


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A   :					Dict	=      16
A   :					Optional[int]	=      32

def    lowercase_	(	_A    :					Accelerator					,      _A    :					int = 16					,      _A    :					str = "bert-base-cased"		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						AutoTokenizer.from_pretrained(_A		)
      lowerCamelCase__	:       List[str]					=						load_dataset("glue"					,      "mrpc"		)

      def tokenize_function(_A    :					Optional[Any]		):
            # max_length=None => use the model max length (it's actually the default)
            lowerCamelCase__	:       int					=						tokenizer(examples["sentence1"]					,      examples["sentence2"]					,      truncation=_A					,      max_length=_A		)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      lowerCamelCase__	:       Tuple					=						datasets.map(
          _A					,      batched=_A					,      remove_columns=["idx", "sentence1", "sentence2"]					,      load_from_cache_file=_A		)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      lowerCamelCase__	:       Dict					=						tokenized_datasets.rename_column("label"					,      "labels"		)

      def collate_fn(_A    :					Any		):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(_A					,      padding="max_length"					,      max_length=128					,      return_tensors="pt"		)
            return tokenizer.pad(_A					,      padding="longest"					,      return_tensors="pt"		)

      # Instantiate dataloaders.
      lowerCamelCase__	:       List[Any]					=						DataLoader(
          tokenized_datasets["train"]					,      shuffle=_A					,      collate_fn=_A					,      batch_size=_A		)
      lowerCamelCase__	:       Optional[Any]					=						DataLoader(
          tokenized_datasets["validation"]					,      shuffle=_A					,      collate_fn=_A					,      batch_size=_A		)

      return train_dataloader, eval_dataloader

def    lowercase_	(	_A    :					List[Any]					,      _A    :					int					,      _A    :					Union[str, Any]					,      _A    :					Union[str, Any]		):

      """simple docstring"""




      model.eval()
      lowerCamelCase__	:       Optional[int]					=						0
      for step, batch in enumerate(_A		):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device		)
            with torch.no_grad():
                  lowerCamelCase__	:       Optional[Any]					=						model(**_A		)
            lowerCamelCase__	:       Union[str, Any]					=						outputs.logits.argmax(dim=-1		)
            # It is slightly faster to call this once, than multiple times
            lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						accelerator.gather(
                (predictions, batch["labels"])		)  # If we are in a multiprocess environment, the last batch has duplicates
            if accelerator.use_distributed:
                  if step == len(_A		) - 1:
                        lowerCamelCase__	:       Any					=						predictions[: len(eval_dataloader.dataset		) - samples_seen]
                        lowerCamelCase__	:       Optional[Any]					=						references[: len(eval_dataloader.dataset		) - samples_seen]
                  else:
                        samples_seen += references.shape[0]
            metric.add_batch(
                predictions=_A					,      references=_A					,      )

      lowerCamelCase__	:       Optional[int]					=						metric.compute()
      return eval_metric["accuracy"]

def    lowercase_	(	_A    :					Optional[Any]					,      _A    :					Dict		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      lowerCamelCase__	:       int					=						config["lr"]
      lowerCamelCase__	:       Optional[Any]					=						int(config["num_epochs"]		)
      lowerCamelCase__	:       Union[str, Any]					=						int(config["seed"]		)
      lowerCamelCase__	:       Any					=						int(config["batch_size"]		)
      lowerCamelCase__	:       Union[str, Any]					=						args.model_name_or_path

      set_seed(_A		)
      lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						get_dataloaders(_A					,      _A					,      _A		)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      lowerCamelCase__	:       List[Any]					=						AutoModelForSequenceClassification.from_pretrained(_A					,      return_dict=_A		)

      # Instantiate optimizer
      lowerCamelCase__	:       Any					=						(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      lowerCamelCase__	:       Optional[int]					=						optimizer_cls(params=model.parameters()					,      lr=_A		)

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase__	:       Union[str, Any]					=						accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            lowerCamelCase__	:       Union[str, Any]					=						1
      lowerCamelCase__	:       Tuple					=						(len(_A		) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            lowerCamelCase__	:       Optional[Any]					=						get_linear_schedule_with_warmup(
                optimizer=_A					,      num_warmup_steps=0					,      num_training_steps=_A					,      )
      else:
            lowerCamelCase__	:       List[Any]					=						DummyScheduler(_A					,      total_num_steps=_A					,      warmup_num_steps=0		)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       List[str]					=						accelerator.prepare(
          _A					,      _A					,      _A					,      _A					,      _A		)

      # We need to keep track of how many total steps we have iterated over
      lowerCamelCase__	:       Optional[Any]					=						0
      # We also need to keep track of the stating epoch so files are named properly
      lowerCamelCase__	:       Tuple					=						0
      lowerCamelCase__	:       Dict					=						evaluate.load("glue"					,      "mrpc"		)
      lowerCamelCase__	:       List[str]					=						num_epochs

      if args.partial_train_epoch is not None:
            lowerCamelCase__	:       Dict					=						args.partial_train_epoch

      if args.resume_from_checkpoint:
            accelerator.load_state(args.resume_from_checkpoint		)
            lowerCamelCase__	:       Dict					=						args.resume_from_checkpoint.split("epoch_"		)[1]
            lowerCamelCase__	:       Optional[int]					=						""
            for char in epoch_string:
                  if char.isdigit():
                        state_epoch_num += char
                  else:
                        break
            lowerCamelCase__	:       Optional[int]					=						int(_A		) + 1
            lowerCamelCase__	:       Union[str, Any]					=						evaluation_loop(_A					,      _A					,      _A					,      _A		)
            accelerator.print("resumed checkpoint performance:"					,      _A		)
            accelerator.print("resumed checkpoint's scheduler's lr:"					,      lr_scheduler.get_lr()[0]		)
            accelerator.print("resumed optimizers's lr:"					,      optimizer.param_groups[0]["lr"]		)
            with open(os.path.join(args.output_dir					,      F"state_{starting_epoch-1}.json"		)					,      "r"		) as f:
                  lowerCamelCase__	:       int					=						json.load(_A		)
                  assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["lr"] == lr_scheduler.get_lr()[0]
                  ), "Scheduler learning rate mismatch, loading from checkpoint failed"
                  assert (
                      resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
                  ), "Optimizer learning rate mismatch, loading from checkpoint failed"
                  assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
                  return

    # Now we train the model
      lowerCamelCase__	:       Optional[int]					=						{}
      for epoch in range(_A					,      _A		):
            model.train()
            for step, batch in enumerate(_A		):
                  lowerCamelCase__	:       Tuple					=						model(**_A		)
                  lowerCamelCase__	:       Union[str, Any]					=						outputs.loss
                  lowerCamelCase__	:       Optional[int]					=						loss / gradient_accumulation_steps
                  accelerator.backward(_A		)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1
            lowerCamelCase__	:       Union[str, Any]					=						F"epoch_{epoch}"
            lowerCamelCase__	:       List[Any]					=						os.path.join(args.output_dir					,      _A		)
            accelerator.save_state(_A		)
            lowerCamelCase__	:       str					=						evaluation_loop(_A					,      _A					,      _A					,      _A		)
            lowerCamelCase__	:       List[Any]					=						accuracy
            lowerCamelCase__	:       Union[str, Any]					=						lr_scheduler.get_lr()[0]
            lowerCamelCase__	:       Any					=						optimizer.param_groups[0]["lr"]
            lowerCamelCase__	:       List[Any]					=						epoch
            lowerCamelCase__	:       Dict					=						overall_step
            accelerator.print(F"epoch {epoch}:"					,      _A		)

            accelerator.wait_for_everyone()
            if accelerator.is_main_process:
                  with open(os.path.join(args.output_dir					,      F"state_{epoch}.json"		)					,      "w"		) as f:
                        json.dump(_A					,      _A		)

def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
      parser.add_argument(
          "--model_name_or_path"					,      type=_A					,      default="bert-base-cased"					,      help="Path to pretrained model or model identifier from huggingface.co/models."					,      required=_A					,      )
      parser.add_argument(
          "--output_dir"					,      type=_A					,      default="."					,      help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."					,      )
      parser.add_argument(
          "--resume_from_checkpoint"					,      type=_A					,      default=_A					,      help="If the training should continue from a checkpoint folder."					,      )
      parser.add_argument(
          "--partial_train_epoch"					,      type=_A					,      default=_A					,      help="If passed, the training will stop after this number of epochs."					,      )
      parser.add_argument(
          "--num_epochs"					,      type=_A					,      default=2					,      help="Number of train epochs."					,      )
      lowerCamelCase__	:       Optional[Any]					=						parser.parse_args()
      lowerCamelCase__	:       int					=						{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

      training_function(_A					,      _A		)


if __name__ == "__main__":
    main()






from __future__ import annotations

import time

import numpy as np

A   :					Dict	=      [8, 5, 9, 7]
A   :					Optional[Any]	=      [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
A   :					Any	=      [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    str      ,   __lowerCamelCase							:    list[int]      ,   __lowerCamelCase							:    list[list[int]]      ,   __lowerCamelCase							:    list[list[int]]      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						claim_vector
             lowerCamelCase__	:       str					=						allocated_resources_table
             lowerCamelCase__	:       int					=						maximum_claim_table





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             return [
                 sum(p_item[i] for p_item in self.__allocated_resources_table )
                 for i in range(len(self.__allocated_resources_table[0] ) )
             ]





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return np.array(self.__claim_vector ) - np.array(
                 self.__processes_resource_summation() )





       def 		lowerCAmelCase  (     self							:    List[str] ):


             '''simple docstring'''



             return [
                 list(np.array(self.__maximum_claim_table[i] ) - np.array(__lowerCamelCase ) )
                 for i, allocated_resource in enumerate(self.__allocated_resources_table )
             ]





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return {self.__need().index(__lowerCamelCase ): i for i in self.__need()}





       def 		lowerCAmelCase  (     self							:    List[str]      ,   **__lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.__need()
             lowerCamelCase__	:       str					=						self.__allocated_resources_table
             lowerCamelCase__	:       List[Any]					=						self.__available_resources()
             lowerCamelCase__	:       str					=						self.__need_index_manager()
             for kw, val in kwargs.items():
                   if kw and val is True:
                         self.__pretty_data()
             print("_" * 50 + "\n" )
             while need_list:
                   lowerCamelCase__	:       int					=						False
                   for each_need in need_list:
                         lowerCamelCase__	:       Dict					=						True
                         for index, need in enumerate(__lowerCamelCase ):
                               if need > available_resources[index]:
                                     lowerCamelCase__	:       str					=						False
                                     break
                         if execution:
                               lowerCamelCase__	:       Tuple					=						True
                               # get the original index of the process from ind_ctrl db
                               for original_need_index, need_clone in need_index_manager.items():
                                     if each_need == need_clone:
                                           lowerCamelCase__	:       Any					=						original_need_index
                               print(f"Process {process_number + 1} is executing." )
                               # remove the process run from stack
                               need_list.remove(__lowerCamelCase )
                               # update available/freed resources stack
                               lowerCamelCase__	:       Union[str, Any]					=						np.array(__lowerCamelCase ) + np.array(
                                   alloc_resources_table[process_number] )
                               print(
                                   "Updated available resource stack for processes: "
                                   + " ".join([str(__lowerCamelCase ) for x in available_resources] ) )
                               break
                   if safe:
                         print("The process is in a safe state.\n" )
                   else:
                         print("System in unsafe state. Aborting...\n" )
                         break





       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             print(" " * 9 + "Allocated Resource Table" )
             for item in self.__allocated_resources_table:
                   print(
                       f"P{self.__allocated_resources_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(" " * 9 + "System Resource Table" )
             for item in self.__maximum_claim_table:
                   print(
                       f"P{self.__maximum_claim_table.index(__lowerCamelCase ) + 1}"
                       + " ".join(f"{it:>8}" for it in item )
                       + "\n" )
             print(
                 "Current Usage by Active Processes: "
                 + " ".join(str(__lowerCamelCase ) for x in self.__claim_vector ) )
             print(
                 "Initial Available Resources:       "
                 + " ".join(str(__lowerCamelCase ) for x in self.__available_resources() ) )
             time.sleep(1 )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


