



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,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      int:
						UpperCAmelCase_:		Tuple       			=					cuda
						UpperCAmelCase_:		Tuple       			=					AutoConfig.from_pretrained(lowerCamelCase__				)
						UpperCAmelCase_:		List[Any]       			=					AutoModelForCTC.from_pretrained(lowerCamelCase__				)
						self.model.eval()
						if self.cuda:
									self.model.to(device="""cuda"""				)
						UpperCAmelCase_:		List[str]       			=					AutoProcessor.from_pretrained(lowerCamelCase__				)
						UpperCAmelCase_:		str       			=					torchaudio.transforms.Resample(lowerCamelCase__,      16000				)
						UpperCAmelCase_:		Any       			=					0
						UpperCAmelCase_:		Optional[Any]       			=					list(self.processor.tokenizer.get_vocab().keys()				)
						for i in range(len(lowerCamelCase__				)				):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
												UpperCAmelCase_:		Tuple       			=					i
						print("""Blank Token id [PAD]/<pad>""",      lowerCamelCase__				)
						UpperCAmelCase_:		Union[str, Any]       			=					blank_id

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
						UpperCAmelCase_:		List[Any]       			=					torchaudio.load(lowerCamelCase__				)
						UpperCAmelCase_:		List[str]       			=					self.resampler(lowerCamelCase__				).squeeze().numpy()
						return speech

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      List[Any]:
						UpperCAmelCase_:		Optional[Any]       			=					self.blank_id
						UpperCAmelCase_:		int       			=					"|".join(item["""sent"""].split(""" """				)				)
						if not os.path.isfile(item["""wav_path"""]				):
									print(item["""wav_path"""],      """not found in wavs directory"""				)

						UpperCAmelCase_:		Optional[int]       			=					self.speech_file_to_array_fn(item["""wav_path"""]				)
						UpperCAmelCase_:		List[Any]       			=					self.processor(lowerCamelCase__,      sampling_rate=16000,      return_tensors="""pt""",      padding=lowerCamelCase__				)
						if self.cuda:
									UpperCAmelCase_:		Union[str, Any]       			=					inputs.to(device="""cuda"""				)

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

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

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

						UpperCAmelCase_:		Optional[Any]       			=					{c: i for i, c in enumerate(lowerCamelCase__				)}
						UpperCAmelCase_:		Dict       			=					[]
						for c in transcript:
									if c in dictionary:
												tokens.append(dictionary[c]				)

						def get_trellis(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=0				):
									UpperCAmelCase_:		Union[str, Any]       			=					emission.size(0				)
									UpperCAmelCase_:		Optional[Any]       			=					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.
									UpperCAmelCase_:		Tuple       			=					torch.full((num_frames + 1, num_tokens + 1),      -float("""inf"""				)				)
									UpperCAmelCase_:		int       			=					0
									for t in range(lowerCamelCase__				):
												UpperCAmelCase_:		Dict       			=					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_:		str       			=					get_trellis(lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__				)







						@dataclass
						class 							_a							:
									A					=  42
									A					=  42
									A					=  42

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

									UpperCAmelCase_:		List[Any]       			=					[]
									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.
												UpperCAmelCase_:		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_:		str       			=					trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

												# 2. Store the path with frame-wise probability.
												UpperCAmelCase_:		Optional[int]       			=					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]

						UpperCAmelCase_:		str       			=					backtrack(lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__				)







						@dataclass
						class 							_a							:
									A					=  42
									A					=  42
									A					=  42
									A					=  42

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

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

						def merge_repeats(SCREAMING_SNAKE_CASE_				):
									UpperCAmelCase_:		List[Any]       			=					0, 0
									UpperCAmelCase_:		Union[str, Any]       			=					[]
									while ia < len(lowerCamelCase__				):
												while ia < len(lowerCamelCase__				) and path[ia].token_index == path[ia].token_index:
															ia += 1
												UpperCAmelCase_:		Dict       			=					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__,      )				)
												UpperCAmelCase_:		Optional[int]       			=					ia
									return segments

						UpperCAmelCase_:		Dict       			=					merge_repeats(lowerCamelCase__				)
						with open(item["""out_path"""],      """w"""				) as out_align:
									for seg in segments:
												out_align.write(str(lowerCamelCase__				) + """\n"""				)

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Union[str, Any]:
						if not os.path.exists(lowerCamelCase__				):
									os.makedirs(lowerCamelCase__				)

						# load text file
						UpperCAmelCase_:		Dict       			=					open(lowerCamelCase__,      encoding="""utf8"""				).readlines()

						UpperCAmelCase_:		int       			=					[]
						for line in lines:
									if len(line.strip().split("""\t"""				)				) != 2:
												print("""Script must be in format: 00001  this is my sentence"""				)
												exit()

									UpperCAmelCase_:		int       			=					line.strip().split("""\t"""				)
									UpperCAmelCase_:		List[Any]       			=					os.path.join(lowerCamelCase__,      wav_name + """.wav"""				)
									UpperCAmelCase_:		List[str]       			=					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    lowerCAmelCase_  ():





			"""simple docstring"""


			UpperCAmelCase_:		List[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=1_6_0_0_0		,	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_:		str       			=					parser.parse_args()

			UpperCAmelCase_:		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 decimal







def    lowerCAmelCase_  ():





			"""simple docstring"""


			UpperCAmelCase_:		Union[str, Any]       			=    0
			UpperCAmelCase_:		Optional[Any]       			=    decimal.Context(prec=1_0_5     )
			for i in range(2		,	1_0_0     ):
						UpperCAmelCase_:		str       			=    decimal.Decimal(lowerCAmelCase__     )
						UpperCAmelCase_:		Any       			=    number.sqrt(lowerCAmelCase__     )
						if len(str(lowerCAmelCase__     )     ) > 1:
									answer += int(str(lowerCAmelCase__     )[0]     )
									UpperCAmelCase_:		str       			=    str(lowerCAmelCase__     )[2:1_0_1]
									answer += sum(int(lowerCAmelCase__     ) for x in sqrt_number_str     )
			return answer


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F'''{solution() = }''')






