





import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor




class   _UpperCAmelCase	:




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




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  List[str]:
							UpperCamelCase__  ,			UpperCamelCase__         =	torchaudio.load(__lowerCAmelCase       )
							UpperCamelCase__         =	self.resampler(__lowerCAmelCase       ).squeeze().numpy()
							return speech




					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  List[str]:
							UpperCamelCase__         =	self.blank_id
							UpperCamelCase__         =	"""|""".join(item["""sent"""].split(""" """       )       )
							if not os.path.isfile(item["""wav_path"""]       ):
									print(item["""wav_path"""]    ,      """not found in wavs directory"""       )

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

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

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

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

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

							def get_trellis(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase=0       ):
									UpperCamelCase__         =	emission.size(0       )
									UpperCamelCase__         =	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__         =	torch.full((num_frames + 1, num_tokens + 1)    ,      -float("""inf"""       )       )
									UpperCamelCase__         =	0
									for t in range(__lowerCAmelCase       ):
											UpperCamelCase__         =	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__         =	get_trellis(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )




							@dataclass
							class   _UpperCAmelCase	:
												__A          =   42
												__A          =   42
												__A          =   42

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

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

											# 2. Store the path with frame-wise probability.
											UpperCamelCase__         =	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__         =	backtrack(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )




							@dataclass
							class   _UpperCAmelCase	:
												__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       )						->  str:
														return self.end - self.start

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

							UpperCamelCase__         =	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    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Any:
							if not os.path.exists(__lowerCAmelCase       ):
									os.makedirs(__lowerCAmelCase       )

							# load text file
							UpperCamelCase__         =	open(__lowerCAmelCase    ,      encoding="""utf8"""       ).readlines()

							UpperCamelCase__         =	[]
							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__         =	line.strip().split("""\t"""       )
									UpperCamelCase__         =	os.path.join(__lowerCAmelCase    ,      wav_name + """.wav"""       )
									UpperCamelCase__         =	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     a						(      )     ->Union[str, Any]:
		UpperCamelCase__         =	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"""       )

		UpperCamelCase__         =	parser.parse_args()

		UpperCamelCase__         =	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()












__lowerCAmelCase     =			{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 120,
    "6": 720,
    "7": 5_040,
    "8": 40_320,
    "9": 362_880,
}

__lowerCAmelCase     =			{145: 145}

__lowerCAmelCase     =			{
    145: 0,
    169: 3,
    36_301: 3,
    1_454: 3,
    871: 2,
    45_361: 2,
    872: 2,
}

def     a						(      _lowercase   :     int       )     ->int:
		if n in CACHE_SUM_DIGIT_FACTORIALS:
				return CACHE_SUM_DIGIT_FACTORIALS[n]
		UpperCamelCase__         =	sum(DIGIT_FACTORIALS[let] for let in str(_lowercase       )       )
		UpperCamelCase__         =	ret
		return ret

def     a						(      _lowercase   :     int ,						_lowercase   :     set | None = None       )     ->int:
		UpperCamelCase__         =	previous or set()
		if n in CHAIN_LENGTH_CACHE:
				return CHAIN_LENGTH_CACHE[n]
		UpperCamelCase__         =	sum_digit_factorials(_lowercase       )
		if next_number in previous:
				UpperCamelCase__         =	0
				return 0
		else:
				previous.add(_lowercase       )
				UpperCamelCase__         =	1 + chain_length(_lowercase ,						_lowercase       )
				UpperCamelCase__         =	ret
				return ret

def     a						(      _lowercase   :     int = 6_0 ,						_lowercase   :     int = 1_0_0_0_0_0_0       )     ->int:
		return sum(1 for i in range(1 ,						_lowercase       ) if chain_length(_lowercase       ) == num_terms       )


if __name__ == "__main__":
				print(f"""{solution() = }""")





