from typing import TYPE_CHECKING

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


__magic_name__								={}

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

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__								=['''NllbTokenizerFast''']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

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




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


__magic_name__								=16
__magic_name__								=32







def  __UpperCamelCase    (						A ,							A = 16							):
     UpperCamelCase__										=      AutoTokenizer.from_pretrained('''bert-base-cased'''							)
     UpperCamelCase__										=      load_dataset('''glue''' ,							'''mrpc'''							)

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

     def collate_fn(A							):
          # 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(
              A ,							padding='''longest''' ,							max_length=A ,							pad_to_multiple_of=A ,							return_tensors='''pt''' ,							)

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

     __magic_name__								=mocked_dataloaders  # noqa: F811







def  __UpperCamelCase    (						A ,							A							):
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							A							) == "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(A							)
     UpperCamelCase__						,       UpperCamelCase__										=      get_dataloaders(A ,							A							)
     # 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=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).
     UpperCamelCase__										=      model.to(accelerator.device							)

     # Instantiate optimizer
     UpperCamelCase__										=      AdamW(params=model.parameters() ,							lr=A							)

     # Instantiate scheduler
     UpperCamelCase__										=      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.
     UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      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							)
               UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.loss
               UpperCamelCase__										=      loss / gradient_accumulation_steps
               accelerator.backward(A							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCamelCase__										=      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():
                    UpperCamelCase__										=      model(**A							)
               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(A							) - 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=A ,							references=A ,							)

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







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      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.'''							)
     UpperCamelCase__										=      parser.parse_args()
     UpperCamelCase__										=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(A ,							A							)


if __name__ == "__main__":
     main()



