

"""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 ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							torchaudio.load(lowerCamelCase__      )
        lowerCAmelCase__      :							List[str]    =							self.resampler(lowerCamelCase__      ).squeeze().numpy()
        return speech






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

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

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

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

        # get the emission probability at frame level
        lowerCAmelCase__      :							List[str]    =							torch.log_softmax(lowerCamelCase__ ,				dim=-1      )
        lowerCAmelCase__      :							Optional[int]    =							emissions[0].cpu().detach()

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

        lowerCAmelCase__      :							Optional[Any]    =							{c: i for i, c in enumerate(lowerCamelCase__      )}
        lowerCAmelCase__      :							Dict    =							[]
        for c in transcript:
         if c in dictionary:
          tokens.append(dictionary[c]      )

        def get_trellis(snake_case ,				snake_case ,				snake_case=0      ):
         lowerCAmelCase__      :							Union[str, Any]    =							emission.size(0      )
         lowerCAmelCase__      :							Optional[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.
         lowerCAmelCase__      :							Tuple    =							torch.full((num_frames + 1, num_tokens + 1) ,				-float("inf"      )      )
         lowerCAmelCase__      :							int    =							0
         for t in range(lowerCamelCase__      ):
          lowerCAmelCase__      :							Dict    =							torch.maximum(
              # Score for staying at the same token
              trellis[t, 1:] + emission[t, blank_id] ,				trellis[t, :-1] + emission[t, tokens] ,				)
         return trellis

        lowerCAmelCase__      :							str    =							get_trellis(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )






        @dataclass
        class  __a				:





               """simple docstring"""


               __UpperCamelCase				:			int
               __UpperCamelCase				:			int
               __UpperCamelCase				:			float

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

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

          # 2. Store the path with frame-wise probability.
          lowerCAmelCase__      :							Optional[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 ,				lowerCamelCase__      )      )

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

        lowerCAmelCase__      :							str    =							backtrack(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      )






        @dataclass
        class  __a				:





               """simple docstring"""


               __UpperCamelCase				:			str
               __UpperCamelCase				:			int
               __UpperCamelCase				:			int
               __UpperCamelCase				:			float






               def __repr__(      self      ):





                """simple docstring"""

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






               @property
               def  SCREAMING_SNAKE_CASE_  (      self      ):





                """simple docstring"""

                return self.end - self.start

        def merge_repeats(snake_case      ):
         lowerCAmelCase__      :							List[Any]    =							0, 0
         lowerCAmelCase__      :							Union[str, Any]    =							[]
         while ia < len(lowerCamelCase__      ):
          while ia < len(lowerCamelCase__      ) and path[ia].token_index == path[ia].token_index:
           ia += 1
          lowerCAmelCase__      :							Dict    =							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__ ,				)      )
          lowerCAmelCase__      :							Optional[int]    =							ia
         return segments

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

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

        # load text file
        lowerCAmelCase__      :							Dict    =							open(lowerCamelCase__ ,				encoding="utf8"      ).readlines()

        lowerCAmelCase__      :							int    =							[]
        for line in lines:
         if len(line.strip().split("\t"      )      ) != 2:
          print("Script must be in format: 00001  this is my sentence"      )
          exit()

         lowerCAmelCase__      :							int    =							line.strip().split("\t"      )
         lowerCAmelCase__      :							List[Any]    =							os.path.join(lowerCamelCase__ ,				wav_name + ".wav"      )
         lowerCAmelCase__      :							List[str]    =							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       (   ) ->	Optional[int]:
 lowerCAmelCase__      :							List[Any]    =							argparse.ArgumentParser()

 parser.add_argument(
     "--model_name"				,				type=lowercase__				,				default="arijitx/wav2vec2-xls-r-300m-bengali"				,				help="wav2vec model name"      )
 parser.add_argument("--wav_dir"				,				type=lowercase__				,				default="./wavs"				,				help="directory containing wavs"      )
 parser.add_argument("--text_file"				,				type=lowercase__				,				default="script.txt"				,				help="file containing text"      )
 parser.add_argument("--input_wavs_sr"				,				type=lowercase__				,				default=1_6_0_0_0				,				help="sampling rate of input audios"      )
 parser.add_argument(
     "--output_dir"				,				type=lowercase__				,				default="./out_alignment"				,				help="output directory containing the alignment files"      )
 parser.add_argument("--cuda"				,				action="store_true"      )

 lowerCAmelCase__      :							str    =							parser.parse_args()

 lowerCAmelCase__      :							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"""

# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	List[str]:
 lowerCAmelCase__      :							Tuple    =							Queue()
 lowerCAmelCase__      :							Optional[int]    =							[False] * len(lowercase__      )
 lowerCAmelCase__      :							Dict    =							[-1] * len(lowercase__      )

 def bfs():
  while not queue.empty():
   lowerCAmelCase__      :							Optional[Any]    =							queue.get()
   lowerCAmelCase__      :							Union[str, Any]    =							True

   for neighbour in graph[u]:
    if neighbour == u:
     return False

    if color[neighbour] == -1:
     lowerCAmelCase__      :							Any    =							1 - color[u]
     queue.put(lowercase__      )

    elif color[neighbour] == color[u]:
     return False

  return True

 for i in range(len(lowercase__      )      ):
  if not visited[i]:
   queue.put(lowercase__      )
   lowerCAmelCase__      :							int    =							0
   if bfs() is False:
    return False

 return True


if __name__ == "__main__":
    # Adjacency List of graph
    print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))






