






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:    List[Any] ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    Any ,      UpperCAmelCase_:    int	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    torchaudio.load(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    self.resampler(UpperCAmelCase_	).squeeze().numpy()
									return speech







			def 				UpperCamelCase (      self:    List[Any] ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    self.blank_id
									_SCREAMING_SNAKE_CASE       =    """|""".join(item["""sent"""].split(""" """	)	)
									if not os.path.isfile(item["""wav_path"""]	):
															print(item["""wav_path"""] ,      """not found in wavs directory"""	)

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

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

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

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

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

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

									_SCREAMING_SNAKE_CASE       =    get_trellis(UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_	)



									@dataclass
									class  __UpperCAmelCase						:
												__snake_case							: int
												__snake_case							: int
												__snake_case							: float

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

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

																					# 2. Store the path with frame-wise probability.
																					_SCREAMING_SNAKE_CASE       =    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]

									_SCREAMING_SNAKE_CASE       =    backtrack(UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_ ,      UpperCAmelCase_	)



									@dataclass
									class  __UpperCAmelCase						:
												__snake_case							: str
												__snake_case							: int
												__snake_case							: int
												__snake_case							: float







												def __repr__(      self:    Tuple	):







																		'''simple docstring'''




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







												@property
												def 				UpperCamelCase (      self:    List[Any]	):







																		'''simple docstring'''




																		return self.end - self.start

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

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







			def 				UpperCamelCase (      self:    Optional[Any] ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    Any	):







									'''simple docstring'''




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

									# load text file
									_SCREAMING_SNAKE_CASE       =    open(UpperCAmelCase_ ,      encoding="""utf8"""	).readlines()

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

															_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    line.strip().split("""\t"""	)
															_SCREAMING_SNAKE_CASE       =    os.path.join(UpperCAmelCase_ ,      wav_name + """.wav"""	)
															_SCREAMING_SNAKE_CASE       =    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 			__lowerCamelCase     (						)				->    Union[str, Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    argparse.ArgumentParser()

						parser.add_argument(
						    """--model_name"""			,type=snake_case__			,default="""arijitx/wav2vec2-xls-r-300m-bengali"""			,help="""wav2vec model name"""						)
						parser.add_argument("""--wav_dir"""			,type=snake_case__			,default="""./wavs"""			,help="""directory containing wavs"""						)
						parser.add_argument("""--text_file"""			,type=snake_case__			,default="""script.txt"""			,help="""file containing text"""						)
						parser.add_argument("""--input_wavs_sr"""			,type=snake_case__			,default=1_60_00			,help="""sampling rate of input audios"""						)
						parser.add_argument(
						    """--output_dir"""			,type=snake_case__			,default="""./out_alignment"""			,help="""output directory containing the alignment files"""						)
						parser.add_argument("""--cuda"""			,action="""store_true"""						)

						_SCREAMING_SNAKE_CASE       =    parser.parse_args()

						_SCREAMING_SNAKE_CASE       =    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

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    len(snake_case__						) - 1
						while left <= right:
												_SCREAMING_SNAKE_CASE       =    (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								_SCREAMING_SNAKE_CASE       =    middle + 1
																		break
												elif sorted_list[middle] < item:
																		_SCREAMING_SNAKE_CASE       =    middle + 1
												else:
																		_SCREAMING_SNAKE_CASE       =    middle - 1
						sorted_list.insert(snake_case__			,snake_case__						)
						return sorted_list

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0						)						)
												else:
																		result.append(right.pop(0						)						)
						return result + left + right

def 			__lowerCamelCase     (						snake_case__						)				->    List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    len(snake_case__						)
						if length <= 1:
												return list_ad
						_SCREAMING_SNAKE_CASE       =    length // 2
						return merge(sortlist_ad(list_ad[:middle]						)			,sortlist_ad(list_ad[middle:]						)						)

def 			__lowerCamelCase     (						snake_case__						)				->    list[int]:






						"""simple docstring"""





						if len(snake_case__						) <= 1:
												return collection
						_SCREAMING_SNAKE_CASE       =    []
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(0			,len(snake_case__						)			,2						):
												if i == len(snake_case__						) - 1:
																		_SCREAMING_SNAKE_CASE       =    True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]						)
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]						)
						_SCREAMING_SNAKE_CASE       =    sortlist_ad(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]						)
						if has_last_odd_item:
												_SCREAMING_SNAKE_CASE       =    collection[-1]
												_SCREAMING_SNAKE_CASE       =    binary_search_insertion(snake_case__			,snake_case__						)
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(len(snake_case__						) - 1						):
												if result[i] == collection[-1] and has_last_odd_item:
																		_SCREAMING_SNAKE_CASE       =    True
												_SCREAMING_SNAKE_CASE       =    sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 2] + binary_search_insertion(result[i + 2 :]			,snake_case__						)
												else:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 1] + binary_search_insertion(result[i + 1 :]			,snake_case__						)

						return result


if __name__ == "__main__":
		UpperCamelCase												=      input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase												=      [int(item) for item in user_input.split(''',''')]
		print(merge_insertion_sort(unsorted))

