






'''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
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
#
########################################################################


UpperCamelCase__ =			16
UpperCamelCase__ =			32




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = 16							):


   """simple docstring"""




   lowercase_	:    Tuple           =     AutoTokenizer.from_pretrained("bert-base-cased"							)
   lowercase_	:    Optional[int]           =     load_dataset("glue"						,		"mrpc"							)

   def tokenize_function(_UpperCamelCase							):
      # max_length=None => use the model max length (it's actually the default)
      lowercase_	:    Optional[int]           =     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():
      lowercase_	:    str           =     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
   lowercase_	:    Optional[Any]           =     tokenized_datasets.rename_column("label"						,		"labels"							)

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

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

   # Instantiate dataloaders.
   lowercase_	:    str           =     DataLoader(
       tokenized_datasets["train"]						,		shuffle=_UpperCamelCase						,		collate_fn=_UpperCamelCase						,		batch_size=_UpperCamelCase							)
   lowercase_	:    List[Any]           =     DataLoader(
       tokenized_datasets["validation"]						,		shuffle=_UpperCamelCase						,		collate_fn=_UpperCamelCase						,		batch_size=_UpperCamelCase							)

   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

 UpperCamelCase__ =			mocked_dataloaders  # noqa: F811




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   if os.environ.get("TESTING_MOCKED_DATALOADERS"						,		_UpperCamelCase							) == "1":
      lowercase_	:    int           =     2
   # New Code #
   lowercase_	:    str           =     int(args.gradient_accumulation_steps							)
   lowercase_	:    int           =     int(args.local_sgd_steps							)
   # Initialize accelerator
   lowercase_	:    List[str]           =     Accelerator(
       cpu=args.cpu						,		mixed_precision=args.mixed_precision						,		gradient_accumulation_steps=_UpperCamelCase							)
   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
   lowercase_	:    Any           =     config["lr"]
   lowercase_	:    int           =     int(config["num_epochs"]							)
   lowercase_	:    str           =     int(config["seed"]							)
   lowercase_	:    Dict           =     int(config["batch_size"]							)

   lowercase_	:    Optional[int]           =     evaluate.load("glue"						,		"mrpc"							)

   set_seed(_UpperCamelCase							)
   lowercase_    ,		lowercase_	:    Optional[int]           =     get_dataloaders(_UpperCamelCase						,		_UpperCamelCase							)
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowercase_	:    Optional[int]           =     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).
   lowercase_	:    Optional[Any]           =     model.to(accelerator.device							)

   # Instantiate optimizer
   lowercase_	:    Union[str, Any]           =     AdamW(params=model.parameters()						,		lr=_UpperCamelCase							)

   # Instantiate scheduler
   lowercase_	:    str           =     get_linear_schedule_with_warmup(
       optimizer=_UpperCamelCase						,		num_warmup_steps=100						,		num_training_steps=(len(_UpperCamelCase							) * 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.
   lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    List[str]           =     accelerator.prepare(
       _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)

   # Now we train the model
   for epoch in range(_UpperCamelCase							):
      model.train()
      with LocalSGD(
          accelerator=_UpperCamelCase						,		model=_UpperCamelCase						,		local_sgd_steps=_UpperCamelCase						,		enabled=local_sgd_steps is not None							) as local_sgd:
         for step, batch in enumerate(_UpperCamelCase							):
            # 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(_UpperCamelCase							):
               lowercase_	:    Union[str, Any]           =     model(**_UpperCamelCase							)
               lowercase_	:    Dict           =     output.loss
               accelerator.backward(_UpperCamelCase							)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      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():
            lowercase_	:    Union[str, Any]           =     model(**_UpperCamelCase							)
         lowercase_	:    List[str]           =     outputs.logits.argmax(dim=-1							)
         lowercase_    ,		lowercase_	:    List[str]           =     accelerator.gather_for_metrics((predictions, batch["labels"])							)
         metric.add_batch(
             predictions=_UpperCamelCase						,		references=_UpperCamelCase						,		)

      lowercase_	:    Optional[Any]           =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    str           =     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."						,		)
   # New Code #
   parser.add_argument(
       "--gradient_accumulation_steps"						,		type=_UpperCamelCase						,		default=1						,		help="The number of minibatches to be ran before gradients are accumulated."						,		)
   parser.add_argument(
       "--local_sgd_steps"						,		type=_UpperCamelCase						,		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."							)
   lowercase_	:    Optional[int]           =     parser.parse_args()
   lowercase_	:    List[str]           =     {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_UpperCamelCase						,		_UpperCamelCase							)


if __name__ == "__main__":
 main()












'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   return LambdaLR(_UpperCamelCase						,		lambda _UpperCamelCase							: 1						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1.0						,		_UpperCamelCase							)							)
      return 1.0

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_    ,		lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

   def create_rules_function(_UpperCamelCase						,		_UpperCamelCase							):
      def rule_func(_UpperCamelCase							) -> float:
         lowercase_	:    Optional[Any]           =     sorted(rules_dict.keys()							)
         for i, sorted_step in enumerate(_UpperCamelCase							):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      return max(
          0.0						,		float(num_training_steps - current_step							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[str]           =     float(current_step - num_warmup_steps							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)
      return max(0.0						,		0.5 * (1.0 + math.cos(math.pi * float(_UpperCamelCase							) * 2.0 * progress							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    Dict           =     float(current_step - num_warmup_steps							) / float(max(1						,		num_training_steps - num_warmup_steps							)							)
      if progress >= 1.0:
         return 0.0
      return max(0.0						,		0.5 * (1.0 + math.cos(math.pi * ((float(_UpperCamelCase							) * progress) % 1.0)							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     optimizer.defaults["lr"]
   if not (lr_init > lr_end):
      raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""							)

   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(_UpperCamelCase						,		step_rules=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""							)

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(_UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""							)

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		num_cycles=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		power=_UpperCamelCase						,		last_epoch=_UpperCamelCase						,		)

   return schedule_func(
       _UpperCamelCase						,		num_warmup_steps=_UpperCamelCase						,		num_training_steps=_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




