



"""simple docstring"""






import argparse
import json
from pathlib import Path

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

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


logging.set_verbosity_info()
__UpperCAmelCase   :   int        =       logging.get_logger(__name__)
def   A   (      _A,  _A=False				):




					"""simple docstring"""
					snake_case_    :Optional[int]										=    []
					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"),
										    ]				)

										# if just the base model, we should remove "vit" from all keys that start with "vit"
										snake_case_    :Any										=    [(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   A   (      _A,  _A,  _A=False				):




					"""simple docstring"""
					for i in range(config.num_hidden_layers				):
										if base_model:
															snake_case_    :Dict										=    ""
										else:
															snake_case_    :Any										=    "vit."
										# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
										snake_case_    :Union[str, Any]										=    state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''				)
										snake_case_    :Union[str, Any]										=    state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''				)
										# next, add query, keys and values (in that order) to the state dict
										snake_case_    :List[Any]										=    in_proj_weight[
										    : config.hidden_size, :
										]
										snake_case_    :List[Any]										=    in_proj_bias[: config.hidden_size]
										snake_case_    :str										=    in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										snake_case_    :Any										=    in_proj_bias[
										    config.hidden_size : config.hidden_size * 2
										]
										snake_case_    :List[str]										=    in_proj_weight[
										    -config.hidden_size :, :
										]
										snake_case_    :List[str]										=    in_proj_bias[-config.hidden_size :]
def   A   (      _A				):




					"""simple docstring"""
					snake_case_    :str										=    ["head.weight", "head.bias"]
					for k in ignore_keys:
										state_dict.pop(_A,  _A				)
def   A   (      _A,  _A,  _A				):




					"""simple docstring"""
					snake_case_    :Optional[int]										=    dct.pop(_A				)
					snake_case_    :int										=    val
def   A   (      ):




					"""simple docstring"""
					snake_case_    :Optional[int]										=    "http://images.cocodataset.org/val2017/000000039769.jpg"
					snake_case_    :str										=    Image.open(requests.get(_A,  stream=_A				).raw				)
					return im
@torch.no_grad()
def   A   (      _A,  _A,  _A=True				):




					"""simple docstring"""
					snake_case_    :Optional[Any]										=    ViTConfig()
					# patch_size
					if model_name[-1] == "8":
										snake_case_    :int										=    8
					# set labels if required
					if not base_model:
										snake_case_    :str										=    1_000
										snake_case_    :List[Any]										=    "huggingface/label-files"
										snake_case_    :List[str]										=    "imagenet-1k-id2label.json"
										snake_case_    :Dict										=    json.load(open(hf_hub_download(_A,  _A,  repo_type="dataset"				),  "r"				)				)
										snake_case_    :List[Any]										=    {int(_A				): v for k, v in idalabel.items()}
										snake_case_    :Optional[int]										=    idalabel
										snake_case_    :Optional[Any]										=    {v: k for k, v in idalabel.items()}
					# size of the architecture
					if model_name in ["dino_vits8", "dino_vits16"]:
										snake_case_    :Tuple										=    384
										snake_case_    :Tuple										=    1_536
										snake_case_    :Union[str, Any]										=    12
										snake_case_    :Optional[Any]										=    6

					# load original model from torch hub
					snake_case_    :Optional[int]										=    torch.hub.load("facebookresearch/dino:main",  _A				)
					original_model.eval()

					# load state_dict of original model, remove and rename some keys
					snake_case_    :Optional[int]										=    original_model.state_dict()
					if base_model:
										remove_classification_head_(_A				)
					snake_case_    :List[str]										=    create_rename_keys(_A,  base_model=_A				)
					for src, dest in rename_keys:
										rename_key(_A,  _A,  _A				)
					read_in_q_k_v(_A,  _A,  _A				)

					# load HuggingFace model
					if base_model:
										snake_case_    :Union[str, Any]										=    ViTModel(_A,  add_pooling_layer=_A				).eval()
					else:
										snake_case_    :Union[str, Any]										=    ViTForImageClassification(_A				).eval()
					model.load_state_dict(_A				)

					# Check outputs on an image, prepared by ViTImageProcessor
					snake_case_    :Dict										=    ViTImageProcessor()
					snake_case_    :Any										=    image_processor(images=prepare_img(),  return_tensors="pt"				)
					snake_case_    :str										=    encoding["pixel_values"]
					snake_case_    :Tuple										=    model(_A				)

					if base_model:
										snake_case_    :Dict										=    original_model(_A				)
										assert torch.allclose(_A,  outputs.last_hidden_state[:, 0, :],  atol=1e-1				)
					else:
										snake_case_    :Union[str, Any]										=    original_model(_A				)
										assert logits.shape == outputs.logits.shape
										assert torch.allclose(_A,  outputs.logits,  atol=1e-3				)

					Path(_A				).mkdir(exist_ok=_A				)
					print(F'''Saving model {model_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__":
				__UpperCAmelCase   :   Optional[int]        =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='dino_vitb16',
				    type=str,
				    help='Name of the model trained with DINO you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)
				parser.add_argument(
				    '--base_model',
				    action='store_true',
				    help='Whether to only convert the base model (no projection head weights).',
				)

				parser.set_defaults(base_model=True)
				__UpperCAmelCase   :   List[str]        =       parser.parse_args()
				convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)



"""simple docstring"""




from collections import deque







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     None:







         """simple docstring"""



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







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				)		->     None:







         """simple docstring"""



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



     def 			lowercase__  (							self   )		->     list[str]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     list[int]:







         """simple docstring"""



         return [q.burst_time for q in queue]



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     deque[Process]:







         """simple docstring"""



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

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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     tuple[deque[Process], deque[Process]]:







         """simple docstring"""



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

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




     def 			lowercase__  (							self   )		->     deque[Process]:







         """simple docstring"""



         for i in range(self.number_of_queues - 1   ):
             a__	,     a__		=			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		:       List[str]     						=       Process('P1', 0, 53)
    a		:       Optional[int]     						=       Process('P2', 0, 17)
    a		:       Union[str, Any]     						=       Process('P3', 0, 68)
    a		:       Optional[int]     						=       Process('P4', 0, 24)
    a		:       Optional[int]     						=       3
    a		:       Optional[Any]     						=       [17, 25]
    a		:       Union[str, 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		:       Union[str, Any]     						=       Process('P1', 0, 53)
    a		:       Optional[int]     						=       Process('P2', 0, 17)
    a		:       Optional[Any]     						=       Process('P3', 0, 68)
    a		:       str     						=       Process('P4', 0, 24)
    a		:       Optional[Any]     						=       3
    a		:       Tuple     						=       [17, 25]
    a		:       int     						=       deque([Pa, Pa, Pa, Pa])
    a		:       Tuple     						=       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()}'''
    )


