





'''simple docstring'''







import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


lowerCAmelCase_					:    int      	=			16
lowerCAmelCase_					:    Tuple      	=			32






def  _lowerCamelCase				( lowercase						:      Accelerator    ,							lowercase						:      DatasetDict    ,							lowercase						:      List[int]    ,							lowercase						:      List[int]    ,							lowercase						:      int = 16						)	->	Tuple:
				_a								= AutoTokenizer.from_pretrained("bert-base-cased"						)
				_a								= DatasetDict(
				    {
				        "train": dataset["train"].select(lowercase						),
				        "validation": dataset["train"].select(lowercase						),
				        "test": dataset["validation"],
				    }						)

				def tokenize_function(lowercase						:      str						):
								# max_length=None => use the model max length (it's actually the default)
								_a								= tokenizer(examples["sentence1"]    ,							examples["sentence2"]    ,							truncation=lowercase    ,							max_length=lowercase						)
								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(
								    lowercase    ,							batched=lowercase    ,							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(lowercase						:      int						):
								# 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(
								    lowercase    ,							padding="longest"    ,							max_length=lowercase    ,							pad_to_multiple_of=lowercase    ,							return_tensors="pt"    ,							)

				# Instantiate dataloaders.
				_a								= DataLoader(
				    tokenized_datasets["train"]    ,							shuffle=lowercase    ,							collate_fn=lowercase    ,							batch_size=lowercase						)
				_a								= DataLoader(
				    tokenized_datasets["validation"]    ,							shuffle=lowercase    ,							collate_fn=lowercase    ,							batch_size=lowercase						)

				_a								= DataLoader(
				    tokenized_datasets["test"]    ,							shuffle=lowercase    ,							collate_fn=lowercase    ,							batch_size=lowercase						)

				return train_dataloader, eval_dataloader, test_dataloader






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      List[Any]						)	->	Union[str, Any]:
				# New Code #
				_a								= []
				# Download the dataset
				_a								= load_dataset("glue"    ,							"mrpc"						)
				# Create our splits
				_a								= StratifiedKFold(n_splits=int(args.num_folds						)						)
				# Initialize accelerator
				_a								= Accelerator(cpu=args.cpu    ,							mixed_precision=args.mixed_precision						)
				# 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"						)

				# If the batch size is too big we use gradient accumulation
				_a								= 1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								_a								= batch_size // MAX_GPU_BATCH_SIZE
								_a								= MAX_GPU_BATCH_SIZE

				set_seed(lowercase						)

				# New Code #
				# Create our folds:
				_a								= kfold.split(np.zeros(datasets["train"].num_rows						)    ,							datasets["train"]["label"]						)
				_a								= []
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(lowercase						):
								_a	,					_a	,					_a								= get_fold_dataloaders(
								    lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							)
								# 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=lowercase						)

								# 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=lowercase						)

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

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

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

												_a								= metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'epoch {epoch}:'    ,							lowercase						)

								# New Code #
								# We also run predictions on the test set at the very end
								_a								= []
								for step, batch in enumerate(lowercase						):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device						)
												with torch.no_grad():
																_a								= model(**lowercase						)
												_a								= outputs.logits
												_a	,					_a								= accelerator.gather_for_metrics((predictions, batch["labels"])						)
												fold_predictions.append(predictions.cpu()						)
												if i == 0:
																# We need all of the test predictions
																test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
								test_predictions.append(torch.cat(lowercase    ,							dim=0						)						)
								# We now need to release all our memory and get rid of the current model, optimizer, etc
								accelerator.free_memory()
				# New Code #
				# Finally we check the accuracy of our folded results:
				_a								= torch.cat(lowercase    ,							dim=0						)
				_a								= torch.stack(lowercase    ,							dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
				_a								= metric.compute(predictions=lowercase    ,							references=lowercase						)
				accelerator.print("Average test metrics from all folds:"    ,							lowercase						)






def  _lowerCamelCase				( )	->	Dict:
				_a								= argparse.ArgumentParser(description="Simple example of training script."						)
				parser.add_argument(
				    "--mixed_precision"    ,							type=lowercase    ,							default=lowercase    ,							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."						)
				# New Code #
				parser.add_argument("--num_folds"    ,							type=lowercase    ,							default=3    ,							help="The number of splits to perform across the dataset"						)
				_a								= parser.parse_args()
				_a								= {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(lowercase    ,							lowercase						)


if __name__ == "__main__":
					main()


import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__a  			=					'sshleifer/bart-tiny-random'
__a  			=					'patrickvonplaten/t5-tiny-random'
@require_torch
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@cached_property
	def 						_lowercase  (       self  :			Any       )       ->     Tuple:
					return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.num_hidden_layers       ,							1       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							self.teacher_config.encoder_layers       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							1       )




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=SCREAMING_SNAKE_CASE_       ,							d=SCREAMING_SNAKE_CASE_       )




