'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
      import torch




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=14					,      lowerCAmelCase__=7					,      lowerCAmelCase__=True					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=True					,      lowerCAmelCase__=99					,      lowerCAmelCase__=32					,      lowerCAmelCase__=5					,      lowerCAmelCase__=4					,      lowerCAmelCase__=37					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=512					,      lowerCAmelCase__=0.02					,      )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :int       =parent
        _UpperCamelCase  :int       =batch_size
        _UpperCamelCase  :str       =seq_length
        _UpperCamelCase  :int       =is_training
        _UpperCamelCase  :Dict       =use_input_mask
        _UpperCamelCase  :List[str]       =use_token_type_ids
        _UpperCamelCase  :int       =use_labels
        _UpperCamelCase  :Tuple       =vocab_size
        _UpperCamelCase  :Optional[Any]       =hidden_size
        _UpperCamelCase  :str       =num_hidden_layers
        _UpperCamelCase  :List[str]       =num_attention_heads
        _UpperCamelCase  :List[str]       =intermediate_size
        _UpperCamelCase  :Any       =hidden_act
        _UpperCamelCase  :Optional[Any]       =hidden_dropout_prob
        _UpperCamelCase  :int       =attention_probs_dropout_prob
        _UpperCamelCase  :str       =max_position_embeddings
        _UpperCamelCase  :Any       =initializer_range
        _UpperCamelCase  :Optional[Any]       =None
        _UpperCamelCase  :Optional[Any]       =vocab_size - 1
        _UpperCamelCase  :Optional[Any]       =vocab_size - 1
        _UpperCamelCase  :Tuple       =vocab_size - 1



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :Dict       =ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size     )

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

        _UpperCamelCase  :Optional[Any]       =GPTaConfig(
            vocab_size=self.vocab_size					,      n_embd=self.hidden_size					,      n_layer=self.num_hidden_layers					,      n_head=self.num_attention_heads					,      n_positions=self.max_position_embeddings					,      use_cache=lowerCAmelCase__					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      )

        return (config, input_ids, input_mask)



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Any       =self.prepare_config_and_inputs()
        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Any       =config_and_inputs
        _UpperCamelCase  :Union[str, Any]       ={"""input_ids""": input_ids, """attention_mask""": attention_mask}
        return config, inputs_dict



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.prepare_config_and_inputs()

        _UpperCamelCase  :int       =floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        _UpperCamelCase  :Union[str, Any]       =ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2     )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =20
        _UpperCamelCase  :Union[str, Any]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :Any       =jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype="""i4"""     )

        _UpperCamelCase  :Any       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )
        _UpperCamelCase  :Tuple       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :str       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :List[str]       =model(
            input_ids[:, -1:]					,      attention_mask=lowerCAmelCase__					,      past_key_values=outputs_cache.past_key_values					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =model(lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :Dict       =20
        _UpperCamelCase  :List[str]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]					,      axis=-1					,      )

        _UpperCamelCase  :int       =model.init_cache(input_ids.shape[0]					,      lowerCAmelCase__     )
        _UpperCamelCase  :List[Any]       =jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1     )[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)     )

        _UpperCamelCase  :Dict       =model(
            input_ids[:, :-1]					,      attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )
        _UpperCamelCase  :Any       =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :str       =model(
            input_ids[:, -1:]					,      past_key_values=outputs_cache.past_key_values					,      attention_mask=lowerCAmelCase__					,      position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Tuple       =model(lowerCAmelCase__					,      attention_mask=lowerCAmelCase__     )

        _UpperCamelCase  :int       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




@require_flax
class    lowerCamelCase__  (   __snake_case       ,				__snake_case       ,				unittest.TestCase     ):
   __UpperCAmelCase           =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
   __UpperCAmelCase           =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()



   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        _UpperCamelCase  :int       =FlaxGPTaModelTester(self     )



   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase       ,      _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   @slow
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =GPTaTokenizer.from_pretrained("""gpt2"""					,      pad_token="""</s>"""					,      padding_side="""left"""     )
        _UpperCamelCase  :Dict       =tokenizer(["""Hello this is a long string""", """Hey"""]					,      return_tensors="""np"""					,      padding=lowerCAmelCase__					,      truncation=lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""     )
        _UpperCamelCase  :int       =False
        _UpperCamelCase  :Optional[int]       =model.config.eos_token_id

        _UpperCamelCase  :Any       =jax.jit(model.generate     )

        _UpperCamelCase  :Dict       =jit_generate(inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]     ).sequences

        _UpperCamelCase  :Optional[int]       =tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =[
            """Hello this is a long string of words. I'm going to start with the first one.\n""",
            """Hey, I'm not sure if I'm going to be able to do""",
        ]

        self.assertListEqual(lowerCAmelCase__					,      lowerCAmelCase__     )



   @is_pt_flax_cross_test
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :str       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                  # prepare inputs
                  _UpperCamelCase  :Optional[Any]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Optional[int]       ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  _UpperCamelCase  :Union[str, Any]       =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  _UpperCamelCase  :Dict       =getattr(lowerCAmelCase__					,      lowerCAmelCase__     )

                  _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =pt_inputs["""input_ids"""].shape
                  _UpperCamelCase  :List[str]       =np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)     )
                  for batch_idx, start_index in enumerate(lowerCAmelCase__     ):
                       _UpperCamelCase  :Tuple       =0
                       _UpperCamelCase  :Dict       =1
                       _UpperCamelCase  :Tuple       =0
                       _UpperCamelCase  :str       =1
                  _UpperCamelCase  :Dict       =pt_model_class(lowerCAmelCase__     ).eval()
                  _UpperCamelCase  :int       =model_class(lowerCAmelCase__					,      dtype=jnp.floataa     )

                  _UpperCamelCase  :Any       =convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      lowerCAmelCase__     )
                  _UpperCamelCase  :List[str]       =fx_state

                  with torch.no_grad():
                       _UpperCamelCase  :Optional[int]       =pt_model(**lowerCAmelCase__     ).to_tuple()

                  _UpperCamelCase  :List[Any]       =fx_model(**lowerCAmelCase__     ).to_tuple()
                  self.assertEqual(len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       pt_model.save_pretrained(lowerCAmelCase__     )
                       _UpperCamelCase  :Any       =model_class.from_pretrained(lowerCAmelCase__					,      from_pt=lowerCAmelCase__     )

                  _UpperCamelCase  :Optional[int]       =fx_model_loaded(**lowerCAmelCase__     ).to_tuple()
                  self.assertEqual(
                      len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output_loaded, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output_loaded[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )



   @is_pt_flax_cross_test
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[str]       =self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                  # prepare inputs
                  _UpperCamelCase  :Optional[int]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Tuple       ={k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

                  # load corresponding PyTorch class
                  _UpperCamelCase  :str       =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                  _UpperCamelCase  :List[Any]       =getattr(lowerCAmelCase__					,      lowerCAmelCase__     )

                  _UpperCamelCase  :Union[str, Any]       =pt_model_class(lowerCAmelCase__     ).eval()
                  _UpperCamelCase  :Dict       =model_class(lowerCAmelCase__					,      dtype=jnp.floataa     )

                  _UpperCamelCase  :Optional[Any]       =load_flax_weights_in_pytorch_model(lowerCAmelCase__					,      fx_model.params     )
                  _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =pt_inputs["""input_ids"""].shape
                  _UpperCamelCase  :Tuple       =np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)     )
                  for batch_idx, start_index in enumerate(lowerCAmelCase__     ):
                       _UpperCamelCase  :List[Any]       =0
                       _UpperCamelCase  :Union[str, Any]       =1
                       _UpperCamelCase  :List[str]       =0
                       _UpperCamelCase  :List[Any]       =1

                  # make sure weights are tied in PyTorch
                  pt_model.tie_weights()

                  with torch.no_grad():
                       _UpperCamelCase  :Tuple       =pt_model(**lowerCAmelCase__     ).to_tuple()

                  _UpperCamelCase  :Tuple       =fx_model(**lowerCAmelCase__     ).to_tuple()
                  self.assertEqual(len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )

                  with tempfile.TemporaryDirectory() as tmpdirname:
                       fx_model.save_pretrained(lowerCAmelCase__     )
                       _UpperCamelCase  :Dict       =pt_model_class.from_pretrained(lowerCAmelCase__					,      from_flax=lowerCAmelCase__     )

                  with torch.no_grad():
                       _UpperCamelCase  :str       =pt_model_loaded(**lowerCAmelCase__     ).to_tuple()

                  self.assertEqual(
                      len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )					,      """Output lengths differ between Flax and PyTorch"""     )
                  for fx_output, pt_output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2     )




   @slow
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase  :Optional[int]       =model_class_name.from_pretrained("""gpt2"""					,      from_pt=lowerCAmelCase__     )
             _UpperCamelCase  :Union[str, Any]       =model(np.ones((1, 1)     )     )
             self.assertIsNotNone(lowerCAmelCase__     )



'''simple docstring'''

import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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
#
########################################################################

_UpperCAmelCase					:			List[Any]				     =					3_2

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase = 1_6):
						__lowerCAmelCase											=					AutoTokenizer.from_pretrained('''bert-base-cased''')
						__lowerCAmelCase											=					load_dataset('''glue''',					'''mrpc''')

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

						def collate_fn(lowerCamelCase):
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__lowerCAmelCase											=					1_6
												elif accelerator.mixed_precision != "no":
																		__lowerCAmelCase											=					8
												else:
																		__lowerCAmelCase											=					None

												return tokenizer.pad(
												    lowerCamelCase,					padding='''longest''',					pad_to_multiple_of=lowerCamelCase,					return_tensors='''pt''',					)

						# Instantiate dataloaders.
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''train'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)
						__lowerCAmelCase											=					DataLoader(
						    tokenized_datasets['''validation'''],					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase,					batch_size=lowerCamelCase)

						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					:			Optional[int]				     =					mocked_dataloaders  # noqa: F811

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS''',					lowerCamelCase) == "1":
												__lowerCAmelCase											=					2
						# Initialize accelerator
						__lowerCAmelCase											=					Accelerator(cpu=args.cpu,					mixed_precision=args.mixed_precision)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__lowerCAmelCase											=					config['''lr''']
						__lowerCAmelCase											=					int(config['''num_epochs'''])
						__lowerCAmelCase											=					int(config['''seed'''])
						__lowerCAmelCase											=					int(config['''batch_size'''])

						__lowerCAmelCase											=					evaluate.load('''glue''',					'''mrpc''')

						# New Code #
						# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
						# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
						# half each time. From this, we can calculate the number of gradient accumulation steps needed
						# and modify the Accelerator object as a result
						@find_executable_batch_size(starting_batch_size=int(lowerCamelCase))
						def inner_training_loop(lowerCamelCase):
												# Since we need to modify the outside accelerator object, we need to bring it
												# to the local scope
												nonlocal accelerator

												# We can calculate the number of gradient accumulation steps based on the current
												# batch size vs the starting batch size
												__lowerCAmelCase											=					observed_batch_size // batch_size

												# And then set it in the Accelerator directly:
												__lowerCAmelCase											=					num_gradient_accumulation_steps

												# Next we need to free all of the stored model references in the Accelerator each time
												accelerator.free_memory()

												# And set the seed so our results are reproducable each reset
												set_seed(lowerCamelCase)

												# Instantiate the model (we build the model here so that the seed also control new weights initialization)
												__lowerCAmelCase											=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',					return_dict=lowerCamelCase)

												# 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).
												__lowerCAmelCase											=					model.to(accelerator.device)

												# Instantiate optimizer
												__lowerCAmelCase											=					AdamW(params=model.parameters(),					lr=lowerCamelCase)
												__lowerCAmelCase							,       __lowerCAmelCase											=					get_dataloaders(lowerCamelCase,					lowerCamelCase)

												# Instantiate scheduler
												__lowerCAmelCase											=					get_linear_schedule_with_warmup(
												    optimizer=lowerCamelCase,					num_warmup_steps=1_0_0,					num_training_steps=(len(lowerCamelCase) * 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.
												__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					accelerator.prepare(
												    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase)

												# Now we train the model
												for epoch in range(lowerCamelCase):
																		model.train()
																		for step, batch in enumerate(lowerCamelCase):
																								# And perform gradient accumulation
																								with accelerator.accumulate(lowerCamelCase):
																														# We could avoid this line since we set the accelerator with `device_placement=True`.
																														batch.to(accelerator.device)
																														__lowerCAmelCase											=					model(**lowerCamelCase)
																														__lowerCAmelCase											=					outputs.loss
																														accelerator.backward(lowerCamelCase)
																														optimizer.step()
																														lr_scheduler.step()
																														optimizer.zero_grad()

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
						inner_training_loop()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser(description='''Simple example of training script.''')
						parser.add_argument(
						    '''--mixed_precision''',					type=lowerCamelCase,					default=lowerCamelCase,					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.''')
						__lowerCAmelCase											=					parser.parse_args()
						# New Code #
						# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
						__lowerCAmelCase											=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
						training_function(lowerCamelCase,					lowerCamelCase)


if __name__ == "__main__":
	main()

