




import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
       import torch




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[str]	     =      """char"""
    lowerCamelCase_      :	Union[str, Any]	     =      """bpe"""
    lowerCamelCase_      :	Optional[Any]	     =      """wp"""




SCREAMING_SNAKE_CASE__       :      Tuple  =   (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      ["""image_processor""", """char_tokenizer"""]
    lowerCamelCase_      :	Optional[int]	     =      """ViTImageProcessor"""
    lowerCamelCase_      :	List[Any]	     =      """MgpstrTokenizer"""







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      int			 =					None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                      " instead."     ,  UpperCamelCase__     ,  )
                  lowerCamelCase   :      List[str]			 =					kwargs.pop("feature_extractor"     )

           lowerCamelCase   :      List[Any]			 =					image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError("You need to specify an `image_processor`."     )
           if tokenizer is None:
                  raise ValueError("You need to specify a `tokenizer`."     )

           lowerCamelCase   :      Any			 =					tokenizer
           lowerCamelCase   :      str			 =					AutoTokenizer.from_pretrained("gpt2"     )
           lowerCamelCase   :      Tuple			 =					AutoTokenizer.from_pretrained("bert-base-uncased"     )

           super().__init__(UpperCamelCase__     ,  UpperCamelCase__     )







    def __call__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  **UpperCamelCase__     )     ->      Optional[int]:
           if images is None and text is None:
                  raise ValueError("You need to specify either an `images` or `text` input to process."     )

           if images is not None:
                  lowerCamelCase   :      Optional[int]			 =					self.image_processor(UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     )
           if text is not None:
                  lowerCamelCase   :      Tuple			 =					self.char_tokenizer(UpperCamelCase__     ,  return_tensors=UpperCamelCase__     ,  **UpperCamelCase__     )

           if text is None:
                  return inputs
           elif images is None:
                  return encodings
           else:
                  lowerCamelCase   :      Dict			 =					encodings["input_ids"]
                  return inputs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      str:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					sequences
           lowerCamelCase   :      Any			 =					char_preds.size(0     )

           lowerCamelCase ,      lowerCamelCase   :      Any			 =					self._decode_helper(UpperCamelCase__     ,  "char"     )
           lowerCamelCase ,      lowerCamelCase   :      List[Any]			 =					self._decode_helper(UpperCamelCase__     ,  "bpe"     )
           lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					self._decode_helper(UpperCamelCase__     ,  "wp"     )

           lowerCamelCase   :      Union[str, Any]			 =					[]
           lowerCamelCase   :      str			 =					[]
           for i in range(UpperCamelCase__     ):
                  lowerCamelCase   :      List[str]			 =					[char_scores[i], bpe_scores[i], wp_scores[i]]
                  lowerCamelCase   :      str			 =					[char_strs[i], bpe_strs[i], wp_strs[i]]
                  lowerCamelCase   :      Optional[int]			 =					scores.index(max(UpperCamelCase__     )     )
                  final_strs.append(strs[max_score_index]     )
                  final_scores.append(scores[max_score_index]     )

           lowerCamelCase   :      Optional[Any]			 =					{}
           lowerCamelCase   :      List[str]			 =					final_strs
           lowerCamelCase   :      str			 =					final_scores
           lowerCamelCase   :      Optional[int]			 =					char_strs
           lowerCamelCase   :      Union[str, Any]			 =					bpe_strs
           lowerCamelCase   :      Tuple			 =					wp_strs
           return out







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Dict:
           if format == DecodeType.CHARACTER:
                  lowerCamelCase   :      Union[str, Any]			 =					self.char_decode
                  lowerCamelCase   :      List[Any]			 =					1
                  lowerCamelCase   :      Union[str, Any]			 =					"[s]"
           elif format == DecodeType.BPE:
                  lowerCamelCase   :      Dict			 =					self.bpe_decode
                  lowerCamelCase   :      List[str]			 =					2
                  lowerCamelCase   :      List[Any]			 =					"#"
           elif format == DecodeType.WORDPIECE:
                  lowerCamelCase   :      Any			 =					self.wp_decode
                  lowerCamelCase   :      List[str]			 =					102
                  lowerCamelCase   :      Any			 =					"[SEP]"
           else:
                  raise ValueError(F'''Format {format} is not supported.'''     )

           lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					[], []
           lowerCamelCase   :      List[Any]			 =					pred_logits.size(0     )
           lowerCamelCase   :      Union[str, Any]			 =					pred_logits.size(1     )
           lowerCamelCase ,      lowerCamelCase   :      Optional[int]			 =					pred_logits.topk(1     ,  dim=-1     ,  largest=UpperCamelCase__     ,  sorted=UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					preds_index.view(-1     ,  UpperCamelCase__     )[:, 1:]
           lowerCamelCase   :      str			 =					decoder(UpperCamelCase__     )
           lowerCamelCase ,      lowerCamelCase   :      str			 =					torch.nn.functional.softmax(UpperCamelCase__     ,  dim=2     ).max(dim=2     )
           lowerCamelCase   :      Optional[int]			 =					preds_max_prob[:, 1:]

           for index in range(UpperCamelCase__     ):
                  lowerCamelCase   :      List[str]			 =					preds_str[index].find(UpperCamelCase__     )
                  lowerCamelCase   :      int			 =					preds_str[index][:pred_eos]
                  lowerCamelCase   :      Optional[Any]			 =					preds_index[index].cpu().tolist()
                  lowerCamelCase   :      List[Any]			 =					pred_index.index(UpperCamelCase__     ) if eos_token in pred_index else -1
                  lowerCamelCase   :      Optional[int]			 =					preds_max_prob[index][: pred_eos_index + 1]
                  lowerCamelCase   :      Union[str, Any]			 =					pred_max_prob.cumprod(dim=0     )[-1] if pred_max_prob.nelement() != 0 else 0.0
                  dec_strs.append(UpperCamelCase__     )
                  conf_scores.append(UpperCamelCase__     )

           return dec_strs, conf_scores







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase   :      Optional[int]			 =					[seq.replace(" "     ,  ""     ) for seq in self.char_tokenizer.batch_decode(UpperCamelCase__     )]
           return decode_strs







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      str:
           return self.bpe_tokenizer.batch_decode(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      Tuple			 =					[seq.replace(" "     ,  ""     ) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase__     )]
           return decode_strs












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

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

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					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()






