
from __future__ import annotations


def       a			(   a		,	a		,	a  )    ->int:




			'''simple docstring'''


			assert n > 1 and a > 0 and greatest_common_divisor(a		,	a  ) == 1
			(SCREAMING_SNAKE_CASE)    	=   extended_gcd(a		,	a  )  # Implemented below
			SCREAMING_SNAKE_CASE    	=   (b * s) % n
			return x


def       a			(   a		,	a  )    ->int:




			'''simple docstring'''


			(SCREAMING_SNAKE_CASE)    	=   extended_euclid(a		,	a  )  # Implemented below
			if b < 0:
						SCREAMING_SNAKE_CASE    	=   (b % n + n) % n
			return b


def       a			(   a		,	a		,	a  )    ->int:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   invert_modulo(a		,	a  )
			SCREAMING_SNAKE_CASE    	=   (b * s) % n
			return x


def       a			(   a		,	a  )    ->tuple[int, int, int]:




			'''simple docstring'''


			assert a >= 0 and b >= 0

			if b == 0:
						SCREAMING_SNAKE_CASE    	=   a, 1, 0
			else:
						(SCREAMING_SNAKE_CASE)    	=   extended_gcd(a		,	a % b  )
						SCREAMING_SNAKE_CASE    	=   q
						SCREAMING_SNAKE_CASE    	=   p - q * (a // b)

			assert a % d == 0 and b % d == 0
			assert d == a * x + b * y

			return (d, x, y)


def       a			(   a		,	a  )    ->tuple[int, int]:




			'''simple docstring'''


			if b == 0:
						return (1, 0)
			(SCREAMING_SNAKE_CASE)    	=   extended_euclid(a		,	a % b  )
			SCREAMING_SNAKE_CASE    	=   a // b
			return (y, x - k * y)


def       a			(   a		,	a  )    ->int:




			'''simple docstring'''


			if a < b:
						SCREAMING_SNAKE_CASE    	=   b, a

			while a % b != 0:
						SCREAMING_SNAKE_CASE    	=   b, a % b

			return b


if __name__ == "__main__":
							from doctest import testmod

							testmod(name='modular_division', verbose=True)
							testmod(name='modular_division2', verbose=True)
							testmod(name='invert_modulo', verbose=True)
							testmod(name='extended_gcd', verbose=True)
							testmod(name='extended_euclid', verbose=True)
							testmod(name='greatest_common_divisor', verbose=True)

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			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






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







	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
				return speech







	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:







				"""simple docstring"""






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

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

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

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

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

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

				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
							SCREAMING_SNAKE_CASE    	=   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.
							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
							SCREAMING_SNAKE_CASE    	=   0
							for t in range(lowercase ):
										SCREAMING_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

				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float

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

							SCREAMING_SNAKE_CASE    	=   []
							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.
										SCREAMING_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.
										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										SCREAMING_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      ,    lowercase ) )

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

				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				str
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float







					def __repr__(    self			:Optional[Any] )		->			str:







								"""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    snake_case__    (    self			:List[str] )		->			Any:







								"""simple docstring"""






								return self.end - self.start

				def merge_repeats(lowercase			:List[str] ):
							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   0, 0
							SCREAMING_SNAKE_CASE    	=   []
							while ia < len(lowercase ):
										while ia < len(lowercase ) and path[ia].token_index == path[ia].token_index:
													ia += 1
										SCREAMING_SNAKE_CASE    	=   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      ,    ) )
										SCREAMING_SNAKE_CASE    	=   ia
							return segments

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







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:







				"""simple docstring"""






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

				# load text file
				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()

				SCREAMING_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()

							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
							SCREAMING_SNAKE_CASE    	=   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			(   )    ->Optional[int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name'''		,	type=a		,	default='''arijitx/wav2vec2-xls-r-300m-bengali'''		,	help='''wav2vec model name'''  )
			parser.add_argument('''--wav_dir'''		,	type=a		,	default='''./wavs'''		,	help='''directory containing wavs'''  )
			parser.add_argument('''--text_file'''		,	type=a		,	default='''script.txt'''		,	help='''file containing text'''  )
			parser.add_argument('''--input_wavs_sr'''		,	type=a		,	default=1_6000		,	help='''sampling rate of input audios'''  )
			parser.add_argument(
			    '''--output_dir'''		,	type=a		,	default='''./out_alignment'''		,	help='''output directory containing the alignment files'''  )
			parser.add_argument('''--cuda'''		,	action='''store_true'''  )

			SCREAMING_SNAKE_CASE    	=   parser.parse_args()

			SCREAMING_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()