





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	,      _A	,      _A	,      _A						):






									'''simple docstring'''







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







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									UpperCamelCase      ,	UpperCamelCase							:		Any							      = torchaudio.load(_A						)
									UpperCamelCase							:		Union[str, Any]							      = self.resampler(_A						).squeeze().numpy()
									return speech







			def      _a			(			self	,      _A						):






									'''simple docstring'''







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

									UpperCamelCase							:		Optional[int]							      = self.speech_file_to_array_fn(item["""wav_path"""]						)
									UpperCamelCase							:		int							      = self.processor(_A	,      sampling_rate=1_6_0_0_0	,      return_tensors="""pt"""	,      padding=_A						)
									if self.cuda:
															UpperCamelCase							:		Any							      = inputs.to(device="""cuda"""						)

									with torch.no_grad():
															UpperCamelCase							:		Optional[Any]							      = self.model(inputs.input_values						).logits

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

									# get labels from vocab
									UpperCamelCase							:		Any							      = ([""""""] + 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(_A						)}
									UpperCamelCase							:		Optional[Any]							      = []
									for c in transcript:
															if c in dictionary:
																					tokens.append(dictionary[c]						)

									def get_trellis(_A	,      _A	,      _A=0						):
															UpperCamelCase							:		List[Any]							      = emission.size(0						)
															UpperCamelCase							:		List[str]							      = len(_A						)

															# 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							:		int							      = torch.full((num_frames + 1, num_tokens + 1)	,      -float("""inf"""						)						)
															UpperCamelCase							:		int							      = 0
															for t in range(_A						):
																					UpperCamelCase							:		Union[str, 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							:		Tuple							      = get_trellis(_A	,      _A	,      _A						)






									@dataclass
									class        lowercase__ :

												"""simple docstring"""
												__lowerCAmelCase    :							int
												__lowerCAmelCase    :							int
												__lowerCAmelCase    :							float

									def backtrack(_A	,      _A	,      _A	,      _A=0						):
															UpperCamelCase							:		Optional[Any]							      = trellis.size(1						) - 1
															UpperCamelCase							:		Dict							      = torch.argmax(trellis[:, j]						).item()

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

																					# 2. Store the path with frame-wise probability.
																					UpperCamelCase							:		Optional[Any]							      = 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	,      _A						)						)

																					# 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(_A	,      _A	,      _A	,      _A						)






									@dataclass
									class        lowercase__ :

												"""simple docstring"""
												__lowerCAmelCase    :							str
												__lowerCAmelCase    :							int
												__lowerCAmelCase    :							int
												__lowerCAmelCase    :							float







												def __repr__(			self						):






																		'''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      _a			(			self						):






																		'''simple docstring'''







																		return self.end - self.start

									def merge_repeats(_A						):
															UpperCamelCase      ,	UpperCamelCase							:		Union[str, Any]							      = 0, 0
															UpperCamelCase							:		List[str]							      = []
															while ia < len(_A						):
																					while ia < len(_A						) and path[ia].token_index == path[ia].token_index:
																											ia += 1
																					UpperCamelCase							:		Dict							      = sum(path[k].score for k in range(_A	,      _A						)						) / (ia - ia)
																					segments.append(
																					    Segment(
																					        transcript[path[ia].token_index]	,      path[ia].time_index	,      path[ia - 1].time_index + 1	,      _A	,      )						)
																					UpperCamelCase							:		Optional[int]							      = ia
															return segments

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







			def      _a			(			self	,      _A	,      _A	,      _A						):






									'''simple docstring'''







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

									# load text file
									UpperCamelCase							:		Dict							      = open(_A	,      encoding="""utf8"""						).readlines()

									UpperCamelCase							:		Tuple							      = []
									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							:		int							      = line.strip().split("""\t"""						)
															UpperCamelCase							:		Any							      = os.path.join(_A	,      wav_name + """.wav"""						)
															UpperCamelCase							:		List[Any]							      = os.path.join(_A	,      wav_name + """.txt"""						)

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

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


def     UpperCamelCase       ():
						UpperCamelCase							:		Any							      = 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=1_6000       ,    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"""    )

						UpperCamelCase							:		Dict							      = parser.parse_args()

						UpperCamelCase							:		Tuple							      = 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_torch_available, is_vision_available


__magic_name__					:				str	= {"""configuration_levit""": ["""LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LevitConfig""", """LevitOnnxConfig"""]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				Optional[int]	= ["""LevitFeatureExtractor"""]
		__magic_name__					:				Any	= ["""LevitImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__					:				List[Any]	= [
		    """LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LevitForImageClassification""",
		    """LevitForImageClassificationWithTeacher""",
		    """LevitModel""",
		    """LevitPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_levit import LevitFeatureExtractor
				from .image_processing_levit import LevitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_levit import (
				    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LevitForImageClassification,
				    LevitForImageClassificationWithTeacher,
				    LevitModel,
				    LevitPreTrainedModel,
				)
else:
		import sys

		__magic_name__					:				Tuple	= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



