



"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowercase__		:    Dict				   =     logging.get_logger(__name__)

lowercase__		:    Tuple				   =     {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""dpt"""



      def __init__(				self    :				str   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=0.0   ,      SCREAMING_SNAKE_CASE_    :				str=0.0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.02   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=3_8_4   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_6   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=3   ,      SCREAMING_SNAKE_CASE_    :				int=False   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=True   ,      SCREAMING_SNAKE_CASE_    :				str=[2, 5, 8, 1_1]   ,      SCREAMING_SNAKE_CASE_    :				int="project"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=[4, 2, 1, 0.5]   ,      SCREAMING_SNAKE_CASE_    :				int=[9_6, 1_9_2, 3_8_4, 7_6_8]   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=2_5_6   ,      SCREAMING_SNAKE_CASE_    :				List[str]=-1   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=False   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=0.4   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2_5_5   ,      SCREAMING_SNAKE_CASE_    :				Any=0.1   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=[1, 1_0_2_4, 2_4, 2_4]   ,      SCREAMING_SNAKE_CASE_    :				List[str]=[0, 1]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=None   ,      **SCREAMING_SNAKE_CASE_    :				List[str]   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Dict								=			hidden_size
        lowerCAmelCase_       :    List[str]								=			is_hybrid

        if self.is_hybrid:
          if backbone_config is None:
            logger.info('Initializing the config with a `BiT` backbone.'	)
            lowerCAmelCase_       :    int								=			{
                'global_padding': 'same',
                'layer_type': 'bottleneck',
                'depths': [3, 4, 9],
                'out_features': ['stage1', 'stage2', 'stage3'],
                'embedding_dynamic_padding': True,
            }
            lowerCAmelCase_       :    str								=			BitConfig(**SCREAMING_SNAKE_CASE_	)
          elif isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            logger.info('Initializing the config with a `BiT` backbone.'	)
            lowerCAmelCase_       :    Tuple								=			BitConfig(**SCREAMING_SNAKE_CASE_	)
          elif isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	):
            lowerCAmelCase_       :    Tuple								=			backbone_config
          else:
            raise ValueError(
                F"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."	)

          lowerCAmelCase_       :    Union[str, Any]								=			backbone_featmap_shape
          lowerCAmelCase_       :    Union[str, Any]								=			neck_ignore_stages

          if readout_type != "project":
            raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'	)
        else:
          lowerCAmelCase_       :    Optional[Any]								=			None
          lowerCAmelCase_       :    Dict								=			None
          lowerCAmelCase_       :    Optional[int]								=			[]

        lowerCAmelCase_       :    Union[str, Any]								=			num_hidden_layers
        lowerCAmelCase_       :    Optional[int]								=			num_attention_heads
        lowerCAmelCase_       :    Union[str, Any]								=			intermediate_size
        lowerCAmelCase_       :    List[Any]								=			hidden_act
        lowerCAmelCase_       :    int								=			hidden_dropout_prob
        lowerCAmelCase_       :    Any								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    int								=			initializer_range
        lowerCAmelCase_       :    List[str]								=			layer_norm_eps
        lowerCAmelCase_       :    List[str]								=			image_size
        lowerCAmelCase_       :    Optional[int]								=			patch_size
        lowerCAmelCase_       :    List[str]								=			num_channels
        lowerCAmelCase_       :    str								=			qkv_bias
        lowerCAmelCase_       :    Optional[int]								=			backbone_out_indices
        if readout_type not in ["ignore", "add", "project"]:
          raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'	)
        lowerCAmelCase_       :    Optional[int]								=			readout_type
        lowerCAmelCase_       :    Union[str, Any]								=			reassemble_factors
        lowerCAmelCase_       :    Union[str, Any]								=			neck_hidden_sizes
        lowerCAmelCase_       :    Dict								=			fusion_hidden_size
        lowerCAmelCase_       :    Any								=			head_in_index
        lowerCAmelCase_       :    Optional[Any]								=			use_batch_norm_in_fusion_residual
        # auxiliary head attributes (semantic segmentation)
        lowerCAmelCase_       :    Union[str, Any]								=			use_auxiliary_head
        lowerCAmelCase_       :    Dict								=			auxiliary_loss_weight
        lowerCAmelCase_       :    Optional[Any]								=			semantic_loss_ignore_index
        lowerCAmelCase_       :    int								=			semantic_classifier_dropout





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        lowerCAmelCase_       :    Dict								=			copy.deepcopy(self.__dict__	)

        if output["backbone_config"] is not None:
          lowerCAmelCase_       :    List[str]								=			self.backbone_config.to_dict()

        lowerCAmelCase_       :    Optional[Any]								=			self.__class__.model_type
        return output










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


lowercase__		:    Optional[int]				   =     1_6
lowercase__		:    List[str]				   =     3_2

def     UpperCamelCase_   (   lowerCAmelCase__				:    Accelerator   ,       lowerCAmelCase__				:    int = 16						)						->       Dict:

  """simple docstring"""




  lowerCAmelCase_       :    Union[str, Any]								=			AutoTokenizer.from_pretrained('bert-base-cased'						)
  lowerCAmelCase_       :    Union[str, Any]								=			load_dataset('glue'   ,       'mrpc'						)

  def tokenize_function(lowerCAmelCase__				:    Tuple						):
    # max_length=None => use the model max length (it's actually the default)
    lowerCAmelCase_       :    Dict								=			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():
    lowerCAmelCase_       :    Dict								=			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
  lowerCAmelCase_       :    int								=			tokenized_datasets.rename_column('label'   ,       'labels'						)

  def collate_fn(lowerCAmelCase__				:    List[str]						):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCAmelCase_       :    Any								=			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_       :    Dict								=			16
    elif accelerator.mixed_precision != "no":
      lowerCAmelCase_       :    str								=			8
    else:
      lowerCAmelCase_       :    str								=			None

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

  # Instantiate dataloaders.
  lowerCAmelCase_       :    List[Any]								=			DataLoader(
      tokenized_datasets['train']   ,       shuffle=lowerCAmelCase__   ,       collate_fn=lowerCAmelCase__   ,       batch_size=lowerCAmelCase__						)
  lowerCAmelCase_       :    Tuple								=			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

     lowercase__		:    int				   =     mocked_dataloaders  # noqa: F811

def     UpperCamelCase_   (   lowerCAmelCase__				:    Optional[int]   ,       lowerCAmelCase__				:    Optional[Any]						)						->       Optional[int]:

  """simple docstring"""




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

  lowerCAmelCase_       :    str								=			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=lowerCAmelCase__						)
  def inner_training_loop(lowerCAmelCase__				:    Optional[int]						):
    # 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(lowerCAmelCase__						)

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

    # Instantiate optimizer
    lowerCAmelCase_       :    List[Any]								=			AdamW(params=model.parameters()   ,       lr=lowerCAmelCase__						)
    lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			get_dataloaders(lowerCAmelCase__   ,       lowerCAmelCase__						)

    # Instantiate scheduler
    lowerCAmelCase_       :    Dict								=			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.
    lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_       :    List[Any]								=			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__						):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device						)
        lowerCAmelCase_       :    List[str]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    Optional[Any]								=			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():
          lowerCAmelCase_       :    Union[str, Any]								=			model(**lowerCAmelCase__						)
        lowerCAmelCase_       :    List[Any]								=			outputs.logits.argmax(dim=-1						)
        lowerCAmelCase_						,lowerCAmelCase_       :    Optional[Any]								=			accelerator.gather_for_metrics((predictions, batch['labels'])						)
        metric.add_batch(
            predictions=lowerCAmelCase__   ,       references=lowerCAmelCase__   ,       )

      lowerCAmelCase_       :    str								=			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     UpperCamelCase_   (   )						->       Optional[Any]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			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.'						)
  lowerCAmelCase_       :    str								=			parser.parse_args()
  lowerCAmelCase_       :    Union[str, Any]								=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
  training_function(lowerCAmelCase__   ,       lowerCAmelCase__						)


if __name__ == "__main__":
     main()





