



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


lowerCAmelCase__		:					int							={
    '''Acehnese Arabic''': '''ace_Arab''',
    '''Acehnese Latin''': '''ace_Latn''',
    '''Mesopotamian Arabic''': '''acm_Arab''',
    '''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
    '''Tunisian Arabic''': '''aeb_Arab''',
    '''Afrikaans''': '''afr_Latn''',
    '''South Levantine Arabic''': '''ajp_Arab''',
    '''Akan''': '''aka_Latn''',
    '''Amharic''': '''amh_Ethi''',
    '''North Levantine Arabic''': '''apc_Arab''',
    '''Modern Standard Arabic''': '''arb_Arab''',
    '''Modern Standard Arabic Romanized''': '''arb_Latn''',
    '''Najdi Arabic''': '''ars_Arab''',
    '''Moroccan Arabic''': '''ary_Arab''',
    '''Egyptian Arabic''': '''arz_Arab''',
    '''Assamese''': '''asm_Beng''',
    '''Asturian''': '''ast_Latn''',
    '''Awadhi''': '''awa_Deva''',
    '''Central Aymara''': '''ayr_Latn''',
    '''South Azerbaijani''': '''azb_Arab''',
    '''North Azerbaijani''': '''azj_Latn''',
    '''Bashkir''': '''bak_Cyrl''',
    '''Bambara''': '''bam_Latn''',
    '''Balinese''': '''ban_Latn''',
    '''Belarusian''': '''bel_Cyrl''',
    '''Bemba''': '''bem_Latn''',
    '''Bengali''': '''ben_Beng''',
    '''Bhojpuri''': '''bho_Deva''',
    '''Banjar Arabic''': '''bjn_Arab''',
    '''Banjar Latin''': '''bjn_Latn''',
    '''Standard Tibetan''': '''bod_Tibt''',
    '''Bosnian''': '''bos_Latn''',
    '''Buginese''': '''bug_Latn''',
    '''Bulgarian''': '''bul_Cyrl''',
    '''Catalan''': '''cat_Latn''',
    '''Cebuano''': '''ceb_Latn''',
    '''Czech''': '''ces_Latn''',
    '''Chokwe''': '''cjk_Latn''',
    '''Central Kurdish''': '''ckb_Arab''',
    '''Crimean Tatar''': '''crh_Latn''',
    '''Welsh''': '''cym_Latn''',
    '''Danish''': '''dan_Latn''',
    '''German''': '''deu_Latn''',
    '''Southwestern Dinka''': '''dik_Latn''',
    '''Dyula''': '''dyu_Latn''',
    '''Dzongkha''': '''dzo_Tibt''',
    '''Greek''': '''ell_Grek''',
    '''English''': '''eng_Latn''',
    '''Esperanto''': '''epo_Latn''',
    '''Estonian''': '''est_Latn''',
    '''Basque''': '''eus_Latn''',
    '''Ewe''': '''ewe_Latn''',
    '''Faroese''': '''fao_Latn''',
    '''Fijian''': '''fij_Latn''',
    '''Finnish''': '''fin_Latn''',
    '''Fon''': '''fon_Latn''',
    '''French''': '''fra_Latn''',
    '''Friulian''': '''fur_Latn''',
    '''Nigerian Fulfulde''': '''fuv_Latn''',
    '''Scottish Gaelic''': '''gla_Latn''',
    '''Irish''': '''gle_Latn''',
    '''Galician''': '''glg_Latn''',
    '''Guarani''': '''grn_Latn''',
    '''Gujarati''': '''guj_Gujr''',
    '''Haitian Creole''': '''hat_Latn''',
    '''Hausa''': '''hau_Latn''',
    '''Hebrew''': '''heb_Hebr''',
    '''Hindi''': '''hin_Deva''',
    '''Chhattisgarhi''': '''hne_Deva''',
    '''Croatian''': '''hrv_Latn''',
    '''Hungarian''': '''hun_Latn''',
    '''Armenian''': '''hye_Armn''',
    '''Igbo''': '''ibo_Latn''',
    '''Ilocano''': '''ilo_Latn''',
    '''Indonesian''': '''ind_Latn''',
    '''Icelandic''': '''isl_Latn''',
    '''Italian''': '''ita_Latn''',
    '''Javanese''': '''jav_Latn''',
    '''Japanese''': '''jpn_Jpan''',
    '''Kabyle''': '''kab_Latn''',
    '''Jingpho''': '''kac_Latn''',
    '''Kamba''': '''kam_Latn''',
    '''Kannada''': '''kan_Knda''',
    '''Kashmiri Arabic''': '''kas_Arab''',
    '''Kashmiri Devanagari''': '''kas_Deva''',
    '''Georgian''': '''kat_Geor''',
    '''Central Kanuri Arabic''': '''knc_Arab''',
    '''Central Kanuri Latin''': '''knc_Latn''',
    '''Kazakh''': '''kaz_Cyrl''',
    '''Kabiyè''': '''kbp_Latn''',
    '''Kabuverdianu''': '''kea_Latn''',
    '''Khmer''': '''khm_Khmr''',
    '''Kikuyu''': '''kik_Latn''',
    '''Kinyarwanda''': '''kin_Latn''',
    '''Kyrgyz''': '''kir_Cyrl''',
    '''Kimbundu''': '''kmb_Latn''',
    '''Northern Kurdish''': '''kmr_Latn''',
    '''Kikongo''': '''kon_Latn''',
    '''Korean''': '''kor_Hang''',
    '''Lao''': '''lao_Laoo''',
    '''Ligurian''': '''lij_Latn''',
    '''Limburgish''': '''lim_Latn''',
    '''Lingala''': '''lin_Latn''',
    '''Lithuanian''': '''lit_Latn''',
    '''Lombard''': '''lmo_Latn''',
    '''Latgalian''': '''ltg_Latn''',
    '''Luxembourgish''': '''ltz_Latn''',
    '''Luba-Kasai''': '''lua_Latn''',
    '''Ganda''': '''lug_Latn''',
    '''Luo''': '''luo_Latn''',
    '''Mizo''': '''lus_Latn''',
    '''Standard Latvian''': '''lvs_Latn''',
    '''Magahi''': '''mag_Deva''',
    '''Maithili''': '''mai_Deva''',
    '''Malayalam''': '''mal_Mlym''',
    '''Marathi''': '''mar_Deva''',
    '''Minangkabau Arabic ''': '''min_Arab''',
    '''Minangkabau Latin''': '''min_Latn''',
    '''Macedonian''': '''mkd_Cyrl''',
    '''Plateau Malagasy''': '''plt_Latn''',
    '''Maltese''': '''mlt_Latn''',
    '''Meitei Bengali''': '''mni_Beng''',
    '''Halh Mongolian''': '''khk_Cyrl''',
    '''Mossi''': '''mos_Latn''',
    '''Maori''': '''mri_Latn''',
    '''Burmese''': '''mya_Mymr''',
    '''Dutch''': '''nld_Latn''',
    '''Norwegian Nynorsk''': '''nno_Latn''',
    '''Norwegian Bokmål''': '''nob_Latn''',
    '''Nepali''': '''npi_Deva''',
    '''Northern Sotho''': '''nso_Latn''',
    '''Nuer''': '''nus_Latn''',
    '''Nyanja''': '''nya_Latn''',
    '''Occitan''': '''oci_Latn''',
    '''West Central Oromo''': '''gaz_Latn''',
    '''Odia''': '''ory_Orya''',
    '''Pangasinan''': '''pag_Latn''',
    '''Eastern Panjabi''': '''pan_Guru''',
    '''Papiamento''': '''pap_Latn''',
    '''Western Persian''': '''pes_Arab''',
    '''Polish''': '''pol_Latn''',
    '''Portuguese''': '''por_Latn''',
    '''Dari''': '''prs_Arab''',
    '''Southern Pashto''': '''pbt_Arab''',
    '''Ayacucho Quechua''': '''quy_Latn''',
    '''Romanian''': '''ron_Latn''',
    '''Rundi''': '''run_Latn''',
    '''Russian''': '''rus_Cyrl''',
    '''Sango''': '''sag_Latn''',
    '''Sanskrit''': '''san_Deva''',
    '''Santali''': '''sat_Olck''',
    '''Sicilian''': '''scn_Latn''',
    '''Shan''': '''shn_Mymr''',
    '''Sinhala''': '''sin_Sinh''',
    '''Slovak''': '''slk_Latn''',
    '''Slovenian''': '''slv_Latn''',
    '''Samoan''': '''smo_Latn''',
    '''Shona''': '''sna_Latn''',
    '''Sindhi''': '''snd_Arab''',
    '''Somali''': '''som_Latn''',
    '''Southern Sotho''': '''sot_Latn''',
    '''Spanish''': '''spa_Latn''',
    '''Tosk Albanian''': '''als_Latn''',
    '''Sardinian''': '''srd_Latn''',
    '''Serbian''': '''srp_Cyrl''',
    '''Swati''': '''ssw_Latn''',
    '''Sundanese''': '''sun_Latn''',
    '''Swedish''': '''swe_Latn''',
    '''Swahili''': '''swh_Latn''',
    '''Silesian''': '''szl_Latn''',
    '''Tamil''': '''tam_Taml''',
    '''Tatar''': '''tat_Cyrl''',
    '''Telugu''': '''tel_Telu''',
    '''Tajik''': '''tgk_Cyrl''',
    '''Tagalog''': '''tgl_Latn''',
    '''Thai''': '''tha_Thai''',
    '''Tigrinya''': '''tir_Ethi''',
    '''Tamasheq Latin''': '''taq_Latn''',
    '''Tamasheq Tifinagh''': '''taq_Tfng''',
    '''Tok Pisin''': '''tpi_Latn''',
    '''Tswana''': '''tsn_Latn''',
    '''Tsonga''': '''tso_Latn''',
    '''Turkmen''': '''tuk_Latn''',
    '''Tumbuka''': '''tum_Latn''',
    '''Turkish''': '''tur_Latn''',
    '''Twi''': '''twi_Latn''',
    '''Central Atlas Tamazight''': '''tzm_Tfng''',
    '''Uyghur''': '''uig_Arab''',
    '''Ukrainian''': '''ukr_Cyrl''',
    '''Umbundu''': '''umb_Latn''',
    '''Urdu''': '''urd_Arab''',
    '''Northern Uzbek''': '''uzn_Latn''',
    '''Venetian''': '''vec_Latn''',
    '''Vietnamese''': '''vie_Latn''',
    '''Waray''': '''war_Latn''',
    '''Wolof''': '''wol_Latn''',
    '''Xhosa''': '''xho_Latn''',
    '''Eastern Yiddish''': '''ydd_Hebr''',
    '''Yoruba''': '''yor_Latn''',
    '''Yue Chinese''': '''yue_Hant''',
    '''Chinese Simplified''': '''zho_Hans''',
    '''Chinese Traditional''': '''zho_Hant''',
    '''Standard Malay''': '''zsm_Latn''',
    '''Zulu''': '''zul_Latn''',
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  '''facebook/nllb-200-distilled-600M'''
						UpperCamelCase__      :			List[str]      =  (
						    '''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
						    '''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
						    '''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
						    '''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
						)
						UpperCamelCase__      :			Dict      =  '''translator'''
						UpperCamelCase__      :			Optional[Any]      =  AutoTokenizer
						UpperCamelCase__      :			List[str]      =  AutoModelForSeqaSeqLM
						UpperCamelCase__      :			Union[str, Any]      =  LANGUAGE_CODES

						UpperCamelCase__      :			Optional[int]      =  ['''text''', '''text''', '''text''']
						UpperCamelCase__      :			Dict      =  ['''text''']






						def 	_A     (   self				,							_A				,							_A				,							_A		):







													'''simple docstring'''

													if src_lang not in self.lang_to_code:
																				raise ValueError(f"""{src_lang} is not a supported language."""		)
													if tgt_lang not in self.lang_to_code:
																				raise ValueError(f"""{tgt_lang} is not a supported language."""		)
													__SCREAMING_SNAKE_CASE									= self.lang_to_code[src_lang]
													__SCREAMING_SNAKE_CASE									= self.lang_to_code[tgt_lang]
													return self.pre_processor._build_translation_inputs(
													    _A				,							return_tensors='pt'				,							src_lang=_A				,							tgt_lang=_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.model.generate(**_A		)






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													return self.post_processor.decode(outputs[0].tolist()				,							skip_special_tokens=_A		)











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__		:					Optional[int]							=True
			from torch.cuda.amp import autocast


lowerCAmelCase__		:					List[Any]							=logging.getLogger(__name__)

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}   ,   )
						UpperCamelCase__      :			Optional[bool]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}	)
						UpperCamelCase__      :			Optional[bool]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Whether to log verbose messages or not.'''}   ,   )
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=2.0   ,   metadata={'''help''': '''Maximum temperature for gumbel softmax.'''}	)
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=0.5   ,   metadata={'''help''': '''Minimum temperature for gumbel softmax.'''}	)
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=0.9_9_9_9_9_5   ,   metadata={'''help''': '''Decay of gumbel temperature during training.'''}	)







def      __lowercase       (			a__							,       a__       )    ->			Dict:
							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									= logging.WARNING
							if model_args.verbose_logging:
														__SCREAMING_SNAKE_CASE									= logging.DEBUG
							elif trainer_utils.is_main_process(training_args.local_rank       ):
														__SCREAMING_SNAKE_CASE									= logging.INFO
							logger.setLevel(a__       )

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
						UpperCamelCase__      :			Optional[str]      =  field(
						    default='''train'''   ,   metadata={
						        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default='''validation'''   ,   metadata={
						        '''help''': (
						            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
						        )
						    }   ,   )
						UpperCamelCase__      :			Optional[str]      =  field(
						    default='''file'''   ,   metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''}   ,   )
						UpperCamelCase__      :			bool      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}	)
						UpperCamelCase__      :			Optional[int]      =  field(
						    default=1   ,   metadata={
						        '''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
						    }   ,   )
						UpperCamelCase__      :			Optional[int]      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''The number of processes to use for the preprocessing.'''}   ,   )
						UpperCamelCase__      :			Optional[float]      =  field(
						    default=2_0.0   ,   metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''}	)







@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			WavaVecaForPreTraining
						UpperCamelCase__      :			WavaVecaFeatureExtractor
						UpperCamelCase__      :			Union[bool, str]      =  "longest"
						UpperCamelCase__      :			Optional[int]      =  None
						UpperCamelCase__      :			Optional[int]      =  None






						def __call__(   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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'				,							)
													__SCREAMING_SNAKE_CASE									= self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]		)

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

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

													# sample randomly masked indices
													__SCREAMING_SNAKE_CASE									= _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_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							_A=1				,							_A=0				,							_A=1.0				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= 0
													__SCREAMING_SNAKE_CASE									= max_gumbel_temp
													__SCREAMING_SNAKE_CASE									= min_gumbel_temp
													__SCREAMING_SNAKE_CASE									= gumbel_temp_decay






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													model.train()
													__SCREAMING_SNAKE_CASE									= self._prepare_inputs(_A		)

													if self.use_amp:
																				with autocast():
																											__SCREAMING_SNAKE_CASE									= self.compute_loss(_A				,							_A		)
													else:
																				__SCREAMING_SNAKE_CASE									= self.compute_loss(_A				,							_A		)

													if self.args.n_gpu > 1 or self.deepspeed:
																				if model.module.config.ctc_loss_reduction == "mean":
																											__SCREAMING_SNAKE_CASE									= loss.mean()
																				elif model.module.config.ctc_loss_reduction == "sum":
																											__SCREAMING_SNAKE_CASE									= 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									= 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      __lowercase       (			)    ->			Tuple:
							# 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									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= parser.parse_args_into_dataclasses()
							configure_logger(a__							,       a__       )

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

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

							# load audio files into numpy arrays
							__SCREAMING_SNAKE_CASE									= 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									= vectorized_datasets.filter(
							    lambda a__       : len(data['speech']       ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate       )       )

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

							# normalize and transform to `BatchFeatures`
							__SCREAMING_SNAKE_CASE									= 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									= 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									= WavaVecaForPreTraining(a__       )

							__SCREAMING_SNAKE_CASE									= DataCollatorForWavaVecaPretraining(model=a__							,       feature_extractor=a__       )

							__SCREAMING_SNAKE_CASE									= 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()






