

'''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   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Optional[Any]        =       cuda
							UpperCAmelCase		:							Any        =       AutoConfig.from_pretrained(snake_case      )
							UpperCAmelCase		:							Any        =       AutoModelForCTC.from_pretrained(snake_case      )
							self.model.eval()
							if self.cuda:
												self.model.to(device="cuda"      )
							UpperCAmelCase		:							Dict        =       AutoProcessor.from_pretrained(snake_case      )
							UpperCAmelCase		:							Optional[int]        =       torchaudio.transforms.Resample(snake_case   ,      1_6_0_0_0      )
							UpperCAmelCase		:							int        =       0
							UpperCAmelCase		:							int        =       list(self.processor.tokenizer.get_vocab().keys()      )
							for i in range(len(snake_case      )      ):
												if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																	UpperCAmelCase		:							str        =       i
							print("Blank Token id [PAD]/<pad>"   ,      snake_case      )
							UpperCAmelCase		:							Dict        =       blank_id







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase ,			UpperCAmelCase		:							Optional[Any]        =       torchaudio.load(snake_case      )
							UpperCAmelCase		:							str        =       self.resampler(snake_case      ).squeeze().numpy()
							return speech







		def 				A_	(			self   ,      snake_case      ):


							'''simple docstring'''

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

							UpperCAmelCase		:							str        =       self.speech_file_to_array_fn(item["wav_path"]      )
							UpperCAmelCase		:							List[str]        =       self.processor(snake_case   ,      sampling_rate=1_6_0_0_0   ,      return_tensors="pt"   ,      padding=snake_case      )
							if self.cuda:
												UpperCAmelCase		:							Any        =       inputs.to(device="cuda"      )

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

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

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

							UpperCAmelCase		:							str        =       {c: i for i, c in enumerate(snake_case      )}
							UpperCAmelCase		:							Tuple        =       []
							for c in transcript:
												if c in dictionary:
																	tokens.append(dictionary[c]      )

							def get_trellis(snake_case   ,      snake_case   ,      snake_case=0      ):
												UpperCAmelCase		:							Any        =       emission.size(0      )
												UpperCAmelCase		:							str        =       len(snake_case      )

												# 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.
												UpperCAmelCase		:							Dict        =       torch.full((num_frames + 1, num_tokens + 1)   ,      -float("inf"      )      )
												UpperCAmelCase		:							Optional[int]        =       0
												for t in range(snake_case      ):
																	UpperCAmelCase		:							List[str]        =       torch.maximum(
																	    # Score for staying at the same token
																	    trellis[t, 1:] + emission[t, blank_id]   ,      trellis[t, :-1] + emission[t, tokens]   ,      )
												return trellis

							UpperCAmelCase		:							Optional[Any]        =       get_trellis(snake_case   ,      snake_case   ,      snake_case      )







							@dataclass
							class   UpperCamelCase__   :


									"""simple docstring"""

									SCREAMING_SNAKE_CASE__  :     int
									SCREAMING_SNAKE_CASE__  :     int
									SCREAMING_SNAKE_CASE__  :     float

							def backtrack(snake_case   ,      snake_case   ,      snake_case   ,      snake_case=0      ):
												UpperCAmelCase		:							Dict        =       trellis.size(1      ) - 1
												UpperCAmelCase		:							Tuple        =       torch.argmax(trellis[:, j]      ).item()

												UpperCAmelCase		:							Optional[int]        =       []
												for t in range(snake_case   ,      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.
																	UpperCAmelCase		:							str        =       trellis[t - 1, j] + emission[t - 1, blank_id]
																	# Score for token changing from C-1 at T-1 to J at T.
																	UpperCAmelCase		:							Union[str, Any]        =       trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																	# 2. Store the path with frame-wise probability.
																	UpperCAmelCase		:							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   ,      snake_case      )      )

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

							UpperCAmelCase		:							List[str]        =       backtrack(snake_case   ,      snake_case   ,      snake_case   ,      snake_case      )







							@dataclass
							class   UpperCamelCase__   :


									"""simple docstring"""

									SCREAMING_SNAKE_CASE__  :     str
									SCREAMING_SNAKE_CASE__  :     int
									SCREAMING_SNAKE_CASE__  :     int
									SCREAMING_SNAKE_CASE__  :     float







									def __repr__(			self      ):


														'''simple docstring'''

														return f"{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"







									@property
									def 				A_	(			self      ):


														'''simple docstring'''

														return self.end - self.start

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

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







		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

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

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

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

												UpperCAmelCase ,			UpperCAmelCase		:							Any        =       line.strip().split("\t"      )
												UpperCAmelCase		:							Optional[int]        =       os.path.join(snake_case   ,      wav_name + ".wav"      )
												UpperCAmelCase		:							Optional[Any]        =       os.path.join(snake_case   ,      wav_name + ".txt"      )

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

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





def 				lowercase (					):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple        =       argparse.ArgumentParser()

					parser.add_argument(
					    "--model_name"			,   type=__magic_name__			,   default="arijitx/wav2vec2-xls-r-300m-bengali"			,   help="wav2vec model name"							)
					parser.add_argument("--wav_dir"			,   type=__magic_name__			,   default="./wavs"			,   help="directory containing wavs"							)
					parser.add_argument("--text_file"			,   type=__magic_name__			,   default="script.txt"			,   help="file containing text"							)
					parser.add_argument("--input_wavs_sr"			,   type=__magic_name__			,   default=1_6000			,   help="sampling rate of input audios"							)
					parser.add_argument(
					    "--output_dir"			,   type=__magic_name__			,   default="./out_alignment"			,   help="output directory containing the alignment files"							)
					parser.add_argument("--cuda"			,   action="store_true"							)

					UpperCAmelCase		:							Optional[int]        =       parser.parse_args()

					UpperCAmelCase		:							Any        =       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'''

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any        =       filter(lambda __magic_name__							: p.requires_grad			,   model.parameters()							)
					UpperCAmelCase		:							Union[str, Any]        =       sum([np.prod(p.size()							) for p in model_parameters]							)
					return params


a       :      int  	=			logging.getLogger(__name__)







class   UpperCamelCase__   (       pl.Callback       ):


		"""simple docstring"""

		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							UpperCAmelCase		:							Union[str, Any]        =       {f"lr_group_{i}": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups      )}
							pl_module.logger.log_metrics(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case   ,      snake_case   ,      snake_case=True      ):


							'''simple docstring'''

							logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"      )
							UpperCAmelCase		:							Optional[Any]        =       trainer.callback_metrics
							trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["log", "progress_bar", "preds"]}      )
							# Log results
							UpperCAmelCase		:							List[str]        =       Path(pl_module.hparams.output_dir      )
							if type_path == "test":
												UpperCAmelCase		:							Dict        =       od / "test_results.txt"
												UpperCAmelCase		:							Dict        =       od / "test_generations.txt"
							else:
												# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
												# If people want this it will be easy enough to add back.
												UpperCAmelCase		:							Union[str, Any]        =       od / f"{type_path}_results/{trainer.global_step:05d}.txt"
												UpperCAmelCase		:							int        =       od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
												results_file.parent.mkdir(exist_ok=snake_case      )
												generations_file.parent.mkdir(exist_ok=snake_case      )
							with open(snake_case   ,      "a+"      ) as writer:
												for key in sorted(snake_case      ):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	UpperCAmelCase		:							Optional[Any]        =       metrics[key]
																	if isinstance(snake_case   ,      torch.Tensor      ):
																						UpperCAmelCase		:							Union[str, Any]        =       val.item()
																	UpperCAmelCase		:							List[str]        =       f"{key}: {val:.6f}\n"
																	writer.write(snake_case      )

							if not save_generations:
												return

							if "preds" in metrics:
												UpperCAmelCase		:							List[str]        =       "\n".join(metrics["preds"]      )
												generations_file.open("w+"      ).write(snake_case      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							try:
												UpperCAmelCase		:							Tuple        =       pl_module.model.model.num_parameters()
							except AttributeError:
												UpperCAmelCase		:							int        =       pl_module.model.num_parameters()

							UpperCAmelCase		:							List[Any]        =       count_trainable_parameters(snake_case      )
							# mp stands for million parameters
							trainer.logger.log_metrics({"n_params": npars, "mp": npars / 1e6, "grad_mp": n_trainable_pars / 1e6}      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							save_json(pl_module.metrics   ,      pl_module.metrics_save_path      )
							return self._write_logs(snake_case   ,      snake_case   ,      "test"      )







		@rank_zero_only
		def 				A_	(			self   ,      snake_case   ,      snake_case      ):


							'''simple docstring'''

							save_json(pl_module.metrics   ,      pl_module.metrics_save_path      )
							# Uncommenting this will save val generations
							# return self._write_logs(trainer, pl_module, "valid")





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__=1			,   __magic_name__=False							):



					'''simple docstring'''







					if metric == "rouge2":
										UpperCAmelCase		:							Dict        =       "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										UpperCAmelCase		:							int        =       "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										UpperCAmelCase		:							Tuple        =       "{val_avg_loss:.4f}-{step_count}"
					else:
										raise NotImplementedError(
										    F"seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to"
										    " this function."							)

					UpperCAmelCase		:							int        =       ModelCheckpoint(
					    dirpath=__magic_name__			,   filename=__magic_name__			,   monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   save_top_k=__magic_name__			,   )
					return checkpoint_callback





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return EarlyStopping(
					    monitor=F"val_{metric}"			,   mode="min" if "loss" in metric else "max"			,   patience=__magic_name__			,   verbose=__magic_name__			,   )
