

'''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 lowerCamelCase					:

				def __init__(    self,		lowercase_,		lowercase_,		lowercase_      )  ->				Union[str, Any]:
					snake_case							       = cuda
					snake_case							       = AutoConfig.from_pretrained(lowerCAmelCase__      )
					snake_case							       = AutoModelForCTC.from_pretrained(lowerCAmelCase__      )
					self.model.eval()
					if self.cuda:
						self.model.to(device='cuda'      )
					snake_case							       = AutoProcessor.from_pretrained(lowerCAmelCase__      )
					snake_case							       = torchaudio.transforms.Resample(lowerCAmelCase__,		16000      )
					snake_case							       = 0
					snake_case							       = list(self.processor.tokenizer.get_vocab().keys()      )
					for i in range(len(lowerCAmelCase__      )      ):
						if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
							snake_case							       = i
					print('Blank Token id [PAD]/<pad>',		lowerCAmelCase__      )
					snake_case							       = blank_id

				def  _lowerCamelCase  (    self,		lowercase_      )  ->				List[Any]:
					snake_case							       = torchaudio.load(lowerCAmelCase__      )
					snake_case							       = self.resampler(lowerCAmelCase__      ).squeeze().numpy()
					return speech

				def  _lowerCamelCase  (    self,		lowercase_      )  ->				Tuple:
					snake_case							       = self.blank_id
					snake_case							       = '''|'''.join(item['sent'].split(' '      )      )
					if not os.path.isfile(item['wav_path']      ):
						print(item['wav_path'],		'not found in wavs directory'      )

					snake_case							       = self.speech_file_to_array_fn(item['wav_path']      )
					snake_case							       = self.processor(lowerCAmelCase__,		sampling_rate=16000,		return_tensors='pt',		padding=lowerCAmelCase__      )
					if self.cuda:
						snake_case							       = inputs.to(device='cuda'      )

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

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

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

					snake_case							       = {c: i for i, c in enumerate(lowerCAmelCase__      )}
					snake_case							       = []
					for c in transcript:
						if c in dictionary:
							tokens.append(dictionary[c]      )

					def get_trellis(lowercase_,		lowercase_,		lowercase_=0      ):
						snake_case							       = emission.size(0      )
						snake_case							       = 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.
						snake_case							       = torch.full((num_frames + 1, num_tokens + 1),		-float('inf'      )      )
						snake_case							       = 0
						for t in range(lowerCAmelCase__      ):
							snake_case							       = torch.maximum(
							    # Score for staying at the same token
							    trellis[t, 1:] + emission[t, blank_id],		trellis[t, :-1] + emission[t, tokens],		)
						return trellis

					snake_case							       = get_trellis(lowerCAmelCase__,		lowerCAmelCase__,		lowerCAmelCase__      )







					@dataclass
					class lowerCamelCase					:
									snake_case_							=      42
									snake_case_							=      42
									snake_case_							=      42

					def backtrack(lowercase_,		lowercase_,		lowercase_,		lowercase_=0      ):
						snake_case							       = trellis.size(1      ) - 1
						snake_case							       = torch.argmax(trellis[:, j]      ).item()

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

							# 2. Store the path with frame-wise probability.
							snake_case							       = 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]

					snake_case							       = backtrack(lowerCAmelCase__,		lowerCAmelCase__,		lowerCAmelCase__,		lowerCAmelCase__      )







					@dataclass
					class lowerCamelCase					:
									snake_case_							=      42
									snake_case_							=      42
									snake_case_							=      42
									snake_case_							=      42

									def __repr__(    self      )  ->				int:
										return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''






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

					def merge_repeats(lowercase_      ):
						snake_case							       = 0, 0
						snake_case							       = []
						while ia < len(lowerCAmelCase__      ):
							while ia < len(lowerCAmelCase__      ) and path[ia].token_index == path[ia].token_index:
								ia += 1
							snake_case							       = 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__,		)      )
							snake_case							       = ia
						return segments

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






				def  _lowerCamelCase  (    self,		lowercase_,		lowercase_,		lowercase_      )  ->				int:



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

					# load text file
					snake_case							       = open(lowerCAmelCase__,		encoding='utf8'      ).readlines()

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

						snake_case							       = line.strip().split('\t'      )
						snake_case							       = os.path.join(lowerCAmelCase__,		wav_name + '.wav'      )
						snake_case							       = 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 			__magic_name__				(       )	->						int:
	snake_case							       = 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_6_0_0_0    ,      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'							)

	snake_case							       = parser.parse_args()

	snake_case							       = 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()












"""simple docstring"""




from __future__ import annotations





def        a_				(							_lowercase   ):
						assert (
						    isinstance(_lowercase       ,						_lowercase   ) and mask > 0
						), F"""mask needs to be positive integer, your input {mask}"""
						_UpperCamelCase			:					Union[str, Any]     =    []
						_UpperCamelCase			:					str     =    mask

						while submask:
												all_submasks.append(_lowercase   )
												_UpperCamelCase			:					int     =    (submask - 1) & mask

						return all_submasks


if __name__ == "__main__":
						import doctest

						doctest.testmod()



