





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 							SCREAMING_SNAKE_CASE__						(    __a   , __a = 16   ):
	snake_case_		:			Dict											=	AutoTokenizer.from_pretrained('bert-base-cased'   )
	snake_case_		:			Any											=	load_dataset('glue'   , 'mrpc'   )

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

	def collate_fn(__a   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		snake_case_		:			Optional[Any]											=	1_28 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			snake_case_		:			Optional[Any]											=	16
		elif accelerator.mixed_precision != "no":
			snake_case_		:			Tuple											=	8
		else:
			snake_case_		:			List[str]											=	None

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

	# Instantiate dataloaders.
	snake_case_		:			int											=	DataLoader(
	    tokenized_datasets['train']   , shuffle=__a   , collate_fn=__a   , batch_size=__a   )
	snake_case_		:			Union[str, Any]											=	DataLoader(
	    tokenized_datasets['validation']   , shuffle=__a   , collate_fn=__a   , batch_size=__a   )

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

	snake_case_		:			Dict											=	evaluate.load('glue'   , 'mrpc'   )

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

	set_seed(__a   )
	snake_case_						,snake_case_		:			List[str]											=	get_dataloaders(__a   , __a   )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	snake_case_		:			Union[str, Any]											=	AutoModelForSequenceClassification.from_pretrained('bert-base-cased'   , return_dict=__a   )

	# 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).
	snake_case_		:			List[Any]											=	model.to(accelerator.device   )

	# Instantiate optimizer
	snake_case_		:			Optional[Any]											=	AdamW(params=model.parameters()   , lr=__a   )

	# Instantiate scheduler
	snake_case_		:			Optional[Any]											=	get_linear_schedule_with_warmup(
	    optimizer=__a   , num_warmup_steps=1_00   , num_training_steps=(len(__a   ) * 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.
	snake_case_						,snake_case_						,snake_case_						,snake_case_						,snake_case_		:			Any											=	accelerator.prepare(
	    __a   , __a   , __a   , __a   , __a   )

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

		model.eval()
		snake_case_		:			str											=	0
		for step, batch in enumerate(__a   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			with torch.no_grad():
				snake_case_		:			Any											=	model(**__a   )
			snake_case_		:			Any											=	outputs.logits.argmax(dim=-1   )
			snake_case_						,snake_case_		:			List[Any]											=	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(__a   ) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					snake_case_		:			Optional[Any]											=	predictions[: len(eval_dataloader.dataset   ) - samples_seen]
					snake_case_		:			Any											=	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=__a   , references=__a   , )

		snake_case_		:			Optional[Any]											=	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""   , __a   )


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Optional[Any]											=	argparse.ArgumentParser(description='Simple example of training script.'   )
	parser.add_argument(
	    '--mixed_precision'   , type=__a   , default=__a   , 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.'   )
	snake_case_		:			Union[str, Any]											=	parser.parse_args()
	snake_case_		:			Dict											=	{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(__a   , __a   )


if __name__ == "__main__":
						main()








import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

							snake_case_		:			Optional[int]											=	tokenizer(_A		,   return_tensors='pt'		,   truncation=_A		,   padding='longest'	).to(_A	)
							snake_case_		:			List[Any]											=	model.generate(
							    input_ids=batch.input_ids		,   num_beams=8		,   )
							snake_case_		:			Dict											=	tokenizer.batch_decode(
							    _A		,   skip_special_tokens=_A		,   clean_up_tokenization_spaces=_A	)
							snake_case_		:			Tuple											=	calculate_bleu(_A		,   _A	)
							print(_A	)
							self.assertGreaterEqual(scores['bleu']		,   _A	)

