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__      :    str          =     logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

lowercase__      :    Dict          =     {
    '''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__      :    Union[str, 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__      :    int          =     sorted(arg_to_scheduler.keys())
lowercase__      :    Any          =     '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''




class        lowercase_  (  pl.LightningModule						):



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE="base"		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=None		,      __SCREAMING_SNAKE_CASE=None		,      **__SCREAMING_SNAKE_CASE		,      )						->Dict:
								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		Path(self.hparams.output_dir    )
								lowerCAmelCase     		=		self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
															lowerCAmelCase     		=		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=__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE		,      )
								else:
															lowerCAmelCase     		=		config

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

								if tokenizer is None:
															lowerCAmelCase     		=		AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,      cache_dir=__SCREAMING_SNAKE_CASE		,      )
								else:
															lowerCAmelCase     		=		tokenizer
								lowerCAmelCase     		=		MODEL_MODES[mode]
								if model is None:
															lowerCAmelCase     		=		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=__SCREAMING_SNAKE_CASE		,      )
								else:
															lowerCAmelCase     		=		model


	def       SCREAMING_SNAKE_CASE_    (							self		,      *__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[Any]:
								lowerCAmelCase     		=		arg_to_scheduler[self.hparams.lr_scheduler]
								lowerCAmelCase     		=		get_schedule_func(
								    self.opt		,      num_warmup_steps=self.hparams.warmup_steps		,      num_training_steps=self.total_steps()    )
								lowerCAmelCase     		=		{'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
								return scheduler


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.model
								lowerCAmelCase     		=		['''bias''', '''LayerNorm.weight''']
								lowerCAmelCase     		=		[
								    {
								        '''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:
															lowerCAmelCase     		=		Adafactor(
															    __SCREAMING_SNAKE_CASE		,      lr=self.hparams.learning_rate		,      scale_parameter=__SCREAMING_SNAKE_CASE		,      relative_step=__SCREAMING_SNAKE_CASE    )

								else:
															lowerCAmelCase     		=		AdamW(
															    __SCREAMING_SNAKE_CASE		,      lr=self.hparams.learning_rate		,      eps=self.hparams.adam_epsilon    )
								lowerCAmelCase     		=		optimizer

								lowerCAmelCase     		=		self.get_lr_scheduler()

								return [optimizer], [scheduler]


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->int:
								return self.validation_step(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								return self.validation_end(__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		max(1		,      self.hparams.gpus    )  # TODO: consider num_tpu_cores
								lowerCAmelCase     		=		self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								if mode == "test":
															lowerCAmelCase     		=		len(self.test_dataloader().dataset    )
								else:
															lowerCAmelCase     		=		self.get_dataloader('''train'''		,      self.hparams.train_batch_size		,      shuffle=__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		len(self.train_dataloader().dataset    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE = False    )						->Optional[Any]:
								raise NotImplementedError('''You must implement this for your task'''    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								return self.train_loader


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								return self.get_dataloader('''dev'''		,      self.hparams.eval_batch_size		,      shuffle=__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[int]:
								return self.get_dataloader('''test'''		,      self.hparams.eval_batch_size		,      shuffle=__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Optional[int]:
								return os.path.join(
								    self.hparams.data_dir		,      '''cached_{}_{}_{}'''.format(
								        __SCREAMING_SNAKE_CASE		,      list(filter(__SCREAMING_SNAKE_CASE		,      self.hparams.model_name_or_path.split('''/'''    )    )    ).pop()		,      str(self.hparams.max_seq_length    )		,      )		,      )


	@pl.utilities.rank_zero_only
	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		self.output_dir.joinpath('''best_tfmr'''    )
								lowerCAmelCase     		=		self.step_count
								self.model.save_pretrained(__SCREAMING_SNAKE_CASE    )
								self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE    )




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




class        lowercase_  (  pl.Callback						):



	"""simple docstring"""





	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		trainer.lr_schedulers[0]['''scheduler''']
								lowerCAmelCase     		=		{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
								pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE    )


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->List[Any]:
								rank_zero_info('''***** Validation results *****'''    )
								lowerCAmelCase     		=		trainer.callback_metrics
								# Log results
								for key in sorted(__SCREAMING_SNAKE_CASE    ):
															if key not in ["log", "progress_bar"]:
																						rank_zero_info('''{} = {}\n'''.format(__SCREAMING_SNAKE_CASE		,      str(metrics[key]    )    )    )




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

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     None:






							"""simple docstring"""




							parser.add_argument(
							    '''--output_dir'''       ,  default=snake_case__       ,  type=snake_case__       ,  required=snake_case__       ,  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=snake_case__       ,  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=snake_case__							)
							parser.add_argument('''--max_grad_norm'''       ,  dest='''gradient_clip_val'''       ,  default=1.0       ,  type=snake_case__       ,  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=snake_case__       ,  default=1       ,  help='''Number of updates steps to accumulate before performing a backward/update pass.'''       ,  )
							parser.add_argument('''--seed'''       ,  type=snake_case__       ,  default=4_2       ,  help='''random seed for initialization'''							)
							parser.add_argument(
							    '''--data_dir'''       ,  default=snake_case__       ,  type=snake_case__       ,  required=snake_case__       ,  help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''       ,  )






def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[]       ,  snake_case__=None       ,  snake_case__=None       ,  **snake_case__       ,  )					->     str:






							"""simple docstring"""




							pl.seed_everything(args.seed							)

							# init model
							lowerCAmelCase     		=		Path(model.hparams.output_dir							)
							odir.mkdir(exist_ok=snake_case__							)

							# add custom checkpoints
							if checkpoint_callback is None:
														lowerCAmelCase     		=		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(snake_case__							)
							if logging_callback is None:
														lowerCAmelCase     		=		LoggingCallback()

							lowerCAmelCase     		=		{}

							# TODO: remove with PyTorch 1.6 since pl uses native amp
							if args.fpaa:
														lowerCAmelCase     		=		1_6
														lowerCAmelCase     		=		args.fpaa_opt_level

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

							lowerCAmelCase     		=		args.accumulate_grad_batches
							lowerCAmelCase     		=		extra_train_kwargs.get('''accelerator'''       ,  snake_case__							)
							lowerCAmelCase     		=		extra_train_kwargs.get('''profiler'''       ,  snake_case__							)

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

							if args.do_train:
														trainer.fit(snake_case__							)

							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        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Any:
								lowerCAmelCase     		=		cuda
								lowerCAmelCase     		=		AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		AutoModelForCTC.from_pretrained(__SCREAMING_SNAKE_CASE    )
								self.model.eval()
								if self.cuda:
															self.model.to(device='''cuda'''    )
								lowerCAmelCase     		=		AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		torchaudio.transforms.Resample(__SCREAMING_SNAKE_CASE		,      16000    )
								lowerCAmelCase     		=		0
								lowerCAmelCase     		=		list(self.processor.tokenizer.get_vocab().keys()    )
								for i in range(len(__SCREAMING_SNAKE_CASE    )    ):
															if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																						lowerCAmelCase     		=		i
								print('''Blank Token id [PAD]/<pad>'''		,      __SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		blank_id


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								lowerCAmelCase		,				lowerCAmelCase     		=		torchaudio.load(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		self.resampler(__SCREAMING_SNAKE_CASE    ).squeeze().numpy()
								return speech


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->str:
								lowerCAmelCase     		=		self.blank_id
								lowerCAmelCase     		=		'''|'''.join(item['''sent'''].split(''' '''    )    )
								if not os.path.isfile(item['''wav_path''']    ):
															print(item['''wav_path''']		,      '''not found in wavs directory'''    )

								lowerCAmelCase     		=		self.speech_file_to_array_fn(item['''wav_path''']    )
								lowerCAmelCase     		=		self.processor(__SCREAMING_SNAKE_CASE		,      sampling_rate=16000		,      return_tensors='''pt'''		,      padding=__SCREAMING_SNAKE_CASE    )
								if self.cuda:
															lowerCAmelCase     		=		inputs.to(device='''cuda'''    )

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

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

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

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

								def get_trellis(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=0    ):
															lowerCAmelCase     		=		emission.size(0    )
															lowerCAmelCase     		=		len(__SCREAMING_SNAKE_CASE    )

															# 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.
															lowerCAmelCase     		=		torch.full((num_frames + 1, num_tokens + 1)		,      -float('''inf'''    )    )
															lowerCAmelCase     		=		0
															for t in range(__SCREAMING_SNAKE_CASE    ):
																						lowerCAmelCase     		=		torch.maximum(
																						    # Score for staying at the same token
																						    trellis[t, 1:] + emission[t, blank_id]		,      trellis[t, :-1] + emission[t, tokens]		,      )
															return trellis

								lowerCAmelCase     		=		get_trellis(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float

								def backtrack(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE=0    ):
															lowerCAmelCase     		=		trellis.size(1    ) - 1
															lowerCAmelCase     		=		torch.argmax(trellis[:, j]    ).item()

															lowerCAmelCase     		=		[]
															for t in range(__SCREAMING_SNAKE_CASE		,      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.
																						lowerCAmelCase     		=		trellis[t - 1, j] + emission[t - 1, blank_id]
																						# Score for token changing from C-1 at T-1 to J at T.
																						lowerCAmelCase     		=		trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																						# 2. Store the path with frame-wise probability.
																						lowerCAmelCase     		=		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		,      __SCREAMING_SNAKE_CASE    )    )

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

								lowerCAmelCase     		=		backtrack(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )




								@dataclass
								class        lowercase_  :



									"""simple docstring"""





									UpperCAmelCase_  : str
									UpperCAmelCase_  : int
									UpperCAmelCase_  : int
									UpperCAmelCase_  : float


									def __repr__(							self    )						->int:
																return F"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"




									@property
									def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
																return self.end - self.start

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

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




	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->Dict:
								if not os.path.exists(__SCREAMING_SNAKE_CASE    ):
															os.makedirs(__SCREAMING_SNAKE_CASE    )

								# load text file
								lowerCAmelCase     		=		open(__SCREAMING_SNAKE_CASE		,      encoding='''utf8'''    ).readlines()

								lowerCAmelCase     		=		[]
								for line in lines:
															if len(line.strip().split('''\t'''    )    ) != 2:
																						print('''Script must be in format: 00001  this is my sentence'''    )
																						exit()

															lowerCAmelCase		,				lowerCAmelCase     		=		line.strip().split('''\t'''    )
															lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      wav_name + '''.wav'''    )
															lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      wav_name + '''.txt'''    )

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

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

def        SCREAMING_SNAKE_CASE_					(						)					->     Optional[Any]:
							lowerCAmelCase     		=		argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name'''       ,  type=snake_case__       ,  default='''arijitx/wav2vec2-xls-r-300m-bengali'''       ,  help='''wav2vec model name'''							)
							parser.add_argument('''--wav_dir'''       ,  type=snake_case__       ,  default='''./wavs'''       ,  help='''directory containing wavs'''							)
							parser.add_argument('''--text_file'''       ,  type=snake_case__       ,  default='''script.txt'''       ,  help='''file containing text'''							)
							parser.add_argument('''--input_wavs_sr'''       ,  type=snake_case__       ,  default=1_6_0_0_0       ,  help='''sampling rate of input audios'''							)
							parser.add_argument(
							    '''--output_dir'''       ,  type=snake_case__       ,  default='''./out_alignment'''       ,  help='''output directory containing the alignment files'''							)
							parser.add_argument('''--cuda'''       ,  action='''store_true'''							)

							lowerCAmelCase     		=		parser.parse_args()

							lowerCAmelCase     		=		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()



