import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 				_lowercase						:






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       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     _a						(						self					,	lowerCamelCase__   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  torchaudio.load(lowerCamelCase__   )
       lowerCAmelCase_:       List[str]         =  self.resampler(lowerCamelCase__   ).squeeze().numpy()
       return speech




    def     _a						(						self					,	lowerCamelCase__   ):
       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(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__=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 				_lowercase						:






           '''simple docstring'''
           SCREAMING_SNAKE_CASE:							int
           SCREAMING_SNAKE_CASE:							int
           SCREAMING_SNAKE_CASE:							float

       def backtrack(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__=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 				_lowercase						:






           '''simple docstring'''
           SCREAMING_SNAKE_CASE:							str
           SCREAMING_SNAKE_CASE:							int
           SCREAMING_SNAKE_CASE:							int
           SCREAMING_SNAKE_CASE:							float




           def __repr__(						self   ):
              return F'''{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'''




           @property
           def     _a						(						self   ):
              return self.end - self.start

       def merge_repeats(lowerCamelCase__   ):
          lowerCAmelCase_     ,  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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       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_     ,  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 		snake_case__  (		):
   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=16000       ,							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()
from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


a     :			Optional[Any]					    =						{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Union[str, Any]					    =						[
     """Constraint""",
     """ConstraintListState""",
     """DisjunctiveConstraint""",
     """PhrasalConstraint""",
 ]
 a     :			Optional[int]					    =						[
     """BeamHypotheses""",
     """BeamScorer""",
     """BeamSearchScorer""",
     """ConstrainedBeamSearchScorer""",
 ]
 a     :			Any					    =						[
     """EpsilonLogitsWarper""",
     """EtaLogitsWarper""",
     """ForcedBOSTokenLogitsProcessor""",
     """ForcedEOSTokenLogitsProcessor""",
     """HammingDiversityLogitsProcessor""",
     """InfNanRemoveLogitsProcessor""",
     """LogitsProcessor""",
     """LogitsProcessorList""",
     """LogitsWarper""",
     """MinLengthLogitsProcessor""",
     """MinNewTokensLengthLogitsProcessor""",
     """NoBadWordsLogitsProcessor""",
     """NoRepeatNGramLogitsProcessor""",
     """PrefixConstrainedLogitsProcessor""",
     """RepetitionPenaltyLogitsProcessor""",
     """SequenceBiasLogitsProcessor""",
     """EncoderRepetitionPenaltyLogitsProcessor""",
     """TemperatureLogitsWarper""",
     """TopKLogitsWarper""",
     """TopPLogitsWarper""",
     """TypicalLogitsWarper""",
     """EncoderNoRepeatNGramLogitsProcessor""",
     """ExponentialDecayLengthPenalty""",
     """LogitNormalization""",
 ]
 a     :			int					    =						[
     """MaxNewTokensCriteria""",
     """MaxLengthCriteria""",
     """MaxTimeCriteria""",
     """StoppingCriteria""",
     """StoppingCriteriaList""",
     """validate_stopping_criteria""",
 ]
 a     :			Dict					    =						[
     """GenerationMixin""",
     """top_k_top_p_filtering""",
     """GreedySearchEncoderDecoderOutput""",
     """GreedySearchDecoderOnlyOutput""",
     """SampleEncoderDecoderOutput""",
     """SampleDecoderOnlyOutput""",
     """BeamSearchEncoderDecoderOutput""",
     """BeamSearchDecoderOnlyOutput""",
     """BeamSampleEncoderDecoderOutput""",
     """BeamSampleDecoderOnlyOutput""",
     """ContrastiveSearchEncoderDecoderOutput""",
     """ContrastiveSearchDecoderOnlyOutput""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Optional[Any]					    =						[
     """TFForcedBOSTokenLogitsProcessor""",
     """TFForcedEOSTokenLogitsProcessor""",
     """TFLogitsProcessor""",
     """TFLogitsProcessorList""",
     """TFLogitsWarper""",
     """TFMinLengthLogitsProcessor""",
     """TFNoBadWordsLogitsProcessor""",
     """TFNoRepeatNGramLogitsProcessor""",
     """TFRepetitionPenaltyLogitsProcessor""",
     """TFTemperatureLogitsWarper""",
     """TFTopKLogitsWarper""",
     """TFTopPLogitsWarper""",
     """TFForceTokensLogitsProcessor""",
     """TFSuppressTokensAtBeginLogitsProcessor""",
     """TFSuppressTokensLogitsProcessor""",
 ]
 a     :			Any					    =						[
     """TFGenerationMixin""",
     """tf_top_k_top_p_filtering""",
     """TFGreedySearchDecoderOnlyOutput""",
     """TFGreedySearchEncoderDecoderOutput""",
     """TFSampleEncoderDecoderOutput""",
     """TFSampleDecoderOnlyOutput""",
     """TFBeamSearchEncoderDecoderOutput""",
     """TFBeamSearchDecoderOnlyOutput""",
     """TFBeamSampleEncoderDecoderOutput""",
     """TFBeamSampleDecoderOnlyOutput""",
     """TFContrastiveSearchEncoderDecoderOutput""",
     """TFContrastiveSearchDecoderOnlyOutput""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a     :			Union[str, Any]					    =						[
     """FlaxForcedBOSTokenLogitsProcessor""",
     """FlaxForcedEOSTokenLogitsProcessor""",
     """FlaxLogitsProcessor""",
     """FlaxLogitsProcessorList""",
     """FlaxLogitsWarper""",
     """FlaxMinLengthLogitsProcessor""",
     """FlaxTemperatureLogitsWarper""",
     """FlaxTopKLogitsWarper""",
     """FlaxTopPLogitsWarper""",
 ]
 a     :			Dict					    =						[
     """FlaxGenerationMixin""",
     """FlaxGreedySearchOutput""",
     """FlaxSampleOutput""",
     """FlaxBeamSearchOutput""",
 ]

if TYPE_CHECKING:
 from .configuration_utils import GenerationConfig
 from .streamers import TextIteratorStreamer, TextStreamer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  from .logits_process import (
      EncoderNoRepeatNGramLogitsProcessor,
      EncoderRepetitionPenaltyLogitsProcessor,
      EpsilonLogitsWarper,
      EtaLogitsWarper,
      ExponentialDecayLengthPenalty,
      ForcedBOSTokenLogitsProcessor,
      ForcedEOSTokenLogitsProcessor,
      HammingDiversityLogitsProcessor,
      InfNanRemoveLogitsProcessor,
      LogitNormalization,
      LogitsProcessor,
      LogitsProcessorList,
      LogitsWarper,
      MinLengthLogitsProcessor,
      MinNewTokensLengthLogitsProcessor,
      NoBadWordsLogitsProcessor,
      NoRepeatNGramLogitsProcessor,
      PrefixConstrainedLogitsProcessor,
      RepetitionPenaltyLogitsProcessor,
      SequenceBiasLogitsProcessor,
      TemperatureLogitsWarper,
      TopKLogitsWarper,
      TopPLogitsWarper,
      TypicalLogitsWarper,
  )
  from .stopping_criteria import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )
  from .utils import (
      BeamSampleDecoderOnlyOutput,
      BeamSampleEncoderDecoderOutput,
      BeamSearchDecoderOnlyOutput,
      BeamSearchEncoderDecoderOutput,
      ContrastiveSearchDecoderOnlyOutput,
      ContrastiveSearchEncoderDecoderOutput,
      GenerationMixin,
      GreedySearchDecoderOnlyOutput,
      GreedySearchEncoderDecoderOutput,
      SampleDecoderOnlyOutput,
      SampleEncoderDecoderOutput,
      top_k_top_p_filtering,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tf_logits_process import (
      TFForcedBOSTokenLogitsProcessor,
      TFForcedEOSTokenLogitsProcessor,
      TFForceTokensLogitsProcessor,
      TFLogitsProcessor,
      TFLogitsProcessorList,
      TFLogitsWarper,
      TFMinLengthLogitsProcessor,
      TFNoBadWordsLogitsProcessor,
      TFNoRepeatNGramLogitsProcessor,
      TFRepetitionPenaltyLogitsProcessor,
      TFSuppressTokensAtBeginLogitsProcessor,
      TFSuppressTokensLogitsProcessor,
      TFTemperatureLogitsWarper,
      TFTopKLogitsWarper,
      TFTopPLogitsWarper,
  )
  from .tf_utils import (
      TFBeamSampleDecoderOnlyOutput,
      TFBeamSampleEncoderDecoderOutput,
      TFBeamSearchDecoderOnlyOutput,
      TFBeamSearchEncoderDecoderOutput,
      TFContrastiveSearchDecoderOnlyOutput,
      TFContrastiveSearchEncoderDecoderOutput,
      TFGenerationMixin,
      TFGreedySearchDecoderOnlyOutput,
      TFGreedySearchEncoderDecoderOutput,
      TFSampleDecoderOnlyOutput,
      TFSampleEncoderDecoderOutput,
      tf_top_k_top_p_filtering,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .flax_logits_process import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxLogitsWarper,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )
  from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
 import sys

 a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)