



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


SCREAMING_SNAKE_CASE     				=						16
SCREAMING_SNAKE_CASE     				=						32







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = 16 )		->							Any:
		UpperCAmelCase_						=				AutoTokenizer.from_pretrained("bert-base-cased" )
		UpperCAmelCase_						=				load_dataset("glue"			,     "mrpc" )

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

		def collate_fn(__SCREAMING_SNAKE_CASE ):
				# 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(
				    __SCREAMING_SNAKE_CASE			,     padding="longest"			,     max_length=__SCREAMING_SNAKE_CASE			,     pad_to_multiple_of=__SCREAMING_SNAKE_CASE			,     return_tensors="pt"			,     )

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

			SCREAMING_SNAKE_CASE     				=						mocked_dataloaders  # noqa: F811







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Any:
		# For testing only
		if os.environ.get("TESTING_MOCKED_DATALOADERS"			,     __SCREAMING_SNAKE_CASE ) == "1":
				UpperCAmelCase_						=				2
		# New Code #
		UpperCAmelCase_						=				int(args.gradient_accumulation_steps )
		# Initialize accelerator
		UpperCAmelCase_						=				Accelerator(
		    cpu=args.cpu			,     mixed_precision=args.mixed_precision			,     gradient_accumulation_steps=__SCREAMING_SNAKE_CASE )
		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
		UpperCAmelCase_						=				config["lr"]
		UpperCAmelCase_						=				int(config["num_epochs"] )
		UpperCAmelCase_						=				int(config["seed"] )
		UpperCAmelCase_						=				int(config["batch_size"] )

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

		set_seed(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_					,						UpperCAmelCase_						=				get_dataloaders(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# 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=__SCREAMING_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).
		UpperCAmelCase_						=				model.to(accelerator.device )

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

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

		# Now we train the model
		for epoch in range(__SCREAMING_SNAKE_CASE ):
				model.train()
				for step, batch in enumerate(__SCREAMING_SNAKE_CASE ):
						# 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(__SCREAMING_SNAKE_CASE ):
								UpperCAmelCase_						=				model(**__SCREAMING_SNAKE_CASE )
								UpperCAmelCase_						=				output.loss
								accelerator.backward(__SCREAMING_SNAKE_CASE )
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

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

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







def snake_case__  (			)		->							str:
		UpperCAmelCase_						=				argparse.ArgumentParser(description="Simple example of training script." )
		parser.add_argument(
		    "--mixed_precision"			,     type=__SCREAMING_SNAKE_CASE			,     default=__SCREAMING_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."			,     )
		# New Code #
		parser.add_argument(
		    "--gradient_accumulation_steps"			,     type=__SCREAMING_SNAKE_CASE			,     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." )
		UpperCAmelCase_						=				parser.parse_args()
		UpperCAmelCase_						=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			main()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTBigCodeForSequenceClassification",
			    "GPTBigCodeForTokenClassification",
			    "GPTBigCodeForCausalLM",
			    "GPTBigCodeModel",
			    "GPTBigCodePreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

