"""simple docstring"""





from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser


@dataclass
class   UpperCAmelCase_			:
      lowerCamelCase__	:					str										=						field(
          metadata={"help": "The output directory where the model will be written."}				,							)
      lowerCamelCase__	:					str										=						field(
          metadata={
              "help": (
                  "The encoder model checkpoint for weights initialization."
                  "Don't set if you want to train an encoder model from scratch."
              )
          }				,							)
      lowerCamelCase__	:					str										=						field(
          metadata={
              "help": (
                  "The decoder model checkpoint for weights initialization."
                  "Don't set if you want to train a decoder model from scratch."
              )
          }				,							)
      lowerCamelCase__	:					Optional[str]										=						field(
          default=_a				,							metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"})
      lowerCamelCase__	:					Optional[str]										=						field(
          default=_a				,							metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"})





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			HfArgumentParser((ModelArguments,)  )
       ((lowercase__)					,   )    :	List[str]     					=			parser.parse_args_into_dataclasses()

       # Load pretrained model and tokenizer

       # Use explicit specified encoder config
       if model_args.encoder_config_name:
              lowercase__    :	Dict     					=			AutoConfig.from_pretrained(model_args.encoder_config_name  )
       # Use pretrained encoder model's config
       else:
              lowercase__    :	Any     					=			AutoConfig.from_pretrained(model_args.encoder_model_name_or_path  )

       # Use explicit specified decoder config
       if model_args.decoder_config_name:
              lowercase__    :	str     					=			AutoConfig.from_pretrained(model_args.decoder_config_name  )
       # Use pretrained decoder model's config
       else:
              lowercase__    :	Tuple     					=			AutoConfig.from_pretrained(model_args.decoder_model_name_or_path  )

       # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
       lowercase__    :	Optional[int]     					=			True
       lowercase__    :	Optional[int]     					=			True

       lowercase__    :	Tuple     					=			FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
           encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path     ,				decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path     ,				encoder_config=_lowerCAmelCase     ,				decoder_config=_lowerCAmelCase     ,				)

       # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
       lowercase__    :	Optional[int]     					=			decoder_config.decoder_start_token_id
       lowercase__    :	str     					=			decoder_config.pad_token_id
       if decoder_start_token_id is None:
              lowercase__    :	Dict     					=			decoder_config.bos_token_id
       if pad_token_id is None:
              lowercase__    :	Tuple     					=			decoder_config.eos_token_id

       # This is necessary to make Flax's generate() work
       lowercase__    :	int     					=			decoder_config.eos_token_id
       lowercase__    :	List[str]     					=			decoder_start_token_id
       lowercase__    :	Any     					=			pad_token_id

       lowercase__    :	List[str]     					=			AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path  )

       lowercase__    :	Tuple     					=			AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path  )
       lowercase__    :	Optional[int]     					=			tokenizer.convert_ids_to_tokens(model.config.pad_token_id  )

       model.save_pretrained(model_args.output_dir  )
       image_processor.save_pretrained(model_args.output_dir  )
       tokenizer.save_pretrained(model_args.output_dir  )


if __name__ == "__main__":
       main()


"""simple docstring"""





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						:				dict  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray     ,				_lowerCAmelCase						:				np.ndarray  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			XGBRegressor(verbosity=0     ,				random_state=42  )
       xgb.fit(_lowerCAmelCase     ,				_lowerCAmelCase  )
       # Predict target for test data
       lowercase__    :	str     					=			xgb.predict(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			predictions.reshape(len(_lowerCAmelCase  )     ,				1  )
       return predictions





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			fetch_california_housing()
       lowercase__					,   lowercase__    :	str     					=			data_handling(_lowerCAmelCase  )
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	str     					=			train_test_split(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				test_size=0.2_5     ,				random_state=1  )
       lowercase__    :	Any     					=			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()

