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


UpperCAmelCase__			=    1_6
UpperCAmelCase__			=    3_2






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase ,lowercase = 16		):







  """simple docstring"""
  _UpperCAmelCase    			=   AutoTokenizer.from_pretrained("""bert-base-cased"""		)
  _UpperCAmelCase    			=   DatasetDict(
      {
          """train""": dataset["""train"""].select(lowercase		),
          """validation""": dataset["""train"""].select(lowercase		),
          """test""": dataset["""validation"""],
      }		)

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

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

  _UpperCAmelCase    			=   DataLoader(
      tokenized_datasets["""test"""] ,shuffle=lowercase ,collate_fn=lowercase ,batch_size=lowercase		)

  return train_dataloader, eval_dataloader, test_dataloader






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  # New Code #
  _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(lowercase		)

  # 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(lowercase		):
    _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   get_fold_dataloaders(
        lowercase ,lowercase ,lowercase ,lowercase ,)
    # 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=lowercase		)

    # 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=lowercase		)

    # Instantiate scheduler
    _UpperCAmelCase    			=   get_linear_schedule_with_warmup(
        optimizer=lowercase ,num_warmup_steps=1_00 ,num_training_steps=(len(lowercase		) * 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(
        lowercase ,lowercase ,lowercase ,lowercase ,lowercase		)

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

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

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

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






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   argparse.ArgumentParser(description="""Simple example of training script."""		)
  parser.add_argument(
      """--mixed_precision""" ,type=lowercase ,default=lowercase ,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=lowercase ,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(lowercase ,lowercase		)


if __name__ == "__main__":
    main()

"""simple docstring"""


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    """kwargs, expected""" ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(lowercase ,i + 1		) for i in range(10		)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4		), range(4 ,7		), range(7 ,10		)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1		), range(1 ,2		), range(2 ,3		)]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _distribute_shards(**lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected""" ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _split_gen_kwargs(lowercase ,lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected""" ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if expected is RuntimeError:
    with pytest.raises(lowercase		):
      _number_of_shards_in_gen_kwargs(lowercase		)
  else:
    _UpperCAmelCase    			=   _number_of_shards_in_gen_kwargs(lowercase		)
    assert out == expected
