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
#
########################################################################


SCREAMING_SNAKE_CASE :	Optional[int]							     =		16
SCREAMING_SNAKE_CASE :	Optional[int]							     =		32



def   __A		(			_A							,				_A = 16					):



 """simple docstring"""


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

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

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

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

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

 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

  SCREAMING_SNAKE_CASE :	Optional[int]							     =		mocked_dataloaders  # noqa: F811



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if os.environ.get("TESTING_MOCKED_DATALOADERS"							,				_A					) == "1":
  __a       =  2
 # New Code #
 __a       =  int(args.gradient_accumulation_steps					)
 __a       =  int(args.local_sgd_steps					)
 # Initialize accelerator
 __a       =  Accelerator(
     cpu=args.cpu							,				mixed_precision=args.mixed_precision							,				gradient_accumulation_steps=_A					)
 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       =  config['''lr''']
 __a       =  int(config["num_epochs"]					)
 __a       =  int(config["seed"]					)
 __a       =  int(config["batch_size"]					)

 __a       =  evaluate.load("glue"							,				"mrpc"					)

 set_seed(_A					)
 __a       =  get_dataloaders(_A							,				_A					)
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __a       =  AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,				return_dict=_A					)

 # 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       =  model.to(accelerator.device					)

 # Instantiate optimizer
 __a       =  AdamW(params=model.parameters()							,				lr=_A					)

 # Instantiate scheduler
 __a       =  get_linear_schedule_with_warmup(
     optimizer=_A							,				num_warmup_steps=100							,				num_training_steps=(len(_A					) * 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       =  accelerator.prepare(
     _A							,				_A							,				_A							,				_A							,				_A					)

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

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

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



def   __A		(			):



 """simple docstring"""


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


if __name__ == "__main__":
  main()



import unittest

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


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


