

"""simple docstring"""


UpperCAmelCase													=8.31_44_62  # Unit - J mol-1 K-1



def   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      if moles < 0 or kelvin < 0 or volume < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""				)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume



def   _A					(	_a	:						float     ,   _a	:						float     ,   _a	:						float				):



      """simple docstring"""






      if moles < 0 or kelvin < 0 or pressure < 0:
            raise ValueError("""Invalid inputs. Enter positive value."""				)
      return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
  from doctest import testmod

  testmod()







"""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"):
  UpperCAmelCase													=True
  from torch.cuda.amp import autocast


UpperCAmelCase													=logging.getLogger(__name__)





@dataclass
class lowerCamelCase__   :



    '''simple docstring'''




    _lowerCamelCase					=   field(
        metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,   )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}    )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   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=0.9_9_9_9_9_5     ,   metadata={'''help''': '''Decay of gumbel temperature during training.'''}    )




def   _A					(	_a	:						ModelArguments     ,   _a	:						TrainingArguments				):



      """simple docstring"""






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





@dataclass
class lowerCamelCase__   :



    '''simple docstring'''




    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}    )
    _lowerCamelCase					=   field(
        default=SCREAMING_SNAKE_CASE     ,   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=SCREAMING_SNAKE_CASE     ,   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=SCREAMING_SNAKE_CASE     ,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}     ,   )
    _lowerCamelCase					=   field(
        default=2_0.0     ,   metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}    )





@dataclass
class lowerCamelCase__   :



    '''simple docstring'''




    _lowerCamelCase					=   42
    _lowerCamelCase					=   42
    _lowerCamelCase					=   "longest"
    _lowerCamelCase					=   None
    _lowerCamelCase					=   None




    def __call__( self ,lowerCamelCase_    )    ->      Dict[str, torch.Tensor]:
          # reformat list to dict and set to pytorch format
          A				  =		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""" ,)
          A				  =		self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]    )

          A				  =		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
                A				  =		self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1    )    ).to(
                    torch.long    )

                A				  =		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
                A				  =		1
                A				  =		attention_mask.flip([-1]    ).cumsum(-1    ).flip([-1]    ).bool()

          # sample randomly masked indices
          A				  =		_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 lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,*lowerCamelCase_ ,lowerCamelCase_=1 ,lowerCamelCase_=0 ,lowerCamelCase_=1.0 ,**lowerCamelCase_    )    ->      Union[str, Any]:
          super().__init__(*lowerCamelCase_ ,**lowerCamelCase_    )
          A				  =		0
          A				  =		max_gumbel_temp
          A				  =		min_gumbel_temp
          A				  =		gumbel_temp_decay




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      torch.Tensor:
          model.train()
          A				  =		self._prepare_inputs(lowerCamelCase_    )

          if self.use_amp:
                with autocast():
                      A				  =		self.compute_loss(lowerCamelCase_ ,lowerCamelCase_    )
          else:
                A				  =		self.compute_loss(lowerCamelCase_ ,lowerCamelCase_    )

          if self.args.n_gpu > 1 or self.deepspeed:
                if model.module.config.ctc_loss_reduction == "mean":
                      A				  =		loss.mean()
                elif model.module.config.ctc_loss_reduction == "sum":
                      A				  =		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:
                A				  =		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   _A					(	):



      """simple docstring"""






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

      A  ,			A  ,			A				  =		parser.parse_args_into_dataclasses()
      configure_logger(_a     ,   _a				)

      # Downloading and loading a dataset from the hub.
      A				  =		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"
            A				  =		DatasetDict()
            A				  =		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     ,   )
            A				  =		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"
            A				  =		DatasetDict()
            A				  =		load_dataset(
                data_args.dataset_name     ,   data_args.dataset_config_name     ,   split="""validation"""     ,   cache_dir=model_args.cache_dir     ,   )
            A				  =		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
      A				  =		WavaVecaFeatureExtractor.from_pretrained(
          model_args.model_name_or_path     ,   cache_dir=model_args.cache_dir     ,   do_normalize=_a				)

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

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

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

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

      # normalize and transform to `BatchFeatures`
      A				  =		vectorized_datasets.map(
          _a     ,   batched=_a     ,   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
      A				  =		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'"""				)

      A				  =		WavaVecaForPreTraining(_a				)

      A				  =		DataCollatorForWavaVecaPretraining(model=_a     ,   feature_extractor=_a				)

      A				  =		WavaVecaPreTrainer(
          model=_a     ,   data_collator=_a     ,   args=_a     ,   train_dataset=vectorized_datasets["""train"""]     ,   eval_dataset=vectorized_datasets["""validation"""]     ,   tokenizer=_a     ,   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()




