






"""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  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   Any:




          '''simple docstring'''




          lowercase__:							Tuple         =     cuda
          lowercase__:							int         =     AutoConfig.from_pretrained(UpperCAmelCase_)
          lowercase__:							Union[str, Any]         =     AutoModelForCTC.from_pretrained(UpperCAmelCase_)
          self.model.eval()
          if self.cuda:
                 self.model.to(device="cuda")
          lowercase__:							int         =     AutoProcessor.from_pretrained(UpperCAmelCase_)
          lowercase__:							Optional[int]         =     torchaudio.transforms.Resample(UpperCAmelCase_					, 16_000)
          lowercase__:							Tuple         =     0
          lowercase__:							List[str]         =     list(self.processor.tokenizer.get_vocab().keys())
          for i in range(len(UpperCAmelCase_)):
                 if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                        lowercase__:							str         =     i
          print("Blank Token id [PAD]/<pad>"					, UpperCAmelCase_)
          lowercase__:							int         =     blank_id





   def __lowercase							(       self					, UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




          lowercase__    ,      lowercase__:							Any         =     torchaudio.load(UpperCAmelCase_)
          lowercase__:							Any         =     self.resampler(UpperCAmelCase_).squeeze().numpy()
          return speech





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




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

          lowercase__:							int         =     self.speech_file_to_array_fn(item["wav_path"])
          lowercase__:							int         =     self.processor(UpperCAmelCase_					, sampling_rate=16_000					, return_tensors="pt"					, padding=UpperCAmelCase_)
          if self.cuda:
                 lowercase__:							Union[str, Any]         =     inputs.to(device="cuda")

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

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

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

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

          def get_trellis(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0):
                 lowercase__:							Any         =     emission.size(0)
                 lowercase__:							List[str]         =     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.
                 lowercase__:							str         =     torch.full((num_frames + 1, num_tokens + 1)					, -float("inf"))
                 lowercase__:							str         =     0
                 for t in range(UpperCAmelCase_):
                        lowercase__:							Optional[int]         =     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__:							str         =     get_trellis(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)







          @dataclass
          class 						_a  :





             '''simple docstring'''






             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42

          def backtrack(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_=0):
                 lowercase__:							List[str]         =     trellis.size(1) - 1
                 lowercase__:							Dict         =     torch.argmax(trellis[:, j]).item()

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

                        # 2. Store the path with frame-wise probability.
                        lowercase__:							int         =     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]

          lowercase__:							Optional[Any]         =     backtrack(UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)







          @dataclass
          class 						_a  :





             '''simple docstring'''






             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42
             UpperCamelCase__     	=							42





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




                    '''simple docstring'''




                    return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""





             @property
             def __lowercase							(       self)			->   Any:




                    '''simple docstring'''




                    return self.end - self.start

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

          lowercase__:							Dict         =     merge_repeats(UpperCAmelCase_)
          with open(item["out_path"]					, "w") as out_align:
                 for seg in segments:
                        out_align.write(str(UpperCAmelCase_) + "\n")





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   List[str]:




          '''simple docstring'''




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

          # load text file
          lowercase__:							List[str]         =     open(UpperCAmelCase_					, 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__:							str         =     line.strip().split("\t")
                 lowercase__:							List[Any]         =     os.path.join(UpperCAmelCase_					, wav_name + ".wav")
                 lowercase__:							List[Any]         =     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(    ):
       """simple docstring"""





       lowercase__:							List[Any]         =     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=16000		,  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"						)

       lowercase__:							Any         =     parser.parse_args()

       lowercase__:							Any         =     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 argparse
import json
import re
import string
import sys
from collections import Counter
def _A		(		lowercase						):







    """simple docstring"""

    def remove_articles(lowercase						):
        return re.sub(R'''\b(a|an|the)\b'''			,       ''' '''			,       lowercase						)

    def white_space_fix(lowercase						):
        return " ".join(text.split()						)

    def remove_punc(lowercase						):
        a											=set(string.punctuation						)
        return "".join(ch for ch in text if ch not in exclude						)

    def lower(lowercase						):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(lowercase						)						)						)						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=normalize_answer(lowercase						).split()
    a											=normalize_answer(lowercase						).split()
    a											=Counter(lowercase						) & Counter(lowercase						)
    a											=sum(common.values()						)
    if num_same == 0:
        return 0
    a											=1.0 * num_same / len(lowercase						)
    a											=1.0 * num_same / len(lowercase						)
    a											=(2 * precision * recall) / (precision + recall)
    return fa
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    return normalize_answer(lowercase						) == normalize_answer(lowercase						)
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=[]
    for ground_truth in ground_truths:
        a											=metric_fn(lowercase			,       lowercase						)
        scores_for_ground_truths.append(lowercase						)
    return max(lowercase						)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=a											=a											=0
    for article in dataset:
        for paragraph in article["paragraphs"]:
            for qa in paragraph["qas"]:
                total += 1
                if qa["id"] not in predictions:
                    a											='''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
                    print(lowercase			,       file=sys.stderr						)
                    continue
                a											=[x['''text'''] for x in qa['''answers''']]
                a											=predictions[qa['''id''']]
                exact_match += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)
                fa += metric_max_over_ground_truths(lowercase			,       lowercase			,       lowercase						)

    a											=100.0 * exact_match / total
    a											=100.0 * fa / total

    return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
       lowerCamelCase_  :  List[Any]     		=    """1.1"""
       lowerCamelCase_  :  Optional[Any]     		=    argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
       parser.add_argument("""dataset_file""", help="""Dataset file""")
       parser.add_argument("""prediction_file""", help="""Prediction File""")
       lowerCamelCase_  :  Optional[Any]     		=    parser.parse_args()
       with open(args.dataset_file) as dataset_file:
              lowerCamelCase_  :  List[str]     		=    json.load(dataset_file)
              if dataset_json["version"] != expected_version:
                     print(
                         """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
                         file=sys.stderr,
                     )
              lowerCamelCase_  :  Dict     		=    dataset_json["""data"""]
       with open(args.prediction_file) as prediction_file:
              lowerCamelCase_  :  Optional[Any]     		=    json.load(prediction_file)
       print(json.dumps(evaluate(dataset, predictions)))