






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__      :	int       	=      1_6
snake_case__      :	int       	=      3_2






def     _snake_case    (__lowercase    ,      __lowercase = 16):
   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_									=     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(
          __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

 snake_case__      :	Union[str, Any]       	=      mocked_dataloaders  # noqa: F811






def     _snake_case    (__lowercase    ,      __lowercase):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'    ,      __lowercase) == "1":
      UpperCamelCase_									=     2
   # Initialize accelerator
   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'])

   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

   set_seed(__lowercase)
   UpperCamelCase_		,      UpperCamelCase_									=     get_dataloaders(__lowercase    ,      __lowercase)
   # 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=100    ,      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_		,      UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     accelerator.prepare(
       __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase    ,      __lowercase)

   # Now we train the model
   for epoch in range(__lowercase):
      model.train()
      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
         UpperCamelCase_									=     loss / gradient_accumulation_steps
         accelerator.backward(__lowercase)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      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)
         with torch.no_grad():
            UpperCamelCase_									=     model(**__lowercase)
         UpperCamelCase_									=     outputs.logits.argmax(dim=-1)
         UpperCamelCase_		,      UpperCamelCase_									=     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(__lowercase) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               UpperCamelCase_									=     predictions[: len(eval_dataloader.dataset) - samples_seen]
               UpperCamelCase_									=     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=__lowercase    ,      references=__lowercase    ,      )

      UpperCamelCase_									=     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""    ,      __lowercase)






def     _snake_case    ():
   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.')
   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_sentencepiece_available,
    is_torch_available,
)


snake_case__      :	List[str]       	=      {
    """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:
 snake_case__      :	List[str]       	=      ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__      :	Any       	=      [
     """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

 snake_case__      :	Optional[int]       	=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





