




'''simple docstring'''







import argparse

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
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A     =16
A     =32





def 						snake_case_							(_a	:							Accelerator    ,   _a	:							int = 1_6     ):
    UpperCAmelCase			       =		AutoTokenizer.from_pretrained('''bert-base-cased'''     )
    UpperCAmelCase			       =		load_dataset('''glue'''    ,   '''mrpc'''     )

    def tokenize_function(_a	:							Tuple     ):
        # 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	:							Optional[int]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        UpperCAmelCase			       =		1_2_8 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			       =		1_6
        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    ,   drop_last=_a     )
    UpperCAmelCase			       =		DataLoader(
        tokenized_datasets['''validation''']    ,   shuffle=_a    ,   collate_fn=_a    ,   batch_size=_a    ,   drop_last=(accelerator.mixed_precision == '''fp8''')    ,   )

    return train_dataloader, eval_dataloader





def 						snake_case_							(_a	:							Optional[Any]    ,   _a	:							Union[str, Any]     ):
    # 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=1_0_0    ,   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()
        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_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 						snake_case_							():
    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''': 4_2, '''batch_size''': 1_6}
    training_function(_a    ,   _a     )


if __name__ == "__main__":
   main()










'''simple docstring'''







import math
from typing import Optional

import numpy as np

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


A     =logging.get_logger(__name__)

A     ={
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}


class 			_a  (	__a ):
       __a				:     Union[str, Any]   				=					"""encodec"""
       def __init__(	self						:	Tuple   ,				lowercase						:	List[str]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,				lowercase						:	Any=24_000   ,				lowercase						:	str=1   ,				lowercase						:	Optional[int]=False   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str=None   ,				lowercase						:	Tuple=128   ,				lowercase						:	Union[str, Any]=32   ,				lowercase						:	Union[str, Any]=1   ,				lowercase						:	Optional[Any]=[8, 5, 4, 2]   ,				lowercase						:	Any="weight_norm"   ,				lowercase						:	Tuple=7   ,				lowercase						:	int=7   ,				lowercase						:	Dict=3   ,				lowercase						:	List[Any]=2   ,				lowercase						:	str=True   ,				lowercase						:	List[str]="reflect"   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	int=1.0   ,				lowercase						:	Dict=1_024   ,				lowercase						:	str=None   ,				lowercase						:	Union[str, Any]=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		target_bandwidths
           UpperCAmelCase			       =		sampling_rate
           UpperCAmelCase			       =		audio_channels
           UpperCAmelCase			       =		normalize
           UpperCAmelCase			       =		chunk_length_s
           UpperCAmelCase			       =		overlap
           UpperCAmelCase			       =		hidden_size
           UpperCAmelCase			       =		num_filters
           UpperCAmelCase			       =		num_residual_layers
           UpperCAmelCase			       =		upsampling_ratios
           UpperCAmelCase			       =		norm_type
           UpperCAmelCase			       =		kernel_size
           UpperCAmelCase			       =		last_kernel_size
           UpperCAmelCase			       =		residual_kernel_size
           UpperCAmelCase			       =		dilation_growth_rate
           UpperCAmelCase			       =		use_causal_conv
           UpperCAmelCase			       =		pad_mode
           UpperCAmelCase			       =		compress
           UpperCAmelCase			       =		num_lstm_layers
           UpperCAmelCase			       =		trim_right_ratio
           UpperCAmelCase			       =		codebook_size
           UpperCAmelCase			       =		codebook_dim if codebook_dim is not None else hidden_size
           UpperCAmelCase			       =		use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
               raise ValueError(
                   f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"				)

           super().__init__(**lowercase				)
       @property
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           if self.chunk_length_s is None:
               return None
           else:
               return int(self.chunk_length_s * self.sampling_rate				)
       @property
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           if self.chunk_length_s is None or self.overlap is None:
               return None
           else:
               return max(1   ,				int((1.0 - self.overlap) * self.chunk_length				)				)
       @property
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		np.prod(self.upsampling_ratios				)
           return math.ceil(self.sampling_rate / hop_length				)







       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)




