






'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Union[str, Any]:
							lowerCamelCase__ :     Optional[Any]	  =	SwinvaConfig()
							lowerCamelCase__ :     Dict	  =	swinva_name.split("""_"""				)

							lowerCamelCase__ :     Tuple	  =	name_split[1]
							if "to" in name_split[3]:
														lowerCamelCase__ :     Optional[int]	  =	int(name_split[3][-3:]				)
							else:
														lowerCamelCase__ :     List[str]	  =	int(name_split[3]				)
							if "to" in name_split[2]:
														lowerCamelCase__ :     Any	  =	int(name_split[2][-2:]				)
							else:
														lowerCamelCase__ :     int	  =	int(name_split[2][6:]				)

							if model_size == "tiny":
														lowerCamelCase__ :     Any	  =	96
														lowerCamelCase__ :     Tuple	  =	(2, 2, 6, 2)
														lowerCamelCase__ :     Any	  =	(3, 6, 12, 24)
							elif model_size == "small":
														lowerCamelCase__ :     int	  =	96
														lowerCamelCase__ :     Tuple	  =	(2, 2, 18, 2)
														lowerCamelCase__ :     Any	  =	(3, 6, 12, 24)
							elif model_size == "base":
														lowerCamelCase__ :     Tuple	  =	128
														lowerCamelCase__ :     Tuple	  =	(2, 2, 18, 2)
														lowerCamelCase__ :     Optional[int]	  =	(4, 8, 16, 32)
							else:
														lowerCamelCase__ :     Any	  =	192
														lowerCamelCase__ :     str	  =	(2, 2, 18, 2)
														lowerCamelCase__ :     int	  =	(6, 12, 24, 48)

							if "to" in swinva_name:
														lowerCamelCase__ :     Any	  =	(12, 12, 12, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														lowerCamelCase__ :     List[str]	  =	21841
														lowerCamelCase__ :     Tuple	  =	"""huggingface/label-files"""
														lowerCamelCase__ :     Optional[int]	  =	"""imagenet-22k-id2label.json"""
														lowerCamelCase__ :     Dict	  =	json.load(open(hf_hub_download(UpperCamelCase    ,      UpperCamelCase    ,      repo_type="""dataset"""				)    ,      """r"""				)				)
														lowerCamelCase__ :     Tuple	  =	{int(UpperCamelCase				): v for k, v in idalabel.items()}
														lowerCamelCase__ :     int	  =	idalabel
														lowerCamelCase__ :     Optional[Any]	  =	{v: k for k, v in idalabel.items()}

							else:
														lowerCamelCase__ :     List[Any]	  =	1000
														lowerCamelCase__ :     int	  =	"""huggingface/label-files"""
														lowerCamelCase__ :     str	  =	"""imagenet-1k-id2label.json"""
														lowerCamelCase__ :     str	  =	json.load(open(hf_hub_download(UpperCamelCase    ,      UpperCamelCase    ,      repo_type="""dataset"""				)    ,      """r"""				)				)
														lowerCamelCase__ :     int	  =	{int(UpperCamelCase				): v for k, v in idalabel.items()}
														lowerCamelCase__ :     Union[str, Any]	  =	idalabel
														lowerCamelCase__ :     Any	  =	{v: k for k, v in idalabel.items()}

							lowerCamelCase__ :     Any	  =	img_size
							lowerCamelCase__ :     List[str]	  =	num_classes
							lowerCamelCase__ :     Union[str, Any]	  =	embed_dim
							lowerCamelCase__ :     Any	  =	depths
							lowerCamelCase__ :     Optional[int]	  =	num_heads
							lowerCamelCase__ :     Tuple	  =	window_size

							return config





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase				)				->	Optional[Any]:
							if "patch_embed.proj" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""patch_embed.proj"""    ,      """embeddings.patch_embeddings.projection"""				)
							if "patch_embed.norm" in name:
														lowerCamelCase__ :     List[Any]	  =	name.replace("""patch_embed.norm"""    ,      """embeddings.norm"""				)
							if "layers" in name:
														lowerCamelCase__ :     Union[str, Any]	  =	"""encoder.""" + name
							if "attn.proj" in name:
														lowerCamelCase__ :     List[Any]	  =	name.replace("""attn.proj"""    ,      """attention.output.dense"""				)
							if "attn" in name:
														lowerCamelCase__ :     List[str]	  =	name.replace("""attn"""    ,      """attention.self"""				)
							if "norm1" in name:
														lowerCamelCase__ :     List[Any]	  =	name.replace("""norm1"""    ,      """layernorm_before"""				)
							if "norm2" in name:
														lowerCamelCase__ :     List[Any]	  =	name.replace("""norm2"""    ,      """layernorm_after"""				)
							if "mlp.fc1" in name:
														lowerCamelCase__ :     Any	  =	name.replace("""mlp.fc1"""    ,      """intermediate.dense"""				)
							if "mlp.fc2" in name:
														lowerCamelCase__ :     int	  =	name.replace("""mlp.fc2"""    ,      """output.dense"""				)
							if "q_bias" in name:
														lowerCamelCase__ :     List[str]	  =	name.replace("""q_bias"""    ,      """query.bias"""				)
							if "k_bias" in name:
														lowerCamelCase__ :     Optional[int]	  =	name.replace("""k_bias"""    ,      """key.bias"""				)
							if "v_bias" in name:
														lowerCamelCase__ :     List[str]	  =	name.replace("""v_bias"""    ,      """value.bias"""				)
							if "cpb_mlp" in name:
														lowerCamelCase__ :     List[str]	  =	name.replace("""cpb_mlp"""    ,      """continuous_position_bias_mlp"""				)
							if name == "norm.weight":
														lowerCamelCase__ :     Optional[int]	  =	"""layernorm.weight"""
							if name == "norm.bias":
														lowerCamelCase__ :     List[Any]	  =	"""layernorm.bias"""

							if "head" in name:
														lowerCamelCase__ :     Dict	  =	name.replace("""head"""    ,      """classifier"""				)
							else:
														lowerCamelCase__ :     Any	  =	"""swinv2.""" + name

							return name





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	str:
							for key in orig_state_dict.copy().keys():
														lowerCamelCase__ :     Optional[Any]	  =	orig_state_dict.pop(UpperCamelCase				)

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					lowerCamelCase__ :     Any	  =	key.split("""."""				)
																					lowerCamelCase__ :     List[str]	  =	int(key_split[1]				)
																					lowerCamelCase__ :     str	  =	int(key_split[3]				)
																					lowerCamelCase__ :     Any	  =	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												lowerCamelCase__ :     str	  =	val[:dim, :]
																												lowerCamelCase__ :     int	  =	val[dim : dim * 2, :]
																												lowerCamelCase__ :     Optional[Any]	  =	val[-dim:, :]
																					else:
																												lowerCamelCase__ :     Optional[Any]	  =	val[:dim]
																												lowerCamelCase__ :     Any	  =	val[
																												    dim : dim * 2
																												]
																												lowerCamelCase__ :     List[Any]	  =	val[-dim:]
														else:
																					lowerCamelCase__ :     Union[str, Any]	  =	val

							return orig_state_dict





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	List[str]:
							lowerCamelCase__ :     Union[str, Any]	  =	timm.create_model(UpperCamelCase    ,      pretrained=UpperCamelCase				)
							timm_model.eval()

							lowerCamelCase__ :     Dict	  =	get_swinva_config(UpperCamelCase				)
							lowerCamelCase__ :     int	  =	SwinvaForImageClassification(UpperCamelCase				)
							model.eval()

							lowerCamelCase__ :     Any	  =	convert_state_dict(timm_model.state_dict()    ,      UpperCamelCase				)
							model.load_state_dict(UpperCamelCase				)

							lowerCamelCase__ :     Optional[int]	  =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

							lowerCamelCase__ :     List[Any]	  =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""    ,      """-"""				)				)				)
							lowerCamelCase__ :     str	  =	Image.open(requests.get(UpperCamelCase    ,      stream=UpperCamelCase				).raw				)
							lowerCamelCase__ :     str	  =	image_processor(images=UpperCamelCase    ,      return_tensors="""pt"""				)

							lowerCamelCase__ :     Optional[Any]	  =	timm_model(inputs["""pixel_values"""]				)
							lowerCamelCase__ :     Optional[Any]	  =	model(**UpperCamelCase				).logits

							assert torch.allclose(UpperCamelCase    ,      UpperCamelCase    ,      atol=1E-3				)

							print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''				)
							model.save_pretrained(UpperCamelCase				)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''				)
							image_processor.save_pretrained(UpperCamelCase				)

							model.push_to_hub(
							    repo_path_or_name=Path(UpperCamelCase    ,      UpperCamelCase				)    ,      organization="""nandwalritik"""    ,      commit_message="""Add model"""    ,      )


if __name__ == "__main__":
			_A     :      List[str]   					=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--swinv2_name''',
			    default='''swinv2_tiny_patch4_window8_256''',
			    type=str,
			    help='''Name of the Swinv2 timm model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_A     :      Optional[Any]   					=parser.parse_args()
			convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









'''simple docstring'''







from collections import deque

class _lowercase     :




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



class _lowercase     :




							def __init__(							self:    Any      ,							UpperCamelCase__:    int      ,							UpperCamelCase__:    list[int]      ,							UpperCamelCase__:    deque[Process]      ,							UpperCamelCase__:    int      ,							):
														# total number of mlfq's queues
														lowerCamelCase__ :     Tuple	  =	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__ :     int	  =	queue
														# current time
														lowerCamelCase__ :     Optional[int]	  =	current_time
														# finished process is in this sequence queue
														lowerCamelCase__ :     deque[Process]	  =	deque()




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	[]
														for i in range(len(self.finish_queue					)					):
																					sequence.append(self.finish_queue[i].process_name					)
														return sequence




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     int	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					waiting_times.append(queue[i].waiting_time					)
														return waiting_times




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     Optional[int]	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					turnaround_times.append(queue[i].turnaround_time					)
														return turnaround_times




							def 		lowerCamelCase_    (							self:    Union[str, Any]      ,							UpperCamelCase__:    list[Process]					):
														lowerCamelCase__ :     List[Any]	  =	[]
														for i in range(len(UpperCamelCase__					)					):
																					completion_times.append(queue[i].stop_time					)
														return completion_times




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    deque[Process]					):
														return [q.burst_time for q in queue]




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Process					):
														process.waiting_time += self.current_time - process.stop_time
														return process.waiting_time




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    deque[Process]					):
														lowerCamelCase__ :     deque[Process]	  =	deque()  # sequence deque of finished process
														while len(UpperCamelCase__					) != 0:
																					lowerCamelCase__ :     List[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 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__ :     Dict	  =	0
																					# set the process's turnaround time because it is finished
																					lowerCamelCase__ :     List[str]	  =	self.current_time - cp.arrival_time
																					# set the completion time
																					lowerCamelCase__ :     int	  =	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:    Union[str, Any]      ,							UpperCamelCase__:    deque[Process]      ,							UpperCamelCase__:    int					):
														lowerCamelCase__ :     deque[Process]	  =	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[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 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__ :     Dict	  =	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__ :     Any	  =	0
																												# set the finish time
																												lowerCamelCase__ :     List[Any]	  =	self.current_time
																												# update the process' turnaround time because it is finished
																												lowerCamelCase__ :     Any	  =	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:    Tuple					):

														#  all queues except last one have round_robin algorithm
														for i in range(self.number_of_queues - 1					):
																					lowerCamelCase__  ,		lowerCamelCase__ :     str	  =	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     :      Optional[Any]   					=Process('''P1''', 0, 53)
			_A     :      List[Any]   					=Process('''P2''', 0, 17)
			_A     :      Any   					=Process('''P3''', 0, 68)
			_A     :      Tuple   					=Process('''P4''', 0, 24)
			_A     :      int   					=3
			_A     :      Tuple   					=[17, 25]
			_A     :      List[Any]   					=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     :      Dict   					=Process('''P1''', 0, 53)
			_A     :      Union[str, Any]   					=Process('''P2''', 0, 17)
			_A     :      int   					=Process('''P3''', 0, 68)
			_A     :      Dict   					=Process('''P4''', 0, 24)
			_A     :      List[str]   					=3
			_A     :      List[Any]   					=[17, 25]
			_A     :      Any   					=deque([Pa, Pa, Pa, Pa])
			_A     :      List[str]   					=MLFQ(number_of_queues, time_slices, queue, 0)
			_A     :      Union[str, 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()}'
			)

