






"""simple docstring"""


import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union






class __a   (   enum.Enum				):
  __lowercase    :    List[Any]						=					'timm'
  __lowercase    :    Optional[Any]						=					'transformers'






def 				a (     UpperCamelCase_							:				Optional[Iterable[str]]     ,	UpperCamelCase_							:				Optional[Iterable[int]]     ,	UpperCamelCase_							:				Optional[Iterable[str]]		)  ->		Union[str, Any]:
 if stage_names is None:
  raise ValueError('Stage_names must be set for transformers backbones'		)

 if out_features is not None:
  if not isinstance(UpperCamelCase_     ,	(list,)		):
   raise ValueError(F"out_features must be a list {type(UpperCamelCase_		)}"		)
  if any(feat not in stage_names for feat in out_features		):
   raise ValueError(F"out_features must be a subset of stage_names: {stage_names} got {out_features}"		)

 if out_indices is not None:
  if not isinstance(UpperCamelCase_     ,	(list, tuple)		):
   raise ValueError(F"out_indices must be a list or tuple, got {type(UpperCamelCase_		)}"		)
  if any(idx >= len(UpperCamelCase_		) for idx in out_indices		):
   raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'		)

 if out_features is not None and out_indices is not None:
  if len(UpperCamelCase_		) != len(UpperCamelCase_		):
   raise ValueError('out_features and out_indices should have the same length if both are set'		)
  if out_features != [stage_names[idx] for idx in out_indices]:
   raise ValueError('out_features and out_indices should correspond to the same stages if both are set'		)




def 				a (     UpperCamelCase_							:				Optional[List[str]]     ,	UpperCamelCase_							:				Optional[Union[List[int], Tuple[int]]]     ,	UpperCamelCase_							:				List[str]     ,	)  ->		Union[str, Any]:
 if out_indices is None and out_features is None:
  __A	:       List[Any]       =	[len(UpperCamelCase_		) - 1]
  __A	:       Union[str, Any]       =	[stage_names[-1]]
 elif out_indices is None and out_features is not None:
  __A	:       int       =	[stage_names.index(UpperCamelCase_		) for layer in out_features]
 elif out_features is None and out_indices is not None:
  __A	:       List[Any]       =	[stage_names[idx] for idx in out_indices]
 return out_features, out_indices




def 				a (     UpperCamelCase_							:				Optional[List[str]]     ,	UpperCamelCase_							:				Optional[Union[List[int], Tuple[int]]]     ,	UpperCamelCase_							:				List[str]     ,	)  ->		Tuple[List[str], List[int]]:
 verify_out_features_out_indices(out_features=UpperCamelCase_     ,	out_indices=UpperCamelCase_     ,	stage_names=UpperCamelCase_		)
 __A	:       int       =	_align_output_features_output_indices(
     out_features=UpperCamelCase_     ,	out_indices=UpperCamelCase_     ,	stage_names=UpperCamelCase_		)
 # Verify that the aligned out_features and out_indices are valid
 verify_out_features_out_indices(out_features=UpperCamelCase_     ,	out_indices=UpperCamelCase_     ,	stage_names=UpperCamelCase_		)
 return output_features, output_indices






class __a   :
  __lowercase    :    Optional[BackboneType]						=					None



  def 						A_				(self  :		List[Any]				,			lowercase_  :		List[Any]   )		->							None:
   if getattr(self				,			'_backbone'				,			lowercase_   ) is None:
    raise ValueError('self._backbone must be set before calling _init_timm_backbone'   )

   # These will diagree with the defaults for the transformers models e.g. for resnet50
   # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
   # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
   __A	:       Dict       =	[stage['module'] for stage in self._backbone.feature_info.info]
   __A	:       Dict       =	[stage['num_chs'] for stage in self._backbone.feature_info.info]
   __A	:       Optional[Any]       =	self._backbone.feature_info.out_indices
   __A	:       Any       =	self._backbone.feature_info.module_name()

   # We verify the out indices and out features are valid
   verify_out_features_out_indices(
       out_features=lowercase_				,			out_indices=lowercase_				,			stage_names=self.stage_names   )
   __A	:       int       =	out_features, out_indices



  def 						A_				(self  :		str				,			lowercase_  :		Optional[Any]   )		->							None:
   __A	:       Union[str, Any]       =	getattr(lowercase_				,			'stage_names'   )
   __A	:       Dict       =	getattr(lowercase_				,			'out_features'				,			lowercase_   )
   __A	:       Union[str, Any]       =	getattr(lowercase_				,			'out_indices'				,			lowercase_   )

   __A	:       List[Any]       =	stage_names
   __A	:       Union[str, Any]       =	get_aligned_output_features_output_indices(
       out_features=lowercase_				,			out_indices=lowercase_				,			stage_names=lowercase_   )
   # Number of channels for each stage. This is set in the transformer backbone model init
   __A	:       Optional[int]       =	None



  def 						A_				(self  :		Union[str, Any]				,			lowercase_  :		List[str]   )		->							None:
   __A	:       Any       =	config

   __A	:       str       =	getattr(lowercase_				,			'use_timm_backbone'				,			lowercase_   )
   __A	:       Optional[Any]       =	BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

   if self.backbone_type == BackboneType.TIMM:
    self._init_timm_backbone(lowercase_   )
   elif self.backbone_type == BackboneType.TRANSFORMERS:
    self._init_transformers_backbone(lowercase_   )
   else:
    raise ValueError(f"backbone_type {self.backbone_type} not supported."   )



  @property
  def 						A_				(self  :		Tuple   )		->							Optional[Any]:
   return self._out_features



  @out_features.setter
  def 						A_				(self  :		Tuple				,			lowercase_  :		List[str]   )		->							Optional[Any]:
   __A	:       int       =	get_aligned_output_features_output_indices(
       out_features=lowercase_				,			out_indices=lowercase_				,			stage_names=self.stage_names   )



  @property
  def 						A_				(self  :		Optional[Any]   )		->							int:
   return self._out_indices



  @out_indices.setter
  def 						A_				(self  :		Union[str, Any]				,			lowercase_  :		Union[Tuple[int], List[int]]   )		->							Union[str, Any]:
   __A	:       str       =	get_aligned_output_features_output_indices(
       out_features=lowercase_				,			out_indices=lowercase_				,			stage_names=self.stage_names   )



  @property
  def 						A_				(self  :		int   )		->							List[Any]:
   # the current backbones will output the number of channels for each stage
   # even if that stage is not in the out_features list.
   return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names   )}



  @property
  def 						A_				(self  :		Tuple   )		->							Optional[Any]:
   return [self.out_feature_channels[name] for name in self.out_features]



  def 						A_				(self  :		List[str]				,			*lowercase_  :		int				,			**lowercase_  :		Any   )		->							List[Any]:
   __A	:       Optional[Any]       =	dict(inspect.signature(self.forward   ).parameters   )
   __A	:       Dict       =	{k: v for k, v in kwargs.items() if k in signature}
   return self(*lowercase_				,			**lowercase_   )



  def 						A_				(self  :		List[str]				,			lowercase_  :		str				,			lowercase_  :		Optional[bool] = None				,			lowercase_  :		Optional[bool] = None				,			lowercase_  :		Optional[bool] = None				,			)		->							Tuple:
   raise NotImplementedError('This method should be implemented by the derived class.'   )



  def 						A_				(self  :		int   )		->							List[Any]:
   __A	:       str       =	super().to_dict()
   __A	:       Optional[Any]       =	output.pop('_out_features'   )
   __A	:       Optional[int]       =	output.pop('_out_indices'   )
   return output






class __a   :



  @property
  def 						A_				(self  :		int   )		->							List[str]:
   return self._out_features



  @out_features.setter
  def 						A_				(self  :		Any				,			lowercase_  :		List[str]   )		->							Optional[int]:
   __A	:       Dict       =	get_aligned_output_features_output_indices(
       out_features=lowercase_				,			out_indices=lowercase_				,			stage_names=self.stage_names   )



  @property
  def 						A_				(self  :		List[str]   )		->							int:
   return self._out_indices



  @out_indices.setter
  def 						A_				(self  :		Tuple				,			lowercase_  :		Union[Tuple[int], List[int]]   )		->							Tuple:
   __A	:       Optional[int]       =	get_aligned_output_features_output_indices(
       out_features=lowercase_				,			out_indices=lowercase_				,			stage_names=self.stage_names   )



  def 						A_				(self  :		Dict   )		->							Any:
   __A	:       Union[str, Any]       =	super().to_dict()
   __A	:       int       =	output.pop('_out_features'   )
   __A	:       List[Any]       =	output.pop('_out_indices'   )
   return output













"""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 __a   :



  def __init__(self  :		Dict				,			lowercase_  :		str				,			lowercase_  :		Optional[Any]				,			lowercase_  :		Tuple   )		->							int:
   __A	:       Union[str, Any]       =	cuda
   __A	:       List[Any]       =	AutoConfig.from_pretrained(lowercase_   )
   __A	:       Any       =	AutoModelForCTC.from_pretrained(lowercase_   )
   self.model.eval()
   if self.cuda:
    self.model.to(device='cuda'   )
   __A	:       Union[str, Any]       =	AutoProcessor.from_pretrained(lowercase_   )
   __A	:       List[str]       =	torchaudio.transforms.Resample(lowercase_				,			16_000   )
   __A	:       Dict       =	0
   __A	:       Optional[int]       =	list(self.processor.tokenizer.get_vocab().keys()   )
   for i in range(len(lowercase_   )   ):
    if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
     __A	:       List[str]       =	i
   print('Blank Token id [PAD]/<pad>'				,			lowercase_   )
   __A	:       List[str]       =	blank_id



  def 						A_				(self  :		List[str]				,			lowercase_  :		Union[str, Any]   )		->							Optional[int]:
   __A    ,					__A	:       Tuple       =	torchaudio.load(lowercase_   )
   __A	:       int       =	self.resampler(lowercase_   ).squeeze().numpy()
   return speech



  def 						A_				(self  :		Any				,			lowercase_  :		Any   )		->							List[Any]:
   __A	:       int       =	self.blank_id
   __A	:       List[str]       =	'|'.join(item['sent'].split(' '   )   )
   if not os.path.isfile(item['wav_path']   ):
    print(item['wav_path']				,			'not found in wavs directory'   )

   __A	:       List[Any]       =	self.speech_file_to_array_fn(item['wav_path']   )
   __A	:       Any       =	self.processor(lowercase_				,			sampling_rate=16_000				,			return_tensors='pt'				,			padding=lowercase_   )
   if self.cuda:
    __A	:       int       =	inputs.to(device='cuda'   )

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

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

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

   __A	:       int       =	{c: i for i, c in enumerate(lowercase_   )}
   __A	:       str       =	[]
   for c in transcript:
    if c in dictionary:
     tokens.append(dictionary[c]   )

   def get_trellis(lowercase_  :		int				,			lowercase_  :		List[Any]				,			lowercase_  :		str=0   ):
    __A	:       List[str]       =	emission.size(0   )
    __A	:       List[Any]       =	len(lowercase_   )

    # 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	:       int       =	torch.full((num_frames + 1, num_tokens + 1)				,			-float('inf'   )   )
    __A	:       str       =	0
    for t in range(lowercase_   ):
     __A	:       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

   __A	:       Dict       =	get_trellis(lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float

   def backtrack(lowercase_  :		Any				,			lowercase_  :		str				,			lowercase_  :		Dict				,			lowercase_  :		int=0   ):
    __A	:       str       =	trellis.size(1   ) - 1
    __A	:       int       =	torch.argmax(trellis[:, j]   ).item()

    __A	:       int       =	[]
    for t in range(lowercase_				,			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	:       List[Any]       =	trellis[t - 1, j] + emission[t - 1, blank_id]
     # Score for token changing from C-1 at T-1 to J at T.
     __A	:       Tuple       =	trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

     # 2. Store the path with frame-wise probability.
     __A	:       Tuple       =	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				,			lowercase_   )   )

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

   __A	:       Any       =	backtrack(lowercase_				,			lowercase_				,			lowercase_				,			lowercase_   )






   @dataclass
   class __a   :
     __lowercase    :    str
     __lowercase    :    int
     __lowercase    :    int
     __lowercase    :    float



     def __repr__(self  :		Optional[Any]   )		->							Union[str, Any]:
      return f"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"



     @property
     def 						A_				(self  :		int   )		->							Dict:
      return self.end - self.start

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

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



  def 						A_				(self  :		int				,			lowercase_  :		Optional[int]				,			lowercase_  :		List[str]				,			lowercase_  :		str   )		->							Optional[Any]:
   if not os.path.exists(lowercase_   ):
    os.makedirs(lowercase_   )

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

   __A	:       int       =	[]
   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	:       Union[str, Any]       =	line.strip().split('\t'   )
    __A	:       List[Any]       =	os.path.join(lowercase_				,			wav_name + '.wav'   )
    __A	:       Optional[Any]       =	os.path.join(lowercase_				,			wav_name + '.txt'   )

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

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






def 				a (     )  ->		Any:
 __A	:       Any       =	argparse.ArgumentParser()

 parser.add_argument(
     '--model_name'     ,	type=UpperCamelCase_     ,	default='arijitx/wav2vec2-xls-r-300m-bengali'     ,	help='wav2vec model name'		)
 parser.add_argument('--wav_dir'     ,	type=UpperCamelCase_     ,	default='./wavs'     ,	help='directory containing wavs'		)
 parser.add_argument('--text_file'     ,	type=UpperCamelCase_     ,	default='script.txt'     ,	help='file containing text'		)
 parser.add_argument('--input_wavs_sr'     ,	type=UpperCamelCase_     ,	default=1_6_0_0_0     ,	help='sampling rate of input audios'		)
 parser.add_argument(
     '--output_dir'     ,	type=UpperCamelCase_     ,	default='./out_alignment'     ,	help='output directory containing the alignment files'		)
 parser.add_argument('--cuda'     ,	action='store_true'		)

 __A	:       Tuple       =	parser.parse_args()

 __A	:       Optional[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()





