






'''simple docstring'''





import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__A							:							List[Any]  							=		"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
__A							:							Dict  							=		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__A							:							List[Any]  							=		re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
__A							:							Union[str, Any]  							=		re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__A							:							int  							=		re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__A							:							List[Any]  							=		[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"				,     lowercase__)
 return [m.group(0) for m in matches]






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 lowerCamelCase__  =     {
     config.replace("Config"				,     ""): model_type for model_type, config in config_maping_names.items()
 }

 # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
 lowerCamelCase__  =     collections.defaultdict(lowercase__)
 lowerCamelCase__  =     collections.defaultdict(lowercase__)
 lowerCamelCase__  =     collections.defaultdict(lowercase__)

 # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
 for attr_name in dir(lowercase__):
  lowerCamelCase__  =     None
  if _re_tf_models.match(lowercase__) is not None:
   lowerCamelCase__  =     tf_models
   lowerCamelCase__  =     _re_tf_models.match(lowercase__).groups()[0]
  elif _re_flax_models.match(lowercase__) is not None:
   lowerCamelCase__  =     flax_models
   lowerCamelCase__  =     _re_flax_models.match(lowercase__).groups()[0]
  elif _re_pt_models.match(lowercase__) is not None:
   lowerCamelCase__  =     pt_models
   lowerCamelCase__  =     _re_pt_models.match(lowercase__).groups()[0]

  if lookup_dict is not None:
   while len(lowercase__) > 0:
    if attr_name in model_prefix_to_model_type:
     lowerCamelCase__  =     True
     break
    # Try again after removing the last word in the name
    lowerCamelCase__  =     "".join(camel_case_split(lowercase__)[:-1])

 lowerCamelCase__  =     set(list(pt_models.keys()) + list(tf_models.keys()) + list(flax_models.keys()))
 lowerCamelCase__  =     list(lowercase__)
 all_models.sort()

 lowerCamelCase__  =     {"model_type": all_models}
 lowerCamelCase__  =     [pt_models[t] for t in all_models]
 lowerCamelCase__  =     [tf_models[t] for t in all_models]
 lowerCamelCase__  =     [flax_models[t] for t in all_models]

 # Now let's use the auto-mapping names to make sure
 lowerCamelCase__  =     {}
 for t in all_models:
  if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
   lowerCamelCase__  =     "AutoProcessor"
  elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
   lowerCamelCase__  =     "AutoTokenizer"
  elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
   lowerCamelCase__  =     "AutoFeatureExtractor"
  else:
   # Default to AutoTokenizer if a model has nothing, for backward compatibility.
   lowerCamelCase__  =     "AutoTokenizer"

 lowerCamelCase__  =     [processors[t] for t in all_models]

 return pd.DataFrame(lowercase__)






def    lowerCamelCase_     (    lowercase__):
 lowerCamelCase__  =     [
     transformers_module.models.auto.modeling_auto,
     transformers_module.models.auto.modeling_tf_auto,
     transformers_module.models.auto.modeling_flax_auto,
 ]
 for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
  lowerCamelCase__  =     [model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
  lowerCamelCase__  =     [auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
  # Loop through all three frameworks
  for module, cls, mapping in zip(lowercase__				,     lowercase__				,     lowercase__):
   # The type of pipeline may not exist in this framework
   if not hasattr(lowercase__				,     lowercase__):
    continue
   # First extract all model_names
   lowerCamelCase__  =     []
   for name in getattr(lowercase__				,     lowercase__).values():
    if isinstance(lowercase__				,     lowercase__):
     model_names.append(lowercase__)
    else:
     model_names.extend(list(lowercase__))

            # Add pipeline tag and auto model class for those models
   table.update({model_name: (pipeline_tag, cls) for model_name in model_names})

 return table






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 lowerCamelCase__  =     get_frameworks_table()
 lowerCamelCase__  =     Dataset.from_pandas(lowercase__)

 lowerCamelCase__  =     hf_hub_download(
     "huggingface/transformers-metadata"				,     "pipeline_tags.json"				,     repo_type="dataset"				,     token=lowercase__)
 lowerCamelCase__  =     Dataset.from_json(lowercase__)
 lowerCamelCase__  =     {
     tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
     for i in range(len(lowercase__))
 }
 lowerCamelCase__  =     update_pipeline_and_auto_class_table(lowercase__)

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 lowerCamelCase__  =     sorted(table.keys())
 lowerCamelCase__  =     pd.DataFrame(
     {
         "model_class": model_classes,
         "pipeline_tag": [table[m][0] for m in model_classes],
         "auto_class": [table[m][1] for m in model_classes],
     })
 lowerCamelCase__  =     Dataset.from_pandas(lowercase__)

 with tempfile.TemporaryDirectory() as tmp_dir:
  frameworks_dataset.to_json(os.path.join(lowercase__				,     "frameworks.json"))
  tags_dataset.to_json(os.path.join(lowercase__				,     "pipeline_tags.json"))

  if commit_sha is not None:
   lowerCamelCase__  =     (
       F'''Update with commit {commit_sha}\n\nSee: '''
       F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
   )
  else:
   lowerCamelCase__  =     "Update"

  upload_folder(
      repo_id="huggingface/transformers-metadata"				,     folder_path=lowercase__				,     repo_type="dataset"				,     token=lowercase__				,     commit_message=lowercase__				,     )






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 lowerCamelCase__  =     transformers_module.pipelines.SUPPORTED_TASKS
 lowerCamelCase__  =     []
 for key in pipeline_tasks:
  if key not in in_table:
   lowerCamelCase__  =     pipeline_tasks[key]["pt"]
   if isinstance(lowercase__				,     (list, tuple)):
    lowerCamelCase__  =     model[0]
   lowerCamelCase__  =     model.__name__
   if model not in in_table.values():
    missing.append(lowercase__)

 if len(lowercase__) > 0:
  lowerCamelCase__  =     ", ".join(lowercase__)
  raise ValueError(
      "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
      F'''`utils/update_metadata.py`: {msg}. Please add them!''')


if __name__ == "__main__":
 __A							:							Any  							=		argparse.ArgumentParser()
 parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
 parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
 parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
 __A							:							Dict  							=		parser.parse_args()

 if args.check_only:
  check_pipeline_tags()
 else:
  update_metadata(args.token, args.commit_sha)









'''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()

