





"""simple docstring"""




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		:    Any, lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    List[Any], lowerCAmelCase__		:    Any							)	->      Union[str, Any]:




										'''simple docstring'''



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






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    Dict							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    torchaudio.load(lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    self.resampler(lowerCAmelCase__							).squeeze().numpy()
										return speech






				def 				snake_case	( self		:    List[str], lowerCAmelCase__		:    Dict							)	->      Tuple:




										'''simple docstring'''



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

										_UpperCamelCase			:					List[Any]     =    self.speech_file_to_array_fn(item['''wav_path''']							)
										_UpperCamelCase			:					Any     =    self.processor(lowerCAmelCase__, sampling_rate=1_6_0_0_0, return_tensors='''pt''', padding=lowerCAmelCase__							)
										if self.cuda:
																_UpperCamelCase			:					Dict     =    inputs.to(device='''cuda'''							)

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

										# get the emission probability at frame level
										_UpperCamelCase			:					str     =    torch.log_softmax(lowerCAmelCase__, dim=-1							)
										_UpperCamelCase			:					List[str]     =    emissions[0].cpu().detach()

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

										_UpperCamelCase			:					Dict     =    {c: i for i, c in enumerate(lowerCAmelCase__							)}
										_UpperCamelCase			:					str     =    []
										for c in transcript:
																if c in dictionary:
																						tokens.append(dictionary[c]							)

										def get_trellis(lowerCAmelCase__		:    Optional[Any], lowerCAmelCase__		:    List[Any], lowerCAmelCase__		:    List[Any]=0							):
																_UpperCamelCase			:					Any     =    emission.size(0							)
																_UpperCamelCase			:					Optional[int]     =    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.
																_UpperCamelCase			:					Tuple     =    torch.full((num_frames + 1, num_tokens + 1), -float('''inf'''							)							)
																_UpperCamelCase			:					int     =    0
																for t in range(lowerCAmelCase__							):
																						_UpperCamelCase			:					List[Any]     =    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			:					int     =    get_trellis(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__							)




										@dataclass
										class  _a						:
														UpperCamelCase								=    42
														UpperCamelCase								=    42
														UpperCamelCase								=    42

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

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

																						# 2. Store the path with frame-wise probability.
																						_UpperCamelCase			:					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]

										_UpperCamelCase			:					List[str]     =    backtrack(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__							)




										@dataclass
										class  _a						:
														UpperCamelCase								=    42
														UpperCamelCase								=    42
														UpperCamelCase								=    42
														UpperCamelCase								=    42






														def __repr__( self		:    Dict							)	->      int:




																				'''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 				snake_case	( self		:    Any							)	->      Dict:




																				'''simple docstring'''



																				return self.end - self.start

										def merge_repeats(lowerCAmelCase__		:    int							):
																_UpperCamelCase   ,							_UpperCamelCase			:					Dict     =    0, 0
																_UpperCamelCase			:					List[str]     =    []
																while ia < len(lowerCAmelCase__							):
																						while ia < len(lowerCAmelCase__							) and path[ia].token_index == path[ia].token_index:
																												ia += 1
																						_UpperCamelCase			:					Any     =    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__, )							)
																						_UpperCamelCase			:					Tuple     =    ia
																return segments

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






				def 				snake_case	( self		:    Dict, lowerCAmelCase__		:    str, lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    Dict							)	->      int:




										'''simple docstring'''



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

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

										_UpperCamelCase			:					List[Any]     =    []
										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			:					Tuple     =    line.strip().split('''\t'''							)
																_UpperCamelCase			:					Any     =    os.path.join(lowerCAmelCase__, wav_name + '''.wav'''							)
																_UpperCamelCase			:					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        a_				(							):
						_UpperCamelCase			:					Union[str, Any]     =    argparse.ArgumentParser()

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

						_UpperCamelCase			:					Optional[Any]     =    parser.parse_args()

						_UpperCamelCase			:					str     =    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 tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Dict:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 5]    ,     _SCREAMING_SNAKE_CASE=[5, 4]    ,     )		->     List[str]:
           pass




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               ConfigMixin.load_config('''dummy_path''' )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   SampleObject()
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           A_     =   SampleObject(_name_or_path='''lalala''' )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           A_     =   SampleObject(c=6 )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           A_     =   SampleObject(1    ,     c=6 )
           A_     =   obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 					__A       (      self )		->     int:
           A_     =   SampleObject()
           A_     =   obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_SCREAMING_SNAKE_CASE )
               A_     =   SampleObject.from_config(SampleObject.load_config(_SCREAMING_SNAKE_CASE ) )
               A_     =   new_obj.config

           # unfreeze configs
           A_     =   dict(_SCREAMING_SNAKE_CASE )
           A_     =   dict(_SCREAMING_SNAKE_CASE )

           assert config.pop('''c''' ) == (2, 5)  # instantiated as tuple
           assert new_config.pop('''c''' ) == [2, 5]  # saved & loaded as list because of json
           config.pop('''_use_default_values''' )
           assert config == new_config

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDIMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     List[str]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerAncestralDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   PNDMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Tuple:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDPMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler'''    ,     prediction_type='''sample'''    ,     beta_end=8    ,     )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger_a:
               A_     =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''    ,     beta_start=88 )

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DPMSolverMultistepScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           A_     =   SampleObject()

           A_     =   {k: v for k, v in config.config.items() if not k.startswith('''_''' )}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys() ) == set(config.config._use_default_values )

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_SCREAMING_SNAKE_CASE )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               A_     =   SampleObjecta.from_config(_SCREAMING_SNAKE_CASE )

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]






