





import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


lowercase				:	Dict      						=  1_6
lowercase				:	List[Any]      						=  3_2



def A_     (		A__		,				A__ = 16		,				A__ = "bert-base-cased"    )  ->		Dict:
       a__						:     List[str]			=    AutoTokenizer.from_pretrained(A__    )
       a__						:     Union[str, Any]			=    load_dataset('glue'		,				'mrpc'    )

       def tokenize_function(A__    ):
              # max_length=None => use the model max length (it's actually the default)
              a__						:     Tuple			=    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
       a__						:     List[str]			=    datasets.map(
           A__		,				batched=A__		,				remove_columns=['idx', 'sentence1', 'sentence2']		,				load_from_cache_file=A__    )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       a__						:     List[str]			=    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.
              if accelerator.distributed_type == DistributedType.TPU:
                     return tokenizer.pad(A__		,				padding='max_length'		,				max_length=128		,				return_tensors='pt'    )
              return tokenizer.pad(A__		,				padding='longest'		,				return_tensors='pt'    )

       # Instantiate dataloaders.
       a__						:     Dict			=    DataLoader(
           tokenized_datasets['train']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )
       a__						:     Tuple			=    DataLoader(
           tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       return train_dataloader, eval_dataloader



def A_     (		A__		,				A__    )  ->		List[str]:
       # Initialize accelerator
       a__						:     Any			=    Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       a__						:     List[Any]			=    config['lr']
       a__						:     int			=    int(config['num_epochs']    )
       a__						:     Optional[Any]			=    int(config['seed']    )
       a__						:     Optional[int]			=    int(config['batch_size']    )
       a__						:     Optional[int]			=    args.model_name_or_path

       set_seed(A__    )
       a__			,							a__						:     str			=    get_dataloaders(A__		,				A__		,				A__    )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       a__						:     Tuple			=    AutoModelForSequenceClassification.from_pretrained(A__		,				return_dict=A__    )

       # Instantiate optimizer
       a__						:     str			=    (
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       a__						:     int			=    optimizer_cls(params=model.parameters()		,				lr=A__    )

       if accelerator.state.deepspeed_plugin is not None:
              a__						:     List[str]			=    accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              a__						:     Dict			=    1
       a__						:     Optional[int]			=    (len(A__    ) * num_epochs) // gradient_accumulation_steps

       # Instantiate scheduler
       if (
           accelerator.state.deepspeed_plugin is None
           or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
       ):
              a__						:     Optional[Any]			=    get_linear_schedule_with_warmup(
                  optimizer=A__		,				num_warmup_steps=0		,				num_training_steps=A__		,				)
       else:
              a__						:     List[str]			=    DummyScheduler(A__		,				total_num_steps=A__		,				warmup_num_steps=0    )

       # 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__			,							a__			,							a__			,							a__			,							a__						:     Union[str, Any]			=    accelerator.prepare(
           A__		,				A__		,				A__		,				A__		,				A__    )

       # We need to keep track of how many total steps we have iterated over
       a__						:     Dict			=    0
       # We also need to keep track of the stating epoch so files are named properly
       a__						:     Optional[Any]			=    0

       # Now we train the model
       a__						:     List[Any]			=    evaluate.load('glue'		,				'mrpc'    )
       a__						:     Optional[int]			=    0
       a__						:     Any			=    {}
       for epoch in range(A__		,				A__    ):
              model.train()
              for step, batch in enumerate(A__    ):
                     a__						:     Union[str, Any]			=    model(**A__    )
                     a__						:     List[Any]			=    outputs.loss
                     a__						:     Dict			=    loss / gradient_accumulation_steps
                     accelerator.backward(A__    )
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     overall_step += 1

              model.eval()
              a__						:     int			=    0
              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__						:     Union[str, Any]			=    model(**A__    )
                     a__						:     Dict			=    outputs.logits.argmax(dim=-1    )
                     # It is slightly faster to call this once, than multiple times
                     a__			,							a__						:     str			=    accelerator.gather(
                         (predictions, batch['labels'])    )  # If we are in a multiprocess environment, the last batch has duplicates
                     if accelerator.use_distributed:
                            if step == len(A__    ) - 1:
                                   a__						:     int			=    predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                                   a__						:     Union[str, Any]			=    references[: len(eval_dataloader.dataset    ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=A__		,				references=A__		,				)

              a__						:     Optional[int]			=    metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(F'epoch {epoch}:'		,				A__    )
              a__						:     Optional[int]			=    eval_metric['accuracy']

              if best_performance < eval_metric["accuracy"]:
                     a__						:     Any			=    eval_metric['accuracy']

       if args.performance_lower_bound is not None:
              assert (
                  args.performance_lower_bound <= best_performance
              ), F'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir		,				'all_results.json'    )		,				'w'    ) as f:
                     json.dump(A__		,				A__    )



def A_     (		)  ->		Tuple:
       a__						:     int			=    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
       parser.add_argument(
           '--model_name_or_path'		,				type=A__		,				default='bert-base-cased'		,				help='Path to pretrained model or model identifier from huggingface.co/models.'		,				required=A__		,				)
       parser.add_argument(
           '--output_dir'		,				type=A__		,				default='.'		,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,				)
       parser.add_argument(
           '--performance_lower_bound'		,				type=A__		,				default=A__		,				help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'		,				)
       parser.add_argument(
           '--num_epochs'		,				type=A__		,				default=3		,				help='Number of train epochs.'		,				)
       a__						:     Optional[Any]			=    parser.parse_args()
       a__						:     List[Any]			=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(A__		,				A__    )


if __name__ == "__main__":
    main()









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer





@require_tf
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          self.assertEqual(len(lowercase) ,  len(lowercase))
          for a, b in zip(lowercase ,  lowercase):
                 self.assertAlmostEqual(lowercase ,  lowercase ,  delta=lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    GradientAccumulator()
          accumulator([tf.constant([1.0, 2.0])])
          accumulator([tf.constant([-2.0, 1.0])])
          accumulator([tf.constant([-1.0, 2.0])])
          with self.assertRaises(lowercase):
                 accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
          self.assertEqual(accumulator.step ,  3)
          self.assertEqual(len(accumulator.gradients) ,  1)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [-2.0, 5.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [0.0, 0.0] ,  tol=1e-2)

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    None
          ops.enable_eager_execution_internal()
          a__						:     Optional[int]			=    tf.config.list_physical_devices('CPU')
          if len(lowercase) == 1:
                 tf.config.set_logical_device_configuration(
                     physical_devices[0] ,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
          a__						:     int			=    tf.config.list_logical_devices(device_type='CPU')
          a__						:     Union[str, Any]			=    tf.distribute.MirroredStrategy(devices=devices[:2])

          with strategy.scope():
                 a__						:     str			=    GradientAccumulator()
                 a__						:     Tuple			=    tf.Variable([4.0, 3.0])
                 a__			,							a__						:     Tuple			=    create_optimizer(5e-5 ,  10 ,  5)
                 a__						:     Tuple			=    tf.Variable([0.0, 0.0] ,  trainable=lowercase)

          def accumulate_on_replica(lowercase):
                 accumulator([gradient])

          def apply_on_replica():
                 optimizer.apply_gradients(list(zip(accumulator.gradients ,  [variable])))

          @tf.function
          def accumulate(lowercase ,  lowercase):
                 with strategy.scope():
                        a__						:     Union[str, Any]			=    strategy.experimental_local_results(lowercase)
                        local_variables[0].assign(lowercase)
                        local_variables[1].assign(lowercase)
                        strategy.run(lowercase ,  args=(gradient_placeholder,))

          @tf.function
          def apply_grad():
                 with strategy.scope():
                        strategy.run(lowercase)

          def _check_local_values(lowercase ,  lowercase):
                 a__						:     Union[str, Any]			=    strategy.experimental_local_results(accumulator._gradients[0])
                 self.assertListAlmostEqual(values[0].value() ,  lowercase ,  tol=1e-2)
                 self.assertListAlmostEqual(values[1].value() ,  lowercase ,  tol=1e-2)

          accumulate([1.0, 2.0] ,  [-1.0, 1.0])
          accumulate([3.0, -1.0] ,  [-1.0, -1.0])
          accumulate([-2.0, 2.0] ,  [3.0, -2.0])
          self.assertEqual(accumulator.step ,  3)
          _check_local_values([2.0, 3.0] ,  [1.0, -2.0])
          apply_grad()
          self.assertListAlmostEqual(variable.value() ,  [4.0, 3.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          _check_local_values([0.0, 0.0] ,  [0.0, 0.0])


