



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       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_											=				cuda
         UpperCamelCase_											=				AutoConfig.from_pretrained(__SCREAMING_SNAKE_CASE      )
         UpperCamelCase_											=				AutoModelForCTC.from_pretrained(__SCREAMING_SNAKE_CASE      )
         self.model.eval()
         if self.cuda:
              self.model.to(device="cuda"      )
         UpperCamelCase_											=				AutoProcessor.from_pretrained(__SCREAMING_SNAKE_CASE      )
         UpperCamelCase_											=				torchaudio.transforms.Resample(__SCREAMING_SNAKE_CASE       ,	1_6000      )
         UpperCamelCase_											=				0
         UpperCamelCase_											=				list(self.processor.tokenizer.get_vocab().keys()      )
         for i in range(len(__SCREAMING_SNAKE_CASE      )      ):
              if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
                   UpperCamelCase_											=				i
         print("Blank Token id [PAD]/<pad>"       ,	__SCREAMING_SNAKE_CASE      )
         UpperCamelCase_											=				blank_id

    def  _lowerCamelCase   (	self       ,	snake_case__      ):

         '''simple docstring'''


         UpperCamelCase_					,      UpperCamelCase_											=				torchaudio.load(__SCREAMING_SNAKE_CASE      )
         UpperCamelCase_											=				self.resampler(__SCREAMING_SNAKE_CASE      ).squeeze().numpy()
         return speech

    def  _lowerCamelCase   (	self       ,	snake_case__      ):

         '''simple docstring'''


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

         UpperCamelCase_											=				self.speech_file_to_array_fn(item["wav_path"]      )
         UpperCamelCase_											=				self.processor(__SCREAMING_SNAKE_CASE       ,	sampling_rate=1_6000       ,	return_tensors="pt"       ,	padding=__SCREAMING_SNAKE_CASE      )
         if self.cuda:
              UpperCamelCase_											=				inputs.to(device="cuda"      )

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

         # get the emission probability at frame level
         UpperCamelCase_											=				torch.log_softmax(__SCREAMING_SNAKE_CASE       ,	dim=-1      )
         UpperCamelCase_											=				emissions[0].cpu().detach()

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

         UpperCamelCase_											=				{c: i for i, c in enumerate(__SCREAMING_SNAKE_CASE      )}
         UpperCamelCase_											=				[]
         for c in transcript:
              if c in dictionary:
                   tokens.append(dictionary[c]      )

         def get_trellis(snake_case__       ,	snake_case__       ,	snake_case__=0      ):
              UpperCamelCase_											=				emission.size(0      )
              UpperCamelCase_											=				len(__SCREAMING_SNAKE_CASE      )

              # 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.
              UpperCamelCase_											=				torch.full((num_frames + 1, num_tokens + 1)       ,	-float("inf"      )      )
              UpperCamelCase_											=				0
              for t in range(__SCREAMING_SNAKE_CASE      ):
                   UpperCamelCase_											=				torch.maximum(
                       # Score for staying at the same token
                       trellis[t, 1:] + emission[t, blank_id]       ,	trellis[t, :-1] + emission[t, tokens]       ,	)
              return trellis

         UpperCamelCase_											=				get_trellis(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE      )


         @dataclass
         class  _lowercase				:
             '''simple docstring'''







             lowercase__				  =  42
             lowercase__				  =  42
             lowercase__				  =  42

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

              UpperCamelCase_											=				[]
              for t in range(__SCREAMING_SNAKE_CASE       ,	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.
                   UpperCamelCase_											=				trellis[t - 1, j] + emission[t - 1, blank_id]
                   # Score for token changing from C-1 at T-1 to J at T.
                   UpperCamelCase_											=				trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

                   # 2. Store the path with frame-wise probability.
                   UpperCamelCase_											=				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       ,	__SCREAMING_SNAKE_CASE      )      )

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

         UpperCamelCase_											=				backtrack(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE      )


         @dataclass
         class  _lowercase				:
             '''simple docstring'''







             lowercase__				  =  42
             lowercase__				  =  42
             lowercase__				  =  42
             lowercase__				  =  42

             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  _lowerCamelCase   (	self      ):

                  '''simple docstring'''


                  return self.end - self.start

         def merge_repeats(snake_case__      ):
              UpperCamelCase_					,      UpperCamelCase_											=				0, 0
              UpperCamelCase_											=				[]
              while ia < len(__SCREAMING_SNAKE_CASE      ):
                   while ia < len(__SCREAMING_SNAKE_CASE      ) and path[ia].token_index == path[ia].token_index:
                        ia += 1
                   UpperCamelCase_											=				sum(path[k].score for k in range(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE      )      ) / (ia - ia)
                   segments.append(
                       Segment(
                           transcript[path[ia].token_index]       ,	path[ia].time_index       ,	path[ia - 1].time_index + 1       ,	__SCREAMING_SNAKE_CASE       ,	)      )
                   UpperCamelCase_											=				ia
              return segments

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

    def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

         '''simple docstring'''


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

         # load text file
         UpperCamelCase_											=				open(__SCREAMING_SNAKE_CASE       ,	encoding="utf8"      ).readlines()

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

              UpperCamelCase_					,      UpperCamelCase_											=				line.strip().split("\t"      )
              UpperCamelCase_											=				os.path.join(__SCREAMING_SNAKE_CASE       ,	wav_name + ".wav"      )
              UpperCamelCase_											=				os.path.join(__SCREAMING_SNAKE_CASE       ,	wav_name + ".txt"      )

              items.append({"sent": sentence, "wav_path": wav_path, "out_path": out_path}      )
         print("Number of samples found in script file"       ,	len(__SCREAMING_SNAKE_CASE      )      )

         for item in tqdm(__SCREAMING_SNAKE_CASE      ):
              self.align_single_sample(__SCREAMING_SNAKE_CASE      )




def 				_lowerCAmelCase      ():
     UpperCamelCase_											=				argparse.ArgumentParser()

     parser.add_argument(
         "--model_name"				,		type=snake_case__				,		default="arijitx/wav2vec2-xls-r-300m-bengali"				,		help="wav2vec model name")
     parser.add_argument("--wav_dir"				,		type=snake_case__				,		default="./wavs"				,		help="directory containing wavs")
     parser.add_argument("--text_file"				,		type=snake_case__				,		default="script.txt"				,		help="file containing text")
     parser.add_argument("--input_wavs_sr"				,		type=snake_case__				,		default=1_60_00				,		help="sampling rate of input audios")
     parser.add_argument(
         "--output_dir"				,		type=snake_case__				,		default="./out_alignment"				,		help="output directory containing the alignment files")
     parser.add_argument("--cuda"				,		action="store_true")

     UpperCamelCase_											=				parser.parse_args()

     UpperCamelCase_											=				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_tokenizers_available, is_torch_available


UpperCAmelCase				:							Optional[int]								={
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Optional[int]								=["""RealmTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase				:							Union[str, Any]								=[
	    """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RealmEmbedder""",
	    """RealmForOpenQA""",
	    """RealmKnowledgeAugEncoder""",
	    """RealmPreTrainedModel""",
	    """RealmReader""",
	    """RealmScorer""",
	    """load_tf_weights_in_realm""",
	]
	UpperCAmelCase				:							List[str]								=["""RealmRetriever"""]


if TYPE_CHECKING:
	from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
	from .tokenization_realm import RealmTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_realm import RealmTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_realm import (
		    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RealmEmbedder,
		    RealmForOpenQA,
		    RealmKnowledgeAugEncoder,
		    RealmPreTrainedModel,
		    RealmReader,
		    RealmScorer,
		    load_tf_weights_in_realm,
		)
		from .retrieval_realm import RealmRetriever


else:
	import sys

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