

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase    :       Any        =							logging.get_logger(__name__)

_lowerCamelCase    :       Tuple        =							"▁"

_lowerCamelCase    :       List[str]        =							{"vocab_file": "sentencepiece.bpe.model"}

_lowerCamelCase    :       int        =							{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

_lowerCamelCase    :       Tuple        =							{
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
_lowerCamelCase    :       Optional[Any]        =							["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]





class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					VOCAB_FILES_NAMES
   UpperCamelCase       					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase       					=					PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase       					=					["""input_ids""", """attention_mask"""]

   UpperCamelCase       					=					[]
   UpperCamelCase       					=					[]



   def __init__(							self							:					List[str],		__A							:					Optional[int],		__A							:					Optional[int]="<s>",		__A							:					str="</s>",		__A							:					List[str]="</s>",		__A							:					int="<s>",		__A							:					Tuple="<unk>",		__A							:					Union[str, Any]="<pad>",		__A							:					Union[str, Any]="<mask>",		__A							:					Any=None,		__A							:					Optional[Any]=None,		__A							:					Any=None,		__A							:					Optional[Dict[str, Any]] = None,		__A							:					Optional[int]=None,		**__A							:					List[Any],		):
     # Mask token behave like a normal word, i.e. include the space before it
     UpperCAmelCase	:							Union[str, Any]						=			AddedToken(__A,		lstrip=__A,		rstrip=__A						) if isinstance(__A,		__A						) else mask_token

     UpperCAmelCase	:							str						=			{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=__A,		eos_token=__A,		unk_token=__A,		sep_token=__A,		cls_token=__A,		pad_token=__A,		mask_token=__A,		tokenizer_file=__A,		src_lang=__A,		tgt_lang=__A,		additional_special_tokens=__A,		sp_model_kwargs=self.sp_model_kwargs,		**__A,		)

     UpperCAmelCase	:							int						=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
     self.sp_model.Load(str(__A						)						)
     UpperCAmelCase	:							List[str]						=			vocab_file

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # Mimic fairseq token-to-id alignment for the first 4 token
     UpperCAmelCase	:							Optional[int]						=			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     UpperCAmelCase	:							List[str]						=			1

     UpperCAmelCase	:							Optional[int]						=			len(self.sp_model						)
     UpperCAmelCase	:							int						=			{
         code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__A						)
     }
     UpperCAmelCase	:							str						=			{v: k for k, v in self.lang_code_to_id.items()}
     UpperCAmelCase	:							List[str]						=			len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

     self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
     UpperCAmelCase	:							List[Any]						=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     UpperCAmelCase	:							Optional[int]						=			list(self.lang_code_to_id.keys()						)

     if additional_special_tokens is not None:
       # Only add those special tokens if they are not already there.
       self._additional_special_tokens.extend(
           [t for t in additional_special_tokens if t not in self._additional_special_tokens]						)

     UpperCAmelCase	:							Any						=			src_lang if src_lang is not None else '''en_XX'''
     UpperCAmelCase	:							Any						=			self.lang_code_to_id[self._src_lang]
     UpperCAmelCase	:							Any						=			tgt_lang
     self.set_src_lang_special_tokens(self._src_lang						)



   def __getstate__(							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Dict						=			self.__dict__.copy()
     UpperCAmelCase	:							Union[str, Any]						=			None
     UpperCAmelCase	:							List[Any]						=			self.sp_model.serialized_model_proto()
     return state



   def __setstate__(							self							:					Dict,		__A							:					Optional[int]						):
     UpperCAmelCase	:							Optional[Any]						=			d

     # for backward compatibility
     if not hasattr(self,		'''sp_model_kwargs'''						):
       UpperCAmelCase	:							Optional[int]						=			{}

     UpperCAmelCase	:							Any						=			spm.SentencePieceProcessor(**self.sp_model_kwargs						)
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)



   @property
   def 	__magic_name__   (							self							:					List[str]						):
     return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token



   @property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return self._src_lang



   @src_lang.setter
   def 	__magic_name__   (							self							:					int,		__A							:					str						):
     UpperCAmelCase	:							int						=			new_src_lang
     self.set_src_lang_special_tokens(self._src_lang						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					List[int],		__A							:					Optional[List[int]] = None,		__A							:					bool = False						):

     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=__A,		token_ids_a=__A,		already_has_special_tokens=__A						)

     UpperCAmelCase	:							List[str]						=			[1] * len(self.prefix_tokens						)
     UpperCAmelCase	:							List[Any]						=			[1] * len(self.suffix_tokens						)
     if token_ids_a is None:
       return prefix_ones + ([0] * len(__A						)) + suffix_ones
     return prefix_ones + ([0] * len(__A						)) + ([0] * len(__A						)) + suffix_ones



   def 	__magic_name__   (							self							:					Tuple,		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					List[int],		__A							:					Optional[List[int]] = None						):
     UpperCAmelCase	:							Any						=			[self.sep_token_id]
     UpperCAmelCase	:							List[Any]						=			[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]



   def 	__magic_name__   (							self							:					List[Any],		__A							:					Any,		__A							:					str,		__A							:					Optional[str],		__A							:					Optional[str],		**__A							:					Tuple						):
     if src_lang is None or tgt_lang is None:
       raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''						)
     UpperCAmelCase	:							List[str]						=			src_lang
     UpperCAmelCase	:							Tuple						=			self(__A,		add_special_tokens=__A,		return_tensors=__A,		**__A						)
     UpperCAmelCase	:							Dict						=			self.convert_tokens_to_ids(__A						)
     UpperCAmelCase	:							Optional[Any]						=			tgt_lang_id
     return inputs



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							Any						=			{self.convert_ids_to_tokens(__A						): i for i in range(self.vocab_size						)}
     vocab.update(self.added_tokens_encoder						)
     return vocab



   def 	__magic_name__   (							self							:					Dict,		__A							:					str						):
     return self.sp_model.encode(__A,		out_type=__A						)



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Tuple						):
     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     UpperCAmelCase	:							int						=			self.sp_model.PieceToId(__A						)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



   def 	__magic_name__   (							self							:					int,		__A							:					int						):
     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset						)



   def 	__magic_name__   (							self							:					str,		__A							:					int						):
     UpperCAmelCase	:							Tuple						=			''''''.join(__A						).replace(__A,		''' '''						).strip()
     return out_string



   def 	__magic_name__   (							self							:					Dict,		__A							:					str,		__A							:					Optional[str] = None						):
     if not os.path.isdir(__A						):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
       return
     UpperCAmelCase	:							List[str]						=			os.path.join(
         __A,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

     if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						) and os.path.isfile(self.vocab_file						):
       copyfile(self.vocab_file,		__A						)
     elif not os.path.isfile(self.vocab_file						):
       with open(__A,		'''wb'''						) as fi:
         UpperCAmelCase	:							Dict						=			self.sp_model.serialized_model_proto()
         fi.write(__A						)

     return (out_vocab_file,)



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[str],		__A							:					str = "en_XX",		__A							:					Optional[List[str]] = None,		__A							:					str = "ro_RO",		**__A							:					Optional[Any],		):
     UpperCAmelCase	:							Union[str, Any]						=			src_lang
     UpperCAmelCase	:							Optional[Any]						=			tgt_lang
     return super().prepare_seqaseq_batch(__A,		__A,		**__A						)



   def 	__magic_name__   (							self							:					Any						):
     return self.set_src_lang_special_tokens(self.src_lang						)



   def 	__magic_name__   (							self							:					Tuple						):
     return self.set_tgt_lang_special_tokens(self.tgt_lang						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Any						):
     UpperCAmelCase	:							Dict						=			self.lang_code_to_id[src_lang]
     UpperCAmelCase	:							int						=			[]
     UpperCAmelCase	:							str						=			[self.eos_token_id, self.cur_lang_code]





   def 	__magic_name__   (							self							:					Tuple,		__A							:					str						):
     UpperCAmelCase	:							str						=			self.lang_code_to_id[lang]
     UpperCAmelCase	:							int						=			[]
     UpperCAmelCase	:							Any						=			[self.eos_token_id, self.cur_lang_code]








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"):
     _lowerCamelCase    :       List[str]        =							True
     from torch.cuda.amp import autocast


_lowerCamelCase    :       Any        =							logging.getLogger(__name__)





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					field(
       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,  )
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  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=0.9_9_9_9_9_5      ,  metadata={"""help""": """Decay of gumbel temperature during training."""}						)






def       a__					(		UpperCAmelCase							:      ModelArguments    ,			UpperCAmelCase							:      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      )]    ,			)
  UpperCAmelCase	:							Any						=			logging.WARNING
  if model_args.verbose_logging:
    UpperCAmelCase	:							Any						=			logging.DEBUG
  elif trainer_utils.is_main_process(training_args.local_rank      ):
    UpperCAmelCase	:							Any						=			logging.INFO
  logger.setLevel(UpperCAmelCase      )





@dataclass
class  __UpperCAmelCase					:
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
   UpperCamelCase       					=					field(
       default=lowerCamelCase__      ,  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=lowerCamelCase__      ,  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=lowerCamelCase__      ,  metadata={"""help""": """The number of processes to use for the preprocessing."""}      ,  )
   UpperCamelCase       					=					field(
       default=2_0.0      ,  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							:					int,		__A							:					List[Dict[str, Union[List[int], torch.Tensor]]]						):
     # reformat list to dict and set to pytorch format
     UpperCAmelCase	:							List[Any]						=			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''',		)
     UpperCAmelCase	:							int						=			self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]						)

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

       UpperCAmelCase	:							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
       UpperCAmelCase	:							Tuple						=			1
       UpperCAmelCase	:							int						=			attention_mask.flip([-1]						).cumsum(-1						).flip([-1]						).bool()

     # sample randomly masked indices
     UpperCAmelCase	:							Dict						=			_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  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Union[str, Any],		*__A							:					int,		__A							:					Dict=1,		__A							:					Any=0,		__A							:					Optional[Any]=1.0,		**__A							:					Any						):
     super().__init__(*__A,		**__A						)
     UpperCAmelCase	:							Any						=			0
     UpperCAmelCase	:							Any						=			max_gumbel_temp
     UpperCAmelCase	:							Optional[Any]						=			min_gumbel_temp
     UpperCAmelCase	:							str						=			gumbel_temp_decay





   def 	__magic_name__   (							self							:					Dict,		__A							:					nn.Module,		__A							:					Dict[str, Union[torch.Tensor, Any]]						):
     model.train()
     UpperCAmelCase	:							List[Any]						=			self._prepare_inputs(__A						)

     if self.use_amp:
       with autocast():
         UpperCAmelCase	:							Optional[Any]						=			self.compute_loss(__A,		__A						)
     else:
       UpperCAmelCase	:							Optional[int]						=			self.compute_loss(__A,		__A						)

     if self.args.n_gpu > 1 or self.deepspeed:
       if model.module.config.ctc_loss_reduction == "mean":
         UpperCAmelCase	:							Optional[Any]						=			loss.mean()
       elif model.module.config.ctc_loss_reduction == "sum":
         UpperCAmelCase	:							str						=			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:
       UpperCAmelCase	:							Any						=			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       a__					(		)  ->						Union[str, 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.

  UpperCAmelCase	:							Optional[Any]						=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

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

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

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

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

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

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

  # normalize and transform to `BatchFeatures`
  UpperCAmelCase	:							Any						=			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
  UpperCAmelCase	:							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\''''      )

  UpperCAmelCase	:							Any						=			WavaVecaForPreTraining(UpperCAmelCase      )

  UpperCAmelCase	:							int						=			DataCollatorForWavaVecaPretraining(model=UpperCAmelCase    ,			feature_extractor=UpperCAmelCase      )

  UpperCAmelCase	:							Any						=			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()





