


'''simple docstring'''




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


_a   :   int												=   logging.getLogger(__name__)
_a   :   Optional[Any]												=   list(MODEL_FOR_MASKED_LM_MAPPING.keys())
_a   :   Dict												=   tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




@dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(__lowercase							)}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "Override some existing default config settings when a model is trained from scratch. Example: "
               "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}						,					)
   _SCREAMING_SNAKE_CASE  :  str          =	field(
       default="main"						,					metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
               "with private models)."
           )
       }						,					)
   def a  (						self       :      List[Any]      ) ->							Tuple:
        if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
             raise ValueError(
                 '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'      )




@dataclass
class 			_lowercase :
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(default=__lowercase						,					metadata={"help": "The input training data file (a text file)."}							)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[str]          =	field(
       default=__lowercase						,					metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}						,					)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={"help": "Overwrite the cached training and evaluation sets"}							)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=5						,					metadata={
           "help": "The percentage of the train set used as validation set in case there's no validation split"
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated. Default to the max input length of the model."
           )
       }						,					)
   _SCREAMING_SNAKE_CASE  :  Optional[int]          =	field(
       default=__lowercase						,					metadata={"help": "The number of processes to use for the preprocessing."}						,					)
   _SCREAMING_SNAKE_CASE  :  float          =	field(
       default=0.15						,					metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}							)
   _SCREAMING_SNAKE_CASE  :  bool          =	field(
       default=__lowercase						,					metadata={
           "help": (
               "Whether to pad all samples to `max_seq_length`. "
               "If False, will pad the samples dynamically when batching to the maximum length in the batch."
           )
       }						,					)
   def a  (						self       :      Optional[Any]      ) ->							Optional[int]:
        if self.train_file is not None:
             __snake_case           =						self.train_file.split('.'      )[-1]
             assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
        if self.validation_file is not None:
             __snake_case           =						self.validation_file.split('.'      )[-1]
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def   _a		(lowercase__			:   Dict							,   lowercase__			:   Dict						)			->			int:






     """simple docstring"""
     with open(lowercase__							,   'r'							,   encoding='utf-8'						) as f:
          __snake_case           =						[json.loads(lowercase__						) for line in f.read().splitlines() if (len(lowercase__						) > 0 and not line.isspace())]
     assert len(lowercase__						) == len(lowercase__						)

     __snake_case           =						{c: dataset[c] for c in dataset.column_names}
     __snake_case           =						refs
     return Dataset.from_dict(lowercase__						)





def   _a		()			->			Tuple:






     """simple docstring"""
     # 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.

     __snake_case           =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('.json'						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __snake_case      ,       __snake_case      ,       __snake_case           =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case      ,       __snake_case      ,       __snake_case           =						parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     __snake_case           =						None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          __snake_case           =						get_last_checkpoint(training_args.output_dir						)
          if last_checkpoint is None and len(os.listdir(training_args.output_dir						)						) > 0:
               raise ValueError(
                   f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                   'Use --overwrite_output_dir to overcome.'						)
          elif last_checkpoint is not None:
               logger.info(
                   f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                   'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'						)

    # Setup logging
     logging.basicConfig(
         format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,   datefmt='%m/%d/%Y %H:%M:%S'							,   handlers=[logging.StreamHandler(sys.stdout						)]							,   )
     logger.setLevel(logging.INFO if is_main_process(training_args.local_rank						) else logging.WARN						)

     # Log on each process the small summary:
     logger.warning(
         f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
         + f'distributed training: {bool(training_args.local_rank != -1						)}, 16-bits training: {training_args.fpaa}'						)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank						):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('Training/evaluation parameters %s'							,   lowercase__						)

     # Set seed before initializing model.
     set_seed(training_args.seed						)

     # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
     # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
     # 'text' is found. You can easily tweak this behavior (see below).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
     if data_args.dataset_name is not None:
          # Downloading and loading a dataset from the hub.
          __snake_case           =						load_dataset(data_args.dataset_name							,   data_args.dataset_config_name						)
          if "validation" not in datasets.keys():
               __snake_case           =						load_dataset(
                   data_args.dataset_name							,   data_args.dataset_config_name							,   split=f'train[:{data_args.validation_split_percentage}%]'							,   )
               __snake_case           =						load_dataset(
                   data_args.dataset_name							,   data_args.dataset_config_name							,   split=f'train[{data_args.validation_split_percentage}%:]'							,   )
     else:
          __snake_case           =						{}
          if data_args.train_file is not None:
               __snake_case           =						data_args.train_file
          if data_args.validation_file is not None:
               __snake_case           =						data_args.validation_file
          __snake_case           =						data_args.train_file.split('.'						)[-1]
          if extension == "txt":
               __snake_case           =						'text'
          __snake_case           =						load_dataset(lowercase__							,   data_files=lowercase__						)
     # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
     # https://huggingface.co/docs/datasets/loading_datasets.html.

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     __snake_case           =						{
         'cache_dir': model_args.cache_dir,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.config_name:
          __snake_case           =						AutoConfig.from_pretrained(model_args.config_name							,   **lowercase__						)
     elif model_args.model_name_or_path:
          __snake_case           =						AutoConfig.from_pretrained(model_args.model_name_or_path							,   **lowercase__						)
     else:
          __snake_case           =						CONFIG_MAPPING[model_args.model_type]()
          logger.warning('You are instantiating a new config instance from scratch.'						)
          if model_args.config_overrides is not None:
               logger.info(f'Overriding config: {model_args.config_overrides}'						)
               config.update_from_string(model_args.config_overrides						)
               logger.info(f'New config: {config}'						)

     __snake_case           =						{
         'cache_dir': model_args.cache_dir,
         'use_fast': model_args.use_fast_tokenizer,
         'revision': model_args.model_revision,
         'use_auth_token': True if model_args.use_auth_token else None,
     }
     if model_args.tokenizer_name:
          __snake_case           =						AutoTokenizer.from_pretrained(model_args.tokenizer_name							,   **lowercase__						)
     elif model_args.model_name_or_path:
          __snake_case           =						AutoTokenizer.from_pretrained(model_args.model_name_or_path							,   **lowercase__						)
     else:
          raise ValueError(
              'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
              'You can do it from another script, save it, and load it from here, using --tokenizer_name.'						)

     if model_args.model_name_or_path:
          __snake_case           =						AutoModelForMaskedLM.from_pretrained(
              model_args.model_name_or_path							,   from_tf=bool('.ckpt' in model_args.model_name_or_path						)							,   config=lowercase__							,   cache_dir=model_args.cache_dir							,   revision=model_args.model_revision							,   use_auth_token=True if model_args.use_auth_token else None							,   )
     else:
          logger.info('Training new model from scratch'						)
          __snake_case           =						AutoModelForMaskedLM.from_config(lowercase__						)

     model.resize_token_embeddings(len(lowercase__						)						)

     # Preprocessing the datasets.
     # First we tokenize all the texts.
     if training_args.do_train:
          __snake_case           =						datasets['train'].column_names
     else:
          __snake_case           =						datasets['validation'].column_names
     __snake_case           =						'text' if 'text' in column_names else column_names[0]

     __snake_case           =						'max_length' if data_args.pad_to_max_length else False

     def tokenize_function(lowercase__			:   Any						):
          # Remove empty lines
          __snake_case           =						[line for line in examples['text'] if len(lowercase__						) > 0 and not line.isspace()]
          return tokenizer(examples['text']							,   padding=lowercase__							,   truncation=lowercase__							,   max_length=data_args.max_seq_length						)

     __snake_case           =						datasets.map(
         lowercase__							,   batched=lowercase__							,   num_proc=data_args.preprocessing_num_workers							,   remove_columns=[text_column_name]							,   load_from_cache_file=not data_args.overwrite_cache							,   )

     # Add the chinese references if provided
     if data_args.train_ref_file is not None:
          __snake_case           =						add_chinese_references(tokenized_datasets['train']							,   data_args.train_ref_file						)
     if data_args.validation_ref_file is not None:
          __snake_case           =						add_chinese_references(
              tokenized_datasets['validation']							,   data_args.validation_ref_file						)
     # If we have ref files, need to avoid it removed by trainer
     __snake_case           =						data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
          __snake_case           =						False

     # Data collator
     # This one will take care of randomly masking the tokens.
     __snake_case           =						DataCollatorForWholeWordMask(tokenizer=lowercase__							,   mlm_probability=data_args.mlm_probability						)

     # Initialize our Trainer
     __snake_case           =						Trainer(
         model=lowercase__							,   args=lowercase__							,   train_dataset=tokenized_datasets['train'] if training_args.do_train else None							,   eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None							,   tokenizer=lowercase__							,   data_collator=lowercase__							,   )

     # Training
     if training_args.do_train:
          if last_checkpoint is not None:
               __snake_case           =						last_checkpoint
          elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path						):
               __snake_case           =						model_args.model_name_or_path
          else:
               __snake_case           =						None
          __snake_case           =						trainer.train(resume_from_checkpoint=lowercase__						)
          trainer.save_model()  # Saves the tokenizer too for easy upload

          __snake_case           =						os.path.join(training_args.output_dir							,   'train_results.txt'						)
          if trainer.is_world_process_zero():
               with open(lowercase__							,   'w'						) as writer:
                    logger.info('***** Train results *****'						)
                    for key, value in sorted(train_result.metrics.items()						):
                         logger.info(f'  {key} = {value}'						)
                         writer.write(f'{key} = {value}\n'						)

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir							,   'trainer_state.json'						)						)

    # Evaluation
     __snake_case           =						{}
     if training_args.do_eval:
          logger.info('*** Evaluate ***'						)

          __snake_case           =						trainer.evaluate()

          __snake_case           =						math.exp(eval_output['eval_loss']						)
          __snake_case           =						perplexity

          __snake_case           =						os.path.join(training_args.output_dir							,   'eval_results_mlm_wwm.txt'						)
          if trainer.is_world_process_zero():
               with open(lowercase__							,   'w'						) as writer:
                    logger.info('***** Eval results *****'						)
                    for key, value in sorted(results.items()						):
                         logger.info(f'  {key} = {value}'						)
                         writer.write(f'{key} = {value}\n'						)

     return results





def   _a		(lowercase__			:   int						)			->			Any:






     """simple docstring"""
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
       main()






"""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[str]  						=			True
   from torch.cuda.amp import autocast


_A     :			Any  						=			logging.getLogger(__name__)

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		)
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},     )
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     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     __magic_name__      ( __snake_case							:			ModelArguments	,					__snake_case							:			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    )]	,					)
       lowercase       :							Optional[int]									=				logging.WARNING
       if model_args.verbose_logging:
              lowercase       :							Union[str, Any]									=				logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank    ):
              lowercase       :							Union[str, Any]									=				logging.INFO
       logger.setLevel(__snake_case    )

@dataclass
class 						a__		:
   __lowerCAmelCase				=							field(
       default=a_,     metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}		)
   __lowerCAmelCase				=							field(
       default=a_,     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=a_,     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=a_,     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 						a__		:
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							"longest"
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							None
   def __call__(     self      ,       _a							):
          # reformat list to dict and set to pytorch format
          lowercase       :							Tuple									=				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"      ,       )
          lowercase       :							str									=				self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]							)

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

                 lowercase       :							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
                 lowercase       :							int									=				1
                 lowercase       :							List[Any]									=				attention_mask.flip([-1]							).cumsum(-1							).flip([-1]							).bool()

          # sample randomly masked indices
          lowercase       :							int									=				_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 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       _a=1      ,       _a=0      ,       _a=1.0      ,       **_a							):
          super().__init__(*_a      ,       **_a							)
          lowercase       :							Tuple									=				0
          lowercase       :							Optional[Any]									=				max_gumbel_temp
          lowercase       :							Union[str, Any]									=				min_gumbel_temp
          lowercase       :							str									=				gumbel_temp_decay

   def      __magic_name__     (     self      ,       _a      ,       _a							):
          model.train()
          lowercase       :							Union[str, Any]									=				self._prepare_inputs(_a							)

          if self.use_amp:
                 with autocast():
                        lowercase       :							Optional[Any]									=				self.compute_loss(_a      ,       _a							)
          else:
                 lowercase       :							List[str]									=				self.compute_loss(_a      ,       _a							)

          if self.args.n_gpu > 1 or self.deepspeed:
                 if model.module.config.ctc_loss_reduction == "mean":
                        lowercase       :							Tuple									=				loss.mean()
                 elif model.module.config.ctc_loss_reduction == "sum":
                        lowercase       :							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:
                 lowercase       :							List[str]									=				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     __magic_name__      ( )					->							List[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.

       lowercase       :							Any									=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )

       lowercase						,       lowercase						,       lowercase       :							Union[str, Any]									=				parser.parse_args_into_dataclasses()
       configure_logger(__snake_case	,					__snake_case    )

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

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

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

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

       def normalize(__snake_case							:			Union[str, Any]    ):
              return feature_extractor(batch["speech"]	,					sampling_rate=feature_extractor.sampling_rate    )

       # normalize and transform to `BatchFeatures`
       lowercase       :							Any									=				vectorized_datasets.map(
           __snake_case	,					batched=__snake_case	,					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
       lowercase       :							Union[str, 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'"    )

       lowercase       :							Optional[Any]									=				WavaVecaForPreTraining(__snake_case    )

       lowercase       :							Optional[int]									=				DataCollatorForWavaVecaPretraining(model=__snake_case	,					feature_extractor=__snake_case    )

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


