





import argparse
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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)
#
# 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				:   Union[str, Any]						       =						16
_lowerCAmelCase				:   Union[str, Any]						       =						32

def   __snake_case    (       _lowerCAmelCase			:			Accelerator					,					_lowerCAmelCase			:			int = 16						)			->    Dict:
   A_       :					Dict        =      AutoTokenizer.from_pretrained("bert-base-cased"						)
   A_       :					Optional[int]        =      load_dataset("glue"					,					"mrpc"						)

   def tokenize_function(_lowerCAmelCase			:			Optional[Any]						):
      # max_length=None => use the model max length (it's actually the default)
      A_       :					Optional[Any]        =      tokenizer(examples["sentence1"]					,					examples["sentence2"]					,					truncation=_lowerCAmelCase					,					max_length=_lowerCAmelCase						)
      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_       :					Optional[Any]        =      datasets.map(
          _lowerCAmelCase					,					batched=_lowerCAmelCase					,					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_       :					int        =      tokenized_datasets.rename_column("label"					,					"labels"						)

   def collate_fn(_lowerCAmelCase			:			Optional[Any]						):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_       :					Union[str, Any]        =      128 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_       :					Optional[int]        =      16
      elif accelerator.mixed_precision != "no":
         A_       :					Optional[int]        =      8
      else:
         A_       :					List[str]        =      None

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

   # Instantiate dataloaders.
   A_       :					Union[str, Any]        =      DataLoader(
       tokenized_datasets["train"]					,					shuffle=_lowerCAmelCase					,					collate_fn=_lowerCAmelCase					,					batch_size=_lowerCAmelCase						)
   A_       :					Any        =      DataLoader(
       tokenized_datasets["validation"]					,					shuffle=_lowerCAmelCase					,					collate_fn=_lowerCAmelCase					,					batch_size=_lowerCAmelCase						)

   return train_dataloader, eval_dataloader


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

 _lowerCAmelCase				:   List[str]						       =						mocked_dataloaders  # noqa: F811

def   __snake_case    (       _lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			int						)			->    int:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"					,					_lowerCAmelCase						) == "1":
      A_       :					Tuple        =      2
   # New Code #
   A_       :					Any        =      int(args.gradient_accumulation_steps						)
   A_       :					Optional[Any]        =      int(args.local_sgd_steps						)
   # Initialize accelerator
   A_       :					List[str]        =      Accelerator(
       cpu=args.cpu					,					mixed_precision=args.mixed_precision					,					gradient_accumulation_steps=_lowerCAmelCase						)
   if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
      raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_       :					Any        =      config["lr"]
   A_       :					str        =      int(config["num_epochs"]						)
   A_       :					str        =      int(config["seed"]						)
   A_       :					Tuple        =      int(config["batch_size"]						)

   A_       :					str        =      evaluate.load("glue"					,					"mrpc"						)

   set_seed(_lowerCAmelCase						)
   A_   ,    A_       :					str        =      get_dataloaders(_lowerCAmelCase					,					_lowerCAmelCase						)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_       :					Optional[int]        =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"					,					return_dict=_lowerCAmelCase						)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   A_       :					Dict        =      model.to(accelerator.device						)

   # Instantiate optimizer
   A_       :					Optional[Any]        =      AdamW(params=model.parameters()					,					lr=_lowerCAmelCase						)

   # Instantiate scheduler
   A_       :					List[Any]        =      get_linear_schedule_with_warmup(
       optimizer=_lowerCAmelCase					,					num_warmup_steps=100					,					num_training_steps=(len(_lowerCAmelCase						) * num_epochs)					,					)

   # 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.
   A_   ,    A_   ,    A_   ,    A_   ,    A_       :					Any        =      accelerator.prepare(
       _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

   # Now we train the model
   for epoch in range(_lowerCAmelCase						):
      model.train()
      with LocalSGD(
          accelerator=_lowerCAmelCase					,					model=_lowerCAmelCase					,					local_sgd_steps=_lowerCAmelCase					,					enabled=local_sgd_steps is not None						) as local_sgd:
         for step, batch in enumerate(_lowerCAmelCase						):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device						)
            # New code #
            # We use the new `accumulate` context manager to perform gradient accumulation
            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
            with accelerator.accumulate(_lowerCAmelCase						):
               A_       :					List[str]        =      model(**_lowerCAmelCase						)
               A_       :					Tuple        =      output.loss
               accelerator.backward(_lowerCAmelCase						)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(_lowerCAmelCase						):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device						)
         with torch.no_grad():
            A_       :					str        =      model(**_lowerCAmelCase						)
         A_       :					List[str]        =      outputs.logits.argmax(dim=-1						)
         A_   ,    A_       :					Any        =      accelerator.gather_for_metrics((predictions, batch["labels"])						)
         metric.add_batch(
             predictions=_lowerCAmelCase					,					references=_lowerCAmelCase					,					)

      A_       :					int        =      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:"					,					_lowerCAmelCase						)

def   __snake_case    (       )			->    str:
   A_       :					List[str]        =      argparse.ArgumentParser(description="Simple example of training script."						)
   parser.add_argument(
       "--mixed_precision"					,					type=_lowerCAmelCase					,					default=_lowerCAmelCase					,					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."					,					)
   # New Code #
   parser.add_argument(
       "--gradient_accumulation_steps"					,					type=_lowerCAmelCase					,					default=1					,					help="The number of minibatches to be ran before gradients are accumulated."					,					)
   parser.add_argument(
       "--local_sgd_steps"					,					type=_lowerCAmelCase					,					default=8					,					help="Number of local SGD steps or None to disable local SGD"						)
   parser.add_argument("--cpu"					,					action="store_true"					,					help="If passed, will train on the CPU."						)
   A_       :					List[Any]        =      parser.parse_args()
   A_       :					List[str]        =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_lowerCAmelCase					,					_lowerCAmelCase						)


if __name__ == "__main__":
 main()







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser

def   __snake_case    (       _lowerCAmelCase			:			Optional[int]=None					,					_lowerCAmelCase			:			List[Any]=None						)			->    Optional[Any]:
   return field(default_factory=lambda: default					,					metadata=_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  field(
         metadata={'''help''': '''The csv file to plot.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Disable logarithmic scale when plotting'''}     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={
             '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
         }     ,					)
     __UpperCamelCase            =  field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}     ,					)
     __UpperCamelCase            =  list_field(
         default=lowerCamelCase__     ,					metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}				)




def   __snake_case    (       _lowerCAmelCase			:			int						)			->    Optional[Any]:
   try:
      int(_lowerCAmelCase						)
      return True
   except ValueError:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    Union[str, Any]:
   try:
      float(_lowerCAmelCase						)
      return True
   except ValueError:
      return False


class        __magic_name__	:





     """simple docstring"""



     def __init__(				self							:Any      ,   snake_case							:List[Any]	):





        '''simple docstring'''




        A_       :					Any        =      args
        A_       :					Dict        =      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}	)

        with open(self.args.csv_file      ,   newline=""	) as csv_file:
           A_       :					Union[str, Any]        =      csv.DictReader(snake_case	)
           for row in reader:
              A_       :					Any        =      row["model"]
              self.result_dict[model_name]["bsz"].append(int(row["batch_size"]	)	)
              self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]	)	)
              if can_convert_to_int(row["result"]	):
                 # value is not None
                 A_       :					List[Any]        =      int(row["result"]	)
              elif can_convert_to_float(row["result"]	):
                 # value is not None
                 A_       :					Optional[Any]        =      float(row["result"]	)





     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        A_   ,    A_       :					List[str]        =      plt.subplots()
        A_       :					List[str]        =      "Time usage" if self.args.is_time else "Memory usage"
        A_       :					Optional[Any]        =      title_str + " for training" if self.args.is_train else title_str + " for inference"

        if not self.args.no_log_scale:
           # set logarithm scales
           ax.set_xscale("log"	)
           ax.set_yscale("log"	)

        for axis in [ax.xaxis, ax.yaxis]:
           axis.set_major_formatter(ScalarFormatter()	)

        for model_name_idx, model_name in enumerate(self.result_dict.keys()	):
           A_       :					int        =      sorted(set(self.result_dict[model_name]["bsz"]	)	)
           A_       :					List[str]        =      sorted(set(self.result_dict[model_name]["seq_len"]	)	)
           A_       :					Dict        =      self.result_dict[model_name]["result"]

           ((A_)   ,    (A_))       :					Optional[Any]        =      (
               (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
           )

           A_       :					List[Any]        =      (
               model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
           )

           for inner_loop_value in inner_loop_array:
              if self.args.plot_along_batch:
                 A_       :					Dict        =      np.asarray(
                     [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,   dtype=snake_case      ,   )
              else:
                 A_       :					Union[str, Any]        =      np.asarray(
                     [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,   dtype=np.floataa      ,   )

              ((A_)   ,    (A_))       :					Optional[Any]        =      (
                  ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
              )

              A_       :					List[Any]        =      np.asarray(snake_case      ,   snake_case	)[: len(snake_case	)]
              plt.scatter(
                  snake_case      ,   snake_case      ,   label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"	)
              plt.plot(snake_case      ,   snake_case      ,   "--"	)

           title_str += f" {label_model_name} vs."

        A_       :					Optional[int]        =      title_str[:-4]
        A_       :					List[str]        =      "Time in s" if self.args.is_time else "Memory in MB"

        # plot
        plt.title(snake_case	)
        plt.xlabel(snake_case	)
        plt.ylabel(snake_case	)
        plt.legend()

        if self.args.figure_png_file is not None:
           plt.savefig(self.args.figure_png_file	)
        else:
           plt.show()




def   __snake_case    (       )			->    Any:
   A_       :					Any        =      HfArgumentParser(_lowerCAmelCase						)
   A_       :					str        =      parser.parse_args_into_dataclasses()[0]
   A_       :					Optional[Any]        =      Plot(args=_lowerCAmelCase						)
   plot.plot()


if __name__ == "__main__":
 main()
