"""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   :    Optional[int]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Optional[int]						,  lowerCamelCase_   :    Dict     )      -> 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        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    str     )      -> List[Any]:
      __a      ,  __a			      =						torchaudio.load(lowerCamelCase_     )
      __a			      =						self.resampler(lowerCamelCase_     ).squeeze().numpy()
      return speech



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    Any     )      -> Any:
      __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_   :    str						,  lowerCamelCase_   :    List[Any]						,  lowerCamelCase_   :    str=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 					a					:
        A_ :				int
        A_ :				int
        A_ :				float

      def backtrack(lowerCamelCase_   :    str						,  lowerCamelCase_   :    Dict						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    Union[str, Any]=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 					a					:
        A_ :				str
        A_ :				int
        A_ :				int
        A_ :				float



        def __repr__(		self   :    str     )      -> Dict:
            return F"""{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"""



        @property
        def        lowerCAmelCase_				(		self   :    Any     )      -> 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        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    str						,  lowerCamelCase_   :    int     )      -> Any:
      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 						UpperCamelCase				(	):
    __a			      =						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"""						)

    __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()

"""simple docstring"""


from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    """cross_attention""",
    """0.20.0""",
    """Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.""",
    standard_warn=False,
)


__A       			=      AttentionProcessor






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Union[str, Any]						,  **lowerCamelCase_   :    str     )      -> int:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Optional[int]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    Union[str, Any]     )      -> List[str]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    List[str]     )      -> Optional[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    str						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Any     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Union[str, Any]						,  *lowerCamelCase_   :    Optional[Any]						,  **lowerCamelCase_   :    Tuple     )      -> Union[str, Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[str]						,  *lowerCamelCase_   :    Dict						,  **lowerCamelCase_   :    List[Any]     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    Tuple						,  *lowerCamelCase_   :    List[str]						,  **lowerCamelCase_   :    Dict     )      -> Any:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )






class 					a					(				A_							):



  def __init__(		self   :    List[Any]						,  *lowerCamelCase_   :    Tuple						,  **lowerCamelCase_   :    Optional[Any]     )      -> List[Any]:
      __a			      =						F"""{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"     )     )} instead."""
      deprecate("""cross_attention"""						,  """0.20.0"""						,  lowerCamelCase_						,  standard_warn=lowerCamelCase_     )
      super().__init__(*lowerCamelCase_						,  **lowerCamelCase_     )
