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


__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			1_6
__SCREAMING_SNAKE_CASE						:   List[Any]         =			3_2
def  snake_case						(__lowercase ,      __lowercase = 16						)	->	List[str]:

						'''simple docstring'''



						_snake_case						:					str    =   AutoTokenizer.from_pretrained("bert-base-cased"						)
						_snake_case						:					Dict    =   load_dataset("glue" ,      "mrpc"						)

						def tokenize_function(__lowercase						):
												# max_length=None => use the model max length (it's actually the default)
												_snake_case						:					List[str]    =   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():
												_snake_case						:					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
						_snake_case						:					Union[str, 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.
												_snake_case						:					Tuple    =   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":
																		_snake_case						:					Any    =   16
												elif accelerator.mixed_precision != "no":
																		_snake_case						:					List[Any]    =   8
												else:
																		_snake_case						:					Optional[int]    =   None

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

						# Instantiate dataloaders.
						_snake_case						:					List[Any]    =   DataLoader(
						    tokenized_datasets["train"] ,      shuffle=__lowercase ,      collate_fn=__lowercase ,      batch_size=__lowercase ,      drop_last=__lowercase						)
						_snake_case						:					Any    =   DataLoader(
						    tokenized_datasets["validation"] ,      shuffle=__lowercase ,      collate_fn=__lowercase ,      batch_size=__lowercase ,      drop_last=(accelerator.mixed_precision == "fp8") ,      )

						return train_dataloader, eval_dataloader
def  snake_case						(__lowercase ,      __lowercase						)	->	int:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision						)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						_snake_case						:					Optional[int]    =   config["lr"]
						_snake_case						:					Union[str, Any]    =   int(config["num_epochs"]						)
						_snake_case						:					Tuple    =   int(config["seed"]						)
						_snake_case						:					Tuple    =   int(config["batch_size"]						)

						_snake_case						:					Tuple    =   evaluate.load("glue" ,      "mrpc"						)

						# If the batch size is too big we use gradient accumulation
						_snake_case						:					Optional[int]    =   1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												_snake_case						:					str    =   batch_size // MAX_GPU_BATCH_SIZE
												_snake_case						:					Union[str, Any]    =   MAX_GPU_BATCH_SIZE

						set_seed(__lowercase						)
						_snake_case    ,_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)
						_snake_case						:					Union[str, 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).
						_snake_case						:					Dict    =   model.to(accelerator.device						)
						# Instantiate optimizer
						_snake_case						:					List[Any]    =   AdamW(params=model.parameters() ,      lr=__lowercase						)

						# Instantiate scheduler
						_snake_case						:					Dict    =   get_linear_schedule_with_warmup(
						    optimizer=__lowercase ,      num_warmup_steps=100 ,      num_training_steps=(len(__lowercase						) * 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.

						_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case						:					Union[str, Any]    =   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						)
																		_snake_case						:					str    =   model(**__lowercase						)
																		_snake_case						:					Tuple    =   outputs.loss
																		_snake_case						:					int    =   loss / gradient_accumulation_steps
																		accelerator.backward(__lowercase						)
																		if step % gradient_accumulation_steps == 0:
																								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():
																								_snake_case						:					List[str]    =   model(**__lowercase						)
																		_snake_case						:					Any    =   outputs.logits.argmax(dim=-1						)
																		_snake_case    ,_snake_case						:					str    =   accelerator.gather_for_metrics((predictions, batch["labels"])						)
																		metric.add_batch(
																		    predictions=__lowercase ,      references=__lowercase ,      )

												_snake_case						:					str    =   metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:""" ,      __lowercase						)
def  snake_case						()	->	Optional[int]:

						'''simple docstring'''



						_snake_case						:					Dict    =   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." ,      )
						parser.add_argument("--cpu" ,      action="store_true" ,      help="If passed, will train on the CPU."						)
						_snake_case						:					int    =   parser.parse_args()
						_snake_case						:					Optional[Any]    =   {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(__lowercase ,      __lowercase						)


if __name__ == "__main__":
				main()
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)