





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__							:      Tuple         =    1_6
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    3_2






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase = 16 ):
   a__						:    Union[str, Any]   		=	AutoTokenizer.from_pretrained("bert-base-cased" )
   a__						:    Optional[int]   		=	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)
      a__						:    Dict   		=	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():
      a__						:    List[str]   		=	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
   a__						:    Optional[int]   		=	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.
      a__						:    Dict   		=	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":
         a__						:    int   		=	16
      elif accelerator.mixed_precision != "no":
         a__						:    Optional[int]   		=	8
      else:
         a__						:    str   		=	None

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

   # Instantiate dataloaders.
   a__						:    str   		=	DataLoader(
       tokenized_datasets["train"]				,			shuffle=lowerCamelCase				,			collate_fn=lowerCamelCase				,			batch_size=lowerCamelCase )
   a__						:    int   		=	DataLoader(
       tokenized_datasets["validation"]				,			shuffle=lowerCamelCase				,			collate_fn=lowerCamelCase				,			batch_size=lowerCamelCase )

   a__						:    int   		=	DataLoader(
       tokenized_datasets["test"]				,			shuffle=lowerCamelCase				,			collate_fn=lowerCamelCase				,			batch_size=lowerCamelCase )

   return train_dataloader, eval_dataloader, test_dataloader






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

   a__						:    Union[str, Any]   		=	evaluate.load("glue"				,			"mrpc" )

   # If the batch size is too big we use gradient accumulation
   a__						:    Dict   		=	1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      a__						:    Union[str, Any]   		=	batch_size // MAX_GPU_BATCH_SIZE
      a__						:    Tuple   		=	MAX_GPU_BATCH_SIZE

   set_seed(lowerCamelCase )

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

      # Instantiate optimizer
      a__						:    Tuple   		=	AdamW(params=model.parameters()				,			lr=lowerCamelCase )

      # Instantiate scheduler
      a__						:    str   		=	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.
      a__		,  a__		,  a__		,  a__		,  a__						:    Tuple   		=	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 )
            a__						:    str   		=	model(**lowerCamelCase )
            a__						:    List[str]   		=	outputs.loss
            a__						:    Dict   		=	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():
               a__						:    str   		=	model(**lowerCamelCase )
            a__						:    int   		=	outputs.logits.argmax(dim=-1 )
            a__		,  a__						:    Union[str, Any]   		=	accelerator.gather_for_metrics((predictions, batch["labels"]) )
            metric.add_batch(
                predictions=lowerCamelCase				,			references=lowerCamelCase				,			)

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






def    _A	(   ):
   a__						:    Dict   		=	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" )
   a__						:    str   		=	parser.parse_args()
   a__						:    Any   		=	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(lowerCamelCase				,			lowerCamelCase )


if __name__ == "__main__":
  main()



"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


_lowercase							=				[
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]



def      lowerCAmelCase__			(		__magic_name__=True      )						->int:
			if with_config:
						return [
						    {
						        "testcase_name": d["dataset"] + "/" + d["config_name"],
						        "dataset": d["dataset"],
						        "config_name": d["config_name"],
						    }
						    for d in DATASETS_ON_HF_GCP
						]
			else:
						return [
						    {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
						]

@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__a       )       )
class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			int          =   None
							_lowerCamelCase					:			int          =   None
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										with TemporaryDirectory() as tmp_dir:
													__lowercase       =       dataset_module_factory(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )

													__lowercase       =       import_main_class(dataset_module.module_path  ,    dataset=_lowerCamelCase       )

													__lowercase       =       builder_cls(
													    cache_dir=_lowerCamelCase  ,    config_name=_lowerCamelCase  ,    hash=dataset_module.hash  ,    )

													__lowercase       =       "/".join(
													    [
													        HF_GCP_BASE_URL,
													        builder_instance._relative_data_dir(with_hash=_lowerCamelCase       ).replace(os.sep  ,    "/"       ),
													        config.DATASET_INFO_FILENAME,
													    ]       )
													__lowercase       =       cached_path(_lowerCamelCase  ,    cache_dir=_lowerCamelCase       )
													self.assertTrue(os.path.exists(_lowerCamelCase       )       )





@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->str:
			__lowercase       =       tmp_path_factory.mktemp("test_hf_gcp"      ) / "test_wikipedia_simple"
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
			__lowercase       =       None
			builder_instance.download_and_prepare()
			__lowercase       =       builder_instance.as_dataset()
			assert ds







@pytest.mark.integration
def      lowerCAmelCase__			(		__magic_name__      )						->Any:
			__lowercase       =       dataset_module_factory("wikipedia"			,   cache_dir=__magic_name__      )
			__lowercase       =       import_main_class(dataset_module.module_path			,   dataset=__magic_name__      )
			__lowercase       =       builder_cls(
			    cache_dir=__magic_name__			,   config_name="20220301.frr"			,   hash=dataset_module.hash			,   )
			__lowercase       =       builder_instance.as_streaming_dataset()
			assert ds
			assert isinstance(__magic_name__			,   __magic_name__      )
			assert "train" in ds
			assert isinstance(ds["train"]			,   __magic_name__      )
			assert next(iter(ds["train"]      )      )





