






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


__UpperCamelCase      :      int     						=						16
__UpperCamelCase      :      int     						=						32





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase = 16					):




	'''simple docstring'''







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

	def tokenize_function(lowerCamelCase					):
		# max_length=None => use the model max length (it's actually the default)
		__lowercase             =  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():
		__lowercase             =  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
	__lowercase             =  tokenized_datasets.rename_column("""label"""				,  """labels"""					)

	def collate_fn(lowerCamelCase					):
		# 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(
		    lowerCamelCase				,  padding="""longest"""				,  max_length=lowerCamelCase				,  pad_to_multiple_of=lowerCamelCase				,  return_tensors="""pt"""				,  )

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

	__lowercase             =  DataLoader(
	    tokenized_datasets["""test"""]				,  shuffle=lowerCamelCase				,  collate_fn=lowerCamelCase				,  batch_size=lowerCamelCase					)

	return train_dataloader, eval_dataloader, test_dataloader





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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

	set_seed(lowerCamelCase					)

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

		# Instantiate optimizer
		__lowercase             =  AdamW(params=model.parameters()				,  lr=lowerCamelCase					)

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

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





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  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"""					)
	__lowercase             =  parser.parse_args()
	__lowercase             =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(lowerCamelCase				,  lowerCamelCase					)


if __name__ == "__main__":
						main()









import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard


@slow
@require_flax
class        __UpperCamelCase    (     unittest.TestCase   ):







	def  _a   (  self      :							Union[str, Any]			)		-> Any:
		"""simple docstring"""







		super().tearDown()
		gc.collect()







	def  _a   (  self      :							List[str]			)		-> int:
		"""simple docstring"""







		__lowercase       ,						__lowercase             =  FlaxStableDiffusionPipeline.from_pretrained(
		    """stabilityai/stable-diffusion-2"""       ,				revision="""bf16"""       ,				dtype=jnp.bfloataa       ,				)

		__lowercase             =  """A painting of a squirrel eating a burger"""
		__lowercase             =  jax.device_count()
		__lowercase             =  num_samples * [prompt]
		__lowercase             =  sd_pipe.prepare_inputs(_lowerCAmelCase			)

		__lowercase             =  replicate(_lowerCAmelCase			)
		__lowercase             =  shard(_lowerCAmelCase			)

		__lowercase             =  jax.random.PRNGKey(0			)
		__lowercase             =  jax.random.split(_lowerCAmelCase       ,				jax.device_count()			)

		__lowercase             =  sd_pipe(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				num_inference_steps=25       ,				jit=_lowerCAmelCase			)[0]
		assert images.shape == (jax.device_count(), 1, 768, 768, 3)

		__lowercase             =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
		__lowercase             =  images[0, 253:256, 253:256, -1]

		__lowercase             =  jnp.asarray(jax.device_get(image_slice.flatten()			)			)
		__lowercase             =  jnp.array([0.4_238, 0.4_414, 0.4_395, 0.4_453, 0.4_629, 0.4_590, 0.4_531, 0.45_508, 0.4_512]			)
		print(F'output_slice: {output_slice}'			)
		assert jnp.abs(output_slice - expected_slice			).max() < 1e-2







	def  _a   (  self      :							str			)		-> List[Any]:
		"""simple docstring"""







		__lowercase             =  """stabilityai/stable-diffusion-2"""
		__lowercase       ,						__lowercase             =  FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCAmelCase       ,				subfolder="""scheduler"""			)
		__lowercase       ,						__lowercase             =  FlaxStableDiffusionPipeline.from_pretrained(
		    _lowerCAmelCase       ,				scheduler=_lowerCAmelCase       ,				revision="""bf16"""       ,				dtype=jnp.bfloataa       ,				)
		__lowercase             =  scheduler_params

		__lowercase             =  """A painting of a squirrel eating a burger"""
		__lowercase             =  jax.device_count()
		__lowercase             =  num_samples * [prompt]
		__lowercase             =  sd_pipe.prepare_inputs(_lowerCAmelCase			)

		__lowercase             =  replicate(_lowerCAmelCase			)
		__lowercase             =  shard(_lowerCAmelCase			)

		__lowercase             =  jax.random.PRNGKey(0			)
		__lowercase             =  jax.random.split(_lowerCAmelCase       ,				jax.device_count()			)

		__lowercase             =  sd_pipe(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase       ,				num_inference_steps=25       ,				jit=_lowerCAmelCase			)[0]
		assert images.shape == (jax.device_count(), 1, 768, 768, 3)

		__lowercase             =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
		__lowercase             =  images[0, 253:256, 253:256, -1]

		__lowercase             =  jnp.asarray(jax.device_get(image_slice.flatten()			)			)
		__lowercase             =  jnp.array([0.4_336, 0.42_969, 0.4_453, 0.4_199, 0.4_297, 0.4_531, 0.4_434, 0.4_434, 0.4_297]			)
		print(F'output_slice: {output_slice}'			)
		assert jnp.abs(output_slice - expected_slice			).max() < 1e-2

