




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor
class 							__lowercase   :



     def __init__(  self   :							Union[str, Any]      ,		lowercase__   :							List[str]      ,		lowercase__   :							str      ,		lowercase__   :							Tuple       ):
       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 				__magic_name__		(  self   :							Dict      ,		lowercase__   :							Optional[int]       ):
       a_		    =  torchaudio.load(UpperCamelCase_       )
       a_		    =  self.resampler(UpperCamelCase_       ).squeeze().numpy()
       return speech



     def 				__magic_name__		(  self   :							Optional[Any]      ,		lowercase__   :							List[str]       ):
       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(lowercase__   :							Optional[Any]      ,		lowercase__   :							List[Any]      ,		lowercase__   :							Dict=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 							__lowercase   :
            _lowerCAmelCase		       =						42
            _lowerCAmelCase		       =						42
            _lowerCAmelCase		       =						42

       def backtrack(lowercase__   :							Union[str, Any]      ,		lowercase__   :							Union[str, Any]      ,		lowercase__   :							str      ,		lowercase__   :							str=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 							__lowercase   :
            _lowerCAmelCase		       =						42
            _lowerCAmelCase		       =						42
            _lowerCAmelCase		       =						42
            _lowerCAmelCase		       =						42



            def __repr__(  self   :							str       ):
              return f"{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"



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

       def merge_repeats(lowercase__   :							str       ):
         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 				__magic_name__		(  self   :							List[str]      ,		lowercase__   :							Optional[int]      ,		lowercase__   :							Optional[Any]      ,		lowercase__   :							Dict       ):
       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__  (							):






  """simple docstring"""


  a_		    =  argparse.ArgumentParser()

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






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SegformerImageProcessor

class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	def __init__(						self				: int					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Any=7					,						UpperCamelCase_				: Optional[Any]=3					,						UpperCamelCase_				: Optional[Any]=30					,						UpperCamelCase_				: List[Any]=400					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: List[str]=None					,						UpperCamelCase_				: str=True					,						UpperCamelCase_				: Tuple=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: int=[0.5, 0.5, 0.5]					,						UpperCamelCase_				: str=False					,						):
			lowerCAmelCase_   :       Tuple    							=size if size is not None else {'''height''': 30, '''width''': 30}
			lowerCAmelCase_   :       List[Any]    							=parent
			lowerCAmelCase_   :       int    							=batch_size
			lowerCAmelCase_   :       int    							=num_channels
			lowerCAmelCase_   :       Union[str, Any]    							=min_resolution
			lowerCAmelCase_   :       Tuple    							=max_resolution
			lowerCAmelCase_   :       Tuple    							=do_resize
			lowerCAmelCase_   :       List[str]    							=size
			lowerCAmelCase_   :       int    							=do_normalize
			lowerCAmelCase_   :       int    							=image_mean
			lowerCAmelCase_   :       str    							=image_std
			lowerCAmelCase_   :       List[Any]    							=do_reduce_labels





	def 	__A      (						self				: Optional[Any]      ):
			return {
			    "do_resize": self.do_resize,
			    "size": self.size,
			    "do_normalize": self.do_normalize,
			    "image_mean": self.image_mean,
			    "image_std": self.image_std,
			    "do_reduce_labels": self.do_reduce_labels,
			}







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Optional[int]    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

		lowerCAmelCase_   :       Optional[Any]    							=Image.open(dataset[0]['''file''']  )
		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[1]['''file''']  )

		return image, map







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Tuple    							=load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,					split='''test'''  )

		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[0]['''file''']  )
		lowerCAmelCase_   :       Tuple    							=Image.open(dataset[1]['''file''']  )
		lowerCAmelCase_   :       Optional[int]    							=Image.open(dataset[2]['''file''']  )
		lowerCAmelCase_   :       Union[str, Any]    							=Image.open(dataset[3]['''file''']  )

		return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 							_snake_case     (  lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		SegformerImageProcessor if is_vision_available() else None




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       List[str]    							=SegformerImageProcessingTester(self      )




	@property
	def 	__A      (						self				: str      ):
			return self.image_processor_tester.prepare_image_processor_dict()




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Optional[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_resize'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''size'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_normalize'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''image_mean'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''image_std'''      )      )
			self.assertTrue(hasattr(UpperCamelCase_					,						'''do_reduce_labels'''      )      )




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       str    							=self.image_processing_class.from_dict(self.image_processor_dict      )
			self.assertEqual(image_processor.size					,						{'''height''': 30, '''width''': 30}      )
			self.assertEqual(image_processor.do_reduce_labels					,						UpperCamelCase_      )

			lowerCAmelCase_   :       Tuple    							=self.image_processing_class.from_dict(self.image_processor_dict					,						size=42					,						reduce_labels=UpperCamelCase_      )
			self.assertEqual(image_processor.size					,						{'''height''': 42, '''width''': 42}      )
			self.assertEqual(image_processor.do_reduce_labels					,						UpperCamelCase_      )




	def 	__A      (						self				: Dict      ):
			pass




	def 	__A      (						self				: Any      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			lowerCAmelCase_   :       Dict    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						Image.Image      )

			# Test not batched input
			lowerCAmelCase_   :       int    							=image_processing(image_inputs[0]					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Union[str, Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			lowerCAmelCase_   :       List[str]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						numpify=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						np.ndarray      )

			# Test not batched input
			lowerCAmelCase_   :       Any    							=image_processing(image_inputs[0]					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       List[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       str    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[int]    							=image_processing(image_inputs[0]					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       str    							=image_processing(UpperCamelCase_					,						return_tensors='''pt'''      ).pixel_values
			self.assertEqual(
			    encoded_images.shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)




	def 	__A      (						self				: str      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       Optional[Any]    							=prepare_image_inputs(self.image_processor_tester					,						equal_resolution=UpperCamelCase_					,						torchify=UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=[]
			for image in image_inputs:
					self.assertIsInstance(UpperCamelCase_					,						torch.Tensor      )
					maps.append(torch.zeros(image.shape[-2:]      ).long()      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(image_inputs[0]					,						maps[0]					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        1,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test batched
			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        self.image_processor_tester.batch_size,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test not batched input (PIL images)
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=prepare_semantic_single_inputs()

			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        1,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        1,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )

			# Test batched input (PIL images)
			lowerCAmelCase_							,		lowerCAmelCase_   :       Any    							=prepare_semantic_batch_inputs()

			lowerCAmelCase_   :       Union[str, Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertEqual(
			    encoding['''pixel_values'''].shape					,						(
			        2,
			        self.image_processor_tester.num_channels,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(
			    encoding['''labels'''].shape					,						(
			        2,
			        self.image_processor_tester.size['''height'''],
			        self.image_processor_tester.size['''width'''],
			    )					,						)
			self.assertEqual(encoding['''labels'''].dtype					,						torch.long      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )





	def 	__A      (						self				: List[str]      ):
			# Initialize image_processing
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )

			# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
			lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=prepare_semantic_single_inputs()
			lowerCAmelCase_   :       Dict    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 150      )

			lowerCAmelCase_   :       Optional[Any]    							=True
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(UpperCamelCase_					,						UpperCamelCase_					,						return_tensors='''pt'''      )
			self.assertTrue(encoding['''labels'''].min().item() >= 0      )
			self.assertTrue(encoding['''labels'''].max().item() <= 255      )






