


"""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							,			a_							,			a_							,			a_   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							cuda
			__snake_case      :		int         =							AutoConfig.from_pretrained(a_   )
			__snake_case      :		Union[str, Any]         =							AutoModelForCTC.from_pretrained(a_   )
			self.model.eval()
			if self.cuda:
				self.model.to(device='''cuda'''   )
			__snake_case      :		Optional[Any]         =							AutoProcessor.from_pretrained(a_   )
			__snake_case      :		Any         =							torchaudio.transforms.Resample(a_							,			1_60_00   )
			__snake_case      :		Tuple         =							0
			__snake_case      :		List[str]         =							list(self.processor.tokenizer.get_vocab().keys()   )
			for i in range(len(a_   )   ):
				if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
					__snake_case      :		Union[str, Any]         =							i
			print('''Blank Token id [PAD]/<pad>'''							,			a_   )
			__snake_case      :		str         =							blank_id



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							torchaudio.load(a_   )
			__snake_case      :		int         =							self.resampler(a_   ).squeeze().numpy()
			return speech



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

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

			__snake_case      :		Union[str, Any]         =							self.speech_file_to_array_fn(item['''wav_path''']   )
			__snake_case      :		int         =							self.processor(a_							,			sampling_rate=1_60_00							,			return_tensors='''pt'''							,			padding=a_   )
			if self.cuda:
				__snake_case      :		Optional[Any]         =							inputs.to(device='''cuda'''   )

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

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

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

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

			def get_trellis(a_							,			a_							,			a_=0   ):
				__snake_case      :		Any         =							emission.size(0   )
				__snake_case      :		Optional[Any]         =							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.
				__snake_case      :		str         =							torch.full((num_frames + 1, num_tokens + 1)							,			-float('''inf'''   )   )
				__snake_case      :		Dict         =							0
				for t in range(a_   ):
					__snake_case      :		Union[str, 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      :		List[Any]         =							get_trellis(a_							,			a_							,			a_   )



			@dataclass
			class      _UpperCAmelCase					:






					'''simple docstring'''

					lowerCamelCase__           =42
					lowerCamelCase__           =42
					lowerCamelCase__           =42

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

				__snake_case      :		List[Any]         =							[]
				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.
					__snake_case      :		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      :		int         =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

					# 2. Store the path with frame-wise probability.
					__snake_case      :		int         =							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]

			__snake_case      :		List[str]         =							backtrack(a_							,			a_							,			a_							,			a_   )



			@dataclass
			class      _UpperCAmelCase					:






					'''simple docstring'''

					lowerCamelCase__           =42
					lowerCamelCase__           =42
					lowerCamelCase__           =42
					lowerCamelCase__           =42



					def __repr__(self   ):





						'''simple docstring'''

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



					@property
					def 							SCREAMING_SNAKE_CASE    (self   ):





						'''simple docstring'''

						return self.end - self.start

			def merge_repeats(a_   ):
				__snake_case      :		Any         =							0, 0
				__snake_case      :		List[Any]         =							[]
				while ia < len(a_   ):
					while ia < len(a_   ) and path[ia].token_index == path[ia].token_index:
						ia += 1
					__snake_case      :		int         =							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_							,			)   )
					__snake_case      :		str         =							ia
				return segments

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



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_   ):





			'''simple docstring'''

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

			# load text file
			__snake_case      :		Optional[int]         =							open(a_							,			encoding='''utf8'''   ).readlines()

			__snake_case      :		List[Any]         =							[]
			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      :		Union[str, Any]         =							line.strip().split('''\t'''   )
				__snake_case      :		Tuple         =							os.path.join(a_							,			wav_name + '''.wav'''   )
				__snake_case      :		Dict         =							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 						lowercase				(	)	->Dict:
	"""simple docstring"""





	__snake_case      :		Dict         =							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=16_000	,  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'''	)

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

	__snake_case      :		List[str]         =							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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


SCREAMING_SNAKE_CASE__     : List[Any]				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : List[str]				=   {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__     : Optional[int]				=   {
    """vocab_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """microsoft/layoutlm-base-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json"""
        ),
        """microsoft/layoutlm-large-uncased""": (
            """https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__     : Tuple				=   {
    """microsoft/layoutlm-base-uncased""": 5_12,
    """microsoft/layoutlm-large-uncased""": 5_12,
}

SCREAMING_SNAKE_CASE__     : List[Any]				=   {
    """microsoft/layoutlm-base-uncased""": {"""do_lower_case""": True},
    """microsoft/layoutlm-large-uncased""": {"""do_lower_case""": True},
}




class 				__lowerCAmelCase(							lowerCAmelCase__      ):
			__snake_case  :		List[str]		=      VOCAB_FILES_NAMES
			__snake_case  :		Dict		=      PRETRAINED_VOCAB_FILES_MAP
			__snake_case  :		Optional[int]		=      PRETRAINED_INIT_CONFIGURATION
			__snake_case  :		Any		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__snake_case  :		str		=      LayoutLMTokenizer






			def __init__(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Dict=None      ,       SCREAMING_SNAKE_CASE  :							int=None      ,       SCREAMING_SNAKE_CASE  :							Tuple=True      ,       SCREAMING_SNAKE_CASE  :							Optional[int]="[UNK]"      ,       SCREAMING_SNAKE_CASE  :							Tuple="[SEP]"      ,       SCREAMING_SNAKE_CASE  :							int="[PAD]"      ,       SCREAMING_SNAKE_CASE  :							List[Any]="[CLS]"      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]="[MASK]"      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=True      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=None      ,       **SCREAMING_SNAKE_CASE  :							Optional[int]      ,       ):




					"""simple docstring"""







					super().__init__(
					    SCREAMING_SNAKE_CASE      ,       tokenizer_file=SCREAMING_SNAKE_CASE      ,       do_lower_case=SCREAMING_SNAKE_CASE      ,       unk_token=SCREAMING_SNAKE_CASE      ,       sep_token=SCREAMING_SNAKE_CASE      ,       pad_token=SCREAMING_SNAKE_CASE      ,       cls_token=SCREAMING_SNAKE_CASE      ,       mask_token=SCREAMING_SNAKE_CASE      ,       tokenize_chinese_chars=SCREAMING_SNAKE_CASE      ,       strip_accents=SCREAMING_SNAKE_CASE      ,       **SCREAMING_SNAKE_CASE      ,       )

					SCREAMING_SNAKE_CASE_						:str										=      json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
					if (
					    normalizer_state.get('lowercase'      ,       SCREAMING_SNAKE_CASE	) != do_lower_case
					    or normalizer_state.get('strip_accents'      ,       SCREAMING_SNAKE_CASE	) != strip_accents
					    or normalizer_state.get('handle_chinese_chars'      ,       SCREAMING_SNAKE_CASE	) != tokenize_chinese_chars
					):
							SCREAMING_SNAKE_CASE_						:List[str]										=      getattr(SCREAMING_SNAKE_CASE      ,       normalizer_state.pop('type'	)	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      do_lower_case
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      strip_accents
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      tokenize_chinese_chars
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      normalizer_class(**SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[str]										=      do_lower_case






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							str=None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

					return output






			def 				_lowercase	(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[int]      ,       SCREAMING_SNAKE_CASE  :							Optional[List[int]] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [self.sep_token_id]
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [self.cls_token_id]
					if token_ids_a is None:
							return len(cls + token_ids_a + sep	) * [0]
					return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]






			def 				_lowercase	(   self  :							str      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      self._tokenizer.model.save(SCREAMING_SNAKE_CASE      ,       name=SCREAMING_SNAKE_CASE	)
					return tuple(SCREAMING_SNAKE_CASE	)
