


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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     # Initialize accelerator
     if args.with_tracking:
          a_     :  str				      =     Accelerator(
              cpu=args.cpu	,	mixed_precision=args.mixed_precision	,	log_with="""all"""	,	project_dir=args.project_dir			)
     else:
          a_     :  Any				      =     Accelerator(cpu=args.cpu	,	mixed_precision=args.mixed_precision			)

     if hasattr(args.checkpointing_steps	,	"""isdigit"""			):
          if args.checkpointing_steps == "epoch":
               a_     :  Any				      =     args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               a_     :  Optional[int]				      =     int(args.checkpointing_steps			)
          else:
               raise ValueError(
                   F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''			)
     else:
          a_     :  Any				      =     None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     a_     :  str				      =     config["""lr"""]
     a_     :  int				      =     int(config["""num_epochs"""]			)
     a_     :  Optional[int]				      =     int(config["""seed"""]			)
     a_     :  Dict				      =     int(config["""batch_size"""]			)

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          a_     :  Union[str, Any]				      =     os.path.split(__UpperCamelCase			)[-1].split("""."""			)[0]
          accelerator.init_trackers(__UpperCamelCase	,	__UpperCamelCase			)

     a_     :  int				      =     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
     a_     :  Tuple				      =     load_dataset("""glue"""	,	"""mrpc"""			)
     a_     :  Optional[int]				      =     evaluate.load("""glue"""	,	"""mrpc"""			)

     def tokenize_function(__UpperCamelCase			):
          # max_length=None => use the model max length (it's actually the default)
          a_     :  List[Any]				      =     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_     :  int				      =     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_     :  Tuple				      =     tokenized_datasets.rename_column("""label"""	,	"""labels"""			)

     # If the batch size is too big we use gradient accumulation
     a_     :  str				      =     1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          a_     :  List[str]				      =     batch_size // MAX_GPU_BATCH_SIZE
          a_     :  int				      =     MAX_GPU_BATCH_SIZE

     def collate_fn(__UpperCamelCase			):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          a_     :  str				      =     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_     :  Dict				      =     1_6
          elif accelerator.mixed_precision != "no":
               a_     :  Optional[Any]				      =     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_     :  Optional[Any]				      =     DataLoader(
         tokenized_datasets["""validation"""]	,	shuffle=__UpperCamelCase	,	collate_fn=__UpperCamelCase	,	batch_size=__UpperCamelCase			)

     set_seed(__UpperCamelCase			)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     a_     :  List[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_     :  int				      =     AdamW(params=model.parameters()	,	lr=__UpperCamelCase			)

     # Instantiate scheduler
     a_     :  List[Any]				      =     get_linear_schedule_with_warmup(
         optimizer=__UpperCamelCase	,	num_warmup_steps=1_0_0	,	num_training_steps=(len(__UpperCamelCase			) * 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.
     a_      ,      a_      ,      a_      ,      a_      ,      a_     :  List[str]				      =     accelerator.prepare(
         __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)

     # We need to keep track of how many total steps we have iterated over
     a_     :  Optional[Any]				      =     0
     # We also need to keep track of the stating epoch so files are named properly
     a_     :  Tuple				      =     0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''			)
               accelerator.load_state(args.resume_from_checkpoint			)
               a_     :  List[str]				      =     os.path.basename(args.resume_from_checkpoint			)
          else:
               # Get the most recent checkpoint
               a_     :  Optional[Any]				      =     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
               dirs.sort(key=os.path.getctime			)
               a_     :  str				      =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          a_     :  Optional[int]				      =     os.path.splitext(__UpperCamelCase			)[0]

          if "epoch" in training_difference:
               a_     :  Tuple				      =     int(training_difference.replace("""epoch_"""	,	""""""			)			) + 1
               a_     :  List[str]				      =     None
          else:
               a_     :  Any				      =     int(training_difference.replace("""step_"""	,	""""""			)			)
               a_     :  Dict				      =     resume_step // len(__UpperCamelCase			)
               resume_step -= starting_epoch * len(__UpperCamelCase			)

    # Now we train the model
     for epoch in range(__UpperCamelCase	,	__UpperCamelCase			):
          model.train()
          if args.with_tracking:
               a_     :  Optional[int]				      =     0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               a_     :  Dict				      =     accelerator.skip_first_batches(__UpperCamelCase	,	__UpperCamelCase			)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               a_     :  Optional[int]				      =     train_dataloader
          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_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
               a_     :  List[str]				      =     outputs.loss
               a_     :  List[str]				      =     loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__UpperCamelCase			)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__UpperCamelCase	,	__UpperCamelCase			):
                    a_     :  Optional[int]				      =     F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              a_     :  Tuple				      =     os.path.join(args.output_dir	,	__UpperCamelCase			)
                         accelerator.save_state(__UpperCamelCase			)

          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_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
               a_     :  Optional[Any]				      =     outputs.logits.argmax(dim=-1			)
               a_      ,      a_     :  List[Any]				      =     accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
               metric.add_batch(
                   predictions=__UpperCamelCase	,	references=__UpperCamelCase	,	)

          a_     :  int				      =     metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''	,	__UpperCamelCase			)
          if args.with_tracking:
               accelerator.log(
                   {
                       """accuracy""": eval_metric["""accuracy"""],
                       """f1""": eval_metric["""f1"""],
                       """train_loss""": total_loss.item() / len(__UpperCamelCase			),
                       """epoch""": epoch,
                   }	,	step=__UpperCamelCase	,	)

          if checkpointing_steps == "epoch":
               a_     :  Optional[int]				      =     F'''epoch_{epoch}'''
               if args.output_dir is not None:
                    a_     :  List[str]				      =     os.path.join(args.output_dir	,	__UpperCamelCase			)
               accelerator.save_state(__UpperCamelCase			)

     if args.with_tracking:
          accelerator.end_training()



def 			_a      (    ):
     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."""			)
     parser.add_argument(
         """--checkpointing_steps"""	,	type=__UpperCamelCase	,	default=__UpperCamelCase	,	help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""	,	)
     parser.add_argument(
         """--resume_from_checkpoint"""	,	type=__UpperCamelCase	,	default=__UpperCamelCase	,	help="""If the training should continue from a checkpoint folder."""	,	)
     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(
         """--output_dir"""	,	type=__UpperCamelCase	,	default="""."""	,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,	)
     parser.add_argument(
         """--project_dir"""	,	type=__UpperCamelCase	,	default="""logs"""	,	help="""Location on where to store experiment tracking logs` and relevent project information"""	,	)
     a_     :  List[Any]				      =     parser.parse_args()
     a_     :  Dict				      =     {"""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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:					Dict      				=			{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					List[Any]      				=			["""LayoutLMTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[Any]      				=			[
        """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """LayoutLMForMaskedLM""",
        """LayoutLMForSequenceClassification""",
        """LayoutLMForTokenClassification""",
        """LayoutLMForQuestionAnswering""",
        """LayoutLMModel""",
        """LayoutLMPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase_						:					Optional[int]      				=			[
        """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFLayoutLMForMaskedLM""",
        """TFLayoutLMForSequenceClassification""",
        """TFLayoutLMForTokenClassification""",
        """TFLayoutLMForQuestionAnswering""",
        """TFLayoutLMMainLayer""",
        """TFLayoutLMModel""",
        """TFLayoutLMPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlm import (
            LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMForMaskedLM,
            LayoutLMForQuestionAnswering,
            LayoutLMForSequenceClassification,
            LayoutLMForTokenClassification,
            LayoutLMModel,
            LayoutLMPreTrainedModel,
        )
    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlm import (
            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMForMaskedLM,
            TFLayoutLMForQuestionAnswering,
            TFLayoutLMForSequenceClassification,
            TFLayoutLMForTokenClassification,
            TFLayoutLMMainLayer,
            TFLayoutLMModel,
            TFLayoutLMPreTrainedModel,
        )

else:
    import sys

    UpperCamelCase_						:					Optional[Any]      				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


