



import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :str  ,      _snake_case  :str=100  ,      _snake_case  :Optional[Any]=" "							)     ->    Tuple:
   _A						     =					text.split(_snake_case							)
   return [character.join(text[i : i + n]							).strip() for i in range(0  ,      len(_snake_case							)  ,      _snake_case							)]

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]							)     ->    Dict:
   _A     ,						_A						     =					[], []
   for title, text in zip(documents['''title''']  ,      documents['''text''']							):
      if text is not None:
         for passage in split_text(_snake_case							):
            titles.append(title if title is not None else ''''''							)
            texts.append(_snake_case							)
   return {"title": titles, "text": texts}

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Union[str, Any]  ,      _snake_case  :Optional[Any]  ,      _snake_case  :Optional[Any]  ,      _snake_case  :Tuple  ,      _snake_case  :Dict  ,      _snake_case  :Tuple							)     ->    Tuple:
   _A						     =					load_dataset(
       '''csv'''  ,      data_files=[csv_path]  ,      split='''train'''  ,      delimiter='''\t'''  ,      column_names=['''title''', '''text''']							)
   _A						     =					kb_dataset.map(
       _snake_case  ,      batched=_snake_case  ,      num_proc=1							)  # if you want you can load already splitted csv.
   _A						     =					[kb_dataset.shard(_snake_case  ,      _snake_case  ,      contiguous=_snake_case							) for i in range(_snake_case							)]
   _A						     =					kb_list[process_num]

   _A						     =					'''data_''' + str(_snake_case							)
   _A						     =					os.path.join(_snake_case  ,      _snake_case							)

   _A						     =					DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''							)
   _A						     =					ctx_encoder.to(device=_snake_case							)

   def embed(
       _snake_case  :dict  ,      _snake_case  :DPRContextEncoder  ,      _snake_case  :DPRContextEncoderTokenizerFast  ,      _snake_case  :Optional[Any]							) -> dict:
      _A						     =					ctx_tokenizer(
          documents['''title''']  ,      documents['''text''']  ,      truncation=_snake_case  ,      padding='''longest'''  ,      return_tensors='''pt'''							)['''input_ids''']
      _A						     =					ctx_encoder(input_ids.to(device=_snake_case							)  ,      return_dict=_snake_case							).pooler_output
      return {"embeddings": embeddings.detach().cpu().numpy()}

   _A						     =					Features(
       {'''text''': Value('''string'''							), '''title''': Value('''string'''							), '''embeddings''': Sequence(Value('''float32'''							)							)}							)  # optional, save as float32 instead of float64 to save space

   _A						     =					data_shrad.map(
       partial(_snake_case  ,      ctx_encoder=_snake_case  ,      ctx_tokenizer=_snake_case  ,      device=_snake_case							)  ,      batched=_snake_case  ,      batch_size=16  ,      features=_snake_case  ,      )
   dataset.save_to_disk(_snake_case							)


def   SCREAMING_SNAKE_CASE_       (  _snake_case  :str  ,      _snake_case  :Dict							)     ->    List[str]:
   _A						     =					[]

   for shard_address in glob(str(_snake_case							) + '''/*/'''							):
      data_shard_list.append(load_from_disk(_snake_case							)							)

   _A						     =					concatenate_datasets(_snake_case							)
   faiss.omp_set_num_threads(96							)

   _A						     =					faiss.IndexHNSWFlat(768  ,      128  ,      faiss.METRIC_INNER_PRODUCT							)
   concat.add_faiss_index('''embeddings'''  ,      custom_index=_snake_case							)
   concat.get_index('''embeddings'''							).save(
       _snake_case							)  # since we load the index in to memory,we can directly update the index in the disk








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

 def 						snake_case_			(						self				:				Tuple ,	__lowerCAmelCase				:				List[Any]			)      ->  str:
    _A     ,						_A						     =					torchaudio.load(__lowerCAmelCase			)
    _A						     =					self.resampler(__lowerCAmelCase			).squeeze().numpy()
    return speech

 def 						snake_case_			(						self				:				Optional[int] ,	__lowerCAmelCase				:				Tuple			)      ->  Tuple:
    _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(__lowerCAmelCase ,	sampling_rate=1_60_00 ,	return_tensors='''pt''' ,	padding=__lowerCAmelCase			)
    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(__lowerCAmelCase ,	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(__lowerCAmelCase			)}
    _A						     =					[]
    for c in transcript:
       if c in dictionary:
          tokens.append(dictionary[c]			)

    def get_trellis(__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				int=0			):
       _A						     =					emission.size(0			)
       _A						     =					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.
       _A						     =					torch.full((num_frames + 1, num_tokens + 1) ,	-float('''inf'''			)			)
       _A						     =					0
       for t in range(__lowerCAmelCase			):
          _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(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)


    @dataclass
    class   lowerCamelCase__  :



     """simple docstring"""

     a__				:   int
     a__				:   int
     a__				:   float

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

       _A						     =					[]
       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.
          _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 ,	__lowerCAmelCase			)			)

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

    _A						     =					backtrack(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)


    @dataclass
    class   lowerCamelCase__  :



     """simple docstring"""

     a__				:   str
     a__				:   int
     a__				:   int
     a__				:   float

     def __repr__(						self				:				Dict			)      ->  Union[str, Any]:
        return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''

     @property
     def 						snake_case_			(						self				:				Dict			)      ->  str:
        return self.end - self.start

    def merge_repeats(__lowerCAmelCase				:				Any			):
       _A     ,						_A						     =					0, 0
       _A						     =					[]
       while ia < len(__lowerCAmelCase			):
          while ia < len(__lowerCAmelCase			) and path[ia].token_index == path[ia].token_index:
             ia += 1
          _A						     =					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 ,	)			)
          _A						     =					ia
       return segments

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

 def 						snake_case_			(						self				:				List[Any] ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				Optional[int]			)      ->  int:
    if not os.path.exists(__lowerCAmelCase			):
       os.makedirs(__lowerCAmelCase			)

    # load text file
    _A						     =					open(__lowerCAmelCase ,	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     ,						_A						     =					line.strip().split('''\t'''			)
       _A						     =					os.path.join(__lowerCAmelCase ,	wav_name + '''.wav'''			)
       _A						     =					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   SCREAMING_SNAKE_CASE_       (  )     ->    Dict:
   _A						     =					argparse.ArgumentParser()

   parser.add_argument(
       '''--model_name'''  ,      type=_snake_case  ,      default='''arijitx/wav2vec2-xls-r-300m-bengali'''  ,      help='''wav2vec model name'''							)
   parser.add_argument('''--wav_dir'''  ,      type=_snake_case  ,      default='''./wavs'''  ,      help='''directory containing wavs'''							)
   parser.add_argument('''--text_file'''  ,      type=_snake_case  ,      default='''script.txt'''  ,      help='''file containing text'''							)
   parser.add_argument('''--input_wavs_sr'''  ,      type=_snake_case  ,      default=16_000  ,      help='''sampling rate of input audios'''							)
   parser.add_argument(
       '''--output_dir'''  ,      type=_snake_case  ,      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()



