





import argparse
import os

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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# 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
#
########################################################################

UpperCamelCase__           =			16
UpperCamelCase__           =			32





def      _UpperCamelCase  (a__ :Accelerator ,      a__ :int = 16							):






							"""simple docstring"""





							UpperCamelCase__					  =      AutoTokenizer.from_pretrained("""bert-base-cased"""							)
							UpperCamelCase__					  =      load_dataset("""glue""" ,      """mrpc"""							)

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

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

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

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

							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

					UpperCamelCase__           =			mocked_dataloaders  # noqa: F811





def      _UpperCamelCase  (a__ :Tuple ,      a__ :Optional[Any]							):






							"""simple docstring"""





							if os.environ.get("""TESTING_MOCKED_DATALOADERS""" ,      a__							) == "1":
														UpperCamelCase__					  =      2
							# Initialize Accelerator

							# New Code #
							# We pass in "all" to `log_with` to grab all available trackers in the environment
							# Note: If using a custom `Tracker` class, should be passed in here such as:
							# >>> log_with = ["all", MyCustomTrackerClassInstance()]
							if args.with_tracking:
														UpperCamelCase__					  =      Accelerator(
														    cpu=args.cpu ,      mixed_precision=args.mixed_precision ,      log_with="""all""" ,      project_dir=args.project_dir							)
							else:
														UpperCamelCase__					  =      Accelerator(cpu=args.cpu ,      mixed_precision=args.mixed_precision							)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							UpperCamelCase__					  =      config["""lr"""]
							UpperCamelCase__					  =      int(config["""num_epochs"""]							)
							UpperCamelCase__					  =      int(config["""seed"""]							)
							UpperCamelCase__					  =      int(config["""batch_size"""]							)
							set_seed(a__							)

							UpperCamelCase__  ,  UpperCamelCase__					  =      get_dataloaders(a__ ,      a__							)
							UpperCamelCase__					  =      evaluate.load("""glue""" ,      """mrpc"""							)

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

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

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

							# Instantiate optimizer
							UpperCamelCase__					  =      AdamW(params=model.parameters() ,      lr=a__							)

							# Instantiate scheduler
							UpperCamelCase__					  =      get_linear_schedule_with_warmup(
							    optimizer=a__ ,      num_warmup_steps=100 ,      num_training_steps=(len(a__							) * 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.
							UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__  ,  UpperCamelCase__					  =      accelerator.prepare(
							    a__ ,      a__ ,      a__ ,      a__ ,      a__							)

							# New Code #
							# We need to initialize the trackers we use. Overall configurations can also be stored
							if args.with_tracking:
														UpperCamelCase__					  =      os.path.split(a__							)[-1].split("""."""							)[0]
														accelerator.init_trackers(a__ ,      a__							)

							# Now we train the model
							for epoch in range(a__							):
														model.train()
														# New Code #
														# For our tracking example, we will log the total loss of each epoch
														if args.with_tracking:
																					UpperCamelCase__					  =      0
														for step, batch in enumerate(a__							):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device							)
																					UpperCamelCase__					  =      model(**a__							)
																					UpperCamelCase__					  =      outputs.loss
																					# New Code #
																					if args.with_tracking:
																												total_loss += loss.detach().float()
																					UpperCamelCase__					  =      loss / gradient_accumulation_steps
																					accelerator.backward(a__							)
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

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

														# New Code #
														# To actually log, we call `Accelerator.log`
														# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
														if args.with_tracking:
																					accelerator.log(
																					    {
																					        """accuracy""": eval_metric["""accuracy"""],
																					        """f1""": eval_metric["""f1"""],
																					        """train_loss""": total_loss.item() / len(a__							),
																					        """epoch""": epoch,
																					    } ,      step=a__ ,      )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
							if args.with_tracking:
														accelerator.end_training()





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      argparse.ArgumentParser(description="""Simple example of training script."""							)
							parser.add_argument(
							    """--mixed_precision""" ,      type=a__ ,      default=a__ ,      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."""							)
							parser.add_argument(
							    """--with_tracking""" ,      action="""store_true""" ,      help="""Whether to load in all available experiment trackers from the environment and use them for logging.""" ,      )
							parser.add_argument(
							    """--project_dir""" ,      type=a__ ,      default="""logs""" ,      help="""Location on where to store experiment tracking logs` and relevent project information""" ,      )
							UpperCamelCase__					  =      parser.parse_args()
							UpperCamelCase__					  =      {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
							training_function(a__ ,      a__							)


if __name__ == "__main__":
					main()











import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""encoder-decoder"""
							snake_case  :   Optional[int]      =							True






							def __init__(    self	,			**__lowerCAmelCase	):
														super().__init__(**__lowerCAmelCase	)
														assert (
														    "encoder" in kwargs and "decoder" in kwargs
														), "Config has to be initialized with encoder and decoder config"
														UpperCamelCase__					  =      kwargs.pop("""encoder"""	)
														UpperCamelCase__					  =      encoder_config.pop("""model_type"""	)
														UpperCamelCase__					  =      kwargs.pop("""decoder"""	)
														UpperCamelCase__					  =      decoder_config.pop("""model_type"""	)

														from ..auto.configuration_auto import AutoConfig

														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      True






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""	)
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      True

														return cls(encoder=encoder_config.to_dict()	,			decoder=decoder_config.to_dict()	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      copy.deepcopy(self.__dict__	)
														UpperCamelCase__					  =      self.encoder.to_dict()
														UpperCamelCase__					  =      self.decoder.to_dict()
														UpperCamelCase__					  =      self.__class__.model_type
														return output




