




"""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
#
########################################################################


a            =							1_6
a            =							3_2





def 							lowercase    (snake_case__      :       Accelerator					,				snake_case__      :       DatasetDict					,				snake_case__      :       List[int]					,				snake_case__      :       List[int]					,				snake_case__      :       int = 16       )   ->     Any:


				'''simple docstring'''






				lowerCAmelCase              = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				lowerCAmelCase              = DatasetDict(
				    {
				        """train""": dataset["""train"""].select(_lowerCamelCase       ),
				        """validation""": dataset["""train"""].select(_lowerCamelCase       ),
				        """test""": dataset["""validation"""],
				    }       )

				def tokenize_function(snake_case__      :       Dict       ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase              = tokenizer(examples["""sentence1"""]					,				examples["""sentence2"""]					,				truncation=_lowerCamelCase					,				max_length=_lowerCamelCase       )
								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(
								    _lowerCamelCase					,				batched=_lowerCamelCase					,				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__      :       Any       ):
								# 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(
								    _lowerCamelCase					,				padding="""longest"""					,				max_length=_lowerCamelCase					,				pad_to_multiple_of=_lowerCamelCase					,				return_tensors="""pt"""					,				)

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

				lowerCAmelCase              = DataLoader(
				    tokenized_datasets["""test"""]					,				shuffle=_lowerCamelCase					,				collate_fn=_lowerCamelCase					,				batch_size=_lowerCamelCase       )

				return train_dataloader, eval_dataloader, test_dataloader





def 							lowercase    (snake_case__      :       List[Any]					,				snake_case__      :       str       )   ->     Optional[int]:


				'''simple docstring'''






				lowerCAmelCase              = []
				# Download the dataset
				lowerCAmelCase              = load_dataset("""glue"""					,				"""mrpc"""       )
				# Create our splits
				lowerCAmelCase              = StratifiedKFold(n_splits=int(args.num_folds       )       )
				# 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"""       )

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

				set_seed(_lowerCamelCase       )

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

								# 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=_lowerCamelCase       )

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

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

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

												lowerCAmelCase              = metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f'''epoch {epoch}:'''					,				_lowerCamelCase       )

								# New Code #
								# We also run predictions on the test set at the very end
								lowerCAmelCase              = []
								for step, batch in enumerate(_lowerCamelCase       ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device       )
												with torch.no_grad():
																lowerCAmelCase              = model(**_lowerCamelCase       )
												lowerCAmelCase              = outputs.logits
												lowerCAmelCase       ,  lowerCAmelCase              = 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(_lowerCamelCase					,				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:
				lowerCAmelCase              = torch.cat(_lowerCamelCase					,				dim=0       )
				lowerCAmelCase              = torch.stack(_lowerCamelCase					,				dim=0       ).sum(dim=0       ).div(int(args.num_folds       )       ).argmax(dim=-1       )
				lowerCAmelCase              = metric.compute(predictions=_lowerCamelCase					,				references=_lowerCamelCase       )
				accelerator.print("""Average test metrics from all folds:"""					,				_lowerCamelCase       )





def 							lowercase    ()   ->     Dict:


				'''simple docstring'''






				lowerCAmelCase              = argparse.ArgumentParser(description="""Simple example of training script."""       )
				parser.add_argument(
				    """--mixed_precision"""					,				type=_lowerCamelCase					,				default=_lowerCamelCase					,				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=_lowerCamelCase					,				default=3					,				help="""The number of splits to perform across the dataset"""       )
				lowerCAmelCase              = parser.parse_args()
				lowerCAmelCase              = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_lowerCamelCase					,				_lowerCamelCase       )


if __name__ == "__main__":
		main()












'''simple docstring'''

import contextlib
import importlib
import io
import unittest

import transformers

# Try to import everything from transformers to ensure every object can be loaded.
from transformers import *  # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
from transformers.utils import ContextManagers, find_labels, is_flax_available, is_tf_available, is_torch_available


if is_torch_available():
  from transformers import BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification

if is_tf_available():
  from transformers import TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification

if is_flax_available():
  from transformers import FlaxBertForPreTraining, FlaxBertForQuestionAnswering, FlaxBertForSequenceClassification


__lowerCAmelCase							:	Dict			=DUMMY_UNKNOWN_IDENTIFIER
# An actual model hosted on huggingface.co

__lowerCAmelCase							:	List[Any]			="main"
# Default branch name
__lowerCAmelCase							:	int			="f2c752cfc5c0ab6f4bdec59acea69eefbee381c2"
# One particular commit (not the top of `main`)
__lowerCAmelCase							:	List[Any]			="aaaaaaa"
# This commit does not exist, so we should 404.

__lowerCAmelCase							:	Optional[int]			="d9e9f15bc825e4b2c9249e9578f884bbcb5e3684"
# Sha-1 of config.json on the top of `main`, for checking purposes
__lowerCAmelCase							:	Dict			="4b243c475af8d0a7754e87d7d096c92e5199ec2fe168a2ee7998e3b8e9bcb1d3"





@contextlib.contextmanager
def 						UpperCamelCase				(							):
    print("Welcome!"       )
    yield
    print("Bye!"       )





@contextlib.contextmanager
def 						UpperCamelCase				(							):
    print("Bonjour!"       )
    yield
    print("Au revoir!"       )






class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :str							)->							List[str]:
     # If the spec is missing, importlib would not be able to import the module dynamically.
     assert transformers.__spec__ is not None
     assert importlib.util.find_spec("transformers"							) is not None






class    UpperCAmelCase	(       unittest.TestCase				):
 @unittest.mock.patch("sys.stdout"						,       new_callable=io.StringIO							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :Union[str, Any]							)->							Any:
     with ContextManagers([]							):
         print("Transformers are awesome!"							)
     # The print statement adds a new line at the end of the output
     self.assertEqual(mock_stdout.getvalue()						,       "Transformers are awesome!\n"							)
 @unittest.mock.patch("sys.stdout"						,       new_callable=io.StringIO							)
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]							)->							Tuple:
     with ContextManagers([context_en()]							):
         print("Transformers are awesome!"							)
     # The output should be wrapped with an English welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()						,       "Welcome!\nTransformers are awesome!\nBye!\n"							)
 @unittest.mock.patch("sys.stdout"						,       new_callable=io.StringIO							)
 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :int							)->							Union[str, Any]:
     with ContextManagers([context_fr(), context_en()]							):
         print("Transformers are awesome!"							)
     # The output should be wrapped with an English and French welcome and goodbye
     self.assertEqual(mock_stdout.getvalue()						,       "Bonjour!\nWelcome!\nTransformers are awesome!\nBye!\nAu revoir!\n"							)
 @require_torch
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["labels", "next_sentence_label"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["start_positions", "end_positions"]							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
 @require_tf
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["labels", "next_sentence_label"]							)
     self.assertEqual(find_labels(lowercase_							)						,       ["start_positions", "end_positions"]							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       ["labels"]							)



 @require_flax
 def      UpperCAmelCase_							(				self      :Dict							)->							str:
     # Flax models don't have labels
     self.assertEqual(find_labels(lowercase_							)						,       []							)
     self.assertEqual(find_labels(lowercase_							)						,       []							)
     self.assertEqual(find_labels(lowercase_							)						,       []							)






     class    UpperCAmelCase	(       UpperCamelCase__				):
      pass

     self.assertEqual(find_labels(lowercase_							)						,       []							)



