 		UpperCamelCase      :






				"""simple docstring"""




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



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



				def  _lowercase					(self    :				Any     ,  _A    :				Dict)   ->     list[int]:
					__snake_case      :							Optional[int]						=			[]
					for i in range(len(lowerCAmelCase__)):
						waiting_times.append(queue[i].waiting_time)
					return waiting_times



				def  _lowercase					(self    :				Tuple     ,  _A    :				Optional[int])   ->     list[int]:
					__snake_case      :							List[Any]						=			[]
					for i in range(len(lowerCAmelCase__)):
						turnaround_times.append(queue[i].turnaround_time)
					return turnaround_times



				def  _lowercase					(self    :				List[str]     ,  _A    :				Union[str, Any])   ->     list[int]:
					__snake_case      :							List[str]						=			[]
					for i in range(len(lowerCAmelCase__)):
						completion_times.append(queue[i].stop_time)
					return completion_times



				def  _lowercase					(self    :				Any     ,  _A    :				int)   ->     list[int]:
					return [q.burst_time for q in queue]



				def  _lowercase					(self    :				Optional[int]     ,  _A    :				Optional[Any])   ->     int:
					process.waiting_time += self.current_time - process.stop_time
					return process.waiting_time



				def  _lowercase					(self    :				str     ,  _A    :				List[str])   ->     deque[Process]:
					__snake_case      :							deque[Process]						=			deque()  # sequence deque of finished process
					while len(lowerCAmelCase__) != 0:
						__snake_case      :							Optional[int]						=			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
						__snake_case      :							str						=			0
						# set the process's turnaround time because it is finished
						__snake_case      :							Union[str, Any]						=			self.current_time - cp.arrival_time
						# set the completion time
						__snake_case      :							str						=			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    :				int     ,  _A    :				int     ,  _A    :				List[str])   ->     tuple[deque[Process], deque[Process]]:
					__snake_case      :							deque[Process]						=			deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(lowerCAmelCase__)):
						__snake_case      :							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(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
							__snake_case      :							Optional[Any]						=			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
							__snake_case      :							int						=			0
							# set the finish time
							__snake_case      :							Any						=			self.current_time
							# update the process' turnaround time because it is finished
							__snake_case      :							str						=			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    :				Tuple)   ->     deque[Process]:

					#  all queues except last one have round_robin algorithm
					for i in range(self.number_of_queues - 1):
						__snake_case      :							Optional[Any]						=			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					:					Any=			Process("P1", 0, 53)
	_a					:					List[str]=			Process("P2", 0, 17)
	_a					:					Optional[int]=			Process("P3", 0, 68)
	_a					:					Union[str, Any]=			Process("P4", 0, 24)
	_a					:					Optional[Any]=			3
	_a					:					List[str]=			[17, 25]
	_a					:					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])})

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






"""simple docstring"""







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


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

@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				List[nn.Module]   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				list   =		field(default_factory=lowercase						)



				def  _lowercase					(self    :				str     ,  _A    :				Optional[Any]     ,  _A    :				Tensor     ,  _A    :				Tensor)   ->     Any:
					__snake_case      :							str						=			len(list(m.modules())) == 1 or isinstance(_A     ,  nn.Convad) or isinstance(_A     ,  nn.BatchNormad)
					if has_not_submodules:
						self.traced.append(_A)



				def __call__(self    :				Dict     ,  _A    :				Tensor)   ->     Optional[Any]:
					for m in self.module.modules():
						self.handles.append(m.register_forward_hook(self._forward_hook))
					self.module(_A)
					[x.remove() for x in self.handles]
					return self






				@property
				def  _lowercase					(self    :				Union[str, Any])   ->     List[str]:
					# check the len of the state_dict keys to see if we have learnable params
					return list(filter(lambda _A: len(list(x.state_dict().keys())) > 0     ,  self.traced))







@dataclass
class 		UpperCamelCase      :
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				nn.Module
				UpperCAmelCase    :				int   =		0
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)
				UpperCAmelCase    :				List   =		field(default_factory=lowercase						)



				def __call__(self    :				List[str]     ,  _A    :				Tensor)   ->     List[Any]:
					__snake_case      :							Any						=			Tracker(self.dest)(_A).parametrized
					__snake_case      :							int						=			Tracker(self.src)(_A).parametrized

					__snake_case      :							List[Any]						=			list(filter(lambda _A: type(_A) not in self.src_skip     ,  _A))
					__snake_case      :							Any						=			list(filter(lambda _A: type(_A) not in self.dest_skip     ,  _A))

					if len(_A) != len(_A):
						raise Exception(
						    f"Numbers of operations are different. Source module has {len(_A)} operations while"
						    f" destination module has {len(_A)}.")

					for dest_m, src_m in zip(_A     ,  _A):
						dest_m.load_state_dict(src_m.state_dict())
						if self.verbose == 1:
							print(f"Transfered from={src_m} to={dest_m}")







def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    ResNetConfig      ,							UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    bool = True  )							->      List[str]:

	'''simple docstring'''





	print(F"Converting {name}..."  )
	with torch.no_grad():
		__snake_case      :							Dict						=			timm.create_model(UpperCAmelCase_      ,							pretrained=UpperCAmelCase_  ).eval()
		__snake_case      :							List[Any]						=			ResNetForImageClassification(UpperCAmelCase_  ).eval()
		__snake_case      :							int						=			ModuleTransfer(src=UpperCAmelCase_      ,							dest=UpperCAmelCase_  )
		__snake_case      :							Optional[Any]						=			torch.randn((1, 3, 2_24, 2_24)  )
		module_transfer(UpperCAmelCase_  )

	assert torch.allclose(from_model(UpperCAmelCase_  )      ,							our_model(UpperCAmelCase_  ).logits  ), "The model logits don't match the original one."

	__snake_case      :							str						=			F"resnet{'-'.join(name.split('resnet'  )  )}"
	print(UpperCAmelCase_  )

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message='Add model'      ,							use_temp_dir=UpperCAmelCase_      ,							)

		# we can use the convnext one
		__snake_case      :							int						=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'  )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name      ,							commit_message='Add image processor'      ,							use_temp_dir=UpperCAmelCase_      ,							)

		print(F"Pushed {checkpoint_name}"  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Path      ,							UpperCAmelCase_   :    str = None      ,							UpperCAmelCase_   :    bool = True  )							->      Union[str, Any]:

	'''simple docstring'''





	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							Optional[Any]						=			10_00
	__snake_case      :							Any						=			(1, num_labels)

	__snake_case      :							List[Any]						=			'huggingface/label-files'
	__snake_case      :							Dict						=			num_labels
	__snake_case      :							Any						=			json.load(open(hf_hub_download(UpperCAmelCase_      ,							UpperCAmelCase_      ,							repo_type='dataset'  )      ,							'r'  )  )
	__snake_case      :							Any						=			{int(UpperCAmelCase_  ): v for k, v in idalabel.items()}

	__snake_case      :							Optional[Any]						=			idalabel
	__snake_case      :							Optional[Any]						=			{v: k for k, v in idalabel.items()}

	__snake_case      :							Optional[int]						=			partial(UpperCAmelCase_      ,							num_labels=UpperCAmelCase_      ,							idalabel=UpperCAmelCase_      ,							labelaid=UpperCAmelCase_  )

	__snake_case      :							str						=			{
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[64, 1_28, 2_56, 5_12]      ,							layer_type='basic'  ),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]      ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]      ,							layer_type='bottleneck'  ),
	}

	if model_name:
		convert_weight_and_push(UpperCAmelCase_      ,							names_to_config[model_name]      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
	return config, expected_shape


if __name__ == "__main__":
	_a					:					Optional[Any]=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default=None,
	    type=str,
	    help=(
	        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
	        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
	    ),
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=Path,
	    required=True,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument(
	    "--push_to_hub",
	    default=True,
	    type=bool,
	    required=False,
	    help="If True, push model and image processor to the hub.",
	)

	_a					:					Union[str, Any]=			parser.parse_args()
	_a					:					Path=			args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





