



import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
    )
    from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=99,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=36,      SCREAMING_SNAKE_CASE_=6,      SCREAMING_SNAKE_CASE_=6,      SCREAMING_SNAKE_CASE_=6,      SCREAMING_SNAKE_CASE_=37,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=512,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=0.02,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=None,      )					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     parent
          UpperCamelCase   :  List[str]						     =     batch_size
          UpperCamelCase   :  Union[str, Any]						     =     seq_length
          UpperCamelCase   :  Tuple						     =     is_training
          UpperCamelCase   :  Optional[int]						     =     use_input_mask
          UpperCamelCase   :  Optional[int]						     =     use_token_type_ids
          UpperCamelCase   :  Dict						     =     use_labels
          UpperCamelCase   :  Any						     =     vocab_size
          UpperCamelCase   :  int						     =     embedding_size
          UpperCamelCase   :  int						     =     hidden_size
          UpperCamelCase   :  int						     =     num_hidden_layers
          UpperCamelCase   :  Tuple						     =     num_hidden_groups
          UpperCamelCase   :  Union[str, Any]						     =     num_attention_heads
          UpperCamelCase   :  Optional[Any]						     =     intermediate_size
          UpperCamelCase   :  int						     =     hidden_act
          UpperCamelCase   :  int						     =     hidden_dropout_prob
          UpperCamelCase   :  int						     =     attention_probs_dropout_prob
          UpperCamelCase   :  Any						     =     max_position_embeddings
          UpperCamelCase   :  Tuple						     =     type_vocab_size
          UpperCamelCase   :  Optional[int]						     =     type_sequence_label_size
          UpperCamelCase   :  str						     =     initializer_range
          UpperCamelCase   :  Optional[int]						     =     num_labels
          UpperCamelCase   :  List[Any]						     =     num_choices
          UpperCamelCase   :  Union[str, Any]						     =     scope
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     ids_tensor([self.batch_size, self.seq_length],      self.vocab_size	)

          UpperCamelCase   :  Optional[Any]						     =     None
          if self.use_input_mask:
             UpperCamelCase   :  Optional[int]						     =     random_attention_mask([self.batch_size, self.seq_length]	)

          UpperCamelCase   :  Dict						     =     None
          if self.use_token_type_ids:
             UpperCamelCase   :  List[str]						     =     ids_tensor([self.batch_size, self.seq_length],      self.type_vocab_size	)

          UpperCamelCase   :  str						     =     None
          UpperCamelCase   :  List[str]						     =     None
          UpperCamelCase   :  Tuple						     =     None
          if self.use_labels:
             UpperCamelCase   :  Dict						     =     ids_tensor([self.batch_size],      self.type_sequence_label_size	)
             UpperCamelCase   :  List[str]						     =     ids_tensor([self.batch_size, self.seq_length],      self.num_labels	)
             UpperCamelCase   :  Optional[int]						     =     ids_tensor([self.batch_size],      self.num_choices	)

          UpperCamelCase   :  List[str]						     =     self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
       def  snake_case_	(      self	)					->		Optional[Any]:
          return AlbertConfig(
              vocab_size=self.vocab_size,      hidden_size=self.hidden_size,      num_hidden_layers=self.num_hidden_layers,      num_attention_heads=self.num_attention_heads,      intermediate_size=self.intermediate_size,      hidden_act=self.hidden_act,      hidden_dropout_prob=self.hidden_dropout_prob,      attention_probs_dropout_prob=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      type_vocab_size=self.type_vocab_size,      initializer_range=self.initializer_range,      num_hidden_groups=self.num_hidden_groups,      )
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[int]:
          UpperCamelCase   :  int						     =     AlbertModel(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  Optional[int]						     =     model(_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase	)
          UpperCamelCase   :  str						     =     model(_UpperCAmelCase,      token_type_ids=_UpperCAmelCase	)
          UpperCamelCase   :  Dict						     =     model(_UpperCAmelCase	)
          self.parent.assertEqual(result.last_hidden_state.shape,      (self.batch_size, self.seq_length, self.hidden_size)	)
          self.parent.assertEqual(result.pooler_output.shape,      (self.batch_size, self.hidden_size)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		str:
          UpperCamelCase   :  Optional[Any]						     =     AlbertForPreTraining(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  str						     =     model(
              _UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase,      labels=_UpperCAmelCase,      sentence_order_label=_UpperCAmelCase,      )
          self.parent.assertEqual(result.prediction_logits.shape,      (self.batch_size, self.seq_length, self.vocab_size)	)
          self.parent.assertEqual(result.sop_logits.shape,      (self.batch_size, config.num_labels)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Any						     =     AlbertForMaskedLM(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  Dict						     =     model(_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase,      labels=_UpperCAmelCase	)
          self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.vocab_size)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  int						     =     AlbertForQuestionAnswering(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  Optional[Any]						     =     model(
              _UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase,      start_positions=_UpperCAmelCase,      end_positions=_UpperCAmelCase,      )
          self.parent.assertEqual(result.start_logits.shape,      (self.batch_size, self.seq_length)	)
          self.parent.assertEqual(result.end_logits.shape,      (self.batch_size, self.seq_length)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Union[str, Any]						     =     self.num_labels
          UpperCamelCase   :  Any						     =     AlbertForSequenceClassification(_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  Any						     =     model(_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase,      labels=_UpperCAmelCase	)
          self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Union[str, Any]						     =     self.num_labels
          UpperCamelCase   :  List[Any]						     =     AlbertForTokenClassification(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  str						     =     model(_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase,      labels=_UpperCAmelCase	)
          self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.seq_length, self.num_labels)	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          UpperCamelCase   :  Optional[int]						     =     self.num_choices
          UpperCamelCase   :  Any						     =     AlbertForMultipleChoice(config=_UpperCAmelCase	)
          model.to(_UpperCAmelCase	)
          model.eval()
          UpperCamelCase   :  Dict						     =     input_ids.unsqueeze(1	).expand(-1,      self.num_choices,      -1	).contiguous()
          UpperCamelCase   :  Union[str, Any]						     =     token_type_ids.unsqueeze(1	).expand(-1,      self.num_choices,      -1	).contiguous()
          UpperCamelCase   :  Optional[int]						     =     input_mask.unsqueeze(1	).expand(-1,      self.num_choices,      -1	).contiguous()
          UpperCamelCase   :  int						     =     model(
              _UpperCAmelCase,      attention_mask=_UpperCAmelCase,      token_type_ids=_UpperCAmelCase,      labels=_UpperCAmelCase,      )
          self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_choices)	)






       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Any						     =     self.prepare_config_and_inputs()
          (
              UpperCamelCase			
          )   :  int						     =     config_and_inputs
          UpperCamelCase   :  List[str]						     =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict






@require_torch
class     lowerCAmelCase_		(  _UpperCAmelCase	,   _UpperCAmelCase	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  (
           (
               AlbertModel,
               AlbertForPreTraining,
               AlbertForMaskedLM,
               AlbertForMultipleChoice,
               AlbertForSequenceClassification,
               AlbertForTokenClassification,
               AlbertForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       UpperCAmelCase__    : str       					=  (
           {
               "feature-extraction": AlbertModel,
               "fill-mask": AlbertForMaskedLM,
               "question-answering": AlbertForQuestionAnswering,
               "text-classification": AlbertForSequenceClassification,
               "token-classification": AlbertForTokenClassification,
               "zero-shot": AlbertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCAmelCase__    : Any       					=  True
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     super()._prepare_for_class(_UpperCAmelCase,      _UpperCAmelCase,      return_labels=_UpperCAmelCase	)

          if return_labels:
             if model_class in get_values(_UpperCAmelCase	):
                UpperCamelCase   :  Dict						     =     torch.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length),      dtype=torch.long,      device=_UpperCAmelCase	)
                UpperCamelCase   :  Optional[int]						     =     torch.zeros(
                    self.model_tester.batch_size,      dtype=torch.long,      device=_UpperCAmelCase	)
          return inputs_dict
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     AlbertModelTester(self	)
          UpperCamelCase   :  Optional[int]						     =     ConfigTester(self,      config_class=_UpperCAmelCase,      hidden_size=37	)
       def  snake_case_	(      self	)					->		Optional[int]:
          self.config_tester.run_common_tests()
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  List[Any]						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Optional[Any]						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Any						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase	)
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Tuple						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*_UpperCAmelCase	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[int]						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Optional[int]						     =     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase	)
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Optional[Any]						     =     self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCamelCase   :  Tuple						     =     type
             self.model_tester.create_and_check_model(*_UpperCAmelCase	)






       @slow
       def  snake_case_	(      self	)					->		Tuple:
          for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase   :  str						     =     AlbertModel.from_pretrained(_UpperCAmelCase	)
             self.assertIsNotNone(_UpperCAmelCase	)






@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       @slow
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     AlbertModel.from_pretrained('albert-base-v2'	)
          UpperCamelCase   :  Dict						     =     torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]	)
          UpperCamelCase   :  Optional[Any]						     =     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
          with torch.no_grad():
             UpperCamelCase   :  List[str]						     =     model(_UpperCAmelCase,      attention_mask=_UpperCAmelCase	)[0]
          UpperCamelCase   :  Any						     =     torch.Size((1, 11, 768)	)
          self.assertEqual(output.shape,      _UpperCAmelCase	)
          UpperCamelCase   :  Optional[Any]						     =     torch.tensor(
              [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]	)

          self.assertTrue(torch.allclose(output[:, 1:4, 1:4],      _UpperCAmelCase,      atol=1e-4	)	)








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 		UpperCamelCase     (       snake_case__	:   Accelerator	,					snake_case__	:   int = 16     )     ->					Dict:
   UpperCamelCase   :  str						     =     AutoTokenizer.from_pretrained('bert-base-cased'     )
   UpperCamelCase   :  str						     =     load_dataset('glue'	,					'mrpc'     )

   def tokenize_function(snake_case__	:   Dict     ):
      # 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   :  Dict						     =     tokenized_datasets.rename_column('label'	,					'labels'     )

   def collate_fn(snake_case__	:   Tuple     ):
      # 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   :  str						     =     16
      elif accelerator.mixed_precision != "no":
         UpperCamelCase   :  Dict						     =     8
      else:
         UpperCamelCase   :  Union[str, 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   :  Optional[int]						     =     DataLoader(
       tokenized_datasets['train']	,					shuffle=snake_case__	,					collate_fn=snake_case__	,					batch_size=snake_case__     )
   UpperCamelCase   :  List[Any]						     =     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

    __UpperCAmelCase	       =	mocked_dataloaders  # noqa: F811
def 		UpperCamelCase     (       snake_case__	:   str	,					snake_case__	:   List[Any]     )     ->					List[str]:
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'	,					snake_case__     ) == "1":
      UpperCamelCase   :  List[str]						     =     2
   # New Code #
   UpperCamelCase   :  Optional[int]						     =     int(args.gradient_accumulation_steps     )
   UpperCamelCase   :  List[str]						     =     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   :  Union[str, Any]						     =     config['lr']
   UpperCamelCase   :  Optional[int]						     =     int(config['num_epochs']     )
   UpperCamelCase   :  List[Any]						     =     int(config['seed']     )
   UpperCamelCase   :  List[str]						     =     int(config['batch_size']     )

   UpperCamelCase   :  Optional[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   :  Tuple						     =     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   :  Dict						     =     model.to(accelerator.device     )

   # Instantiate optimizer
   UpperCamelCase   :  List[str]						     =     AdamW(params=model.parameters()	,					lr=snake_case__     )

   # Instantiate scheduler
   UpperCamelCase   :  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   :  str						     =     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   :  Union[str, Any]						     =     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   :  Dict						     =     model(**snake_case__     )
         UpperCamelCase   :  Any						     =     outputs.logits.argmax(dim=-1     )
         UpperCamelCase     ,					UpperCamelCase   :  Any						     =     accelerator.gather_for_metrics((predictions, batch['labels'])     )
         metric.add_batch(
             predictions=snake_case__	,					references=snake_case__	,					)

      UpperCamelCase   :  List[Any]						     =     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""	,					snake_case__     )
def 		UpperCamelCase     (       )     ->					Dict:
   UpperCamelCase   :  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   :  str						     =     parser.parse_args()
   UpperCamelCase   :  Tuple						     =     {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(snake_case__	,					snake_case__     )


if __name__ == "__main__":
    main()



