






'''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 					lowercase							:


    '''simple docstring'''

    def __init__(       self  :  Optional[int]					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  List[str]					,  __lowerCamelCase  :  Union[str, Any]  )				->				Any:

     '''simple docstring'''




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

    def  a__					(       self  :  int					,  __lowerCamelCase  :  Dict  )				->				List[Any]:

     '''simple docstring'''




     lowerCamelCase__	,						lowerCamelCase__	=  torchaudio.load(__lowerCamelCase  )
     lowerCamelCase__	=  self.resampler(__lowerCamelCase  ).squeeze().numpy()
     return speech

    def  a__					(       self  :  Dict					,  __lowerCamelCase  :  Dict  )				->				Optional[Any]:

     '''simple docstring'''




     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(__lowerCamelCase					,  sampling_rate=16000					,  return_tensors="pt"					,  padding=__lowerCamelCase  )
     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(__lowerCamelCase					,  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(__lowerCamelCase  )}
     lowerCamelCase__	=  []
     for c in transcript:
      if c in dictionary:
       tokens.append(dictionary[c]  )

     def get_trellis(__lowerCamelCase  :  Dict					,  __lowerCamelCase  :  Tuple					,  __lowerCamelCase  :  Union[str, Any]=0  ):
      lowerCamelCase__	=  emission.size(0  )
      lowerCamelCase__	=  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.
      lowerCamelCase__	=  torch.full((num_frames + 1, num_tokens + 1)					,  -float("inf"  )  )
      lowerCamelCase__	=  0
      for t in range(__lowerCamelCase  ):
       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(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  )
     @dataclass
     class 					lowercase							:


         '''simple docstring'''

         lowerCAmelCase__   					=				42
         lowerCAmelCase__   					=				42
         lowerCAmelCase__   					=				42

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

      lowerCamelCase__	=  []
      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.
       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					,  __lowerCamelCase  )  )

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

     lowerCamelCase__	=  backtrack(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase  )
     @dataclass
     class 					lowercase							:


         '''simple docstring'''

         lowerCAmelCase__   					=				42
         lowerCAmelCase__   					=				42
         lowerCAmelCase__   					=				42
         lowerCAmelCase__   					=				42

         def __repr__(       self  :  Union[str, Any]  )				->				int:

          '''simple docstring'''




          return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''




         @property
         def  a__					(       self  :  str  )				->				int:

          '''simple docstring'''




          return self.end - self.start

     def merge_repeats(__lowerCamelCase  :  List[str]  ):
      lowerCamelCase__	,						lowerCamelCase__	=  0, 0
      lowerCamelCase__	=  []
      while ia < len(__lowerCamelCase  ):
       while ia < len(__lowerCamelCase  ) and path[ia].token_index == path[ia].token_index:
        ia += 1
       lowerCamelCase__	=  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					,  )  )
       lowerCamelCase__	=  ia
      return segments

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




    def  a__					(       self  :  List[str]					,  __lowerCamelCase  :  int					,  __lowerCamelCase  :  Optional[int]					,  __lowerCamelCase  :  List[str]  )				->				Any:

     '''simple docstring'''




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

     # load text file
     lowerCamelCase__	=  open(__lowerCamelCase					,  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(__lowerCamelCase					,  wav_name + ".wav"  )
      lowerCamelCase__	=  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  lowerCamelCase_  (     ):
 lowerCamelCase__	=  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=16000					,  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")

 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()









'''simple docstring'''





from math import cos, sin






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	=  float(lowercase__)
 return [[scaling_factor * int(x == y) for x in range(2)] for y in range(2)]






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	=  cos(lowercase__), sin(lowercase__)
 return [[c, -s], [s, c]]






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	=  cos(lowercase__), sin(lowercase__)
 lowerCamelCase__	=  c * s
 return [[c * c, cs], [cs, s * s]]






def  lowerCamelCase_  (     lowercase__):
 lowerCamelCase__	,						lowerCamelCase__	=  cos(lowercase__), sin(lowercase__)
 lowerCamelCase__	=  c * s
 return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"""    {scaling(5) = }""")
print(F"""  {rotation(45) = }""")
print(F"""{projection(45) = }""")
print(F"""{reflection(45) = }""")

