import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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
#
########################################################################


_lowercase:	List[str]       			=		1_6
_lowercase:	int       			=		3_2






def _lowerCamelCase  (		snake_case     , snake_case = 16  ):
							_lowerCAmelCase							=							AutoTokenizer.from_pretrained('bert-base-cased'  )
							_lowerCAmelCase							=							load_dataset('glue'     , 'mrpc'  )

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

							def collate_fn(snake_case  ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_lowerCAmelCase							=							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":
																					_lowerCAmelCase							=							16
														elif accelerator.mixed_precision != "no":
																					_lowerCAmelCase							=							8
														else:
																					_lowerCAmelCase							=							None

														return tokenizer.pad(
														    snake_case     , padding='longest'     , max_length=snake_case     , pad_to_multiple_of=snake_case     , return_tensors='pt'     , )

							# Instantiate dataloaders.
							_lowerCAmelCase							=							DataLoader(
							    tokenized_datasets['train']     , shuffle=snake_case     , collate_fn=snake_case     , batch_size=snake_case  )
							_lowerCAmelCase							=							DataLoader(
							    tokenized_datasets['validation']     , shuffle=snake_case     , collate_fn=snake_case     , batch_size=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

				_lowercase:	str       			=		mocked_dataloaders  # noqa: F811






def _lowerCamelCase  (		snake_case     , snake_case  ):
							# For testing only
							if os.environ.get('TESTING_MOCKED_DATALOADERS'     , snake_case  ) == "1":
														_lowerCAmelCase							=							2
							# Initialize accelerator
							_lowerCAmelCase							=							Accelerator(cpu=args.cpu     , mixed_precision=args.mixed_precision  )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowerCAmelCase							=							config['lr']
							_lowerCAmelCase							=							int(config['num_epochs']  )
							_lowerCAmelCase							=							int(config['seed']  )
							_lowerCAmelCase							=							int(config['batch_size']  )

							_lowerCAmelCase							=							evaluate.load('glue'     , 'mrpc'  )

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=snake_case  )
							def inner_training_loop(snake_case  ):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(snake_case  )

														# Instantiate the model (we build the model here so that the seed also control new weights initialization)
														_lowerCAmelCase							=							AutoModelForSequenceClassification.from_pretrained('bert-base-cased'     , return_dict=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).
														_lowerCAmelCase							=							model.to(accelerator.device  )

														# Instantiate optimizer
														_lowerCAmelCase							=							AdamW(params=model.parameters()     , lr=snake_case  )
														_lowerCAmelCase			,		_lowerCAmelCase							=							get_dataloaders(snake_case     , snake_case  )

														# Instantiate scheduler
														_lowerCAmelCase							=							get_linear_schedule_with_warmup(
														    optimizer=snake_case     , num_warmup_steps=100     , num_training_steps=(len(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.
														_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							accelerator.prepare(
														    snake_case     , snake_case     , snake_case     , snake_case     , snake_case  )

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

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

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

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
							inner_training_loop()






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							argparse.ArgumentParser(description='Simple example of training script.'  )
							parser.add_argument(
							    '--mixed_precision'     , type=snake_case     , default=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.'  )
							_lowerCAmelCase							=							parser.parse_args()
							_lowerCAmelCase							=							{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(snake_case     , snake_case  )


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


_lowercase:	str       			=		'''sshleifer/bart-tiny-random'''
_lowercase:	Union[str, Any]       			=		'''patrickvonplaten/t5-tiny-random'''



@require_torch
class        lowerCamelCase__				(   unittest.TestCase				):





			@cached_property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										return AutoConfig.from_pretrained(lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	int						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.num_hidden_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=lowercase__						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  self.teacher_config.encoder_layers						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										_lowerCAmelCase			,		*_lowerCAmelCase							=							create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=1  ,  d=1						)
										self.assertEqual(student.config.encoder_layers  ,  1						)
										self.assertEqual(student.config.decoder_layers  ,  1						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										with self.assertRaises(lowercase__						):
																	create_student_by_copying_alternating_layers(lowercase__  ,  tempfile.mkdtemp()  ,  e=lowercase__  ,  d=lowercase__						)






