





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      Any					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '			)
         with self.assertRaises(lowerCAmelCase			):
               DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      int					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)





"""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      :							Any,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Dict,     lowerCAmelCase      :							Optional[Any]			)    ->       Optional[Any]:
         lowercase  :      Optional[Any]					=   cuda
         lowercase  :      Optional[Any]					=   AutoConfig.from_pretrained(lowerCAmelCase			)
         lowercase  :      str					=   AutoModelForCTC.from_pretrained(lowerCAmelCase			)
         self.model.eval()
         if self.cuda:
               self.model.to(device='cuda'			)
         lowercase  :      Tuple					=   AutoProcessor.from_pretrained(lowerCAmelCase			)
         lowercase  :      Tuple					=   torchaudio.transforms.Resample(lowerCAmelCase,     16000			)
         lowercase  :      Any					=   0
         lowercase  :      Tuple					=   list(self.processor.tokenizer.get_vocab().keys()			)
         for i in range(len(lowerCAmelCase			)			):
               if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                     lowercase  :      Dict					=   i
         print('Blank Token id [PAD]/<pad>',     lowerCAmelCase			)
         lowercase  :      List[str]					=   blank_id


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							Optional[int]			)    ->       Optional[Any]:
         lowercase	,    lowercase  :      Any					=   torchaudio.load(lowerCAmelCase			)
         lowercase  :      List[Any]					=   self.resampler(lowerCAmelCase			).squeeze().numpy()
         return speech


   def        lowercase				(							self      :							Tuple,     lowerCAmelCase      :							Union[str, Any]			)    ->       Union[str, Any]:
         lowercase  :      List[Any]					=   self.blank_id
         lowercase  :      Any					=   '|'.join(item['sent'].split(' '			)			)
         if not os.path.isfile(item['wav_path']			):
               print(item['wav_path'],     'not found in wavs directory'			)

         lowercase  :      Dict					=   self.speech_file_to_array_fn(item['wav_path']			)
         lowercase  :      List[str]					=   self.processor(lowerCAmelCase,     sampling_rate=16000,     return_tensors='pt',     padding=lowerCAmelCase			)
         if self.cuda:
               lowercase  :      Union[str, Any]					=   inputs.to(device='cuda'			)

         with torch.no_grad():
               lowercase  :      List[Any]					=   self.model(inputs.input_values			).logits

         # get the emission probability at frame level
         lowercase  :      Dict					=   torch.log_softmax(lowerCAmelCase,     dim=-1			)
         lowercase  :      Optional[int]					=   emissions[0].cpu().detach()

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

         lowercase  :      Optional[int]					=   {c: i for i, c in enumerate(lowerCAmelCase			)}
         lowercase  :      Union[str, Any]					=   []
         for c in transcript:
               if c in dictionary:
                     tokens.append(dictionary[c]			)

         def get_trellis(lowerCAmelCase      :							List[str],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Union[str, Any]=0			):
               lowercase  :      Any					=   emission.size(0			)
               lowercase  :      Union[str, Any]					=   len(lowerCAmelCase			)

               # 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.
               lowercase  :      Optional[int]					=   torch.full((num_frames + 1, num_tokens + 1),     -float('inf'			)			)
               lowercase  :      str					=   0
               for t in range(lowerCAmelCase			):
                     lowercase  :      Optional[Any]					=   torch.maximum(
                         # Score for staying at the same token
                         trellis[t, 1:] + emission[t, blank_id],     trellis[t, :-1] + emission[t, tokens],     )
               return trellis

         lowercase  :      Any					=   get_trellis(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42

         def backtrack(lowerCAmelCase      :							Any,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							str,     lowerCAmelCase      :							Any=0			):
               lowercase  :      Tuple					=   trellis.size(1			) - 1
               lowercase  :      Optional[int]					=   torch.argmax(trellis[:, j]			).item()

               lowercase  :      Any					=   []
               for t in range(lowerCAmelCase,     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.
                     lowercase  :      Optional[Any]					=   trellis[t - 1, j] + emission[t - 1, blank_id]
                     # Score for token changing from C-1 at T-1 to J at T.
                     lowercase  :      List[str]					=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                     # 2. Store the path with frame-wise probability.
                     lowercase  :      str					=   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,     lowerCAmelCase			)			)

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

         lowercase  :      int					=   backtrack(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)


         @dataclass
         class   a__		:
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42
            _lowerCamelCase												=				42


            def __repr__(							self      :							Union[str, Any]			)    ->       Optional[Any]:
                  return f'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''






            @property
            def        lowercase				(							self      :							Tuple			)    ->       int:
                  return self.end - self.start

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

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






   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							int			)    ->       Optional[Any]:
         if not os.path.exists(lowerCAmelCase			):
               os.makedirs(lowerCAmelCase			)

         # load text file
         lowercase  :      Dict					=   open(lowerCAmelCase,     encoding='utf8'			).readlines()

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

               lowercase	,    lowercase  :      int					=   line.strip().split('\t'			)
               lowercase  :      List[Any]					=   os.path.join(lowerCAmelCase,     wav_name + '.wav'			)
               lowercase  :      List[Any]					=   os.path.join(lowerCAmelCase,     wav_name + '.txt'			)

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

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




def   lowercase__     (			)     ->			Dict:






      '''simple docstring'''


      lowercase  :      Union[str, 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_60_00	,      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'				)

      lowercase  :      List[Any]					=   parser.parse_args()

      lowercase  :      Dict					=   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()
