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


UpperCAmelCase       =					16
UpperCAmelCase       =					32






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE = 16				):
	lowercase	    =       AutoTokenizer.from_pretrained('bert-base-cased'				)
	lowercase	    =       load_dataset('glue'					,				'mrpc'				)

	def tokenize_function(__SCREAMING_SNAKE_CASE				):
		# max_length=None => use the model max length (it's actually the default)
		lowercase	    =       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():
		lowercase	    =       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
	lowercase	    =       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.
		lowercase	    =       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":
			lowercase	    =       16
		elif accelerator.mixed_precision != "no":
			lowercase	    =       8
		else:
			lowercase	    =       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.
	lowercase	    =       DataLoader(
	    tokenized_datasets['train']					,				shuffle=__SCREAMING_SNAKE_CASE					,				collate_fn=__SCREAMING_SNAKE_CASE					,				batch_size=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       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

	UpperCAmelCase       =					mocked_dataloaders  # noqa: F811






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

	lowercase	    =       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=__SCREAMING_SNAKE_CASE				)
	def inner_training_loop(__SCREAMING_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(__SCREAMING_SNAKE_CASE				)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase	    =       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).
		lowercase	    =       model.to(accelerator.device				)

		# Instantiate optimizer
		lowercase	    =       AdamW(params=model.parameters()					,				lr=__SCREAMING_SNAKE_CASE				)
		lowercase    ,   lowercase	    =       get_dataloaders(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

		# Instantiate scheduler
		lowercase	    =       get_linear_schedule_with_warmup(
		    optimizer=__SCREAMING_SNAKE_CASE					,				num_warmup_steps=100					,				num_training_steps=(len(__SCREAMING_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.
		lowercase    ,   lowercase    ,   lowercase    ,   lowercase    ,   lowercase	    =       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				)
				lowercase	    =       model(**__SCREAMING_SNAKE_CASE				)
				lowercase	    =       outputs.loss
				accelerator.backward(__SCREAMING_SNAKE_CASE				)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			model.eval()
			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():
					lowercase	    =       model(**__SCREAMING_SNAKE_CASE				)
				lowercase	    =       outputs.logits.argmax(dim=-1				)
				lowercase    ,   lowercase	    =       accelerator.gather_for_metrics((predictions, batch['labels'])				)
				metric.add_batch(
				    predictions=__SCREAMING_SNAKE_CASE					,				references=__SCREAMING_SNAKE_CASE					,				)

			lowercase	    =       metric.compute()
			# Use accelerator.print to print only on the main process.
			accelerator.print(F'''epoch {epoch}:'''					,				__SCREAMING_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 				UpperCAmelCase_						(				):
	lowercase	    =       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.'				)
	lowercase	    =       parser.parse_args()
	lowercase	    =       {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
	main()








'''simple docstring'''




import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Union[str, Any]						)->	Optional[Any]:
        snake_case 						=   FlaxMTaForConditionalGeneration.from_pretrained("""google/mt5-small"""						)
        snake_case 						=   AutoTokenizer.from_pretrained("""google/mt5-small"""						)

        snake_case 						=   tokenizer("""Hello there"""   ,       return_tensors="""np"""						).input_ids
        snake_case 						=   tokenizer("""Hi I am"""   ,       return_tensors="""np"""						).input_ids

        snake_case 						=   shift_tokens_right(__snake_case   ,       model.config.pad_token_id   ,       model.config.decoder_start_token_id						)

        snake_case 						=   model(__snake_case   ,       decoder_input_ids=__snake_case						).logits
        snake_case 						=   optax.softmax_cross_entropy(__snake_case   ,       onehot(__snake_case   ,       logits.shape[-1]						)						).mean()

        snake_case 						=   -(labels.shape[-1] * loss.item())

        snake_case 						=   -84.91_27
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1e-4						)

