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

SCREAMING_SNAKE_CASE_            =    16
SCREAMING_SNAKE_CASE_            =    32




def      lowercase__    (       lowerCAmelCase					:       Accelerator			,					lowerCAmelCase					:       int = 16			)			->   int:






    """simple docstring"""




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

    def tokenize_function(lowerCAmelCase					:       str			):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase          =       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():
        UpperCAmelCase          =       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
    UpperCAmelCase          =       tokenized_datasets.rename_column('label'			,					'labels'			)

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

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

    # Instantiate dataloaders.
    UpperCAmelCase          =       DataLoader(
        tokenized_datasets['train']			,					shuffle=lowerCAmelCase			,					collate_fn=lowerCAmelCase			,					batch_size=lowerCAmelCase			)
    UpperCAmelCase          =       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

   SCREAMING_SNAKE_CASE_            =    mocked_dataloaders  # noqa: F811




def      lowercase__    (       lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[str]			)			->   Dict:






    """simple docstring"""




    if os.environ.get('TESTING_MOCKED_DATALOADERS'			,					lowerCAmelCase			) == "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(lowerCAmelCase			)

    UpperCAmelCase      ,				UpperCAmelCase          =       get_dataloaders(lowerCAmelCase			,					lowerCAmelCase			)
    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=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).
    UpperCAmelCase          =       model.to(accelerator.device			)

    # Instantiate optimizer
    UpperCAmelCase          =       AdamW(params=model.parameters()			,					lr=lowerCAmelCase			)

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

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

    # Now we train the model
    for epoch in range(lowerCAmelCase			):
        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(lowerCAmelCase			):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device			)
            UpperCAmelCase          =       model(**lowerCAmelCase			)
            UpperCAmelCase          =       outputs.loss
            # New Code #
            if args.with_tracking:
                total_loss += loss.detach().float()
            UpperCAmelCase          =       loss / gradient_accumulation_steps
            accelerator.backward(lowerCAmelCase			)
            if step % gradient_accumulation_steps == 0:
                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` (the default).
            batch.to(accelerator.device			)
            with torch.no_grad():
                UpperCAmelCase          =       model(**lowerCAmelCase			)
            UpperCAmelCase          =       outputs.logits.argmax(dim=-1			)
            UpperCAmelCase      ,				UpperCAmelCase          =       accelerator.gather_for_metrics((predictions, batch['labels'])			)
            metric.add_batch(
                predictions=lowerCAmelCase			,					references=lowerCAmelCase			,					)

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

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

    # 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      lowercase__    (       )			->   List[str]:






    """simple docstring"""




    UpperCAmelCase          =       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.'			)
    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=lowerCAmelCase			,					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(lowerCAmelCase			,					lowerCAmelCase			)


if __name__ == "__main__":
   main()





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''SpeechT5Tokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SpeechT5ForSpeechToText''',
       '''SpeechT5ForSpeechToSpeech''',
       '''SpeechT5ForTextToSpeech''',
       '''SpeechT5Model''',
       '''SpeechT5PreTrainedModel''',
       '''SpeechT5HifiGan''',
   ]

if TYPE_CHECKING:
   from .configuration_speechta import (
       SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
       SpeechTaConfig,
       SpeechTaHifiGanConfig,
   )
   from .feature_extraction_speechta import SpeechTaFeatureExtractor
   from .processing_speechta import SpeechTaProcessor

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_speechta import SpeechTaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_speechta import (
          SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
          SpeechTaForSpeechToSpeech,
          SpeechTaForSpeechToText,
          SpeechTaForTextToSpeech,
          SpeechTaHifiGan,
          SpeechTaModel,
          SpeechTaPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



