

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor






class 			__magic_name__					:





			def __init__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						__a						   =cuda
						__a						   =AutoConfig.from_pretrained(__snake_case      )
						__a						   =AutoModelForCTC.from_pretrained(__snake_case      )
						self.model.eval()
						if self.cuda:
									self.model.to(device='cuda'      )
						__a						   =AutoProcessor.from_pretrained(__snake_case      )
						__a						   =torchaudio.transforms.Resample(__snake_case      ,						1_6000      )
						__a						   =0
						__a						   =list(self.processor.tokenizer.get_vocab().keys()      )
						for i in range(len(__snake_case      )      ):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
												__a						   =i
						print('Blank Token id [PAD]/<pad>'      ,						__snake_case      )
						__a						   =blank_id





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						__a   ,					__a						   =torchaudio.load(__snake_case      )
						__a						   =self.resampler(__snake_case      ).squeeze().numpy()
						return speech





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




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

						__a						   =self.speech_file_to_array_fn(item['wav_path']      )
						__a						   =self.processor(__snake_case      ,						sampling_rate=1_6000      ,						return_tensors='pt'      ,						padding=__snake_case      )
						if self.cuda:
									__a						   =inputs.to(device='cuda'      )

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

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

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

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

						def get_trellis(__snake_case      ,						__snake_case      ,						__snake_case=0      ):
									__a						   =emission.size(0      )
									__a						   =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.
									__a						   =torch.full((num_frames + 1, num_tokens + 1)      ,						-float('inf'      )      )
									__a						   =0
									for t in range(__snake_case      ):
												__a						   =torch.maximum(
												    # Score for staying at the same token
												    trellis[t, 1:] + emission[t, blank_id]      ,						trellis[t, :-1] + emission[t, tokens]      ,						)
									return trellis

						__a						   =get_trellis(__snake_case      ,						__snake_case      ,						__snake_case      )






						@dataclass
						class 			__magic_name__					:
									SCREAMING_SNAKE_CASE							     =		42
									SCREAMING_SNAKE_CASE							     =		42
									SCREAMING_SNAKE_CASE							     =		42

						def backtrack(__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case=0      ):
									__a						   =trellis.size(1      ) - 1
									__a						   =torch.argmax(trellis[:, j]      ).item()

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

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

						__a						   =backtrack(__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      )






						@dataclass
						class 			__magic_name__					:
									SCREAMING_SNAKE_CASE							     =		42
									SCREAMING_SNAKE_CASE							     =		42
									SCREAMING_SNAKE_CASE							     =		42
									SCREAMING_SNAKE_CASE							     =		42





									def __repr__(	self      )     ->				Any:




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





									@property
									def 	__magic_name__					(	self      )     ->				int:




												'''simple docstring'''
												return self.end - self.start

						def merge_repeats(__snake_case      ):
									__a   ,					__a						   =0, 0
									__a						   =[]
									while ia < len(__snake_case      ):
												while ia < len(__snake_case      ) and path[ia].token_index == path[ia].token_index:
															ia += 1
												__a						   =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      ,						)      )
												__a						   =ia
									return segments

						__a						   =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 	__magic_name__					(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						if not os.path.exists(__snake_case      ):
									os.makedirs(__snake_case      )

						# load text file
						__a						   =open(__snake_case      ,						encoding='utf8'      ).readlines()

						__a						   =[]
						for line in lines:
									if len(line.strip().split('\t'      )      ) != 2:
												print('Script must be in format: 00001  this is my sentence'      )
												exit()

									__a   ,					__a						   =line.strip().split('\t'      )
									__a						   =os.path.join(__snake_case      ,						wav_name + '.wav'      )
									__a						   =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  UpperCamelCase_(							):




			"""simple docstring"""






			__a						   =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=16000 ,  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'			)

			__a						   =parser.parse_args()

			__a						   =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()









# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import tensorflow as tf






class 			__magic_name__					(    TensorFormatter[Mapping, 'tf.Tensor', Mapping]   ):





			def __init__(	self      ,						__snake_case=None      ,						**__snake_case      )     ->				Union[str, Any]:




						'''simple docstring'''
						super().__init__(features=__snake_case      )
						__a						   =tf_tensor_kwargs
						import tensorflow as tf  # noqa: import tf at initialization





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				int:




						'''simple docstring'''
						import tensorflow as tf

						if isinstance(__snake_case      ,						__snake_case      ) and column:
									if all(
									    isinstance(__snake_case      ,						tf.Tensor      ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column      ):
												return tf.stack(__snake_case      )
									elif all(
									    isinstance(__snake_case      ,						(tf.Tensor, tf.RaggedTensor)      ) and x.ndim == 1 and x.dtype == column[0].dtype
									    for x in column      ):
												# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
												return tf.ragged.stack(__snake_case      )

						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Any:




						'''simple docstring'''
						import tensorflow as tf

						if value is None:
									return value

						__a						   ={}

						if isinstance(__snake_case      ,						(np.number, np.ndarray)      ) and np.issubdtype(value.dtype      ,						np.integer      ):
									__a						   ={'dtype': tf.intaa}
						elif isinstance(__snake_case      ,						(np.number, np.ndarray)      ) and np.issubdtype(value.dtype      ,						np.floating      ):
									__a						   ={'dtype': tf.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(__snake_case      ,						PIL.Image.Image      ):
												__a						   =np.asarray(__snake_case      )

						return tf.convert_to_tensor(__snake_case      ,						**{**default_dtype, **self.tf_tensor_kwargs}      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Dict:




						'''simple docstring'''
						import tensorflow as tf

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
									import torch

									if isinstance(__snake_case      ,						torch.Tensor      ):
												return self._tensorize(data_struct.detach().cpu().numpy()[()]      )
						if hasattr(__snake_case      ,						'__array__'      ) and not isinstance(__snake_case      ,						tf.Tensor      ):
									__a						   =data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__snake_case      ,						np.ndarray      ):
									if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
												return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						elif isinstance(__snake_case      ,						(list, tuple)      ):
									return self._consolidate([self.recursive_tensorize(__snake_case      ) for substruct in data_struct]      )
						return self._tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						return map_nested(self._recursive_tensorize      ,						__snake_case      ,						map_list=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_row(__snake_case      )
						__a						   =self.python_features_decoder.decode_row(__snake_case      )
						return self.recursive_tensorize(__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				"tf.Tensor":




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_column(__snake_case      )
						__a						   =self.python_features_decoder.decode_column(__snake_case      ,						pa_table.column_names[0]      )
						__a						   =self.recursive_tensorize(__snake_case      )
						__a						   =self._consolidate(__snake_case      )
						return column





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Mapping:




						'''simple docstring'''
						__a						   =self.numpy_arrow_extractor().extract_batch(__snake_case      )
						__a						   =self.python_features_decoder.decode_batch(__snake_case      )
						__a						   =self.recursive_tensorize(__snake_case      )
						for column_name in batch:
									__a						   =self._consolidate(batch[column_name]      )
						return batch






