




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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


_snake_case            = 16
_snake_case            = 32







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 16  ):

      '''simple docstring'''





      _A							:  List[Any]    =		AutoTokenizer.from_pretrained("""bert-base-cased"""  )
      _A							:  Optional[int]    =		load_dataset("""glue""","""mrpc"""  )

      def tokenize_function(snake_case_  ):
            # max_length=None => use the model max length (it's actually the default)
            _A							:  List[Any]    =		tokenizer(examples["""sentence1"""],examples["""sentence2"""],truncation=A__,max_length=A__  )
            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(
                A__,batched=A__,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							:  int    =		tokenized_datasets.rename_column("""label""","""labels"""  )

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

            return tokenizer.pad(
                A__,padding="""longest""",max_length=A__,pad_to_multiple_of=A__,return_tensors="""pt""",)

      # Instantiate dataloaders.
      _A							:  Dict    =		DataLoader(
          tokenized_datasets["""train"""],shuffle=A__,collate_fn=A__,batch_size=A__  )
      _A							:  Any    =		DataLoader(
          tokenized_datasets["""validation"""],shuffle=A__,collate_fn=A__,batch_size=A__  )

      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

  _snake_case            = mocked_dataloaders  # noqa: F811







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):

      '''simple docstring'''





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

      _A							:  Optional[int]    =		evaluate.load("""glue""","""mrpc"""  )

      # 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							:  Tuple    =		batch_size // MAX_GPU_BATCH_SIZE
            _A							:  Dict    =		MAX_GPU_BATCH_SIZE

      set_seed(A__  )
      _A    ,     _A							:  List[Any]    =		get_dataloaders(A__,A__  )
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      _A							:  Optional[int]    =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",return_dict=A__  )

      # 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							:  Tuple    =		model.to(accelerator.device  )

      # Instantiate optimizer
      _A							:  Tuple    =		AdamW(params=model.parameters(),lr=A__  )

      # Instantiate scheduler
      _A							:  Optional[Any]    =		get_linear_schedule_with_warmup(
          optimizer=A__,num_warmup_steps=100,num_training_steps=(len(A__  ) * 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							:  Optional[int]    =		accelerator.prepare(
          A__,A__,A__,A__,A__  )

      # Now we train the model
      for epoch in range(A__  ):
            model.train()
            for step, batch in enumerate(A__  ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device  )
                  _A							:  int    =		model(**A__  )
                  _A							:  List[str]    =		outputs.loss
                  _A							:  Tuple    =		loss / gradient_accumulation_steps
                  accelerator.backward(A__  )
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            _A							:  Union[str, Any]    =		0
            for step, batch in enumerate(A__  ):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device  )
                  with torch.no_grad():
                        _A							:  Optional[int]    =		model(**A__  )
                  _A							:  Any    =		outputs.logits.argmax(dim=-1  )
                  _A    ,     _A							:  Optional[int]    =		accelerator.gather((predictions, batch["""labels"""])  )
                  # New Code #
                  # First we check if it's a distributed system
                  if accelerator.use_distributed:
                        # Then see if we're on the last batch of our eval dataloader
                        if step == len(A__  ) - 1:
                              # Last batch needs to be truncated on distributed systems as it contains additional samples
                              _A							:  List[Any]    =		predictions[: len(eval_dataloader.dataset  ) - samples_seen]
                              _A							:  List[str]    =		references[: len(eval_dataloader.dataset  ) - samples_seen]
                        else:
                              # Otherwise we add the number of samples seen
                              samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
                  metric.add_batch(
                      predictions=A__,references=A__,)

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







def        lowerCAmelCase_			(  ):

      '''simple docstring'''





      _A							:  str    =		argparse.ArgumentParser(description="""Simple example of training script."""  )
      parser.add_argument(
          """--mixed_precision""",type=A__,default=A__,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."""  )
      _A							:  List[Any]    =		parser.parse_args()
      _A							:  Union[str, Any]    =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(A__,A__  )


if __name__ == "__main__":
  main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case            = {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  _snake_case            = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

