


import functools
import operator

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


_snake_case					:		List[Any]      =    logging.get_logger(__name__)

_snake_case					:		Dict      =    {
    """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     a		(__A				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  'unispeech-sat'

							def __init__(							self	:  Dict   ,     lowerCamelCase	:  Tuple=32   ,     lowerCamelCase	:  int=768   ,     lowerCamelCase	:  Optional[int]=12   ,     lowerCamelCase	:  Tuple=12   ,     lowerCamelCase	:  Optional[int]=3072   ,     lowerCamelCase	:  int="gelu"   ,     lowerCamelCase	:  Dict=0.1   ,     lowerCamelCase	:  str=0.1   ,     lowerCamelCase	:  List[Any]=0.1   ,     lowerCamelCase	:  Optional[int]=0.0   ,     lowerCamelCase	:  str=0.0   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  List[str]=0.1   ,     lowerCamelCase	:  int=0.02   ,     lowerCamelCase	:  List[str]=1E-5   ,     lowerCamelCase	:  Union[str, Any]="group"   ,     lowerCamelCase	:  Optional[Any]="gelu"   ,     lowerCamelCase	:  Optional[int]=(512, 512, 512, 512, 512, 512, 512)   ,     lowerCamelCase	:  Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)   ,     lowerCamelCase	:  Dict=(10, 3, 3, 3, 3, 2, 2)   ,     lowerCamelCase	:  Any=False   ,     lowerCamelCase	:  Tuple=128   ,     lowerCamelCase	:  Dict=16   ,     lowerCamelCase	:  List[Any]=False   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  List[Any]=0.05   ,     lowerCamelCase	:  Optional[int]=10   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  Union[str, Any]=0.0   ,     lowerCamelCase	:  List[str]=10   ,     lowerCamelCase	:  List[Any]=0   ,     lowerCamelCase	:  int=320   ,     lowerCamelCase	:  Optional[int]=2   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  str=100   ,     lowerCamelCase	:  Union[str, Any]=256   ,     lowerCamelCase	:  Tuple=256   ,     lowerCamelCase	:  int=0.1   ,     lowerCamelCase	:  int="mean"   ,     lowerCamelCase	:  List[str]=False   ,     lowerCamelCase	:  List[Any]=False   ,     lowerCamelCase	:  Optional[Any]=256   ,     lowerCamelCase	:  Optional[Any]=(512, 512, 512, 512, 1500)   ,     lowerCamelCase	:  List[Any]=(5, 3, 3, 1, 1)   ,     lowerCamelCase	:  Tuple=(1, 2, 3, 1, 1)   ,     lowerCamelCase	:  Dict=512   ,     lowerCamelCase	:  int=0   ,     lowerCamelCase	:  List[Any]=1   ,     lowerCamelCase	:  List[Any]=2   ,     lowerCamelCase	:  int=504   ,     **lowerCamelCase	:  Optional[int]   ,     )     ->   str:
														super().__init__(**__lowercase   ,     pad_token_id=__lowercase   ,     bos_token_id=__lowercase   ,     eos_token_id=__lowercase    )
														__snake_case		:      Union[str, Any]										=       hidden_size
														__snake_case		:      List[Any]										=       feat_extract_norm
														__snake_case		:      Optional[Any]										=       feat_extract_activation
														__snake_case		:      Dict										=       list(__lowercase    )
														__snake_case		:      Optional[Any]										=       list(__lowercase    )
														__snake_case		:      List[str]										=       list(__lowercase    )
														__snake_case		:      Union[str, Any]										=       conv_bias
														__snake_case		:      Optional[Any]										=       num_conv_pos_embeddings
														__snake_case		:      List[str]										=       num_conv_pos_embedding_groups
														__snake_case		:      Union[str, Any]										=       len(self.conv_dim    )
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      List[str]										=       intermediate_size
														__snake_case		:      Union[str, Any]										=       hidden_act
														__snake_case		:      Optional[Any]										=       num_attention_heads
														__snake_case		:      int										=       hidden_dropout
														__snake_case		:      Tuple										=       attention_dropout
														__snake_case		:      str										=       activation_dropout
														__snake_case		:      Dict										=       feat_proj_dropout
														__snake_case		:      List[str]										=       final_dropout
														__snake_case		:      str										=       layerdrop
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      Optional[int]										=       initializer_range
														__snake_case		:      Union[str, Any]										=       vocab_size
														__snake_case		:      List[str]										=       num_clusters
														__snake_case		:      List[Any]										=       do_stable_layer_norm
														__snake_case		:      str										=       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
														__snake_case		:      Tuple										=       apply_spec_augment
														__snake_case		:      Optional[Any]										=       mask_time_prob
														__snake_case		:      str										=       mask_time_length
														__snake_case		:      List[Any]										=       mask_time_min_masks
														__snake_case		:      List[Any]										=       mask_feature_prob
														__snake_case		:      str										=       mask_feature_length
														__snake_case		:      Union[str, Any]										=       mask_feature_min_masks

														# parameters for pretraining with codevector quantized representations
														__snake_case		:      List[Any]										=       num_codevectors_per_group
														__snake_case		:      Tuple										=       num_codevector_groups
														__snake_case		:      Optional[Any]										=       contrastive_logits_temperature
														__snake_case		:      Dict										=       feat_quantizer_dropout
														__snake_case		:      Optional[int]										=       num_negatives
														__snake_case		:      Optional[int]										=       codevector_dim
														__snake_case		:      Optional[int]										=       proj_codevector_dim
														__snake_case		:      List[Any]										=       diversity_loss_weight

														# ctc loss
														__snake_case		:      Union[str, Any]										=       ctc_loss_reduction
														__snake_case		:      List[str]										=       ctc_zero_infinity

														# SequenceClassification-specific parameter. Feel free to ignore for other classes.
														__snake_case		:      Optional[Any]										=       classifier_proj_size

														# XVector-specific parameters. Feel free to ignore for other classes.
														__snake_case		:      List[str]										=       list(__lowercase    )
														__snake_case		:      int										=       list(__lowercase    )
														__snake_case		:      List[str]										=       list(__lowercase    )
														__snake_case		:      Optional[int]										=       xvector_output_dim






							@property
							def    __snake_case (							self	:  Tuple    )     ->   Dict:
														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

_snake_case					:		Union[str, Any]      =    datasets.load_iris()

_snake_case					:		Tuple      =    np.array(data["data"])
_snake_case					:		int      =    np.array(data["target"])
_snake_case					:		int      =    data["target_names"]

_snake_case ,				_snake_case ,				_snake_case ,				_snake_case					:		Any      =    train_test_split(X, y)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return np.linalg.norm(np.array(__lowerCamelCase     ) - np.array(__lowerCamelCase     )     )







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

