



import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor



class 						_snake_case					:





 """simple docstring"""



 def __init__(  self		,			a		,			a		,			a    )		->							str:




   """simple docstring"""







   _A							=			cuda
   _A							=			AutoConfig.from_pretrained(UpperCAmelCase_    )
   _A							=			AutoModelForCTC.from_pretrained(UpperCAmelCase_    )
   self.model.eval()
   if self.cuda:
     self.model.to(device='''cuda'''    )
   _A							=			AutoProcessor.from_pretrained(UpperCAmelCase_    )
   _A							=			torchaudio.transforms.Resample(UpperCAmelCase_		,			1_6_0_0_0    )
   _A							=			0
   _A							=			list(self.processor.tokenizer.get_vocab().keys()    )
   for i in range(len(UpperCAmelCase_    )    ):
     if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
       _A							=			i
   print('''Blank Token id [PAD]/<pad>'''		,			UpperCAmelCase_    )
   _A							=			blank_id
 def lowercase_						(  self		,			a    )		->							List[Any]:




   """simple docstring"""







   _A							=			torchaudio.load(UpperCAmelCase_    )
   _A							=			self.resampler(UpperCAmelCase_    ).squeeze().numpy()
   return speech
 def lowercase_						(  self		,			a    )		->							str:




   """simple docstring"""







   _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(UpperCAmelCase_		,			sampling_rate=1_6_0_0_0		,			return_tensors='''pt'''		,			padding=UpperCAmelCase_    )
   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(UpperCAmelCase_		,			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(UpperCAmelCase_    )}
   _A							=			[]
   for c in transcript:
     if c in dictionary:
       tokens.append(dictionary[c]    )

   def get_trellis(a		,			a		,			a=0    ):
     _A							=			emission.size(0    )
     _A							=			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.
     _A							=			torch.full((num_frames + 1, num_tokens + 1)		,			-float('''inf'''    )    )
     _A							=			0
     for t in range(UpperCAmelCase_    ):
       _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(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_    )



   @dataclass
   class 						_snake_case					:





    """simple docstring"""



    lowerCamelCase_								=	42
    lowerCamelCase_								=	42
    lowerCamelCase_								=	42

   def backtrack(a		,			a		,			a		,			a=0    ):
     _A							=			trellis.size(1    ) - 1
     _A							=			torch.argmax(trellis[:, j]    ).item()

     _A							=			[]
     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.
       _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		,			UpperCAmelCase_    )    )

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

   _A							=			backtrack(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_    )



   @dataclass
   class 						_snake_case					:





    """simple docstring"""



    lowerCamelCase_								=	42
    lowerCamelCase_								=	42
    lowerCamelCase_								=	42
    lowerCamelCase_								=	42
    def __repr__(  self    )		->							Union[str, Any]:




      """simple docstring"""







      return f'''{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}'''



    @property
    def lowercase_						(  self    )		->							List[str]:




      """simple docstring"""







      return self.end - self.start

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

   _A							=			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		,			a		,			a		,			a    )		->							Optional[int]:




   """simple docstring"""







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

   # load text file
   _A							=			open(UpperCAmelCase_		,			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							=			line.strip().split('''\t'''    )
     _A							=			os.path.join(UpperCAmelCase_		,			wav_name + '''.wav'''    )
     _A							=			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 			UpperCAmelCase__       (							)      ->       Optional[Any]:
  _A							=			argparse.ArgumentParser()

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

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


UpperCAmelCase:   int       			=							{
    """configuration_utils""": ["""GenerationConfig"""],
    """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase:   List[Any]       			=							[
		    """Constraint""",
		    """ConstraintListState""",
		    """DisjunctiveConstraint""",
		    """PhrasalConstraint""",
		]
		UpperCAmelCase:   Optional[Any]       			=							[
		    """BeamHypotheses""",
		    """BeamScorer""",
		    """BeamSearchScorer""",
		    """ConstrainedBeamSearchScorer""",
		]
		UpperCAmelCase:   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""",
		]
		UpperCAmelCase:   str       			=							[
		    """MaxNewTokensCriteria""",
		    """MaxLengthCriteria""",
		    """MaxTimeCriteria""",
		    """StoppingCriteria""",
		    """StoppingCriteriaList""",
		    """validate_stopping_criteria""",
		]
		UpperCAmelCase:   Optional[Any]       			=							[
		    """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:
		UpperCAmelCase:   Any       			=							[
		    """TFForcedBOSTokenLogitsProcessor""",
		    """TFForcedEOSTokenLogitsProcessor""",
		    """TFLogitsProcessor""",
		    """TFLogitsProcessorList""",
		    """TFLogitsWarper""",
		    """TFMinLengthLogitsProcessor""",
		    """TFNoBadWordsLogitsProcessor""",
		    """TFNoRepeatNGramLogitsProcessor""",
		    """TFRepetitionPenaltyLogitsProcessor""",
		    """TFTemperatureLogitsWarper""",
		    """TFTopKLogitsWarper""",
		    """TFTopPLogitsWarper""",
		    """TFForceTokensLogitsProcessor""",
		    """TFSuppressTokensAtBeginLogitsProcessor""",
		    """TFSuppressTokensLogitsProcessor""",
		]
		UpperCAmelCase:   Tuple       			=							[
		    """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:
		UpperCAmelCase:   Tuple       			=							[
		    """FlaxForcedBOSTokenLogitsProcessor""",
		    """FlaxForcedEOSTokenLogitsProcessor""",
		    """FlaxLogitsProcessor""",
		    """FlaxLogitsProcessorList""",
		    """FlaxLogitsWarper""",
		    """FlaxMinLengthLogitsProcessor""",
		    """FlaxTemperatureLogitsWarper""",
		    """FlaxTopKLogitsWarper""",
		    """FlaxTopPLogitsWarper""",
		]
		UpperCAmelCase:   int       			=							[
		    """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

		UpperCAmelCase:   Optional[Any]       			=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



