






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Optional[Any]									=['''image_processor''', '''tokenizer''']
       a    :		Union[str, Any]									='''BlipImageProcessor'''
       a    :		Union[str, Any]									='''AutoTokenizer'''





       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         super().__init__(_lowerCamelCase  ,      _lowerCamelCase							)

         # add QFormer tokenizer
         UpperCamelCase_: Tuple   				=    qformer_tokenizer





       def __call__( self  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = False  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = 0  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = False  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         if images is None and text is None:
           raise ValueError('You have to specify at least images or text.'							)

         UpperCamelCase_: Tuple   				=    BatchFeature()

         if text is not None:
           UpperCamelCase_: Optional[int]   				=    self.tokenizer(
               text=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase  ,      max_length=_lowerCamelCase  ,      stride=_lowerCamelCase  ,      pad_to_multiple_of=_lowerCamelCase  ,      return_attention_mask=_lowerCamelCase  ,      return_overflowing_tokens=_lowerCamelCase  ,      return_special_tokens_mask=_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      return_token_type_ids=_lowerCamelCase  ,      return_length=_lowerCamelCase  ,      verbose=_lowerCamelCase  ,      return_tensors=_lowerCamelCase  ,      **_lowerCamelCase  ,      )
           encoding.update(_lowerCamelCase							)
           UpperCamelCase_: Union[str, Any]   				=    self.qformer_tokenizer(
               text=_lowerCamelCase  ,      add_special_tokens=_lowerCamelCase  ,      padding=_lowerCamelCase  ,      truncation=_lowerCamelCase  ,      max_length=_lowerCamelCase  ,      stride=_lowerCamelCase  ,      pad_to_multiple_of=_lowerCamelCase  ,      return_attention_mask=_lowerCamelCase  ,      return_overflowing_tokens=_lowerCamelCase  ,      return_special_tokens_mask=_lowerCamelCase  ,      return_offsets_mapping=_lowerCamelCase  ,      return_token_type_ids=_lowerCamelCase  ,      return_length=_lowerCamelCase  ,      verbose=_lowerCamelCase  ,      return_tensors=_lowerCamelCase  ,      **_lowerCamelCase  ,      )
           UpperCamelCase_: Dict   				=    qformer_text_encoding.pop('input_ids'							)
           UpperCamelCase_: int   				=    qformer_text_encoding.pop('attention_mask'							)

         if images is not None:
           UpperCamelCase_: int   				=    self.image_processor(_lowerCamelCase  ,      return_tensors=_lowerCamelCase							)
           encoding.update(_lowerCamelCase							)

         return encoding





       def       _a   ( self  ,      *_lowerCamelCase  ,      **_lowerCamelCase							):
         return self.tokenizer.batch_decode(*_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      *_lowerCamelCase  ,      **_lowerCamelCase							):
         return self.tokenizer.decode(*_lowerCamelCase  ,      **_lowerCamelCase							)





       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.tokenizer.model_input_names
         UpperCamelCase_: Union[str, Any]   				=    self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)





       def       _a   ( self  ,      _lowerCamelCase  ,      **_lowerCamelCase							):
         if os.path.isfile(_lowerCamelCase							):
           raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''							)
         os.makedirs(_lowerCamelCase  ,      exist_ok=_lowerCamelCase							)
         UpperCamelCase_: List[str]   				=    os.path.join(_lowerCamelCase  ,      'qformer_tokenizer'							)
         self.qformer_tokenizer.save_pretrained(_lowerCamelCase							)
         return super().save_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)





       @classmethod
       def       _a   ( cls  ,      _lowerCamelCase  ,      **_lowerCamelCase							):
         UpperCamelCase_: Tuple   				=    AutoTokenizer.from_pretrained(_lowerCamelCase  ,      subfolder='qformer_tokenizer'							)
         UpperCamelCase_: int   				=    cls._get_arguments_from_pretrained(_lowerCamelCase  ,      **_lowerCamelCase							)
         args.append(_lowerCamelCase							)
         return cls(*_lowerCamelCase							)







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   snake_case (UpperCAmelCase__  )	->    tuple:
  return (data["data"], data["target"])

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    np.ndarray:
  UpperCamelCase_: Dict   				=    XGBRegressor(verbosity=0     ,   random_state=4_2  )
  xgb.fit(UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Predict target for test data
  UpperCamelCase_: int   				=    xgb.predict(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    predictions.reshape(len(UpperCAmelCase__  )     ,   1  )
  return predictions

def   snake_case ()	->    None:
  UpperCamelCase_: Union[str, Any]   				=    fetch_california_housing()
  UpperCamelCase_				,UpperCamelCase_: Tuple   				=    data_handling(UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_: Optional[Any]   				=    train_test_split(
      UpperCAmelCase__     ,   UpperCAmelCase__     ,   test_size=0.25     ,   random_state=1  )
  UpperCamelCase_: Union[str, Any]   				=    xgboost(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )
  print(F'''Mean Square Error  : {mean_squared_error(UpperCAmelCase__     ,   UpperCAmelCase__  )}'''  )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()