'''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 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = cuda
       UpperCAmelCase_				:  List[str]  = AutoConfig.from_pretrained(snake_case_		)
       UpperCAmelCase_				:  List[Any]  = AutoModelForCTC.from_pretrained(snake_case_		)
       self.model.eval()
       if self.cuda:
            self.model.to(device='cuda'		)
       UpperCAmelCase_				:  Optional[int]  = AutoProcessor.from_pretrained(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = torchaudio.transforms.Resample(snake_case_ ,						1_6_0_0_0		)
       UpperCAmelCase_				:  str  = 0
       UpperCAmelCase_				:  Tuple  = list(self.processor.tokenizer.get_vocab().keys()		)
       for i in range(len(snake_case_		)		):
            if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                 UpperCAmelCase_				:  List[str]  = i
       print('Blank Token id [PAD]/<pad>' ,						snake_case_		)
       UpperCAmelCase_				:  Tuple  = blank_id


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = torchaudio.load(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = self.resampler(snake_case_		).squeeze().numpy()
       return speech


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[Any]  = self.speech_file_to_array_fn(item['wav_path']		)
       UpperCAmelCase_				:  Optional[Any]  = self.processor(snake_case_ ,						sampling_rate=1_6_0_0_0 ,						return_tensors='pt' ,						padding=snake_case_		)
       if self.cuda:
            UpperCAmelCase_				:  Any  = inputs.to(device='cuda'		)

       with torch.no_grad():
            UpperCAmelCase_				:  Optional[int]  = self.model(inputs.input_values		).logits

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

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

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

       def get_trellis(snake_case_ ,						snake_case_ ,						snake_case_=0		):
            UpperCAmelCase_				:  Union[str, Any]  = emission.size(0		)
            UpperCAmelCase_				:  List[Any]  = 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_				:  Any  = torch.full((num_frames + 1, num_tokens + 1) ,						-float('inf'		)		)
            UpperCAmelCase_				:  Any  = 0
            for t in range(snake_case_		):
                 UpperCAmelCase_				:  str  = 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_				:  Tuple  = get_trellis(snake_case_ ,						snake_case_ ,						snake_case_		)






       @dataclass
       class 	__SCREAMING_SNAKE_CASE		:







         '''simple docstring'''






         lowerCamelCase_						:int
         lowerCamelCase_						:int
         lowerCamelCase_						:float

       def backtrack(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_=0		):
            UpperCAmelCase_				:  Optional[Any]  = trellis.size(1		) - 1
            UpperCAmelCase_				:  str  = torch.argmax(trellis[:, j]		).item()

            UpperCAmelCase_				:  Union[str, Any]  = []
            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_				:  Union[str, Any]  = trellis[t - 1, j] + emission[t - 1, blank_id]
                 # Score for token changing from C-1 at T-1 to J at T.
                 UpperCAmelCase_				:  Optional[int]  = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                 # 2. Store the path with frame-wise probability.
                 UpperCAmelCase_				:  List[Any]  = 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_				:  Tuple  = backtrack(snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		)






       @dataclass
       class 	__SCREAMING_SNAKE_CASE		:







         '''simple docstring'''






         lowerCamelCase_						:str
         lowerCamelCase_						:int
         lowerCamelCase_						:int
         lowerCamelCase_						:float


         def __repr__(					self		):




              '''simple docstring'''

              return F'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''






         @property
         def _UpperCamelCase	(					self		):




              '''simple docstring'''

              return self.end - self.start

       def merge_repeats(snake_case_		):
            UpperCAmelCase_  ,     UpperCAmelCase_				:  Tuple  = 0, 0
            UpperCAmelCase_				:  Union[str, Any]  = []
            while ia < len(snake_case_		):
                 while ia < len(snake_case_		) and path[ia].token_index == path[ia].token_index:
                      ia += 1
                 UpperCAmelCase_				:  Optional[int]  = 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_				:  str  = ia
            return segments

       UpperCAmelCase_				:  Optional[Any]  = 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 ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

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

       # load text file
       UpperCAmelCase_				:  Optional[int]  = open(snake_case_ ,						encoding='utf8'		).readlines()

       UpperCAmelCase_				:  Dict  = []
       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_				:  List[str]  = line.strip().split('\t'		)
            UpperCAmelCase_				:  int  = os.path.join(snake_case_ ,						wav_name + '.wav'		)
            UpperCAmelCase_				:  Any  = 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    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = 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=16000      ,	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_				:  Optional[int]  = parser.parse_args()

     UpperCAmelCase_				:  Optional[int]  = 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 re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = min(re.search('^ +'      ,	lowerCamelCase_  ).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(lowerCamelCase_  )]  )


snake_case__	:     Dict      =    '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case__	:     Union[str, Any]      =    '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case__	:     int      =    '''\
# Begin of markdown

Some cool dataset card
'''






class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       with tempfile.TemporaryDirectory() as tmp_dir:
            UpperCAmelCase_				:  Tuple  = Path(snake_case_		) / 'README.md'
            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Optional[Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  List[str]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertEqual(snake_case_ ,						{}		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  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            - question-answering\n            task_ids:\n            - open-domain-qa\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  int  = _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(snake_case_		):
            DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  str  = _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(snake_case_		)






