




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 lowercase    (  SCREAMING_SNAKE_CASE )			->							tuple:


			'''simple docstring'''







			return (data["data"], data["target"])

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							np.ndarray:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						XGBRegressor(verbosity=0       ,    random_state=42 )
			xgb.fit(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			# Predict target for test data
			SCREAMING_SNAKE_CASE_								=						xgb.predict(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						predictions.reshape(len(SCREAMING_SNAKE_CASE )       ,    1 )
			return predictions

def lowercase    (  )			->							None:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						fetch_california_housing()
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						data_handling(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						train_test_split(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    test_size=0.25       ,    random_state=1 )
			SCREAMING_SNAKE_CASE_								=						xgboost(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			# Error printing
			print(F'Mean Absolute Error : {mean_absolute_error(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )}' )
			print(F'Mean Square Error  : {mean_squared_error(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
