



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_		   =				logging.get_logger(__name__)


if is_vision_available():
     import PIL


class   lowerCamelCase__  (					UpperCamelCase_):



 """simple docstring"""

 a__				:   Dict   =     ["""pixel_values"""]

 def __init__(						self				:				str ,	__lowerCAmelCase				:				List[str] = True ,	__lowerCAmelCase				:				List[Any] = None ,	__lowerCAmelCase				:				Union[str, Any] = PILImageResampling.BICUBIC ,	__lowerCAmelCase				:				str = True ,	__lowerCAmelCase				:				Dict = None ,	__lowerCAmelCase				:				Tuple = True ,	__lowerCAmelCase				:				str = 1 / 2_55 ,	__lowerCAmelCase				:				str = True ,	__lowerCAmelCase				:				int = None ,	__lowerCAmelCase				:				Dict = None ,	__lowerCAmelCase				:				str = True ,	**__lowerCAmelCase				:				Optional[Any] ,	)      ->  None:
    super().__init__(**__SCREAMING_SNAKE_CASE			)
    _A						     =					size if size is not None else {'''shortest_edge''': 2_24}
    _A						     =					get_size_dict(__SCREAMING_SNAKE_CASE ,	default_to_square=__SCREAMING_SNAKE_CASE			)
    _A						     =					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
    _A						     =					get_size_dict(__SCREAMING_SNAKE_CASE			)

    _A						     =					do_resize
    _A						     =					size
    _A						     =					resample
    _A						     =					do_center_crop
    _A						     =					crop_size
    _A						     =					do_rescale
    _A						     =					rescale_factor
    _A						     =					do_normalize
    _A						     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
    _A						     =					image_std if image_std is not None else OPENAI_CLIP_STD
    _A						     =					do_convert_rgb

 def 						snake_case_			(						self				:				Union[str, Any] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				List[Any] = PILImageResampling.BICUBIC ,	__lowerCAmelCase				:				Any = None ,	**__lowerCAmelCase				:				int ,	)      ->  np.ndarray:
    _A						     =					get_size_dict(__SCREAMING_SNAKE_CASE ,	default_to_square=__SCREAMING_SNAKE_CASE			)
    _A						     =					get_resize_output_image_size(
        __SCREAMING_SNAKE_CASE ,	size=(size['''height'''], size['''width''']) ,	default_to_square=__SCREAMING_SNAKE_CASE			)
    return resize(__SCREAMING_SNAKE_CASE ,	size=__SCREAMING_SNAKE_CASE ,	resample=__SCREAMING_SNAKE_CASE ,	data_format=__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE			)

 def 						snake_case_			(						self				:				List[str] ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				int ,	__lowerCAmelCase				:				Optional[Any] = None ,	**__lowerCAmelCase				:				Tuple ,	)      ->  np.ndarray:
    _A						     =					get_size_dict(__SCREAMING_SNAKE_CASE			)
    return center_crop(__SCREAMING_SNAKE_CASE ,	size=(size['''height'''], size['''width''']) ,	data_format=__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE			)

 def 						snake_case_			(						self				:				Optional[Any] ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				List[Any] ,	__lowerCAmelCase				:				Optional[Any] = None ,	**__lowerCAmelCase				:				int ,	)      ->  List[str]:
    return rescale(__SCREAMING_SNAKE_CASE ,	scale=__SCREAMING_SNAKE_CASE ,	data_format=__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE			)

 def 						snake_case_			(						self				:				int ,	__lowerCAmelCase				:				Dict ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				int ,	__lowerCAmelCase				:				Dict = None ,	**__lowerCAmelCase				:				List[Any] ,	)      ->  np.ndarray:
    return normalize(__SCREAMING_SNAKE_CASE ,	mean=__SCREAMING_SNAKE_CASE ,	std=__SCREAMING_SNAKE_CASE ,	data_format=__SCREAMING_SNAKE_CASE ,	**__SCREAMING_SNAKE_CASE			)

 def 						snake_case_			(						self				:				Tuple ,	__lowerCAmelCase				:				str ,	__lowerCAmelCase				:				Union[str, Any] = None ,	__lowerCAmelCase				:				Any = None ,	__lowerCAmelCase				:				str = None ,	__lowerCAmelCase				:				List[str] = None ,	__lowerCAmelCase				:				int = None ,	__lowerCAmelCase				:				Dict = None ,	__lowerCAmelCase				:				Union[str, Any] = None ,	__lowerCAmelCase				:				Optional[int] = None ,	__lowerCAmelCase				:				Union[str, Any] = None ,	__lowerCAmelCase				:				Optional[int] = None ,	__lowerCAmelCase				:				List[str] = None ,	__lowerCAmelCase				:				Optional[Any] = None ,	__lowerCAmelCase				:				str = ChannelDimension.FIRST ,	**__lowerCAmelCase				:				Union[str, Any] ,	)      ->  PIL.Image.Image:
    _A						     =					do_resize if do_resize is not None else self.do_resize
    _A						     =					size if size is not None else self.size
    _A						     =					get_size_dict(__SCREAMING_SNAKE_CASE ,	default_to_square=__SCREAMING_SNAKE_CASE			)
    _A						     =					resample if resample is not None else self.resample
    _A						     =					do_center_crop if do_center_crop is not None else self.do_center_crop
    _A						     =					crop_size if crop_size is not None else self.crop_size
    _A						     =					get_size_dict(__SCREAMING_SNAKE_CASE			)
    _A						     =					do_rescale if do_rescale is not None else self.do_rescale
    _A						     =					rescale_factor if rescale_factor is not None else self.rescale_factor
    _A						     =					do_normalize if do_normalize is not None else self.do_normalize
    _A						     =					image_mean if image_mean is not None else self.image_mean
    _A						     =					image_std if image_std is not None else self.image_std
    _A						     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    _A						     =					make_list_of_images(__SCREAMING_SNAKE_CASE			)

    if not valid_images(__SCREAMING_SNAKE_CASE			):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

    if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize is True.'''			)

    if do_center_crop and crop_size is None:
       raise ValueError('''Crop size must be specified if do_center_crop is True.'''			)

    if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and std must be specified if do_normalize is True.'''			)

    # PIL RGBA images are converted to RGB
    if do_convert_rgb:
       _A						     =					[convert_to_rgb(__SCREAMING_SNAKE_CASE			) for image in images]

    # All transformations expect numpy arrays.
    _A						     =					[to_numpy_array(__SCREAMING_SNAKE_CASE			) for image in images]

    if do_resize:
       _A						     =					[self.resize(image=__SCREAMING_SNAKE_CASE ,	size=__SCREAMING_SNAKE_CASE ,	resample=__SCREAMING_SNAKE_CASE			) for image in images]

    if do_center_crop:
       _A						     =					[self.center_crop(image=__SCREAMING_SNAKE_CASE ,	size=__SCREAMING_SNAKE_CASE			) for image in images]

    if do_rescale:
       _A						     =					[self.rescale(image=__SCREAMING_SNAKE_CASE ,	scale=__SCREAMING_SNAKE_CASE			) for image in images]

    if do_normalize:
       _A						     =					[self.normalize(image=__SCREAMING_SNAKE_CASE ,	mean=__SCREAMING_SNAKE_CASE ,	std=__SCREAMING_SNAKE_CASE			) for image in images]

    _A						     =					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE ,	__SCREAMING_SNAKE_CASE			) for image in images]

    _A						     =					{'''pixel_values''': images}
    return BatchFeature(data=__SCREAMING_SNAKE_CASE ,	tensor_type=__SCREAMING_SNAKE_CASE			)








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()



