



"""simple docstring"""
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__       ):



  @register_to_config
  def __init__(						self :					Dict       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					float       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					bool = False       ,		)     ->					Optional[int]:


     '''simple docstring'''




     super().__init__()

     SCREAMING_SNAKE_CASE_: Tuple     			=nn.Embedding(lowerCAmelCase       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=nn.Embedding(lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=False

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=nn.Dropout(p=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=TaConfig(
         vocab_size=lowerCAmelCase       ,		d_model=lowerCAmelCase       ,		num_heads=lowerCAmelCase       ,		d_kv=lowerCAmelCase       ,		d_ff=lowerCAmelCase       ,		dropout_rate=lowerCAmelCase       ,		feed_forward_proj=lowerCAmelCase       ,		is_decoder=lowerCAmelCase       ,		is_encoder_decoder=lowerCAmelCase       ,		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=nn.ModuleList()
     for lyr_num in range(lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=TaBlock(lowerCAmelCase		)
        self.encoders.append(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=TaLayerNorm(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=nn.Dropout(p=lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Dict		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.token_embedder(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=encoder_input_tokens.shape[1]
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.arange(lowerCAmelCase       ,		device=encoder_input_tokens.device		)
     x += self.position_encoding(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=self.dropout_pre(lowerCAmelCase		)

     # inverted the attention mask
     SCREAMING_SNAKE_CASE_: List[Any]     			=encoder_input_tokens.size()
     SCREAMING_SNAKE_CASE_: Dict     			=self.get_extended_attention_mask(lowerCAmelCase       ,		lowerCAmelCase		)

     for lyr in self.encoders:
        SCREAMING_SNAKE_CASE_: Dict     			=lyr(lowerCAmelCase       ,		lowerCAmelCase		)[0]
     SCREAMING_SNAKE_CASE_: int     			=self.layer_norm(lowerCAmelCase		)

     return self.dropout_post(lowerCAmelCase		), encoder_inputs_mask








"""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      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

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


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



