






import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    cuda
												lowerCAmelCase__         =    AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
												lowerCAmelCase__         =    AutoModelForCTC.from_pretrained(SCREAMING_SNAKE_CASE_ )
												self.model.eval()
												if self.cuda:
																		self.model.to(device='cuda' )
												lowerCAmelCase__         =    AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE_ )
												lowerCAmelCase__         =    torchaudio.transforms.Resample(SCREAMING_SNAKE_CASE_  ,    1_60_00 )
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    list(self.processor.tokenizer.get_vocab().keys() )
												for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
																		if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																								lowerCAmelCase__         =    i
												print('Blank Token id [PAD]/<pad>'  ,    SCREAMING_SNAKE_CASE_ )
												lowerCAmelCase__         =    blank_id






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    torchaudio.load(SCREAMING_SNAKE_CASE_ )
												lowerCAmelCase__         =    self.resampler(SCREAMING_SNAKE_CASE_ ).squeeze().numpy()
												return speech






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






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

												lowerCAmelCase__         =    self.speech_file_to_array_fn(item['wav_path'] )
												lowerCAmelCase__         =    self.processor(SCREAMING_SNAKE_CASE_  ,    sampling_rate=1_60_00  ,    return_tensors='pt'  ,    padding=SCREAMING_SNAKE_CASE_ )
												if self.cuda:
																		lowerCAmelCase__         =    inputs.to(device='cuda' )

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

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

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

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

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

												lowerCAmelCase__         =    get_trellis(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )


												@dataclass
												class      __SCREAMING_SNAKE_CASE       :
																		_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       42
																		_SCREAMING_SNAKE_CASE						:							Union[str, Any]				=       42
																		_SCREAMING_SNAKE_CASE						:							List[str]				=       42

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

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

																								# 2. Store the path with frame-wise probability.
																								lowerCAmelCase__         =    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  ,    SCREAMING_SNAKE_CASE_ ) )

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

												lowerCAmelCase__         =    backtrack(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )


												@dataclass
												class      __SCREAMING_SNAKE_CASE       :
																		_SCREAMING_SNAKE_CASE						:							List[Any]				=       42
																		_SCREAMING_SNAKE_CASE						:							str				=       42
																		_SCREAMING_SNAKE_CASE						:							Union[str, Any]				=       42
																		_SCREAMING_SNAKE_CASE						:							int				=       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    UpperCamelCase__   (						self ):






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

												def merge_repeats(_UpperCamelCase ):
																		lowerCAmelCase__    ,							lowerCAmelCase__         =    0, 0
																		lowerCAmelCase__         =    []
																		while ia < len(SCREAMING_SNAKE_CASE_ ):
																								while ia < len(SCREAMING_SNAKE_CASE_ ) and path[ia].token_index == path[ia].token_index:
																														ia += 1
																								lowerCAmelCase__         =    sum(path[k].score for k in range(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ ) ) / (ia - ia)
																								segments.append(
																								    Segment(
																								        transcript[path[ia].token_index]  ,    path[ia].time_index  ,    path[ia - 1].time_index + 1  ,    SCREAMING_SNAKE_CASE_  ,    ) )
																								lowerCAmelCase__         =    ia
																		return segments

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







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if not os.path.exists(SCREAMING_SNAKE_CASE_ ):
																		os.makedirs(SCREAMING_SNAKE_CASE_ )

												# load text file
												lowerCAmelCase__         =    open(SCREAMING_SNAKE_CASE_  ,    encoding='utf8' ).readlines()

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

																		lowerCAmelCase__    ,							lowerCAmelCase__         =    line.strip().split('\t' )
																		lowerCAmelCase__         =    os.path.join(SCREAMING_SNAKE_CASE_  ,    wav_name + '.wav' )
																		lowerCAmelCase__         =    os.path.join(SCREAMING_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(SCREAMING_SNAKE_CASE_ ) )

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







def 						_UpperCamelCase (     )	-> List[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    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=1_6000     ,							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'		)

						lowerCAmelCase__         =    parser.parse_args()

						lowerCAmelCase__         =    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()







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


lowerCamelCase_				:							List[str]       =    logging.get_logger(__name__)







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			["""pixel_values"""]

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 1 / 255							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = True							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   size if size is not None else {'height': 346, 'width': 346}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else {'height': 289, 'width': 289}
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

            __snake_case						       =   do_resize
            __snake_case						       =   size
            __snake_case						       =   resample
            __snake_case						       =   do_center_crop
            __snake_case						       =   crop_size
            __snake_case						       =   do_rescale
            __snake_case						       =   rescale_factor
            __snake_case						       =   rescale_offset
            __snake_case						       =   do_normalize
            __snake_case						       =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __snake_case						       =   image_std if image_std is not None else IMAGENET_STANDARD_STD
            __snake_case						       =   include_top

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = PIL.Image.NEAREST							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
            return resize(
                SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       resample=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            if "height" not in size or "width" not in size:
                   raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''    )
            return center_crop(SCREAMING_SNAKE_CASE_							,       size=(size['height'], size['width'])							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = True							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            if offset:
                   __snake_case						       =   (image - 1_2_7.5) * scale
                   if data_format is not None:
                          __snake_case						       =   to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )
                   __snake_case						       =   rescaled_image.astype(np.floataa    )
            else:
                   __snake_case						       =   rescale(SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )
            return rescaled_image

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       **SCREAMING_SNAKE_CASE_							,       ):
            return normalize(SCREAMING_SNAKE_CASE_							,       mean=SCREAMING_SNAKE_CASE_							,       std=SCREAMING_SNAKE_CASE_							,       data_format=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   do_resize if do_resize is not None else self.do_resize
            __snake_case						       =   resample if resample is not None else self.resample
            __snake_case						       =   do_center_crop if do_center_crop is not None else self.do_center_crop
            __snake_case						       =   do_rescale if do_rescale is not None else self.do_rescale
            __snake_case						       =   rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case						       =   rescale_offset if rescale_offset is not None else self.rescale_offset
            __snake_case						       =   do_normalize if do_normalize is not None else self.do_normalize
            __snake_case						       =   image_mean if image_mean is not None else self.image_mean
            __snake_case						       =   image_std if image_std is not None else self.image_std
            __snake_case						       =   include_top if include_top is not None else self.include_top

            __snake_case						       =   size if size is not None else self.size
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   crop_size if crop_size is not None else self.crop_size
            __snake_case						       =   get_size_dict(SCREAMING_SNAKE_CASE_							,       param_name='crop_size'    )

            __snake_case						       =   make_list_of_images(SCREAMING_SNAKE_CASE_    )

            if not valid_images(SCREAMING_SNAKE_CASE_    ):
                   raise ValueError(
                       'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                       'torch.Tensor, tf.Tensor or jax.ndarray.'    )

            if do_resize and size is None or resample is None:
                   raise ValueError('Size and resample must be specified if do_resize is True.'    )

            if do_center_crop and crop_size is None:
                   raise ValueError('Crop size must be specified if do_center_crop is True.'    )

            if do_rescale and rescale_factor is None:
                   raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

            if do_normalize and (image_mean is None or image_std is None):
                   raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

            # All transformations expect numpy arrays.
            __snake_case						       =   [to_numpy_array(SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_resize:
                   __snake_case						       =   [self.resize(image=SCREAMING_SNAKE_CASE_							,       size=SCREAMING_SNAKE_CASE_							,       resample=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_center_crop:
                   __snake_case						       =   [self.center_crop(image=SCREAMING_SNAKE_CASE_							,       size=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_rescale:
                   __snake_case						       =   [self.rescale(image=SCREAMING_SNAKE_CASE_							,       scale=SCREAMING_SNAKE_CASE_							,       offset=SCREAMING_SNAKE_CASE_    ) for image in images]

            if do_normalize:
                   __snake_case						       =   [self.normalize(image=SCREAMING_SNAKE_CASE_							,       mean=SCREAMING_SNAKE_CASE_							,       std=SCREAMING_SNAKE_CASE_    ) for image in images]

            if include_top:
                   __snake_case						       =   [self.normalize(image=SCREAMING_SNAKE_CASE_							,       mean=[0, 0, 0]							,       std=SCREAMING_SNAKE_CASE_    ) for image in images]

            __snake_case						       =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ) for image in images]

            __snake_case						       =   {'pixel_values': images}
            return BatchFeature(data=SCREAMING_SNAKE_CASE_							,       tensor_type=SCREAMING_SNAKE_CASE_    )
