



'''simple docstring'''



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

import sentencepiece as spm

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


lowerCAmelCase_     :       List[Any]								=						logging.get_logger(__name__)

lowerCAmelCase_     :       Optional[Any]								=						{'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_     :       Optional[int]								=						{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

lowerCAmelCase_     :       Union[str, Any]								=						{
    'camembert-base': 512,
}

lowerCAmelCase_     :       List[Any]								=						'▁'





class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   List[Any]	     =				VOCAB_FILES_NAMES
						_lowerCAmelCase     :   Dict	     =				PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase     :   str	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase     :   Any	     =				['input_ids', 'attention_mask']







						def __init__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      int="<s>"				,    lowerCAmelCase__   :      Optional[int]="</s>"				,    lowerCAmelCase__   :      Any="</s>"				,    lowerCAmelCase__   :      str="<s>"				,    lowerCAmelCase__   :      List[Any]="<unk>"				,    lowerCAmelCase__   :      Optional[Any]="<pad>"				,    lowerCAmelCase__   :      Optional[Any]="<mask>"				,    lowerCAmelCase__   :      Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]				,    lowerCAmelCase__   :      Optional[Dict[str, Any]] = None				,    **lowerCAmelCase__   :      str				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			AddedToken(lowerCAmelCase__				,    lstrip=lowerCAmelCase__				,    rstrip=lowerCAmelCase__				) if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				) else mask_token

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

													super().__init__(
													    bos_token=lowerCAmelCase__				,    eos_token=lowerCAmelCase__				,    unk_token=lowerCAmelCase__				,    sep_token=lowerCAmelCase__				,    cls_token=lowerCAmelCase__				,    pad_token=lowerCAmelCase__				,    mask_token=lowerCAmelCase__				,    additional_special_tokens=lowerCAmelCase__				,    sp_model_kwargs=self.sp_model_kwargs				,    **lowerCAmelCase__				,    )
													SCREAMING_SNAKE_CASE					:      int     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(str(lowerCAmelCase__				)				)
													SCREAMING_SNAKE_CASE					:      Tuple     					=			vocab_file
													# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
													# sentencepiece vocabulary (this is the case for <s> and </s>
													SCREAMING_SNAKE_CASE					:      int     					=			{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
													SCREAMING_SNAKE_CASE					:      int     					=			len(self.fairseq_tokens_to_ids				)
													SCREAMING_SNAKE_CASE					:      str     					=			len(self.sp_model				) + len(self.fairseq_tokens_to_ids				)
													SCREAMING_SNAKE_CASE					:      List[str]     					=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				):

													"""simple docstring"""

													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[self.cls_token_id]
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				,    lowerCAmelCase__   :      bool = False				):

													"""simple docstring"""
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=lowerCAmelCase__				,    token_ids_a=lowerCAmelCase__				,    already_has_special_tokens=lowerCAmelCase__				)

													if token_ids_a is None:
																				return [1] + ([0] * len(lowerCAmelCase__				)) + [1]
													return [1] + ([0] * len(lowerCAmelCase__				)) + [1, 1] + ([0] * len(lowerCAmelCase__				)) + [1]







						def 					__lowercase    (  self   :      int				,    lowerCAmelCase__   :      List[int]				,    lowerCAmelCase__   :      Optional[List[int]] = None				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			[self.sep_token_id]
													SCREAMING_SNAKE_CASE					:      str     					=			[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]







						@property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return len(self.fairseq_tokens_to_ids				) + len(self.sp_model				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			{self.convert_ids_to_tokens(lowerCAmelCase__				): i for i in range(self.vocab_size				)}
													vocab.update(self.added_tokens_encoder				)
													return vocab







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      str				):

													"""simple docstring"""
													return self.sp_model.encode(lowerCAmelCase__				,    out_type=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Tuple				,    lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													if token in self.fairseq_tokens_to_ids:
																				return self.fairseq_tokens_to_ids[token]
													elif self.sp_model.PieceToId(lowerCAmelCase__				) == 0:
																				# Convert sentence piece unk token to fairseq unk token index
																				return self.unk_token_id
													return self.fairseq_offset + self.sp_model.PieceToId(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													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 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			[]
													SCREAMING_SNAKE_CASE					:      str     					=			''''''
													SCREAMING_SNAKE_CASE					:      List[str]     					=			False
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											if not prev_is_special:
																																		out_string += " "
																											out_string += self.sp_model.decode(lowerCAmelCase__				) + token
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			True
																											SCREAMING_SNAKE_CASE					:      int     					=			[]
																				else:
																											current_sub_tokens.append(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			False
													out_string += self.sp_model.decode(lowerCAmelCase__				)
													return out_string.strip()







						def __getstate__(  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.__dict__.copy()
													SCREAMING_SNAKE_CASE					:      Any     					=			None
													return state







						def __setstate__(  self   :      str				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			d

													# for backward compatibility
													if not hasattr(self				,    '''sp_model_kwargs'''				):
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			{}

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
													self.sp_model.Load(self.vocab_file				)







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Optional[str] = None				):

													"""simple docstring"""
													if not os.path.isdir(lowerCAmelCase__				):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""				)
																				return
													SCREAMING_SNAKE_CASE					:      List[str]     					=			os.path.join(
													    lowerCAmelCase__				,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

													if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase__				) and os.path.isfile(self.vocab_file				):
																				copyfile(self.vocab_file				,    lowerCAmelCase__				)
													elif not os.path.isfile(self.vocab_file				):
																				with open(lowerCAmelCase__				,    '''wb'''				) as fi:
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self.sp_model.serialized_model_proto()
																											fi.write(lowerCAmelCase__				)

													return (out_vocab_file,)









'''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'):
							lowerCAmelCase_     :       Union[str, Any]								=						True
							from torch.cuda.amp import autocast


lowerCAmelCase_     :       Union[str, Any]								=						logging.getLogger(__name__)
@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   str	     =				field(
						    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}    ,				)
						_lowerCAmelCase     :   Optional[bool]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}			)
						_lowerCAmelCase     :   Optional[bool]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Whether to log verbose messages or not.'}    ,				)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=2.0    ,				metadata={'help': 'Maximum temperature for gumbel softmax.'}			)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=0.5    ,				metadata={'help': 'Minimum temperature for gumbel softmax.'}			)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=0.99_9995    ,				metadata={'help': 'Decay of gumbel temperature during training.'}			)


def 					UpperCAmelCase   (						A  :				ModelArguments					,		A  :				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					:      List[Any]     					=			logging.WARNING
							if model_args.verbose_logging:
														SCREAMING_SNAKE_CASE					:      Dict     					=			logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank				):
														SCREAMING_SNAKE_CASE					:      Any     					=			logging.INFO
							logger.setLevel(A				)
@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   str	     =				field(
						    default=snake_case_    ,				metadata={'help': 'The name of the dataset to use (via the datasets library).'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='train'    ,				metadata={
						        'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
						    }    ,				)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='validation'    ,				metadata={
						        'help': (
						            'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
						        )
						    }    ,				)
						_lowerCAmelCase     :   Optional[str]	     =				field(
						    default='file'    ,				metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}    ,				)
						_lowerCAmelCase     :   bool	     =				field(
						    default=snake_case_    ,				metadata={'help': 'Overwrite the cached preprocessed datasets or not.'}			)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=1    ,				metadata={
						        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
						    }    ,				)
						_lowerCAmelCase     :   Optional[int]	     =				field(
						    default=snake_case_    ,				metadata={'help': 'The number of processes to use for the preprocessing.'}    ,				)
						_lowerCAmelCase     :   Optional[float]	     =				field(
						    default=20.0    ,				metadata={'help': 'Filter audio files that are longer than `max_duration_in_seconds` seconds'}			)


@dataclass
class 	lowerCamelCase_							:
						_lowerCAmelCase     :   WavaVecaForPreTraining
						_lowerCAmelCase     :   WavaVecaFeatureExtractor
						_lowerCAmelCase     :   Union[bool, str]	     =				"longest"
						_lowerCAmelCase     :   Optional[int]	     =				None
						_lowerCAmelCase     :   Optional[int]	     =				None







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

													"""simple docstring"""
													# reformat list to dict and set to pytorch format
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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					:      Optional[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					:      List[Any]     					=			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					:      Optional[int]     					=			1
																				SCREAMING_SNAKE_CASE					:      Dict     					=			attention_mask.flip([-1]				).cumsum(-1				).flip([-1]				).bool()

													# sample randomly masked indices
													SCREAMING_SNAKE_CASE					:      List[str]     					=			_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_							(					snake_case_			):







						def __init__(  self   :      str				,    *lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      Any=1				,    lowerCAmelCase__   :      Dict=0				,    lowerCAmelCase__   :      Tuple=1.0				,    **lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													super().__init__(*lowerCAmelCase__				,    **lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			0
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			max_gumbel_temp
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			min_gumbel_temp
													SCREAMING_SNAKE_CASE					:      Tuple     					=			gumbel_temp_decay







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      nn.Module				,    lowerCAmelCase__   :      Dict[str, Union[torch.Tensor, Any]]				):

													"""simple docstring"""
													model.train()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self._prepare_inputs(lowerCAmelCase__				)

													if self.use_amp:
																				with autocast():
																											SCREAMING_SNAKE_CASE					:      Any     					=			self.compute_loss(lowerCAmelCase__				,    lowerCAmelCase__				)
													else:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			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					:      Any     					=			loss.mean()
																				elif model.module.config.ctc_loss_reduction == "sum":
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			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					:      List[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 					UpperCAmelCase   (						):
							# 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					:      Optional[int]     					=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)

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

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

							def prepare_dataset(A  :				str				):
														# 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[Any]     					=			datasets.map(
							    A					,		num_proc=data_args.preprocessing_num_workers					,		remove_columns=datasets['''train'''].column_names				)

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

							def normalize(A  :				List[Any]				):
														return feature_extractor(batch['''speech''']					,		sampling_rate=feature_extractor.sampling_rate				)

							# normalize and transform to `BatchFeatures`
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			vectorized_datasets.map(
							    A					,		batched=A					,		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					:      Tuple     					=			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					:      str     					=			WavaVecaForPreTraining(A				)

							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			DataCollatorForWavaVecaPretraining(model=A					,		feature_extractor=A				)

							SCREAMING_SNAKE_CASE					:      Dict     					=			WavaVecaPreTrainer(
							    model=A					,		data_collator=A					,		args=A					,		train_dataset=vectorized_datasets['''train''']					,		eval_dataset=vectorized_datasets['''validation''']					,		tokenizer=A					,		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()




