




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


__UpperCamelCase      : Any					      =   logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__UpperCamelCase      : List[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
__UpperCamelCase      : Dict					      =   {
    "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
}
__UpperCamelCase      : Optional[int]					      =   sorted(arg_to_scheduler.keys())
__UpperCamelCase      : Tuple					      =   "{" + ", ".join(arg_to_scheduler_choices) + "}"

class 	__lowerCAmelCase			(			pl.LightningModule				):
       def __init__(		self		:Union[str, Any]	,   __magic_name__		:Dict	,   __magic_name__		:List[Any]=None	,   __magic_name__		:Union[str, Any]="base"	,   __magic_name__		:List[str]=None	,   __magic_name__		:Any=None	,   __magic_name__		:List[str]=None	,   **__magic_name__		:Tuple	,   ):







        '''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(lowercase_       )
        a		       =							0
        a		       =							Path(self.hparams.output_dir       )
        a		       =							self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
         a		       =							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=lowercase_	,   **lowercase_	,   )
        else:
         a		       =							config

        a		       =							('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
        for p in extra_model_params:
         if getattr(self.hparams	,   lowercase_	,   lowercase_       ):
          assert hasattr(self.config	,   lowercase_       ), F'model config doesn\'t have a `{p}` attribute'
          setattr(self.config	,   lowercase_	,   getattr(self.hparams	,   lowercase_       )       )

        if tokenizer is None:
         a		       =							AutoTokenizer.from_pretrained(
             self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,   cache_dir=lowercase_	,   )
        else:
         a		       =							tokenizer
        a		       =							MODEL_MODES[mode]
        if model is None:
         a		       =							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=lowercase_	,   )
        else:
         a		       =							model
       def    lowerCamelCase__							(		self		:List[str]	,   *__magic_name__		:Optional[Any]	,   **__magic_name__		:Dict       ):







        '''simple docstring'''







        a		       =							self.model_type.from_pretrained(*lowercase_	,   **lowercase_       )
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        a		       =							arg_to_scheduler[self.hparams.lr_scheduler]
        a		       =							get_schedule_func(
            self.opt	,   num_warmup_steps=self.hparams.warmup_steps	,   num_training_steps=self.total_steps()       )
        a		       =							{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
        return scheduler
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        a		       =							self.model
        a		       =							['''bias''', '''LayerNorm.weight''']
        a		       =							[
            {
                '''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		       =							Adafactor(
             lowercase_	,   lr=self.hparams.learning_rate	,   scale_parameter=lowercase_	,   relative_step=lowercase_       )

        else:
         a		       =							AdamW(
             lowercase_	,   lr=self.hparams.learning_rate	,   eps=self.hparams.adam_epsilon       )
        a		       =							optimizer

        a		       =							self.get_lr_scheduler()

        return [optimizer], [scheduler]
       def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:Dict	,   __magic_name__		:Optional[int]       ):







        '''simple docstring'''







        return self.validation_step(lowercase_	,   lowercase_       )
       def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:List[str]       ):







        '''simple docstring'''







        return self.validation_end(lowercase_       )
       def    lowerCamelCase__							(		self		:str       ):







        '''simple docstring'''







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







        '''simple docstring'''







        if mode == "test":
         a		       =							len(self.test_dataloader().dataset       )
        else:
         a		       =							self.get_dataloader("""train"""	,   self.hparams.train_batch_size	,   shuffle=lowercase_       )
         a		       =							len(self.train_dataloader().dataset       )
       def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:int	,   __magic_name__		:Tuple	,   __magic_name__		:Optional[Any] = False       ):







        '''simple docstring'''







        raise NotImplementedError("""You must implement this for your task"""       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        return self.train_loader
       def    lowerCamelCase__							(		self		:Optional[int]       ):







        '''simple docstring'''







        return self.get_dataloader("""dev"""	,   self.hparams.eval_batch_size	,   shuffle=lowercase_       )
       def    lowerCamelCase__							(		self		:Dict       ):







        '''simple docstring'''







        return self.get_dataloader("""test"""	,   self.hparams.eval_batch_size	,   shuffle=lowercase_       )
       def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:str       ):







        '''simple docstring'''







        return os.path.join(
            self.hparams.data_dir	,   """cached_{}_{}_{}""".format(
                lowercase_	,   list(filter(lowercase_	,   self.hparams.model_name_or_path.split("""/"""       )       )       ).pop()	,   str(self.hparams.max_seq_length       )	,   )	,   )
       @pl.utilities.rank_zero_only
       def    lowerCamelCase__							(		self		:Tuple	,   __magic_name__		:str       ):







        '''simple docstring'''







        a		       =							self.output_dir.joinpath("""best_tfmr"""       )
        a		       =							self.step_count
        self.model.save_pretrained(lowercase_       )
        self.tokenizer.save_pretrained(lowercase_       )





       @staticmethod
       def    lowerCamelCase__							(		__magic_name__		:Tuple	,   __magic_name__		:Dict       ):







        '''simple docstring'''







        parser.add_argument(
            """--model_name_or_path"""	,   default=lowercase_	,   type=lowercase_	,   required=lowercase_	,   help="""Path to pretrained model or model identifier from huggingface.co/models"""	,   )
        parser.add_argument(
            """--config_name"""	,   default=""""""	,   type=lowercase_	,   help="""Pretrained config name or path if not the same as model_name"""       )
        parser.add_argument(
            """--tokenizer_name"""	,   default=lowercase_	,   type=lowercase_	,   help="""Pretrained tokenizer name or path if not the same as model_name"""	,   )
        parser.add_argument(
            """--cache_dir"""	,   default=""""""	,   type=lowercase_	,   help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""	,   )
        parser.add_argument(
            """--encoder_layerdrop"""	,   type=lowercase_	,   help="""Encoder layer dropout probability (Optional). Goes into model.config"""	,   )
        parser.add_argument(
            """--decoder_layerdrop"""	,   type=lowercase_	,   help="""Decoder layer dropout probability (Optional). Goes into model.config"""	,   )
        parser.add_argument(
            """--dropout"""	,   type=lowercase_	,   help="""Dropout probability (Optional). Goes into model.config"""	,   )
        parser.add_argument(
            """--attention_dropout"""	,   type=lowercase_	,   help="""Attention dropout probability (Optional). Goes into model.config"""	,   )
        parser.add_argument("""--learning_rate"""	,   default=5E-5	,   type=lowercase_	,   help="""The initial learning rate for Adam."""       )
        parser.add_argument(
            """--lr_scheduler"""	,   default="""linear"""	,   choices=lowercase_	,   metavar=lowercase_	,   type=lowercase_	,   help="""Learning rate scheduler"""	,   )
        parser.add_argument("""--weight_decay"""	,   default=0.0	,   type=lowercase_	,   help="""Weight decay if we apply some."""       )
        parser.add_argument("""--adam_epsilon"""	,   default=1E-8	,   type=lowercase_	,   help="""Epsilon for Adam optimizer."""       )
        parser.add_argument("""--warmup_steps"""	,   default=0	,   type=lowercase_	,   help="""Linear warmup over warmup_steps."""       )
        parser.add_argument("""--num_workers"""	,   default=4	,   type=lowercase_	,   help="""kwarg passed to DataLoader"""       )
        parser.add_argument("""--num_train_epochs"""	,   dest="""max_epochs"""	,   default=3	,   type=lowercase_       )
        parser.add_argument("""--train_batch_size"""	,   default=32	,   type=lowercase_       )
        parser.add_argument("""--eval_batch_size"""	,   default=32	,   type=lowercase_       )
        parser.add_argument("""--adafactor"""	,   action="""store_true"""       )







class 	__lowerCAmelCase			(			pl.Callback				):
       def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:str	,   __magic_name__		:str       ):







        '''simple docstring'''







        a		       =							trainer.lr_schedulers[0]['''scheduler''']
        a		       =							{F'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
        pl_module.logger.log_metrics(lowercase_       )
       def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Optional[int]	,   __magic_name__		:List[Any]       ):







        '''simple docstring'''







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





       def    lowerCamelCase__							(		self		:int	,   __magic_name__		:Dict	,   __magic_name__		:Optional[int]       ):







        '''simple docstring'''







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







def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> None:
 #  To allow all pl args uncomment the following line
 #  parser = pl.Trainer.add_argparse_args(parser)
 parser.add_argument(
     """--output_dir"""							,    default=lowercase_							,    type=lowercase_							,    required=lowercase_							,    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=lowercase_							,    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=lowercase_					)
 parser.add_argument("""--max_grad_norm"""							,    dest="""gradient_clip_val"""							,    default=1.0							,    type=lowercase_							,    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=lowercase_							,    default=1							,    help="""Number of updates steps to accumulate before performing a backward/update pass."""							,    )
 parser.add_argument("""--seed"""							,    type=lowercase_							,    default=42							,    help="""random seed for initialization"""					)
 parser.add_argument(
     """--data_dir"""							,    default=lowercase_							,    type=lowercase_							,    required=lowercase_							,    help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""							,    )





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase=None							,    __lowerCamelCase=True							,    __lowerCamelCase=[]							,    __lowerCamelCase=None							,    __lowerCamelCase=None							,    **__lowerCamelCase							,    )   -> Dict:
 pl.seed_everything(args.seed					)

 # init model
 a		       =							Path(model.hparams.output_dir					)
 odir.mkdir(exist_ok=lowercase_					)

 # add custom checkpoints
 if checkpoint_callback is None:
  a		       =							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(lowercase_					)
 if logging_callback is None:
  a		       =							LoggingCallback()

 a		       =							{}

 # TODO: remove with PyTorch 1.6 since pl uses native amp
 if args.fpaa:
  a		       =							16
  a		       =							args.fpaa_opt_level

 if args.gpus > 1:
  a		       =							'''ddp'''

 a		       =							args.accumulate_grad_batches
 a		       =							extra_train_kwargs.get("""accelerator"""							,    lowercase_					)
 a		       =							extra_train_kwargs.get("""profiler"""							,    lowercase_					)

 a		       =							pl.Trainer.from_argparse_args(
     lowercase_							,    weights_summary=lowercase_							,    callbacks=[logging_callback] + extra_callbacks							,    logger=lowercase_							,    checkpoint_callback=lowercase_							,    **lowercase_							,    )

 if args.do_train:
  trainer.fit(lowercase_					)

 return trainer







import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 	__lowerCAmelCase			:
							def __init__(		self		:Dict	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							cuda
								a		       =							AutoConfig.from_pretrained(__magic_name__       )
								a		       =							AutoModelForCTC.from_pretrained(__magic_name__       )
								self.model.eval()
								if self.cuda:
									self.model.to(device="""cuda"""       )
								a		       =							AutoProcessor.from_pretrained(__magic_name__       )
								a		       =							torchaudio.transforms.Resample(__magic_name__	,   1_6000       )
								a		       =							0
								a		       =							list(self.processor.tokenizer.get_vocab().keys()       )
								for i in range(len(__magic_name__       )       ):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										a		       =							i
								print("""Blank Token id [PAD]/<pad>"""	,   __magic_name__       )
								a		       =							blank_id
							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a       ,				a		       =							torchaudio.load(__magic_name__       )
								a		       =							self.resampler(__magic_name__       ).squeeze().numpy()
								return speech
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a		       =							self.blank_id
								a		       =							"""|""".join(item["""sent"""].split(""" """       )       )
								if not os.path.isfile(item["""wav_path"""]       ):
									print(item["""wav_path"""]	,   """not found in wavs directory"""       )

								a		       =							self.speech_file_to_array_fn(item["""wav_path"""]       )
								a		       =							self.processor(__magic_name__	,   sampling_rate=1_6000	,   return_tensors="""pt"""	,   padding=__magic_name__       )
								if self.cuda:
									a		       =							inputs.to(device="""cuda"""       )

								with torch.no_grad():
									a		       =							self.model(inputs.input_values       ).logits

								# get the emission probability at frame level
								a		       =							torch.log_softmax(__magic_name__	,   dim=-1       )
								a		       =							emissions[0].cpu().detach()

								# get labels from vocab
								a		       =							([""""""] + list(self.processor.tokenizer.get_vocab().keys()       ))[
								    :-1
								]  # logits don't align with the tokenizer's vocab

								a		       =							{c: i for i, c in enumerate(__magic_name__       )}
								a		       =							[]
								for c in transcript:
									if c in dictionary:
										tokens.append(dictionary[c]       )

								def get_trellis(__magic_name__		:Optional[Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:Any=0       ):
									a		       =							emission.size(0       )
									a		       =							len(__magic_name__       )

									# Trellis has extra diemsions for both time axis and tokens.
									# The extra dim for tokens represents <SoS> (start-of-sentence)
									# The extra dim for time axis is for simplification of the code.
									a		       =							torch.full((num_frames + 1, num_tokens + 1)	,   -float("""inf"""       )       )
									a		       =							0
									for t in range(__magic_name__       ):
										a		       =							torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]	,   trellis[t, :-1] + emission[t, tokens]	,   )
									return trellis

								a		       =							get_trellis(__magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42

								def backtrack(__magic_name__		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:int	,   __magic_name__		:str=0       ):
									a		       =							trellis.size(1       ) - 1
									a		       =							torch.argmax(trellis[:, j]       ).item()

									a		       =							[]
									for t in range(__magic_name__	,   0	,   -1       ):
										# 1. Figure out if the current position was stay or change
										# Note (again):
										# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
										# Score for token staying the same from time frame J-1 to T.
										a		       =							trellis[t - 1, j] + emission[t - 1, blank_id]
										# Score for token changing from C-1 at T-1 to J at T.
										a		       =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										a		       =							emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
										# Return token index and time index in non-trellis coordinate.
										path.append(Point(j - 1	,   t - 1	,   __magic_name__       )       )

										# 3. Update the token
										if changed > stayed:
											j -= 1
											if j == 0:
												break
									else:
										raise ValueError("""Failed to align"""       )
									return path[::-1]

								a		       =							backtrack(__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															def __repr__(		self		:Union[str, Any]       ):







																'''simple docstring'''







																return F'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'





															@property
															def    lowerCamelCase__							(		self		:int       ):







																'''simple docstring'''







																return self.end - self.start

								def merge_repeats(__magic_name__		:int       ):
									a       ,				a		       =							0, 0
									a		       =							[]
									while ia < len(__magic_name__       ):
										while ia < len(__magic_name__       ) and path[ia].token_index == path[ia].token_index:
											ia += 1
										a		       =							sum(path[k].score for k in range(__magic_name__	,   __magic_name__       )       ) / (ia - ia)
										segments.append(
										    Segment(
										        transcript[path[ia].token_index]	,   path[ia].time_index	,   path[ia - 1].time_index + 1	,   __magic_name__	,   )       )
										a		       =							ia
									return segments

								a		       =							merge_repeats(__magic_name__       )
								with open(item["""out_path"""]	,   """w"""       ) as out_align:
									for seg in segments:
										out_align.write(str(__magic_name__       ) + """\n"""       )





							def    lowerCamelCase__							(		self		:int	,   __magic_name__		:str	,   __magic_name__		:List[Any]	,   __magic_name__		:Optional[int]       ):







								'''simple docstring'''







								if not os.path.exists(__magic_name__       ):
									os.makedirs(__magic_name__       )

								# load text file
								a		       =							open(__magic_name__	,   encoding="""utf8"""       ).readlines()

								a		       =							[]
								for line in lines:
									if len(line.strip().split("""\t"""       )       ) != 2:
										print("""Script must be in format: 00001  this is my sentence"""       )
										exit()

									a       ,				a		       =							line.strip().split("""\t"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.wav"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.txt"""       )

									items.append({"""sent""": sentence, """wav_path""": wav_path, """out_path""": out_path}       )
								print("""Number of samples found in script file"""	,   len(__magic_name__       )       )

								for item in tqdm(__magic_name__       ):
									self.align_single_sample(__magic_name__       )







def 	__A	(			)   -> Union[str, Any]:
	a		       =							argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name"""							,    type=__lowerCamelCase							,    default="""arijitx/wav2vec2-xls-r-300m-bengali"""							,    help="""wav2vec model name"""					)
	parser.add_argument("""--wav_dir"""							,    type=__lowerCamelCase							,    default="""./wavs"""							,    help="""directory containing wavs"""					)
	parser.add_argument("""--text_file"""							,    type=__lowerCamelCase							,    default="""script.txt"""							,    help="""file containing text"""					)
	parser.add_argument("""--input_wavs_sr"""							,    type=__lowerCamelCase							,    default=1_6000							,    help="""sampling rate of input audios"""					)
	parser.add_argument(
	    """--output_dir"""							,    type=__lowerCamelCase							,    default="""./out_alignment"""							,    help="""output directory containing the alignment files"""					)
	parser.add_argument("""--cuda"""							,    action="""store_true"""					)

	a		       =							parser.parse_args()

	a		       =							WavaVecaAligner(args.model_name							,    args.input_wavs_sr							,    args.cuda					)
	aligner.align_data(args.wav_dir							,    args.text_file							,    args.output_dir					)


if __name__ == "__main__":
		main()

