

'''simple docstring'''







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									=     16
__lowerCamelCase									=     32
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__ = 16					)      ->      str:
       A_						       =   AutoTokenizer.from_pretrained("""bert-base-cased"""					)
       A_						       =   load_dataset("""glue""",							"""mrpc"""					)

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

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

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

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

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

       A_						       =   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=UpperCAmelCase__					)
       def inner_training_loop(UpperCAmelCase__					):
              # 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(UpperCAmelCase__					)

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              A_						       =   AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",							return_dict=UpperCAmelCase__					)

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

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

              # Instantiate scheduler
              A_						       =   get_linear_schedule_with_warmup(
                  optimizer=UpperCAmelCase__,							num_warmup_steps=1_00,							num_training_steps=(len(UpperCAmelCase__					) * 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.
              A_       ,      A_       ,      A_       ,      A_       ,      A_						       =   accelerator.prepare(
                  UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)

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

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

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

    # 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    UpperCAmelCase__     (   )      ->      Optional[Any]:
       A_						       =   argparse.ArgumentParser(description="""Simple example of training script."""					)
       parser.add_argument(
           """--mixed_precision""",							type=UpperCAmelCase__,							default=UpperCAmelCase__,							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."""					)
       A_						       =   parser.parse_args()
       A_						       =   {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
       training_function(UpperCAmelCase__,							UpperCAmelCase__					)


if __name__ == "__main__":
      main()



'''simple docstring'''







import os
import sys
import unittest


__lowerCamelCase									=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__lowerCamelCase									=     os.path.join(git_repo_path, '''src''', '''transformers''')

__lowerCamelCase									=     '''
{0} = None
'''

__lowerCamelCase									=     '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__lowerCamelCase									=     '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''


class        A__   (   unittest.TestCase      ):



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""							)
           self.assertIsNone(UpperCamelCase__							)

           A_						       =   find_backend("""    if not is_tokenizers_available():"""							)
           self.assertEqual(UpperCamelCase__		,					"""tokenizers"""							)

           A_						       =   find_backend("""    if not is_tensorflow_text_available():"""							)
           self.assertEqual(UpperCamelCase__		,					"""tensorflow_text"""							)

           A_						       =   find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""							)
           self.assertEqual(UpperCamelCase__		,					"""sentencepiece_and_tokenizers"""							)

           A_						       =   find_backend(
               """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""							)
           self.assertEqual(UpperCamelCase__		,					"""sentencepiece_and_tensorflow_text"""							)

           A_						       =   find_backend(
               """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""							)
           self.assertEqual(UpperCamelCase__		,					"""sentencepiece_and_tokenizers_and_vision"""							)



    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn("""torch"""		,					UpperCamelCase__							)
           self.assertIn("""tensorflow_text"""		,					UpperCamelCase__							)
           self.assertIn("""sentencepiece_and_tokenizers"""		,					UpperCamelCase__							)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn("""BertModel"""		,					objects["""torch"""]							)
           self.assertIn("""TFBertModel"""		,					objects["""tf"""]							)
           self.assertIn("""FlaxBertModel"""		,					objects["""flax"""]							)
           self.assertIn("""BertModel"""		,					objects["""torch"""]							)
           self.assertIn("""TFBertTokenizer"""		,					objects["""tensorflow_text"""]							)
           self.assertIn("""convert_slow_tokenizer"""		,					objects["""sentencepiece_and_tokenizers"""]							)



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   create_dummy_object("""CONSTANT"""		,					"""'torch'"""							)
           self.assertEqual(UpperCamelCase__		,					"""\nCONSTANT = None\n"""							)

           A_						       =   create_dummy_object("""function"""		,					"""'torch'"""							)
           self.assertEqual(
               UpperCamelCase__		,					"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""							)

           A_						       =   """
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
           A_						       =   create_dummy_object("""FakeClass"""		,					"""'torch'"""							)
           self.assertEqual(UpperCamelCase__		,					UpperCamelCase__							)







    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
           A_						       =   create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}							)
           self.assertEqual(dummy_files["""torch"""]		,					UpperCamelCase__							)
