



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       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : DatasetDict      ,lowerCAmelCase_       : List[int]      ,lowerCAmelCase_       : List[int]      ,lowerCAmelCase_       : int = 16    )				->			Union[str, Any]:






						'''simple docstring'''



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

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

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

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

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

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

						return train_dataloader, eval_dataloader, test_dataloader



def 	__a					(   lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Union[str, Any]    )				->			str:






						'''simple docstring'''



						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(lowerCAmelCase_    )

						# 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(lowerCAmelCase_    ):
												UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       get_fold_dataloaders(
												    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,)
												# 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=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).
												UpperCAmelCase_=       model.to(accelerator.device    )

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

												# Instantiate scheduler
												UpperCAmelCase_=       get_linear_schedule_with_warmup(
												    optimizer=lowerCAmelCase_      ,num_warmup_steps=1_00      ,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.
												UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       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    )
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																								UpperCAmelCase_=       outputs.loss
																								UpperCAmelCase_=       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():
																														UpperCAmelCase_=       model(**lowerCAmelCase_    )
																								UpperCAmelCase_=       outputs.logits.argmax(dim=-1    )
																								UpperCAmelCase_,       UpperCAmelCase_=       accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
																								metric.add_batch(
																								    predictions=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

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



def 	__a					(   )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       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"""    )
						UpperCAmelCase_=       parser.parse_args()
						UpperCAmelCase_=       {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()









from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						pyspark.sql.DataFrame						,  __UpperCAmelCase     :						Optional[NamedSplit] = None						,  __UpperCAmelCase     :						Optional[Features] = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						str = None						,  __UpperCAmelCase     :						bool = True						,  __UpperCAmelCase     :						str = "arrow"						,  **__UpperCAmelCase     :						str						,  )      ->			Dict:
							super().__init__(
							    split=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  keep_in_memory=__UpperCAmelCase						,  streaming=__UpperCAmelCase						,  **__UpperCAmelCase						,  )
							UpperCAmelCase_=       load_from_cache_file
							UpperCAmelCase_=       file_format
							UpperCAmelCase_=       Spark(
							    df=__UpperCAmelCase						,  features=__UpperCAmelCase						,  cache_dir=__UpperCAmelCase						,  working_dir=__UpperCAmelCase						,  **__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Dict:
							if self.streaming:
													return self.builder.as_streaming_dataset(split=self.split					)
							UpperCAmelCase_=       None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
							self.builder.download_and_prepare(
							    download_mode=__UpperCAmelCase						,  file_format=self._file_format						,  )
							return self.builder.as_dataset(split=self.split					)




