

'''simple docstring'''
from collections import deque







class 							A__    :





  def __init__(		self      :	List[Any]							,					_a      :	str							,					_a      :	int							,					_a      :	int )	->    None:





       '''simple docstring'''






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







class 							A__    :





  def __init__(		self      :	List[str]							,					_a      :	int							,					_a      :	list[int]							,					_a      :	deque[Process]							,					_a      :	int							,					)	->    None:





       '''simple docstring'''






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





  def 						A  (		self      :	Union[str, Any] )	->    list[str]:





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					_a      :	list[Process] )	->    list[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	List[str]							,					_a      :	list[Process] )	->    list[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	str							,					_a      :	list[Process] )	->    list[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	Tuple							,					_a      :	deque[Process] )	->    list[int]:





       '''simple docstring'''






       return [q.burst_time for q in queue]





  def 						A  (		self      :	Optional[int]							,					_a      :	Process )	->    int:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[int]							,					_a      :	deque[Process] )	->    deque[Process]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =deque()  # sequence deque of finished process
       while len(_a ) != 0:
            _SCREAMING_SNAKE_CASE							    =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(_a )
            # update current time
            self.current_time += cp.burst_time
            # finish the process and set the process's burst-time 0
            _SCREAMING_SNAKE_CASE							    =0
            # set the process's turnaround time because it is finished
            _SCREAMING_SNAKE_CASE							    =self.current_time - cp.arrival_time
            # set the completion time
            _SCREAMING_SNAKE_CASE							    =self.current_time
            # add the process to queue that has finished queue
            finished.append(_a )

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





  def 						A  (		self      :	Any							,					_a      :	deque[Process]							,					_a      :	int )	->    tuple[deque[Process], deque[Process]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =deque()  # sequence deque of terminated process
       # just for 1 cycle and unfinished processes will go back to queue
       for _ in range(len(_a ) ):
            _SCREAMING_SNAKE_CASE							    =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(_a )
            # 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							    =self.current_time
                 # locate the process behind the queue because it is not finished
                 ready_queue.append(_a )
            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							    =0
                 # set the finish time
                 _SCREAMING_SNAKE_CASE							    =self.current_time
                 # update the process' turnaround time because it is finished
                 _SCREAMING_SNAKE_CASE							    =self.current_time - cp.arrival_time
                 # add the process to queue that has finished queue
                 finished.append(_a )

       self.finish_queue.extend(_a )  # 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 ):
            _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =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 :      Tuple					 =     Process("P1", 0, 5_3)
     lowerCamelCase :      str					 =     Process("P2", 0, 1_7)
     lowerCamelCase :      Any					 =     Process("P3", 0, 6_8)
     lowerCamelCase :      Any					 =     Process("P4", 0, 2_4)
     lowerCamelCase :      Optional[int]					 =     3
     lowerCamelCase :      Dict					 =     [1_7, 2_5]
     lowerCamelCase :      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])})

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

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

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> str:
      snake_case__  :    Union[str, Any]    						=      []
      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"deit.encoder.layer.{i}.layernorm_before.weight")		)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")		)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")		)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")		)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")		)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ("""cls_token""", """deit.embeddings.cls_token"""),
              ("""dist_token""", """deit.embeddings.distillation_token"""),
              ("""patch_embed.proj.weight""", """deit.embeddings.patch_embeddings.projection.weight"""),
              ("""patch_embed.proj.bias""", """deit.embeddings.patch_embeddings.projection.bias"""),
              ("""pos_embed""", """deit.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 "deit" from all keys that start with "deit"
            snake_case__  :    List[Any]    						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]		)

      return rename_keys





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> Union[str, Any]:
      for i in range(config.num_hidden_layers		):
            if base_model:
                  snake_case__  :    Tuple    						=      """"""
            else:
                  snake_case__  :    Dict    						=      """deit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            snake_case__  :    Optional[Any]    						=      state_dict.pop(f"blocks.{i}.attn.qkv.weight"		)
            snake_case__  :    Tuple    						=      state_dict.pop(f"blocks.{i}.attn.qkv.bias"		)
            # next, add query, keys and values (in that order) to the state dict
            snake_case__  :    Any    						=      in_proj_weight[
                : config.hidden_size, :
            ]
            snake_case__  :    Optional[int]    						=      in_proj_bias[: config.hidden_size]
            snake_case__  :    Any    						=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            snake_case__  :    str    						=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            snake_case__  :    List[str]    						=      in_proj_weight[
                -config.hidden_size :, :
            ]
            snake_case__  :    Tuple    						=      in_proj_bias[-config.hidden_size :]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    str    						=      dct.pop(_lowerCAmelCase		)
      snake_case__  :    Tuple    						=      val





def 						__snake_case(				)		-> Tuple:
      snake_case__  :    Union[str, Any]    						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__  :    Optional[int]    						=      Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase		).raw		)
      return im





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case__  :    Union[str, Any]    						=      False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case__  :    int    						=      1_000
      snake_case__  :    Any    						=      """huggingface/label-files"""
      snake_case__  :    Optional[Any]    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    Tuple    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    List[Any]    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    List[Any]    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      int(deit_name[-6:-4]		)
      snake_case__  :    Optional[Any]    						=      int(deit_name[-3:]		)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""		):
            snake_case__  :    Tuple    						=      192
            snake_case__  :    Union[str, Any]    						=      768
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Union[str, Any]    						=      3
      elif deit_name[9:].startswith("""small"""		):
            snake_case__  :    str    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    str    						=      12
            snake_case__  :    int    						=      6
      if deit_name[9:].startswith("""base"""		):
            pass
      elif deit_name[4:].startswith("""large"""		):
            snake_case__  :    Union[str, Any]    						=      1_024
            snake_case__  :    Any    						=      4_096
            snake_case__  :    List[Any]    						=      24
            snake_case__  :    Tuple    						=      16

      # load original model from timm
      snake_case__  :    List[Any]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[Any]    						=      timm_model.state_dict()
      snake_case__  :    Optional[int]    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # load HuggingFace model
      snake_case__  :    Optional[Any]    						=      DeiTForImageClassificationWithTeacher(_lowerCAmelCase		).eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case__  :    List[Any]    						=      int(
          (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case__  :    Optional[Any]    						=      DeiTImageProcessor(size=_lowerCAmelCase     ,   crop_size=config.image_size		)
      snake_case__  :    str    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Optional[Any]    						=      encoding["""pixel_values"""]
      snake_case__  :    Tuple    						=      model(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      timm_model(_lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase     ,   outputs.logits     ,   atol=1e-3		)

      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)
      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT 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          =		parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
