




"""simple docstring"""







import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      List[Any]:
       UpperCamelCase										=  cuda
       UpperCamelCase										=  AutoConfig.from_pretrained(lowerCamelCase_)
       UpperCamelCase										=  AutoModelForCTC.from_pretrained(lowerCamelCase_)
       self.model.eval()
       if self.cuda:
           self.model.to(device='''cuda''')
       UpperCamelCase										=  AutoProcessor.from_pretrained(lowerCamelCase_)
       UpperCamelCase										=  torchaudio.transforms.Resample(lowerCamelCase_					,				1_6_0_0_0)
       UpperCamelCase										=  0
       UpperCamelCase										=  list(self.processor.tokenizer.get_vocab().keys())
       for i in range(len(lowerCamelCase_)):
           if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
               UpperCamelCase										=  i
       print('''Blank Token id [PAD]/<pad>'''					,				lowerCamelCase_)
       UpperCamelCase										=  blank_id







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase					,  UpperCamelCase										=  torchaudio.load(lowerCamelCase_)
       UpperCamelCase										=  self.resampler(lowerCamelCase_).squeeze().numpy()
       return speech







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Dict:
       UpperCamelCase										=  self.blank_id
       UpperCamelCase										=  '''|'''.join(item['''sent'''].split(''' '''))
       if not os.path.isfile(item['''wav_path''']):
           print(item['''wav_path''']					,				'''not found in wavs directory''')

       UpperCamelCase										=  self.speech_file_to_array_fn(item['''wav_path'''])
       UpperCamelCase										=  self.processor(lowerCamelCase_					,				sampling_rate=1_6_0_0_0					,				return_tensors='''pt'''					,				padding=lowerCamelCase_)
       if self.cuda:
           UpperCamelCase										=  inputs.to(device='''cuda''')

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

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

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

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

       def get_trellis(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_=0):
           UpperCamelCase										=  emission.size(0)
           UpperCamelCase										=  len(lowerCamelCase_)

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

       UpperCamelCase										=  get_trellis(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)

       @dataclass
       class 							snake_case_ :







          """simple docstring"""




          A_										=					42
          A_										=					42
          A_										=					42

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

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

               # 2. Store the path with frame-wise probability.
               UpperCamelCase										=  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					,				lowerCamelCase_))

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

       UpperCamelCase										=  backtrack(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)

       @dataclass
       class 							snake_case_ :







          """simple docstring"""




          A_										=					42
          A_										=					42
          A_										=					42
          A_										=					42







          def __repr__(		self)							->      List[Any]:
              return F'{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'







          @property
          def      UpperCAmelCase__ (		self)							->      Any:
              return self.end - self.start

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

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







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      str:
       if not os.path.exists(lowerCamelCase_):
           os.makedirs(lowerCamelCase_)

       # load text file
       UpperCamelCase										=  open(lowerCamelCase_					,				encoding='''utf8''').readlines()

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

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

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

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





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  argparse.ArgumentParser()

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

    UpperCamelCase										=  parser.parse_args()

    UpperCamelCase										=  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()





"""simple docstring"""







import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--is_encoder_named_decoder',
      action='store_true',
      help='If decoder has to be renamed to encoder in PyTorch model.',
  )
  parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
  parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )