





'''simple docstring'''






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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

A__       :     str          =16
A__       :     int          =32
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase = 16						):





      """simple docstring"""


      _lowerCAmelCase							  =							AutoTokenizer.from_pretrained("""bert-base-cased"""						)
      _lowerCAmelCase							  =							load_dataset("""glue"""							,			"""mrpc"""						)

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

      def collate_fn(lowerCAmelCase						):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            _lowerCAmelCase							  =							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":
                  _lowerCAmelCase							  =							16
            elif accelerator.mixed_precision != "no":
                  _lowerCAmelCase							  =							8
            else:
                  _lowerCAmelCase							  =							None

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

      # Instantiate dataloaders.
      _lowerCAmelCase							  =							DataLoader(
          tokenized_datasets["""train"""]							,			shuffle=lowerCAmelCase							,			collate_fn=lowerCAmelCase							,			batch_size=lowerCAmelCase						)
      _lowerCAmelCase							  =							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

      A__       :     Any          =mocked_dataloaders  # noqa: F811
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			lowerCAmelCase						) == "1":
            _lowerCAmelCase							  =							2
      # Initialize accelerator
      _lowerCAmelCase							  =							Accelerator(cpu=args.cpu							,			mixed_precision=args.mixed_precision						)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      _lowerCAmelCase							  =							config["""lr"""]
      _lowerCAmelCase							  =							int(config["""num_epochs"""]						)
      _lowerCAmelCase							  =							int(config["""seed"""]						)
      _lowerCAmelCase							  =							int(config["""batch_size"""]						)

      # New Code #
      # Parse out whether we are saving every epoch or after a certain number of batches
      if hasattr(args.checkpointing_steps							,			"""isdigit"""						):
            if args.checkpointing_steps == "epoch":
                  _lowerCAmelCase							  =							args.checkpointing_steps
            elif args.checkpointing_steps.isdigit():
                  _lowerCAmelCase							  =							int(args.checkpointing_steps						)
            else:
                  raise ValueError(
                      f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."						)
      else:
            _lowerCAmelCase							  =							None

      set_seed(lowerCAmelCase						)

      _lowerCAmelCase  ,							_lowerCAmelCase							  =							get_dataloaders(lowerCAmelCase							,			lowerCAmelCase						)
      _lowerCAmelCase							  =							evaluate.load("""glue"""							,			"""mrpc"""						)

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

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _lowerCAmelCase							  =							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).
      _lowerCAmelCase							  =							model.to(accelerator.device						)

      # Instantiate optimizer
      _lowerCAmelCase							  =							AdamW(params=model.parameters()							,			lr=lowerCAmelCase						)

      # Instantiate scheduler
      _lowerCAmelCase							  =							get_linear_schedule_with_warmup(
          optimizer=lowerCAmelCase							,			num_warmup_steps=1_00							,			num_training_steps=(len(lowerCAmelCase						) * num_epochs) // gradient_accumulation_steps							,			)

      # 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							  =							accelerator.prepare(
          lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						)

      # New Code #
      # We need to keep track of how many total steps we have iterated over
      _lowerCAmelCase							  =							0
      # We also need to keep track of the stating epoch so files are named properly
      _lowerCAmelCase							  =							0

      # We need to load the checkpoint back in before training here with `load_state`
      # The total number of epochs is adjusted based on where the state is being loaded from,
      # as we assume continuation of the same training script
      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						)
                  _lowerCAmelCase							  =							os.path.basename(args.resume_from_checkpoint						)
            else:
                  # Get the most recent checkpoint
                  _lowerCAmelCase							  =							[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
                  dirs.sort(key=os.path.getctime						)
                  _lowerCAmelCase							  =							dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
            # Extract `epoch_{i}` or `step_{i}`
            _lowerCAmelCase							  =							os.path.splitext(lowerCAmelCase						)[0]

            if "epoch" in training_difference:
                  _lowerCAmelCase							  =							int(training_difference.replace("""epoch_"""							,			""""""						)						) + 1
                  _lowerCAmelCase							  =							None
            else:
                  _lowerCAmelCase							  =							int(training_difference.replace("""step_"""							,			""""""						)						)
                  _lowerCAmelCase							  =							resume_step // len(lowerCAmelCase						)
                  resume_step -= starting_epoch * len(lowerCAmelCase						)

    # Now we train the model
      for epoch in range(lowerCAmelCase							,			lowerCAmelCase						):
            model.train()
            # New Code #
            if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                  # We need to skip steps until we reach the resumed step
                  _lowerCAmelCase							  =							accelerator.skip_first_batches(lowerCAmelCase							,			lowerCAmelCase						)
                  overall_step += resume_step
            else:
                  # After the first iteration though, we need to go back to the original dataloader
                  _lowerCAmelCase							  =							train_dataloader
            for step, batch in enumerate(lowerCAmelCase						):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device						)
                  _lowerCAmelCase							  =							model(**lowerCAmelCase						)
                  _lowerCAmelCase							  =							outputs.loss
                  _lowerCAmelCase							  =							loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase						)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()
                  # New Code #
                  overall_step += 1

                  # New Code #
                  # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                  # These are saved to folders named `step_{overall_step}`
                  # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                  # If mixed precision was used, will also save a "scalar.bin" file
                  if isinstance(lowerCAmelCase							,			lowerCAmelCase						):
                        _lowerCAmelCase							  =							f"step_{overall_step}"
                        if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                    _lowerCAmelCase							  =							os.path.join(args.output_dir							,			lowerCAmelCase						)
                              accelerator.save_state(lowerCAmelCase						)

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

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

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `epoch_{epoch}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if checkpointing_steps == "epoch":
                  _lowerCAmelCase							  =							f"epoch_{epoch}"
                  if args.output_dir is not None:
                        _lowerCAmelCase							  =							os.path.join(args.output_dir							,			lowerCAmelCase						)
                  accelerator.save_state(lowerCAmelCase						)
def      UpperCamelCase__   (	):





      """simple docstring"""


      _lowerCAmelCase							  =							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."""							,			)
      parser.add_argument("""--cpu"""							,			action="""store_true"""							,			help="""If passed, will train on the CPU."""						)
      parser.add_argument(
          """--checkpointing_steps"""							,			type=lowerCAmelCase							,			default=lowerCAmelCase							,			help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""							,			)
      parser.add_argument(
          """--output_dir"""							,			type=lowerCAmelCase							,			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=lowerCAmelCase							,			default=lowerCAmelCase							,			help="""If the training should continue from a checkpoint folder."""							,			)
      _lowerCAmelCase							  =							parser.parse_args()
      _lowerCAmelCase							  =							{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(lowerCAmelCase							,			lowerCAmelCase						)


if __name__ == "__main__":
      main()








'''simple docstring'''






import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 				UpperCAmelCase  (    nn.Linear     ):




      def __init__(    self    :				str    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				bool = True    ,    __snake_case    :				str = "constant"    ,    __snake_case    :				float = 0.0    ,    __snake_case    :				str = "topK"    ,    )       ->   int:
            super(__snake_case    ,    self						).__init__(in_features=__snake_case    ,    out_features=__snake_case    ,    bias=__snake_case						)
            assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
            _lowerCAmelCase							  =							pruning_method

            if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                  _lowerCAmelCase							  =							mask_scale
                  _lowerCAmelCase							  =							mask_init
                  _lowerCAmelCase							  =							nn.Parameter(torch.empty(self.weight.size()						)						)
                  self.init_mask()




      def        lowercase__					(    self    :				Any						)       ->   str:
            if self.mask_init == "constant":
                  init.constant_(self.mask_scores    ,    val=self.mask_scale						)
            elif self.mask_init == "uniform":
                  init.uniform_(self.mask_scores    ,    a=-self.mask_scale    ,    b=self.mask_scale						)
            elif self.mask_init == "kaiming":
                  init.kaiming_uniform_(self.mask_scores    ,    a=math.sqrt(5						)						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				torch.tensor    ,    __snake_case    :				float						)       ->   Union[str, Any]:
            # Get the mask
            if self.pruning_method == "topK":
                  _lowerCAmelCase							  =							TopKBinarizer.apply(self.mask_scores    ,    __snake_case						)
            elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                  _lowerCAmelCase							  =							"""sigmoied""" in self.pruning_method
                  _lowerCAmelCase							  =							ThresholdBinarizer.apply(self.mask_scores    ,    __snake_case    ,    __snake_case						)
            elif self.pruning_method == "magnitude":
                  _lowerCAmelCase							  =							MagnitudeBinarizer.apply(self.weight    ,    __snake_case						)
            elif self.pruning_method == "l0":
                  _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							-0.1, 1.1, 2 / 3
                  if self.training:
                        _lowerCAmelCase							  =							torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01    ,    0.99_99						)
                        _lowerCAmelCase							  =							torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                  else:
                        _lowerCAmelCase							  =							torch.sigmoid(self.mask_scores						)
                  _lowerCAmelCase							  =							s * (r - l) + l
                  _lowerCAmelCase							  =							s_bar.clamp(min=0.0    ,    max=1.0						)
            # Mask weights with computed mask
            _lowerCAmelCase							  =							mask * self.weight
            # Compute output (linear layer) with masked weights
            return nn.functional.linear(__snake_case    ,    __snake_case    ,    self.bias						)






