


'''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
#
# 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)
#
# 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
#
########################################################################


a_  :    Union[str, Any]      =			16
a_  :    List[str]      =			32





def __snake_case (				UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 # Initialize accelerator
 if args.with_tracking:
  lowerCamelCase_								=  Accelerator(
      cpu=args.cpu ,    mixed_precision=args.mixed_precision ,    log_with="all" ,    project_dir=args.project_dir		)
 else:
  lowerCamelCase_								=  Accelerator(cpu=args.cpu ,    mixed_precision=args.mixed_precision		)

 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
 # 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"]		)

 # We need to initialize the trackers we use, and also store our configuration
 if args.with_tracking:
  lowerCamelCase_								=  os.path.split(UpperCAmelCase_		)[-1].split("."		)[0]
  accelerator.init_trackers(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  AutoTokenizer.from_pretrained("bert-base-cased"		)
 lowerCamelCase_								=  load_dataset("glue" ,    "mrpc"		)
 lowerCamelCase_								=  evaluate.load("glue" ,    "mrpc"		)

 def tokenize_function(UpperCAmelCase_						:						Tuple		):
  # max_length=None => use the model max length (it's actually the default)
  lowerCamelCase_								=  tokenizer(examples["sentence1"] ,    examples["sentence2"] ,    truncation=UpperCAmelCase_ ,    max_length=UpperCAmelCase_		)
  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(
      UpperCAmelCase_ ,    batched=UpperCAmelCase_ ,    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"		)

 # 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

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

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

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

 set_seed(UpperCAmelCase_		)

 # 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=UpperCAmelCase_		)

 # 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=UpperCAmelCase_		)

 # Instantiate scheduler
 lowerCamelCase_								=  get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase_ ,    num_warmup_steps=100 ,    num_training_steps=(len(UpperCAmelCase_		) * 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(
     UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 # 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

 # 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		)
   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(UpperCAmelCase_		)[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(UpperCAmelCase_		)
   resume_step -= starting_epoch * len(UpperCAmelCase_		)

    # Now we train the model
 for epoch in range(UpperCAmelCase_ ,    UpperCAmelCase_		):
  model.train()
  if args.with_tracking:
   lowerCamelCase_								=  0
  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(UpperCAmelCase_ ,    UpperCAmelCase_		)
   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(UpperCAmelCase_		):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device		)
   lowerCamelCase_								=  model(**UpperCAmelCase_		)
   lowerCamelCase_								=  outputs.loss
   lowerCamelCase_								=  loss / gradient_accumulation_steps
   # We keep track of the loss at each epoch
   if args.with_tracking:
    total_loss += loss.detach().float()
   accelerator.backward(UpperCAmelCase_		)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1

   if isinstance(UpperCAmelCase_ ,    UpperCAmelCase_		):
    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 ,    UpperCAmelCase_		)
     accelerator.save_state(UpperCAmelCase_		)

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

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

  if checkpointing_steps == "epoch":
   lowerCamelCase_								=  F'''epoch_{epoch}'''
   if args.output_dir is not None:
    lowerCamelCase_								=  os.path.join(args.output_dir ,    UpperCAmelCase_		)
   accelerator.save_state(UpperCAmelCase_		)

 if args.with_tracking:
  accelerator.end_training()





def __snake_case (				):
 lowerCamelCase_								=  argparse.ArgumentParser(description="Simple example of training script."		)
 parser.add_argument(
     "--mixed_precision" ,    type=UpperCAmelCase_ ,    default=UpperCAmelCase_ ,    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=UpperCAmelCase_ ,    default=UpperCAmelCase_ ,    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=UpperCAmelCase_ ,    default=UpperCAmelCase_ ,    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=UpperCAmelCase_ ,    default="." ,    help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,    )
 parser.add_argument(
     "--project_dir" ,    type=UpperCAmelCase_ ,    default="logs" ,    help="Location on where to store experiment tracking logs` and relevent project information" ,    )
 lowerCamelCase_								=  parser.parse_args()
 lowerCamelCase_								=  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(UpperCAmelCase_ ,    UpperCAmelCase_		)


if __name__ == "__main__":
     main()







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_  :    Union[str, Any]      =			logging.get_logger(__name__)

a_  :    int      =			{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "nystromformer"






  def __init__(   self      ,  UpperCamelCase=3_0000      ,  UpperCamelCase=768      ,  UpperCamelCase=12      ,  UpperCamelCase=12      ,  UpperCamelCase=3072      ,  UpperCamelCase="gelu_new"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=510      ,  UpperCamelCase=2      ,  UpperCamelCase=64      ,  UpperCamelCase=64      ,  UpperCamelCase=65      ,  UpperCamelCase=False      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-5      ,  UpperCamelCase=1      ,  UpperCamelCase=0      ,  UpperCamelCase=2      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  segment_means_seq_len
   lowerCamelCase_								=  num_landmarks
   lowerCamelCase_								=  conv_kernel_size
   lowerCamelCase_								=  inv_coeff_init_option
   lowerCamelCase_								=  layer_norm_eps
   super().__init__(pad_token_id=UpperCamelCase      ,  bos_token_id=UpperCamelCase      ,  eos_token_id=UpperCamelCase      ,  **UpperCamelCase					)



