'''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 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = cuda
       UpperCAmelCase_				:  List[str]  = AutoConfig.from_pretrained(snake_case_		)
       UpperCAmelCase_				:  List[Any]  = AutoModelForCTC.from_pretrained(snake_case_		)
       self.model.eval()
       if self.cuda:
            self.model.to(device='cuda'		)
       UpperCAmelCase_				:  Optional[int]  = AutoProcessor.from_pretrained(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = torchaudio.transforms.Resample(snake_case_ ,						1_6_0_0_0		)
       UpperCAmelCase_				:  str  = 0
       UpperCAmelCase_				:  Tuple  = list(self.processor.tokenizer.get_vocab().keys()		)
       for i in range(len(snake_case_		)		):
            if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                 UpperCAmelCase_				:  List[str]  = i
       print('Blank Token id [PAD]/<pad>' ,						snake_case_		)
       UpperCAmelCase_				:  Tuple  = blank_id


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = torchaudio.load(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = self.resampler(snake_case_		).squeeze().numpy()
       return speech


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[Any]  = self.speech_file_to_array_fn(item['wav_path']		)
       UpperCAmelCase_				:  Optional[Any]  = self.processor(snake_case_ ,						sampling_rate=1_6_0_0_0 ,						return_tensors='pt' ,						padding=snake_case_		)
       if self.cuda:
            UpperCAmelCase_				:  Any  = inputs.to(device='cuda'		)

       with torch.no_grad():
            UpperCAmelCase_				:  Optional[int]  = self.model(inputs.input_values		).logits

       # get the emission probability at frame level
       UpperCAmelCase_				:  List[str]  = torch.log_softmax(snake_case_ ,						dim=-1		)
       UpperCAmelCase_				:  Dict  = emissions[0].cpu().detach()

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

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

       def get_trellis(snake_case_ ,						snake_case_ ,						snake_case_=0		):
            UpperCAmelCase_				:  Union[str, Any]  = emission.size(0		)
            UpperCAmelCase_				:  List[Any]  = 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.
            UpperCAmelCase_				:  Any  = torch.full((num_frames + 1, num_tokens + 1) ,						-float('inf'		)		)
            UpperCAmelCase_				:  Any  = 0
            for t in range(snake_case_		):
                 UpperCAmelCase_				:  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

       UpperCAmelCase_				:  Tuple  = get_trellis(snake_case_ ,						snake_case_ ,						snake_case_		)






       @dataclass
       class 	__SCREAMING_SNAKE_CASE		:







         '''simple docstring'''






         lowerCamelCase_						:int
         lowerCamelCase_						:int
         lowerCamelCase_						:float

       def backtrack(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_=0		):
            UpperCAmelCase_				:  Optional[Any]  = trellis.size(1		) - 1
            UpperCAmelCase_				:  str  = torch.argmax(trellis[:, j]		).item()

            UpperCAmelCase_				:  Union[str, Any]  = []
            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.
                 UpperCAmelCase_				:  Union[str, Any]  = trellis[t - 1, j] + emission[t - 1, blank_id]
                 # Score for token changing from C-1 at T-1 to J at T.
                 UpperCAmelCase_				:  Optional[int]  = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                 # 2. Store the path with frame-wise probability.
                 UpperCAmelCase_				:  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 ,						snake_case_		)		)

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

       UpperCAmelCase_				:  Tuple  = backtrack(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)






       @dataclass
       class 	__SCREAMING_SNAKE_CASE		:







         '''simple docstring'''






         lowerCamelCase_						:str
         lowerCamelCase_						:int
         lowerCamelCase_						:int
         lowerCamelCase_						:float


         def __repr__(					self		):




              '''simple docstring'''

              return F'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''






         @property
         def _UpperCamelCase	(					self		):




              '''simple docstring'''

              return self.end - self.start

       def merge_repeats(snake_case_		):
            UpperCAmelCase_  ,     UpperCAmelCase_				:  Tuple  = 0, 0
            UpperCAmelCase_				:  Union[str, Any]  = []
            while ia < len(snake_case_		):
                 while ia < len(snake_case_		) and path[ia].token_index == path[ia].token_index:
                      ia += 1
                 UpperCAmelCase_				:  Optional[int]  = 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_ ,						)		)
                 UpperCAmelCase_				:  str  = ia
            return segments

       UpperCAmelCase_				:  Optional[Any]  = 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 _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

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

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

       UpperCAmelCase_				:  Dict  = []
       for line in lines:
            if len(line.strip().split('\t'		)		) != 2:
                 print('Script must be in format: 00001  this is my sentence'		)
                 exit()

            UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = line.strip().split('\t'		)
            UpperCAmelCase_				:  int  = os.path.join(snake_case_ ,						wav_name + '.wav'		)
            UpperCAmelCase_				:  Any  = 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    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = argparse.ArgumentParser()

     parser.add_argument(
         '--model_name'      ,	type=lowerCamelCase_      ,	default='arijitx/wav2vec2-xls-r-300m-bengali'      ,	help='wav2vec model name'  )
     parser.add_argument('--wav_dir'      ,	type=lowerCamelCase_      ,	default='./wavs'      ,	help='directory containing wavs'  )
     parser.add_argument('--text_file'      ,	type=lowerCamelCase_      ,	default='script.txt'      ,	help='file containing text'  )
     parser.add_argument('--input_wavs_sr'      ,	type=lowerCamelCase_      ,	default=16000      ,	help='sampling rate of input audios'  )
     parser.add_argument(
         '--output_dir'      ,	type=lowerCamelCase_      ,	default='./out_alignment'      ,	help='output directory containing the alignment files'  )
     parser.add_argument('--cuda'      ,	action='store_true'  )

     UpperCAmelCase_				:  Optional[int]  = parser.parse_args()

     UpperCAmelCase_				:  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()









from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray						)       ->       np.ndarray:

 """simple docstring"""






 __lowerCAmelCase:  Any        = np.stack((horizontal_flow, vertical_flow)       ,   2						)

 # Create a grid of all pixel coordinates and subtract the flow to get the
 # target pixels coordinates
 __lowerCAmelCase:  List[str]        = np.stack(
     np.meshgrid(np.arange(0       ,   image.shape[1]						)       ,   np.arange(0       ,   image.shape[0]						)						)       ,   2						)
 __lowerCAmelCase:  int        = np.round(grid - flow						).astype(np.intaa						)

 # Find the locations outside of the original image
 __lowerCAmelCase:  Dict        = (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]						))
 __lowerCAmelCase:  str        = 0

 __lowerCAmelCase:  List[str]        = image[grid[:, :, 1], grid[:, :, 0]]

 # Set pixels at invalid locations to 0
 __lowerCAmelCase:  List[str]        = 0

 return warped







def _a					(			SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     np.ndarray       ,   SCREAMING_SNAKE_CASE						:     SupportsIndex       ,   SCREAMING_SNAKE_CASE						:     float | None = None       ,   )       ->       tuple[np.ndarray, np.ndarray]:

 """simple docstring"""






 if alpha is None:
  __lowerCAmelCase:  Tuple        = 0.1

 # Initialize flow
 __lowerCAmelCase:  Optional[int]        = np.zeros_like(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = np.zeros_like(SCREAMING_SNAKE_CASE						)

 # Prepare kernels for the calculation of the derivatives and the average velocity
 __lowerCAmelCase:  List[Any]        = np.array([[-1, 1], [-1, 1]]						) * 0.2_5
 __lowerCAmelCase:  int        = np.array([[-1, -1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Tuple        = np.array([[1, 1], [1, 1]]						) * 0.2_5
 __lowerCAmelCase:  Dict        = np.array(
     [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]						)

 # Iteratively refine the flow
 for _ in range(SCREAMING_SNAKE_CASE						):
  __lowerCAmelCase:  Optional[int]        = warp(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[str]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  List[Any]        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						) + convolve(SCREAMING_SNAKE_CASE       ,   -kernel_t						)

  __lowerCAmelCase:  Any        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Dict        = convolve(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  # This updates the flow as proposed in the paper (Step 12)
  __lowerCAmelCase:  Tuple        = (
      derivative_x * avg_horizontal_velocity
      + derivative_y * avg_vertical_velocity
      + derivative_t
  )
  __lowerCAmelCase:  int        = update / (alpha**2 + derivative_x**2 + derivative_y**2)

  __lowerCAmelCase:  List[Any]        = avg_horizontal_velocity - derivative_x * update
  __lowerCAmelCase:  Union[str, Any]        = avg_vertical_velocity - derivative_y * update

 return horizontal_flow, vertical_flow


if __name__ == "__main__":
  import doctest

  doctest.testmod()





