


import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase	: Any  				=logging.get_logger(__name__)

__lowerCAmelCase	: Tuple  				={
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		'''unispeech-sat'''


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=32			,			lowerCAmelCase__			:int=768			,			lowerCAmelCase__			:Optional[int]=12			,			lowerCAmelCase__			:str=12			,			lowerCAmelCase__			:Dict=3_072			,			lowerCAmelCase__			:int="gelu"			,			lowerCAmelCase__			:str=0.1			,			lowerCAmelCase__			:Optional[Any]=0.1			,			lowerCAmelCase__			:Dict=0.1			,			lowerCAmelCase__			:str=0.0			,			lowerCAmelCase__			:str=0.0			,			lowerCAmelCase__			:Optional[int]=0.1			,			lowerCAmelCase__			:List[Any]=0.1			,			lowerCAmelCase__			:List[Any]=0.02			,			lowerCAmelCase__			:Union[str, Any]=1E-5			,			lowerCAmelCase__			:Any="group"			,			lowerCAmelCase__			:Union[str, Any]="gelu"			,			lowerCAmelCase__			:List[str]=(512, 512, 512, 512, 512, 512, 512)			,			lowerCAmelCase__			:Any=(5, 2, 2, 2, 2, 2, 2)			,			lowerCAmelCase__			:Optional[int]=(10, 3, 3, 3, 3, 2, 2)			,			lowerCAmelCase__			:str=False			,			lowerCAmelCase__			:Optional[int]=128			,			lowerCAmelCase__			:Optional[int]=16			,			lowerCAmelCase__			:str=False			,			lowerCAmelCase__			:Optional[int]=True			,			lowerCAmelCase__			:str=0.05			,			lowerCAmelCase__			:Any=10			,			lowerCAmelCase__			:str=2			,			lowerCAmelCase__			:Tuple=0.0			,			lowerCAmelCase__			:List[Any]=10			,			lowerCAmelCase__			:Tuple=0			,			lowerCAmelCase__			:List[str]=320			,			lowerCAmelCase__			:Optional[Any]=2			,			lowerCAmelCase__			:int=0.1			,			lowerCAmelCase__			:Optional[int]=100			,			lowerCAmelCase__			:Dict=256			,			lowerCAmelCase__			:Optional[Any]=256			,			lowerCAmelCase__			:Optional[int]=0.1			,			lowerCAmelCase__			:Optional[int]="mean"			,			lowerCAmelCase__			:Any=False			,			lowerCAmelCase__			:str=False			,			lowerCAmelCase__			:List[Any]=256			,			lowerCAmelCase__			:int=(512, 512, 512, 512, 1_500)			,			lowerCAmelCase__			:Optional[Any]=(5, 3, 3, 1, 1)			,			lowerCAmelCase__			:Optional[int]=(1, 2, 3, 1, 1)			,			lowerCAmelCase__			:Any=512			,			lowerCAmelCase__			:List[Any]=0			,			lowerCAmelCase__			:List[Any]=1			,			lowerCAmelCase__			:List[str]=2			,			lowerCAmelCase__			:int=504			,			**lowerCAmelCase__			:Any			,			)		->			Any:
										super().__init__(**lowerCAmelCase__			,			pad_token_id=lowerCAmelCase__			,			bos_token_id=lowerCAmelCase__			,			eos_token_id=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     Any					=		feat_extract_norm
										__SCREAMING_SNAKE_CASE						:     str					=		feat_extract_activation
										__SCREAMING_SNAKE_CASE						:     int					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     int					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		conv_bias
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_conv_pos_embeddings
										__SCREAMING_SNAKE_CASE						:     Any					=		num_conv_pos_embedding_groups
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		len(self.conv_dim	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     Dict					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     str					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     str					=		hidden_dropout
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		attention_dropout
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		activation_dropout
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		feat_proj_dropout
										__SCREAMING_SNAKE_CASE						:     Any					=		final_dropout
										__SCREAMING_SNAKE_CASE						:     str					=		layerdrop
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:     List[str]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     int					=		num_clusters
										__SCREAMING_SNAKE_CASE						:     Any					=		do_stable_layer_norm
										__SCREAMING_SNAKE_CASE						:     Tuple					=		use_weighted_layer_sum

										if (
										    (len(self.conv_stride	) != self.num_feat_extract_layers)
										    or (len(self.conv_kernel	) != self.num_feat_extract_layers)
										    or (len(self.conv_dim	) != self.num_feat_extract_layers)
										):
																raise ValueError(
																    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
																    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
																    f''' {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,'''
																    f''' `len(config.conv_kernel) = {len(self.conv_kernel	)}`.'''	)

										# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
										__SCREAMING_SNAKE_CASE						:     Tuple					=		apply_spec_augment
										__SCREAMING_SNAKE_CASE						:     Dict					=		mask_time_prob
										__SCREAMING_SNAKE_CASE						:     Tuple					=		mask_time_length
										__SCREAMING_SNAKE_CASE						:     Dict					=		mask_time_min_masks
										__SCREAMING_SNAKE_CASE						:     str					=		mask_feature_prob
										__SCREAMING_SNAKE_CASE						:     Tuple					=		mask_feature_length
										__SCREAMING_SNAKE_CASE						:     Tuple					=		mask_feature_min_masks

										# parameters for pretraining with codevector quantized representations
										__SCREAMING_SNAKE_CASE						:     str					=		num_codevectors_per_group
										__SCREAMING_SNAKE_CASE						:     Dict					=		num_codevector_groups
										__SCREAMING_SNAKE_CASE						:     str					=		contrastive_logits_temperature
										__SCREAMING_SNAKE_CASE						:     List[str]					=		feat_quantizer_dropout
										__SCREAMING_SNAKE_CASE						:     str					=		num_negatives
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		codevector_dim
										__SCREAMING_SNAKE_CASE						:     Tuple					=		proj_codevector_dim
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		diversity_loss_weight

										# ctc loss
										__SCREAMING_SNAKE_CASE						:     Tuple					=		ctc_loss_reduction
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		ctc_zero_infinity

										# SequenceClassification-specific parameter. Feel free to ignore for other classes.
										__SCREAMING_SNAKE_CASE						:     Dict					=		classifier_proj_size

										# XVector-specific parameters. Feel free to ignore for other classes.
										__SCREAMING_SNAKE_CASE						:     Any					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		xvector_output_dim



				@property
				def 		__magic_name__(       self			:Optional[int]	)		->			Optional[Any]:
										return functools.reduce(operator.mul			,			self.conv_stride			,			1	)




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						return np.linalg.norm(np.array(lowercase__      ) - np.array(lowercase__      )      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__=5      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		zip(lowercase__    ,       lowercase__      )
						# List of distances of all points from the point to be classified
						__SCREAMING_SNAKE_CASE						:     Dict					=		[]
						for data_point in data:
												__SCREAMING_SNAKE_CASE						:     Tuple					=		euclidean_distance(data_point[0]    ,       lowercase__      )
												distances.append((distance, data_point[1])      )
						# Choosing 'k' points with the least distances.
						__SCREAMING_SNAKE_CASE						:     int					=		[i[1] for i in sorted(lowercase__      )[:k]]
						# Most commonly occurring class among them
						# is the class into which the point is classified
						__SCREAMING_SNAKE_CASE						:     Any					=		Counter(lowercase__      ).most_common(1      )[0][0]
						return classes[result]


if __name__ == "__main__":
						print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
