





import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Tuple      ,    snake_case    :		Optional[int]      ,    snake_case    :		Dict      ,    snake_case    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					cuda
      __lowerCAmelCase								=					AutoConfig.from_pretrained(snake_case				)
      __lowerCAmelCase								=					AutoModelForCTC.from_pretrained(snake_case				)
      self.model.eval()
      if self.cuda:
        self.model.to(device='''cuda'''				)
      __lowerCAmelCase								=					AutoProcessor.from_pretrained(snake_case				)
      __lowerCAmelCase								=					torchaudio.transforms.Resample(snake_case      ,    16_000				)
      __lowerCAmelCase								=					0
      __lowerCAmelCase								=					list(self.processor.tokenizer.get_vocab().keys()				)
      for i in range(len(snake_case				)				):
        if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
          __lowerCAmelCase								=					i
      print('''Blank Token id [PAD]/<pad>'''      ,    snake_case				)
      __lowerCAmelCase								=					blank_id





    def 						A		( self    :		List[Any]      ,    snake_case    :		Tuple				):

      """simple docstring"""





      __lowerCAmelCase						,    __lowerCAmelCase								=					torchaudio.load(snake_case				)
      __lowerCAmelCase								=					self.resampler(snake_case				).squeeze().numpy()
      return speech





    def 						A		( self    :		Any      ,    snake_case    :		Optional[int]				):

      """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(snake_case      ,    sampling_rate=16_000      ,    return_tensors='''pt'''      ,    padding=snake_case				)
      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(snake_case      ,    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(snake_case				)}
      __lowerCAmelCase								=					[]
      for c in transcript:
        if c in dictionary:
          tokens.append(dictionary[c]				)

      def get_trellis(snake_case    :		str      ,    snake_case    :		Optional[Any]      ,    snake_case    :		str=0				):
        __lowerCAmelCase								=					emission.size(0				)
        __lowerCAmelCase								=					len(snake_case				)

        # 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(snake_case				):
          __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(snake_case      ,    snake_case      ,    snake_case				)

      @dataclass
      class 	__snake_case					:






          '''simple docstring'''
          _lowerCAmelCase  =						42
          _lowerCAmelCase  =						42
          _lowerCAmelCase  =						42

      def backtrack(snake_case    :		str      ,    snake_case    :		int      ,    snake_case    :		Any      ,    snake_case    :		Dict=0				):
        __lowerCAmelCase								=					trellis.size(1				) - 1
        __lowerCAmelCase								=					torch.argmax(trellis[:, j]				).item()

        __lowerCAmelCase								=					[]
        for t in range(snake_case      ,    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      ,    snake_case				)				)

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

      __lowerCAmelCase								=					backtrack(snake_case      ,    snake_case      ,    snake_case      ,    snake_case				)

      @dataclass
      class 	__snake_case					:






          '''simple docstring'''
          _lowerCAmelCase  =						42
          _lowerCAmelCase  =						42
          _lowerCAmelCase  =						42
          _lowerCAmelCase  =						42





          def __repr__( self    :		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    :		Tuple				):

            """simple docstring"""





            return self.end - self.start

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

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





    def 						A		( self    :		int      ,    snake_case    :		List[str]      ,    snake_case    :		str      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





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

      # load text file
      __lowerCAmelCase								=					open(snake_case      ,    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(snake_case      ,    wav_name + '''.wav'''				)
        __lowerCAmelCase								=					os.path.join(snake_case      ,    wav_name + '''.txt'''				)

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

      for item in tqdm(snake_case				):
        self.align_single_sample(snake_case				)


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __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=1_60_00  ,				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()







import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__lowercase				    =      logging.get_logger(__name__)

if is_vision_available():
      import PIL.Image
      from PIL import Image
      from PIL.Image import Image as ImageType
else:
      __lowercase				    =      object

if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

class 	__snake_case					:






    '''simple docstring'''





    def __init__( self    :		Optional[int]      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					value





    def __str__( self    :		List[Any]				):

      """simple docstring"""





      return self.to_string()





    def 						A		( self    :		int				):

      """simple docstring"""





      logger.error(
          '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''				)
      return self._value





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      logger.error(
          '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''				)
      return str(self._value				)


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''





    def 						A		( self    :		int				):

      """simple docstring"""





      return self._value





    def 						A		( self    :		List[str]				):

      """simple docstring"""





      return self._value


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''





    def __init__( self    :		int      ,    snake_case    :		Optional[int]				):

      """simple docstring"""





      super().__init__(snake_case				)

      if not is_vision_available():
        raise ImportError('''PIL must be installed in order to handle images.'''				)

      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None

      if isinstance(snake_case      ,    snake_case				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    (str, pathlib.Path)				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    torch.Tensor				):
        __lowerCAmelCase								=					value
      else:
        raise ValueError(f'''Unsupported type for {self.__class__.__name__}: {type(snake_case				)}'''				)





    def 						A		( self    :		Tuple      ,    snake_case    :		Union[str, Any]=None      ,    snake_case    :		int=None				):

      """simple docstring"""





      from IPython.display import Image, display

      display(Image(self.to_string()				)				)





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      if self._raw is not None:
        return self._raw

      if self._path is not None:
        __lowerCAmelCase								=					Image.open(self._path				)
        return self._raw





    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      if self._path is not None:
        return self._path

      if self._raw is not None:
        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.png'''				)
        self._raw.save(self._path				)

        return self._path

      if self._tensor is not None:
        __lowerCAmelCase								=					self._tensor.cpu().detach().numpy()

        # There is likely simpler than load into image into save
        __lowerCAmelCase								=					Image.fromarray((array * 255).astype(np.uinta				)				)

        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.png'''				)

        img.save(self._path				)

        return self._path


class 	__snake_case					(      _a ):






    '''simple docstring'''





    def __init__( self    :		List[Any]      ,    snake_case    :		int      ,    snake_case    :		Any=16_000				):

      """simple docstring"""





      super().__init__(snake_case				)

      if not is_soundfile_availble():
        raise ImportError('''soundfile must be installed in order to handle audio.'''				)

      __lowerCAmelCase								=					None
      __lowerCAmelCase								=					None

      __lowerCAmelCase								=					samplerate

      if isinstance(snake_case      ,    (str, pathlib.Path)				):
        __lowerCAmelCase								=					value
      elif isinstance(snake_case      ,    torch.Tensor				):
        __lowerCAmelCase								=					value
      else:
        raise ValueError(f'''Unsupported audio type: {type(snake_case				)}'''				)





    def 						A		( self    :		Optional[int]      ,    snake_case    :		Tuple=None      ,    snake_case    :		Tuple=None				):

      """simple docstring"""





      from IPython.display import Audio, display

      display(Audio(self.to_string()      ,    rate=self.samplerate				)				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      if self._tensor is not None:
        return self._tensor

      if self._path is not None:
        __lowerCAmelCase						,    __lowerCAmelCase								=					sf.read(self._path				)
        __lowerCAmelCase								=					torch.tensor(snake_case				)
        return self._tensor





    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      if self._path is not None:
        return self._path

      if self._tensor is not None:
        __lowerCAmelCase								=					tempfile.mkdtemp()
        __lowerCAmelCase								=					os.path.join(snake_case      ,    str(uuid.uuida()				) + '''.wav'''				)
        sf.write(self._path      ,    self._tensor      ,    samplerate=self.samplerate				)
        return self._path


__lowercase				    =      {"""text""": AgentText, """image""": AgentImage, """audio""": AgentAudio}
__lowercase				    =      {str: AgentText}

if is_vision_available():
      __lowercase				    =      AgentImage


def        __SCREAMING_SNAKE_CASE	(				*__lowercase:		List[Any]  ,				**__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					[(arg.to_raw() if isinstance(__lowercase  ,				__lowercase) else arg) for arg in args]
  __lowerCAmelCase								=					{k: (v.to_raw() if isinstance(__lowercase  ,				__lowercase) else v) for k, v in kwargs.items()}
  return args, kwargs




def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		List[Any]=None):



  '''simple docstring'''






  if isinstance(__lowercase  ,				__lowercase):
    __lowerCAmelCase								=					{}
    for i, (k, v) in enumerate(outputs.items()):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          __lowerCAmelCase								=					AGENT_TYPE_MAPPING[output_types[i]](__lowercase)
        else:
          __lowerCAmelCase								=					AgentType(__lowercase)

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__lowercase  ,				_k):
            __lowerCAmelCase								=					_v(__lowercase)
        if k not in decoded_outputs:
          __lowerCAmelCase								=					AgentType[v]

  elif isinstance(__lowercase  ,				(list, tuple)):
    __lowerCAmelCase								=					type(__lowercase)()
    for i, v in enumerate(__lowercase):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__lowercase))
        else:
          decoded_outputs.append(AgentType(__lowercase))
      else:
        # If the class does not have defined output, then we map according to the type
        __lowerCAmelCase								=					False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__lowercase  ,				_k):
            decoded_outputs.append(_v(__lowercase))
            __lowerCAmelCase								=					True

        if not found:
          decoded_outputs.append(AgentType(__lowercase))

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      __lowerCAmelCase								=					AGENT_TYPE_MAPPING[output_types[0]](__lowercase)

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(__lowercase  ,				_k):
          return _v(__lowercase)
      return AgentType(__lowercase)

  return decoded_outputs
