

def       a__					(		UpperCAmelCase							:      str    ,			UpperCAmelCase							:      list[str]      )  ->						str:
  UpperCAmelCase	:							Any						=			''''''
  for word_or_phrase in separated:
    if not isinstance(UpperCAmelCase    ,			UpperCAmelCase      ):
      raise Exception('''join() accepts only strings to be joined'''      )
    joined += word_or_phrase + separator
  return joined.strip(UpperCAmelCase      )


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

     testmod()








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"):
     _lowerCamelCase    :       List[str]        =							True
     from torch.cuda.amp import autocast


_lowerCamelCase    :       Any        =							logging.getLogger(__name__)





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to log verbose messages or not."""}      ,  )
   UpperCamelCase       					=					field(
       default=2.0      ,  metadata={"""help""": """Maximum temperature for gumbel softmax."""}						)
   UpperCamelCase       					=					field(
       default=0.5      ,  metadata={"""help""": """Minimum temperature for gumbel softmax."""}						)
   UpperCamelCase       					=					field(
       default=0.9_9_9_9_9_5      ,  metadata={"""help""": """Decay of gumbel temperature during training."""}						)






def       a__					(		UpperCAmelCase							:      ModelArguments    ,			UpperCAmelCase							:      TrainingArguments      )  ->						Any:
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,			datefmt='''%m/%d/%Y %H:%M:%S'''    ,			handlers=[logging.StreamHandler(sys.stdout      )]    ,			)
  UpperCAmelCase	:							Any						=			logging.WARNING
  if model_args.verbose_logging:
    UpperCAmelCase	:							Any						=			logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank      ):
    UpperCAmelCase	:							Any						=			logging.INFO
  logger.setLevel(UpperCAmelCase      )





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
   UpperCamelCase       					=					field(
       default="""train"""      ,  metadata={
           """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
       }      ,  )
   UpperCamelCase       					=					field(
       default="""validation"""      ,  metadata={
           """help""": (
               """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
           )
       }      ,  )
   UpperCamelCase       					=					field(
       default="""file"""      ,  metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}						)
   UpperCamelCase       					=					field(
       default=1      ,  metadata={
           """help""": """The percentage of the train set used as validation set in case there's no validation split"""
       }      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The number of processes to use for the preprocessing."""}      ,  )
   UpperCamelCase       					=					field(
       default=2_0.0      ,  metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}						)






@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					42
   UpperCamelCase       					=					42
   UpperCamelCase       					=					"longest"
   UpperCamelCase       					=					None
   UpperCamelCase       					=					None



   def __call__(							self							:					int,		__A							:					List[Dict[str, Union[List[int], torch.Tensor]]]						):
     # reformat list to dict and set to pytorch format
     UpperCAmelCase	:							List[Any]						=			self.feature_extractor.pad(
         __A,		max_length=self.max_length,		padding=self.padding,		pad_to_multiple_of=self.pad_to_multiple_of,		return_tensors='''pt''',		)
     UpperCAmelCase	:							int						=			self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]						)

     UpperCAmelCase	:							Tuple						=			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
       UpperCAmelCase	:							Tuple						=			self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1						)						).to(
           torch.long						)

       UpperCAmelCase	:							Dict						=			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
       UpperCAmelCase	:							Tuple						=			1
       UpperCAmelCase	:							int						=			attention_mask.flip([-1]						).cumsum(-1						).flip([-1]						).bool()

     # sample randomly masked indices
     UpperCAmelCase	:							Dict						=			_compute_mask_indices(
         (batch_size, mask_indices_seq_length),		self.model.config.mask_time_prob,		self.model.config.mask_time_length,		attention_mask=__A,		min_masks=2,		)

     return batch






class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Union[str, Any],		*__A							:					int,		__A							:					Dict=1,		__A							:					Any=0,		__A							:					Optional[Any]=1.0,		**__A							:					Any						):
     super().__init__(*__A,		**__A						)
     UpperCAmelCase	:							Any						=			0
     UpperCAmelCase	:							Any						=			max_gumbel_temp
     UpperCAmelCase	:							Optional[Any]						=			min_gumbel_temp
     UpperCAmelCase	:							str						=			gumbel_temp_decay





   def 	__magic_name__   (							self							:					Dict,		__A							:					nn.Module,		__A							:					Dict[str, Union[torch.Tensor, Any]]						):
     model.train()
     UpperCAmelCase	:							List[Any]						=			self._prepare_inputs(__A						)

     if self.use_amp:
       with autocast():
         UpperCAmelCase	:							Optional[Any]						=			self.compute_loss(__A,		__A						)
     else:
       UpperCAmelCase	:							Optional[int]						=			self.compute_loss(__A,		__A						)

     if self.args.n_gpu > 1 or self.deepspeed:
       if model.module.config.ctc_loss_reduction == "mean":
         UpperCAmelCase	:							Optional[Any]						=			loss.mean()
       elif model.module.config.ctc_loss_reduction == "sum":
         UpperCAmelCase	:							str						=			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:
       UpperCAmelCase	:							Any						=			loss / self.args.gradient_accumulation_steps

     if self.use_amp:
       self.scaler.scale(__A						).backward()
     elif self.use_apex:
       with amp.scale_loss(__A,		self.optimizer						) as scaled_loss:
         scaled_loss.backward()
     elif self.deepspeed:
       self.deepspeed.backward(__A						)
     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__					(		)  ->						Union[str, Any]:
  # 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.

  UpperCAmelCase	:							Optional[Any]						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

  UpperCAmelCase			,						UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			parser.parse_args_into_dataclasses()
  configure_logger(UpperCAmelCase    ,			UpperCAmelCase      )

  # Downloading and loading a dataset from the hub.
  UpperCAmelCase	:							int						=			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"
    UpperCAmelCase	:							Union[str, Any]						=			DatasetDict()
    UpperCAmelCase	:							Any						=			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    ,			)
    UpperCAmelCase	:							Tuple						=			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"
    UpperCAmelCase	:							Optional[Any]						=			DatasetDict()
    UpperCAmelCase	:							List[Any]						=			load_dataset(
        data_args.dataset_name    ,			data_args.dataset_config_name    ,			split='''validation'''    ,			cache_dir=model_args.cache_dir    ,			)
    UpperCAmelCase	:							int						=			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
  UpperCAmelCase	:							Optional[Any]						=			WavaVecaFeatureExtractor.from_pretrained(
      model_args.model_name_or_path    ,			cache_dir=model_args.cache_dir    ,			do_normalize=UpperCAmelCase      )

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

  # load audio files into numpy arrays
  UpperCAmelCase	:							str						=			datasets.map(
      UpperCAmelCase    ,			num_proc=data_args.preprocessing_num_workers    ,			remove_columns=datasets['''train'''].column_names      )

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

  def normalize(UpperCAmelCase							:      Dict      ):
    return feature_extractor(batch['''speech''']    ,			sampling_rate=feature_extractor.sampling_rate      )

  # normalize and transform to `BatchFeatures`
  UpperCAmelCase	:							Any						=			vectorized_datasets.map(
      UpperCAmelCase    ,			batched=UpperCAmelCase    ,			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
  UpperCAmelCase	:							Optional[int]						=			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\''''      )

  UpperCAmelCase	:							Any						=			WavaVecaForPreTraining(UpperCAmelCase      )

  UpperCAmelCase	:							int						=			DataCollatorForWavaVecaPretraining(model=UpperCAmelCase    ,			feature_extractor=UpperCAmelCase      )

  UpperCAmelCase	:							Any						=			WavaVecaPreTrainer(
      model=UpperCAmelCase    ,			data_collator=UpperCAmelCase    ,			args=UpperCAmelCase    ,			train_dataset=vectorized_datasets['''train''']    ,			eval_dataset=vectorized_datasets['''validation''']    ,			tokenizer=UpperCAmelCase    ,			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()





