




import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor





class 		A__				:






	def __init__(	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							cuda
						__magic_name__					:							Union[str, Any]		   =							AutoConfig.from_pretrained(lowerCamelCase__						)
						__magic_name__					:							List[Any]		   =							AutoModelForCTC.from_pretrained(lowerCamelCase__						)
						self.model.eval()
						if self.cuda:
											self.model.to(device='''cuda'''						)
						__magic_name__					:							int		   =							AutoProcessor.from_pretrained(lowerCamelCase__						)
						__magic_name__					:							Optional[int]		   =							torchaudio.transforms.Resample(lowerCamelCase__				,     16000						)
						__magic_name__					:							Optional[int]		   =							0
						__magic_name__					:							Any		   =							list(self.processor.tokenizer.get_vocab().keys()						)
						for i in range(len(lowerCamelCase__						)						):
											if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																__magic_name__					:							Union[str, Any]		   =							i
						print('''Blank Token id [PAD]/<pad>'''				,     lowerCamelCase__						)
						__magic_name__					:							List[Any]		   =							blank_id






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Any:




						"""simple docstring"""






						__magic_name__   , __magic_name__					:							List[Any]		   =							torchaudio.load(lowerCamelCase__						)
						__magic_name__					:							Tuple		   =							self.resampler(lowerCamelCase__						).squeeze().numpy()
						return speech






	def  lowercase      (	self				,     lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							self.blank_id
						__magic_name__					:							int		   =							'''|'''.join(item['''sent'''].split(''' '''						)						)
						if not os.path.isfile(item['''wav_path''']						):
											print(item['''wav_path''']				,     '''not found in wavs directory'''						)

						__magic_name__					:							Tuple		   =							self.speech_file_to_array_fn(item['''wav_path''']						)
						__magic_name__					:							Tuple		   =							self.processor(lowerCamelCase__				,     sampling_rate=16000				,     return_tensors='''pt'''				,     padding=lowerCamelCase__						)
						if self.cuda:
											__magic_name__					:							Union[str, Any]		   =							inputs.to(device='''cuda'''						)

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

						# get the emission probability at frame level
						__magic_name__					:							Optional[Any]		   =							torch.log_softmax(lowerCamelCase__				,     dim=-1						)
						__magic_name__					:							Dict		   =							emissions[0].cpu().detach()

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

						__magic_name__					:							List[str]		   =							{c: i for i, c in enumerate(lowerCamelCase__						)}
						__magic_name__					:							int		   =							[]
						for c in transcript:
											if c in dictionary:
																tokens.append(dictionary[c]						)

						def get_trellis(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase=0						):
											__magic_name__					:							List[Any]		   =							emission.size(0						)
											__magic_name__					:							str		   =							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.
											__magic_name__					:							Dict		   =							torch.full((num_frames + 1, num_tokens + 1)				,     -float('''inf'''						)						)
											__magic_name__					:							List[str]		   =							0
											for t in range(lowerCamelCase__						):
																__magic_name__					:							List[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

						__magic_name__					:							int		   =							get_trellis(lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__						)





						@dataclass
						class 		A__				:
							lowerCamelCase__		: int
							lowerCamelCase__		: int
							lowerCamelCase__		: float

						def backtrack(lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase=0						):
											__magic_name__					:							List[Any]		   =							trellis.size(1						) - 1
											__magic_name__					:							int		   =							torch.argmax(trellis[:, j]						).item()

											__magic_name__					:							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.
																__magic_name__					:							int		   =							trellis[t - 1, j] + emission[t - 1, blank_id]
																# Score for token changing from C-1 at T-1 to J at T.
																__magic_name__					:							List[Any]		   =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

																# 2. Store the path with frame-wise probability.
																__magic_name__					:							List[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				,     lowerCamelCase__						)						)

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

						__magic_name__					:							Dict		   =							backtrack(lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__						)





						@dataclass
						class 		A__				:
							lowerCamelCase__		: str
							lowerCamelCase__		: int
							lowerCamelCase__		: int
							lowerCamelCase__		: float






							def __repr__(	self						)      ->   Dict:




												"""simple docstring"""






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






							@property
							def  lowercase      (	self						)      ->   Optional[int]:




												"""simple docstring"""






												return self.end - self.start

						def merge_repeats(lowerCamelCase						):
											__magic_name__   , __magic_name__					:							Dict		   =							0, 0
											__magic_name__					:							Optional[Any]		   =							[]
											while ia < len(lowerCamelCase__						):
																while ia < len(lowerCamelCase__						) and path[ia].token_index == path[ia].token_index:
																					ia += 1
																__magic_name__					:							List[str]		   =							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__				,     )						)
																__magic_name__					:							Any		   =							ia
											return segments

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






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase				,     lowerCamelCase						)      ->   int:




						"""simple docstring"""






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

						# load text file
						__magic_name__					:							Dict		   =							open(lowerCamelCase__				,     encoding='''utf8'''						).readlines()

						__magic_name__					:							Tuple		   =							[]
						for line in lines:
											if len(line.strip().split('''\t'''						)						) != 2:
																print('''Script must be in format: 00001  this is my sentence'''						)
																exit()

											__magic_name__   , __magic_name__					:							Any		   =							line.strip().split('''\t'''						)
											__magic_name__					:							Union[str, Any]		   =							os.path.join(lowerCamelCase__				,     wav_name + '''.wav'''						)
											__magic_name__					:							Dict		   =							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  lowerCAmelCase  (	)  ->str:






					"""simple docstring"""






					__magic_name__					:							int		   =							argparse.ArgumentParser()

					parser.add_argument(
					    '''--model_name''',	type=a__,	default='''arijitx/wav2vec2-xls-r-300m-bengali''',	help='''wav2vec model name'''       )
					parser.add_argument('''--wav_dir''',	type=a__,	default='''./wavs''',	help='''directory containing wavs'''       )
					parser.add_argument('''--text_file''',	type=a__,	default='''script.txt''',	help='''file containing text'''       )
					parser.add_argument('''--input_wavs_sr''',	type=a__,	default=1_6000,	help='''sampling rate of input audios'''       )
					parser.add_argument(
					    '''--output_dir''',	type=a__,	default='''./out_alignment''',	help='''output directory containing the alignment files'''       )
					parser.add_argument('''--cuda''',	action='''store_true'''       )

					__magic_name__					:							str		   =							parser.parse_args()

					__magic_name__					:							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'''
import unittest

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BlipImageProcessor






class        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__=7					,		lowerCamelCase__=3					,		lowerCamelCase__=18					,		lowerCamelCase__=30					,		lowerCamelCase__=400					,		lowerCamelCase__=True					,		lowerCamelCase__=None					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,		lowerCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,		lowerCamelCase__=True					,		)    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						size if size is not None else {"""height""": 20, """width""": 20}
           __lowercase							=						parent
           __lowercase							=						batch_size
           __lowercase							=						num_channels
           __lowercase							=						image_size
           __lowercase							=						min_resolution
           __lowercase							=						max_resolution
           __lowercase							=						do_resize
           __lowercase							=						size
           __lowercase							=						do_normalize
           __lowercase							=						image_mean
           __lowercase							=						image_std
           __lowercase							=						do_pad
           __lowercase							=						do_convert_rgb

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           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_convert_rgb": self.do_convert_rgb,
               "do_pad": self.do_pad,
           }




       def     snake_case__			(    self					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False     )    ->					Tuple:

           """simple docstring"""



           assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

           if equal_resolution:
               __lowercase							=						[]
               for i in range(self.batch_size     ):
                   image_inputs.append(
                       np.random.randint(
                           255					,		size=(self.num_channels, self.max_resolution, self.max_resolution)					,		dtype=np.uinta     )     )
           else:
               __lowercase							=						[]
               for i in range(self.batch_size     ):
                   __lowercase				,__lowercase							=						np.random.choice(np.arange(self.min_resolution					,		self.max_resolution     )					,		2     )
                   image_inputs.append(np.random.randint(255					,		size=(self.num_channels, width, height)					,		dtype=np.uinta     )     )

           if not numpify and not torchify:
               # PIL expects the channel dimension as last dimension
               __lowercase							=						[Image.fromarray(np.moveaxis(lowerCamelCase__					,		0					,		-1     )     ) for x in image_inputs]

           if torchify:
               __lowercase							=						[torch.from_numpy(lowerCamelCase__     ) for x in image_inputs]

           return image_inputs






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Optional[int]    =					BlipImageProcessor if is_vision_available() else None

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						BlipImageProcessingTester(self     )

       @property
       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_resize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""size"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_mean"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_std"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_convert_rgb"""     )     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		Image.Image     )

           # Test not batched input
           __lowercase							=						image_processor(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
           __lowercase							=						image_processor(lowerCamelCase__					,		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     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random numpy tensors
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__					,		numpify=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		np.ndarray     )

           # Test not batched input
           __lowercase							=						image_processor(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
           __lowercase							=						image_processor(lowerCamelCase__					,		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     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PyTorch tensors
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__					,		torchify=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		torch.Tensor     )

           # Test not batched input
           __lowercase							=						image_processor(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
           __lowercase							=						image_processor(lowerCamelCase__					,		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"""],
               )					,		)






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Union[str, Any]    =					BlipImageProcessor if is_vision_available() else None

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						BlipImageProcessingTester(self					,		num_channels=4     )
           __lowercase							=						3

       @property
       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_resize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""size"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_mean"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_std"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_convert_rgb"""     )     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		Image.Image     )

           # Test not batched input
           __lowercase							=						image_processor(image_inputs[0]					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)




