




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor







class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> str:
     __A				 =       cuda
     __A				 =       AutoConfig.from_pretrained(__UpperCamelCase     )
     __A				 =       AutoModelForCTC.from_pretrained(__UpperCamelCase     )
     self.model.eval()
     if self.cuda:
        self.model.to(device='''cuda'''     )
     __A				 =       AutoProcessor.from_pretrained(__UpperCamelCase     )
     __A				 =       torchaudio.transforms.Resample(__UpperCamelCase  ,    1_60_00     )
     __A				 =       0
     __A				 =       list(self.processor.tokenizer.get_vocab().keys()     )
     for i in range(len(__UpperCamelCase     )     ):
        if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
           __A				 =       i
     print('''Blank Token id [PAD]/<pad>'''  ,    __UpperCamelCase     )
     __A				 =       blank_id

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> int:
     __A				 =       torchaudio.load(__UpperCamelCase     )
     __A				 =       self.resampler(__UpperCamelCase     ).squeeze().numpy()
     return speech

  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase     )-> int:
     __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(__UpperCamelCase  ,    sampling_rate=1_60_00  ,    return_tensors='''pt'''  ,    padding=__UpperCamelCase     )
     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(__UpperCamelCase  ,    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(__UpperCamelCase     )}
     __A				 =       []
     for c in transcript:
        if c in dictionary:
           tokens.append(dictionary[c]     )

     def get_trellis(UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase=0     ):
        __A				 =       emission.size(0     )
        __A				 =       len(__UpperCamelCase     )

        # 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(__UpperCamelCase     ):
           __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(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase     )







     @dataclass
     class 						_lowerCAmelCase:

       """simple docstring"""







       lowerCamelCase__      					=   42
       lowerCamelCase__      					=   42
       lowerCamelCase__      					=   42

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

        __A				 =       []
        for t in range(__UpperCamelCase  ,    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  ,    __UpperCamelCase     )     )

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

     __A				 =       backtrack(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase     )







     @dataclass
     class 						_lowerCAmelCase:

       """simple docstring"""







       lowerCamelCase__      					=   42
       lowerCamelCase__      					=   42
       lowerCamelCase__      					=   42
       lowerCamelCase__      					=   42

       def __repr__( self     )-> Union[str, Any]:
          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     )-> Optional[Any]:
          return self.end - self.start

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

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





  def 		SCREAMING_SNAKE_CASE__	( self  ,    UpperCAmelCase  ,    UpperCAmelCase  ,    UpperCAmelCase     )-> Tuple:







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

     # load text file
     __A				 =       open(__UpperCamelCase  ,    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				 =       line.strip().split('''\t'''     )
        __A				 =       os.path.join(__UpperCamelCase  ,    wav_name + '''.wav'''     )
        __A				 =       os.path.join(__UpperCamelCase  ,    wav_name + '''.txt'''     )

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

     for item in tqdm(__UpperCamelCase     ):
        self.align_single_sample(__UpperCamelCase     )
def 				__UpperCamelCase  (		)  ->						Optional[Any]:


   '''simple docstring'''







   __A				 =       argparse.ArgumentParser()

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase   :							Dict     =	logging.get_logger(__name__)

_UpperCamelCase   :							Optional[int]     =	{
    """MCG-NJU/videomae-base""": """https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json""",
}







class 						_lowerCAmelCase(						_a):

  """simple docstring"""







  lowerCamelCase__      					=   '''videomae'''

  def __init__( self  ,    UpperCAmelCase=2_24  ,    UpperCAmelCase=16  ,    UpperCAmelCase=3  ,    UpperCAmelCase=16  ,    UpperCAmelCase=2  ,    UpperCAmelCase=7_68  ,    UpperCAmelCase=12  ,    UpperCAmelCase=12  ,    UpperCAmelCase=30_72  ,    UpperCAmelCase="gelu"  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.0  ,    UpperCAmelCase=0.02  ,    UpperCAmelCase=1e-12  ,    UpperCAmelCase=True  ,    UpperCAmelCase=True  ,    UpperCAmelCase=6  ,    UpperCAmelCase=3_84  ,    UpperCAmelCase=4  ,    UpperCAmelCase=15_36  ,    UpperCAmelCase=True  ,    **UpperCAmelCase  ,    )-> Any:
     super().__init__(**UpperCAmelCase     )

     __A				 =       image_size
     __A				 =       patch_size
     __A				 =       num_channels
     __A				 =       num_frames
     __A				 =       tubelet_size

     __A				 =       hidden_size
     __A				 =       num_hidden_layers
     __A				 =       num_attention_heads
     __A				 =       intermediate_size
     __A				 =       hidden_act
     __A				 =       hidden_dropout_prob
     __A				 =       attention_probs_dropout_prob
     __A				 =       initializer_range
     __A				 =       layer_norm_eps
     __A				 =       qkv_bias
     __A				 =       use_mean_pooling

     __A				 =       decoder_num_attention_heads
     __A				 =       decoder_hidden_size
     __A				 =       decoder_num_hidden_layers
     __A				 =       decoder_intermediate_size
     __A				 =       norm_pix_loss

