






import os
import tempfile
import unittest

from transformers import FlaubertConfig, 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 (
				    FlaubertForMultipleChoice,
				    FlaubertForQuestionAnswering,
				    FlaubertForQuestionAnsweringSimple,
				    FlaubertForSequenceClassification,
				    FlaubertForTokenClassification,
				    FlaubertModel,
				    FlaubertWithLMHeadModel,
				)
				from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a=13		,       a=7		,       a=True		,       a=True		,       a=True		,       a=True		,       a=True		,       a=False		,       a=False		,       a=False		,       a=2		,       a=99		,       a=0		,       a=32		,       a=5		,       a=4		,       a=0.1		,       a=0.1		,       a=512		,       a=12		,       a=2		,       a=0.02		,       a=3		,       a=4		,       a="last"		,       a=None		,       a=None		,       )   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			parent
						SCREAMING_SNAKE_CASE							=			batch_size
						SCREAMING_SNAKE_CASE							=			seq_length
						SCREAMING_SNAKE_CASE							=			is_training
						SCREAMING_SNAKE_CASE							=			use_input_lengths
						SCREAMING_SNAKE_CASE							=			use_token_type_ids
						SCREAMING_SNAKE_CASE							=			use_labels
						SCREAMING_SNAKE_CASE							=			gelu_activation
						SCREAMING_SNAKE_CASE							=			sinusoidal_embeddings
						SCREAMING_SNAKE_CASE							=			causal
						SCREAMING_SNAKE_CASE							=			asm
						SCREAMING_SNAKE_CASE							=			n_langs
						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			n_special
						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			hidden_dropout_prob
						SCREAMING_SNAKE_CASE							=			attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE							=			max_position_embeddings
						SCREAMING_SNAKE_CASE							=			type_vocab_size
						SCREAMING_SNAKE_CASE							=			type_sequence_label_size
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			num_labels
						SCREAMING_SNAKE_CASE							=			num_choices
						SCREAMING_SNAKE_CASE							=			summary_type
						SCREAMING_SNAKE_CASE							=			use_proj
						SCREAMING_SNAKE_CASE							=			scope






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.vocab_size)
						SCREAMING_SNAKE_CASE							=			random_attention_mask([self.batch_size, self.seq_length])

						SCREAMING_SNAKE_CASE							=			None
						if self.use_input_lengths:
									SCREAMING_SNAKE_CASE							=			(
									    ids_tensor([self.batch_size]		,       vocab_size=2) + self.seq_length - 2
									)  # small variation of seq_length

						SCREAMING_SNAKE_CASE							=			None
						if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.n_langs)

						SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			None
						if self.use_labels:
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size]		,       self.type_sequence_label_size)
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size, self.seq_length]		,       self.num_labels)
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size]		,       2).float()
									SCREAMING_SNAKE_CASE							=			ids_tensor([self.batch_size]		,       self.num_choices)

						SCREAMING_SNAKE_CASE							=			self.get_config()

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						return FlaubertConfig(
						    vocab_size=self.vocab_size		,       n_special=self.n_special		,       emb_dim=self.hidden_size		,       n_layers=self.num_hidden_layers		,       n_heads=self.num_attention_heads		,       dropout=self.hidden_dropout_prob		,       attention_dropout=self.attention_probs_dropout_prob		,       gelu_activation=self.gelu_activation		,       sinusoidal_embeddings=self.sinusoidal_embeddings		,       asm=self.asm		,       causal=self.causal		,       n_langs=self.n_langs		,       max_position_embeddings=self.max_position_embeddings		,       initializer_range=self.initializer_range		,       summary_type=self.summary_type		,       use_proj=self.use_proj		,       )






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaubertModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE							=			model(a		,       lengths=a		,       langs=a)
						SCREAMING_SNAKE_CASE							=			model(a		,       langs=a)
						SCREAMING_SNAKE_CASE							=			model(a)
						self.parent.assertEqual(result.last_hidden_state.shape		,       (self.batch_size, self.seq_length, self.hidden_size))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							str:
						SCREAMING_SNAKE_CASE							=			FlaubertWithLMHeadModel(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a		,       token_type_ids=a		,       labels=a)
						self.parent.assertEqual(result.loss.shape		,       ())
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.seq_length, self.vocab_size))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Any:
						SCREAMING_SNAKE_CASE							=			FlaubertForQuestionAnsweringSimple(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a)

						SCREAMING_SNAKE_CASE							=			model(a		,       start_positions=a		,       end_positions=a)
						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  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaubertForQuestionAnswering(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a)

						SCREAMING_SNAKE_CASE							=			model(
						    a		,       start_positions=a		,       end_positions=a		,       cls_index=a		,       is_impossible=a		,       p_mask=a		,       )

						SCREAMING_SNAKE_CASE							=			model(
						    a		,       start_positions=a		,       end_positions=a		,       cls_index=a		,       is_impossible=a		,       )

						((SCREAMING_SNAKE_CASE)					,    )							=			result_with_labels.to_tuple()

						SCREAMING_SNAKE_CASE							=			model(a		,       start_positions=a		,       end_positions=a)

						((SCREAMING_SNAKE_CASE)					,    )							=			result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape		,       ())
						self.parent.assertEqual(result.start_top_log_probs.shape		,       (self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(result.start_top_index.shape		,       (self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(
						    result.end_top_log_probs.shape		,       (self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(
						    result.end_top_index.shape		,       (self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(result.cls_logits.shape		,       (self.batch_size,))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							int:
						SCREAMING_SNAKE_CASE							=			FlaubertForSequenceClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE							=			model(a)
						SCREAMING_SNAKE_CASE							=			model(a		,       labels=a)

						self.parent.assertEqual(result.loss.shape		,       ())
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size))






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.num_labels
						SCREAMING_SNAKE_CASE							=			FlaubertForTokenClassification(a)
						model.to(a)
						model.eval()

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






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       a		,       )   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.num_choices
						SCREAMING_SNAKE_CASE							=			FlaubertForMultipleChoice(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE							=			input_ids.unsqueeze(1).expand(-1		,       self.num_choices		,       -1).contiguous()
						SCREAMING_SNAKE_CASE							=			token_type_ids.unsqueeze(1).expand(-1		,       self.num_choices		,       -1).contiguous()
						SCREAMING_SNAKE_CASE							=			input_mask.unsqueeze(1).expand(-1		,       self.num_choices		,       -1).contiguous()
						SCREAMING_SNAKE_CASE							=			model(
						    a		,       attention_mask=a		,       token_type_ids=a		,       labels=a		,       )
						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.num_choices))







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    (
						    SCREAMING_SNAKE_CASE
						)					,    
						)							=			config_and_inputs
						SCREAMING_SNAKE_CASE							=			{
						    'input_ids': input_ids,
						    'token_type_ids': token_type_ids,
						    'lengths': input_lengths,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict



@require_torch
class 		_snake_case	(	A__ ,     A__ ,     unittest.TestCase       ):
			_lowercase		:     Any        =				(
			    (
			        FlaubertModel,
			        FlaubertWithLMHeadModel,
			        FlaubertForQuestionAnswering,
			        FlaubertForQuestionAnsweringSimple,
			        FlaubertForSequenceClassification,
			        FlaubertForTokenClassification,
			        FlaubertForMultipleChoice,
			    )
			    if is_torch_available()
			    else ()
			)
			_lowercase		:     Any        =				(
			    {
			        '''feature-extraction''': FlaubertModel,
			        '''fill-mask''': FlaubertWithLMHeadModel,
			        '''question-answering''': FlaubertForQuestionAnsweringSimple,
			        '''text-classification''': FlaubertForSequenceClassification,
			        '''token-classification''': FlaubertForTokenClassification,
			        '''zero-shot''': FlaubertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a		,       a)   ->							Optional[int]:
						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith('Fast')
						):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

						return False






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a=False)   ->							Any:
						SCREAMING_SNAKE_CASE							=			super()._prepare_for_class(a		,       a		,       return_labels=a)

						if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
												SCREAMING_SNAKE_CASE							=			torch.zeros(
												    self.model_tester.batch_size		,       dtype=torch.long		,       device=a)
												SCREAMING_SNAKE_CASE							=			torch.zeros(
												    self.model_tester.batch_size		,       dtype=torch.long		,       device=a)

						return inputs_dict






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			FlaubertModelTester(self)
						SCREAMING_SNAKE_CASE							=			ConfigTester(self		,       config_class=a		,       emb_dim=37)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						self.config_tester.run_common_tests()






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_model(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[Any]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_lm_head(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_simple_qa(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_qa(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_sequence_classif(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_token_classif(*a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_multiple_choice(*a)






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE							=			FlaubertModel.from_pretrained(a)
									self.assertIsNotNone(a)







			@slow
			@require_torch_gpu
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
												return

									SCREAMING_SNAKE_CASE							=			True
									SCREAMING_SNAKE_CASE							=			model_class(config=a)

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

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



@require_torch
class 		_snake_case	(	unittest.TestCase       ):






			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			FlaubertModel.from_pretrained('flaubert/flaubert_base_cased')
						SCREAMING_SNAKE_CASE							=			torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
						with torch.no_grad():
									SCREAMING_SNAKE_CASE							=			model(a)[0]
						SCREAMING_SNAKE_CASE							=			torch.Size((1, 11, 768))
						self.assertEqual(output.shape		,       a)
						SCREAMING_SNAKE_CASE							=			torch.tensor(
						    [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]])

						self.assertTrue(torch.allclose(output[:, :3, :3]		,       a		,       atol=1E-4))












"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


lowerCAmelCase__    						=			16
lowerCAmelCase__    						=			32





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase = 16       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained('bert-base-cased'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    load_dataset('glue',						'mrpc'       )

				def tokenize_function(lowerCamelCase       ):
								# max_length=None => use the model max length (it's actually the default)
								_SCREAMING_SNAKE_CASE   :		int       		=    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():
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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
				_SCREAMING_SNAKE_CASE   :		Dict       		=    tokenized_datasets.rename_column('label',						'labels'       )

				def collate_fn(lowerCamelCase       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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":
												_SCREAMING_SNAKE_CASE   :		List[str]       		=    16
								elif accelerator.mixed_precision != "no":
												_SCREAMING_SNAKE_CASE   :		Dict       		=    8
								else:
												_SCREAMING_SNAKE_CASE   :		str       		=    None

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

				# Instantiate dataloaders.
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    DataLoader(
				    tokenized_datasets['train'],						shuffle=lowerCamelCase,						collate_fn=lowerCamelCase,						batch_size=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    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

			lowerCAmelCase__    						=			mocked_dataloaders  # noqa: F811





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

				_SCREAMING_SNAKE_CASE   :		Tuple       		=    evaluate.load('glue',						'mrpc'       )

				# New Code #
				# We now can define an inner training loop function. It should take a batch size as the only parameter,
				# and build the dataloaders in there.
				# It also gets our decorator
				@find_executable_batch_size(starting_batch_size=lowerCamelCase       )
				def inner_training_loop(lowerCamelCase       ):
								# And now just move everything below under this function
								# We need to bring in the Accelerator object from earlier
								nonlocal accelerator
								# And reset all of its attributes that could hold onto any memory:
								accelerator.free_memory()

								# Then we can declare the model, optimizer, and everything else:
								set_seed(lowerCamelCase       )

								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    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).
								_SCREAMING_SNAKE_CASE   :		Any       		=    model.to(accelerator.device       )

								# Instantiate optimizer
								_SCREAMING_SNAKE_CASE   :		str       		=    AdamW(params=model.parameters(),						lr=lowerCamelCase       )
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_dataloaders(lowerCamelCase,						lowerCamelCase       )

								# Instantiate scheduler
								_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    get_linear_schedule_with_warmup(
								    optimizer=lowerCamelCase,						num_warmup_steps=100,						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.
								_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    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       ):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device       )
																_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(**lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[str]       		=    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():
																				_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(**lowerCamelCase       )
																_SCREAMING_SNAKE_CASE   :		List[Any]       		=    outputs.logits.argmax(dim=-1       )
																_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    accelerator.gather_for_metrics((predictions, batch['labels'])       )
																metric.add_batch(
																    predictions=lowerCamelCase,						references=lowerCamelCase,						)

												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		str       		=    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.'       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    parser.parse_args()
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(lowerCamelCase,						lowerCamelCase       )


if __name__ == "__main__":
			main()

