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      :    Dict		,							_UpperCamelCase      :    Optional[Any]		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[Any]					)    -> Union[str, Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    List[str]					)    -> List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				torchaudio.load(__a					)
												SCREAMING_SNAKE_CASE				=				self.resampler(__a					).squeeze().numpy()
												return speech




							def 		__snake_case(  self      :    Tuple		,							_UpperCamelCase      :    Optional[int]					)    -> Union[str, 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(__a		,							sampling_rate=16_000		,							return_tensors="pt"		,							padding=__a					)
												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(__a		,							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(__a					)}
												SCREAMING_SNAKE_CASE				=				[]
												for c in transcript:
																	if c in dictionary:
																						tokens.append(dictionary[c]					)

												def get_trellis(_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Optional[Any]=0					):
																	SCREAMING_SNAKE_CASE				=				emission.size(0					)
																	SCREAMING_SNAKE_CASE				=				len(__a					)

																	# 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(__a					):
																						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(__a		,							__a		,							__a					)


												@dataclass
												class   lowercase			:
																			lowercase__  :				List[Any]									=							42
																			lowercase__  :				int									=							42
																			lowercase__  :				int									=							42

												def backtrack(_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Union[str, Any]		,							_UpperCamelCase      :    List[str]		,							_UpperCamelCase      :    Dict=0					):
																	SCREAMING_SNAKE_CASE				=				trellis.size(1					) - 1
																	SCREAMING_SNAKE_CASE				=				torch.argmax(trellis[:, j]					).item()

																	SCREAMING_SNAKE_CASE				=				[]
																	for t in range(__a		,							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		,							__a					)					)

																						# 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(__a		,							__a		,							__a		,							__a					)


												@dataclass
												class   lowercase			:
																			lowercase__  :				str									=							42
																			lowercase__  :				Tuple									=							42
																			lowercase__  :				str									=							42
																			lowercase__  :				Optional[Any]									=							42




																			def __repr__(  self      :    Dict					)    -> List[Any]:



																								'''simple docstring'''

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





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



																								'''simple docstring'''

																								return self.end - self.start

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

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





							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    Any		,							_UpperCamelCase      :    Union[str, Any]		,							_UpperCamelCase      :    Dict					)    -> Optional[Any]:



												'''simple docstring'''

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

												# load text file
												SCREAMING_SNAKE_CASE				=				open(__a		,							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(__a		,							wav_name + ".wav"					)
																	SCREAMING_SNAKE_CASE				=				os.path.join(__a		,							wav_name + ".txt"					)

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

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





def 		__lowerCamelCase							():
					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_6_0_0_0					,       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()







import os
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				os.path.dirname(os.path.realpath(snake_case__   )   )
 UpperCAmelCase__							=				os.path.join(snake_case__  ,    'triangle.txt'   )

 with open(snake_case__   ) as in_file:
  UpperCAmelCase__							=				[[int(snake_case__   ) for i in line.split()] for line in in_file]

 while len(snake_case__   ) != 1:
  UpperCAmelCase__							=				triangle.pop()
  UpperCAmelCase__							=				triangle[-1]
  for j in range(len(snake_case__   ) - 1   ):
   curr_row[j] += max(last_row[j]  ,    last_row[j + 1]   )
 return triangle[0][0]


if __name__ == "__main__":
 print(solution())



