

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


a_    :						str    		=      logging.getLogger(__name__)




@dataclass
class      UpperCamelCase :
    __UpperCamelCase         =field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
    __UpperCamelCase         =field(
        default=SCREAMING_SNAKE_CASE	,			metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}	,			)
    __UpperCamelCase         =field(
        default=SCREAMING_SNAKE_CASE	,			metadata={"help": "Whether to freeze the feature extractor layers of the model."}   )
    __UpperCamelCase         =field(
        default=SCREAMING_SNAKE_CASE	,			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=0.999995	,			metadata={"help": "Decay of gumbel temperature during training."}   )


def        __lowerCAmelCase    (       _UpperCamelCase		:  ModelArguments		,							_UpperCamelCase		:  TrainingArguments	)		->							Any:
 '''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	)]		,							)
 SCREAMING_SNAKE_CASE								=			logging.WARNING
 if model_args.verbose_logging:
  SCREAMING_SNAKE_CASE								=			logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank	):
  SCREAMING_SNAKE_CASE								=			logging.INFO
 logger.setLevel(_UpperCamelCase	)




@dataclass
class      UpperCamelCase :
    __UpperCamelCase         =field(
        default=SCREAMING_SNAKE_CASE	,			metadata={"help": "The name of the dataset to use (via the datasets library)."}   )
    __UpperCamelCase         =field(
        default=SCREAMING_SNAKE_CASE	,			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=SCREAMING_SNAKE_CASE	,			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=SCREAMING_SNAKE_CASE	,			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      UpperCamelCase :
    __UpperCamelCase         =42
    __UpperCamelCase         =42
    __UpperCamelCase         ="longest"
    __UpperCamelCase         =None
    __UpperCamelCase         =None

    def __call__(			self :      Tuple							,    snake_case__ :      List[Dict[str, Union[List[int], torch.Tensor]]]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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'							,    )
     SCREAMING_SNAKE_CASE								=			self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]    )

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

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

     # sample randomly masked indices
     SCREAMING_SNAKE_CASE								=			_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      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Union[str, Any]							,    *snake_case__ :      List[Any]							,    snake_case__ :      Union[str, Any]=1							,    snake_case__ :      Any=0							,    snake_case__ :      Any=1.0							,    **snake_case__ :      Dict    ):

     """simple docstring"""






     super().__init__(*snake_case__							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			0
     SCREAMING_SNAKE_CASE								=			max_gumbel_temp
     SCREAMING_SNAKE_CASE								=			min_gumbel_temp
     SCREAMING_SNAKE_CASE								=			gumbel_temp_decay




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      nn.Module							,    snake_case__ :      Dict[str, Union[torch.Tensor, Any]]    ):

     """simple docstring"""






     model.train()
     SCREAMING_SNAKE_CASE								=			self._prepare_inputs(snake_case__    )

     if self.use_amp:
      with autocast():
       SCREAMING_SNAKE_CASE								=			self.compute_loss(snake_case__							,    snake_case__    )
     else:
      SCREAMING_SNAKE_CASE								=			self.compute_loss(snake_case__							,    snake_case__    )

     if self.args.n_gpu > 1 or self.deepspeed:
      if model.module.config.ctc_loss_reduction == "mean":
       SCREAMING_SNAKE_CASE								=			loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       SCREAMING_SNAKE_CASE								=			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:
      SCREAMING_SNAKE_CASE								=			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        __lowerCAmelCase    (       )		->							str:
 '''simple docstring'''


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

 SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			parser.parse_args_into_dataclasses()
 configure_logger(_UpperCamelCase		,							_UpperCamelCase	)

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

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

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

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

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

 # normalize and transform to `BatchFeatures`
 SCREAMING_SNAKE_CASE								=			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
 SCREAMING_SNAKE_CASE								=			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\''	)

 SCREAMING_SNAKE_CASE								=			WavaVecaForPreTraining(_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			DataCollatorForWavaVecaPretraining(model=_UpperCamelCase		,							feature_extractor=_UpperCamelCase	)

 SCREAMING_SNAKE_CASE								=			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()




def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							list[str]:
 '''simple docstring'''


 return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase	) - ngram_size + 1	)]


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

      testmod()

