






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        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Tuple							=      'char'
      a    :					Tuple							=      'bpe'
      a    :					Optional[Any]							=      'wp'


a						:			Tuple     	=					(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class        a       (					lowercase__  ):



      """simple docstring"""


      a    :					Tuple							=      ['image_processor', 'char_tokenizer']
      a    :					Optional[Any]							=      'ViTImageProcessor'
      a    :					int							=      'MgpstrTokenizer'
      def __init__( self   :     int						, __lowercase   :     List[str]=None						, __lowercase   :     List[str]=None						, **__lowercase   :     List[Any]   )				-> Any:
        __UpperCAmelCase				:      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."""						, __lowercase						, )
          __UpperCAmelCase				:      Union[str, Any]	 =    kwargs.pop("""feature_extractor"""   )

        __UpperCAmelCase				:      Optional[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`."""   )

        __UpperCAmelCase				:      str	 =    tokenizer
        __UpperCAmelCase				:      Optional[Any]	 =    AutoTokenizer.from_pretrained("""gpt2"""   )
        __UpperCAmelCase				:      Any	 =    AutoTokenizer.from_pretrained("""bert-base-uncased"""   )

        super().__init__(__lowercase						, __lowercase   )
      def __call__( self   :     List[Any]						, __lowercase   :     Optional[Any]=None						, __lowercase   :     str=None						, __lowercase   :     List[str]=None						, **__lowercase   :     Tuple   )				-> Union[str, Any]:
        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:
          __UpperCAmelCase				:      Union[str, Any]	 =    self.image_processor(__lowercase						, return_tensors=__lowercase						, **__lowercase   )
        if text is not None:
          __UpperCAmelCase				:      Any	 =    self.char_tokenizer(__lowercase						, return_tensors=__lowercase						, **__lowercase   )

        if text is None:
          return inputs
        elif images is None:
          return encodings
        else:
          __UpperCAmelCase				:      Tuple	 =    encodings["""input_ids"""]
          return inputs
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     List[str]   )				-> int:
        __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    sequences
        __UpperCAmelCase				:      str	 =    char_preds.size(0   )

        __UpperCAmelCase ,					__UpperCAmelCase				:      List[str]	 =    self._decode_helper(__lowercase						, """char"""   )
        __UpperCAmelCase ,					__UpperCAmelCase				:      List[Any]	 =    self._decode_helper(__lowercase						, """bpe"""   )
        __UpperCAmelCase ,					__UpperCAmelCase				:      int	 =    self._decode_helper(__lowercase						, """wp"""   )

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

        __UpperCAmelCase				:      Union[str, Any]	 =    {}
        __UpperCAmelCase				:      List[str]	 =    final_strs
        __UpperCAmelCase				:      Optional[int]	 =    final_scores
        __UpperCAmelCase				:      List[str]	 =    char_strs
        __UpperCAmelCase				:      Any	 =    bpe_strs
        __UpperCAmelCase				:      Tuple	 =    wp_strs
        return out
      def       UpperCAmelCase ( self   :     Dict						, __lowercase   :     str						, __lowercase   :     Tuple   )				-> Any:
        if format == DecodeType.CHARACTER:
          __UpperCAmelCase				:      str	 =    self.char_decode
          __UpperCAmelCase				:      Optional[int]	 =    1
          __UpperCAmelCase				:      List[str]	 =    """[s]"""
        elif format == DecodeType.BPE:
          __UpperCAmelCase				:      List[Any]	 =    self.bpe_decode
          __UpperCAmelCase				:      List[str]	 =    2
          __UpperCAmelCase				:      str	 =    """#"""
        elif format == DecodeType.WORDPIECE:
          __UpperCAmelCase				:      Any	 =    self.wp_decode
          __UpperCAmelCase				:      Tuple	 =    102
          __UpperCAmelCase				:      str	 =    """[SEP]"""
        else:
          raise ValueError(f"""Format {format} is not supported."""   )

        __UpperCAmelCase ,					__UpperCAmelCase				:      str	 =    [], []
        __UpperCAmelCase				:      Any	 =    pred_logits.size(0   )
        __UpperCAmelCase				:      Optional[Any]	 =    pred_logits.size(1   )
        __UpperCAmelCase ,					__UpperCAmelCase				:      Optional[Any]	 =    pred_logits.topk(1						, dim=-1						, largest=__lowercase						, sorted=__lowercase   )
        __UpperCAmelCase				:      Dict	 =    preds_index.view(-1						, __lowercase   )[:, 1:]
        __UpperCAmelCase				:      Optional[Any]	 =    decoder(__lowercase   )
        __UpperCAmelCase ,					__UpperCAmelCase				:      Union[str, Any]	 =    torch.nn.functional.softmax(__lowercase						, dim=2   ).max(dim=2   )
        __UpperCAmelCase				:      Any	 =    preds_max_prob[:, 1:]

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

        return dec_strs, conf_scores
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     str   )				-> Tuple:
        __UpperCAmelCase				:      int	 =    [seq.replace(""" """						, """"""   ) for seq in self.char_tokenizer.batch_decode(__lowercase   )]
        return decode_strs
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     Dict   )				-> Union[str, Any]:
        return self.bpe_tokenizer.batch_decode(__lowercase   )




      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Union[str, Any]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Optional[Any]	 =    [seq.replace(""" """						, """"""   ) for seq in self.wp_tokenizer.batch_decode(__lowercase   )]
        return decode_strs







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowerCAmelCase	:   int    =   datasets.load_iris()

_lowerCAmelCase	:   Tuple    =   np.array(data["data"])
_lowerCAmelCase	:   Any    =   np.array(data["target"])
_lowerCAmelCase	:   Union[str, Any]    =   data["target_names"]

_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase,		_lowerCAmelCase	:   Optional[Any]    =   train_test_split(X, y)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Tuple:



				"""simple docstring"""





				return np.linalg.norm(np.array(snake_case__					) - np.array(snake_case__					)					)







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__=5					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  zip(snake_case__    ,			snake_case__					)
				# List of distances of all points from the point to be classified
				lowerCAmelCase__		  =  []
				for data_point in data:
								lowerCAmelCase__		  =  euclidean_distance(data_point[0]    ,			snake_case__					)
								distances.append((distance, data_point[1])					)
				# Choosing 'k' points with the least distances.
				lowerCAmelCase__		  =  [i[1] for i in sorted(snake_case__					)[:k]]
				# Most commonly occurring class among them
				# is the class into which the point is classified
				lowerCAmelCase__		  =  Counter(snake_case__					).most_common(1					)[0][0]
				return classes[result]


if __name__ == "__main__":
	print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
