






"""simple docstring"""


import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_UpperCamelCase   :						Optional[Any]     						= logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class __a   (   snake_case_				):
  __lowercase    :    torch.FloatTensor
  __lowercase    :    Optional[torch.FloatTensor]						=					None






def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				Any=0.999     ,	UpperCamelCase_							:				Dict="cosine"     ,	)  ->		Optional[Any]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCamelCase_							:				Tuple		):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCamelCase_							:				Dict		):
   return math.exp(t * -12.0		)

 else:
  raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

 __A	:       Any       =	[]
 for i in range(UpperCamelCase_		):
  __A	:       Optional[Any]       =	i / num_diffusion_timesteps
  __A	:       Tuple       =	(i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCamelCase_		) / alpha_bar_fn(UpperCamelCase_		)     ,	UpperCamelCase_		)		)
 return torch.tensor(UpperCamelCase_     ,	dtype=torch.floataa		)






class __a   (   snake_case_ ,snake_case_				):
  __lowercase    :    str						=					[e.name for e in KarrasDiffusionSchedulers]
  __lowercase    :    str						=					1



  @register_to_config
  def __init__(self  :		Tuple				,			lowercase_  :		int = 1_000				,			lowercase_  :		float = 0.0001				,			lowercase_  :		float = 0.02				,			lowercase_  :		str = "linear"				,			lowercase_  :		Optional[Union[np.ndarray, List[float]]] = None				,			lowercase_  :		str = "epsilon"				,			lowercase_  :		str = "linear"				,			lowercase_  :		Optional[bool] = False				,			lowercase_  :		str = "linspace"				,			lowercase_  :		int = 0				,			)		->							Optional[Any]:
   if trained_betas is not None:
    __A	:       str       =	torch.tensor(lowercase_				,			dtype=torch.floataa   )
   elif beta_schedule == "linear":
    __A	:       Any       =	torch.linspace(lowercase_				,			lowercase_				,			lowercase_				,			dtype=torch.floataa   )
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __A	:       Tuple       =	(
        torch.linspace(beta_start**0.5				,			beta_end**0.5				,			lowercase_				,			dtype=torch.floataa   ) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __A	:       List[Any]       =	betas_for_alpha_bar(lowercase_   )
   else:
    raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"   )

   __A	:       Optional[Any]       =	1.0 - self.betas
   __A	:       Optional[Any]       =	torch.cumprod(self.alphas				,			dim=0   )

   __A	:       List[Any]       =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
   __A	:       List[str]       =	np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
   __A	:       Dict       =	torch.from_numpy(lowercase_   )

   # setable values
   __A	:       Optional[Any]       =	None
   __A	:       str       =	np.linspace(0				,			num_train_timesteps - 1				,			lowercase_				,			dtype=lowercase_   )[::-1].copy()
   __A	:       Any       =	torch.from_numpy(lowercase_   )
   __A	:       Any       =	False
   __A	:       Optional[Any]       =	use_karras_sigmas



  @property
  def 						A_				(self  :		str   )		->							List[str]:
   # standard deviation of the initial noise distribution
   if self.config.timestep_spacing in ["linspace", "trailing"]:
    return self.sigmas.max()

   return (self.sigmas.max() ** 2 + 1) ** 0.5



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		Union[float, torch.FloatTensor]   )		->							torch.FloatTensor:
   if isinstance(lowercase_				,			torch.Tensor   ):
    __A	:       str       =	timestep.to(self.timesteps.device   )
   __A	:       str       =	(self.timesteps == timestep).nonzero().item()
   __A	:       Tuple       =	self.sigmas[step_index]

   __A	:       Any       =	sample / ((sigma**2 + 1) ** 0.5)

   __A	:       Union[str, Any]       =	True
   return sample



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		int				,			lowercase_  :		Union[str, torch.device] = None   )		->							List[Any]:
   __A	:       Tuple       =	num_inference_steps

   # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
   if self.config.timestep_spacing == "linspace":
    __A	:       Optional[int]       =	np.linspace(0				,			self.config.num_train_timesteps - 1				,			lowercase_				,			dtype=lowercase_   )[
        ::-1
    ].copy()
   elif self.config.timestep_spacing == "leading":
    __A	:       Tuple       =	self.config.num_train_timesteps // self.num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # casting to int to avoid issues when num_inference_step is power of 3
    __A	:       int       =	(np.arange(0				,			lowercase_   ) * step_ratio).round()[::-1].copy().astype(lowercase_   )
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    __A	:       Any       =	self.config.num_train_timesteps / self.num_inference_steps
    # creates integer timesteps by multiplying by ratio
    # casting to int to avoid issues when num_inference_step is power of 3
    __A	:       int       =	(np.arange(self.config.num_train_timesteps				,			0				,			-step_ratio   )).round().copy().astype(lowercase_   )
    timesteps -= 1
   else:
    raise ValueError(
        f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."   )

   __A	:       Optional[int]       =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
   __A	:       List[str]       =	np.log(lowercase_   )

   if self.config.interpolation_type == "linear":
    __A	:       Any       =	np.interp(lowercase_				,			np.arange(0				,			len(lowercase_   )   )				,			lowercase_   )
   elif self.config.interpolation_type == "log_linear":
    __A	:       int       =	torch.linspace(np.log(sigmas[-1]   )				,			np.log(sigmas[0]   )				,			num_inference_steps + 1   ).exp()
   else:
    raise ValueError(
        f"{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"
        ' \'linear\' or \'log_linear\''   )

   if self.use_karras_sigmas:
    __A	:       int       =	self._convert_to_karras(in_sigmas=lowercase_				,			num_inference_steps=self.num_inference_steps   )
    __A	:       List[str]       =	np.array([self._sigma_to_t(lowercase_				,			lowercase_   ) for sigma in sigmas]   )

   __A	:       str       =	np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
   __A	:       str       =	torch.from_numpy(lowercase_   ).to(device=lowercase_   )
   if str(lowercase_   ).startswith('mps'   ):
    # mps does not support float64
    __A	:       str       =	torch.from_numpy(lowercase_   ).to(lowercase_				,			dtype=torch.floataa   )
   else:
    __A	:       Any       =	torch.from_numpy(lowercase_   ).to(device=lowercase_   )



  def 						A_				(self  :		Optional[int]				,			lowercase_  :		Dict				,			lowercase_  :		Optional[int]   )		->							Optional[Any]:
   # get log sigma
   __A	:       str       =	np.log(lowercase_   )

   # get distribution
   __A	:       Tuple       =	log_sigma - log_sigmas[:, np.newaxis]

   # get sigmas range
   __A	:       Any       =	np.cumsum((dists >= 0)				,			axis=0   ).argmax(axis=0   ).clip(max=log_sigmas.shape[0] - 2   )
   __A	:       Union[str, Any]       =	low_idx + 1

   __A	:       List[str]       =	log_sigmas[low_idx]
   __A	:       Optional[int]       =	log_sigmas[high_idx]

   # interpolate sigmas
   __A	:       Optional[int]       =	(low - log_sigma) / (low - high)
   __A	:       Tuple       =	np.clip(lowercase_				,			0				,			1   )

   # transform interpolation to time range
   __A	:       Optional[Any]       =	(1 - w) * low_idx + w * high_idx
   __A	:       Any       =	t.reshape(sigma.shape   )
   return t



  def 						A_				(self  :		List[str]				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		List[str]   )		->							torch.FloatTensor:
   __A	:       float       =	in_sigmas[-1].item()
   __A	:       float       =	in_sigmas[0].item()

   __A	:       Optional[Any]       =	7.0  # 7.0 is the value used in the paper
   __A	:       int       =	np.linspace(0				,			1				,			lowercase_   )
   __A	:       Any       =	sigma_min ** (1 / rho)
   __A	:       Optional[int]       =	sigma_max ** (1 / rho)
   __A	:       int       =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas



  def 						A_				(self  :		Any				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		Union[float, torch.FloatTensor]				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		float = 0.0				,			lowercase_  :		float = 0.0				,			lowercase_  :		float = float('inf'   )				,			lowercase_  :		float = 1.0				,			lowercase_  :		Optional[torch.Generator] = None				,			lowercase_  :		bool = True				,			)		->							Union[EulerDiscreteSchedulerOutput, Tuple]:

   if (
       isinstance(lowercase_				,			lowercase_   )
       or isinstance(lowercase_				,			torch.IntTensor   )
       or isinstance(lowercase_				,			torch.LongTensor   )
   ):
    raise ValueError(
        (
            'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
            ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
            ' one of the `scheduler.timesteps` as a timestep.'
        )				,			)

   if not self.is_scale_input_called:
    logger.warning(
        'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
        'See `StableDiffusionPipeline` for a usage example.'   )

   if isinstance(lowercase_				,			torch.Tensor   ):
    __A	:       List[str]       =	timestep.to(self.timesteps.device   )

   __A	:       Tuple       =	(self.timesteps == timestep).nonzero().item()
   __A	:       Optional[int]       =	self.sigmas[step_index]

   __A	:       Dict       =	min(s_churn / (len(self.sigmas   ) - 1)				,			2**0.5 - 1   ) if s_tmin <= sigma <= s_tmax else 0.0

   __A	:       int       =	randn_tensor(
       model_output.shape				,			dtype=model_output.dtype				,			device=model_output.device				,			generator=lowercase_   )

   __A	:       Tuple       =	noise * s_noise
   __A	:       Optional[int]       =	sigma * (gamma + 1)

   if gamma > 0:
    __A	:       str       =	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   # NOTE: "original_sample" should not be an expected prediction_type but is left in for
   # backwards compatibility
   if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
    __A	:       Dict       =	model_output
   elif self.config.prediction_type == "epsilon":
    __A	:       List[Any]       =	sample - sigma_hat * model_output
   elif self.config.prediction_type == "v_prediction":
    # * c_out + input * c_skip
    __A	:       str       =	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
   else:
    raise ValueError(
        f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"   )

   # 2. Convert to an ODE derivative
   __A	:       List[Any]       =	(sample - pred_original_sample) / sigma_hat

   __A	:       Any       =	self.sigmas[step_index + 1] - sigma_hat

   __A	:       int       =	sample + derivative * dt

   if not return_dict:
    return (prev_sample,)

   return EulerDiscreteSchedulerOutput(prev_sample=lowercase_				,			pred_original_sample=lowercase_   )



  def 						A_				(self  :		int				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		torch.FloatTensor				,			lowercase_  :		torch.FloatTensor				,			)		->							torch.FloatTensor:
   # Make sure sigmas and timesteps have the same device and dtype as original_samples
   __A	:       str       =	self.sigmas.to(device=original_samples.device				,			dtype=original_samples.dtype   )
   if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_   ):
    # mps does not support float64
    __A	:       Optional[int]       =	self.timesteps.to(original_samples.device				,			dtype=torch.floataa   )
    __A	:       Union[str, Any]       =	timesteps.to(original_samples.device				,			dtype=torch.floataa   )
   else:
    __A	:       Any       =	self.timesteps.to(original_samples.device   )
    __A	:       Union[str, Any]       =	timesteps.to(original_samples.device   )

   __A	:       Any       =	[(schedule_timesteps == t).nonzero().item() for t in timesteps]

   __A	:       int       =	sigmas[step_indices].flatten()
   while len(sigma.shape   ) < len(original_samples.shape   ):
    __A	:       Optional[Any]       =	sigma.unsqueeze(-1   )

   __A	:       int       =	original_samples + noise * sigma
   return noisy_samples



  def __len__(self  :		int   )		->							int:
   return self.config.num_train_timesteps













"""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()





