


'''simple docstring'''

import functools
import operator

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


a_						:       Dict      			=				logging.get_logger(__name__)

a_						:       Optional[Any]      			=				{
    """microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}





class   snake_case					(					lowercase    ):





		"""simple docstring"""




		_lowerCamelCase						=						"wavlm"






		def __init__(					self    ,						UpperCamelCase=32    ,						UpperCamelCase=768    ,						UpperCamelCase=12    ,						UpperCamelCase=12    ,						UpperCamelCase=3072    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.0    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.1    ,						UpperCamelCase=0.02    ,						UpperCamelCase=1e-5    ,						UpperCamelCase="group"    ,						UpperCamelCase="gelu"    ,						UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)    ,						UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)    ,						UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)    ,						UpperCamelCase=False    ,						UpperCamelCase=128    ,						UpperCamelCase=16    ,						UpperCamelCase=320    ,						UpperCamelCase=800    ,						UpperCamelCase=False    ,						UpperCamelCase=True    ,						UpperCamelCase=0.05    ,						UpperCamelCase=10    ,						UpperCamelCase=2    ,						UpperCamelCase=0.0    ,						UpperCamelCase=10    ,						UpperCamelCase=320    ,						UpperCamelCase=2    ,						UpperCamelCase=0.1    ,						UpperCamelCase=100    ,						UpperCamelCase=256    ,						UpperCamelCase=256    ,						UpperCamelCase=0.1    ,						UpperCamelCase="mean"    ,						UpperCamelCase=False    ,						UpperCamelCase=False    ,						UpperCamelCase=256    ,						UpperCamelCase=(512, 512, 512, 512, 1500)    ,						UpperCamelCase=(5, 3, 3, 1, 1)    ,						UpperCamelCase=(1, 2, 3, 1, 1)    ,						UpperCamelCase=512    ,						UpperCamelCase=80    ,						UpperCamelCase=0    ,						UpperCamelCase=1    ,						UpperCamelCase=2    ,						UpperCamelCase=False    ,						UpperCamelCase=3    ,						UpperCamelCase=2    ,						UpperCamelCase=3    ,						UpperCamelCase=None    ,						**UpperCamelCase    ,						):







			"""simple docstring"""
			super().__init__(**UpperCamelCase    ,						pad_token_id=UpperCamelCase    ,						bos_token_id=UpperCamelCase    ,						eos_token_id=UpperCamelCase  )
			lowerCamelCase_  				=						hidden_size
			lowerCamelCase_  				=						feat_extract_norm
			lowerCamelCase_  				=						feat_extract_activation
			lowerCamelCase_  				=						list(UpperCamelCase  )
			lowerCamelCase_  				=						list(UpperCamelCase  )
			lowerCamelCase_  				=						list(UpperCamelCase  )
			lowerCamelCase_  				=						conv_bias
			lowerCamelCase_  				=						num_buckets
			lowerCamelCase_  				=						max_bucket_distance
			lowerCamelCase_  				=						num_conv_pos_embeddings
			lowerCamelCase_  				=						num_conv_pos_embedding_groups
			lowerCamelCase_  				=						len(self.conv_dim  )
			lowerCamelCase_  				=						num_hidden_layers
			lowerCamelCase_  				=						intermediate_size
			lowerCamelCase_  				=						hidden_act
			lowerCamelCase_  				=						num_attention_heads
			lowerCamelCase_  				=						hidden_dropout
			lowerCamelCase_  				=						attention_dropout
			lowerCamelCase_  				=						activation_dropout
			lowerCamelCase_  				=						feat_proj_dropout
			lowerCamelCase_  				=						final_dropout
			lowerCamelCase_  				=						layerdrop
			lowerCamelCase_  				=						layer_norm_eps
			lowerCamelCase_  				=						initializer_range
			lowerCamelCase_  				=						num_ctc_classes
			lowerCamelCase_  				=						vocab_size
			lowerCamelCase_  				=						do_stable_layer_norm
			lowerCamelCase_  				=						use_weighted_layer_sum
			lowerCamelCase_  				=						classifier_proj_size

			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
			lowerCamelCase_  				=						apply_spec_augment
			lowerCamelCase_  				=						mask_time_prob
			lowerCamelCase_  				=						mask_time_length
			lowerCamelCase_  				=						mask_time_min_masks
			lowerCamelCase_  				=						mask_feature_prob
			lowerCamelCase_  				=						mask_feature_length

			# parameters for pretraining with codevector quantized representations
			lowerCamelCase_  				=						num_codevectors_per_group
			lowerCamelCase_  				=						num_codevector_groups
			lowerCamelCase_  				=						contrastive_logits_temperature
			lowerCamelCase_  				=						num_negatives
			lowerCamelCase_  				=						codevector_dim
			lowerCamelCase_  				=						proj_codevector_dim
			lowerCamelCase_  				=						diversity_loss_weight

			# ctc loss
			lowerCamelCase_  				=						ctc_loss_reduction
			lowerCamelCase_  				=						ctc_zero_infinity

			# adapter
			lowerCamelCase_  				=						add_adapter
			lowerCamelCase_  				=						adapter_kernel_size
			lowerCamelCase_  				=						adapter_stride
			lowerCamelCase_  				=						num_adapter_layers
			lowerCamelCase_  				=						output_hidden_size or hidden_size

			# SequenceClassification-specific parameter. Feel free to ignore for other classes.
			lowerCamelCase_  				=						classifier_proj_size

			# XVector-specific parameters. Feel free to ignore for other classes.
			lowerCamelCase_  				=						list(UpperCamelCase  )
			lowerCamelCase_  				=						list(UpperCamelCase  )
			lowerCamelCase_  				=						list(UpperCamelCase  )
			lowerCamelCase_  				=						xvector_output_dim







		@property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			return functools.reduce(operator.mul    ,						self.conv_stride    ,						1  )







'''simple docstring'''

import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier





def __snake_case      (   UpperCAmelCase_		:		dict	):
 return (data["data"], data["target"])





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray	):
 lowerCamelCase_  				=						XGBClassifier()
 classifier.fit(UpperCAmelCase_      ,       UpperCAmelCase_	)
 return classifier





def __snake_case      (   ):
 lowerCamelCase_  				=						load_iris()
 lowerCamelCase_				,lowerCamelCase_  				=						data_handling(UpperCAmelCase_	)
 lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						train_test_split(
     UpperCAmelCase_      ,       UpperCAmelCase_      ,       test_size=0.25	)

 lowerCamelCase_  				=						iris["target_names"]

 # Create an XGBoost Classifier from the training data
 lowerCamelCase_  				=						xgboost(UpperCAmelCase_      ,       UpperCAmelCase_	)

 # Display the confusion matrix of the classifier with both training and test sets
 ConfusionMatrixDisplay.from_estimator(
     UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       display_labels=UpperCAmelCase_      ,       cmap="Blues"      ,       normalize="true"      ,       )
 plt.title("Normalized Confusion Matrix - IRIS Dataset"	)
 plt.show()


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()



