
"""simple docstring"""




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCAmelCase__      :    int    							= 1_6
UpperCAmelCase__      :    int    							= 3_2

def lowercase_  (   _snake_case   ,_snake_case = 16						):
							SCREAMING_SNAKE_CASE__						:       Dict           =   AutoTokenizer.from_pretrained("""bert-base-cased"""						)
							SCREAMING_SNAKE_CASE__						:       Tuple           =   load_dataset("""glue"""   ,"""mrpc"""						)

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

							def collate_fn(_snake_case						):
														# 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__						:       Optional[Any]           =   16
														elif accelerator.mixed_precision != "no":
																					SCREAMING_SNAKE_CASE__						:       Any           =   8
														else:
																					SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   None

														return tokenizer.pad(
														    _snake_case   ,padding="""longest"""   ,max_length=_snake_case   ,pad_to_multiple_of=_snake_case   ,return_tensors="""pt"""   ,)

							# Instantiate dataloaders.
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   DataLoader(
							    tokenized_datasets["""train"""]   ,shuffle=_snake_case   ,collate_fn=_snake_case   ,batch_size=_snake_case						)
							SCREAMING_SNAKE_CASE__						:       int           =   DataLoader(
							    tokenized_datasets["""validation"""]   ,shuffle=_snake_case   ,collate_fn=_snake_case   ,batch_size=_snake_case						)

							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__      :    Union[str, Any]    							= mocked_dataloaders  # noqa: F811

def lowercase_  (   _snake_case   ,_snake_case						):
							# For testing only
							if os.environ.get("""TESTING_MOCKED_DATALOADERS"""   ,_snake_case						) == "1":
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   2
							# Initialize accelerator
							SCREAMING_SNAKE_CASE__						:       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__						:       List[str]           =   config["""lr"""]
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   int(config["""num_epochs"""]						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   int(config["""seed"""]						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   int(config["""batch_size"""]						)

							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   evaluate.load("""glue"""   ,"""mrpc"""						)

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

							set_seed(_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   get_dataloaders(_snake_case   ,_snake_case						)
							# 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=_snake_case						)

							# 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__						:       Dict           =   model.to(accelerator.device						)

							# Instantiate optimizer
							SCREAMING_SNAKE_CASE__						:       Dict           =   AdamW(params=model.parameters()   ,lr=_snake_case						)

							# Instantiate scheduler
							SCREAMING_SNAKE_CASE__						:       Dict           =   get_linear_schedule_with_warmup(
							    optimizer=_snake_case   ,num_warmup_steps=100   ,num_training_steps=(len(_snake_case						) * 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.
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   accelerator.prepare(
							    _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						)

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

														model.eval()
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   0
														for step, batch in enumerate(_snake_case						):
																					# 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(**_snake_case						)
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   outputs.logits.argmax(dim=-1						)
																					SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[str]           =   accelerator.gather((predictions, batch["""labels"""])						)
																					# New Code #
																					# First we check if it's a distributed system
																					if accelerator.use_distributed:
																												# Then see if we're on the last batch of our eval dataloader
																												if step == len(_snake_case						) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			SCREAMING_SNAKE_CASE__						:       Any           =   predictions[: len(eval_dataloader.dataset						) - samples_seen]
																																			SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   references[: len(eval_dataloader.dataset						) - samples_seen]
																												else:
																																			# Otherwise we add the number of samples seen
																																			samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
																					metric.add_batch(
																					    predictions=_snake_case   ,references=_snake_case   ,)

														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(f'''epoch {epoch}:'''   ,_snake_case						)

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       str           =   argparse.ArgumentParser(description="""Simple example of training script."""						)
							parser.add_argument(
							    """--mixed_precision"""   ,type=_snake_case   ,default=_snake_case   ,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__						:       Dict           =   parser.parse_args()
							SCREAMING_SNAKE_CASE__						:       List[str]           =   {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(_snake_case   ,_snake_case						)


if __name__ == "__main__":
					main()






"""simple docstring"""




def lowercase_  (   _snake_case						):
							if a < 0:
														raise ValueError("""Input value must be a positive integer"""						)
							elif isinstance(_snake_case   ,_snake_case						):
														raise TypeError("""Input value must be a 'int' type"""						)
							return bin(_snake_case						).count("""1"""						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




