




"""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 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[int]:
 return int(x / 2**20		)




class 			UpperCAmelCase_						:




    def __enter__(					self		: Any   )   ->							int:




     '''simple docstring'''

     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 							__magic_name__							(					self		: Tuple   )   ->							Optional[Any]:




     '''simple docstring'''

     return self.process.memory_info().rss




    def 							__magic_name__							(					self		: int   )   ->							List[str]:




     '''simple docstring'''

     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		: Any			,				*snake_case_		: Dict   )   ->							Optional[Any]:




     '''simple docstring'''

     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 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	List[Any]:
 # For testing only
 if os.environ.get("TESTING_MOCKED_DATALOADERS"    ,				lowercase_		) == "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=lowercase_    ,				ranka_only=lowercase_		)    ,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_    ,				ranka_only=lowercase_		)    ,				)

 # 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=lowercase_    ,				)
 else:
  A__ =       Accelerator(fsdp_plugin=lowercase_		)
 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(lowercase_		)
  accelerator.init_trackers("fsdp_glue_no_trainer"    ,				lowercase_		)

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

 def tokenize_function(lowercase_		):
  # max_length=None => use the model max length (it's actually the default)
  A__ =       tokenizer(examples["sentence1"]    ,				examples["sentence2"]    ,				truncation=lowercase_    ,				max_length=lowercase_		)
  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(
      lowercase_    ,				batched=lowercase_    ,				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(lowercase_		):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  A__ =       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":
   A__ =       16
  elif accelerator.mixed_precision != "no":
   A__ =       8
  else:
   A__ =       None

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

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

 set_seed(lowercase_		)

 # 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=lowercase_    ,				low_cpu_mem_usage=lowercase_		)

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

 # 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=lowercase_    ,				weight_decay=2E-4		)

 # Instantiate scheduler
 A__ =       get_linear_schedule_with_warmup(
     optimizer=lowercase_    ,				num_warmup_steps=10    ,				num_training_steps=(len(lowercase_		) * 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(
     lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)

 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(lowercase_		)[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(lowercase_		)
   # If resuming by step, we also need to know exactly how far into the DataLoader we went
   A__ =       (num_epochs * len(lowercase_		)) - resume_step

    # Now we train the model
 for epoch in range(lowercase_		):
  # 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(lowercase_		):
    # 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(**lowercase_		)
    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(lowercase_		)
    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(lowercase_    ,				lowercase_		):
     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    ,				lowercase_		)
      accelerator.save_state(lowercase_		)
        # 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=lowercase_    ,				)

  # New Code #
  # context manager to track the peak memory usage during the evaluation
  with TorchTracemalloc() as tracemalloc:
   model.eval()
   for step, batch in enumerate(lowercase_		):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device		)
    with torch.no_grad():
     A__ =       model(**lowercase_		)
    A__ =       outputs.logits.argmax(dim=-1		)
    A__,   A__ =       accelerator.gather_for_metrics((predictions, batch["labels"])		)
    metric.add_batch(
        predictions=lowercase_    ,				references=lowercase_    ,				)

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

   if checkpointing_steps == "epoch":
    A__ =       f"""epoch_{epoch}"""
    if args.output_dir is not None:
     A__ =       os.path.join(args.output_dir    ,				lowercase_		)
    accelerator.save_state(lowercase_		)
        # 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=lowercase_    ,				)

 if args.with_tracking:
  accelerator.end_training()







def 						_SCREAMING_SNAKE_CASE     (							)		->	Dict:
 A__ =       argparse.ArgumentParser(description="Simple example of training script."		)
 parser.add_argument(
     "--mixed_precision"    ,				type=lowercase_    ,				default=lowercase_    ,				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=lowercase_    ,				default=lowercase_    ,				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=lowercase_    ,				default=lowercase_    ,				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=lowercase_    ,				default="."    ,				help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,				)
 parser.add_argument(
     "--logging_dir"    ,				type=lowercase_    ,				default="logs"    ,				help="Location on where to store experiment tracking logs`"    ,				)
 parser.add_argument(
     "--model_name_or_path"    ,				type=lowercase_    ,				help="Path to pretrained model or model identifier from huggingface.co/models."    ,				required=lowercase_    ,				)
 A__ =       parser.parse_args()
 A__ =       {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(lowercase_    ,				lowercase_		)


if __name__ == "__main__":
      main()











"""simple docstring"""



from manim import *




class 			UpperCAmelCase_						(		A_							):




    def 							__magic_name__							(					self		: Any   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       Rectangle(height=0.5			,				width=0.5   )
     A__ =       Rectangle(height=0.46			,				width=0.46   ).set_stroke(width=0   )
     A__ =       Rectangle(height=0.25			,				width=0.25   )

     A__ =       [mem.copy() for i in range(6   )]
     A__ =       [mem.copy() for i in range(6   )]
     A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       VGroup(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       Text("CPU"			,				font_size=24   )
     A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
     cpu.move_to([-2.5, -0.5, 0]   )
     self.add(snake_case_   )

     A__ =       [mem.copy() for i in range(4   )]
     A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       Text("GPU"			,				font_size=24   )
     A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
     gpu.move_to([-1, -1, 0]   )
     self.add(snake_case_   )

     A__ =       [mem.copy() for i in range(6   )]
     A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )

     A__ =       Text("Model"			,				font_size=24   )
     A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
     model.move_to([3, -1.0, 0]   )
     self.add(snake_case_   )

     A__ =       []
     A__ =       []

     for i, rect in enumerate(snake_case_   ):
      rect.set_stroke(snake_case_   )

      A__ =       Rectangle(height=0.46 / 4			,				width=0.46 / 3   ).set_stroke(width=0.0   ).set_fill(snake_case_			,				opacity=0.7   )

      if i == 0:
       cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT   )			,				buff=0.02			,				direction=snake_case_   )
       cpu_target.set_x(cpu_target.get_x() + 0.1   )
      elif i == 3:
       cpu_target.next_to(model_cpu_arr[0]			,				direction=snake_case_			,				buff=0.0   )
      else:
       cpu_target.next_to(model_cpu_arr[i - 1]			,				direction=snake_case_			,				buff=0.0   )
      self.add(snake_case_   )
      model_cpu_arr.append(snake_case_   )

     self.add(*snake_case_			,				*snake_case_   )

     A__ =       [meta_mem.copy() for i in range(6   )]
     A__ =       [meta_mem.copy() for i in range(6   )]
     A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       VGroup(*snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       VGroup(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0   )
     A__ =       Text("Disk"			,				font_size=24   )
     A__ =       Group(snake_case_			,				snake_case_   ).arrange(snake_case_			,				buff=0.5			,				aligned_edge=snake_case_   )
     disk.move_to([-4.0, -1.25, 0]   )
     self.add(snake_case_			,				snake_case_   )

     A__ =       []

     for i in range(6   ):
      A__ =       fill.copy().set_fill(snake_case_			,				opacity=0.8   )
      target.move_to(disk_left_col_base[i]   ).scale(0.5   )
      cpu_disk_arr.append(snake_case_   )

     self.add(*snake_case_   )

     A__ =       Square(side_length=2.2   )
     key.move_to([-5, 2, 0]   )

     A__ =       MarkupText(
         F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""			,				font_size=18			,				)

     key_text.move_to([-5, 2.4, 0]   )

     self.add(snake_case_			,				snake_case_   )

     A__ =       MarkupText(
         F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""			,				font_size=18			,				)

     blue_text.next_to(snake_case_			,				DOWN * 2.4			,				aligned_edge=key_text.get_left()   )
     self.add(snake_case_   )

     A__ =       MarkupText(F"""The offloaded weights are all sent to the CPU."""			,				font_size=24   )
     step_a.move_to([2, 2, 0]   )

     self.play(Write(snake_case_			,				run_time=3   )   )

     for i in range(6   ):
      A__ =       cpu_disk_arr[i]
      A__ =       rect.copy().set_fill(snake_case_			,				opacity=0.8   ).scale(2.0   )
      cpa.generate_target()
      cpa.target.move_to(model_base[i]   )

      if i == 0:
       rect.set_fill(snake_case_			,				opacity=0.8   )
       rect.generate_target()
       rect.target.move_to(cpu_left_col_base[0]   ).scale(2.0   )

       self.remove(
           *snake_case_			,				*snake_case_			,				)

      else:
       rect.generate_target()
       rect.target.move_to(cpu_left_col_base[i]   ).scale(2.0   )
      self.play(MoveToTarget(snake_case_   )			,				MoveToTarget(snake_case_   )			,				model_base[i].animate.set_stroke(snake_case_   )   )
     self.play(FadeOut(snake_case_   )   )

     A__ =       MarkupText(
         F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""			,				font_size=24			,				)
     step_a.move_to([2, 2, 0]   )

     self.play(Write(snake_case_			,				run_time=3   )   )

     A__ =       []
     A__ =       []
     for i in range(6   ):
      A__ =       Arrow(start=snake_case_			,				end=snake_case_			,				color=snake_case_			,				buff=0.5   )
      a.next_to(model_base[i].get_left()			,				snake_case_			,				buff=0.2   )
      arrows.append(snake_case_   )
      animations.append(Write(snake_case_   )   )
     self.play(*snake_case_   )
     self.wait()





