






'''simple docstring'''





import logging

from transformers.configuration_utils import PretrainedConfig


__A							:							str  							=		logging.getLogger(__name__)
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"masked_bert"

    def __init__(					self     :     str				,     __lowerCamelCase     :     int=30522				,     __lowerCamelCase     :     Optional[Any]=768				,     __lowerCamelCase     :     List[Any]=12				,     __lowerCamelCase     :     List[str]=12				,     __lowerCamelCase     :     List[str]=3072				,     __lowerCamelCase     :     List[str]="gelu"				,     __lowerCamelCase     :     Dict=0.1				,     __lowerCamelCase     :     Optional[int]=0.1				,     __lowerCamelCase     :     Any=512				,     __lowerCamelCase     :     Tuple=2				,     __lowerCamelCase     :     List[str]=0.0_2				,     __lowerCamelCase     :     List[Any]=1E-12				,     __lowerCamelCase     :     List[Any]=0				,     __lowerCamelCase     :     List[str]="topK"				,     __lowerCamelCase     :     str="constant"				,     __lowerCamelCase     :     Union[str, Any]=0.0				,     **__lowerCamelCase     :     int				,     )							->							int:

     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase				,     **__lowerCamelCase     )

     lowerCamelCase__  =     vocab_size
     lowerCamelCase__  =     hidden_size
     lowerCamelCase__  =     num_hidden_layers
     lowerCamelCase__  =     num_attention_heads
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     intermediate_size
     lowerCamelCase__  =     hidden_dropout_prob
     lowerCamelCase__  =     attention_probs_dropout_prob
     lowerCamelCase__  =     max_position_embeddings
     lowerCamelCase__  =     type_vocab_size
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
     lowerCamelCase__  =     pruning_method
     lowerCamelCase__  =     mask_init
     lowerCamelCase__  =     mask_scale









'''simple docstring'''





import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
 from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
 __A							:							List[Any]  							=		True
 from torch.cuda.amp import autocast


__A							:							List[Any]  							=		logging.getLogger(__name__)
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Whether to log verbose messages or not."} ,       )
    lowerCAmelCase__      				=							field(
        default=2.0 ,       metadata={"help": "Maximum temperature for gumbel softmax."}      )
    lowerCAmelCase__      				=							field(
        default=0.5 ,       metadata={"help": "Minimum temperature for gumbel softmax."}      )
    lowerCAmelCase__      				=							field(
        default=0.999995 ,       metadata={"help": "Decay of gumbel temperature during training."}      )






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,     datefmt="%m/%d/%Y %H:%M:%S"				,     handlers=[logging.StreamHandler(sys.stdout)]				,     )
 lowerCamelCase__  =     logging.WARNING
 if model_args.verbose_logging:
  lowerCamelCase__  =     logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank):
  lowerCamelCase__  =     logging.INFO
 logger.setLevel(lowercase__)
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The name of the dataset to use (via the datasets library)."}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
    lowerCAmelCase__      				=							field(
        default="train" ,       metadata={
            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
        } ,       )
    lowerCAmelCase__      				=							field(
        default="validation" ,       metadata={
            "help": (
                "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
            )
        } ,       )
    lowerCAmelCase__      				=							field(
        default="file" ,       metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"} ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."}      )
    lowerCAmelCase__      				=							field(
        default=1 ,       metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        } ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The number of processes to use for the preprocessing."} ,       )
    lowerCAmelCase__      				=							field(
        default=20.0 ,       metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}      )
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							"longest"
    lowerCAmelCase__      				=							None
    lowerCAmelCase__      				=							None

    def __call__(					self     :     Optional[int]				,     __lowerCamelCase     :     List[Dict[str, Union[List[int], torch.Tensor]]]     )							->							Dict[str, torch.Tensor]:

     '''simple docstring'''




     lowerCamelCase__  =     self.feature_extractor.pad(
         __lowerCamelCase				,     max_length=self.max_length				,     padding=self.padding				,     pad_to_multiple_of=self.pad_to_multiple_of				,     return_tensors="pt"				,     )
     lowerCamelCase__  =     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]     )

     lowerCamelCase__  =     batch["input_values"].shape[0]

     # make sure that no loss is computed on padded inputs
     if batch["attention_mask"] is not None:
      # compute real output lengths according to convolution formula
      lowerCamelCase__  =     self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1     )     ).to(
          torch.long     )

      lowerCamelCase__  =     torch.zeros(
          (batch_size, mask_indices_seq_length)				,     dtype=torch.long				,     device=batch["input_values"].device     )

      # these two operations makes sure that all values
      # before the output lengths indices are attended to
      lowerCamelCase__  =     1
      lowerCamelCase__  =     attention_mask.flip([-1]     ).cumsum(-1     ).flip([-1]     ).bool()

     # sample randomly masked indices
     lowerCamelCase__  =     _compute_mask_indices(
         (batch_size, mask_indices_seq_length)				,     self.model.config.mask_time_prob				,     self.model.config.mask_time_length				,     attention_mask=__lowerCamelCase				,     min_masks=2				,     )

     return batch
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     *__lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int=1				,     __lowerCamelCase     :     Optional[Any]=0				,     __lowerCamelCase     :     str=1.0				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     super().__init__(*__lowerCamelCase				,     **__lowerCamelCase     )
     lowerCamelCase__  =     0
     lowerCamelCase__  =     max_gumbel_temp
     lowerCamelCase__  =     min_gumbel_temp
     lowerCamelCase__  =     gumbel_temp_decay




    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     nn.Module				,     __lowerCamelCase     :     Dict[str, Union[torch.Tensor, Any]]     )							->							torch.Tensor:

     '''simple docstring'''




     model.train()
     lowerCamelCase__  =     self._prepare_inputs(__lowerCamelCase     )

     if self.use_amp:
      with autocast():
       lowerCamelCase__  =     self.compute_loss(__lowerCamelCase				,     __lowerCamelCase     )
     else:
      lowerCamelCase__  =     self.compute_loss(__lowerCamelCase				,     __lowerCamelCase     )

     if self.args.n_gpu > 1 or self.deepspeed:
      if model.module.config.ctc_loss_reduction == "mean":
       lowerCamelCase__  =     loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       lowerCamelCase__  =     loss.sum() / (inputs["mask_time_indices"]).sum()
      else:
       raise ValueError(f'''{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'''     )

     if self.args.gradient_accumulation_steps > 1:
      lowerCamelCase__  =     loss / self.args.gradient_accumulation_steps

     if self.use_amp:
      self.scaler.scale(__lowerCamelCase     ).backward()
     elif self.use_apex:
      with amp.scale_loss(__lowerCamelCase				,     self.optimizer     ) as scaled_loss:
       scaled_loss.backward()
     elif self.deepspeed:
      self.deepspeed.backward(__lowerCamelCase     )
     else:
      loss.backward()

     self.num_update_step += 1
     # make sure gumbel softmax temperature is decayed
     if self.args.n_gpu > 1 or self.deepspeed:
      model.module.set_gumbel_temperature(
          max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step				,     self.min_gumbel_temp     )     )
     else:
      model.set_gumbel_temperature(
          max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step				,     self.min_gumbel_temp     )     )

     return loss.detach()






def    lowerCamelCase_     (    ):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.

 lowerCamelCase__  =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))

 lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     parser.parse_args_into_dataclasses()
 configure_logger(lowercase__				,     lowercase__)

 # Downloading and loading a dataset from the hub.
 lowerCamelCase__  =     load_dataset(data_args.dataset_name				,     data_args.dataset_config_name				,     cache_dir=model_args.cache_dir)

 if "validation" not in datasets.keys():
  # make sure only "validation" and "train" keys remain"
  lowerCamelCase__  =     DatasetDict()
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split=F'''{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'''				,     cache_dir=model_args.cache_dir				,     )
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split=F'''{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'''				,     cache_dir=model_args.cache_dir				,     )
 else:
  # make sure only "validation" and "train" keys remain"
  lowerCamelCase__  =     DatasetDict()
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split="validation"				,     cache_dir=model_args.cache_dir				,     )
  lowerCamelCase__  =     load_dataset(
      data_args.dataset_name				,     data_args.dataset_config_name				,     split=F'''{data_args.train_split_name}'''				,     cache_dir=model_args.cache_dir				,     )

 # only normalized-inputs-training is supported
 lowerCamelCase__  =     WavaVecaFeatureExtractor.from_pretrained(
     model_args.model_name_or_path				,     cache_dir=model_args.cache_dir				,     do_normalize=lowercase__)

 def prepare_dataset(lowercase__):
  # check that all files have the correct sampling rate
  lowerCamelCase__  ,	lowerCamelCase__  =     librosa.load(batch[data_args.speech_file_column]				,     sr=feature_extractor.sampling_rate)
  return batch

 # load audio files into numpy arrays
 lowerCamelCase__  =     datasets.map(
     lowercase__				,     num_proc=data_args.preprocessing_num_workers				,     remove_columns=datasets["train"].column_names)

 # filter audio files that are too long
 lowerCamelCase__  =     vectorized_datasets.filter(
     lambda lowercase__: len(data["speech"]) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate))

 def normalize(lowercase__):
  return feature_extractor(batch["speech"]				,     sampling_rate=feature_extractor.sampling_rate)

 # normalize and transform to `BatchFeatures`
 lowerCamelCase__  =     vectorized_datasets.map(
     lowercase__				,     batched=lowercase__				,     num_proc=data_args.preprocessing_num_workers				,     load_from_cache_file=not data_args.overwrite_cache				,     remove_columns=vectorized_datasets["train"].column_names				,     )

 # pretraining is only supported for "newer" stable layer norm architecture
 # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
 lowerCamelCase__  =     WavaVecaConfig.from_pretrained(
     model_args.model_name_or_path				,     cache_dir=model_args.cache_dir				,     gradient_checkpointing=training_args.gradient_checkpointing				,     )

 if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
  raise ValueError(
      "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
      " ``config.feat_extract_norm='layer'")

 lowerCamelCase__  =     WavaVecaForPreTraining(lowercase__)

 lowerCamelCase__  =     DataCollatorForWavaVecaPretraining(model=lowercase__				,     feature_extractor=lowercase__)

 lowerCamelCase__  =     WavaVecaPreTrainer(
     model=lowercase__				,     data_collator=lowercase__				,     args=lowercase__				,     train_dataset=vectorized_datasets["train"]				,     eval_dataset=vectorized_datasets["validation"]				,     tokenizer=lowercase__				,     max_gumbel_temp=model_args.max_gumbel_temperature				,     min_gumbel_temp=model_args.min_gumbel_temperature				,     gumbel_temp_decay=model_args.gumbel_temperature_decay				,     )
 trainer.train()


if __name__ == "__main__":
 main()

