

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


_a:    Optional[int] 							=			16
_a:    Union[str, Any] 							=			32



def       __lowerCAmelCase      (						A       ,     A = 16    ):
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained("bert-base-cased"    )
	UpperCAmelCase_       				=			load_dataset("glue"       ,     "mrpc"    )

	def tokenize_function(A    ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_       				=			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():
		UpperCAmelCase_       				=			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
	UpperCAmelCase_       				=			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.
		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(
		    A       ,     padding="longest"       ,     max_length=A       ,     pad_to_multiple_of=A       ,     return_tensors="pt"       ,     )

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

				_a:    Tuple 							=			mocked_dataloaders  # noqa: F811



def       __lowerCAmelCase      (						A       ,     A    ):
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,     A    ) == "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=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
	UpperCAmelCase_       				=			config["lr"]
	UpperCAmelCase_       				=			int(config["num_epochs"]    )
	UpperCAmelCase_       				=			int(config["seed"]    )
	UpperCAmelCase_       				=			int(config["batch_size"]    )

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

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

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

	# Instantiate scheduler
	UpperCAmelCase_       				=			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.
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			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    ):
				UpperCAmelCase_       				=			model(**A    )
				UpperCAmelCase_       				=			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():
				UpperCAmelCase_       				=			model(**A    )
			UpperCAmelCase_       				=			outputs.logits.argmax(dim=-1    )
			UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.gather_for_metrics((predictions, batch["labels"])    )
			metric.add_batch(
			    predictions=A       ,     references=A       ,     )

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



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			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."    )
	UpperCAmelCase_       				=			parser.parse_args()
	UpperCAmelCase_       				=			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(A       ,     A    )


if __name__ == "__main__":
				main()


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_a:    int 							=			logging.getLogger(__name__)



def       __lowerCAmelCase      (						A       ,     A    ):
	return (preds == labels).mean()

@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(
	    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained config name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,     )


@dataclass
class 						__UpperCamelCase    :
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()						)}						)
	SCREAMING_SNAKE_CASE__    				=     field(metadata={'help': 'Should contain the data files for the task.'}						)
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=1_28     ,     metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated, sequences shorter will be padded.'
	        )
	    }     ,     )
	SCREAMING_SNAKE_CASE__    				=     field(
	    default=lowercase     ,     metadata={'help': 'Overwrite the cached training and evaluation sets'}						)



def       __lowerCAmelCase      (						):
	# See all possible arguments in src/transformers/training_args.py
	# or by passing the --help flag to this script.
	# We now keep distinct sets of args, for a cleaner separation of concerns.

	UpperCAmelCase_       				=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			parser.parse_args_into_dataclasses()

	if (
	    os.path.exists(training_args.output_dir    )
	    and os.listdir(training_args.output_dir    )
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
		    " --overwrite_output_dir to overcome."    )

	# Setup logging
	logging.basicConfig(
	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,     datefmt="%m/%d/%Y %H:%M:%S"       ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN       ,     )
	logger.warning(
	    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"       ,     training_args.local_rank       ,     training_args.device       ,     training_args.n_gpu       ,     bool(training_args.local_rank != -1    )       ,     training_args.fpaa       ,     )
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank    ):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info("Training/evaluation parameters %s"       ,     A    )

	# Set seed
	set_seed(training_args.seed    )

	try:
		UpperCAmelCase_       				=			processors[data_args.task_name]()
		UpperCAmelCase_       				=			processor.get_labels()
		UpperCAmelCase_       				=			len(A    )
	except KeyError:
		raise ValueError("Task not found: %s" % (data_args.task_name)    )

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	UpperCAmelCase_       				=			AutoConfig.from_pretrained(
	    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,     num_labels=A       ,     finetuning_task=data_args.task_name       ,     cache_dir=model_args.cache_dir       ,     )
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained(
	    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path       ,     cache_dir=model_args.cache_dir       ,     )
	UpperCAmelCase_       				=			AutoModelForMultipleChoice.from_pretrained(
	    model_args.model_name_or_path       ,     from_tf=bool(".ckpt" in model_args.model_name_or_path    )       ,     config=A       ,     cache_dir=model_args.cache_dir       ,     )

	# Get datasets
	UpperCAmelCase_       				=			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir       ,     tokenizer=A       ,     task=data_args.task_name       ,     max_seq_length=data_args.max_seq_length       ,     overwrite_cache=data_args.overwrite_cache       ,     mode=Split.train       ,     )
	    if training_args.do_train
	    else None
	)
	UpperCAmelCase_       				=			(
	    MultipleChoiceDataset(
	        data_dir=data_args.data_dir       ,     tokenizer=A       ,     task=data_args.task_name       ,     max_seq_length=data_args.max_seq_length       ,     overwrite_cache=data_args.overwrite_cache       ,     mode=Split.dev       ,     )
	    if training_args.do_eval
	    else None
	)

	def compute_metrics(A    ) -> Dict:
		UpperCAmelCase_       				=			np.argmax(p.predictions       ,     axis=1    )
		return {"acc": simple_accuracy(A       ,     p.label_ids    )}

	# Data collator
	UpperCAmelCase_       				=			DataCollatorWithPadding(A       ,     pad_to_multiple_of=8    ) if training_args.fpaa else None

	# Initialize our Trainer
	UpperCAmelCase_       				=			Trainer(
	    model=A       ,     args=A       ,     train_dataset=A       ,     eval_dataset=A       ,     compute_metrics=A       ,     data_collator=A       ,     )

	# Training
	if training_args.do_train:
		trainer.train(
		    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
	UpperCAmelCase_       				=			{}
	if training_args.do_eval:
		logger.info("*** Evaluate ***"    )

		UpperCAmelCase_       				=			trainer.evaluate()

		UpperCAmelCase_       				=			os.path.join(training_args.output_dir       ,     "eval_results.txt"    )
		if trainer.is_world_master():
			with open(A       ,     "w"    ) as writer:
				logger.info("***** Eval results *****"    )
				for key, value in result.items():
					logger.info("  %s = %s"       ,     A       ,     A    )
					writer.write("%s = %s\n" % (key, value)    )

				results.update(A    )

	return results



def       __lowerCAmelCase      (						A    ):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
				main()