

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


SCREAMING_SNAKE_CASE     				=						16
SCREAMING_SNAKE_CASE     				=						32







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = 16 )		->							Union[str, Any]:
		UpperCAmelCase_						=				AutoTokenizer.from_pretrained("bert-base-cased" )
		UpperCAmelCase_						=				DatasetDict(
		    {
		        "train": dataset["train"].select(_lowercase ),
		        "validation": dataset["train"].select(_lowercase ),
		        "test": dataset["validation"],
		    } )

		def tokenize_function(__SCREAMING_SNAKE_CASE ):
				# max_length=None => use the model max length (it's actually the default)
				UpperCAmelCase_						=				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():
				UpperCAmelCase_						=				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
		UpperCAmelCase_						=				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.
				UpperCAmelCase_						=				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_						=				16
				elif accelerator.mixed_precision != "no":
						UpperCAmelCase_						=				8
				else:
						UpperCAmelCase_						=				None

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

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

		UpperCAmelCase_						=				DataLoader(
		    tokenized_datasets["test"]			,     shuffle=_lowercase			,     collate_fn=_lowercase			,     batch_size=_lowercase )

		return train_dataloader, eval_dataloader, test_dataloader







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Union[str, Any]:
		UpperCAmelCase_						=				[]
		# Download the dataset
		UpperCAmelCase_						=				load_dataset("glue"			,     "mrpc" )
		# Create our splits
		UpperCAmelCase_						=				StratifiedKFold(n_splits=int(args.num_folds ) )
		# Initialize accelerator
		UpperCAmelCase_						=				Accelerator(cpu=args.cpu			,     mixed_precision=args.mixed_precision )
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		UpperCAmelCase_						=				config["lr"]
		UpperCAmelCase_						=				int(config["num_epochs"] )
		UpperCAmelCase_						=				int(config["seed"] )
		UpperCAmelCase_						=				int(config["batch_size"] )

		UpperCAmelCase_						=				evaluate.load("glue"			,     "mrpc" )

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

		set_seed(_lowercase )

		# New Code #
		# Create our folds:
		UpperCAmelCase_						=				kfold.split(np.zeros(datasets["train"].num_rows )			,     datasets["train"]["label"] )
		UpperCAmelCase_						=				[]
		# Iterate over them
		for i, (train_idxs, valid_idxs) in enumerate(_lowercase ):
				UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				get_fold_dataloaders(
				    _lowercase			,     _lowercase			,     _lowercase			,     _lowercase			,     )
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				UpperCAmelCase_						=				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).
				UpperCAmelCase_						=				model.to(accelerator.device )

				# Instantiate optimizer
				UpperCAmelCase_						=				AdamW(params=model.parameters()			,     lr=_lowercase )

				# Instantiate scheduler
				UpperCAmelCase_						=				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.
				UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				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 )
								UpperCAmelCase_						=				model(**_lowercase )
								UpperCAmelCase_						=				outputs.loss
								UpperCAmelCase_						=				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():
										UpperCAmelCase_						=				model(**_lowercase )
								UpperCAmelCase_						=				outputs.logits.argmax(dim=-1 )
								UpperCAmelCase_					,						UpperCAmelCase_						=				accelerator.gather_for_metrics((predictions, batch["labels"]) )
								metric.add_batch(
								    predictions=_lowercase			,     references=_lowercase			,     )

						UpperCAmelCase_						=				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
				UpperCAmelCase_						=				[]
				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():
								UpperCAmelCase_						=				model(**_lowercase )
						UpperCAmelCase_						=				outputs.logits
						UpperCAmelCase_					,						UpperCAmelCase_						=				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:
		UpperCAmelCase_						=				torch.cat(_lowercase			,     dim=0 )
		UpperCAmelCase_						=				torch.stack(_lowercase			,     dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
		UpperCAmelCase_						=				metric.compute(predictions=_lowercase			,     references=_lowercase )
		accelerator.print("Average test metrics from all folds:"			,     _lowercase )







def snake_case__  (			)		->							Union[str, Any]:
		UpperCAmelCase_						=				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" )
		UpperCAmelCase_						=				parser.parse_args()
		UpperCAmelCase_						=				{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(_lowercase			,     _lowercase )


if __name__ == "__main__":
			main()






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

