



import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor



class 	__lowerCAmelCase       :




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      cuda
    __lowerCamelCase  							=      AutoConfig.from_pretrained(UpperCamelCase__			)
    __lowerCamelCase  							=      AutoModelForCTC.from_pretrained(UpperCamelCase__			)
    self.model.eval()
    if self.cuda:
       self.model.to(device='''cuda'''			)
    __lowerCamelCase  							=      AutoProcessor.from_pretrained(UpperCamelCase__			)
    __lowerCamelCase  							=      torchaudio.transforms.Resample(UpperCamelCase__		,			16000			)
    __lowerCamelCase  							=      0
    __lowerCamelCase  							=      list(self.processor.tokenizer.get_vocab().keys()			)
    for i in range(len(UpperCamelCase__			)			):
       if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
          __lowerCamelCase  							=      i
    print('''Blank Token id [PAD]/<pad>'''		,			UpperCamelCase__			)
    __lowerCamelCase  							=      blank_id




 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase  							=      torchaudio.load(UpperCamelCase__			)
    __lowerCamelCase  							=      self.resampler(UpperCamelCase__			).squeeze().numpy()
    return speech




 def     lowerCamelCase (  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(UpperCamelCase__		,			sampling_rate=16000		,			return_tensors='''pt'''		,			padding=UpperCamelCase__			)
    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(UpperCamelCase__		,			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(UpperCamelCase__			)}
    __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(UpperCamelCase__			)

       # 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(UpperCamelCase__			):
          __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(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__			)



    @dataclass
    class 	__lowerCAmelCase       :
     lowerCAmelCase__       =					4_2
     lowerCAmelCase__       =					4_2
     lowerCAmelCase__       =					4_2

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

       __lowerCamelCase  							=      []
       for t in range(UpperCamelCase__		,			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		,			UpperCamelCase__			)			)

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

    __lowerCamelCase  							=      backtrack(UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__			)



    @dataclass
    class 	__lowerCAmelCase       :
     lowerCAmelCase__       =					4_2
     lowerCAmelCase__       =					4_2
     lowerCAmelCase__       =					4_2
     lowerCAmelCase__       =					4_2




     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     lowerCamelCase (  self			):



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

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

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




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''






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

    # load text file
    __lowerCamelCase  							=      open(UpperCamelCase__		,			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(UpperCamelCase__		,			wav_name + '''.wav'''			)
       __lowerCamelCase  							=      os.path.join(UpperCamelCase__		,			wav_name + '''.txt'''			)

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

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





def 						a__     (						):
   __lowerCamelCase  							=      argparse.ArgumentParser()

   parser.add_argument(
       '''--model_name'''	,type=A_	,default='''arijitx/wav2vec2-xls-r-300m-bengali'''	,help='''wav2vec model name'''							)
   parser.add_argument('''--wav_dir'''	,type=A_	,default='''./wavs'''	,help='''directory containing wavs'''							)
   parser.add_argument('''--text_file'''	,type=A_	,default='''script.txt'''	,help='''file containing text'''							)
   parser.add_argument('''--input_wavs_sr'''	,type=A_	,default=1_60_00	,help='''sampling rate of input audios'''							)
   parser.add_argument(
       '''--output_dir'''	,type=A_	,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()











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


a_								=   logging.get_logger(__name__)

a_								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_								=   {
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

a_								=   {
    """allenai/longformer-base-4096""": 4_096,
    """allenai/longformer-large-4096""": 4_096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4_096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4_096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4_096,
}



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					VOCAB_FILES_NAMES
 lowerCAmelCase__       =					PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__       =					["""input_ids""", """attention_mask"""]
 lowerCAmelCase__       =					LongformerTokenizer




 def __init__(  self		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase="replace"		,			__UpperCAmelCase="<s>"		,			__UpperCAmelCase="</s>"		,			__UpperCAmelCase="</s>"		,			__UpperCAmelCase="<s>"		,			__UpperCAmelCase="<unk>"		,			__UpperCAmelCase="<pad>"		,			__UpperCAmelCase="<mask>"		,			__UpperCAmelCase=False		,			__UpperCAmelCase=True		,			**__UpperCAmelCase		,			):



    '''simple docstring'''
    super().__init__(
        __UpperCAmelCase		,			__UpperCAmelCase		,			tokenizer_file=__UpperCAmelCase		,			errors=__UpperCAmelCase		,			bos_token=__UpperCAmelCase		,			eos_token=__UpperCAmelCase		,			sep_token=__UpperCAmelCase		,			cls_token=__UpperCAmelCase		,			unk_token=__UpperCAmelCase		,			pad_token=__UpperCAmelCase		,			mask_token=__UpperCAmelCase		,			add_prefix_space=__UpperCAmelCase		,			trim_offsets=__UpperCAmelCase		,			**__UpperCAmelCase		,			)

    __lowerCamelCase  							=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
    if pre_tok_state.get('''add_prefix_space'''		,			__UpperCAmelCase			) != add_prefix_space:
       __lowerCamelCase  							=      getattr(__UpperCAmelCase		,			pre_tok_state.pop('''type'''			)			)
       __lowerCamelCase  							=      add_prefix_space
       __lowerCamelCase  							=      pre_tok_class(**__UpperCAmelCase			)

    __lowerCamelCase  							=      add_prefix_space

    __lowerCamelCase  							=      '''post_processor'''
    __lowerCamelCase  							=      getattr(self.backend_tokenizer		,			__UpperCAmelCase		,			__UpperCAmelCase			)
    if tokenizer_component_instance:
       __lowerCamelCase  							=      json.loads(tokenizer_component_instance.__getstate__()			)

       # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
       if "sep" in state:
          __lowerCamelCase  							=      tuple(state['''sep''']			)
       if "cls" in state:
          __lowerCamelCase  							=      tuple(state['''cls''']			)

       __lowerCamelCase  							=      False

       if state.get('''add_prefix_space'''		,			__UpperCAmelCase			) != add_prefix_space:
          __lowerCamelCase  							=      add_prefix_space
          __lowerCamelCase  							=      True

       if state.get('''trim_offsets'''		,			__UpperCAmelCase			) != trim_offsets:
          __lowerCamelCase  							=      trim_offsets
          __lowerCamelCase  							=      True

       if changes_to_apply:
          __lowerCamelCase  							=      getattr(__UpperCAmelCase		,			state.pop('''type'''			)			)
          __lowerCamelCase  							=      component_class(**__UpperCAmelCase			)
          setattr(self.backend_tokenizer		,			__UpperCAmelCase		,			__UpperCAmelCase			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    if self._mask_token is None:
       if self.verbose:
          logger.error('''Using mask_token, but it is not set yet.'''			)
       return None
    return str(self._mask_token			)




 @mask_token.setter
 def     lowerCamelCase (  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      AddedToken(__UpperCAmelCase		,			lstrip=__UpperCAmelCase		,			rstrip=__UpperCAmelCase			) if isinstance(__UpperCAmelCase		,			__UpperCAmelCase			) else value
    __lowerCamelCase  							=      value




 def     lowerCamelCase (  self		,			*__UpperCAmelCase		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      kwargs.get('''is_split_into_words'''		,			__UpperCAmelCase			)
    assert self.add_prefix_space or not is_split_into_words, (
        F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._batch_encode_plus(*__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			*__UpperCAmelCase		,			**__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      kwargs.get('''is_split_into_words'''		,			__UpperCAmelCase			)

    assert self.add_prefix_space or not is_split_into_words, (
        F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
        "to use it with pretokenized inputs."
    )

    return super()._encode_plus(*__UpperCAmelCase		,			**__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      self._tokenizer.model.save(__UpperCAmelCase		,			name=__UpperCAmelCase			)
    return tuple(__UpperCAmelCase			)




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase=None			):



    '''simple docstring'''
    __lowerCamelCase  							=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
       return output

    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      [self.sep_token_id]
    __lowerCamelCase  							=      [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 + sep + token_ids_a + sep			) * [0]






