





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,							A,							A,							A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    torchaudio.load(snake_case_   )
            _UpperCamelCase														=    self.resampler(snake_case_   ).squeeze().numpy()
            return speech

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

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

            _UpperCamelCase														=    self.speech_file_to_array_fn(item['''wav_path''']   )
            _UpperCamelCase														=    self.processor(snake_case_,							sampling_rate=1_6000,							return_tensors='''pt''',							padding=snake_case_   )
            if self.cuda:
                   _UpperCamelCase														=    inputs.to(device='''cuda'''   )

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

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

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

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

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

            _UpperCamelCase														=    get_trellis(snake_case_,							snake_case_,							snake_case_   )

            @dataclass
            class 			__A:
                 __A							=							42
                 __A							=							42
                 __A							=							42

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

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

                          # 2. Store the path with frame-wise probability.
                          _UpperCamelCase														=    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,							snake_case_   )   )

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

            _UpperCamelCase														=    backtrack(snake_case_,							snake_case_,							snake_case_,							snake_case_   )

            @dataclass
            class 			__A:
                 __A							=							42
                 __A							=							42
                 __A							=							42
                 __A							=							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(A   ):
                   _UpperCamelCase							,		_UpperCamelCase														=    0, 0
                   _UpperCamelCase														=    []
                   while ia < len(snake_case_   ):
                          while ia < len(snake_case_   ) and path[ia].token_index == path[ia].token_index:
                                 ia += 1
                          _UpperCamelCase														=    sum(path[k].score for k in range(snake_case_,							snake_case_   )   ) / (ia - ia)
                          segments.append(
                              Segment(
                                  transcript[path[ia].token_index],							path[ia].time_index,							path[ia - 1].time_index + 1,							snake_case_,							)   )
                          _UpperCamelCase														=    ia
                   return segments

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



     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """simple docstring"""

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

            # load text file
            _UpperCamelCase														=    open(snake_case_,							encoding='''utf8'''   ).readlines()

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

                   _UpperCamelCase							,		_UpperCamelCase														=    line.strip().split('''\t'''   )
                   _UpperCamelCase														=    os.path.join(snake_case_,							wav_name + '''.wav'''   )
                   _UpperCamelCase														=    os.path.join(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(snake_case_   )   )

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






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    argparse.ArgumentParser()

       parser.add_argument(
           '''--model_name'''     ,							type=lowerCAmelCase     ,							default='''arijitx/wav2vec2-xls-r-300m-bengali'''     ,							help='''wav2vec model name'''       )
       parser.add_argument('''--wav_dir'''     ,							type=lowerCAmelCase     ,							default='''./wavs'''     ,							help='''directory containing wavs'''       )
       parser.add_argument('''--text_file'''     ,							type=lowerCAmelCase     ,							default='''script.txt'''     ,							help='''file containing text'''       )
       parser.add_argument('''--input_wavs_sr'''     ,							type=lowerCAmelCase     ,							default=16_000     ,							help='''sampling rate of input audios'''       )
       parser.add_argument(
           '''--output_dir'''     ,							type=lowerCAmelCase     ,							default='''./out_alignment'''     ,							help='''output directory containing the alignment files'''       )
       parser.add_argument('''--cuda'''     ,							action='''store_true'''       )

       _UpperCamelCase														=    parser.parse_args()

       _UpperCamelCase														=    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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__       :			Union[str, Any]=						logging.get_logger(__name__)

A__       :			Dict=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




