
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			:     List[Any]           =   16
__lowerCAmelCase			:     Any           =   32

def a__    (       A_,     A_,     A_,     A_,     A_ = 16					):






  '''simple docstring'''





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

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

  def collate_fn(A_					):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __magic_name__				     =       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":
      __magic_name__				     =       16
    elif accelerator.mixed_precision != "no":
      __magic_name__				     =       8
    else:
      __magic_name__				     =       None

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

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

  __magic_name__				     =       DataLoader(
      tokenized_datasets["""test"""],     shuffle=A_,     collate_fn=A_,     batch_size=A_					)

  return train_dataloader, eval_dataloader, test_dataloader

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       []
  # Download the dataset
  __magic_name__				     =       load_dataset("""glue""",     """mrpc"""					)
  # Create our splits
  __magic_name__				     =       StratifiedKFold(n_splits=int(args.num_folds					)					)
  # Initialize accelerator
  __magic_name__				     =       Accelerator(cpu=args.cpu,     mixed_precision=args.mixed_precision					)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __magic_name__				     =       config["""lr"""]
  __magic_name__				     =       int(config["""num_epochs"""]					)
  __magic_name__				     =       int(config["""seed"""]					)
  __magic_name__				     =       int(config["""batch_size"""]					)

  __magic_name__				     =       evaluate.load("""glue""",     """mrpc"""					)

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

  set_seed(A_					)

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

    # 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).
    __magic_name__				     =       model.to(accelerator.device					)

    # Instantiate optimizer
    __magic_name__				     =       AdamW(params=model.parameters(),     lr=A_					)

    # Instantiate scheduler
    __magic_name__				     =       get_linear_schedule_with_warmup(
        optimizer=A_,     num_warmup_steps=100,     num_training_steps=(len(A_					) * 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.
    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       accelerator.prepare(
        A_,     A_,     A_,     A_,     A_					)

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

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

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

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

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       argparse.ArgumentParser(description="""Simple example of training script."""					)
  parser.add_argument(
      """--mixed_precision""",     type=A_,     default=A_,     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=A_,     default=3,     help="""The number of splits to perform across the dataset"""					)
  __magic_name__				     =       parser.parse_args()
  __magic_name__				     =       {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A_,     A_					)


if __name__ == "__main__":
    main()







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           if self.timesteps is None:
                 raise ValueError(
                     """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""				)

           # TODO(Patrick) better comments + non-PyTorch
           # postprocess model score
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

           _A							:     str			=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
           _A							:     List[str]			=						beta_t.flatten()
           while len(beta_t.shape				) < len(x.shape				):
                 _A							:     Union[str, Any]			=						beta_t.unsqueeze(-1				)
           _A							:     Tuple			=						-0.5 * beta_t * x

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

