






'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__A							:							Optional[int]  							=		logging.get_logger(__name__)


__A							:							Tuple  							=		{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
__A							:							Union[str, Any]  							=		{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

__A							:							List[Any]  							=		{
    """facebook/bart-base""": 10_24,
    """facebook/bart-large""": 10_24,
    """facebook/bart-large-mnli""": 10_24,
    """facebook/bart-large-cnn""": 10_24,
    """facebook/bart-large-xsum""": 10_24,
    """yjernite/bart_eli5""": 10_24,
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							VOCAB_FILES_NAMES
    lowerCAmelCase__      				=							PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase__      				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase__      				=							["input_ids", "attention_mask"]
    lowerCAmelCase__      				=							BartTokenizer

    def __init__(					self     :     str				,     __lowerCamelCase     :     List[Any]=None				,     __lowerCamelCase     :     Optional[int]=None				,     __lowerCamelCase     :     Optional[int]=None				,     __lowerCamelCase     :     List[str]="replace"				,     __lowerCamelCase     :     Union[str, Any]="<s>"				,     __lowerCamelCase     :     Union[str, Any]="</s>"				,     __lowerCamelCase     :     Union[str, Any]="</s>"				,     __lowerCamelCase     :     List[str]="<s>"				,     __lowerCamelCase     :     Optional[Any]="<unk>"				,     __lowerCamelCase     :     List[str]="<pad>"				,     __lowerCamelCase     :     Optional[int]="<mask>"				,     __lowerCamelCase     :     Tuple=False				,     __lowerCamelCase     :     int=True				,     **__lowerCamelCase     :     Any				,     )							->							Optional[Any]:

     '''simple docstring'''




     super().__init__(
         __lowerCamelCase				,     __lowerCamelCase				,     tokenizer_file=__lowerCamelCase				,     errors=__lowerCamelCase				,     bos_token=__lowerCamelCase				,     eos_token=__lowerCamelCase				,     sep_token=__lowerCamelCase				,     cls_token=__lowerCamelCase				,     unk_token=__lowerCamelCase				,     pad_token=__lowerCamelCase				,     mask_token=__lowerCamelCase				,     add_prefix_space=__lowerCamelCase				,     trim_offsets=__lowerCamelCase				,     **__lowerCamelCase				,     )

     lowerCamelCase__  =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
     if pre_tok_state.get("add_prefix_space"				,     __lowerCamelCase     ) != add_prefix_space:
      lowerCamelCase__  =     getattr(__lowerCamelCase				,     pre_tok_state.pop("type"     )     )
      lowerCamelCase__  =     add_prefix_space
      lowerCamelCase__  =     pre_tok_class(**__lowerCamelCase     )

     lowerCamelCase__  =     add_prefix_space

     # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
     lowerCamelCase__  =     "post_processor"
     lowerCamelCase__  =     getattr(self.backend_tokenizer				,     __lowerCamelCase				,     __lowerCamelCase     )
     if tokenizer_component_instance:
      lowerCamelCase__  =     json.loads(tokenizer_component_instance.__getstate__()     )

      # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
      if "sep" in state:
       lowerCamelCase__  =     tuple(state["sep"]     )
      if "cls" in state:
       lowerCamelCase__  =     tuple(state["cls"]     )

      lowerCamelCase__  =     False

      if state.get("add_prefix_space"				,     __lowerCamelCase     ) != add_prefix_space:
       lowerCamelCase__  =     add_prefix_space
       lowerCamelCase__  =     True

      if state.get("trim_offsets"				,     __lowerCamelCase     ) != trim_offsets:
       lowerCamelCase__  =     trim_offsets
       lowerCamelCase__  =     True

      if changes_to_apply:
       lowerCamelCase__  =     getattr(__lowerCamelCase				,     state.pop("type"     )     )
       lowerCamelCase__  =     component_class(**__lowerCamelCase     )
       setattr(self.backend_tokenizer				,     __lowerCamelCase				,     __lowerCamelCase     )

    @property
    def    a__				(					self     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     if self._mask_token is None:
      if self.verbose:
       logger.error("Using mask_token, but it is not set yet."     )
      return None
     return str(self._mask_token     )

    @mask_token.setter
    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     List[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     AddedToken(__lowerCamelCase				,     lstrip=__lowerCamelCase				,     rstrip=__lowerCamelCase     ) if isinstance(__lowerCamelCase				,     __lowerCamelCase     ) else value
     lowerCamelCase__  =     value

    def    a__				(					self     :     List[str]				,     *__lowerCamelCase     :     Dict				,     **__lowerCamelCase     :     List[str]     )							->							BatchEncoding:

     '''simple docstring'''




     lowerCamelCase__  =     kwargs.get("is_split_into_words"				,     __lowerCamelCase     )

     if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."     )

     return super()._batch_encode_plus(*__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     str				,     *__lowerCamelCase     :     int				,     **__lowerCamelCase     :     Tuple     )							->							BatchEncoding:

     '''simple docstring'''




     lowerCamelCase__  =     kwargs.get("is_split_into_words"				,     __lowerCamelCase     )

     if is_split_into_words and not self.add_prefix_space:
      raise ValueError(
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."     )

     return super()._encode_plus(*__lowerCamelCase				,     **__lowerCamelCase     )

    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[str] = None     )							->							Tuple[str]:

     '''simple docstring'''




     lowerCamelCase__  =     self._tokenizer.model.save(__lowerCamelCase				,     name=__lowerCamelCase     )
     return tuple(__lowerCamelCase     )

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     Optional[int]=None     )							->							Union[str, Any]:

     '''simple docstring'''




     lowerCamelCase__  =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
     if token_ids_a is None:
      return output

     return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




    def    a__				(					self     :     List[str]				,     __lowerCamelCase     :     List[int]				,     __lowerCamelCase     :     Optional[List[int]] = None     )							->							List[int]:

     '''simple docstring'''




     lowerCamelCase__  =     [self.sep_token_id]
     lowerCamelCase__  =     [self.cls_token_id]

     if token_ids_a is None:
      return len(cls + token_ids_a + sep     ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]









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


__A							:							List[Any]  							=		logging.getLogger(__name__)
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,       )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       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    lowerCamelCase_     (    lowercase__				,     lowercase__):
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,     datefmt="%m/%d/%Y %H:%M:%S"				,     handlers=[logging.StreamHandler(sys.stdout)]				,     )
 lowerCamelCase__  =     logging.WARNING
 if model_args.verbose_logging:
  lowerCamelCase__  =     logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank):
  lowerCamelCase__  =     logging.INFO
 logger.setLevel(lowercase__)
@dataclass
class 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       metadata={"help": "The name of the dataset to use (via the datasets library)."}      )
    lowerCAmelCase__      				=							field(
        default=_lowerCamelCase ,       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=_lowerCamelCase ,       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=_lowerCamelCase ,       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 				lowercase       :


    '''simple docstring'''

    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							42
    lowerCAmelCase__      				=							"longest"
    lowerCAmelCase__      				=							None
    lowerCAmelCase__      				=							None

    def __call__(					self     :     Optional[int]				,     __lowerCamelCase     :     List[Dict[str, Union[List[int], torch.Tensor]]]     )							->							Dict[str, torch.Tensor]:

     '''simple docstring'''




     lowerCamelCase__  =     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"				,     )
     lowerCamelCase__  =     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]     )

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

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

     # sample randomly masked indices
     lowerCamelCase__  =     _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 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    def __init__(					self     :     str				,     *__lowerCamelCase     :     Union[str, Any]				,     __lowerCamelCase     :     int=1				,     __lowerCamelCase     :     Optional[Any]=0				,     __lowerCamelCase     :     str=1.0				,     **__lowerCamelCase     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     super().__init__(*__lowerCamelCase				,     **__lowerCamelCase     )
     lowerCamelCase__  =     0
     lowerCamelCase__  =     max_gumbel_temp
     lowerCamelCase__  =     min_gumbel_temp
     lowerCamelCase__  =     gumbel_temp_decay




    def    a__				(					self     :     Tuple				,     __lowerCamelCase     :     nn.Module				,     __lowerCamelCase     :     Dict[str, Union[torch.Tensor, Any]]     )							->							torch.Tensor:

     '''simple docstring'''




     model.train()
     lowerCamelCase__  =     self._prepare_inputs(__lowerCamelCase     )

     if self.use_amp:
      with autocast():
       lowerCamelCase__  =     self.compute_loss(__lowerCamelCase				,     __lowerCamelCase     )
     else:
      lowerCamelCase__  =     self.compute_loss(__lowerCamelCase				,     __lowerCamelCase     )

     if self.args.n_gpu > 1 or self.deepspeed:
      if model.module.config.ctc_loss_reduction == "mean":
       lowerCamelCase__  =     loss.mean()
      elif model.module.config.ctc_loss_reduction == "sum":
       lowerCamelCase__  =     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:
      lowerCamelCase__  =     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    lowerCamelCase_     (    ):
 # 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.

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

 lowerCamelCase__  ,	lowerCamelCase__  ,	lowerCamelCase__  =     parser.parse_args_into_dataclasses()
 configure_logger(lowercase__				,     lowercase__)

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

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

 # load audio files into numpy arrays
 lowerCamelCase__  =     datasets.map(
     lowercase__				,     num_proc=data_args.preprocessing_num_workers				,     remove_columns=datasets["train"].column_names)

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

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

 # normalize and transform to `BatchFeatures`
 lowerCamelCase__  =     vectorized_datasets.map(
     lowercase__				,     batched=lowercase__				,     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
 lowerCamelCase__  =     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'")

 lowerCamelCase__  =     WavaVecaForPreTraining(lowercase__)

 lowerCamelCase__  =     DataCollatorForWavaVecaPretraining(model=lowercase__				,     feature_extractor=lowercase__)

 lowerCamelCase__  =     WavaVecaPreTrainer(
     model=lowercase__				,     data_collator=lowercase__				,     args=lowercase__				,     train_dataset=vectorized_datasets["train"]				,     eval_dataset=vectorized_datasets["validation"]				,     tokenizer=lowercase__				,     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()

