





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


_UpperCAmelCase				:       str	   =				16
_UpperCAmelCase				:       str	   =				32


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase = 16 )			->     str:
    lowerCamelCase__    :      int					 =		AutoTokenizer.from_pretrained('bert-base-cased' )
    lowerCamelCase__    :      Union[str, Any]					 =		load_dataset('glue'						,	'mrpc' )

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

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

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

    # Instantiate dataloaders.
    lowerCamelCase__    :      List[str]					 =		DataLoader(
        tokenized_datasets['train']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	drop_last=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		DataLoader(
        tokenized_datasets['validation']						,	shuffle=_UpperCAmelCase						,	collate_fn=_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	drop_last=(accelerator.mixed_precision == 'fp8')						,	)

    return train_dataloader, eval_dataloader


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    # Initialize accelerator
    lowerCamelCase__    :      Union[str, Any]					 =		Accelerator(cpu=args.cpu						,	mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    lowerCamelCase__    :      int					 =		config['lr']
    lowerCamelCase__    :      Tuple					 =		int(config['num_epochs'] )
    lowerCamelCase__    :      Union[str, Any]					 =		int(config['seed'] )
    lowerCamelCase__    :      List[str]					 =		int(config['batch_size'] )

    lowerCamelCase__    :      List[str]					 =		evaluate.load('glue'						,	'mrpc' )

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

    set_seed(_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      Dict					 =		get_dataloaders(_UpperCAmelCase						,	_UpperCAmelCase )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCamelCase__    :      Any					 =		AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,	return_dict=_UpperCAmelCase )

    # 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).
    lowerCamelCase__    :      Dict					 =		model.to(accelerator.device )
    # Instantiate optimizer
    lowerCamelCase__    :      int					 =		AdamW(params=model.parameters()						,	lr=_UpperCAmelCase )

    # Instantiate scheduler
    lowerCamelCase__    :      Dict					 =		get_linear_schedule_with_warmup(
        optimizer=_UpperCAmelCase						,	num_warmup_steps=100						,	num_training_steps=(len(_UpperCAmelCase ) * 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.

    lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      int					 =		accelerator.prepare(
        _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase )

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

        model.eval()
        for step, batch in enumerate(_UpperCAmelCase ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            with torch.no_grad():
                lowerCamelCase__    :      List[Any]					 =		model(**_UpperCAmelCase )
            lowerCamelCase__    :      Any					 =		outputs.logits.argmax(dim=-1 )
            lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		accelerator.gather_for_metrics((predictions, batch['labels']) )
            metric.add_batch(
                predictions=_UpperCAmelCase						,	references=_UpperCAmelCase						,	)

        lowerCamelCase__    :      List[Any]					 =		metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"""epoch {epoch}:"""						,	_UpperCAmelCase )


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


if __name__ == "__main__":
    main()






import functools
import operator

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


__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Union[str, Any]						=      """wav2vec2"""







    def __init__(	self , __lowercase=32 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=1E-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(512, 512, 512, 512, 512, 512, 512) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=128 , __lowercase=16 , __lowercase=False , __lowercase=True , __lowercase=0.05 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=0 , __lowercase=320 , __lowercase=2 , __lowercase=0.1 , __lowercase=100 , __lowercase=256 , __lowercase=256 , __lowercase=0.1 , __lowercase="sum" , __lowercase=False , __lowercase=False , __lowercase=256 , __lowercase=(512, 512, 512, 512, 1_500) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=512 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , __lowercase=None , **__lowercase , )       ->	int:
        super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase)
        __UpperCamelCase     :Any			  =		hidden_size
        __UpperCamelCase     :int			  =		feat_extract_norm
        __UpperCamelCase     :Tuple			  =		feat_extract_activation
        __UpperCamelCase     :Union[str, Any]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :int			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		conv_bias
        __UpperCamelCase     :Optional[int]			  =		num_conv_pos_embeddings
        __UpperCamelCase     :Dict			  =		num_conv_pos_embedding_groups
        __UpperCamelCase     :Any			  =		len(self.conv_dim)
        __UpperCamelCase     :List[str]			  =		num_hidden_layers
        __UpperCamelCase     :int			  =		intermediate_size
        __UpperCamelCase     :str			  =		hidden_act
        __UpperCamelCase     :Any			  =		num_attention_heads
        __UpperCamelCase     :int			  =		hidden_dropout
        __UpperCamelCase     :Tuple			  =		attention_dropout
        __UpperCamelCase     :List[str]			  =		activation_dropout
        __UpperCamelCase     :Optional[Any]			  =		feat_proj_dropout
        __UpperCamelCase     :Any			  =		final_dropout
        __UpperCamelCase     :Any			  =		layerdrop
        __UpperCamelCase     :str			  =		layer_norm_eps
        __UpperCamelCase     :Optional[Any]			  =		initializer_range
        __UpperCamelCase     :List[str]			  =		vocab_size
        __UpperCamelCase     :str			  =		do_stable_layer_norm
        __UpperCamelCase     :Union[str, Any]			  =		use_weighted_layer_sum

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase     :List[Any]			  =		apply_spec_augment
        __UpperCamelCase     :Tuple			  =		mask_time_prob
        __UpperCamelCase     :int			  =		mask_time_length
        __UpperCamelCase     :Dict			  =		mask_time_min_masks
        __UpperCamelCase     :str			  =		mask_feature_prob
        __UpperCamelCase     :List[str]			  =		mask_feature_length
        __UpperCamelCase     :Union[str, Any]			  =		mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase     :Optional[Any]			  =		num_codevectors_per_group
        __UpperCamelCase     :List[Any]			  =		num_codevector_groups
        __UpperCamelCase     :Tuple			  =		contrastive_logits_temperature
        __UpperCamelCase     :Optional[int]			  =		feat_quantizer_dropout
        __UpperCamelCase     :Optional[int]			  =		num_negatives
        __UpperCamelCase     :List[Any]			  =		codevector_dim
        __UpperCamelCase     :str			  =		proj_codevector_dim
        __UpperCamelCase     :List[str]			  =		diversity_loss_weight

        # ctc loss
        __UpperCamelCase     :Tuple			  =		ctc_loss_reduction
        __UpperCamelCase     :Tuple			  =		ctc_zero_infinity

        # adapter
        __UpperCamelCase     :List[str]			  =		add_adapter
        __UpperCamelCase     :Tuple			  =		adapter_kernel_size
        __UpperCamelCase     :str			  =		adapter_stride
        __UpperCamelCase     :Tuple			  =		num_adapter_layers
        __UpperCamelCase     :Tuple			  =		output_hidden_size or hidden_size
        __UpperCamelCase     :Optional[Any]			  =		adapter_attn_dim

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase     :Optional[Any]			  =		classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase     :Optional[int]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :List[Any]			  =		list(__lowercase)
        __UpperCamelCase     :str			  =		xvector_output_dim







    @property
    def 			UpperCamelCase__   (	self)       ->	List[str]:
        return functools.reduce(operator.mul , self.conv_stride , 1)





