






"""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
#
########################################################################


A_      =							16
A_      =							32




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Accelerator,  UpperCAmelCase__    :   int = 1_6 )						->Tuple:
						A__  :					Optional[Any]											=					AutoTokenizer.from_pretrained("""bert-base-cased""" )
						A__  :					str											=					load_dataset("""glue""",  """mrpc""" )

						def tokenize_function(UpperCAmelCase__    :   Any ):
												# max_length=None => use the model max length (it's actually the default)
												A__  :					Dict											=					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__  :					Tuple											=					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__  :					Union[str, Any]											=					tokenized_datasets.rename_column("""label""",  """labels""" )

						def collate_fn(UpperCAmelCase__    :   Optional[Any] ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												A__  :					int											=					1_2_8 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__  :					Optional[int]											=					1_6
												elif accelerator.mixed_precision != "no":
																		A__  :					List[str]											=					8
												else:
																		A__  :					Dict											=					None

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

						# Instantiate dataloaders.
						A__  :					List[str]											=					DataLoader(
						    tokenized_datasets["""train"""],  shuffle=UpperCAmelCase__,  collate_fn=UpperCAmelCase__,  batch_size=UpperCAmelCase__ )
						A__  :					List[str]											=					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

						A_      =							mocked_dataloaders  # noqa: F811




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

						A__  :					int											=					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__    :   Union[str, Any] ):
												# 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__  :					Optional[Any]											=					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__  :					Any											=					model.to(accelerator.device )

												# Instantiate optimizer
												A__  :					Optional[Any]											=					AdamW(params=model.parameters(),  lr=UpperCAmelCase__ )
												A__					,					A__  :					Optional[int]											=					get_dataloaders(UpperCAmelCase__,  UpperCAmelCase__ )

												# Instantiate scheduler
												A__  :					Tuple											=					get_linear_schedule_with_warmup(
												    optimizer=UpperCAmelCase__,  num_warmup_steps=1_0_0,  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__  :					Any											=					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__  :					Any											=					model(**UpperCAmelCase__ )
																								A__  :					Any											=					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__  :					Union[str, Any]											=					model(**UpperCAmelCase__ )
																								A__  :					int											=					outputs.logits.argmax(dim=-1 )
																								A__					,					A__  :					Any											=					accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
																								metric.add_batch(
																								    predictions=UpperCAmelCase__,  references=UpperCAmelCase__,  )

																		A__  :					Tuple											=					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        _lowerCAmelCase						(							)						->Optional[Any]:
						A__  :					Any											=					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__  :					Union[str, Any]											=					parser.parse_args()
						A__  :					Optional[int]											=					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
						training_function(UpperCAmelCase__,  UpperCAmelCase__ )


if __name__ == "__main__":
						main()














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_      =							{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =							['''LlamaTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =							['''LlamaTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =							[
						    '''LlamaForCausalLM''',
						    '''LlamaModel''',
						    '''LlamaPreTrainedModel''',
						    '''LlamaForSequenceClassification''',
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

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






