






'''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 _lowercase      :




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							Any     ,				UpperCamelCase__:							Any     ,				UpperCamelCase__:							int	):
														lowerCamelCase__   :      Dict			    =			cuda
														lowerCamelCase__   :      int			    =			AutoConfig.from_pretrained(UpperCamelCase__	)
														lowerCamelCase__   :      str			    =			AutoModelForCTC.from_pretrained(UpperCamelCase__	)
														self.model.eval()
														if self.cuda:
																					self.model.to(device="""cuda"""	)
														lowerCamelCase__   :      Any			    =			AutoProcessor.from_pretrained(UpperCamelCase__	)
														lowerCamelCase__   :      Any			    =			torchaudio.transforms.Resample(UpperCamelCase__     ,				16_000	)
														lowerCamelCase__   :      Tuple			    =			0
														lowerCamelCase__   :      str			    =			list(self.processor.tokenizer.get_vocab().keys()	)
														for i in range(len(UpperCamelCase__	)	):
																					if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																												lowerCamelCase__   :      str			    =			i
														print("""Blank Token id [PAD]/<pad>"""     ,				UpperCamelCase__	)
														lowerCamelCase__   :      Union[str, Any]			    =			blank_id




							def 						lowerCamelCase_							(				self:							int     ,				UpperCamelCase__:							int	):
														lowerCamelCase__    ,						lowerCamelCase__   :      Optional[int]			    =			torchaudio.load(UpperCamelCase__	)
														lowerCamelCase__   :      List[Any]			    =			self.resampler(UpperCamelCase__	).squeeze().numpy()
														return speech




							def 						lowerCamelCase_							(				self:							Optional[int]     ,				UpperCamelCase__:							List[Any]	):
														lowerCamelCase__   :      str			    =			self.blank_id
														lowerCamelCase__   :      List[Any]			    =			"""|""".join(item["""sent"""].split(""" """	)	)
														if not os.path.isfile(item["""wav_path"""]	):
																					print(item["""wav_path"""]     ,				"""not found in wavs directory"""	)

														lowerCamelCase__   :      Union[str, Any]			    =			self.speech_file_to_array_fn(item["""wav_path"""]	)
														lowerCamelCase__   :      Any			    =			self.processor(UpperCamelCase__     ,				sampling_rate=16_000     ,				return_tensors="""pt"""     ,				padding=UpperCamelCase__	)
														if self.cuda:
																					lowerCamelCase__   :      int			    =			inputs.to(device="""cuda"""	)

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

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

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

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

														def get_trellis(UpperCamelCase__:							Any     ,				UpperCamelCase__:							Dict     ,				UpperCamelCase__:							str=0	):
																					lowerCamelCase__   :      Optional[Any]			    =			emission.size(0	)
																					lowerCamelCase__   :      Any			    =			len(UpperCamelCase__	)

																					# 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__   :      Optional[Any]			    =			torch.full((num_frames + 1, num_tokens + 1)     ,				-float("""inf"""	)	)
																					lowerCamelCase__   :      int			    =			0
																					for t in range(UpperCamelCase__	):
																												lowerCamelCase__   :      int			    =			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__   :      List[str]			    =			get_trellis(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)

														@dataclass
														class _lowercase      :
																					a							 =			42
																					a							 =			42
																					a							 =			42

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

																					lowerCamelCase__   :      Any			    =			[]
																					for t in range(UpperCamelCase__     ,				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__   :      int			    =			trellis[t - 1, j] + emission[t - 1, blank_id]
																												# Score for token changing from C-1 at T-1 to J at T.
																												lowerCamelCase__   :      Tuple			    =			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     ,				UpperCamelCase__	)	)

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

														lowerCamelCase__   :      Union[str, Any]			    =			backtrack(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__	)

														@dataclass
														class _lowercase      :
																					a							 =			42
																					a							 =			42
																					a							 =			42
																					a							 =			42




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




																					@property
																					def 						lowerCamelCase_							(				self:							List[str]	):
																												return self.end - self.start

														def merge_repeats(UpperCamelCase__:							str	):
																					lowerCamelCase__    ,						lowerCamelCase__   :      Optional[Any]			    =			0, 0
																					lowerCamelCase__   :      List[str]			    =			[]
																					while ia < len(UpperCamelCase__	):
																												while ia < len(UpperCamelCase__	) and path[ia].token_index == path[ia].token_index:
																																			ia += 1
																												lowerCamelCase__   :      Dict			    =			sum(path[k].score for k in range(UpperCamelCase__     ,				UpperCamelCase__	)	) / (ia - ia)
																												segments.append(
																												    Segment(
																												        transcript[path[ia].token_index]     ,				path[ia].time_index     ,				path[ia - 1].time_index + 1     ,				UpperCamelCase__     ,				)	)
																												lowerCamelCase__   :      int			    =			ia
																					return segments

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




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							str     ,				UpperCamelCase__:							List[Any]     ,				UpperCamelCase__:							str	):
														if not os.path.exists(UpperCamelCase__	):
																					os.makedirs(UpperCamelCase__	)

														# load text file
														lowerCamelCase__   :      Union[str, Any]			    =			open(UpperCamelCase__     ,				encoding="""utf8"""	).readlines()

														lowerCamelCase__   :      List[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__   :      Union[str, Any]			    =			line.strip().split("""\t"""	)
																					lowerCamelCase__   :      Dict			    =			os.path.join(UpperCamelCase__     ,				wav_name + """.wav"""	)
																					lowerCamelCase__   :      str			    =			os.path.join(UpperCamelCase__     ,				wav_name + """.txt"""	)

																					items.append({"""sent""": sentence, """wav_path""": wav_path, """out_path""": out_path}	)
														print("""Number of samples found in script file"""     ,				len(UpperCamelCase__	)	)

														for item in tqdm(UpperCamelCase__	):
																					self.align_single_sample(UpperCamelCase__	)





def 					SCREAMING_SNAKE_CASE_			() ->			List[Any]:
							lowerCamelCase__   :      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=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""" )

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

							lowerCamelCase__   :      Tuple			    =			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 __future__ import annotations

_lowerCamelCase							={1: 1}







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     if n in COLLATZ_SEQUENCE_LENGTHS:
          return COLLATZ_SEQUENCE_LENGTHS[n]
     SCREAMING_SNAKE_CASE 		=n // 2 if n % 2 == 0 else 3 * n + 1
     SCREAMING_SNAKE_CASE 		=collatz_sequence_length(lowerCAmelCase_     ) + 1
     SCREAMING_SNAKE_CASE 		=sequence_length
     return sequence_length







def      snake_case__							(  lowerCAmelCase_ = 1000000     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=max((collatz_sequence_length(lowerCAmelCase_     ), i) for i in range(1,       lowerCAmelCase_     )     )
     return result[1]


if __name__ == "__main__":
 print(solution(int(input().strip())))

