




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				:Union[str, Any]	, lowerCamelCase				:Any	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[Any] )       ->     str:
       UpperCAmelCase__								=  cuda
       UpperCAmelCase__								=  AutoConfig.from_pretrained(__UpperCamelCase )
       UpperCAmelCase__								=  AutoModelForCTC.from_pretrained(__UpperCamelCase )
       self.model.eval()
       if self.cuda:
        self.model.to(device="cuda" )
       UpperCAmelCase__								=  AutoProcessor.from_pretrained(__UpperCamelCase )
       UpperCAmelCase__								=  torchaudio.transforms.Resample(__UpperCamelCase	, 1_6000 )
       UpperCAmelCase__								=  0
       UpperCAmelCase__								=  list(self.processor.tokenizer.get_vocab().keys() )
       for i in range(len(__UpperCamelCase ) ):
        if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
         UpperCAmelCase__								=  i
       print("Blank Token id [PAD]/<pad>"	, __UpperCamelCase )
       UpperCAmelCase__								=  blank_id
      def 		UpperCAmelCase_  (     self				:str	, lowerCamelCase				:Any )       ->     int:
       UpperCAmelCase__								=  torchaudio.load(__UpperCamelCase )
       UpperCAmelCase__								=  self.resampler(__UpperCamelCase ).squeeze().numpy()
       return speech
      def 		UpperCAmelCase_  (     self				:str	, lowerCamelCase				:List[str] )       ->     int:
       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(__UpperCamelCase	, sampling_rate=1_6000	, return_tensors="pt"	, padding=__UpperCamelCase )
       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(__UpperCamelCase	, 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(__UpperCamelCase )}
       UpperCAmelCase__								=  []
       for c in transcript:
        if c in dictionary:
         tokens.append(dictionary[c] )

       def get_trellis(lowerCamelCase				:str	, lowerCamelCase				:List[Any]	, lowerCamelCase				:int=0 ):
        UpperCAmelCase__								=  emission.size(0 )
        UpperCAmelCase__								=  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.
        UpperCAmelCase__								=  torch.full((num_frames + 1, num_tokens + 1)	, -float("inf" ) )
        UpperCAmelCase__								=  0
        for t in range(__UpperCamelCase ):
         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(__UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase )
       @dataclass
       class      _UpperCamelCase :
             UpperCAmelCase_            =						42
             UpperCAmelCase_            =						42
             UpperCAmelCase_            =						42

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

        UpperCAmelCase__								=  []
        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.
         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	, __UpperCamelCase ) )

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

       UpperCAmelCase__								=  backtrack(__UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase	, __UpperCamelCase )
       @dataclass
       class      _UpperCamelCase :
             UpperCAmelCase_            =						42
             UpperCAmelCase_            =						42
             UpperCAmelCase_            =						42
             UpperCAmelCase_            =						42
             def __repr__(     self				:Tuple )       ->     Union[str, Any]:
              return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''







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

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

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







      def 		UpperCAmelCase_  (     self				:str	, lowerCamelCase				:str	, lowerCamelCase				:str	, lowerCamelCase				:Tuple )       ->     Tuple:







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

       # load text file
       UpperCAmelCase__								=  open(__UpperCamelCase	, 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__								=  line.strip().split("\t" )
        UpperCAmelCase__								=  os.path.join(__UpperCamelCase	, wav_name + ".wav" )
        UpperCAmelCase__								=  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"""







 UpperCAmelCase__								=  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_6000  ,							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"		)

 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()











from __future__ import annotations

from string import ascii_letters



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				str | None = None		):




 """simple docstring"""







 UpperCAmelCase__								=  alphabet or ascii_letters

 # The final result string
 UpperCAmelCase__								=  ""

 for character in input_string:
  if character not in alpha:
   # Append without encryption if character is not in the alphabet
   result += character
  else:
   # Get the index of the new key and make sure it isn't too large
   UpperCAmelCase__								=  (alpha.index(_lowerCAmelCase		) + key) % len(_lowerCAmelCase		)

   # Append the encoded character to the alphabet
   result += alpha[new_key]

 return result



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				str | None = None		):




 """simple docstring"""







 key *= -1

 return encrypt(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				str | None = None		):




 """simple docstring"""







 UpperCAmelCase__								=  alphabet or ascii_letters

 # To store data on all the combinations
 UpperCAmelCase__								=  {}

 # Cycle through each combination
 for key in range(1  ,							len(_lowerCAmelCase		) + 1		):
  # Decrypt the message and store the result in the data
  UpperCAmelCase__								=  decrypt(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)

 return brute_force_data


if __name__ == "__main__":
  while True:
    print(F'''\n{"-" * 1_0}\n Menu\n{"-" * 1_0}''')
    print(*["1.Encrypt", "2.Decrypt", "3.BruteForce", "4.Quit"], sep="\n")

    # get user input
    _lowerCAmelCase  :				List[Any]						=				input("\nWhat would you like to do?: ").strip() or "4"

    # run functions based on what the user chose
    if choice not in ("1", "2", "3", "4"):
      print("Invalid choice, please enter a valid choice")
    elif choice == "1":
      _lowerCAmelCase  :				Any						=				input("Please enter the string to be encrypted: ")
      _lowerCAmelCase  :				str						=				int(input("Please enter off-set: ").strip())

      print(encrypt(input_string, key))
    elif choice == "2":
      _lowerCAmelCase  :				Optional[Any]						=				input("Please enter the string to be decrypted: ")
      _lowerCAmelCase  :				Any						=				int(input("Please enter off-set: ").strip())

      print(decrypt(input_string, key))
    elif choice == "3":
      _lowerCAmelCase  :				int						=				input("Please enter the string to be decrypted: ")
      _lowerCAmelCase  :				Dict						=				brute_force(input_string)

      for key, value in brute_force_data.items():
        print(F'''Key: {key} | Message: {value}''')

    elif choice == "4":
      print("Goodbye.")
      break





