


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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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
#
########################################################################

_SCREAMING_SNAKE_CASE :					Any			=	32







def 						_lowercase       (     __lowerCamelCase			:					Accelerator		,__lowerCamelCase			:					int = 16			)  ->   Optional[Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Dict								=     AutoTokenizer.from_pretrained('''bert-base-cased'''			)
     UpperCamelCase__							:  int								=     load_dataset('''glue'''		,'''mrpc'''			)

     def tokenize_function(__lowerCamelCase			:					str			):
          # max_length=None => use the model max length (it's actually the default)
          UpperCamelCase__							:  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():
          UpperCamelCase__							:  List[Any]								=     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
     UpperCamelCase__							:  List[str]								=     tokenized_datasets.rename_column('''label'''		,'''labels'''			)

     def collate_fn(__lowerCamelCase			:					List[str]			):
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               UpperCamelCase__							:  List[Any]								=     16
          elif accelerator.mixed_precision != "no":
               UpperCamelCase__							:  int								=     8
          else:
               UpperCamelCase__							:  Union[str, Any]								=     None

          return tokenizer.pad(
              __lowerCamelCase		,padding='''longest'''		,pad_to_multiple_of=__lowerCamelCase		,return_tensors='''pt'''		,)

     # Instantiate dataloaders.
     UpperCamelCase__							:  Optional[Any]								=     DataLoader(
         tokenized_datasets['''train''']		,shuffle=__lowerCamelCase		,collate_fn=__lowerCamelCase		,batch_size=__lowerCamelCase			)
     UpperCamelCase__							:  Any								=     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

   _SCREAMING_SNAKE_CASE :					List[Any]			=	mocked_dataloaders  # noqa: F811







def 						_lowercase       (     __lowerCamelCase			:					Union[str, Any]		,__lowerCamelCase			:					Union[str, Any]			)  ->   int:





     '''simple docstring'''





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

     UpperCamelCase__							:  Any								=     evaluate.load('''glue'''		,'''mrpc'''			)

     # New Code #
     # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
     # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
     # half each time. From this, we can calculate the number of gradient accumulation steps needed
     # and modify the Accelerator object as a result
     @find_executable_batch_size(starting_batch_size=int(__lowerCamelCase			)			)
     def inner_training_loop(__lowerCamelCase			:					Tuple			):
          # Since we need to modify the outside accelerator object, we need to bring it
          # to the local scope
          nonlocal accelerator

          # We can calculate the number of gradient accumulation steps based on the current
          # batch size vs the starting batch size
          UpperCamelCase__							:  str								=     observed_batch_size // batch_size

          # And then set it in the Accelerator directly:
          UpperCamelCase__							:  Optional[Any]								=     num_gradient_accumulation_steps

          # Next we need to free all of the stored model references in the Accelerator each time
          accelerator.free_memory()

          # And set the seed so our results are reproducable each reset
          set_seed(__lowerCamelCase			)

          # Instantiate the model (we build the model here so that the seed also control new weights initialization)
          UpperCamelCase__							:  List[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).
          UpperCamelCase__							:  Optional[int]								=     model.to(accelerator.device			)

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

          # Instantiate scheduler
          UpperCamelCase__							:  Tuple								=     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.
          UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__	,UpperCamelCase__							:  List[str]								=     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			):
                    # And perform gradient accumulation
                    with accelerator.accumulate(__lowerCamelCase			):
                         # We could avoid this line since we set the accelerator with `device_placement=True`.
                         batch.to(accelerator.device			)
                         UpperCamelCase__							:  str								=     model(**__lowerCamelCase			)
                         UpperCamelCase__							:  str								=     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():
                         UpperCamelCase__							:  Dict								=     model(**__lowerCamelCase			)
                    UpperCamelCase__							:  int								=     outputs.logits.argmax(dim=-1			)
                    UpperCamelCase__	,UpperCamelCase__							:  str								=     accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
                    metric.add_batch(
                        predictions=__lowerCamelCase		,references=__lowerCamelCase		,)

               UpperCamelCase__							:  Optional[int]								=     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 						_lowercase       (     )  ->   str:





     '''simple docstring'''





     UpperCamelCase__							:  List[str]								=     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.'''			)
     UpperCamelCase__							:  Optional[int]								=     parser.parse_args()
     # New Code #
     # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
     UpperCamelCase__							:  List[Any]								=     {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 256}
     training_function(__lowerCamelCase		,__lowerCamelCase			)


if __name__ == "__main__":
   main()








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """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

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




