




'''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   _snake_case:






 def __init__(self					:     str						,		a					:     str						,		a					:     List[str]						,		a					:     Dict						)					->      Union[str, Any]:






    """simple docstring"""


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






 def 	_UpperCamelCase   (self					:     Dict						,		a					:     Union[str, Any]						)					->      Optional[Any]:






    """simple docstring"""


    A__     , A__         =    torchaudio.load(a						)
    A__         =    self.resampler(a						).squeeze().numpy()
    return speech






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Union[str, Any]						)					->      Any:






    """simple docstring"""


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

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

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

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

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

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

    def get_trellis(a					:     int						,		a					:     Optional[Any]						,		a					:     Union[str, Any]=0						):
       A__         =    emission.size(0						)
       A__         =    len(a						)

       # 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__         =    torch.full((num_frames + 1, num_tokens + 1)						,		-float('inf'						)						)
       A__         =    0
       for t in range(a						):
          A__         =    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__         =    get_trellis(a						,		a						,		a						)

    @dataclass
    class   _snake_case:
     __snake_case:       int
     __snake_case:       int
     __snake_case:       float

    def backtrack(a					:     str						,		a					:     Any						,		a					:     Any						,		a					:     List[Any]=0						):
       A__         =    trellis.size(1						) - 1
       A__         =    torch.argmax(trellis[:, j]						).item()

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

          # 2. Store the path with frame-wise probability.
          A__         =    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						,		a						)						)

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

    A__         =    backtrack(a						,		a						,		a						,		a						)

    @dataclass
    class   _snake_case:
     __snake_case:       str
     __snake_case:       int
     __snake_case:       int
     __snake_case:       float






     def __repr__(self					:     int						)					->      List[Any]:






        """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					:     Dict						)					->      Optional[Any]:






        """simple docstring"""


        return self.end - self.start

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

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






 def 	_UpperCamelCase   (self					:     str						,		a					:     Optional[Any]						,		a					:     Optional[int]						,		a					:     List[Any]						)					->      str:






    """simple docstring"""


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

    # load text file
    A__         =    open(a						,		encoding='utf8'						).readlines()

    A__         =    []
    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__         =    line.strip().split('\t'						)
       A__         =    os.path.join(a						,		wav_name + '.wav'						)
       A__         =    os.path.join(a						,		wav_name + '.txt'						)

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

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







def 	_A			(   ):






   '''simple docstring'''







   A__         =    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=16000			,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__         =    parser.parse_args()

   A__         =    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'''







import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


lowerCAmelCase_    	=							logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
lowerCAmelCase_    	=							direct_transformers_import(Path(__file__).parent)


lowerCAmelCase_    	=							{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}

class   _snake_case( UpperCAmelCase			):
 __snake_case:       str									=				['''feature_extractor''', '''tokenizer''']
 # Names need to be attr_class for attr in attributes
 __snake_case:       Optional[Any]									=				None
 __snake_case:       List[str]									=				None
 __snake_case:       Any									=				None






 def __init__(self					:     List[Any]						,		*a					:     Union[str, Any]						,		**a					:     Optional[int]						)					->      Any:






    """simple docstring"""


    for key in kwargs:
       if key not in self.attributes:
          raise TypeError(f"""Unexpected keyword argument {key}."""						)
    for arg, attribute_name in zip(a						,		self.attributes						):
       if attribute_name in kwargs:
          raise TypeError(f"""Got multiple values for argument {attribute_name}."""						)
       else:
          A__         =    arg

    if len(a						) != len(self.attributes						):
       raise ValueError(
           f"""This processor requires {len(self.attributes						)} arguments: {", ".join(self.attributes						)}. Got """
           f"""{len(a						)} arguments instead."""						)

    # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
    for attribute_name, arg in kwargs.items():
       A__         =    getattr(self						,		f"""{attribute_name}_class"""						)
       # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
       A__         =    AUTO_TO_BASE_CLASS_MAPPING.get(a						,		a						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) for n in class_name if n is not None						)
       else:
          A__         =    getattr(a						,		a						)

       if not isinstance(a						,		a						):
          raise ValueError(
              f"""Received a {type(a						).__name__} for argument {attribute_name}, but a {class_name} was expected."""						)

       setattr(self						,		a						,		a						)






 def __repr__(self					:     Optional[int]						)					->      Any:






    """simple docstring"""


    A__         =    [f"""- {name}: {repr(getattr(self						,		a						)						)}""" for name in self.attributes]
    A__         =    '\n'.join(a						)
    return f"""{self.__class__.__name__}:\n{attributes_repr}"""






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple						,		a					:     bool = False						,		**a					:     int						)					->      List[str]:






    """simple docstring"""


    os.makedirs(a						,		exist_ok=a						)

    if push_to_hub:
       A__         =    kwargs.pop('commit_message'						,		a						)
       A__         =    kwargs.pop('repo_id'						,		save_directory.split(os.path.sep						)[-1]						)
       A__         =    self._create_repo(a						,		**a						)
       A__         =    self._get_files_timestamps(a						)
    # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
    # loaded from the Hub.
    if self._auto_class is not None:
       A__         =    [getattr(self						,		a						) for attribute_name in self.attributes]
       A__         =    [(a.init_kwargs if isinstance(a						,		a						) else a) for a in attrs]
       custom_object_save(self						,		a						,		config=a						)

    for attribute_name in self.attributes:
       A__         =    getattr(self						,		a						)
       # Include the processor class in the attribute config so this processor can then be reloaded with the
       # `AutoProcessor` API.
       if hasattr(a						,		'_set_processor_class'						):
          attribute._set_processor_class(self.__class__.__name__						)
       attribute.save_pretrained(a						)

    if self._auto_class is not None:
       # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
       for attribute_name in self.attributes:
          A__         =    getattr(self						,		a						)
          if isinstance(a						,		a						):
             del attribute.init_kwargs["auto_map"]

    if push_to_hub:
       self._upload_modified_files(
           a						,		a						,		a						,		commit_message=a						,		token=kwargs.get('use_auth_token'						)						,		)






 @classmethod
 def 	_UpperCamelCase   (cls					:     int						,		a					:     Union[str, os.PathLike]						,		a					:     Optional[Union[str, os.PathLike]] = None						,		a					:     bool = False						,		a					:     bool = False						,		a					:     Optional[Union[str, bool]] = None						,		a					:     str = "main"						,		**a					:     Union[str, Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    A__         =    cache_dir
    A__         =    force_download
    A__         =    local_files_only
    A__         =    revision

    A__         =    kwargs.pop('use_auth_token'						,		a						)
    if use_auth_token is not None:
       warnings.warn(
           'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'						,		a						)
       if token is not None:
          raise ValueError(
              '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'						)
       A__         =    use_auth_token

    if token is not None:
       # change to `token` in a follow-up PR
       A__         =    token

    A__         =    cls._get_arguments_from_pretrained(a						,		**a						)
    return cls(*a						)






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[int]						,		a					:     Union[str, Any]="AutoProcessor"						)					->      Any:






    """simple docstring"""


    if not isinstance(a						,		a						):
       A__         =    auto_class.__name__

    import transformers.models.auto as auto_module

    if not hasattr(a						,		a						):
       raise ValueError(f"""{auto_class} is not a valid auto class."""						)

    A__         =    auto_class






 @classmethod
 def 	_UpperCamelCase   (cls					:     Optional[Any]						,		a					:     Optional[Any]						,		**a					:     Tuple						)					->      int:






    """simple docstring"""


    A__         =    []
    for attribute_name in cls.attributes:
       A__         =    getattr(cls						,		f"""{attribute_name}_class"""						)
       if isinstance(a						,		a						):
          A__         =    tuple(getattr(a						,		a						) if n is not None else None for n in class_name						)
          A__         =    kwargs.get('use_fast'						,		a						)
          if use_fast and classes[1] is not None:
             A__         =    classes[1]
          else:
             A__         =    classes[0]
       else:
          A__         =    getattr(a						,		a						)

       args.append(attribute_class.from_pretrained(a						,		**a						)						)
    return args






 @property
 def 	_UpperCamelCase   (self					:     str						)					->      List[str]:






    """simple docstring"""


    A__         =    getattr(self						,		self.attributes[0]						)
    return getattr(a						,		'model_input_names'						,		a						)




lowerCAmelCase_    	=							copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
    lowerCAmelCase_    	=							ProcessorMixin.push_to_hub.__doc__.format(
        object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
    )


