
import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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					   =1_6
_lowerCamelCase					   =3_2





def     _a					(						lowerCamelCase,	lowerCamelCase = 16							):
 lowerCamelCase		:	Any							=      AutoTokenizer.from_pretrained("""bert-base-cased"""							)
 lowerCamelCase		:	Tuple							=      load_dataset("""glue""",	"""mrpc"""							)

 def tokenize_function(lowerCamelCase							):
  # max_length=None => use the model max length (it's actually the default)
  lowerCamelCase		:	Tuple							=      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():
  lowerCamelCase		:	Optional[int]							=      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
 lowerCamelCase		:	Union[str, Any]							=      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.
  lowerCamelCase		:	List[Any]							=      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":
   lowerCamelCase		:	Union[str, Any]							=      16
  elif accelerator.mixed_precision != "no":
   lowerCamelCase		:	Optional[int]							=      8
  else:
   lowerCamelCase		:	Tuple							=      None

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

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

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      _lowerCamelCase					   =mocked_dataloaders  # noqa: F811





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS""",	lowerCamelCase							) == "1":
  lowerCamelCase		:	Optional[Any]							=      2
 # Initialize accelerator
 lowerCamelCase		:	int							=      Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision							)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase		:	Union[str, Any]							=      config["""lr"""]
 lowerCamelCase		:	Any							=      int(config["""num_epochs"""]							)
 lowerCamelCase		:	int							=      int(config["""seed"""]							)
 lowerCamelCase		:	Optional[int]							=      int(config["""batch_size"""]							)

 lowerCamelCase		:	Tuple							=      evaluate.load("""glue""",	"""mrpc"""							)

 # New Code #
 # We now can define an inner training loop function. It should take a batch size as the only parameter,
 # and build the dataloaders in there.
 # It also gets our decorator
 @find_executable_batch_size(starting_batch_size=lowerCamelCase							)
 def inner_training_loop(lowerCamelCase							):
  # And now just move everything below under this function
  # We need to bring in the Accelerator object from earlier
  nonlocal accelerator
  # And reset all of its attributes that could hold onto any memory:
  accelerator.free_memory()

  # Then we can declare the model, optimizer, and everything else:
  set_seed(lowerCamelCase							)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase		:	str							=      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).
  lowerCamelCase		:	str							=      model.to(accelerator.device							)

  # Instantiate optimizer
  lowerCamelCase		:	Dict							=      AdamW(params=model.parameters(),	lr=lowerCamelCase							)
  lowerCamelCase			,    lowerCamelCase		:	Dict							=      get_dataloaders(lowerCamelCase,	lowerCamelCase							)

  # Instantiate scheduler
  lowerCamelCase		:	List[str]							=      get_linear_schedule_with_warmup(
      optimizer=lowerCamelCase,	num_warmup_steps=100,	num_training_steps=(len(lowerCamelCase							) * num_epochs),	)

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

   lowerCamelCase		:	Tuple							=      metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(F'''epoch {epoch}:''',	lowerCamelCase							)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()





def     _a					(						):
 lowerCamelCase		:	Any							=      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."""							)
 lowerCamelCase		:	Optional[Any]							=      parser.parse_args()
 lowerCamelCase		:	str							=      {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
 training_function(lowerCamelCase,	lowerCamelCase							)


if __name__ == "__main__":
      main()






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




