

'''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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_     :       Any								=						logging.get_logger(__name__)
def 					UpperCAmelCase   (						A  :				str					,		A  :				Any=False				):
							SCREAMING_SNAKE_CASE					:      Tuple     					=			[]
							for i in range(config.num_hidden_layers				):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")				)
														rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")				)
														rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")				)
														rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")				)
														rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")				)
														rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")				)
														rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")				)
														rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")				)
														rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")				)
														rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")				)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('''cls_token''', '''vit.embeddings.cls_token'''),
							        ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
							        ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
							        ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
							    ]				)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''layernorm.weight'''),
														        ('''norm.bias''', '''layernorm.bias'''),
														        ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
														        ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
														    ]				)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														SCREAMING_SNAKE_CASE					:      Dict     					=			[(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''				) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ('''norm.weight''', '''vit.layernorm.weight'''),
														        ('''norm.bias''', '''vit.layernorm.bias'''),
														        ('''head.weight''', '''classifier.weight'''),
														        ('''head.bias''', '''classifier.bias'''),
														    ]				)

							return rename_keys
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				str					,		A  :				Tuple=False				):




							for i in range(config.num_hidden_layers				):
														if base_model:
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			''''''
														else:
																					SCREAMING_SNAKE_CASE					:      str     					=			'''vit.'''
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE					:      Dict     					=			state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
														SCREAMING_SNAKE_CASE					:      int     					=			state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE					:      int     					=			in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE					:      Any     					=			in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE					:      List[str]     					=			in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE					:      Tuple     					=			in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE					:      int     					=			in_proj_bias[-config.hidden_size :]
def 					UpperCAmelCase   (						A  :				str				):
							SCREAMING_SNAKE_CASE					:      int     					=			['''head.weight''', '''head.bias''']
							for k in ignore_keys:
														state_dict.pop(__A					,		__A				)
def 					UpperCAmelCase   (						A  :				Optional[Any]					,		A  :				str					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      Tuple     					=			dct.pop(__A				)
							SCREAMING_SNAKE_CASE					:      Any     					=			val
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							SCREAMING_SNAKE_CASE					:      List[Any]     					=			Image.open(requests.get(__A					,		stream=__A				).raw				)
							return im
@torch.no_grad()
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				str				):
							SCREAMING_SNAKE_CASE					:      str     					=			ViTConfig()
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			False
							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							if vit_name[-5:] == "in21k":
														SCREAMING_SNAKE_CASE					:      Dict     					=			True
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			int(vit_name[-12:-10]				)
														SCREAMING_SNAKE_CASE					:      Tuple     					=			int(vit_name[-9:-6]				)
							else:
														SCREAMING_SNAKE_CASE					:      Dict     					=			1000
														SCREAMING_SNAKE_CASE					:      int     					=			'''huggingface/label-files'''
														SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''imagenet-1k-id2label.json'''
														SCREAMING_SNAKE_CASE					:      Any     					=			json.load(open(hf_hub_download(__A					,		__A					,		repo_type='''dataset'''				)					,		'''r'''				)				)
														SCREAMING_SNAKE_CASE					:      List[str]     					=			{int(__A				): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE					:      List[Any]     					=			idalabel
														SCREAMING_SNAKE_CASE					:      Tuple     					=			{v: k for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			int(vit_name[-6:-4]				)
														SCREAMING_SNAKE_CASE					:      int     					=			int(vit_name[-3:]				)
							# size of the architecture
							if "deit" in vit_name:
														if vit_name[9:].startswith('''tiny'''				):
																					SCREAMING_SNAKE_CASE					:      str     					=			192
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			768
																					SCREAMING_SNAKE_CASE					:      int     					=			12
																					SCREAMING_SNAKE_CASE					:      List[str]     					=			3
														elif vit_name[9:].startswith('''small'''				):
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			384
																					SCREAMING_SNAKE_CASE					:      Tuple     					=			1536
																					SCREAMING_SNAKE_CASE					:      Dict     					=			12
																					SCREAMING_SNAKE_CASE					:      int     					=			6
														else:
																					pass
							else:
														if vit_name[4:].startswith('''small'''				):
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			768
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			2304
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			8
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			8
														elif vit_name[4:].startswith('''base'''				):
																					pass
														elif vit_name[4:].startswith('''large'''				):
																					SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			1024
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			4096
																					SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			24
																					SCREAMING_SNAKE_CASE					:      int     					=			16
														elif vit_name[4:].startswith('''huge'''				):
																					SCREAMING_SNAKE_CASE					:      Dict     					=			1280
																					SCREAMING_SNAKE_CASE					:      int     					=			5120
																					SCREAMING_SNAKE_CASE					:      Optional[int]     					=			32
																					SCREAMING_SNAKE_CASE					:      int     					=			16

																					 # load original model from timm
							SCREAMING_SNAKE_CASE					:      int     					=			timm.create_model(__A					,		pretrained=__A				)
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			timm_model.state_dict()
							if base_model:
														remove_classification_head_(__A				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			create_rename_keys(__A					,		__A				)
							for src, dest in rename_keys:
														rename_key(__A					,		__A					,		__A				)
							read_in_q_k_v(__A					,		__A					,		__A				)

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														SCREAMING_SNAKE_CASE					:      Tuple     					=			ViTModel(__A				).eval()
							else:
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			ViTForImageClassification(__A				).eval()
							model.load_state_dict(__A				)

							# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
							if "deit" in vit_name:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			DeiTImageProcessor(size=config.image_size				)
							else:
														SCREAMING_SNAKE_CASE					:      int     					=			ViTImageProcessor(size=config.image_size				)
							SCREAMING_SNAKE_CASE					:      str     					=			image_processor(images=prepare_img()					,		return_tensors='''pt'''				)
							SCREAMING_SNAKE_CASE					:      Dict     					=			encoding['''pixel_values''']
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(__A				)

							if base_model:
														SCREAMING_SNAKE_CASE					:      str     					=			timm_model.forward_features(__A				)
														assert timm_pooled_output.shape == outputs.pooler_output.shape
														assert torch.allclose(__A					,		outputs.pooler_output					,		atol=1e-3				)
							else:
														SCREAMING_SNAKE_CASE					:      str     					=			timm_model(__A				)
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(__A					,		outputs.logits					,		atol=1e-3				)

							Path(__A				).mkdir(exist_ok=__A				)
							print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(__A				)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
							image_processor.save_pretrained(__A				)


if __name__ == "__main__":
							lowerCAmelCase_     :       Optional[Any]								=						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--vit_name',
							    default='vit_base_patch16_224',
							    type=str,
							    help='Name of the ViT 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.'
							)

							lowerCAmelCase_     :       int								=						parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









'''simple docstring'''



from collections import deque
class 	lowerCamelCase_							:







						def __init__(  self   :      Tuple				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			process_name  # process name
													SCREAMING_SNAKE_CASE					:      int     					=			arrival_time  # arrival time of the process
													# completion time of finished process or last interrupted time
													SCREAMING_SNAKE_CASE					:      List[str]     					=			arrival_time
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			burst_time  # remaining burst time
													SCREAMING_SNAKE_CASE					:      List[str]     					=			0  # total time of the process wait in ready queue
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			0  # time from arrival time to completion time


class 	lowerCamelCase_							:







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      list[int]				,    lowerCAmelCase__   :      deque[Process]				,    lowerCAmelCase__   :      int				,    ):

													"""simple docstring"""
													# total number of mlfq's queues
													SCREAMING_SNAKE_CASE					:      str     					=			number_of_queues
													# time slice of queues that round robin algorithm applied
													SCREAMING_SNAKE_CASE					:      int     					=			time_slices
													# unfinished process is in this ready_queue
													SCREAMING_SNAKE_CASE					:      str     					=			queue
													# current time
													SCREAMING_SNAKE_CASE					:      Any     					=			current_time
													# finished process is in this sequence queue
													SCREAMING_SNAKE_CASE					:      deque[Process]     					=			deque()







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[]
													for i in range(len(self.finish_queue				)				):
																				sequence.append(self.finish_queue[i].process_name				)
													return sequence







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				waiting_times.append(queue[i].waiting_time				)
													return waiting_times







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				turnaround_times.append(queue[i].turnaround_time				)
													return turnaround_times







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      list[Process]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[]
													for i in range(len(lowerCAmelCase__				)				):
																				completion_times.append(queue[i].stop_time				)
													return completion_times







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      deque[Process]				):

													"""simple docstring"""
													return [q.burst_time for q in queue]







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      Process				):

													"""simple docstring"""
													process.waiting_time += self.current_time - process.stop_time
													return process.waiting_time







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      deque[Process]				):

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

													"""simple docstring"""
													SCREAMING_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__				)				):
																				SCREAMING_SNAKE_CASE					:      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 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
																											SCREAMING_SNAKE_CASE					:      Union[str, 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
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
																											# set the finish time
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			self.current_time
																											# update the process' turnaround time because it is finished
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			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   :      Union[str, Any]				):

													"""simple docstring"""

													#  all queues except last one have round_robin algorithm
													for i in range(self.number_of_queues - 1				):
																				SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[int]     					=			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

							lowerCAmelCase_     :       str								=						Process('P1', 0, 53)
							lowerCAmelCase_     :       Optional[int]								=						Process('P2', 0, 17)
							lowerCAmelCase_     :       Optional[Any]								=						Process('P3', 0, 68)
							lowerCAmelCase_     :       Optional[int]								=						Process('P4', 0, 24)
							lowerCAmelCase_     :       List[str]								=						3
							lowerCAmelCase_     :       str								=						[17, 25]
							lowerCAmelCase_     :       Optional[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])})

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




