
"""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
# 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_       :    int						      =			16
UpperCamelCase_       :    Any						      =			32
def 				A_  (__a					, __a = 16					):

					'''simple docstring'''





					A_								=    AutoTokenizer.from_pretrained("bert-base-cased"					)
					A_								=    load_dataset("glue"					, "mrpc"					)

					def tokenize_function(__a					):
										# 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(__a					):
										# 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					)
					A_								=    DataLoader(
					    tokenized_datasets["validation"]					, shuffle=__a					, collate_fn=__a					, batch_size=__a					)

					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_       :    str						      =			mocked_dataloaders  # noqa: F811
def 				A_  (__a					, __a					):

					'''simple docstring'''





					if os.environ.get("TESTING_MOCKED_DATALOADERS"					, __a					) == "1":
										A_								=    2
					# New Code #
					A_								=    int(args.gradient_accumulation_steps					)
					# Initialize accelerator
					A_								=    Accelerator(
					    cpu=args.cpu					, mixed_precision=args.mixed_precision					, gradient_accumulation_steps=__a					)
					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
					A_								=    config["lr"]
					A_								=    int(config["num_epochs"]					)
					A_								=    int(config["seed"]					)
					A_								=    int(config["batch_size"]					)

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

					set_seed(__a					)
					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)					, )

					# 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_			,     A_			,     A_			,     A_			,     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					)
															# 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(__a					):
																				A_								=    model(**__a					)
																				A_								=    output.loss
																				accelerator.backward(__a					)
																				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_			,     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 				A_  ():

					'''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."					, )
					# New Code #
					parser.add_argument(
					    "--gradient_accumulation_steps"					, type=__a					, 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."					)
					A_								=    parser.parse_args()
					A_								=    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(__a					, __a					)


if __name__ == "__main__":
		main()






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase_       :    List[str]						      =			{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Tuple						      =			[
		    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SEWForCTC''',
		    '''SEWForSequenceClassification''',
		    '''SEWModel''',
		    '''SEWPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_sew import (
				    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SEWForCTC,
				    SEWForSequenceClassification,
				    SEWModel,
				    SEWPreTrainedModel,
				)


else:
		import sys

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




