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
# and perform 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		:       str									=     16
__UpperCamelCase		:       Dict									=     32
def       A	(    _lowercase		,  _lowercase = 16						):
						SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoTokenizer.from_pretrained('''bert-base-cased'''						)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							load_dataset('''glue'''		,  '''mrpc'''						)

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

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

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE							:    Any							=							DataLoader(
						    tokenized_datasets['''train''']		,  shuffle=_lowercase		,  collate_fn=_lowercase		,  batch_size=_lowercase						)
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							DataLoader(
						    tokenized_datasets['''validation''']		,  shuffle=_lowercase		,  collate_fn=_lowercase		,  batch_size=_lowercase						)

						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[Any]									=     mocked_dataloaders  # noqa: F811
def       A	(    _lowercase		,  _lowercase						):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,  _lowercase						) == "1":
												SCREAMING_SNAKE_CASE							:    int							=							2
						# New Code #
						SCREAMING_SNAKE_CASE							:    int							=							int(args.gradient_accumulation_steps						)
						# Initialize accelerator
						SCREAMING_SNAKE_CASE							:    int							=							Accelerator(
						    cpu=args.cpu		,  mixed_precision=args.mixed_precision		,  gradient_accumulation_steps=_lowercase						)
						if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
												raise NotImplementedError(
												    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						SCREAMING_SNAKE_CASE							:    List[Any]							=							config['''lr''']
						SCREAMING_SNAKE_CASE							:    Any							=							int(config['''num_epochs''']						)
						SCREAMING_SNAKE_CASE							:    Dict							=							int(config['''seed''']						)
						SCREAMING_SNAKE_CASE							:    Any							=							int(config['''batch_size''']						)

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							evaluate.load('''glue'''		,  '''mrpc'''						)

						set_seed(_lowercase						)
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							get_dataloaders(_lowercase		,  _lowercase						)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''		,  return_dict=_lowercase						)

						# 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							:    str							=							model.to(accelerator.device						)

						# Instantiate optimizer
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							AdamW(params=model.parameters()		,  lr=_lowercase						)

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE							:    int							=							get_linear_schedule_with_warmup(
						    optimizer=_lowercase		,  num_warmup_steps=100		,  num_training_steps=(len(_lowercase						) * 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							:    Dict							=							accelerator.prepare(
						    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase		,  _lowercase						)

						# Now we train the model
						for epoch in range(_lowercase						):
												model.train()
												for step, batch in enumerate(_lowercase						):
																		# 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(_lowercase						):
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model(**_lowercase						)
																								SCREAMING_SNAKE_CASE							:    Any							=							output.loss
																								accelerator.backward(_lowercase						)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												model.eval()
												for step, batch in enumerate(_lowercase						):
																		# 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							:    Any							=							model(**_lowercase						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							outputs.logits.argmax(dim=-1						)
																		SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    int							=							accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
																		metric.add_batch(
																		    predictions=_lowercase		,  references=_lowercase		,  )

												SCREAMING_SNAKE_CASE							:    List[Any]							=							metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"""epoch {epoch}:"""		,  _lowercase						)
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							argparse.ArgumentParser(description='''Simple example of training script.'''						)
						parser.add_argument(
						    '''--mixed_precision'''		,  type=_lowercase		,  default=_lowercase		,  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=_lowercase		,  default=1		,  help='''The number of minibatches to be ran before gradients are accumulated.'''		,  )
						parser.add_argument('''--cpu'''		,  action='''store_true'''		,  help='''If passed, will train on the CPU.'''						)
						SCREAMING_SNAKE_CASE							:    List[Any]							=							parser.parse_args()
						SCREAMING_SNAKE_CASE							:    Any							=							{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(_lowercase		,  _lowercase						)


if __name__ == "__main__":
					main()






import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
					import tensorflow as tf

					from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments





@require_tf
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												for model_result in results.values():
																		for batch_size, sequence_length in zip(model_result['''bs''']    ,		model_result['''ss''']						):
																								SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model_result['''result'''][batch_size][sequence_length]
																								self.assertIsNotNone(UpperCamelCase__						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sgugger/tiny-distilbert-classification'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		only_pretrain_model=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Any						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    str							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    int							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    str							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		[config]						)
												SCREAMING_SNAKE_CASE							:    str							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_train_result						)
												self.check_results_dict_not_empty(results.memory_train_result						)


						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							'''patrickvonplaten/t5-tiny-random'''
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							AutoConfig.from_pretrained(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Dict							=							TensorFlowBenchmark(UpperCamelCase__    ,		configs=[config]						)
												SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						@unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''						)						) == 0    ,		'''Cannot do xla on CPU.'''						)
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							'''sshleifer/tiny-gpt2'''
												SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
												    models=[MODEL_ID]    ,		training=UpperCamelCase__    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		use_xla=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
												SCREAMING_SNAKE_CASE							:    Any							=							TensorFlowBenchmark(UpperCamelCase__						)
												SCREAMING_SNAKE_CASE							:    List[Any]							=							benchmark.run()
												self.check_results_dict_not_empty(results.time_inference_result						)
												self.check_results_dict_not_empty(results.memory_inference_result						)


						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							'''sshleifer/tiny-gpt2'''
												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		save_to_csv=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		inference_time_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)    ,		inference_memory_csv_file=os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)    ,		env_info_csv_file=os.path.join(UpperCamelCase__    ,		'''env.csv'''						)    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		benchmark.run()
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_time.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''inf_mem.csv'''						)						).exists()						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''env.csv'''						)						).exists()						)



						def        __A       (       self	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							'''sshleifer/tiny-gpt2'''

												def _check_summary_is_not_empty(UpperCamelCase__	:				Dict						):
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''sequential'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''cumulative'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''current'''						)						)
																		self.assertTrue(hasattr(UpperCamelCase__    ,		'''total'''						)						)

												with tempfile.TemporaryDirectory() as tmp_dir:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							TensorFlowBenchmarkArguments(
																		    models=[MODEL_ID]    ,		inference=UpperCamelCase__    ,		sequence_lengths=[8]    ,		batch_sizes=[1]    ,		log_filename=os.path.join(UpperCamelCase__    ,		'''log.txt'''						)    ,		log_print=UpperCamelCase__    ,		trace_memory_line_by_line=UpperCamelCase__    ,		eager_mode=UpperCamelCase__    ,		multi_process=UpperCamelCase__    ,		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							TensorFlowBenchmark(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							benchmark.run()
																		_check_summary_is_not_empty(result.inference_summary						)
																		self.assertTrue(Path(os.path.join(UpperCamelCase__    ,		'''log.txt'''						)						).exists()						)





