


import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class A						:






      def __init__(      self		:  Tuple    ,     lowercase_		:  Tuple    ,     lowercase_		:  int    ,     lowercase_		:  List[Any]				)      ->		Tuple:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Dict				)      ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			,  _lowerCamelCase :	Union[str, Any]						=torchaudio.load(lowercase_				)
             _lowerCamelCase :	int						=self.resampler(lowercase_				).squeeze().numpy()
             return speech






      def  lowerCamelCase (      self		:  int    ,     lowercase_		:  Dict				)      ->		Any:
             """simple docstring"""




             _lowerCamelCase :	Tuple						=self.blank_id
             _lowerCamelCase :	List[str]						='|'.join(item['sent'].split(' '				)				)
             if not os.path.isfile(item['wav_path']				):
                    print(item['wav_path']    ,     'not found in wavs directory'				)

             _lowerCamelCase :	Union[str, Any]						=self.speech_file_to_array_fn(item['wav_path']				)
             _lowerCamelCase :	List[Any]						=self.processor(lowercase_    ,     sampling_rate=1_6000    ,     return_tensors='pt'    ,     padding=lowercase_				)
             if self.cuda:
                    _lowerCamelCase :	Tuple						=inputs.to(device='cuda'				)

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

             # get the emission probability at frame level
             _lowerCamelCase :	Optional[Any]						=torch.log_softmax(lowercase_    ,     dim=-1				)
             _lowerCamelCase :	Optional[int]						=emissions[0].cpu().detach()

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

             _lowerCamelCase :	Dict						={c: i for i, c in enumerate(lowercase_				)}
             _lowerCamelCase :	Any						=[]
             for c in transcript:
                    if c in dictionary:
                           tokens.append(dictionary[c]				)

             def get_trellis(lowercase_		:  str    ,     lowercase_		:  int    ,     lowercase_		:  str=0				):
                    _lowerCamelCase :	List[str]						=emission.size(0				)
                    _lowerCamelCase :	List[str]						=len(lowercase_				)

                    # 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 :	int						=torch.full((num_frames + 1, num_tokens + 1)    ,     -float('inf'				)				)
                    _lowerCamelCase :	Optional[int]						=0
                    for t in range(lowercase_				):
                           _lowerCamelCase :	Optional[int]						=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 :	int						=get_trellis(lowercase_    ,     lowercase_    ,     lowercase_				)




             @dataclass
             class A						:
                   UpperCamelCase__							:						int
                   UpperCamelCase__							:						int
                   UpperCamelCase__							:						float

             def backtrack(lowercase_		:  Tuple    ,     lowercase_		:  Dict    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Optional[Any]=0				):
                    _lowerCamelCase :	int						=trellis.size(1				) - 1
                    _lowerCamelCase :	Optional[Any]						=torch.argmax(trellis[:, j]				).item()

                    _lowerCamelCase :	Optional[Any]						=[]
                    for t in range(lowercase_    ,     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 :	Optional[Any]						=trellis[t - 1, j] + emission[t - 1, blank_id]
                           # Score for token changing from C-1 at T-1 to J at T.
                           _lowerCamelCase :	str						=trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                           # 2. Store the path with frame-wise probability.
                           _lowerCamelCase :	List[str]						=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    ,     lowercase_				)				)

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

             _lowerCamelCase :	Union[str, Any]						=backtrack(lowercase_    ,     lowercase_    ,     lowercase_    ,     lowercase_				)




             @dataclass
             class A						:
                   UpperCamelCase__							:						str
                   UpperCamelCase__							:						int
                   UpperCamelCase__							:						int
                   UpperCamelCase__							:						float






                   def __repr__(      self		:  List[Any]				)      ->		Optional[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		:  Tuple				)      ->		List[Any]:
                          """simple docstring"""




                          return self.end - self.start

             def merge_repeats(lowercase_		:  str				):
                    _lowerCamelCase			,  _lowerCamelCase :	Optional[Any]						=0, 0
                    _lowerCamelCase :	Tuple						=[]
                    while ia < len(lowercase_				):
                           while ia < len(lowercase_				) and path[ia].token_index == path[ia].token_index:
                                  ia += 1
                           _lowerCamelCase :	Union[str, Any]						=sum(path[k].score for k in range(lowercase_    ,     lowercase_				)				) / (ia - ia)
                           segments.append(
                               Segment(
                                   transcript[path[ia].token_index]    ,     path[ia].time_index    ,     path[ia - 1].time_index + 1    ,     lowercase_    ,     )				)
                           _lowerCamelCase :	List[Any]						=ia
                    return segments

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






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  Dict    ,     lowercase_		:  Tuple    ,     lowercase_		:  List[Any]				)      ->		Any:
             """simple docstring"""




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

             # load text file
             _lowerCamelCase :	List[str]						=open(lowercase_    ,     encoding='utf8'				).readlines()

             _lowerCamelCase :	List[Any]						=[]
             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 :	List[str]						=line.strip().split('\t'				)
                    _lowerCamelCase :	Optional[int]						=os.path.join(lowercase_    ,     wav_name + '.wav'				)
                    _lowerCamelCase :	Union[str, Any]						=os.path.join(lowercase_    ,     wav_name + '.txt'				)

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

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




def 			a_   (					):
       '''simple docstring'''





       _lowerCamelCase :	Union[str, Any]						=argparse.ArgumentParser()

       parser.add_argument(
           '--model_name'	,    type=SCREAMING_SNAKE_CASE__	,    default='arijitx/wav2vec2-xls-r-300m-bengali'	,    help='wav2vec model name'  )
       parser.add_argument('--wav_dir'	,    type=SCREAMING_SNAKE_CASE__	,    default='./wavs'	,    help='directory containing wavs'  )
       parser.add_argument('--text_file'	,    type=SCREAMING_SNAKE_CASE__	,    default='script.txt'	,    help='file containing text'  )
       parser.add_argument('--input_wavs_sr'	,    type=SCREAMING_SNAKE_CASE__	,    default=16_000	,    help='sampling rate of input audios'  )
       parser.add_argument(
           '--output_dir'	,    type=SCREAMING_SNAKE_CASE__	,    default='./out_alignment'	,    help='output directory containing the alignment files'  )
       parser.add_argument('--cuda'	,    action='store_true'  )

       _lowerCamelCase :	Dict						=parser.parse_args()

       _lowerCamelCase :	Dict						=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()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase		      = {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = ['DebertaV2TokenizerFast']

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDebertaV2ForMaskedLM',
     'TFDebertaV2ForQuestionAnswering',
     'TFDebertaV2ForSequenceClassification',
     'TFDebertaV2ForTokenClassification',
     'TFDebertaV2Model',
     'TFDebertaV2PreTrainedModel',
 ]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase		      = [
     'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DebertaV2ForMaskedLM',
     'DebertaV2ForMultipleChoice',
     'DebertaV2ForQuestionAnswering',
     'DebertaV2ForSequenceClassification',
     'DebertaV2ForTokenClassification',
     'DebertaV2Model',
     'DebertaV2PreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_deberta_va import (
     DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DebertaVaConfig,
     DebertaVaOnnxConfig,
 )
 from .tokenization_deberta_va import DebertaVaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta_va import (
      TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
      TFDebertaVaPreTrainedModel,
  )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta_va import (
      DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaVaForMaskedLM,
      DebertaVaForMultipleChoice,
      DebertaVaForQuestionAnswering,
      DebertaVaForSequenceClassification,
      DebertaVaForTokenClassification,
      DebertaVaModel,
      DebertaVaPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase		      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


