






"""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  UpperCamelCase    :


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


 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		Union[str, Any]							)      -> str:
   _a   ,						_a						: str    =			torchaudio.load(UpperCAmelCase__							)
   _a						: Optional[Any]    =			self.resampler(UpperCAmelCase__							).squeeze().numpy()
   return speech


 def _lowercase			(  self	:		List[str]							,		UpperCAmelCase__	:		int							)      -> int:
   _a						: Any    =			self.blank_id
   _a						: List[str]    =			"""|""".join(item["""sent"""].split(""" """							)							)
   if not os.path.isfile(item["""wav_path"""]							):
     print(item["""wav_path"""]							,		"""not found in wavs directory"""							)

   _a						: int    =			self.speech_file_to_array_fn(item["""wav_path"""]							)
   _a						: Any    =			self.processor(UpperCAmelCase__							,		sampling_rate=16000							,		return_tensors="""pt"""							,		padding=UpperCAmelCase__							)
   if self.cuda:
     _a						: List[str]    =			inputs.to(device="""cuda"""							)

   with torch.no_grad():
     _a						: List[str]    =			self.model(inputs.input_values							).logits

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

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

   _a						: Dict    =			{c: i for i, c in enumerate(UpperCAmelCase__							)}
   _a						: int    =			[]
   for c in transcript:
     if c in dictionary:
       tokens.append(dictionary[c]							)

   def get_trellis(UpperCAmelCase__	:		List[Any]							,		UpperCAmelCase__	:		Any							,		UpperCAmelCase__	:		int=0							):
     _a						: Optional[int]    =			emission.size(0							)
     _a						: Optional[int]    =			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						: Any    =			torch.full((num_frames + 1, num_tokens + 1)							,		-float("""inf"""							)							)
     _a						: Tuple    =			0
     for t in range(UpperCAmelCase__							):
       _a						: Dict    =			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						: List[Any]    =			get_trellis(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							)






   @dataclass
   class  UpperCamelCase    :
    UpperCamelCase      : int
    UpperCamelCase      : int
    UpperCamelCase      : float

   def backtrack(UpperCAmelCase__	:		Union[str, Any]							,		UpperCAmelCase__	:		Dict							,		UpperCAmelCase__	:		Dict							,		UpperCAmelCase__	:		Tuple=0							):
     _a						: Optional[Any]    =			trellis.size(1							) - 1
     _a						: List[str]    =			torch.argmax(trellis[:, j]							).item()

     _a						: Optional[int]    =			[]
     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						: str    =			trellis[t - 1, j] + emission[t - 1, blank_id]
       # Score for token changing from C-1 at T-1 to J at T.
       _a						: int    =			trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

       # 2. Store the path with frame-wise probability.
       _a						: Union[str, Any]    =			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						: List[str]    =			backtrack(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							)






   @dataclass
   class  UpperCamelCase    :
    UpperCamelCase      : str
    UpperCamelCase      : int
    UpperCamelCase      : int
    UpperCamelCase      : float


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







    @property
    def _lowercase			(  self	:		List[str]							)      -> Optional[Any]:
      return self.end - self.start

   def merge_repeats(UpperCAmelCase__	:		Optional[int]							):
     _a   ,						_a						: int    =			0, 0
     _a						: Tuple    =			[]
     while ia < len(UpperCAmelCase__							):
       while ia < len(UpperCAmelCase__							) and path[ia].token_index == path[ia].token_index:
         ia += 1
       _a						: Any    =			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						: Optional[int]    =			ia
     return segments

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







 def _lowercase			(  self	:		str							,		UpperCAmelCase__	:		Optional[int]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		int							)      -> Optional[Any]:
   if not os.path.exists(UpperCAmelCase__							):
     os.makedirs(UpperCAmelCase__							)

   # load text file
   _a						: Optional[int]    =			open(UpperCAmelCase__							,		encoding="""utf8"""							).readlines()

   _a						: Optional[int]    =			[]
   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						: List[str]    =			line.strip().split("""\t"""							)
     _a						: Optional[Any]    =			os.path.join(UpperCAmelCase__							,		wav_name + """.wav"""							)
     _a						: str    =			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     lowerCAmelCase__  (  ):



  '''simple docstring'''
  _a						: Optional[int]    =			argparse.ArgumentParser()

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

  _a						: List[str]    =			parser.parse_args()

  _a						: Tuple    =			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"""




# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

