


"""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
#
########################################################################


__SCREAMING_SNAKE_CASE					=						16
__SCREAMING_SNAKE_CASE					=						32



def 						__a      (  a					):





       """simple docstring"""







       return int(x / 2**2_0					)





class __snake_case		:







   """simple docstring"""




   def __enter__(		self							:Dict       ):
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          _a          =				torch.cuda.memory_allocated()
          _a          =				psutil.Process()

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




   def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
          return self.process.memory_info().rss




   def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
          _a          =				-1

          while True:
                 _a          =				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							:Optional[Any]				,  *UpperCamelCase__							:Optional[int]       ):
          _a          =				False

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

          _a          =				self.cpu_mem_used()
          _a          =				bamb(self.cpu_end - self.cpu_begin       )
          _a          =				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

   __SCREAMING_SNAKE_CASE					=						mocked_dataloaders  # noqa: F811



def 						__a      (  a,		a					):





       """simple docstring"""







       if os.environ.get("TESTING_MOCKED_DATALOADERS",		a					) == "1":
              _a          =				2

       # New Code #
       # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
       _a          =				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:
              _a          =				Accelerator(
                  cpu=args.cpu,		mixed_precision=args.mixed_precision,		log_with="wandb",		project_dir=args.logging_dir,		fsdp_plugin=a,		)
       else:
              _a          =				Accelerator(fsdp_plugin=a					)
       accelerator.print(accelerator.distributed_type					)

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

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

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

       def tokenize_function(a					):
              # max_length=None => use the model max length (it's actually the default)
              _a          =				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():
              _a          =				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
       _a          =				tokenized_datasets.rename_column("label",		"labels"					)

       # If the batch size is too big we use gradient accumulation
       _a          =				1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              _a          =				batch_size // MAX_GPU_BATCH_SIZE
              _a          =				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.
              _a          =				1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     _a          =				1_6
              elif accelerator.mixed_precision != "no":
                     _a          =				8
              else:
                     _a          =				None

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

       # Instantiate dataloaders.
       _a          =				DataLoader(
           tokenized_datasets["train"],		shuffle=a,		collate_fn=a,		batch_size=a					)
       _a          =				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)
       _a          =				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
       _a          =				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
       _a          =				torch.optim.AdamW(params=model.parameters(),		lr=a,		weight_decay=2e-4					)

       # Instantiate scheduler
       _a          =				get_linear_schedule_with_warmup(
           optimizer=a,		num_warmup_steps=1_0,		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.
       _a   , _a   , _a   , _a          =				accelerator.prepare(
           a,		a,		a,		a					)

       _a          =				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					)
                     _a          =				os.path.basename(args.resume_from_checkpoint					)
              else:
                     # Get the most recent checkpoint
                     _a          =				[f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
                     dirs.sort(key=os.path.getctime					)
                     _a          =				dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
              # Extract `epoch_{i}` or `step_{i}`
              _a          =				os.path.splitext(a					)[0]

              if "epoch" in training_difference:
                     num_epochs -= int(training_difference.replace("epoch_",		""					)					)
                     _a          =				None
              else:
                     _a          =				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
                     _a          =				(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:
                            _a          =				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					)
                            _a          =				model(**a					)
                            _a          =				outputs.loss
                            _a          =				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					):
                                   _a          =				F'step_{overall_step}'
                                   if overall_step % checkpointing_steps == 0:
                                          if args.output_dir is not None:
                                                 _a          =				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():
                                   _a          =				model(**a					)
                            _a          =				outputs.logits.argmax(dim=-1					)
                            _a   , _a          =				accelerator.gather_for_metrics((predictions, batch["labels"])					)
                            metric.add_batch(
                                predictions=a,		references=a,		)

                     _a          =				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":
                            _a          =				F'epoch_{epoch}'
                            if args.output_dir is not None:
                                   _a          =				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"""







       _a          =				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,		)
       _a          =				parser.parse_args()
       _a          =				{"lr": 2e-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
       training_function(a,		a					)


if __name__ == "__main__":
   main()




"""simple docstring"""




from __future__ import annotations



def 						__a      (  a,		a					):





       """simple docstring"""







       assert len(a					) == len(a					)

       _a          =				len(a					)
       _a          =				list(range(a					)					)
       _a          =				[-1] * n  # who the donor has donated to
       _a          =				[-1] * n  # who the recipient has received from
       _a          =				[0] * n

       while unmatched_donors:
              _a          =				unmatched_donors[0]
              _a          =				donor_pref[donor]
              _a          =				donor_preference[num_donations[donor]]
              num_donations[donor] += 1
              _a          =				recipient_pref[recipient]
              _a          =				rec_record[recipient]

              if prev_donor != -1:
                     if rec_preference.index(a					) > rec_preference.index(a					):
                            _a          =				donor
                            _a          =				recipient
                            unmatched_donors.append(a					)
                            unmatched_donors.remove(a					)
              else:
                     _a          =				donor
                     _a          =				recipient
                     unmatched_donors.remove(a					)
       return donor_record
