




import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


_lowerCamelCase	:		List[str]							       =   16
_lowerCamelCase	:		int							       =   32







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ = 16						)   ->       List[str]:



       """simple docstring"""

       A__       =    AutoTokenizer.from_pretrained('''bert-base-cased'''						)
       A__       =    DatasetDict(
           {
               '''train''': dataset['''train'''].select(lowercase_						),
               '''validation''': dataset['''train'''].select(lowercase_						),
               '''test''': dataset['''validation'''],
           }						)

       def tokenize_function(lowercase_						):
              # max_length=None => use the model max length (it's actually the default)
              A__       =    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():
              A__       =    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
       A__       =    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.
              A__       =    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__       =    16
              elif accelerator.mixed_precision != "no":
                     A__       =    8
              else:
                     A__       =    None

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

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

       A__       =    DataLoader(
           tokenized_datasets['''test'''] ,				shuffle=lowercase_ ,				collate_fn=lowercase_ ,				batch_size=lowercase_						)

       return train_dataloader, eval_dataloader, test_dataloader







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       A__       =    []
       # Download the dataset
       A__       =    load_dataset('''glue''' ,				'''mrpc'''						)
       # Create our splits
       A__       =    StratifiedKFold(n_splits=int(args.num_folds						)						)
       # Initialize accelerator
       A__       =    Accelerator(cpu=args.cpu ,				mixed_precision=args.mixed_precision						)
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       A__       =    config['''lr''']
       A__       =    int(config['''num_epochs''']						)
       A__       =    int(config['''seed''']						)
       A__       =    int(config['''batch_size''']						)

       A__       =    evaluate.load('''glue''' ,				'''mrpc'''						)

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

       set_seed(lowercase_						)

       # New Code #
       # Create our folds:
       A__       =    kfold.split(np.zeros(datasets['''train'''].num_rows						) ,				datasets['''train''']['''label''']						)
       A__       =    []
       # Iterate over them
       for i, (train_idxs, valid_idxs) in enumerate(lowercase_						):
              A__ ,							A__ ,							A__       =    get_fold_dataloaders(
                  lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				)
              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              A__       =    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).
              A__       =    model.to(accelerator.device						)

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

              # Instantiate scheduler
              A__       =    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.
              A__ ,							A__ ,							A__ ,							A__ ,							A__       =    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						)
                            A__       =    model(**lowercase_						)
                            A__       =    outputs.loss
                            A__       =    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`.
                            batch.to(accelerator.device						)
                            with torch.no_grad():
                                   A__       =    model(**lowercase_						)
                            A__       =    outputs.logits.argmax(dim=-1						)
                            A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                            metric.add_batch(
                                predictions=lowercase_ ,				references=lowercase_ ,				)

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

              # New Code #
              # We also run predictions on the test set at the very end
              A__       =    []
              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():
                            A__       =    model(**lowercase_						)
                     A__       =    outputs.logits
                     A__ ,							A__       =    accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
                     fold_predictions.append(predictions.cpu()						)
                     if i == 0:
                            # We need all of the test predictions
                            test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
              test_predictions.append(torch.cat(lowercase_ ,				dim=0						)						)
              # We now need to release all our memory and get rid of the current model, optimizer, etc
              accelerator.free_memory()
       # New Code #
       # Finally we check the accuracy of our folded results:
       A__       =    torch.cat(lowercase_ ,				dim=0						)
       A__       =    torch.stack(lowercase_ ,				dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
       A__       =    metric.compute(predictions=lowercase_ ,				references=lowercase_						)
       accelerator.print('''Average test metrics from all folds:''' ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       )   ->       Tuple:



       """simple docstring"""

       A__       =    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.'''						)
       # New Code #
       parser.add_argument('''--num_folds''' ,				type=lowercase_ ,				default=3 ,				help='''The number of splits to perform across the dataset'''						)
       A__       =    parser.parse_args()
       A__       =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_ ,				lowercase_						)


if __name__ == "__main__":
  main()










from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)

lowerCamelCase	:							Dict				={
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = '''speech_to_text_2'''
 _lowerCAmelCase       :       Dict        = ['''past_key_values''']
 _lowerCAmelCase       :       Any        = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1_00_00							,   SCREAMING_SNAKE_CASE					:   List[Any]=6							,   SCREAMING_SNAKE_CASE					:   List[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Dict=4							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="relu"							,   SCREAMING_SNAKE_CASE					:   Tuple=2_56							,   SCREAMING_SNAKE_CASE					:   List[str]=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0_2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Any=1							,   SCREAMING_SNAKE_CASE					:   List[Any]=0							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   str=10_24							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   Optional[Any]       				=  d_model
      UpperCamelCase__			:   Optional[Any]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   List[str]       				=  dropout
      UpperCamelCase__			:   int       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Union[str, Any]       				=  activation_function
      UpperCamelCase__			:   Tuple       				=  init_std
      UpperCamelCase__			:   Optional[int]       				=  decoder_layerdrop
      UpperCamelCase__			:   Dict       				=  use_cache
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Union[str, Any]       				=  scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase__			:   Optional[Any]       				=  max_target_positions

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE							,   bos_token_id=SCREAMING_SNAKE_CASE							,   eos_token_id=SCREAMING_SNAKE_CASE							,   decoder_start_token_id=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )