






"""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
#
########################################################################


_lowerCAmelCase	:	Any				=      16
_lowerCAmelCase	:	Any				=      32


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Accelerator			,					snake_case       : int = 16				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    List[str]							=							AutoTokenizer.from_pretrained("bert-base-cased"				)
     UpperCAmelCase__						:    Dict							=							load_dataset("glue"			,					"mrpc"				)

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

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

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

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

     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[Any]				=      mocked_dataloaders  # noqa: F811


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple			,					snake_case       : Union[str, Any]				)->		int:






     '''simple docstring'''



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

     UpperCAmelCase__						:    Union[str, Any]							=							evaluate.load("glue"			,					"mrpc"				)

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

     # 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).
     UpperCAmelCase__						:    Optional[Any]							=							model.to(accelerator.device				)

     # Instantiate optimizer
     UpperCAmelCase__						:    Optional[int]							=							AdamW(params=model.parameters()			,					lr=snake_case				)

     # Instantiate scheduler
     UpperCAmelCase__						:    Optional[Any]							=							get_linear_schedule_with_warmup(
         optimizer=snake_case			,					num_warmup_steps=100			,					num_training_steps=(len(snake_case				) * 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.
     UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							accelerator.prepare(
         snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)

     # Now we train the model
     for epoch in range(snake_case				):
          model.train()
          with LocalSGD(
              accelerator=snake_case			,					model=snake_case			,					local_sgd_steps=snake_case			,					enabled=local_sgd_steps is not None				) as local_sgd:
               for step, batch in enumerate(snake_case				):
                    # 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(snake_case				):
                         UpperCAmelCase__						:    int							=							model(**snake_case				)
                         UpperCAmelCase__						:    Tuple							=							output.loss
                         accelerator.backward(snake_case				)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

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

          UpperCAmelCase__						:    Tuple							=							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'			,					snake_case				)


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[int]:






     '''simple docstring'''



     UpperCAmelCase__						:    List[Any]							=							argparse.ArgumentParser(description="Simple example of training script."				)
     parser.add_argument(
         "--mixed_precision"			,					type=snake_case			,					default=snake_case			,					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=snake_case			,					default=1			,					help="The number of minibatches to be ran before gradients are accumulated."			,					)
     parser.add_argument(
         "--local_sgd_steps"			,					type=snake_case			,					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."				)
     UpperCAmelCase__						:    Tuple							=							parser.parse_args()
     UpperCAmelCase__						:    List[Any]							=							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(snake_case			,					snake_case				)


if __name__ == "__main__":
 main()










"""simple docstring"""



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


