




'''simple docstring'''





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp
@slow
@require_flax
class        __snake_case  (unittest.TestCase		):



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[Any]      )  ->    int:





								'''simple docstring'''




								return f"gaussian_noise_s={seed}_shape={'_'.join([str(_UpperCAmelCase      ) for s in shape]      )}.npy"



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    str:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		Any=0		,	_UpperCAmelCase      :		Any=(4, 4, 64, 64)		,	_UpperCAmelCase      :		List[str]=False      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Any										=      jnp.bfloataa if fpaa else jnp.floataa
								_lowerCAmelCase    :    Optional[Any]										=      jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase		,	_UpperCAmelCase      )      )		,	dtype=_UpperCAmelCase      )
								return image



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]=False		,	_UpperCAmelCase      :		List[str]="CompVis/stable-diffusion-v1-4"      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      jnp.bfloataa if fpaa else jnp.floataa
								_lowerCAmelCase    :    str										=      """bf16""" if fpaa else None

								_lowerCAmelCase    :    Any										=      FlaxUNetaDConditionModel.from_pretrained(
								    _UpperCAmelCase		,	subfolder="""unet"""		,	dtype=_UpperCAmelCase		,	revision=_UpperCAmelCase      )
								return model, params



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		List[str]=0		,	_UpperCAmelCase      :		Optional[int]=(4, 77, 768)		,	_UpperCAmelCase      :		str=False      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      jnp.bfloataa if fpaa else jnp.floataa
								_lowerCAmelCase    :    List[Any]										=      jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase		,	_UpperCAmelCase      )      )		,	dtype=_UpperCAmelCase      )
								return hidden_states



				@parameterized.expand(
				    [
				        # fmt: off
				        [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
				        [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
				        [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
				        [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
				        # fmt: on
				    ]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		List[Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[int]										=      self.get_latents(_UpperCAmelCase		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      self.get_encoder_hidden_states(_UpperCAmelCase		,	fpaa=_UpperCAmelCase      )

								_lowerCAmelCase    :    Tuple										=      model.apply(
								    {"""params""": params}		,	_UpperCAmelCase		,	jnp.array(_UpperCAmelCase		,	dtype=jnp.intaa      )		,	encoder_hidden_states=_UpperCAmelCase		,	).sample

								assert sample.shape == latents.shape

								_lowerCAmelCase    :    Dict										=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )		,	dtype=jnp.floataa      )
								_lowerCAmelCase    :    Optional[Any]										=      jnp.array(_UpperCAmelCase		,	dtype=jnp.floataa      )

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
								assert jnp.allclose(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-2      )



				@parameterized.expand(
				    [
				        # fmt: off
				        [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
				        [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
				        [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
				        [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
				        # fmt: on
				    ]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      self.get_latents(_UpperCAmelCase		,	shape=(4, 4, 96, 96)		,	fpaa=_UpperCAmelCase      )
								_lowerCAmelCase    :    Union[str, Any]										=      self.get_encoder_hidden_states(_UpperCAmelCase		,	shape=(4, 77, 1024)		,	fpaa=_UpperCAmelCase      )

								_lowerCAmelCase    :    List[str]										=      model.apply(
								    {"""params""": params}		,	_UpperCAmelCase		,	jnp.array(_UpperCAmelCase		,	dtype=jnp.intaa      )		,	encoder_hidden_states=_UpperCAmelCase		,	).sample

								assert sample.shape == latents.shape

								_lowerCAmelCase    :    Tuple										=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())      )		,	dtype=jnp.floataa      )
								_lowerCAmelCase    :    Dict										=      jnp.array(_UpperCAmelCase		,	dtype=jnp.floataa      )

								# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
								assert jnp.allclose(_UpperCAmelCase		,	_UpperCAmelCase		,	atol=1E-2      )







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


_lowerCamelCase      :  Any      						=						1_6
_lowerCamelCase      :  Tuple      						=						3_2




def 							_UpperCAmelCase  (UpperCamelCase_ : Accelerator      ,    UpperCamelCase_ : DatasetDict      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : List[int]      ,    UpperCamelCase_ : int = 16       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Any										=      AutoTokenizer.from_pretrained("""bert-base-cased"""       )
				_lowerCAmelCase    :    int										=      DatasetDict(
				    {
				        """train""": dataset["""train"""].select(UpperCamelCase_       ),
				        """validation""": dataset["""train"""].select(UpperCamelCase_       ),
				        """test""": dataset["""validation"""],
				    }       )

				def tokenize_function(UpperCamelCase_ : str       ):
								# max_length=None => use the model max length (it's actually the default)
								_lowerCAmelCase    :    List[Any]										=      tokenizer(examples["""sentence1"""]      ,    examples["""sentence2"""]      ,    truncation=UpperCamelCase_      ,    max_length=UpperCamelCase_       )
								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    :    List[str]										=      datasets.map(
								    UpperCamelCase_      ,    batched=UpperCamelCase_      ,    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    :    Optional[Any]										=      tokenized_datasets.rename_column("""label"""      ,    """labels"""       )

				def collate_fn(UpperCamelCase_ : Optional[Any]       ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								_lowerCAmelCase    :    List[str]										=      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    :    Optional[Any]										=      16
								elif accelerator.mixed_precision != "no":
												_lowerCAmelCase    :    int										=      8
								else:
												_lowerCAmelCase    :    Dict										=      None

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

				# Instantiate dataloaders.
				_lowerCAmelCase    :    Optional[Any]										=      DataLoader(
				    tokenized_datasets["""train"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )
				_lowerCAmelCase    :    Tuple										=      DataLoader(
				    tokenized_datasets["""validation"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				_lowerCAmelCase    :    List[str]										=      DataLoader(
				    tokenized_datasets["""test"""]      ,    shuffle=UpperCamelCase_      ,    collate_fn=UpperCamelCase_      ,    batch_size=UpperCamelCase_       )

				return train_dataloader, eval_dataloader, test_dataloader




def 							_UpperCAmelCase  (UpperCamelCase_ : Any      ,    UpperCamelCase_ : Optional[int]       ):
				'''simple docstring'''
				# New Code #
				_lowerCAmelCase    :    Dict										=      []
				# Download the dataset
				_lowerCAmelCase    :    Optional[int]										=      load_dataset("""glue"""      ,    """mrpc"""       )
				# Create our splits
				_lowerCAmelCase    :    Any										=      StratifiedKFold(n_splits=int(args.num_folds       )       )
				# Initialize accelerator
				_lowerCAmelCase    :    int										=      Accelerator(cpu=args.cpu      ,    mixed_precision=args.mixed_precision       )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_lowerCAmelCase    :    List[str]										=      config["""lr"""]
				_lowerCAmelCase    :    Any										=      int(config["""num_epochs"""]       )
				_lowerCAmelCase    :    Dict										=      int(config["""seed"""]       )
				_lowerCAmelCase    :    Union[str, Any]										=      int(config["""batch_size"""]       )

				_lowerCAmelCase    :    Optional[Any]										=      evaluate.load("""glue"""      ,    """mrpc"""       )

				# If the batch size is too big we use gradient accumulation
				_lowerCAmelCase    :    Optional[Any]										=      1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								_lowerCAmelCase    :    Optional[Any]										=      batch_size // MAX_GPU_BATCH_SIZE
								_lowerCAmelCase    :    int										=      MAX_GPU_BATCH_SIZE

				set_seed(UpperCamelCase_       )

				# New Code #
				# Create our folds:
				_lowerCAmelCase    :    Optional[int]										=      kfold.split(np.zeros(datasets["""train"""].num_rows       )      ,    datasets["""train"""]["""label"""]       )
				_lowerCAmelCase    :    Any										=      []
				# Iterate over them
				for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase_       ):
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      get_fold_dataloaders(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    )
								# Instantiate the model (we build the model here so that the seed also control new weights initialization)
								_lowerCAmelCase    :    int										=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,    return_dict=UpperCamelCase_       )

								# 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    :    Union[str, Any]										=      model.to(accelerator.device       )

								# Instantiate optimizer
								_lowerCAmelCase    :    Any										=      AdamW(params=model.parameters()      ,    lr=UpperCamelCase_       )

								# Instantiate scheduler
								_lowerCAmelCase    :    Tuple										=      get_linear_schedule_with_warmup(
								    optimizer=UpperCamelCase_      ,    num_warmup_steps=100      ,    num_training_steps=(len(UpperCamelCase_       ) * 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    :    List[str]										=      accelerator.prepare(
								    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_       )

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

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

												_lowerCAmelCase    :    List[Any]										=      metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"epoch {epoch}:"      ,    UpperCamelCase_       )

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




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				_lowerCAmelCase    :    Optional[int]										=      argparse.ArgumentParser(description="""Simple example of training script."""       )
				parser.add_argument(
				    """--mixed_precision"""      ,    type=UpperCamelCase_      ,    default=UpperCamelCase_      ,    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=UpperCamelCase_      ,    default=3      ,    help="""The number of splits to perform across the dataset"""       )
				_lowerCAmelCase    :    Optional[Any]										=      parser.parse_args()
				_lowerCAmelCase    :    List[Any]										=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(UpperCamelCase_      ,    UpperCamelCase_       )


if __name__ == "__main__":
	main()

