




from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
__UpperCamelCase      : List[Any]					      =   load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
__UpperCamelCase      ,						__UpperCamelCase      ,						__UpperCamelCase      ,						__UpperCamelCase      : Optional[int]					      =   train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

__UpperCamelCase      : Dict					      =   KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
__UpperCamelCase      : str					      =   [[1, 2, 1, 4], [2, 3, 4, 5]]

__UpperCamelCase      : Union[str, Any]					      =   knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)







import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor

class 	__lowerCAmelCase			:
							def __init__(		self		:Dict	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:str       ):







								'''simple docstring'''







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







								'''simple docstring'''







								a       ,				a		       =							torchaudio.load(__magic_name__       )
								a		       =							self.resampler(__magic_name__       ).squeeze().numpy()
								return speech
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								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(__magic_name__	,   sampling_rate=1_6000	,   return_tensors="""pt"""	,   padding=__magic_name__       )
								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(__magic_name__	,   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(__magic_name__       )}
								a		       =							[]
								for c in transcript:
									if c in dictionary:
										tokens.append(dictionary[c]       )

								def get_trellis(__magic_name__		:Optional[Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:Any=0       ):
									a		       =							emission.size(0       )
									a		       =							len(__magic_name__       )

									# 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(__magic_name__       ):
										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(__magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42

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

									a		       =							[]
									for t in range(__magic_name__	,   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	,   __magic_name__       )       )

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

								a		       =							backtrack(__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															def __repr__(		self		:Union[str, Any]       ):







																'''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		:int       ):







																'''simple docstring'''







																return self.end - self.start

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

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





							def    lowerCamelCase__							(		self		:int	,   __magic_name__		:str	,   __magic_name__		:List[Any]	,   __magic_name__		:Optional[int]       ):







								'''simple docstring'''







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

								# load text file
								a		       =							open(__magic_name__	,   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       ,				a		       =							line.strip().split("""\t"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.wav"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.txt"""       )

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

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







def 	__A	(			)   -> Union[str, Any]:
	a		       =							argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name"""							,    type=__lowerCamelCase							,    default="""arijitx/wav2vec2-xls-r-300m-bengali"""							,    help="""wav2vec model name"""					)
	parser.add_argument("""--wav_dir"""							,    type=__lowerCamelCase							,    default="""./wavs"""							,    help="""directory containing wavs"""					)
	parser.add_argument("""--text_file"""							,    type=__lowerCamelCase							,    default="""script.txt"""							,    help="""file containing text"""					)
	parser.add_argument("""--input_wavs_sr"""							,    type=__lowerCamelCase							,    default=1_6000							,    help="""sampling rate of input audios"""					)
	parser.add_argument(
	    """--output_dir"""							,    type=__lowerCamelCase							,    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()

