






"""simple docstring"""


from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like




def 				a (     UpperCamelCase_							:				Any     ,	UpperCamelCase_							:				int=3		)  ->		int:
 __A	:       Optional[int]       =	int(mask / 2		)
 # copy image size
 __A	:       List[str]       =	zeros_like(UpperCamelCase_		)
 for i in range(UpperCamelCase_     ,	gray_img.shape[0] - bd		):
  for j in range(UpperCamelCase_     ,	gray_img.shape[1] - bd		):
   # get mask according with mask
   __A	:       Optional[int]       =	ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
   # calculate mask median
   __A	:       Dict       =	sort(UpperCamelCase_		)[inta(divide((multiply(UpperCamelCase_     ,	UpperCamelCase_		))     ,	2		) + 1		)]
   __A	:       Optional[Any]       =	median
 return median_img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase   :						str     						= imread("""../image_data/lena.jpg""")
   # turn image in gray scale value
   _UpperCamelCase   :						List[str]     						= cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   _UpperCamelCase   :						Optional[Any]     						= median_filter(gray, 3)
   _UpperCamelCase   :						Optional[int]     						= median_filter(gray, 5)

   # show result images
   imshow("""median filter with 3x3 mask""", medianaxa)
   imshow("""median filter with 5x5 mask""", medianaxa)
   waitKey(0)













"""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 __a   :



  def __init__(self  :		Dict				,			lowercase_  :		str				,			lowercase_  :		Optional[Any]				,			lowercase_  :		Tuple   )		->							int:
   __A	:       Union[str, Any]       =	cuda
   __A	:       List[Any]       =	AutoConfig.from_pretrained(lowercase_   )
   __A	:       Any       =	AutoModelForCTC.from_pretrained(lowercase_   )
   self.model.eval()
   if self.cuda:
    self.model.to(device='cuda'   )
   __A	:       Union[str, Any]       =	AutoProcessor.from_pretrained(lowercase_   )
   __A	:       List[str]       =	torchaudio.transforms.Resample(lowercase_				,			16_000   )
   __A	:       Dict       =	0
   __A	:       Optional[int]       =	list(self.processor.tokenizer.get_vocab().keys()   )
   for i in range(len(lowercase_   )   ):
    if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
     __A	:       List[str]       =	i
   print('Blank Token id [PAD]/<pad>'				,			lowercase_   )
   __A	:       List[str]       =	blank_id



  def 						A_				(self  :		List[str]				,			lowercase_  :		Union[str, Any]   )		->							Optional[int]:
   __A    ,					__A	:       Tuple       =	torchaudio.load(lowercase_   )
   __A	:       int       =	self.resampler(lowercase_   ).squeeze().numpy()
   return speech



  def 						A_				(self  :		Any				,			lowercase_  :		Any   )		->							List[Any]:
   __A	:       int       =	self.blank_id
   __A	:       List[str]       =	'|'.join(item['sent'].split(' '   )   )
   if not os.path.isfile(item['wav_path']   ):
    print(item['wav_path']				,			'not found in wavs directory'   )

   __A	:       List[Any]       =	self.speech_file_to_array_fn(item['wav_path']   )
   __A	:       Any       =	self.processor(lowercase_				,			sampling_rate=16_000				,			return_tensors='pt'				,			padding=lowercase_   )
   if self.cuda:
    __A	:       int       =	inputs.to(device='cuda'   )

   with torch.no_grad():
    __A	:       Dict       =	self.model(inputs.input_values   ).logits

   # get the emission probability at frame level
   __A	:       Any       =	torch.log_softmax(lowercase_				,			dim=-1   )
   __A	:       Tuple       =	emissions[0].cpu().detach()

   # get labels from vocab
   __A	:       Tuple       =	([''] + list(self.processor.tokenizer.get_vocab().keys()   ))[
       :-1
   ]  # logits don't align with the tokenizer's vocab

   __A	:       int       =	{c: i for i, c in enumerate(lowercase_   )}
   __A	:       str       =	[]
   for c in transcript:
    if c in dictionary:
     tokens.append(dictionary[c]   )

   def get_trellis(lowercase_  :		int				,			lowercase_  :		List[Any]				,			lowercase_  :		str=0   ):
    __A	:       List[str]       =	emission.size(0   )
    __A	:       List[Any]       =	len(lowercase_   )

    # 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	:       int       =	torch.full((num_frames + 1, num_tokens + 1)				,			-float('inf'   )   )
    __A	:       str       =	0
    for t in range(lowercase_   ):
     __A	:       List[str]       =	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	:       Dict       =	get_trellis(lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float

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

    __A	:       int       =	[]
    for t in range(lowercase_				,			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	:       List[Any]       =	trellis[t - 1, j] + emission[t - 1, blank_id]
     # Score for token changing from C-1 at T-1 to J at T.
     __A	:       Tuple       =	trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

     # 2. Store the path with frame-wise probability.
     __A	:       Tuple       =	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				,			lowercase_   )   )

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

   __A	:       Any       =	backtrack(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    str
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float



     def __repr__(self  :		Optional[Any]   )		->							Union[str, Any]:
      return f"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"



     @property
     def 						A_				(self  :		int   )		->							Dict:
      return self.end - self.start

   def merge_repeats(lowercase_  :		Dict   ):
    __A    ,					__A	:       List[str]       =	0, 0
    __A	:       Optional[int]       =	[]
    while ia < len(lowercase_   ):
     while ia < len(lowercase_   ) and path[ia].token_index == path[ia].token_index:
      ia += 1
     __A	:       Optional[Any]       =	sum(path[k].score for k in range(lowercase_				,			lowercase_   )   ) / (ia - ia)
     segments.append(
         Segment(
             transcript[path[ia].token_index]				,			path[ia].time_index				,			path[ia - 1].time_index + 1				,			lowercase_				,			)   )
     __A	:       Any       =	ia
    return segments

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



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

   # load text file
   __A	:       Optional[int]       =	open(lowercase_				,			encoding='utf8'   ).readlines()

   __A	:       int       =	[]
   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	:       Union[str, Any]       =	line.strip().split('\t'   )
    __A	:       List[Any]       =	os.path.join(lowercase_				,			wav_name + '.wav'   )
    __A	:       Optional[Any]       =	os.path.join(lowercase_				,			wav_name + '.txt'   )

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

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






def 				a (     )  ->		Any:
 __A	:       Any       =	argparse.ArgumentParser()

 parser.add_argument(
     '--model_name'     ,	type=UpperCamelCase_     ,	default='arijitx/wav2vec2-xls-r-300m-bengali'     ,	help='wav2vec model name'		)
 parser.add_argument('--wav_dir'     ,	type=UpperCamelCase_     ,	default='./wavs'     ,	help='directory containing wavs'		)
 parser.add_argument('--text_file'     ,	type=UpperCamelCase_     ,	default='script.txt'     ,	help='file containing text'		)
 parser.add_argument('--input_wavs_sr'     ,	type=UpperCamelCase_     ,	default=1_6_0_0_0     ,	help='sampling rate of input audios'		)
 parser.add_argument(
     '--output_dir'     ,	type=UpperCamelCase_     ,	default='./out_alignment'     ,	help='output directory containing the alignment files'		)
 parser.add_argument('--cuda'     ,	action='store_true'		)

 __A	:       Tuple       =	parser.parse_args()

 __A	:       Optional[Any]       =	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()





