




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

a_   		=     16
a_   		=     32




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = 16      )       ->	Optional[Any]:

    """simple docstring"""


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

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

    def collate_fn(lowercase_      ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase  		=						1_28 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  		=						16
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase  		=						8
        else:
            __UpperCamelCase  		=						None

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

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

    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

 a_   		=     mocked_dataloaders  # noqa: F811




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	List[Any]:

    """simple docstring"""


    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,     lowercase_      ) == "1":
        __UpperCamelCase  		=						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  		=						Accelerator(
            cpu=args.cpu					,     mixed_precision=args.mixed_precision					,     log_with='''all'''					,     project_dir=args.project_dir      )
    else:
        __UpperCamelCase  		=						Accelerator(cpu=args.cpu					,     mixed_precision=args.mixed_precision      )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase  		=						config["""lr"""]
    __UpperCamelCase  		=						int(config['''num_epochs''']      )
    __UpperCamelCase  		=						int(config['''seed''']      )
    __UpperCamelCase  		=						int(config['''batch_size''']      )
    set_seed(lowercase_      )

    __UpperCamelCase  		=						get_dataloaders(lowercase_					,     lowercase_      )
    __UpperCamelCase  		=						evaluate.load('''glue'''					,     '''mrpc'''      )

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

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

    # 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  		=						model.to(accelerator.device      )

    # Instantiate optimizer
    __UpperCamelCase  		=						AdamW(params=model.parameters()					,     lr=lowercase_      )

    # Instantiate scheduler
    __UpperCamelCase  		=						get_linear_schedule_with_warmup(
        optimizer=lowercase_					,     num_warmup_steps=1_00					,     num_training_steps=(len(lowercase_      ) * 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  		=						accelerator.prepare(
        lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )

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

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

        model.eval()
        for step, batch in enumerate(lowercase_      ):
            # 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  		=						model(**lowercase_      )
            __UpperCamelCase  		=						outputs.logits.argmax(dim=-1      )
            __UpperCamelCase  		=						accelerator.gather_for_metrics((predictions, batch['''labels'''])      )
            metric.add_batch(
                predictions=lowercase_					,     references=lowercase_					,     )

        __UpperCamelCase  		=						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"					,     lowercase_      )

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

    # 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 					__SCREAMING_SNAKE_CASE   (  )       ->	Union[str, Any]:

    """simple docstring"""


    __UpperCamelCase  		=						argparse.ArgumentParser(description='''Simple example of training script.'''      )
    parser.add_argument(
        '''--mixed_precision'''					,     type=lowercase_					,     default=lowercase_					,     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=lowercase_					,     default='''logs'''					,     help='''Location on where to store experiment tracking logs` and relevent project information'''					,     )
    __UpperCamelCase  		=						parser.parse_args()
    __UpperCamelCase  		=						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(lowercase_					,     lowercase_      )


if __name__ == "__main__":
 main()












from typing import TYPE_CHECKING

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


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     ["LongformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

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






