
'''simple docstring'''

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
snake_case									=    logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")

snake_case									=    list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
snake_case									=    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class    lowerCAmelCase       :
 A_				:				Optional[str]	=			field(
     default="""cifar10"""    ,  metadata={"""help""": """Name of a dataset from the datasets package"""}  )
 A_				:				Optional[str]	=			field(
     default=UpperCamelCase_    ,  metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
 A_				:				Optional[str]	=			field(
     default=UpperCamelCase_    ,  metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}    ,  )
 A_				:				Optional[str]	=			field(default=UpperCamelCase_    ,  metadata={"""help""": """A folder containing the training data."""}  )
 A_				:				Optional[str]	=			field(default=UpperCamelCase_    ,  metadata={"""help""": """A folder containing the validation data."""}  )
 A_				:				Optional[float]	=			field(
     default=0.15    ,  metadata={"""help""": """Percent to split off of train for validation."""}  )
 A_				:				int	=			field(default=32    ,  metadata={"""help""": """The size of the square patches to use for masking."""}  )
 A_				:				float	=			field(
     default=0.6    ,  metadata={"""help""": """Percentage of patches to mask."""}    ,  )
 A_				:				Optional[int]	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     }    ,  )
 A_				:				Optional[int]	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
             """value if set."""
         )
     }    ,  )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	{}
  if self.train_dir is not None:
   lowerCAmelCase__     :   str     		=	self.train_dir
  if self.validation_dir is not None:
   lowerCAmelCase__     :   Tuple     		=	self.validation_dir
  lowerCAmelCase__     :   Dict     		=	data_files if data_files else None




@dataclass
class    lowerCAmelCase       :
 A_				:				str	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a """
             """checkpoint identifier on the hub. """
             """Don't set if you want to train a model from scratch."""
         )
     }    ,  )
 A_				:				Optional[str]	=			field(
     default=UpperCamelCase_    ,  metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(UpperCamelCase_  )}    ,  )
 A_				:				Optional[str]	=			field(
     default=UpperCamelCase_    ,  metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
 A_				:				Optional[str]	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """Override some existing default config settings when a model is trained from scratch. Example: """
             """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
         )
     }    ,  )
 A_				:				Optional[str]	=			field(
     default=UpperCamelCase_    ,  metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}    ,  )
 A_				:				str	=			field(
     default="""main"""    ,  metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,  )
 A_				:				str	=			field(default=UpperCamelCase_    ,  metadata={"""help""": """Name or path of preprocessor config."""}  )
 A_				:				bool	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
             """with private models)."""
         )
     }    ,  )
 A_				:				Optional[int]	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
         )
     }    ,  )
 A_				:				Optional[int]	=			field(
     default=UpperCamelCase_    ,  metadata={
         """help""": (
             """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
         )
     }    ,  )
 A_				:				Optional[int]	=			field(
     default=UpperCamelCase_    ,  metadata={"""help""": """Stride to use for the encoder."""}    ,  )




class    lowerCAmelCase       :


 def __init__(    self  : Any			,							a__  : Dict=192			,							a__  : Optional[int]=32			,							a__  : List[Any]=4			,							a__  : Optional[Any]=0.6    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	input_size
  lowerCAmelCase__     :   str     		=	mask_patch_size
  lowerCAmelCase__     :   List[Any]     		=	model_patch_size
  lowerCAmelCase__     :   Optional[Any]     		=	mask_ratio

  if self.input_size % self.mask_patch_size != 0:
   raise ValueError("Input size must be divisible by mask patch size"    )
  if self.mask_patch_size % self.model_patch_size != 0:
   raise ValueError("Mask patch size must be divisible by model patch size"    )

  lowerCAmelCase__     :   List[Any]     		=	self.input_size // self.mask_patch_size
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mask_patch_size // self.model_patch_size

  lowerCAmelCase__     :   Any     		=	self.rand_size**2
  lowerCAmelCase__     :   Optional[Any]     		=	int(np.ceil(self.token_count * self.mask_ratio    )    )


 def __call__(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	np.random.permutation(self.token_count    )[: self.mask_count]
  lowerCAmelCase__     :   Optional[Any]     		=	np.zeros(self.token_count			,							dtype=a__    )
  lowerCAmelCase__     :   Union[str, Any]     		=	1

  lowerCAmelCase__     :   Dict     		=	mask.reshape((self.rand_size, self.rand_size)    )
  lowerCAmelCase__     :   Dict     		=	mask.repeat(self.scale			,							axis=0    ).repeat(self.scale			,							axis=1    )

  return torch.tensor(mask.flatten()    )



def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	torch.stack([example["pixel_values"] for example in examples]			)
 lowerCAmelCase__     :   Optional[int]     		=	torch.stack([example["mask"] for example in examples]			)
 return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 lowerCAmelCase__     :   Any     		=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
 if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   List[Any]     		=	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
 else:
  lowerCAmelCase__		,     lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("run_mim"      ,			lowerCamelCase_      ,			lowerCamelCase_			)

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      ,			datefmt="%m/%d/%Y %H:%M:%S"      ,			handlers=[logging.StreamHandler(sys.stdout			)]      ,			)

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 lowerCAmelCase__     :   int     		=	training_args.get_process_log_level()
 logger.setLevel(lowerCamelCase_			)
 transformers.utils.logging.set_verbosity(lowerCamelCase_			)
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
     + f'''distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'''			)
 logger.info(f'''Training/evaluation parameters {training_args}'''			)

 # Detecting last checkpoint.
 lowerCAmelCase__     :   List[str]     		=	None
 if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
  lowerCAmelCase__     :   Optional[Any]     		=	get_last_checkpoint(training_args.output_dir			)
  if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
   raise ValueError(
       f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
       "Use --overwrite_output_dir to overcome."			)
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
   logger.info(
       f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
       "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."			)

    # Initialize our dataset.
 lowerCAmelCase__     :   List[Any]     		=	load_dataset(
     data_args.dataset_name      ,			data_args.dataset_config_name      ,			data_files=data_args.data_files      ,			cache_dir=model_args.cache_dir      ,			use_auth_token=True if model_args.use_auth_token else None      ,			)

 # If we don't have a validation split, split off a percentage of train as validation.
 lowerCAmelCase__     :   int     		=	None if "validation" in ds.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split      ,			lowerCamelCase_			) and data_args.train_val_split > 0.0:
  lowerCAmelCase__     :   int     		=	ds["train"].train_test_split(data_args.train_val_split			)
  lowerCAmelCase__     :   Dict     		=	split["train"]
  lowerCAmelCase__     :   List[Any]     		=	split["test"]

 # Create config
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 lowerCAmelCase__     :   Any     		=	{
     "cache_dir": model_args.cache_dir,
     "revision": model_args.model_revision,
     "use_auth_token": True if model_args.use_auth_token else None,
 }
 if model_args.config_name_or_path:
  lowerCAmelCase__     :   Optional[Any]     		=	AutoConfig.from_pretrained(model_args.config_name_or_path      ,			**lowerCamelCase_			)
 elif model_args.model_name_or_path:
  lowerCAmelCase__     :   int     		=	AutoConfig.from_pretrained(model_args.model_name_or_path      ,			**lowerCamelCase_			)
 else:
  lowerCAmelCase__     :   Any     		=	CONFIG_MAPPING[model_args.model_type]()
  logger.warning("You are instantiating a new config instance from scratch."			)
  if model_args.config_overrides is not None:
   logger.info(f'''Overriding config: {model_args.config_overrides}'''			)
   config.update_from_string(model_args.config_overrides			)
   logger.info(f'''New config: {config}'''			)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
 if hasattr(lowerCamelCase_      ,			"decoder_type"			):
  lowerCAmelCase__     :   Optional[Any]     		=	"simmim"

 # adapt config
 lowerCAmelCase__     :   int     		=	model_args.image_size if model_args.image_size is not None else config.image_size
 lowerCAmelCase__     :   Any     		=	model_args.patch_size if model_args.patch_size is not None else config.patch_size
 lowerCAmelCase__     :   int     		=	(
     model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
 )

 config.update(
     {
         "image_size": model_args.image_size,
         "patch_size": model_args.patch_size,
         "encoder_stride": model_args.encoder_stride,
     }			)

 # create image processor
 if model_args.image_processor_name:
  lowerCAmelCase__     :   Union[str, Any]     		=	AutoImageProcessor.from_pretrained(model_args.image_processor_name      ,			**lowerCamelCase_			)
 elif model_args.model_name_or_path:
  lowerCAmelCase__     :   List[str]     		=	AutoImageProcessor.from_pretrained(model_args.model_name_or_path      ,			**lowerCamelCase_			)
 else:
  lowerCAmelCase__     :   Dict     		=	{
      conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
  }
  lowerCAmelCase__     :   Union[str, Any]     		=	IMAGE_PROCESSOR_TYPES[model_args.model_type]()

 # create model
 if model_args.model_name_or_path:
  lowerCAmelCase__     :   Optional[int]     		=	AutoModelForMaskedImageModeling.from_pretrained(
      model_args.model_name_or_path      ,			from_tf=bool(".ckpt" in model_args.model_name_or_path			)      ,			config=lowerCamelCase_      ,			cache_dir=model_args.cache_dir      ,			revision=model_args.model_revision      ,			use_auth_token=True if model_args.use_auth_token else None      ,			)
 else:
  logger.info("Training new model from scratch"			)
  lowerCAmelCase__     :   Any     		=	AutoModelForMaskedImageModeling.from_config(lowerCamelCase_			)

 if training_args.do_train:
  lowerCAmelCase__     :   Tuple     		=	ds["train"].column_names
 else:
  lowerCAmelCase__     :   Union[str, Any]     		=	ds["validation"].column_names

 if data_args.image_column_name is not None:
  lowerCAmelCase__     :   Optional[Any]     		=	data_args.image_column_name
 elif "image" in column_names:
  lowerCAmelCase__     :   Tuple     		=	"image"
 elif "img" in column_names:
  lowerCAmelCase__     :   Tuple     		=	"img"
 else:
  lowerCAmelCase__     :   List[Any]     		=	column_names[0]

 # transformations as done in original SimMIM paper
 # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
 lowerCAmelCase__     :   List[str]     		=	Compose(
     [
         Lambda(lambda lowerCamelCase_			: img.convert("RGB"			) if img.mode != "RGB" else img			),
         RandomResizedCrop(model_args.image_size      ,			scale=(0.67, 1.0)      ,			ratio=(3.0 / 4.0, 4.0 / 3.0)			),
         RandomHorizontalFlip(),
         ToTensor(),
         Normalize(mean=image_processor.image_mean      ,			std=image_processor.image_std			),
     ]			)

 # create mask generator
 lowerCAmelCase__     :   str     		=	MaskGenerator(
     input_size=model_args.image_size      ,			mask_patch_size=data_args.mask_patch_size      ,			model_patch_size=model_args.patch_size      ,			mask_ratio=data_args.mask_ratio      ,			)

 def preprocess_images(lowerCamelCase_			):
  lowerCAmelCase__     :   List[Any]     		=	[transforms(lowerCamelCase_			) for image in examples[image_column_name]]
  lowerCAmelCase__     :   Optional[Any]     		=	[mask_generator() for i in range(len(examples[image_column_name]			)			)]

  return examples

 if training_args.do_train:
  if "train" not in ds:
   raise ValueError("--do_train requires a train dataset"			)
  if data_args.max_train_samples is not None:
   lowerCAmelCase__     :   Optional[int]     		=	ds["train"].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
  # Set the training transforms
  ds["train"].set_transform(lowerCamelCase_			)

 if training_args.do_eval:
  if "validation" not in ds:
   raise ValueError("--do_eval requires a validation dataset"			)
  if data_args.max_eval_samples is not None:
   lowerCAmelCase__     :   List[Any]     		=	(
       ds["validation"].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
   )
  # Set the validation transforms
  ds["validation"].set_transform(lowerCamelCase_			)

 # Initialize our trainer
 lowerCAmelCase__     :   List[Any]     		=	Trainer(
     model=lowerCamelCase_      ,			args=lowerCamelCase_      ,			train_dataset=ds["train"] if training_args.do_train else None      ,			eval_dataset=ds["validation"] if training_args.do_eval else None      ,			tokenizer=lowerCamelCase_      ,			data_collator=lowerCamelCase_      ,			)

 # Training
 if training_args.do_train:
  lowerCAmelCase__     :   List[Any]     		=	None
  if training_args.resume_from_checkpoint is not None:
   lowerCAmelCase__     :   str     		=	training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   lowerCAmelCase__     :   Tuple     		=	last_checkpoint
  lowerCAmelCase__     :   str     		=	trainer.train(resume_from_checkpoint=lowerCamelCase_			)
  trainer.save_model()
  trainer.log_metrics("train"      ,			train_result.metrics			)
  trainer.save_metrics("train"      ,			train_result.metrics			)
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  lowerCAmelCase__     :   List[Any]     		=	trainer.evaluate()
  trainer.log_metrics("eval"      ,			lowerCamelCase_			)
  trainer.save_metrics("eval"      ,			lowerCamelCase_			)

 # Write model card and (optionally) push to hub
 lowerCAmelCase__     :   Optional[Any]     		=	{
     "finetuned_from": model_args.model_name_or_path,
     "tasks": "masked-image-modeling",
     "dataset": data_args.dataset_name,
     "tags": ["masked-image-modeling"],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**lowerCamelCase_			)
 else:
  trainer.create_model_card(**lowerCamelCase_			)


if __name__ == "__main__":
     main()







'''simple docstring'''

from __future__ import annotations

import pandas as pd


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Tuple     		=	[0] * no_of_processes
 lowerCAmelCase__     :   Any     		=	[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i]

 lowerCAmelCase__     :   str     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	0
 lowerCAmelCase__     :   Optional[Any]     		=	9_9_9_9_9_9_9_9_9
 lowerCAmelCase__     :   int     		=	0
 lowerCAmelCase__     :   str     		=	False

 # Process until all processes are completed
 while complete != no_of_processes:
  for j in range(lowerCamelCase_			):
   if arrival_time[j] <= increment_time and remaining_time[j] > 0:
    if remaining_time[j] < minm:
     lowerCAmelCase__     :   Any     		=	remaining_time[j]
     lowerCAmelCase__     :   str     		=	j
     lowerCAmelCase__     :   List[Any]     		=	True

  if not check:
   increment_time += 1
   continue
  remaining_time[short] -= 1

  lowerCAmelCase__     :   Optional[Any]     		=	remaining_time[short]
  if minm == 0:
   lowerCAmelCase__     :   Optional[int]     		=	9_9_9_9_9_9_9_9_9

  if remaining_time[short] == 0:
   complete += 1
   lowerCAmelCase__     :   int     		=	False

   # Find finish time of current process
   lowerCAmelCase__     :   Tuple     		=	increment_time + 1

   # Calculate waiting time
   lowerCAmelCase__     :   Dict     		=	finish_time - arrival_time[short]
   lowerCAmelCase__     :   Optional[Any]     		=	finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCAmelCase__     :   Tuple     		=	0

        # Increment time
  increment_time += 1
 return waiting_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[Any]     		=	[0] * no_of_processes
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Dict     		=	burst_time[i] + waiting_time[i]
 return turn_around_time


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Union[str, Any]     		=	0
 lowerCAmelCase__     :   Optional[int]     		=	0
 for i in range(lowerCamelCase_			):
  lowerCAmelCase__     :   Tuple     		=	total_waiting_time + waiting_time[i]
  lowerCAmelCase__     :   str     		=	total_turn_around_time + turn_around_time[i]
 print(f'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''			)
 print("Average turn around time ="      ,			total_turn_around_time / no_of_processes			)


if __name__ == "__main__":
     print("""Enter how many process you want to analyze""")
     snake_case									=    int(input())
     snake_case									=    [0] * no_of_processes
     snake_case									=    [0] * no_of_processes
     snake_case									=    list(range(1, no_of_processes + 1))

     for i in range(no_of_processes):
          print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
          snake_case					,					snake_case									=    map(int, input().split())

     snake_case									=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     snake_case									=    burst_time
     snake_case									=    no_of_processes
     snake_case									=    waiting_time
     snake_case									=    calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     snake_case									=    pd.DataFrame(
         list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
         columns=[
             """Process""",
             """BurstTime""",
             """ArrivalTime""",
             """WaitingTime""",
             """TurnAroundTime""",
         ],
     )

     # Printing the dataFrame
     pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
     print(fcfs)





