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_			:






   def __init__( self							:	Tuple      ,		lowercase_							:	List[str]      ,		lowercase_							:	Tuple      ,		lowercase_							:	List[Any]    )   ->		Optional[Any]:
       lowercase__					:  List[Any]          =							cuda
       lowercase__					:  List[str]          =							AutoConfig.from_pretrained(UpperCAmelCase__    )
       lowercase__					:  List[Any]          =							AutoModelForCTC.from_pretrained(UpperCAmelCase__    )
       self.model.eval()
       if self.cuda:
           self.model.to(device="cuda"    )
       lowercase__					:  Optional[Any]          =							AutoProcessor.from_pretrained(UpperCAmelCase__    )
       lowercase__					:  List[str]          =							torchaudio.transforms.Resample(UpperCAmelCase__      ,		1_60_00    )
       lowercase__					:  Union[str, Any]          =							0
       lowercase__					:  Optional[int]          =							list(self.processor.tokenizer.get_vocab().keys()    )
       for i in range(len(UpperCAmelCase__    )    ):
           if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
               lowercase__					:  List[Any]          =							i
       print("Blank Token id [PAD]/<pad>"      ,		UpperCAmelCase__    )
       lowercase__					:  List[Any]          =							blank_id






   def  __UpperCamelCase	( self							:	Any      ,		lowercase_							:	str    )   ->		int:
       lowercase__					:  Tuple          =							torchaudio.load(UpperCAmelCase__    )
       lowercase__					:  Any          =							self.resampler(UpperCAmelCase__    ).squeeze().numpy()
       return speech






   def  __UpperCamelCase	( self							:	Tuple      ,		lowercase_							:	Optional[int]    )   ->		Dict:
       lowercase__					:  List[Any]          =							self.blank_id
       lowercase__					:  List[str]          =							'''|'''.join(item["sent"].split(" "    )    )
       if not os.path.isfile(item["wav_path"]    ):
           print(item["wav_path"]      ,		"not found in wavs directory"    )

       lowercase__					:  Optional[Any]          =							self.speech_file_to_array_fn(item["wav_path"]    )
       lowercase__					:  Optional[int]          =							self.processor(UpperCAmelCase__      ,		sampling_rate=1_60_00      ,		return_tensors="pt"      ,		padding=UpperCAmelCase__    )
       if self.cuda:
           lowercase__					:  int          =							inputs.to(device="cuda"    )

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

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

       # get labels from vocab
       lowercase__					:  Any          =							([''''''] + 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__					:  List[str]          =							[]
       for c in transcript:
           if c in dictionary:
               tokens.append(dictionary[c]    )

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



       @dataclass
       class     snake_case_			:
          __A      :						Any   =							42
          __A      :						Tuple   =							42
          __A      :						str   =							42

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

           lowercase__					:  int          =							[]
           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__					:  Any          =							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[Any]          =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

               # 2. Store the path with frame-wise probability.
               lowercase__					:  str          =							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__					:  int          =							backtrack(UpperCAmelCase__      ,		UpperCAmelCase__      ,		UpperCAmelCase__      ,		UpperCAmelCase__    )



       @dataclass
       class     snake_case_			:
          __A      :						int   =							42
          __A      :						Any   =							42
          __A      :						Any   =							42
          __A      :						List[Any]   =							42






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







          @property
          def  __UpperCamelCase	( self							:	int    )   ->		List[str]:
              return self.end - self.start

       def merge_repeats(lowercase_							:	List[Any]    ):
           lowercase__					:  Any          =							0, 0
           lowercase__					:  List[Any]          =							[]
           while ia < len(UpperCAmelCase__    ):
               while ia < len(UpperCAmelCase__    ) and path[ia].token_index == path[ia].token_index:
                   ia += 1
               lowercase__					:  Dict          =							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__					:  Dict          =							ia
           return segments

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







   def  __UpperCamelCase	( self							:	int      ,		lowercase_							:	Tuple      ,		lowercase_							:	Tuple      ,		lowercase_							:	Any    )   ->		Tuple:
       if not os.path.exists(UpperCAmelCase__    ):
           os.makedirs(UpperCAmelCase__    )

       # load text file
       lowercase__					:  List[str]          =							open(UpperCAmelCase__      ,		encoding="utf8"    ).readlines()

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

           lowercase__					:  Dict          =							line.strip().split("\t"    )
           lowercase__					:  Dict          =							os.path.join(UpperCAmelCase__      ,		wav_name + ".wav"    )
           lowercase__					:  Tuple          =							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      lowercase_							(     ):
    lowercase__					:  str          =							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=1_6000						,      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")

    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'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
