
'''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      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     UpperCamelCase__							:       str	)		->       List[str]:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       Optional[Any]   ,     UpperCamelCase__							:       int	)		->       Dict:






				"""simple docstring"""







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

				snake_case   :    List[Any]			       =		self.speech_file_to_array_fn(item['''wav_path''']	)
				snake_case   :    List[str]			       =		self.processor(UpperCamelCase__   ,     sampling_rate=1_6000   ,     return_tensors='''pt'''   ,     padding=UpperCamelCase__	)
				if self.cuda:
						snake_case   :    str			       =		inputs.to(device='''cuda'''	)

				with torch.no_grad():
						snake_case   :    List[Any]			       =		self.model(inputs.input_values	).logits

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

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

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

				def get_trellis(UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       str=0	):
						snake_case   :    List[str]			       =		emission.size(0	)
						snake_case   :    List[str]			       =		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.
						snake_case   :    List[Any]			       =		torch.full((num_frames + 1, num_tokens + 1)   ,     -float('''inf'''	)	)
						snake_case   :    Optional[int]			       =		0
						for t in range(UpperCamelCase__	):
								snake_case   :    Any			       =		torch.maximum(
								    # Score for staying at the same token
								    trellis[t, 1:] + emission[t, blank_id]   ,     trellis[t, :-1] + emission[t, tokens]   ,     )
						return trellis

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






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42

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

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

								# 2. Store the path with frame-wise probability.
								snake_case   :    Optional[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   ,     UpperCamelCase__	)	)

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

				snake_case   :    Any			       =		backtrack(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42



						def __repr__(	self							:       Dict	)		->       int:






								"""simple docstring"""







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



						@property
						def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






								"""simple docstring"""







								return self.end - self.start

				def merge_repeats(UpperCamelCase__							:       str	):
						snake_case       ,snake_case   :    Union[str, Any]			       =		0, 0
						snake_case   :    List[Any]			       =		[]
						while ia < len(UpperCamelCase__	):
								while ia < len(UpperCamelCase__	) and path[ia].token_index == path[ia].token_index:
										ia += 1
								snake_case   :    Dict			       =		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__   ,     )	)
								snake_case   :    int			       =		ia
						return segments

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



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       List[str]	)		->       List[str]:






				"""simple docstring"""







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

				# load text file
				snake_case   :    List[Any]			       =		open(UpperCamelCase__   ,     encoding='''utf8'''	).readlines()

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

						snake_case       ,snake_case   :    Dict			       =		line.strip().split('''\t'''	)
						snake_case   :    Tuple			       =		os.path.join(UpperCamelCase__   ,     wav_name + '''.wav'''	)
						snake_case   :    str			       =		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 					_UpperCamelCase		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		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'''       )

		snake_case   :    Tuple			       =		parser.parse_args()

		snake_case   :    Tuple			       =		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"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							=logging.get_logger(__name__)

a							={
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Optional[int]  							=       '''mpnet'''

				def __init__(      self	, _lowerCamelCase=30527	, _lowerCamelCase=768	, _lowerCamelCase=12	, _lowerCamelCase=12	, _lowerCamelCase=3072	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=0.0_2	, _lowerCamelCase=1E-12	, _lowerCamelCase=32	, _lowerCamelCase=1	, _lowerCamelCase=0	, _lowerCamelCase=2	, **_lowerCamelCase	, ):
										super().__init__(pad_token_id=_lowerCamelCase	, bos_token_id=_lowerCamelCase	, eos_token_id=_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=layer_norm_eps
										lowerCamelCase__					=relative_attention_num_buckets


