



from sklearn.metrics import precision_score

import datasets


UpperCAmelCase_		   =				"""
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
"""


UpperCAmelCase_		   =				"""
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to 'warn'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - 'warn': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

    Example 1-A simple binary example
        >>> precision_metric = datasets.load_metric(\"precision\")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'precision': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> precision_metric = datasets.load_metric(\"precision\")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['precision'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric(\"precision\")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(results)
        {'precision': 0.23529411764705882}

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'precision': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'precision': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'precision': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results['precision']])
        [0.67, 0.0, 0.0]
"""


UpperCAmelCase_		   =				"""
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,     _KWARGS_DESCRIPTION)
class   lowerCamelCase__  (					datasets.Metric):



 """simple docstring"""

 def 						snake_case_			(						self				:				List[Any]			)      ->  int:
    return datasets.MetricInfo(
        description=_DESCRIPTION ,	citation=_CITATION ,	inputs_description=_KWARGS_DESCRIPTION ,	features=datasets.Features(
            {
                '''predictions''': datasets.Sequence(datasets.Value('''int32'''			)			),
                '''references''': datasets.Sequence(datasets.Value('''int32'''			)			),
            }
            if self.config_name == '''multilabel'''
            else {
                '''predictions''': datasets.Value('''int32'''			),
                '''references''': datasets.Value('''int32'''			),
            }			) ,	reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html'''] ,	)

 def 						snake_case_			(						self				:				Any ,	__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				Any ,	__lowerCAmelCase				:				int=None ,	__lowerCAmelCase				:				Optional[Any]=1 ,	__lowerCAmelCase				:				Tuple="binary" ,	__lowerCAmelCase				:				Optional[Any]=None ,	__lowerCAmelCase				:				Union[str, Any]="warn" ,	)      ->  int:
    _A						     =					precision_score(
        __lowerCAmelCase ,	__lowerCAmelCase ,	labels=__lowerCAmelCase ,	pos_label=__lowerCAmelCase ,	average=__lowerCAmelCase ,	sample_weight=__lowerCAmelCase ,	zero_division=__lowerCAmelCase ,	)
    return {"precision": float(__lowerCAmelCase			) if score.size == 1 else score}








import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor


class   lowerCamelCase__  :



 """simple docstring"""

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

 def 						snake_case_			(						self				:				Tuple ,	__lowerCAmelCase				:				List[Any]			)      ->  str:
    _A     ,						_A						     =					torchaudio.load(__lowerCAmelCase			)
    _A						     =					self.resampler(__lowerCAmelCase			).squeeze().numpy()
    return speech

 def 						snake_case_			(						self				:				Optional[int] ,	__lowerCAmelCase				:				Tuple			)      ->  Tuple:
    _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(__lowerCAmelCase ,	sampling_rate=1_60_00 ,	return_tensors='''pt''' ,	padding=__lowerCAmelCase			)
    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(__lowerCAmelCase ,	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(__lowerCAmelCase			)}
    _A						     =					[]
    for c in transcript:
       if c in dictionary:
          tokens.append(dictionary[c]			)

    def get_trellis(__lowerCAmelCase				:				List[str] ,	__lowerCAmelCase				:				Optional[int] ,	__lowerCAmelCase				:				int=0			):
       _A						     =					emission.size(0			)
       _A						     =					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.
       _A						     =					torch.full((num_frames + 1, num_tokens + 1) ,	-float('''inf'''			)			)
       _A						     =					0
       for t in range(__lowerCAmelCase			):
          _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(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)


    @dataclass
    class   lowerCamelCase__  :



     """simple docstring"""

     a__				:   int
     a__				:   int
     a__				:   float

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

       _A						     =					[]
       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.
          _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 ,	__lowerCAmelCase			)			)

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

    _A						     =					backtrack(__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase ,	__lowerCAmelCase			)


    @dataclass
    class   lowerCamelCase__  :



     """simple docstring"""

     a__				:   str
     a__				:   int
     a__				:   int
     a__				:   float

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

     @property
     def 						snake_case_			(						self				:				Dict			)      ->  str:
        return self.end - self.start

    def merge_repeats(__lowerCAmelCase				:				Any			):
       _A     ,						_A						     =					0, 0
       _A						     =					[]
       while ia < len(__lowerCAmelCase			):
          while ia < len(__lowerCAmelCase			) and path[ia].token_index == path[ia].token_index:
             ia += 1
          _A						     =					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 ,	)			)
          _A						     =					ia
       return segments

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

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

    # load text file
    _A						     =					open(__lowerCAmelCase ,	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(__lowerCAmelCase ,	wav_name + '''.wav'''			)
       _A						     =					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   SCREAMING_SNAKE_CASE_       (  )     ->    Dict:
   _A						     =					argparse.ArgumentParser()

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



