





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        =				1_6
_UpperCAmelCase        =				3_2




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = 16  )     ->		Dict:
							UpperCamelCase_          =  AutoTokenizer.from_pretrained("bert-base-cased"  )
							UpperCamelCase_          =  load_dataset("glue"					,		"mrpc"  )

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

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

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

							# Instantiate dataloaders.
							UpperCamelCase_          =  DataLoader(
							    tokenized_datasets["train"]					,		shuffle=UpperCamelCase_					,		collate_fn=UpperCamelCase_					,		batch_size=UpperCamelCase_  )
							UpperCamelCase_          =  DataLoader(
							    tokenized_datasets["validation"]					,		shuffle=UpperCamelCase_					,		collate_fn=UpperCamelCase_					,		batch_size=UpperCamelCase_  )

							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        =				mocked_dataloaders  # noqa: F811




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		str:

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

							UpperCamelCase_          =  evaluate.load("glue"					,		"mrpc"  )

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

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

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

							# Instantiate optimizer
							UpperCamelCase_          =  AdamW(params=model.parameters()					,		lr=UpperCamelCase_  )

							# Instantiate scheduler
							UpperCamelCase_          =  get_linear_schedule_with_warmup(
							    optimizer=UpperCamelCase_					,		num_warmup_steps=100					,		num_training_steps=(len(UpperCamelCase_  ) * 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.
							UpperCamelCase_       , UpperCamelCase_       , UpperCamelCase_       , UpperCamelCase_       , UpperCamelCase_          =  accelerator.prepare(
							    UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )

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

														model.eval()
														UpperCamelCase_          =  0
														for step, batch in enumerate(UpperCamelCase_  ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device  )
																					with torch.no_grad():
																												UpperCamelCase_          =  model(**UpperCamelCase_  )
																					UpperCamelCase_          =  outputs.logits.argmax(dim=-1  )
																					UpperCamelCase_       , UpperCamelCase_          =  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(UpperCamelCase_  ) - 1:
																																			# Last batch needs to be truncated on distributed systems as it contains additional samples
																																			UpperCamelCase_          =  predictions[: len(eval_dataloader.dataset  ) - samples_seen]
																																			UpperCamelCase_          =  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=UpperCamelCase_					,		references=UpperCamelCase_					,		)

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




def  lowerCAmelCase_ (							)     ->		Dict:
							UpperCamelCase_          =  argparse.ArgumentParser(description="Simple example of training script."  )
							parser.add_argument(
							    "--mixed_precision"					,		type=UpperCamelCase_					,		default=UpperCamelCase_					,		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."  )
							UpperCamelCase_          =  parser.parse_args()
							UpperCamelCase_          =  {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(UpperCamelCase_					,		UpperCamelCase_  )


if __name__ == "__main__":
					main()








_UpperCAmelCase        =				{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

