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


__lowercase											=      16
__lowercase											=      32



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = 16			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	AutoTokenizer.from_pretrained('''bert-base-cased'''			)
    __UpperCamelCase  :Optional[Any]    						=	load_dataset('''glue'''						,    '''mrpc'''			)

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

    def collate_fn(SCREAMING_SNAKE_CASE			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __UpperCamelCase  :Union[str, Any]    						=	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  :Any    						=	16
        elif accelerator.mixed_precision != "no":
            __UpperCamelCase  :List[Any]    						=	8
        else:
            __UpperCamelCase  :str    						=	None

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

    # Instantiate dataloaders.
    __UpperCamelCase  :Union[str, Any]    						=	DataLoader(
        tokenized_datasets['''train''']						,    shuffle=SCREAMING_SNAKE_CASE						,    collate_fn=SCREAMING_SNAKE_CASE						,    batch_size=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[Any]    						=	DataLoader(
        tokenized_datasets['''validation''']						,    shuffle=SCREAMING_SNAKE_CASE						,    collate_fn=SCREAMING_SNAKE_CASE						,    batch_size=SCREAMING_SNAKE_CASE			)

    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

    __lowercase											=      mocked_dataloaders  # noqa: F811



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,    SCREAMING_SNAKE_CASE			) == "1":
        __UpperCamelCase  :str    						=	2
    # Initialize accelerator
    __UpperCamelCase  :Dict    						=	Accelerator(cpu=args.cpu						,    mixed_precision=args.mixed_precision			)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __UpperCamelCase  :Dict    						=	config['''lr''']
    __UpperCamelCase  :Union[str, Any]    						=	int(config['''num_epochs''']			)
    __UpperCamelCase  :Optional[int]    						=	int(config['''seed''']			)
    __UpperCamelCase  :Dict    						=	int(config['''batch_size''']			)

    __UpperCamelCase  :List[Any]    						=	evaluate.load('''glue'''						,    '''mrpc'''			)

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

    set_seed(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase						, __UpperCamelCase  :List[Any]    						=	get_dataloaders(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __UpperCamelCase  :str    						=	AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,    return_dict=SCREAMING_SNAKE_CASE			)

    # 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  :int    						=	model.to(accelerator.device			)

    # Instantiate optimizer
    __UpperCamelCase  :Any    						=	AdamW(params=model.parameters()						,    lr=SCREAMING_SNAKE_CASE			)

    # Instantiate scheduler
    __UpperCamelCase  :Any    						=	get_linear_schedule_with_warmup(
        optimizer=SCREAMING_SNAKE_CASE						,    num_warmup_steps=100						,    num_training_steps=(len(SCREAMING_SNAKE_CASE			) * 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  :int    						=	accelerator.prepare(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Now we train the model
    for epoch in range(SCREAMING_SNAKE_CASE			):
        model.train()
        for step, batch in enumerate(SCREAMING_SNAKE_CASE			):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device			)
            __UpperCamelCase  :Dict    						=	model(**SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Any    						=	outputs.loss
            __UpperCamelCase  :List[Any]    						=	loss / gradient_accumulation_steps
            accelerator.backward(SCREAMING_SNAKE_CASE			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        __UpperCamelCase  :int    						=	0
        for step, batch in enumerate(SCREAMING_SNAKE_CASE			):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device			)
            with torch.no_grad():
                __UpperCamelCase  :Optional[Any]    						=	model(**SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Any    						=	outputs.logits.argmax(dim=-1			)
            __UpperCamelCase						, __UpperCamelCase  :List[Any]    						=	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(SCREAMING_SNAKE_CASE			) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    __UpperCamelCase  :Any    						=	predictions[: len(eval_dataloader.dataset			) - samples_seen]
                    __UpperCamelCase  :str    						=	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=SCREAMING_SNAKE_CASE						,    references=SCREAMING_SNAKE_CASE						,    )

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



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	argparse.ArgumentParser(description='''Simple example of training script.'''			)
    parser.add_argument(
        '''--mixed_precision'''						,    type=SCREAMING_SNAKE_CASE						,    default=SCREAMING_SNAKE_CASE						,    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  :Optional[Any]    						=	parser.parse_args()
    __UpperCamelCase  :Any    						=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    main()






from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


__lowercase											=      datasets.utils.logging.get_logger(__name__)


class 			lowerCamelCase_  (     folder_based_builder.FolderBasedBuilderConfig				):






    '''simple docstring'''






    a__			:		bool      			=   None
    a__			:		bool      			=   None


class 			lowerCamelCase_  (     folder_based_builder.FolderBasedBuilder				):






    '''simple docstring'''






    a__			:		List[Any]      			=   datasets.Audio()
    a__			:		int      			=   """audio"""
    a__			:		str      			=   AudioFolderConfig
    a__			:		List[str]  # definition at the bottom of the script
    a__			:		int      			=   AudioClassification(audio_column="""audio"""		,			label_column="""label"""				)
__lowercase											=      [
    '''.aiff''',
    '''.au''',
    '''.avr''',
    '''.caf''',
    '''.flac''',
    '''.htk''',
    '''.svx''',
    '''.mat4''',
    '''.mat5''',
    '''.mpc2k''',
    '''.ogg''',
    '''.paf''',
    '''.pvf''',
    '''.raw''',
    '''.rf64''',
    '''.sd2''',
    '''.sds''',
    '''.ircam''',
    '''.voc''',
    '''.w64''',
    '''.wav''',
    '''.nist''',
    '''.wavex''',
    '''.wve''',
    '''.xi''',
    '''.mp3''',
    '''.opus''',
]
__lowercase											=      AUDIO_EXTENSIONS





