




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


A   =       16
A   =       32

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase = 16							)			->			Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Tuple		=					AutoTokenizer.from_pretrained("bert-base-cased"							)
			__UpperCAmelCase	: str		=					load_dataset("glue"				,       "mrpc"							)

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

			def collate_fn(UpperCamelCase							):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__UpperCAmelCase	: Optional[int]		=					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	: Union[str, Any]		=					16
						elif accelerator.mixed_precision != "no":
									__UpperCAmelCase	: Optional[Any]		=					8
						else:
									__UpperCAmelCase	: str		=					None

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

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

			return train_dataloader, eval_dataloader

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


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

			__UpperCAmelCase	: str		=					evaluate.load("glue"				,       "mrpc"							)

			# If the batch size is too big we use gradient accumulation
			__UpperCAmelCase	: List[str]		=					1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__UpperCAmelCase	: Tuple		=					batch_size // MAX_GPU_BATCH_SIZE
						__UpperCAmelCase	: str		=					MAX_GPU_BATCH_SIZE

			set_seed(UpperCamelCase							)
			__UpperCAmelCase	: Tuple		=					get_dataloaders(UpperCamelCase				,       UpperCamelCase							)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__UpperCAmelCase	: str		=					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,       return_dict=UpperCamelCase							)

			# 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	: Tuple		=					model.to(accelerator.device							)
			# Instantiate optimizer
			__UpperCAmelCase	: Union[str, Any]		=					AdamW(params=model.parameters()				,       lr=UpperCamelCase							)

			# Instantiate scheduler
			__UpperCAmelCase	: List[str]		=					get_linear_schedule_with_warmup(
			    optimizer=UpperCamelCase				,       num_warmup_steps=100				,       num_training_steps=(len(UpperCamelCase							) * 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	: str		=					accelerator.prepare(
			    UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			# Now we train the model
			for epoch in range(UpperCamelCase							):
						model.train()
						for step, batch in enumerate(UpperCamelCase							):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device							)
									__UpperCAmelCase	: Optional[int]		=					model(**UpperCamelCase							)
									__UpperCAmelCase	: Union[str, Any]		=					outputs.loss
									__UpperCAmelCase	: Union[str, Any]		=					loss / gradient_accumulation_steps
									accelerator.backward(UpperCamelCase							)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

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

						__UpperCAmelCase	: int		=					metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f"epoch {epoch}:"				,       UpperCamelCase							)

def 					_UpperCamelCase    (			)			->			Dict:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					argparse.ArgumentParser(description="Simple example of training script."							)
			parser.add_argument(
			    "--mixed_precision"				,       type=UpperCamelCase				,       default=UpperCamelCase				,       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	: Union[str, Any]		=					parser.parse_args()
			__UpperCAmelCase	: Optional[Any]		=					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(UpperCamelCase				,       UpperCamelCase							)


if __name__ == "__main__":
					main()








"""simple docstring"""





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format



class      a__				:
			lowercase_    =	None


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)
						__UpperCAmelCase	: Any		=					json.loads(feat_extract.to_json_string())
						for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]      ,  UpperCamelCase_)


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Dict		=					os.path.join(UpperCamelCase_      ,  "feat_extract.json")
									feat_extract_first.to_json_file(UpperCamelCase_)
									__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class.from_json_file(UpperCamelCase_)

						self.assertEqual(feat_extract_second.to_dict()      ,  feat_extract_first.to_dict())


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					self.feature_extraction_class(**self.feat_extract_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
									__UpperCAmelCase	: Tuple		=					feat_extract_first.save_pretrained(UpperCamelCase_)[0]
									check_json_file_has_correct_format(UpperCamelCase_)
									__UpperCAmelCase	: List[Any]		=					self.feature_extraction_class.from_pretrained(UpperCamelCase_)

						self.assertEqual(feat_extract_second.to_dict()      ,  feat_extract_first.to_dict())


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.feature_extraction_class()
						self.assertIsNotNone(UpperCamelCase_)


