



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__magic_name__       =     logging.get_logger(__name__)

__magic_name__       =     {
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}


class 	a__       (       _snake_case       ):


					"""simple docstring"""



					A__  :				Tuple					 =				'''layoutlm'''

					def __init__(		self     :Optional[Any]      ,  lowercase__     :List[Any]=3_0522      ,  lowercase__     :List[Any]=768      ,  lowercase__     :Optional[int]=12      ,  lowercase__     :List[str]=12      ,  lowercase__     :Optional[Any]=3072      ,  lowercase__     :str="gelu"      ,  lowercase__     :Union[str, Any]=0.1      ,  lowercase__     :str=0.1      ,  lowercase__     :Union[str, Any]=512      ,  lowercase__     :str=2      ,  lowercase__     :List[str]=0.02      ,  lowercase__     :List[Any]=1E-12      ,  lowercase__     :str=0      ,  lowercase__     :Tuple="absolute"      ,  lowercase__     :List[str]=True      ,  lowercase__     :str=1024      ,  **lowercase__     :Union[str, Any]      ,  ):
										super().__init__(pad_token_id=_lowerCAmelCase      ,  **_lowerCAmelCase    )
										lowercase						   =       vocab_size
										lowercase						   =       hidden_size
										lowercase						   =       num_hidden_layers
										lowercase						   =       num_attention_heads
										lowercase						   =       hidden_act
										lowercase						   =       intermediate_size
										lowercase						   =       hidden_dropout_prob
										lowercase						   =       attention_probs_dropout_prob
										lowercase						   =       max_position_embeddings
										lowercase						   =       type_vocab_size
										lowercase						   =       initializer_range
										lowercase						   =       layer_norm_eps
										lowercase						   =       position_embedding_type
										lowercase						   =       use_cache
										lowercase						   =       max_ad_position_embeddings




class 	a__       (       _snake_case       ):


					"""simple docstring"""



					def __init__(		self     :Tuple      ,  lowercase__     :PretrainedConfig      ,  lowercase__     :str = "default"      ,  lowercase__     :List[PatchingSpec] = None      ,  ):
										super().__init__(_lowerCAmelCase      ,  task=_lowerCAmelCase      ,  patching_specs=_lowerCAmelCase    )
										lowercase						   =       config.max_ad_position_embeddings - 1

					@property
					def 				__UpperCAmelCase		(		self     :Dict    ):
										return OrderedDict(
										    [
										        ('input_ids', {0: 'batch', 1: 'sequence'}),
										        ('bbox', {0: 'batch', 1: 'sequence'}),
										        ('attention_mask', {0: 'batch', 1: 'sequence'}),
										        ('token_type_ids', {0: 'batch', 1: 'sequence'}),
										    ]    )



					def 				__UpperCAmelCase		(		self     :Union[str, Any]      ,  lowercase__     :PreTrainedTokenizer      ,  lowercase__     :int = -1      ,  lowercase__     :int = -1      ,  lowercase__     :bool = False      ,  lowercase__     :Optional[TensorType] = None      ,  ):
										lowercase						   =       super().generate_dummy_inputs(
										    _lowerCAmelCase      ,  batch_size=_lowerCAmelCase      ,  seq_length=_lowerCAmelCase      ,  is_pair=_lowerCAmelCase      ,  framework=_lowerCAmelCase    )

										# Generate a dummy bbox
										lowercase						   =       [48, 84, 73, 128]

										if not framework == TensorType.PYTORCH:
															raise NotImplementedError('Exporting LayoutLM to ONNX is currently only supported for PyTorch.'    )

										if not is_torch_available():
															raise ValueError('Cannot generate dummy inputs without PyTorch installed.'    )
										import torch

										lowercase   ,      lowercase						   =       input_dict['input_ids'].shape
										lowercase						   =       torch.tensor([*[box] * seq_length]    ).tile(_lowerCAmelCase      ,  1      ,  1    )
										return input_dict








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 checkpointing 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:					Dict     =16
_UpperCamelCase:					Optional[int]     =32



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Accelerator		,				__SCREAMING_SNAKE_CASE					:  int = 16    ):






  """simple docstring"""




  _lowerCAmelCase						=		AutoTokenizer.from_pretrained('bert-base-cased'    )
  _lowerCAmelCase						=		load_dataset('glue'		,				'mrpc'    )

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

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

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

  # Instantiate dataloaders.
  _lowerCAmelCase						=		DataLoader(
      tokenized_datasets['train']		,				shuffle=__SCREAMING_SNAKE_CASE		,				collate_fn=__SCREAMING_SNAKE_CASE		,				batch_size=__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		DataLoader(
      tokenized_datasets['validation']		,				shuffle=__SCREAMING_SNAKE_CASE		,				collate_fn=__SCREAMING_SNAKE_CASE		,				batch_size=__SCREAMING_SNAKE_CASE    )

  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:					int     =mocked_dataloaders  # noqa: F811



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




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

  # New Code #
  # Parse out whether we are saving every epoch or after a certain number of batches
  if hasattr(args.checkpointing_steps		,				'isdigit'    ):
    if args.checkpointing_steps == "epoch":
      _lowerCAmelCase						=		args.checkpointing_steps
    elif args.checkpointing_steps.isdigit():
      _lowerCAmelCase						=		int(args.checkpointing_steps    )
    else:
      raise ValueError(
          f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
  else:
    _lowerCAmelCase						=		None

  set_seed(__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase	,       _lowerCAmelCase						=		get_dataloaders(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
  _lowerCAmelCase						=		evaluate.load('glue'		,				'mrpc'    )

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

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

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

  # Instantiate optimizer
  _lowerCAmelCase						=		AdamW(params=model.parameters()		,				lr=__SCREAMING_SNAKE_CASE    )

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

  # New Code #
  # We need to keep track of how many total steps we have iterated over
  _lowerCAmelCase						=		0
  # We also need to keep track of the stating epoch so files are named properly
  _lowerCAmelCase						=		0

  # We need to load the checkpoint back in before training here with `load_state`
  # The total number of epochs is adjusted based on where the state is being loaded from,
  # as we assume continuation of the same training script
  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    )
      _lowerCAmelCase						=		os.path.basename(args.resume_from_checkpoint    )
    else:
      # Get the most recent checkpoint
      _lowerCAmelCase						=		[f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
      dirs.sort(key=os.path.getctime    )
      _lowerCAmelCase						=		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    _lowerCAmelCase						=		os.path.splitext(__SCREAMING_SNAKE_CASE    )[0]

    if "epoch" in training_difference:
      _lowerCAmelCase						=		int(training_difference.replace('epoch_'		,				''    )    ) + 1
      _lowerCAmelCase						=		None
    else:
      _lowerCAmelCase						=		int(training_difference.replace('step_'		,				''    )    )
      _lowerCAmelCase						=		resume_step // len(__SCREAMING_SNAKE_CASE    )
      resume_step -= starting_epoch * len(__SCREAMING_SNAKE_CASE    )

    # Now we train the model
  for epoch in range(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
    model.train()
    # New Code #
    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
      _lowerCAmelCase						=		accelerator.skip_first_batches(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )
      overall_step += resume_step
    else:
      # After the first iteration though, we need to go back to the original dataloader
      _lowerCAmelCase						=		train_dataloader
    for step, batch in enumerate(__SCREAMING_SNAKE_CASE    ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device    )
      _lowerCAmelCase						=		model(**__SCREAMING_SNAKE_CASE    )
      _lowerCAmelCase						=		outputs.loss
      _lowerCAmelCase						=		loss / gradient_accumulation_steps
      accelerator.backward(__SCREAMING_SNAKE_CASE    )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()
      # New Code #
      overall_step += 1

      # New Code #
      # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
      # These are saved to folders named `step_{overall_step}`
      # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
      # If mixed precision was used, will also save a "scalar.bin" file
      if isinstance(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    ):
        _lowerCAmelCase						=		f'''step_{overall_step}'''
        if overall_step % checkpointing_steps == 0:
          if args.output_dir is not None:
            _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
          accelerator.save_state(__SCREAMING_SNAKE_CASE    )

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

    _lowerCAmelCase						=		metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:'''		,				__SCREAMING_SNAKE_CASE    )

    # New Code #
    # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
    # These are saved to folders named `epoch_{epoch}`
    # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
    # If mixed precision was used, will also save a "scalar.bin" file
    if checkpointing_steps == "epoch":
      _lowerCAmelCase						=		f'''epoch_{epoch}'''
      if args.output_dir is not None:
        _lowerCAmelCase						=		os.path.join(args.output_dir		,				__SCREAMING_SNAKE_CASE    )
      accelerator.save_state(__SCREAMING_SNAKE_CASE    )



def 	_a  (					):






  """simple docstring"""




  _lowerCAmelCase						=		argparse.ArgumentParser(description='Simple example of training script.'    )
  parser.add_argument(
      '--mixed_precision'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				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=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'		,				)
  parser.add_argument(
      '--output_dir'		,				type=__SCREAMING_SNAKE_CASE		,				default='.'		,				help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'		,				)
  parser.add_argument(
      '--resume_from_checkpoint'		,				type=__SCREAMING_SNAKE_CASE		,				default=__SCREAMING_SNAKE_CASE		,				help='If the training should continue from a checkpoint folder.'		,				)
  _lowerCAmelCase						=		parser.parse_args()
  _lowerCAmelCase						=		{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(__SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()
