

import argparse

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
#
# 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
#
########################################################################


lowercase_			=  16
lowercase_			=  32
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Accelerator   ,      SCREAMING_SNAKE_CASE__      :     int = 16				)							->				str:





				'''simple docstring'''






				A__   				=       AutoTokenizer.from_pretrained('bert-base-cased'				)
				A__   				=       load_dataset('glue'   ,      'mrpc'				)

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

				def collate_fn(SCREAMING_SNAKE_CASE__      :     int				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								A__   				=       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":
												A__   				=       16
								elif accelerator.mixed_precision != "no":
												A__   				=       8
								else:
												A__   				=       None

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

				# Instantiate dataloaders.
				A__   				=       DataLoader(
				    tokenized_datasets['train']   ,      shuffle=__a   ,      collate_fn=__a   ,      batch_size=__a   ,      drop_last=__a				)
				A__   				=       DataLoader(
				    tokenized_datasets['validation']   ,      shuffle=__a   ,      collate_fn=__a   ,      batch_size=__a   ,      drop_last=(accelerator.mixed_precision == 'fp8')   ,      )

				return train_dataloader, eval_dataloader
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				List[str]:





				'''simple docstring'''






				A__   				=       Accelerator(cpu=args.cpu   ,      mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				A__   				=       config['lr']
				A__   				=       int(config['num_epochs']				)
				A__   				=       int(config['seed']				)
				A__   				=       int(config['batch_size']				)

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

				# If the batch size is too big we use gradient accumulation
				A__   				=       1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								A__   				=       batch_size // MAX_GPU_BATCH_SIZE
								A__   				=       MAX_GPU_BATCH_SIZE

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

				# 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).
				A__   				=       model.to(accelerator.device				)
				# Instantiate optimizer
				A__   				=       AdamW(params=model.parameters()   ,      lr=__a				)

				# Instantiate scheduler
				A__   				=       get_linear_schedule_with_warmup(
				    optimizer=__a   ,      num_warmup_steps=100   ,      num_training_steps=(len(__a				) * 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.

				A__   				=       accelerator.prepare(
				    __a   ,      __a   ,      __a   ,      __a   ,      __a				)

				# Now we train the model
				for epoch in range(__a				):
								model.train()
								for step, batch in enumerate(__a				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												A__   				=       model(**__a				)
												A__   				=       outputs.loss
												A__   				=       loss / gradient_accumulation_steps
												accelerator.backward(__a				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

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

								A__   				=       metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'epoch {epoch}:'   ,      __a				)
def 						_snake_case(   )							->				Tuple:





				'''simple docstring'''






				A__   				=       argparse.ArgumentParser(description='Simple example of training script.'				)
				parser.add_argument(
				    '--mixed_precision'   ,      type=__a   ,      default=__a   ,      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.'				)
				A__   				=       parser.parse_args()
				A__   				=       {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(__a   ,      __a				)


if __name__ == "__main__":
					main()




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str		)->   List[Any]:





        '''simple docstring'''







        for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['bs'],model_result['ss']		):
                A__   				=       model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(lowercase_		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sgugger/tiny-distilbert-classification'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    @unittest.skipIf(torch_device == 'cpu','Cant do half precision'		)
    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__   				=       None
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    @unittest.skipIf(torch_device == 'cpu','Can\'t do half precision'		)
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    def 						snake_case__					(				self			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    def 						snake_case__					(				self			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,save_to_csv=lowercase_,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(lowercase_,'inf_time.csv'		),train_memory_csv_file=os.path.join(lowercase_,'train_mem.csv'		),inference_memory_csv_file=os.path.join(lowercase_,'inf_mem.csv'		),train_time_csv_file=os.path.join(lowercase_,'train_time.csv'		),env_info_csv_file=os.path.join(lowercase_,'env.csv'		),multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            benchmark.run()
            self.assertTrue(Path(os.path.join(lowercase_,'inf_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'inf_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'env.csv'		)		).exists()		)


    def 						snake_case__					(				self			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_			:	Optional[Any]		):
            self.assertTrue(hasattr(lowercase_,'sequential'		)		)
            self.assertTrue(hasattr(lowercase_,'cumulative'		)		)
            self.assertTrue(hasattr(lowercase_,'current'		)		)
            self.assertTrue(hasattr(lowercase_,'total'		)		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],log_filename=os.path.join(lowercase_,'log.txt'		),log_print=lowercase_,trace_memory_line_by_line=lowercase_,multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            A__   				=       benchmark.run()
            _check_summary_is_not_empty(result.inference_summary		)
            _check_summary_is_not_empty(result.train_summary		)
            self.assertTrue(Path(os.path.join(lowercase_,'log.txt'		)		).exists()		)

