import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor





class 			__snake_case  :




					def __init__(       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  str:



									'''simple docstring'''







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




					def       __a      (       self   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     int 							=				torchaudio.load(__UpperCamelCase		)
									snake_case__ :     Dict 							=				self.resampler(__UpperCamelCase		).squeeze().numpy()
									return speech




					def       __a      (       self   ,	__UpperCamelCase		)					->  int:



									'''simple docstring'''







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

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

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

									# get the emission probability at frame level
									snake_case__ :     List[Any] 							=				torch.log_softmax(__UpperCamelCase   ,	dim=-1		)
									snake_case__ :     List[Any] 							=				emissions[0].cpu().detach()

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

									snake_case__ :     List[Any] 							=				{c: i for i, c in enumerate(__UpperCamelCase		)}
									snake_case__ :     str 							=				[]
									for c in transcript:
													if c in dictionary:
																	tokens.append(dictionary[c]		)

									def get_trellis(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase=0		):
													snake_case__ :     Tuple 							=				emission.size(0		)
													snake_case__ :     Optional[int] 							=				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.
													snake_case__ :     Tuple 							=				torch.full((num_frames + 1, num_tokens + 1)   ,	-float('inf'		)		)
													snake_case__ :     Optional[Any] 							=				0
													for t in range(__UpperCamelCase		):
																	snake_case__ :     Tuple 							=				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__ :     List[Any] 							=				get_trellis(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)





									@dataclass
									class 			__snake_case  :
														__lowerCamelCase	 =       42
														__lowerCamelCase	 =       42
														__lowerCamelCase	 =       42

									def backtrack(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase=0		):
													snake_case__ :     Any 							=				trellis.size(1		) - 1
													snake_case__ :     Dict 							=				torch.argmax(trellis[:, j]		).item()

													snake_case__ :     List[str] 							=				[]
													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.
																	snake_case__ :     List[Any] 							=				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__ :     Dict 							=				trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																	# 2. Store the path with frame-wise probability.
																	snake_case__ :     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   ,	__UpperCamelCase		)		)

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

									snake_case__ :     Any 							=				backtrack(__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)





									@dataclass
									class 			__snake_case  :
														__lowerCamelCase	 =       42
														__lowerCamelCase	 =       42
														__lowerCamelCase	 =       42
														__lowerCamelCase	 =       42




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



																		'''simple docstring'''







																		return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""




														@property
														def       __a      (       self		)					->  Optional[Any]:



																		'''simple docstring'''







																		return self.end - self.start

									def merge_repeats(__UpperCamelCase		):
													snake_case__							,  snake_case__ :     Union[str, Any] 							=				0, 0
													snake_case__ :     List[Any] 							=				[]
													while ia < len(__UpperCamelCase		):
																	while ia < len(__UpperCamelCase		) and path[ia].token_index == path[ia].token_index:
																					ia += 1
																	snake_case__ :     Union[str, Any] 							=				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   ,	)		)
																	snake_case__ :     Optional[Any] 							=				ia
													return segments

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




					def       __a      (       self   ,	__UpperCamelCase   ,	__UpperCamelCase   ,	__UpperCamelCase		)					->  Tuple:



									'''simple docstring'''







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

									# load text file
									snake_case__ :     List[str] 							=				open(__UpperCamelCase   ,	encoding='utf8'		).readlines()

									snake_case__ :     Union[str, Any] 							=				[]
									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__							,  snake_case__ :     Union[str, Any] 							=				line.strip().split('\t'		)
													snake_case__ :     List[Any] 							=				os.path.join(__UpperCamelCase   ,	wav_name + '.wav'		)
													snake_case__ :     int 							=				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 			UpperCamelCase__						(    )       -> Optional[Any]:
				snake_case__ :     List[str] 							=				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'   )

				snake_case__ :     Tuple 							=				parser.parse_args()

				snake_case__ :     Dict 							=				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()



def 			UpperCamelCase__						(    A__   )       -> int:
				if number != int(A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if number < 0:
								raise ValueError('factorial() not defined for negative values'   )
				snake_case__ :     Optional[Any] 							=				1
				for i in range(1      ,			number + 1   ):
								value *= i
				return value
def 			UpperCamelCase__						(    A__   )       -> int:
				if not isinstance(A__      ,			A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if n < 0:
								raise ValueError('factorial() not defined for negative values'   )
				return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__					:       Any		=       int(input('''Enter a positive integer: ''').strip() or 0)
				print(F'''factorial{n} is {factorial(n)}''')


