


'''simple docstring'''





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
#
########################################################################

__lowerCamelCase												=						16
__lowerCamelCase												=						32


def        a__     (					UpperCamelCase_						:      Accelerator,   UpperCamelCase_						:      int = 16						):
   UpperCAmelCase__	:str      =      AutoTokenizer.from_pretrained('''bert-base-cased'''						)
   UpperCAmelCase__	:Union[str, Any]      =      load_dataset('''glue''',   '''mrpc'''						)

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

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

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

   # Instantiate dataloaders.
   UpperCAmelCase__	:List[str]      =      DataLoader(
       tokenized_datasets['''train'''],   shuffle=UpperCamelCase_,   collate_fn=UpperCamelCase_,   batch_size=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[Any]      =      DataLoader(
       tokenized_datasets['''validation'''],   shuffle=UpperCamelCase_,   collate_fn=UpperCamelCase_,   batch_size=UpperCamelCase_						)

   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

    __lowerCamelCase												=						mocked_dataloaders  # noqa: F811


def        a__     (					UpperCamelCase_						:      int,   UpperCamelCase_						:      List[Any]						):
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS''',   UpperCamelCase_						) == "1":
      UpperCAmelCase__	:List[Any]      =      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__	:Optional[Any]      =      Accelerator(
          cpu=args.cpu,   mixed_precision=args.mixed_precision,   log_with='''all''',   project_dir=args.project_dir						)
   else:
      UpperCAmelCase__	:Any      =      Accelerator(cpu=args.cpu,   mixed_precision=args.mixed_precision						)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase__	:List[Any]      =      config['''lr''']
   UpperCAmelCase__	:List[Any]      =      int(config['''num_epochs''']						)
   UpperCAmelCase__	:Optional[Any]      =      int(config['''seed''']						)
   UpperCAmelCase__	:Optional[int]      =      int(config['''batch_size''']						)
   set_seed(UpperCamelCase_						)

   UpperCAmelCase__   ,						UpperCAmelCase__	:Dict      =      get_dataloaders(UpperCamelCase_,   UpperCamelCase_						)
   UpperCAmelCase__	:int      =      evaluate.load('''glue''',   '''mrpc'''						)

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

   # 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=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).
   UpperCAmelCase__	:str      =      model.to(accelerator.device						)

   # Instantiate optimizer
   UpperCAmelCase__	:Tuple      =      AdamW(params=model.parameters(),   lr=UpperCamelCase_						)

   # Instantiate scheduler
   UpperCAmelCase__	:Optional[int]      =      get_linear_schedule_with_warmup(
       optimizer=UpperCamelCase_,   num_warmup_steps=100,   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.
   UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__   ,						UpperCAmelCase__	:Dict      =      accelerator.prepare(
       UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_,   UpperCamelCase_						)

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

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

      model.eval()
      for step, batch in enumerate(UpperCamelCase_						):
         # 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__	:Tuple      =      model(**UpperCamelCase_						)
         UpperCAmelCase__	:Union[str, Any]      =      outputs.logits.argmax(dim=-1						)
         UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
         metric.add_batch(
             predictions=UpperCamelCase_,   references=UpperCamelCase_,   )

      UpperCAmelCase__	:Optional[int]      =      metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:''',   UpperCamelCase_						)

      # 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(UpperCamelCase_						),
                 '''epoch''': epoch,
             },   step=UpperCamelCase_,   )

    # 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        a__     (					):
   UpperCAmelCase__	:Union[str, 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(
       '''--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=UpperCamelCase_,   default='''logs''',   help='''Location on where to store experiment tracking logs` and relevent project information''',   )
   UpperCAmelCase__	:Union[str, Any]      =      parser.parse_args()
   UpperCAmelCase__	:List[Any]      =      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(UpperCamelCase_,   UpperCamelCase_						)


if __name__ == "__main__":
    main()







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase												=						{
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						['''MaskFormerFeatureExtractor''']
    __lowerCamelCase												=						['''MaskFormerImageProcessor''']


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase												=						[
        '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MaskFormerForInstanceSegmentation''',
        '''MaskFormerModel''',
        '''MaskFormerPreTrainedModel''',
    ]
    __lowerCamelCase												=						[
        '''MaskFormerSwinBackbone''',
        '''MaskFormerSwinModel''',
        '''MaskFormerSwinPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
    from .configuration_maskformer_swin import MaskFormerSwinConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_maskformer import MaskFormerFeatureExtractor
        from .image_processing_maskformer import MaskFormerImageProcessor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_maskformer import (
            MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            MaskFormerForInstanceSegmentation,
            MaskFormerModel,
            MaskFormerPreTrainedModel,
        )
        from .modeling_maskformer_swin import (
            MaskFormerSwinBackbone,
            MaskFormerSwinModel,
            MaskFormerSwinPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase												=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)



