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


lowercase_     :   str 				=				logging.getLogger(__name__)






@dataclass
class 		_lowerCamelCase		:
    __a  =      field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Whether to freeze the feature extractor layers of the model."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Whether to log verbose messages or not."}    ,     )
    __a  =      field(
        default=2.0    ,     metadata={"help": "Maximum temperature for gumbel softmax."}    )
    __a  =      field(
        default=0.5    ,     metadata={"help": "Minimum temperature for gumbel softmax."}    )
    __a  =      field(
        default=0.99_9995    ,     metadata={"help": "Decay of gumbel temperature during training."}    )







def 			A__      (	snake_case_ :		ModelArguments       ,				snake_case_ :		TrainingArguments      ):
 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__:   Optional[int]=      logging.WARNING
 if model_args.verbose_logging:
  SCREAMING_SNAKE_CASE__:   Union[str, Any]=      logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank      ):
  SCREAMING_SNAKE_CASE__:   Any=      logging.INFO
 logger.setLevel(snake_case_      )






@dataclass
class 		_lowerCamelCase		:
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "The name of the dataset to use (via the datasets library)."}    )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}    )
    __a  =      field(
        default="train"    ,     metadata={
            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
        }    ,     )
    __a  =      field(
        default="validation"    ,     metadata={
            "help": (
                "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
            )
        }    ,     )
    __a  =      field(
        default="file"    ,     metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "Overwrite the cached preprocessed datasets or not."}    )
    __a  =      field(
        default=1    ,     metadata={
            "help": "The percentage of the train set used as validation set in case there's no validation split"
        }    ,     )
    __a  =      field(
        default=UpperCamelCase_    ,     metadata={"help": "The number of processes to use for the preprocessing."}    ,     )
    __a  =      field(
        default=20.0    ,     metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}    )






@dataclass
class 		_lowerCamelCase		:
    __a  =      42
    __a  =      42
    __a  =      "longest"
    __a  =      None
    __a  =      None
    def __call__(  self , lowerCAmelCase   )       ->					Dict[str, torch.Tensor]:
     # reformat list to dict and set to pytorch format
     SCREAMING_SNAKE_CASE__:   str=      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''' , )
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]   )

     SCREAMING_SNAKE_CASE__:   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
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1   )   ).to(
          torch.long   )

      SCREAMING_SNAKE_CASE__:   int=      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__:   Tuple=      1
      SCREAMING_SNAKE_CASE__:   Any=      attention_mask.flip([-1]   ).cumsum(-1   ).flip([-1]   ).bool()

     # sample randomly masked indices
     SCREAMING_SNAKE_CASE__:   Dict=      _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		(   UpperCamelCase_    ):
    def __init__(  self , *lowerCAmelCase , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=1.0 , **lowerCAmelCase   )       ->					Optional[int]:
     super().__init__(*lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      0
     SCREAMING_SNAKE_CASE__:   Optional[int]=      max_gumbel_temp
     SCREAMING_SNAKE_CASE__:   Any=      min_gumbel_temp
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      gumbel_temp_decay



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase   )       ->					torch.Tensor:
     model.train()
     SCREAMING_SNAKE_CASE__:   Any=      self._prepare_inputs(lowerCAmelCase   )

     if self.use_amp:
      with autocast():
       SCREAMING_SNAKE_CASE__:   Dict=      self.compute_loss(lowerCAmelCase , lowerCAmelCase   )
     else:
      SCREAMING_SNAKE_CASE__:   Dict=      self.compute_loss(lowerCAmelCase , lowerCAmelCase   )

     if self.args.n_gpu > 1 or self.deepspeed:
      if model.module.config.ctc_loss_reduction == "mean":
       SCREAMING_SNAKE_CASE__:   Dict=      loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       SCREAMING_SNAKE_CASE__:   Optional[int]=      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__:   Any=      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__      (	):
 # 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.

 SCREAMING_SNAKE_CASE__:   str=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

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

 # Downloading and loading a dataset from the hub.
 SCREAMING_SNAKE_CASE__:   List[str]=      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__:   int=      DatasetDict()
  SCREAMING_SNAKE_CASE__:   List[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       ,				)
  SCREAMING_SNAKE_CASE__:   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"
  SCREAMING_SNAKE_CASE__:   List[str]=      DatasetDict()
  SCREAMING_SNAKE_CASE__:   Dict=      load_dataset(
      data_args.dataset_name       ,				data_args.dataset_config_name       ,				split='''validation'''       ,				cache_dir=model_args.cache_dir       ,				)
  SCREAMING_SNAKE_CASE__:   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
 SCREAMING_SNAKE_CASE__:   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_ :		Any      ):
  # check that all files have the correct sampling rate
  SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   List[str]=      librosa.load(batch[data_args.speech_file_column]       ,				sr=feature_extractor.sampling_rate      )
  return batch

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

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

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

 # normalize and transform to `BatchFeatures`
 SCREAMING_SNAKE_CASE__:   List[str]=      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
 SCREAMING_SNAKE_CASE__:   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\''''      )

 SCREAMING_SNAKE_CASE__:   List[str]=      WavaVecaForPreTraining(snake_case_      )

 SCREAMING_SNAKE_CASE__:   str=      DataCollatorForWavaVecaPretraining(model=snake_case_       ,				feature_extractor=snake_case_      )

 SCREAMING_SNAKE_CASE__:   Tuple=      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()


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

