






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__       		=  16
lowerCamelCase__       		=  32






def 	UpperCamelCase	(							snake_case__  : Accelerator						,snake_case__  : DatasetDict						,snake_case__  : List[int]						,snake_case__  : List[int]						,snake_case__  : int = 16	):




     '''simple docstring'''






     __snake_case      :Tuple 							=    AutoTokenizer.from_pretrained("""bert-base-cased"""	)
     __snake_case      :Dict 							=    DatasetDict(
         {
             """train""": dataset["""train"""].select(snake_case__	),
             """validation""": dataset["""train"""].select(snake_case__	),
             """test""": dataset["""validation"""],
         }	)

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

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

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

     # Instantiate dataloaders.
     __snake_case      :List[Any] 							=    DataLoader(
         tokenized_datasets["""train"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=snake_case__	)
     __snake_case      :Dict 							=    DataLoader(
         tokenized_datasets["""validation"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=snake_case__	)

     __snake_case      :Dict 							=    DataLoader(
         tokenized_datasets["""test"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=snake_case__	)

     return train_dataloader, eval_dataloader, test_dataloader






def 	UpperCamelCase	(							snake_case__  : Optional[int]						,snake_case__  : Any	):




     '''simple docstring'''






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

     __snake_case      :List[Any] 							=    evaluate.load("""glue"""						,"""mrpc"""	)

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

     set_seed(snake_case__	)

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

          # 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).
          __snake_case      :List[str] 							=    model.to(accelerator.device	)

          # Instantiate optimizer
          __snake_case      :Optional[int] 							=    AdamW(params=model.parameters()						,lr=snake_case__	)

          # Instantiate scheduler
          __snake_case      :int 							=    get_linear_schedule_with_warmup(
              optimizer=snake_case__						,num_warmup_steps=100						,num_training_steps=(len(snake_case__	) * 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.
          __snake_case							,			__snake_case							,			__snake_case							,			__snake_case							,			__snake_case      :Dict 							=    accelerator.prepare(
              snake_case__						,snake_case__						,snake_case__						,snake_case__						,snake_case__	)

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

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

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

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






def 	UpperCamelCase	(							):




     '''simple docstring'''






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


if __name__ == "__main__":
     main()










def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     if digit_amount > 0:
          return round(number - int(snake_case__	)						,snake_case__	)
     return number - int(snake_case__	)


if __name__ == "__main__":
     print(decimal_isolate(1.5_3, 0))
     print(decimal_isolate(3_5.3_4_5, 1))
     print(decimal_isolate(3_5.3_4_5, 2))
     print(decimal_isolate(3_5.3_4_5, 3))
     print(decimal_isolate(-1_4.7_8_9, 3))
     print(decimal_isolate(0, 2))
     print(decimal_isolate(-1_4.1_2_3, 1))
     print(decimal_isolate(-1_4.1_2_3, 2))
     print(decimal_isolate(-1_4.1_2_3, 3))


