


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_						:					Any      ,       lowercase_						:					Tuple      ,       lowercase_						:					Optional[int]			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		Dict          =cuda
             _lowerCamelCase	:		int          =AutoConfig.from_pretrained(lowercase_			)
             _lowerCamelCase	:		Dict          =AutoModelForCTC.from_pretrained(lowercase_			)
             self.model.eval()
             if self.cuda:
                    self.model.to(device='cuda'			)
             _lowerCamelCase	:		List[Any]          =AutoProcessor.from_pretrained(lowercase_			)
             _lowerCamelCase	:		List[Any]          =torchaudio.transforms.Resample(lowercase_      ,       1_6000			)
             _lowerCamelCase	:		Optional[Any]          =0
             _lowerCamelCase	:		Dict          =list(self.processor.tokenizer.get_vocab().keys()			)
             for i in range(len(lowercase_			)			):
                    if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                           _lowerCamelCase	:		List[str]          =i
             print('Blank Token id [PAD]/<pad>'      ,       lowercase_			)
             _lowerCamelCase	:		Dict          =blank_id






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					List[str]			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		List[Any]          =torchaudio.load(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =self.resampler(lowercase_			).squeeze().numpy()
             return speech






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					Tuple			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.blank_id
             _lowerCamelCase	:		Optional[Any]          ='|'.join(item['sent'].split(' '			)			)
             if not os.path.isfile(item['wav_path']			):
                    print(item['wav_path']      ,       'not found in wavs directory'			)

             _lowerCamelCase	:		str          =self.speech_file_to_array_fn(item['wav_path']			)
             _lowerCamelCase	:		Tuple          =self.processor(lowercase_      ,       sampling_rate=1_6000      ,       return_tensors='pt'      ,       padding=lowercase_			)
             if self.cuda:
                    _lowerCamelCase	:		Optional[Any]          =inputs.to(device='cuda'			)

             with torch.no_grad():
                    _lowerCamelCase	:		List[Any]          =self.model(inputs.input_values			).logits

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

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

             _lowerCamelCase	:		Any          ={c: i for i, c in enumerate(lowercase_			)}
             _lowerCamelCase	:		Tuple          =[]
             for c in transcript:
                    if c in dictionary:
                           tokens.append(dictionary[c]			)

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




             @dataclass
             class A  :
                   UpperCamelCase__							:  int
                   UpperCamelCase__							:  int
                   UpperCamelCase__							:  float

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

                    _lowerCamelCase	:		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.
                           _lowerCamelCase	:		str          =trellis[t - 1, j] + emission[t - 1, blank_id]
                           # Score for token changing from C-1 at T-1 to J at T.
                           _lowerCamelCase	:		Dict          =trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                           # 2. Store the path with frame-wise probability.
                           _lowerCamelCase	:		List[Any]          =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]

             _lowerCamelCase	:		Dict          =backtrack(lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_			)




             @dataclass
             class A  :
                   UpperCamelCase__							:  str
                   UpperCamelCase__							:  int
                   UpperCamelCase__							:  int
                   UpperCamelCase__							:  float






                   def __repr__( self						:					Tuple			) ->						List[str]:
                          """simple docstring"""




                          return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''






                   @property
                   def 	lowerCamelCase	( self						:					Optional[Any]			) ->						List[Any]:
                          """simple docstring"""




                          return self.end - self.start

             def merge_repeats(lowercase_						:					List[str]			):
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =0, 0
                    _lowerCamelCase	:		Optional[Any]          =[]
                    while ia < len(lowercase_			):
                           while ia < len(lowercase_			) and path[ia].token_index == path[ia].token_index:
                                  ia += 1
                           _lowerCamelCase	:		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_      ,       )			)
                           _lowerCamelCase	:		int          =ia
                    return segments

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






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Dict      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




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

             # load text file
             _lowerCamelCase	:		int          =open(lowercase_      ,       encoding='utf8'			).readlines()

             _lowerCamelCase	:		str          =[]
             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	:		Optional[Any]          =line.strip().split('\t'			)
                    _lowerCamelCase	:		Tuple          =os.path.join(lowercase_      ,       wav_name + '.wav'			)
                    _lowerCamelCase	:		Union[str, 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_				(   ):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =argparse.ArgumentParser()

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

       _lowerCamelCase	:		Dict          =parser.parse_args()

       _lowerCamelCase	:		Optional[int]          =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 numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
       _lowerCamelCase	:		Dict          =np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

       _lowerCamelCase	:		Optional[Any]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       _lowerCamelCase	:		Optional[int]          =np.abs(img_convolve(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)					)
       # modify the pix within [0, 255]
       _lowerCamelCase	:		Optional[int]          =dst_x * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =dst_y * 255 / np.max(SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Tuple          =np.sqrt((np.square(SCREAMING_SNAKE_CASE__					)) + (np.square(SCREAMING_SNAKE_CASE__					))					)
       _lowerCamelCase	:		int          =dst_xy * 255 / np.max(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		int          =dst_xy.astype(np.uinta					)

       _lowerCamelCase	:		Optional[int]          =np.arctana(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       return dst, theta


if __name__ == "__main__":
 # read original image
 lowerCamelCase						 =	imread('../image_data/lena.jpg')
 # turn image in gray scale value
 lowerCamelCase						 =	cvtColor(img, COLOR_BGR2GRAY)

 lowerCamelCase ,    lowerCamelCase						 =	sobel_filter(gray)

 # show result images
 imshow('sobel filter', sobel_grad)
 imshow('sobel theta', sobel_theta)
 waitKey(0)


