



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


SCREAMING_SNAKE_CASE     				=						16
SCREAMING_SNAKE_CASE     				=						32







def snake_case__   (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = 16 )		->							int:
		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 )		->							str:
		# For testing only
		if os.environ.get("TESTING_MOCKED_DATALOADERS"			,     __SCREAMING_SNAKE_CASE ) == "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(__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) // 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(
		    __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 )
						UpperCAmelCase_						=				model(**__SCREAMING_SNAKE_CASE )
						UpperCAmelCase_						=				outputs.loss
						UpperCAmelCase_						=				loss / gradient_accumulation_steps
						accelerator.backward(__SCREAMING_SNAKE_CASE )
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				UpperCAmelCase_						=				0
				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((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(__SCREAMING_SNAKE_CASE ) - 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=__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__   (			)		->							Tuple:
		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."			,     )
		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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["XLNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["XLNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLNetForMultipleChoice",
			    "XLNetForQuestionAnswering",
			    "XLNetForQuestionAnsweringSimple",
			    "XLNetForSequenceClassification",
			    "XLNetForTokenClassification",
			    "XLNetLMHeadModel",
			    "XLNetModel",
			    "XLNetPreTrainedModel",
			    "load_tf_weights_in_xlnet",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXLNetForMultipleChoice",
			    "TFXLNetForQuestionAnsweringSimple",
			    "TFXLNetForSequenceClassification",
			    "TFXLNetForTokenClassification",
			    "TFXLNetLMHeadModel",
			    "TFXLNetMainLayer",
			    "TFXLNetModel",
			    "TFXLNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

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

