


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       lowercase_					(lowercase__      ,					lowercase__      ,					lowercase__   ):





				@register_to_config
				def __init__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_ = False       ,  )  -> str:
											super().__init__()

											a__      =nn.Embedding(lowercase_       ,  lowercase_)

											a__      =nn.Embedding(lowercase_       ,  lowercase_)
											a__      =False

											a__      =nn.Dropout(p=lowercase_)

											a__      =TaConfig(
											    vocab_size=lowercase_       ,  d_model=lowercase_       ,  num_heads=lowercase_       ,  d_kv=lowercase_       ,  d_ff=lowercase_       ,  dropout_rate=lowercase_       ,  feed_forward_proj=lowercase_       ,  is_decoder=lowercase_       ,  is_encoder_decoder=lowercase_       ,  )

											a__      =nn.ModuleList()
											for lyr_num in range(lowercase_):
																		a__      =TaBlock(lowercase_)
																		self.encoders.append(lowercase_)

											a__      =TaLayerNorm(lowercase_)
											a__      =nn.Dropout(p=lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> Tuple:
											a__      =self.token_embedder(lowercase_)

											a__      =encoder_input_tokens.shape[1]
											a__      =torch.arange(lowercase_       ,  device=encoder_input_tokens.device)
											x += self.position_encoding(lowercase_)

											a__      =self.dropout_pre(lowercase_)

											# inverted the attention mask
											a__      =encoder_input_tokens.size()
											a__      =self.get_extended_attention_mask(lowercase_       ,  lowercase_)

											for lyr in self.encoders:
																		a__      =lyr(lowercase_       ,  lowercase_)[0]
											a__      =self.layer_norm(lowercase_)

											return self.dropout_post(lowercase_), encoder_inputs_mask






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def        __a			(   __lowerCAmelCase   )     ->     tuple:
					return (data["data"], data["target"])

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     np.ndarray:
					SCREAMING_SNAKE_CASE							:     int						=				XGBRegressor(verbosity=0      ,	random_state=42   )
					xgb.fit(__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Predict target for test data
					SCREAMING_SNAKE_CASE							:     List[Any]						=				xgb.predict(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     str						=				predictions.reshape(len(__lowerCAmelCase   )      ,	1   )
					return predictions

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Dict						=				fetch_california_housing()
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				data_handling(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				train_test_split(
					    __lowerCAmelCase      ,	__lowerCAmelCase      ,	test_size=0.25      ,	random_state=1   )
					SCREAMING_SNAKE_CASE							:     int						=				xgboost(__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )
					# Error printing
					print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )
					print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase      ,	__lowerCAmelCase   )}'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)
			main()