
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


lowercase		:						Union[str, Any] =					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowercase		:						Union[str, Any] =					{
    """base""": AutoModel,
    """sequence-classification""": AutoModelForSequenceClassification,
    """question-answering""": AutoModelForQuestionAnswering,
    """pretraining""": AutoModelForPreTraining,
    """token-classification""": AutoModelForTokenClassification,
    """language-modeling""": AutoModelWithLMHead,
    """summarization""": AutoModelForSeqaSeqLM,
    """translation""": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
lowercase		:						List[Any] =					{
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
lowercase		:						Union[str, Any] =					sorted(arg_to_scheduler.keys())
lowercase		:						Optional[Any] =					"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""




class    _snake_case  (						pl.LightningModule       ):






							def __init__(					self						:			Optional[int]						, snake_case__						:			List[Any]						, snake_case__						:			Tuple=None						, snake_case__						:			Optional[Any]="base"						, snake_case__						:			List[str]=None						, snake_case__						:			List[Any]=None						, snake_case__						:			Optional[Any]=None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



												super().__init__()
												# TODO: move to self.save_hyperparameters()
												# self.save_hyperparameters()
												# can also expand arguments into trainer signature for easier reading

												self.save_hyperparameters(lowerCAmelCase__    )
												__a:						List[str]		    =					0
												__a:						Dict		    =					Path(self.hparams.output_dir    )
												__a:						Dict		    =					self.hparams.cache_dir if self.hparams.cache_dir else None
												if config is None:
																	__a:						List[Any]		    =					AutoConfig.from_pretrained(
																	    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path						, **({"""num_labels""": num_labels} if num_labels is not None else {})						, cache_dir=lowerCAmelCase__						, **lowerCAmelCase__						, )
												else:
																	__a:						PretrainedConfig		    =					config

												__a:						Union[str, Any]		    =					("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
												for p in extra_model_params:
																	if getattr(self.hparams						, lowerCAmelCase__						, lowerCAmelCase__    ):
																						assert hasattr(self.config						, lowerCAmelCase__    ), f"""model config doesn\'t have a `{p}` attribute"""
																						setattr(self.config						, lowerCAmelCase__						, getattr(self.hparams						, lowerCAmelCase__    )    )

												if tokenizer is None:
																	__a:						Optional[Any]		    =					AutoTokenizer.from_pretrained(
																	    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path						, cache_dir=lowerCAmelCase__						, )
												else:
																	__a:						PreTrainedTokenizer		    =					tokenizer
												__a:						int		    =					MODEL_MODES[mode]
												if model is None:
																	__a:						List[Any]		    =					self.model_type.from_pretrained(
																	    self.hparams.model_name_or_path						, from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path    )						, config=self.config						, cache_dir=lowerCAmelCase__						, )
												else:
																	__a:						Optional[Any]		    =					model






							def 							__lowercase  (					self						:			Optional[int]						, *snake_case__						:			Optional[Any]						, **snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												__a:						int		    =					self.model_type.from_pretrained(*lowerCAmelCase__						, **lowerCAmelCase__    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						str		    =					arg_to_scheduler[self.hparams.lr_scheduler]
												__a:						Optional[Any]		    =					get_schedule_func(
												    self.opt						, num_warmup_steps=self.hparams.warmup_steps						, num_training_steps=self.total_steps()    )
												__a:						Optional[int]		    =					{"scheduler": scheduler, "interval": "step", "frequency": 1}
												return scheduler






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						List[Any]		    =					self.model
												__a:						Any		    =					["bias", "LayerNorm.weight"]
												__a:						Dict		    =					[
												    {
												        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay    )],
												        "weight_decay": self.hparams.weight_decay,
												    },
												    {
												        "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay    )],
												        "weight_decay": 0.0,
												    },
												]
												if self.hparams.adafactor:
																	__a:						List[Any]		    =					Adafactor(
																	    lowerCAmelCase__						, lr=self.hparams.learning_rate						, scale_parameter=lowerCAmelCase__						, relative_step=lowerCAmelCase__    )

												else:
																	__a:						List[str]		    =					AdamW(
																	    lowerCAmelCase__						, lr=self.hparams.learning_rate						, eps=self.hparams.adam_epsilon    )
												__a:						int		    =					optimizer

												__a:						Any		    =					self.get_lr_scheduler()

												return [optimizer], [scheduler]






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			int						, snake_case__						:			int    ):






												"""simple docstring"""



												return self.validation_step(lowerCAmelCase__						, lowerCAmelCase__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			Tuple    ):






												"""simple docstring"""



												return self.validation_end(lowerCAmelCase__    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						List[str]		    =					max(1						, self.hparams.gpus    )  # TODO: consider num_tpu_cores
												__a:						Any		    =					self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
												return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs






							def 							__lowercase  (					self						:			str						, snake_case__						:			int    ):






												"""simple docstring"""



												if mode == "test":
																	__a:						str		    =					len(self.test_dataloader().dataset    )
												else:
																	__a:						Tuple		    =					self.get_dataloader("""train"""						, self.hparams.train_batch_size						, shuffle=lowerCAmelCase__    )
																	__a:						List[Any]		    =					len(self.train_dataloader().dataset    )






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			Union[str, Any]						, snake_case__						:			str						, snake_case__						:			Union[str, Any] = False    ):






												"""simple docstring"""



												raise NotImplementedError("""You must implement this for your task"""    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												return self.train_loader






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												return self.get_dataloader("""dev"""						, self.hparams.eval_batch_size						, shuffle=lowerCAmelCase__    )






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												return self.get_dataloader("""test"""						, self.hparams.eval_batch_size						, shuffle=lowerCAmelCase__    )






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			Tuple    ):






												"""simple docstring"""



												return os.path.join(
												    self.hparams.data_dir						, """cached_{}_{}_{}""".format(
												        lowerCAmelCase__						, list(filter(lowerCAmelCase__						, self.hparams.model_name_or_path.split("""/"""    )    )    ).pop()						, str(self.hparams.max_seq_length    )						, )						, )






							@pl.utilities.rank_zero_only
							def 							__lowercase  (					self						:			Any						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												__a:						List[str]		    =					self.output_dir.joinpath("""best_tfmr"""    )
												__a:						Optional[Any]		    =					self.step_count
												self.model.save_pretrained(lowerCAmelCase__    )
												self.tokenizer.save_pretrained(lowerCAmelCase__    )






							@staticmethod
							def 							__lowercase  (					snake_case__						:			Dict						, snake_case__						:			Optional[int]    ):






												"""simple docstring"""



												parser.add_argument(
												    """--model_name_or_path"""						, default=lowerCAmelCase__						, type=lowerCAmelCase__						, required=lowerCAmelCase__						, help="""Path to pretrained model or model identifier from huggingface.co/models"""						, )
												parser.add_argument(
												    """--config_name"""						, default=""""""						, type=lowerCAmelCase__						, help="""Pretrained config name or path if not the same as model_name"""    )
												parser.add_argument(
												    """--tokenizer_name"""						, default=lowerCAmelCase__						, type=lowerCAmelCase__						, help="""Pretrained tokenizer name or path if not the same as model_name"""						, )
												parser.add_argument(
												    """--cache_dir"""						, default=""""""						, type=lowerCAmelCase__						, help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""						, )
												parser.add_argument(
												    """--encoder_layerdrop"""						, type=lowerCAmelCase__						, help="""Encoder layer dropout probability (Optional). Goes into model.config"""						, )
												parser.add_argument(
												    """--decoder_layerdrop"""						, type=lowerCAmelCase__						, help="""Decoder layer dropout probability (Optional). Goes into model.config"""						, )
												parser.add_argument(
												    """--dropout"""						, type=lowerCAmelCase__						, help="""Dropout probability (Optional). Goes into model.config"""						, )
												parser.add_argument(
												    """--attention_dropout"""						, type=lowerCAmelCase__						, help="""Attention dropout probability (Optional). Goes into model.config"""						, )
												parser.add_argument("""--learning_rate"""						, default=5e-5						, type=lowerCAmelCase__						, help="""The initial learning rate for Adam."""    )
												parser.add_argument(
												    """--lr_scheduler"""						, default="""linear"""						, choices=lowerCAmelCase__						, metavar=lowerCAmelCase__						, type=lowerCAmelCase__						, help="""Learning rate scheduler"""						, )
												parser.add_argument("""--weight_decay"""						, default=0.0						, type=lowerCAmelCase__						, help="""Weight decay if we apply some."""    )
												parser.add_argument("""--adam_epsilon"""						, default=1e-8						, type=lowerCAmelCase__						, help="""Epsilon for Adam optimizer."""    )
												parser.add_argument("""--warmup_steps"""						, default=0						, type=lowerCAmelCase__						, help="""Linear warmup over warmup_steps."""    )
												parser.add_argument("""--num_workers"""						, default=4						, type=lowerCAmelCase__						, help="""kwarg passed to DataLoader"""    )
												parser.add_argument("""--num_train_epochs"""						, dest="""max_epochs"""						, default=3						, type=lowerCAmelCase__    )
												parser.add_argument("""--train_batch_size"""						, default=3_2						, type=lowerCAmelCase__    )
												parser.add_argument("""--eval_batch_size"""						, default=3_2						, type=lowerCAmelCase__    )
												parser.add_argument("""--adafactor"""						, action="""store_true"""    )






class    _snake_case  (						pl.Callback       ):






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			Any						, snake_case__						:			List[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					trainer.lr_schedulers[0]["scheduler"]
												__a:						Dict		    =					{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
												pl_module.logger.log_metrics(lowerCAmelCase__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			List[Any]						, snake_case__						:			List[str]    ):






												"""simple docstring"""



												rank_zero_info("""***** Validation results *****"""    )
												__a:						int		    =					trainer.callback_metrics
												# Log results
												for key in sorted(lowerCAmelCase__    ):
																	if key not in ["log", "progress_bar"]:
																						rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__						, str(metrics[key]    )    )    )






							def 							__lowercase  (					self						:			List[Any]						, snake_case__						:			Union[str, Any]						, snake_case__						:			Optional[int]    ):






												"""simple docstring"""



												rank_zero_info("""***** Test results *****"""    )
												__a:						Dict		    =					trainer.callback_metrics
												# Log and save results to file
												__a:						int		    =					os.path.join(pl_module.hparams.output_dir						, """test_results.txt"""    )
												with open(lowerCAmelCase__						, """w"""    ) as writer:
																	for key in sorted(lowerCAmelCase__    ):
																						if key not in ["log", "progress_bar"]:
																											rank_zero_info("""{} = {}\n""".format(lowerCAmelCase__						, str(metrics[key]    )    )    )
																											writer.write("""{} = {}\n""".format(lowerCAmelCase__						, str(metrics[key]    )    )    )






def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					parser.add_argument(
					    """--output_dir"""	,				default=lowerCamelCase_	,				type=lowerCamelCase_	,				required=lowerCamelCase_	,				help="""The output directory where the model predictions and checkpoints will be written."""	,				)
					parser.add_argument(
					    """--fp16"""	,				action="""store_true"""	,				help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""	,				)

					parser.add_argument(
					    """--fp16_opt_level"""	,				type=lowerCamelCase_	,				default="""O2"""	,				help=(
					        """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
					        """See details at https://nvidia.github.io/apex/amp.html"""
					    )	,				)
					parser.add_argument("""--n_tpu_cores"""	,				dest="""tpu_cores"""	,				type=lowerCamelCase_ )
					parser.add_argument("""--max_grad_norm"""	,				dest="""gradient_clip_val"""	,				default=1.0	,				type=lowerCamelCase_	,				help="""Max gradient norm""" )
					parser.add_argument("""--do_train"""	,				action="""store_true"""	,				help="""Whether to run training.""" )
					parser.add_argument("""--do_predict"""	,				action="""store_true"""	,				help="""Whether to run predictions on the test set.""" )
					parser.add_argument(
					    """--gradient_accumulation_steps"""	,				dest="""accumulate_grad_batches"""	,				type=lowerCamelCase_	,				default=1	,				help="""Number of updates steps to accumulate before performing a backward/update pass."""	,				)
					parser.add_argument("""--seed"""	,				type=lowerCamelCase_	,				default=42	,				help="""random seed for initialization""" )
					parser.add_argument(
					    """--data_dir"""	,				default=lowerCamelCase_	,				type=lowerCamelCase_	,				required=lowerCamelCase_	,				help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""	,				)




def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_=None	,				lowerCamelCase_=True	,				lowerCamelCase_=[]	,				lowerCamelCase_=None	,				lowerCamelCase_=None	,				**lowerCamelCase_	,				)     ->			int:


					'''simple docstring'''
					pl.seed_everything(args.seed )

					# init model
					__a:						Dict		    =					Path(model.hparams.output_dir )
					odir.mkdir(exist_ok=lowerCamelCase_ )

					# add custom checkpoints
					if checkpoint_callback is None:
										__a:						Tuple		    =					pl.callbacks.ModelCheckpoint(
										    filepath=args.output_dir	,				prefix="""checkpoint"""	,				monitor="""val_loss"""	,				mode="""min"""	,				save_top_k=1 )
					if early_stopping_callback:
										extra_callbacks.append(lowerCamelCase_ )
					if logging_callback is None:
										__a:						str		    =					LoggingCallback()

					__a:						Dict		    =					{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										__a:						Union[str, Any]		    =					16
										__a:						Tuple		    =					args.fpaa_opt_level

					if args.gpus > 1:
										__a:						Union[str, Any]		    =					"ddp"

					__a:						Dict		    =					args.accumulate_grad_batches
					__a:						Any		    =					extra_train_kwargs.get("""accelerator"""	,				lowerCamelCase_ )
					__a:						Optional[int]		    =					extra_train_kwargs.get("""profiler"""	,				lowerCamelCase_ )

					__a:						int		    =					pl.Trainer.from_argparse_args(
					    lowerCamelCase_	,				weights_summary=lowerCamelCase_	,				callbacks=[logging_callback] + extra_callbacks	,				logger=lowerCamelCase_	,				checkpoint_callback=lowerCamelCase_	,				**lowerCamelCase_	,				)

					if args.do_train:
										trainer.fit(lowerCamelCase_ )

					return trainer


"""simple docstring"""
import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
			from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
			lowercase		:						str =					True
			from torch.cuda.amp import autocast


lowercase		:						int =					logging.getLogger(__name__)




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to log verbose messages or not.'''} ,				)






def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,				datefmt="""%m/%d/%Y %H:%M:%S"""	,				handlers=[logging.StreamHandler(sys.stdout )]	,				)
					__a:						List[Any]		    =					logging.WARNING
					if model_args.verbose_logging:
										__a:						Dict		    =					logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank ):
										__a:						Optional[int]		    =					logging.INFO
					logger.setLevel(lowerCamelCase_ )




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''train''' ,				metadata={
							        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''validation''' ,				metadata={
							        '''help''': (
							            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
							        )
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''text''' ,				metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''file''' ,				metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''librispeech''' ,				metadata={
							        '''help''': (
							            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
							            ''' \'buckwalter\'.'''
							        )
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The number of processes to use for the preprocessing.'''} ,				)






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						False
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						"|"
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			List[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



												if name == "librispeech":
																	return cls()
												if name == "timit":
																	return cls(
																	    do_lower_case=snake_case__						, translation_table=str.maketrans({"""-""": """ """}    )						, )
												if name == "buckwalter":
																	__a:						List[Any]		    =					{
																	    """-""": """ """,  # sometimes used to represent pauses
																	    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
																	}
																	return cls(
																	    vocab_file=pathlib.Path(snake_case__    ).parent.joinpath("""vocab/buckwalter.json"""    )						, word_delimiter_token="""/"""						, translation_table=str.maketrans(snake_case__    )						, words_to_remove={"""sil"""}						, untransliterator=arabic.buckwalter.untransliterate						, )
												raise ValueError(f"""Unsupported orthography: '{name}'."""    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			str    ):






												"""simple docstring"""



												if len(self.translation_table    ) > 0:
																	__a:						Optional[int]		    =					text.translate(self.translation_table    )
												if len(self.words_to_remove    ) == 0:
																	__a:						Tuple		    =					""" """.join(text.split()    )  # clean up whitespaces
												else:
																	__a:						int		    =					""" """.join(w for w in text.split() if w not in self.words_to_remove    )  # and clean up whilespaces
												return text






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			ModelArguments    ):






												"""simple docstring"""



												__a:						Dict		    =					WavaVecaFeatureExtractor.from_pretrained(
												    model_args.model_name_or_path						, cache_dir=model_args.cache_dir    )
												if self.vocab_file:
																	__a:						Dict		    =					WavaVecaCTCTokenizer(
																	    self.vocab_file						, cache_dir=model_args.cache_dir						, do_lower_case=self.do_lower_case						, word_delimiter_token=self.word_delimiter_token						, )
												else:
																	__a:						Dict		    =					WavaVecaCTCTokenizer.from_pretrained(
																	    model_args.model_name_or_path						, cache_dir=model_args.cache_dir						, do_lower_case=self.do_lower_case						, word_delimiter_token=self.word_delimiter_token						, )
												return WavaVecaProcessor(snake_case__						, snake_case__    )






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						True
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






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






												"""simple docstring"""



												__a:						Optional[int]		    =					[{"""input_values""": feature["""input_values"""]} for feature in features]
												__a:						Optional[Any]		    =					[{"""input_ids""": feature["""labels"""]} for feature in features]

												__a:						Optional[Any]		    =					self.processor.pad(
												    snake_case__						, padding=self.padding						, max_length=self.max_length						, pad_to_multiple_of=self.pad_to_multiple_of						, return_tensors="""pt"""						, )
												__a:						str		    =					self.processor.pad(
												    labels=snake_case__						, padding=self.padding						, max_length=self.max_length_labels						, pad_to_multiple_of=self.pad_to_multiple_of_labels						, return_tensors="""pt"""						, )

												# replace padding with -100 to ignore loss correctly
												__a:						Any		    =					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )						, -1_0_0    )

												__a:						List[Any]		    =					labels

												return batch






class    _snake_case  (						_lowercase       ):






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






												"""simple docstring"""



												model.train()
												__a:						int		    =					self._prepare_inputs(snake_case__    )

												if self.use_amp:
																	with autocast():
																						__a:						List[str]		    =					self.compute_loss(snake_case__						, snake_case__    )
												else:
																	__a:						Tuple		    =					self.compute_loss(snake_case__						, snake_case__    )

												if self.args.n_gpu > 1:
																	if model.module.config.ctc_loss_reduction == "mean":
																						__a:						Tuple		    =					loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																						__a:						List[str]		    =					loss.sum() / (inputs["""labels"""] >= 0).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:
																	__a:						Any		    =					loss / self.args.gradient_accumulation_steps

												if self.use_amp:
																	self.scaler.scale(snake_case__    ).backward()
												elif self.use_apex:
																	with amp.scale_loss(snake_case__						, self.optimizer    ) as scaled_loss:
																						scaled_loss.backward()
												elif self.deepspeed:
																	self.deepspeed.backward(snake_case__    )
												else:
																	loss.backward()

												return loss.detach()






def     lowerCamelCase_    (	)     ->			List[Any]:


					'''simple docstring'''
					# 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.

					__a:						int		    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

					__a    ,				__a    ,				__a:						List[str]		    =					parser.parse_args_into_dataclasses()
					configure_logger(lowerCamelCase_	,				lowerCamelCase_ )

					__a:						Union[str, Any]		    =					Orthography.from_name(data_args.orthography.lower() )
					__a:						List[str]		    =					orthography.create_processor(lowerCamelCase_ )
					__a:						int		    =					WavaVecaForCTC.from_pretrained(
					    model_args.model_name_or_path	,				cache_dir=model_args.cache_dir	,				gradient_checkpointing=training_args.gradient_checkpointing	,				vocab_size=len(processor.tokenizer )	,				)

					__a:						int		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.train_split_name )
					__a:						Tuple		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.validation_split_name )

					__a:						List[Any]		    =					datasets.load_metric("""wer""" )
					__a:						Optional[Any]		    =					processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
					__a:						int		    =					"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(lowerCamelCase_ ) == 1 )
					__a:						int		    =					re.compile(  # remove characters not in vocabulary
					    rf"""[^\s{re.escape(lowerCamelCase_ )}]"""	,				flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0	,				)
					__a:						Optional[Any]		    =					[]

					def prepare_example(lowerCamelCase_ ):  # TODO(elgeish) make use of multiprocessing?
										__a    ,				__a:						Tuple		    =					librosa.load(example[data_args.speech_file_column]	,				sr=lowerCamelCase_ )
										if data_args.max_duration_in_seconds is not None:
															__a:						List[Any]		    =					len(example["""speech"""] ) / example["""sampling_rate"""]
										# Normalize and clean up text; order matters!
										__a:						Tuple		    =					orthography.preprocess_for_training(example[data_args.target_text_column] )
										__a:						Any		    =					vocabulary_text_cleaner.sub(""""""	,				lowerCamelCase_ )
										if updated_text != example[data_args.target_text_column]:
															text_updates.append((example[data_args.target_text_column], updated_text) )
															__a:						List[Any]		    =					updated_text
										return example

					__a:						str		    =					train_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )
					__a:						Union[str, Any]		    =					val_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )

					if data_args.max_duration_in_seconds is not None:

										def filter_by_max_duration(lowerCamelCase_ ):
															return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

										__a:						List[Any]		    =					len(lowerCamelCase_ )
										__a:						List[str]		    =					len(lowerCamelCase_ )
										__a:						int		    =					train_dataset.filter(lowerCamelCase_	,				remove_columns=["""duration_in_seconds"""] )
										__a:						Optional[int]		    =					val_dataset.filter(lowerCamelCase_	,				remove_columns=["""duration_in_seconds"""] )
										if len(lowerCamelCase_ ) > old_train_size:
															logger.warning(
															    f"""Filtered out {len(lowerCamelCase_ ) - old_train_size} train example(s) longer than"""
															    f""" {data_args.max_duration_in_seconds} second(s).""" )
										if len(lowerCamelCase_ ) > old_val_size:
															logger.warning(
															    f"""Filtered out {len(lowerCamelCase_ ) - old_val_size} validation example(s) longer than"""
															    f""" {data_args.max_duration_in_seconds} second(s).""" )
					logger.info(f"""Split sizes: {len(lowerCamelCase_ )} train and {len(lowerCamelCase_ )} validation.""" )

					logger.warning(f"""Updated {len(lowerCamelCase_ )} transcript(s) using '{data_args.orthography}' orthography rules.""" )
					if logger.isEnabledFor(logging.DEBUG ):
										for original_text, updated_text in text_updates:
															logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\"""" )
					__a:						List[str]		    =					None

					def prepare_dataset(lowerCamelCase_ ):
										# check that all files have the correct sampling rate
										assert (
										    len(set(batch["""sampling_rate"""] ) ) == 1
										), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

										__a:						Tuple		    =					processor(
										    audio=batch["""speech"""]	,				text=batch[data_args.target_text_column]	,				sampling_rate=batch["""sampling_rate"""][0] )
										batch.update(lowerCamelCase_ )
										return batch

					__a:						Union[str, Any]		    =					train_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)
					__a:						Tuple		    =					val_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)

					__a:						Union[str, Any]		    =					DataCollatorCTCWithPadding(processor=lowerCamelCase_	,				padding=lowerCamelCase_ )

					def compute_metrics(lowerCamelCase_ ):
										__a:						Any		    =					pred.predictions
										__a:						List[Any]		    =					np.argmax(lowerCamelCase_	,				axis=-1 )

										__a:						int		    =					processor.tokenizer.pad_token_id

										__a:						List[Any]		    =					processor.batch_decode(lowerCamelCase_ )
										# we do not want to group tokens when computing the metrics
										__a:						str		    =					processor.batch_decode(pred.label_ids	,				group_tokens=lowerCamelCase_ )
										if logger.isEnabledFor(logging.DEBUG ):
															for reference, predicted in zip(lowerCamelCase_	,				lowerCamelCase_ ):
																				logger.debug(f"""reference: \"{reference}\"""" )
																				logger.debug(f"""predicted: \"{predicted}\"""" )
																				if orthography.untransliterator is not None:
																									logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(lowerCamelCase_ )}\"""" )
																									logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(lowerCamelCase_ )}\"""" )

										__a:						Optional[int]		    =					wer_metric.compute(predictions=lowerCamelCase_	,				references=lowerCamelCase_ )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					__a:						Any		    =					CTCTrainer(
					    model=lowerCamelCase_	,				data_collator=lowerCamelCase_	,				args=lowerCamelCase_	,				compute_metrics=lowerCamelCase_	,				train_dataset=lowerCamelCase_	,				eval_dataset=lowerCamelCase_	,				tokenizer=processor.feature_extractor	,				)

					trainer.train()


if __name__ == "__main__":
			main()
