
'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def A (__lowerCamelCase :Any						):
						return DownloadCommand(args.model					,      args.cache_dir					,      args.force					,      args.trust_remote_code						)





class     UpperCAmelCase_	(						lowerCamelCase__					):






						'''simple docstring'''






						@staticmethod
						def    _lowercase      (   _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parser.add_parser("""download""" )
												download_parser.add_argument(
												    """--cache-dir"""       ,  type=_lowercase       ,  default=_lowercase       ,  help="""Path to location to store the models""" )
												download_parser.add_argument(
												    """--force"""       ,  action="""store_true"""       ,  help="""Force the model to be download even if already in cache-dir""" )
												download_parser.add_argument(
												    """--trust-remote-code"""       ,  action="""store_true"""       ,  help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""       ,  )
												download_parser.add_argument("""model"""       ,  type=_lowercase       ,  help="""Name of the model to download""" )
												download_parser.set_defaults(func=_lowercase )




						def __init__(   self       ,  _lowercase       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model
												_lowerCAmelCase    		=					cache
												_lowerCAmelCase    		=					force
												_lowerCAmelCase    		=					trust_remote_code




						def    _lowercase      (   self ):

												"""simple docstring"""



												from ..models.auto import AutoModel, AutoTokenizer

												AutoModel.from_pretrained(
												    self._model       ,  cache_dir=self._cache       ,  force_download=self._force       ,  trust_remote_code=self._trust_remote_code )
												AutoTokenizer.from_pretrained(
												    self._model       ,  cache_dir=self._cache       ,  force_download=self._force       ,  trust_remote_code=self._trust_remote_code )







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				:   Optional[Any]						       =						True
 from torch.cuda.amp import autocast


_lowerCAmelCase				:   Dict						       =						logging.getLogger(__name__)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __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=0.99_9995     ,					metadata={'''help''': '''Decay of gumbel temperature during training.'''}				)




def   __snake_case    (       _lowerCAmelCase			:			ModelArguments					,					_lowerCAmelCase			:			TrainingArguments						)			->    str:
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,					datefmt="%m/%d/%Y %H:%M:%S"					,					handlers=[logging.StreamHandler(sys.stdout						)]					,					)
   A_       :					Optional[int]        =      logging.WARNING
   if model_args.verbose_logging:
      A_       :					Dict        =      logging.DEBUG
   elif trainer_utils.is_main_process(training_args.local_rank						):
      A_       :					int        =      logging.INFO
   logger.setLevel(_lowerCAmelCase						)


@dataclass
class        __magic_name__	:





     """simple docstring"""



     __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=20.0     ,					metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}				)




@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  "longest"
     __UpperCamelCase            =  None
     __UpperCamelCase            =  None



     def __call__(				self							:Optional[Any]      ,   snake_case							:List[Dict[str, Union[List[int], torch.Tensor]]]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.feature_extractor.pad(
            snake_case      ,   max_length=self.max_length      ,   padding=self.padding      ,   pad_to_multiple_of=self.pad_to_multiple_of      ,   return_tensors="pt"      ,   )
        A_       :					int        =      self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]	)

        A_       :					List[str]        =      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_       :					List[str]        =      self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1	)	).to(
               torch.long	)

           A_       :					Union[str, Any]        =      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_       :					str        =      1
           A_       :					Union[str, Any]        =      attention_mask.flip([-1]	).cumsum(-1	).flip([-1]	).bool()

        # sample randomly masked indices
        A_       :					Optional[int]        =      _compute_mask_indices(
            (batch_size, mask_indices_seq_length)      ,   self.model.config.mask_time_prob      ,   self.model.config.mask_time_length      ,   attention_mask=snake_case      ,   min_masks=2      ,   )

        return batch




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     def __init__(				self							:int      ,   *snake_case							:Any      ,   snake_case							:Any=1      ,   snake_case							:Dict=0      ,   snake_case							:Dict=1.0      ,   **snake_case							:Any	):





        '''simple docstring'''




        super().__init__(*snake_case      ,   **snake_case	)
        A_       :					Union[str, Any]        =      0
        A_       :					Dict        =      max_gumbel_temp
        A_       :					Optional[int]        =      min_gumbel_temp
        A_       :					List[Any]        =      gumbel_temp_decay





     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]      ,   snake_case							:nn.Module      ,   snake_case							:Dict[str, Union[torch.Tensor, Any]]	):





        '''simple docstring'''




        model.train()
        A_       :					List[str]        =      self._prepare_inputs(snake_case	)

        if self.use_amp:
           with autocast():
              A_       :					List[str]        =      self.compute_loss(snake_case      ,   snake_case	)
        else:
           A_       :					str        =      self.compute_loss(snake_case      ,   snake_case	)

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

        if self.use_amp:
           self.scaler.scale(snake_case	).backward()
        elif self.use_apex:
           with amp.scale_loss(snake_case      ,   self.optimizer	) as scaled_loss:
              scaled_loss.backward()
        elif self.deepspeed:
           self.deepspeed.backward(snake_case	)
        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   __snake_case    (       )			->    Tuple:
   # 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.

   A_       :					List[str]        =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

   A_   ,    A_   ,    A_       :					Dict        =      parser.parse_args_into_dataclasses()
   configure_logger(_lowerCAmelCase					,					_lowerCAmelCase						)

   # Downloading and loading a dataset from the hub.
   A_       :					Optional[Any]        =      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_       :					Tuple        =      DatasetDict()
      A_       :					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					,					)
      A_       :					Union[str, 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					,					)
   else:
      # make sure only "validation" and "train" keys remain"
      A_       :					Any        =      DatasetDict()
      A_       :					Optional[Any]        =      load_dataset(
          data_args.dataset_name					,					data_args.dataset_config_name					,					split="validation"					,					cache_dir=model_args.cache_dir					,					)
      A_       :					List[str]        =      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_       :					Union[str, Any]        =      WavaVecaFeatureExtractor.from_pretrained(
       model_args.model_name_or_path					,					cache_dir=model_args.cache_dir					,					do_normalize=_lowerCAmelCase						)

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

   # load audio files into numpy arrays
   A_       :					List[Any]        =      datasets.map(
       _lowerCAmelCase					,					num_proc=data_args.preprocessing_num_workers					,					remove_columns=datasets["train"].column_names						)

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

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

   # normalize and transform to `BatchFeatures`
   A_       :					Any        =      vectorized_datasets.map(
       _lowerCAmelCase					,					batched=_lowerCAmelCase					,					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_       :					Any        =      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_       :					int        =      WavaVecaForPreTraining(_lowerCAmelCase						)

   A_       :					str        =      DataCollatorForWavaVecaPretraining(model=_lowerCAmelCase					,					feature_extractor=_lowerCAmelCase						)

   A_       :					List[Any]        =      WavaVecaPreTrainer(
       model=_lowerCAmelCase					,					data_collator=_lowerCAmelCase					,					args=_lowerCAmelCase					,					train_dataset=vectorized_datasets["train"]					,					eval_dataset=vectorized_datasets["validation"]					,					tokenizer=_lowerCAmelCase					,					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()
