
'''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       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								_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      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			torchaudio.load(UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			self.resampler(UpperCamelCase   ).squeeze().numpy()
								return speech




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_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       ,    UpperCamelCase       ,    UpperCamelCase=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       lowerCAmelCase			:
										a				:       int
										a				:       int
										a				:       float

								def backtrack(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=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       lowerCAmelCase			:
										a				:       str
										a				:       int
										a				:       int
										a				:       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      lowercase							(					self   ):
																return self.end - self.start

								def merge_repeats(UpperCamelCase   ):
														_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      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   ):
								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       _a		(						):
						_SCREAMING_SNAKE_CASE					=			argparse.ArgumentParser()

						parser.add_argument(
						    "--model_name"						,      type=_SCREAMING_SNAKE_CASE						,      default="arijitx/wav2vec2-xls-r-300m-bengali"						,      help="wav2vec model name"	)
						parser.add_argument("--wav_dir"						,      type=_SCREAMING_SNAKE_CASE						,      default="./wavs"						,      help="directory containing wavs"	)
						parser.add_argument("--text_file"						,      type=_SCREAMING_SNAKE_CASE						,      default="script.txt"						,      help="file containing text"	)
						parser.add_argument("--input_wavs_sr"						,      type=_SCREAMING_SNAKE_CASE						,      default=1_6000						,      help="sampling rate of input audios"	)
						parser.add_argument(
						    "--output_dir"						,      type=_SCREAMING_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()

'''simple docstring'''







class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase=None   ):
								_SCREAMING_SNAKE_CASE					=			data
								_SCREAMING_SNAKE_CASE					=			None




		def __repr__(					self   ):
								_SCREAMING_SNAKE_CASE					=			""
								_SCREAMING_SNAKE_CASE					=			self
								while temp:
														string_rep += F'<{temp.data}> ---> '
														_SCREAMING_SNAKE_CASE					=			temp.next
								string_rep += "<END>"
								return string_rep



def       _a		(						_SCREAMING_SNAKE_CASE		: Any	):

						# if elements_list is empty
						if not elements_list:
												raise Exception("The Elements List is empty"	)

						# Set first element as Head
						_SCREAMING_SNAKE_CASE					=			Node(elements_list[0]	)
						_SCREAMING_SNAKE_CASE					=			head
						# Loop through elements from position 1
						for data in elements_list[1:]:
												_SCREAMING_SNAKE_CASE					=			Node(_SCREAMING_SNAKE_CASE	)
												_SCREAMING_SNAKE_CASE					=			current.next
						return head


_snake_case					: List[Any]     			=					[1, 3, 5, 32, 44, 12, 43]
print(F"List: {list_data}")
print("""Creating Linked List from List.""")
_snake_case					: List[Any]     			=					make_linked_list(list_data)
print("""Linked List:""")




print(linked_list)