



"""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    UpperCamelCase     :




	"""simple docstring"""






	def __init__(			self							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			_lowercase							:      Tuple			=					cuda
			_lowercase							:      Optional[Any]			=					AutoConfig.from_pretrained(UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					AutoModelForCTC.from_pretrained(UpperCAmelCase_					)
			self.model.eval()
			if self.cuda:
					self.model.to(device="""cuda"""					)
			_lowercase							:      Tuple			=					AutoProcessor.from_pretrained(UpperCAmelCase_					)
			_lowercase							:      str			=					torchaudio.transforms.Resample(UpperCAmelCase_							,1_60_00					)
			_lowercase							:      Union[str, Any]			=					0
			_lowercase							:      Union[str, Any]			=					list(self.processor.tokenizer.get_vocab().keys()					)
			for i in range(len(UpperCAmelCase_					)					):
					if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
							_lowercase							:      Optional[Any]			=					i
			print("""Blank Token id [PAD]/<pad>"""							,UpperCAmelCase_					)
			_lowercase							:      List[Any]			=					blank_id






	def 					lowerCamelCase__ (			self							,UpperCAmelCase_					):
			_lowercase			,   _lowercase							:      Union[str, Any]			=					torchaudio.load(UpperCAmelCase_					)
			_lowercase							:      int			=					self.resampler(UpperCAmelCase_					).squeeze().numpy()
			return speech






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

			_lowercase							:      int			=					self.speech_file_to_array_fn(item["""wav_path"""]					)
			_lowercase							:      Tuple			=					self.processor(UpperCAmelCase_							,sampling_rate=1_60_00							,return_tensors="""pt"""							,padding=UpperCAmelCase_					)
			if self.cuda:
					_lowercase							:      str			=					inputs.to(device="""cuda"""					)

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

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

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

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

			def get_trellis(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_=0					):
					_lowercase							:      Dict			=					emission.size(0					)
					_lowercase							:      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.
					_lowercase							:      Any			=					torch.full((num_frames + 1, num_tokens + 1)							,-float("""inf"""					)					)
					_lowercase							:      Optional[Any]			=					0
					for t in range(UpperCAmelCase_					):
							_lowercase							:      Optional[Any]			=					torch.maximum(
							    # Score for staying at the same token
							    trellis[t, 1:] + emission[t, blank_id]							,trellis[t, :-1] + emission[t, tokens]							,)
					return trellis

			_lowercase							:      int			=					get_trellis(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)





			@dataclass
			class    UpperCamelCase     :




				"""simple docstring"""
				SCREAMING_SNAKE_CASE_				:    int
				SCREAMING_SNAKE_CASE_				:    int
				SCREAMING_SNAKE_CASE_				:    float

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

					_lowercase							:      List[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.
							_lowercase							:      Tuple			=					trellis[t - 1, j] + emission[t - 1, blank_id]
							# Score for token changing from C-1 at T-1 to J at T.
							_lowercase							:      List[str]			=					trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

							# 2. Store the path with frame-wise probability.
							_lowercase							:      Union[str, 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]

			_lowercase							:      Optional[Any]			=					backtrack(UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					)





			@dataclass
			class    UpperCamelCase     :




				"""simple docstring"""
				SCREAMING_SNAKE_CASE_				:    str
				SCREAMING_SNAKE_CASE_				:    int
				SCREAMING_SNAKE_CASE_				:    int
				SCREAMING_SNAKE_CASE_				:    float






				def __repr__(			self					):
						return f"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""






				@property
				def 					lowerCamelCase__ (			self					):
						return self.end - self.start

			def merge_repeats(UpperCAmelCase_					):
					_lowercase			,   _lowercase							:      str			=					0, 0
					_lowercase							:      int			=					[]
					while ia < len(UpperCAmelCase_					):
							while ia < len(UpperCAmelCase_					) and path[ia].token_index == path[ia].token_index:
									ia += 1
							_lowercase							:      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_							,)					)
							_lowercase							:      Union[str, Any]			=					ia
					return segments

			_lowercase							:      List[Any]			=					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							,UpperCAmelCase_							,UpperCAmelCase_							,UpperCAmelCase_					):
			if not os.path.exists(UpperCAmelCase_					):
					os.makedirs(UpperCAmelCase_					)

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

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

					_lowercase			,   _lowercase							:      Any			=					line.strip().split("""\t"""					)
					_lowercase							:      Tuple			=					os.path.join(UpperCAmelCase_							,wav_name + """.wav"""					)
					_lowercase							:      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       __SCREAMING_SNAKE_CASE      (   ):
		_lowercase							:      str			=					argparse.ArgumentParser()

		parser.add_argument(
		    """--model_name"""					,							type=__UpperCAmelCase					,							default="""arijitx/wav2vec2-xls-r-300m-bengali"""					,							help="""wav2vec model name"""	)
		parser.add_argument("""--wav_dir"""					,							type=__UpperCAmelCase					,							default="""./wavs"""					,							help="""directory containing wavs"""	)
		parser.add_argument("""--text_file"""					,							type=__UpperCAmelCase					,							default="""script.txt"""					,							help="""file containing text"""	)
		parser.add_argument("""--input_wavs_sr"""					,							type=__UpperCAmelCase					,							default=16000					,							help="""sampling rate of input audios"""	)
		parser.add_argument(
		    """--output_dir"""					,							type=__UpperCAmelCase					,							default="""./out_alignment"""					,							help="""output directory containing the alignment files"""	)
		parser.add_argument("""--cuda"""					,							action="""store_true"""	)

		_lowercase							:      Optional[Any]			=					parser.parse_args()

		_lowercase							:      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()





def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				for _ in range(
				    0   ,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
								for j in range(0   ,			no_of_elements - 1  ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							lowerCamelCase_  :       Union[str, Any]					      =     [int(x) for x in input().split()]  # inputing elements of the list in one line
							lowerCamelCase_  :       Union[str, Any]					      =     double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)






