"""simple docstring"""


import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCAmelCase_:       List[Any]			     =       1_6
lowerCAmelCase_:       Union[str, Any]			     =       3_2







def        __a (		A   ):






     '''simple docstring'''



     return int(x / 2**20   )






class  a__     :


   def __enter__(  self	):
        '''simple docstring'''



        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        lowercase__								=				torch.cuda.memory_allocated()
        lowercase__								=				psutil.Process()

        lowercase__								=				self.cpu_mem_used()
        lowercase__								=				True
        lowercase__								=				threading.Thread(target=self.peak_monitor_func	)
        lowercase__								=				True
        peak_monitor_thread.start()
        return self


   def 						snake_case__						(  self	):
        '''simple docstring'''



        return self.process.memory_info().rss


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				-1

        while True:
             lowercase__								=				max(self.cpu_mem_used(),					self.cpu_peak	)

             # can't sleep or will not catch the peak right (this comment is here on purpose)
             # time.sleep(0.001) # 1msec

             if not self.peak_monitoring:
                  break


   def __exit__(  self,					*_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				False

        gc.collect()
        torch.cuda.empty_cache()
        lowercase__								=				torch.cuda.memory_allocated()
        lowercase__								=				torch.cuda.max_memory_allocated()
        lowercase__								=				bamb(self.end - self.begin	)
        lowercase__								=				bamb(self.peak - self.begin	)

        lowercase__								=				self.cpu_mem_used()
        lowercase__								=				bamb(self.cpu_end - self.cpu_begin	)
        lowercase__								=				bamb(self.cpu_peak - self.cpu_begin	)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 lowerCAmelCase_:       List[Any]			     =       mocked_dataloaders  # noqa: F811







def        __a (		A     ,					A   ):






     '''simple docstring'''



     if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,					A   ) == "1":
          lowercase__								=				2

     # New Code #
     # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
     lowercase__								=				FullyShardedDataParallelPlugin(
         state_dict_config=FullStateDictConfig(offload_to_cpu=A     ,					ranka_only=A   )     ,					optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=A     ,					ranka_only=A   )     ,					)

     # Initialize accelerator
     if args.with_tracking:
          lowercase__								=				Accelerator(
              cpu=args.cpu     ,					mixed_precision=args.mixed_precision     ,					log_with="wandb"     ,					project_dir=args.logging_dir     ,					fsdp_plugin=A     ,					)
     else:
          lowercase__								=				Accelerator(fsdp_plugin=A   )
     accelerator.print(accelerator.distributed_type   )

     if hasattr(args.checkpointing_steps     ,					"isdigit"   ):
          if args.checkpointing_steps == "epoch":
               lowercase__								=				args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               lowercase__								=				int(args.checkpointing_steps   )
          else:
               raise ValueError(
                   f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''   )
     else:
          lowercase__								=				None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase__								=				config["lr"]
     lowercase__								=				int(config["num_epochs"]   )
     lowercase__								=				int(config["seed"]   )
     lowercase__								=				int(config["batch_size"]   )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          lowercase__								=				vars(A   )
          accelerator.init_trackers("fsdp_glue_no_trainer"     ,					A   )

     lowercase__								=				AutoTokenizer.from_pretrained(args.model_name_or_path   )
     lowercase__								=				load_dataset("glue"     ,					"mrpc"   )
     lowercase__								=				evaluate.load("glue"     ,					"mrpc"   )

     def tokenize_function(A   ):
          # max_length=None => use the model max length (it's actually the default)
          lowercase__								=				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
     # starting with the main process first:
     with accelerator.main_process_first():
          lowercase__								=				datasets.map(
              A     ,					batched=A     ,					remove_columns=["idx", "sentence1", "sentence2"]     ,					)

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

     # If the batch size is too big we use gradient accumulation
     lowercase__								=				1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          lowercase__								=				batch_size // MAX_GPU_BATCH_SIZE
          lowercase__								=				MAX_GPU_BATCH_SIZE

     def collate_fn(A   ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          lowercase__								=				1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               lowercase__								=				16
          elif accelerator.mixed_precision != "no":
               lowercase__								=				8
          else:
               lowercase__								=				None

          return tokenizer.pad(
              A     ,					padding="longest"     ,					max_length=A     ,					pad_to_multiple_of=A     ,					return_tensors="pt"     ,					)

     # Instantiate dataloaders.
     lowercase__								=				DataLoader(
         tokenized_datasets["train"]     ,					shuffle=A     ,					collate_fn=A     ,					batch_size=A   )
     lowercase__								=				DataLoader(
         tokenized_datasets["validation"]     ,					shuffle=A     ,					collate_fn=A     ,					batch_size=A   )

     set_seed(A   )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase__								=				AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path     ,					return_dict=A     ,					low_cpu_mem_usage=A   )

     # New Code #
     # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
     lowercase__								=				accelerator.prepare(A   )
     accelerator.print(A   )

     # Instantiate optimizer
     # New Code #
     # For FSDP feature, at present it doesn't support multiple parameter groups,
     # so we need to create a single parameter group for the whole model
     lowercase__								=				torch.optim.AdamW(params=model.parameters()     ,					lr=A     ,					weight_decay=2e-4   )

     # Instantiate scheduler
     lowercase__								=				get_linear_schedule_with_warmup(
         optimizer=A     ,					num_warmup_steps=10     ,					num_training_steps=(len(A   ) * num_epochs) // gradient_accumulation_steps     ,					)

     # New Code #
     # For FSDP feature, prepare everything except the model as we have already prepared the model
     # before creating the optimizer
     # 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.
     lowercase__			,    lowercase__			,    lowercase__			,    lowercase__								=				accelerator.prepare(
         A     ,					A     ,					A     ,					A   )

     lowercase__								=				0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}'''   )
               accelerator.load_state(args.resume_from_checkpoint   )
               lowercase__								=				os.path.basename(args.resume_from_checkpoint   )
          else:
               # Get the most recent checkpoint
               lowercase__								=				[f.name for f in os.scandir(os.getcwd()   ) if f.is_dir()]
               dirs.sort(key=os.path.getctime   )
               lowercase__								=				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          lowercase__								=				os.path.splitext(A   )[0]

          if "epoch" in training_difference:
               num_epochs -= int(training_difference.replace("epoch_"     ,					""   )   )
               lowercase__								=				None
          else:
               lowercase__								=				int(training_difference.replace("step_"     ,					""   )   )
               num_epochs -= resume_step // len(A   )
               # If resuming by step, we also need to know exactly how far into the DataLoader we went
               lowercase__								=				(num_epochs * len(A   )) - resume_step

    # Now we train the model
     for epoch in range(A   ):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
               model.train()
               if args.with_tracking:
                    lowercase__								=				0
               for step, batch in enumerate(A   ):
                    # We need to skip steps until we reach the resumed step
                    if args.resume_from_checkpoint and epoch == 0:
                         if resume_step is not None and step < resume_step:
                              pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device   )
                    lowercase__								=				model(**A   )
                    lowercase__								=				outputs.loss
                    lowercase__								=				loss / gradient_accumulation_steps
                    # We keep track of the loss at each epoch
                    if args.with_tracking:
                         total_loss += loss.detach().float()
                    accelerator.backward(A   )
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # accelerator.print(lr_scheduler.get_lr())

                    overall_step += 1

                    if isinstance(A     ,					A   ):
                         lowercase__								=				f'''step_{overall_step}'''
                         if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                   lowercase__								=				os.path.join(args.output_dir     ,					A   )
                              accelerator.save_state(A   )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
          accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
          accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
          accelerator.print(
              "Total Peak Memory consumed during the train (max): {}".format(
                  tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       "train_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin   ),
                   }     ,					step=A     ,					)

          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
               model.eval()
               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():
                         lowercase__								=				model(**A   )
                    lowercase__								=				outputs.logits.argmax(dim=-1   )
                    lowercase__			,    lowercase__								=				accelerator.gather_for_metrics((predictions, batch["labels"])   )
                    metric.add_batch(
                        predictions=A     ,					references=A     ,					)

               lowercase__								=				metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(f'''epoch {epoch}:'''     ,					A   )
               if args.with_tracking:
                    accelerator.log(
                        {
                            "accuracy": eval_metric["accuracy"],
                            "f1": eval_metric["f1"],
                            "train_loss": total_loss.item() / len(A   ),
                        }     ,					step=A     ,					)

               if checkpointing_steps == "epoch":
                    lowercase__								=				f'''epoch_{epoch}'''
                    if args.output_dir is not None:
                         lowercase__								=				os.path.join(args.output_dir     ,					A   )
                    accelerator.save_state(A   )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print("Memory before entering the eval : {}".format(bamb(tracemalloc.begin   )   )   )
          accelerator.print("Memory consumed at the end of the eval (end-begin): {}".format(tracemalloc.used   )   )
          accelerator.print("Peak Memory consumed during the eval (max-begin): {}".format(tracemalloc.peaked   )   )
          accelerator.print(
              "Total Peak Memory consumed during the eval (max): {}".format(tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       "eval_total_peak_memory": tracemalloc.peaked + bamb(tracemalloc.begin   ),
                   }     ,					step=A     ,					)

     if args.with_tracking:
          accelerator.end_training()







def        __a (		):






     '''simple docstring'''



     lowercase__								=				argparse.ArgumentParser(description="Simple example of training script."   )
     parser.add_argument(
         "--mixed_precision"     ,					type=A     ,					default=A     ,					choices=["no", "fp16", "bf16", "fp8"]     ,					help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU."     ,					)
     parser.add_argument("--cpu"     ,					action="store_true"     ,					help="If passed, will train on the CPU."   )
     parser.add_argument(
         "--checkpointing_steps"     ,					type=A     ,					default=A     ,					help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."     ,					)
     parser.add_argument(
         "--resume_from_checkpoint"     ,					type=A     ,					default=A     ,					help="If the training should continue from a checkpoint folder."     ,					)
     parser.add_argument(
         "--with_tracking"     ,					action="store_true"     ,					help="Whether to load in all available experiment trackers from the environment and use them for logging."     ,					)
     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(
         "--logging_dir"     ,					type=A     ,					default="logs"     ,					help="Location on where to store experiment tracking logs`"     ,					)
     parser.add_argument(
         "--model_name_or_path"     ,					type=A     ,					help="Path to pretrained model or model identifier from huggingface.co/models."     ,					required=A     ,					)
     lowercase__								=				parser.parse_args()
     lowercase__								=				{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(A     ,					A   )


if __name__ == "__main__":
 main()





"""simple docstring"""


def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				sorted(A     ,					key=lambda A   : value[2]     ,					reverse=A   )

     # Create a list of size equal to the maximum deadline
     # and initialize it with -1
     lowercase__								=				max(A     ,					key=lambda A   : value[1]   )[1]
     lowercase__								=				[-1] * max_deadline

     # Finding the maximum profit and the count of jobs
     lowercase__								=				0
     lowercase__								=				0
     for job in jobs:
          # Find a free time slot for this job
          # (Note that we start from the last possible slot)
          for i in range(job[1] - 1     ,					-1     ,					-1   ):
               if time_slots[i] == -1:
                    lowercase__								=				job[0]
                    count += 1
                    max_profit += job[2]
                    break
     return [count, max_profit]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




