



import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
     DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     DistilBertForMaskedLM,
     DistilBertForMultipleChoice,
     DistilBertForQuestionAnswering,
     DistilBertForSequenceClassification,
     DistilBertForTokenClassification,
     DistilBertModel,
 )

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=13     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=7     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Dict=True     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     str=99     ,		__SCREAMING_SNAKE_CASE							:     Any=32     ,		__SCREAMING_SNAKE_CASE							:     List[str]=5     ,		__SCREAMING_SNAKE_CASE							:     Tuple=4     ,		__SCREAMING_SNAKE_CASE							:     Tuple=37     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     str=0.1     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=512     ,		__SCREAMING_SNAKE_CASE							:     Dict=16     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=2     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.02     ,		__SCREAMING_SNAKE_CASE							:     int=3     ,		__SCREAMING_SNAKE_CASE							:     List[str]=4     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		):


           '''simple docstring'''

           __a						       =				parent
           __a						       =				batch_size
           __a						       =				seq_length
           __a						       =				is_training
           __a						       =				use_input_mask
           __a						       =				use_token_type_ids
           __a						       =				use_labels
           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				intermediate_size
           __a						       =				hidden_act
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				type_vocab_size
           __a						       =				type_sequence_label_size
           __a						       =				initializer_range
           __a						       =				num_labels
           __a						       =				num_choices
           __a						       =				scope



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size)

           __a						       =				None
           if self.use_input_mask:
               __a						       =				random_attention_mask([self.batch_size, self.seq_length])

           __a						       =				None
           __a						       =				None
           __a						       =				None
           if self.use_labels:
               __a						       =				ids_tensor([self.batch_size]     ,		self.type_sequence_label_size)
               __a						       =				ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels)
               __a						       =				ids_tensor([self.batch_size]     ,		self.num_choices)

           __a						       =				self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           return DistilBertConfig(
               vocab_size=self.vocab_size     ,		dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		hidden_dim=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		)



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a						       =				DistilBertModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size))



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				DistilBertForMaskedLM(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size))



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				DistilBertForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(
               __SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		start_positions=__SCREAMING_SNAKE_CASE     ,		end_positions=__SCREAMING_SNAKE_CASE)
           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 		_lowerCamelCase			(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				self.num_labels
           __a						       =				DistilBertForSequenceClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels))



       def 		_lowerCamelCase			(		self							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				self.num_labels
           __a						       =				DistilBertForTokenClassification(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels))



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				self.num_choices
           __a						       =				DistilBertForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a						       =				input_ids.unsqueeze(1).expand(-1     ,		self.num_choices     ,		-1).contiguous()
           __a						       =				input_mask.unsqueeze(1).expand(-1     ,		self.num_choices     ,		-1).contiguous()
           __a						       =				model(
               __SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		labels=__SCREAMING_SNAKE_CASE     ,		)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices))





       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				self.prepare_config_and_inputs()
           ((__a)       ,   (__a)       ,   (__a)       ,   (__a)       ,   (__a)       ,   (__a))						       =				config_and_inputs
           __a						       =				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
           return config, inputs_dict


@require_torch
class     _A       (	__UpperCAmelCase      ,__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Optional[int]											=						(
           (
               DistilBertModel,
               DistilBertForMaskedLM,
               DistilBertForMultipleChoice,
               DistilBertForQuestionAnswering,
               DistilBertForSequenceClassification,
               DistilBertForTokenClassification,
           )
           if is_torch_available()
           else None
       )
       UpperCamelCase__  :    List[str]											=						(
           {
               '''feature-extraction''': DistilBertModel,
               '''fill-mask''': DistilBertForMaskedLM,
               '''question-answering''': DistilBertForQuestionAnswering,
               '''text-classification''': DistilBertForSequenceClassification,
               '''token-classification''': DistilBertForTokenClassification,
               '''zero-shot''': DistilBertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       UpperCamelCase__  :    str											=						True
       UpperCamelCase__  :    List[Any]											=						True
       UpperCamelCase__  :    Dict											=						True
       UpperCamelCase__  :    Union[str, Any]											=						True



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				DistilBertModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__SCREAMING_SNAKE_CASE     ,		dim=37)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_model(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_masked_lm(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_question_answering(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_sequence_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_token_classification(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_multiple_choice(*__SCREAMING_SNAKE_CASE)



       @slow
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a						       =				DistilBertModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





       @slow
       @require_torch_gpu
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
               # BertForMultipleChoice behaves incorrectly in JIT environments.
               if model_class == DistilBertForMultipleChoice:
                   return

               __a						       =				True
               __a						       =				model_class(config=__SCREAMING_SNAKE_CASE)

               __a						       =				self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				torch.jit.trace(
                   __SCREAMING_SNAKE_CASE     ,		(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

               with tempfile.TemporaryDirectory() as tmp:
                   torch.jit.save(__SCREAMING_SNAKE_CASE     ,		os.path.join(__SCREAMING_SNAKE_CASE     ,		'''traced_model.pt'''))
                   __a						       =				torch.jit.load(os.path.join(__SCREAMING_SNAKE_CASE     ,		'''traced_model.pt''')     ,		map_location=__SCREAMING_SNAKE_CASE)
                   loaded(inputs_dict['''input_ids'''].to(__SCREAMING_SNAKE_CASE)     ,		inputs_dict['''attention_mask'''].to(__SCREAMING_SNAKE_CASE))


@require_torch
class     _A       (	unittest.TestCase    ):



       @slow
       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				DistilBertModel.from_pretrained('''distilbert-base-uncased''')
           __a						       =				torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]])
           __a						       =				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)[0]
           __a						       =				torch.Size((1, 11, 768))
           self.assertEqual(output.shape     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				torch.tensor(
               [[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]])

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






'''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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

__a: Optional[Any]          =   16
__a: Any          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Dict  =					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[Any]  =					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__				:    List[Any]  =					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__				:    str  =					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__				:    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":
   lowercase__				:    List[str]  =					16
  elif accelerator.mixed_precision != "no":
   lowercase__				:    Dict  =					8
  else:
   lowercase__				:    Optional[int]  =					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__				:    Optional[int]  =					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

  __a: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     UpperCAmelCase     ) == "1":
  lowercase__				:    Optional[int]  =					2
 # Initialize Accelerator

 # New Code #
 # We pass in "all" to `log_with` to grab all available trackers in the environment
 # Note: If using a custom `Tracker` class, should be passed in here such as:
 # >>> log_with = ["all", MyCustomTrackerClassInstance()]
 if args.with_tracking:
  lowercase__				:    Union[str, Any]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    Any  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    Optional[int]  =					int(config['''num_epochs''']     )
 lowercase__				:    Optional[Any]  =					int(config['''seed''']     )
 lowercase__				:    int  =					int(config['''batch_size''']     )
 set_seed(UpperCAmelCase     )

 lowercase__ ,							lowercase__				:    str  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					evaluate.load('''glue'''		,     '''mrpc'''     )

 # If the batch size is too big we use gradient accumulation
 lowercase__				:    Any  =					1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  lowercase__				:    Union[str, Any]  =					batch_size // MAX_GPU_BATCH_SIZE
  lowercase__				:    Any  =					MAX_GPU_BATCH_SIZE

 # 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__				:    List[str]  =					model.to(accelerator.device     )

 # Instantiate optimizer
 lowercase__				:    List[Any]  =					AdamW(params=model.parameters()		,     lr=UpperCAmelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase		,     num_warmup_steps=100		,     num_training_steps=(len(UpperCAmelCase     ) * num_epochs) // gradient_accumulation_steps		,     )

 # 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     )

 # New Code #
 # We need to initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  lowercase__				:    Optional[Any]  =					os.path.split(UpperCAmelCase     )[-1].split('''.'''     )[0]
  accelerator.init_trackers(UpperCAmelCase		,     UpperCAmelCase     )

 # Now we train the model
 for epoch in range(UpperCAmelCase     ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowercase__				:    str  =					0
  for step, batch in enumerate(UpperCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    List[str]  =					outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowercase__				:    List[str]  =					loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase     )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

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

  lowercase__				:    int  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

  # New Code #
  # To actually log, we call `Accelerator.log`
  # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': eval_metric['''accuracy'''],
           '''f1''': eval_metric['''f1'''],
           '''train_loss''': total_loss.item() / len(UpperCAmelCase     ),
           '''epoch''': epoch,
       }		,     step=UpperCAmelCase		,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
 if args.with_tracking:
  accelerator.end_training()
def 			__UpperCamelCase       (   ):
 lowercase__				:    Any  =					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.'''		,     )
 parser.add_argument('''--cpu'''		,     action='''store_true'''		,     help='''If passed, will train on the CPU.'''     )
 parser.add_argument(
     '''--with_tracking'''		,     action='''store_true'''		,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,     )
 parser.add_argument(
     '''--project_dir'''		,     type=UpperCAmelCase		,     default='''logs'''		,     help='''Location on where to store experiment tracking logs` and relevent project information'''		,     )
 lowercase__				:    str  =					parser.parse_args()
 lowercase__				:    Tuple  =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(UpperCAmelCase		,     UpperCAmelCase     )


if __name__ == "__main__":
  main()

