'''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
#
########################################################################


_A			:      str					      =				16
_A			:      Dict					      =				32







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






 '''simple docstring'''

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

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

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

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

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

 __lowerCAmelCase											=						DataLoader(
     tokenized_datasets["""test"""]						,		shuffle=SCREAMING_SNAKE_CASE__						,		collate_fn=SCREAMING_SNAKE_CASE__						,		batch_size=SCREAMING_SNAKE_CASE__      )

 return train_dataloader, eval_dataloader, test_dataloader







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       Dict      )					->     str:






 '''simple docstring'''

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

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

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

 set_seed(SCREAMING_SNAKE_CASE__      )

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

  # Instantiate optimizer
  __lowerCAmelCase											=						AdamW(params=model.parameters()						,		lr=SCREAMING_SNAKE_CASE__      )

  # Instantiate scheduler
  __lowerCAmelCase											=						get_linear_schedule_with_warmup(
      optimizer=SCREAMING_SNAKE_CASE__						,		num_warmup_steps=1_00						,		num_training_steps=(len(SCREAMING_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.
  __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=						accelerator.prepare(
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__      )

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

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

   __lowerCAmelCase											=						metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f"""epoch {epoch}:"""						,		SCREAMING_SNAKE_CASE__      )

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







def      UpperCamelCase_						(					)					->     Optional[Any]:






 '''simple docstring'''

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


if __name__ == "__main__":
 main()










from typing import TYPE_CHECKING

from ...utils import _LazyModule


_A		       =						{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
   from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





