

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A     						=      logging.get_logger(__name__)

__A     						=      {
    '''mnaylor/mega-base-wikitext''': '''https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json''',
}



class     lowercase_       (    __lowercase	):
      UpperCamelCase_     :						Union[str, Any]		 =							"mega"



      def __init__(     self :							Optional[int]     ,   A__ :							List[Any]=30522     ,   A__ :							str=128     ,   A__ :							Union[str, Any]=4     ,   A__ :							Union[str, Any]=256     ,   A__ :							int=16     ,   A__ :							Union[str, Any]=True     ,   A__ :							int=64     ,   A__ :							Tuple=False     ,   A__ :							str=-1     ,   A__ :							Union[str, Any]=None     ,   A__ :							List[Any]=True     ,   A__ :							List[str]="scalenorm"     ,   A__ :							List[Any]=True     ,   A__ :							Dict="silu"     ,   A__ :							Any="softmax"     ,   A__ :							Union[str, Any]=0.1     ,   A__ :							Union[str, Any]=0.1     ,   A__ :							List[str]=0.1     ,   A__ :							Dict=False     ,   A__ :							Union[str, Any]=True     ,   A__ :							Tuple=256     ,   A__ :							List[Any]=True     ,   A__ :							Optional[Any]=0.1     ,   A__ :							Any=2048     ,   A__ :							int=False     ,   A__ :							Tuple=2     ,   A__ :							List[str]=0.02     ,   A__ :							List[Any]=0.2     ,   A__ :							List[Any]=0.02     ,   A__ :							Optional[int]=1.0     ,   A__ :							str=1     ,   A__ :							List[Any]=0     ,   A__ :							Dict=2     ,   A__ :							Optional[int]="rotary"     ,   A__ :							Any=None     ,   A__ :							Tuple=True     ,   A__ :							List[str]=True     ,   **A__ :							Optional[int]     ,   )       ->	int:
        super().__init__(pad_token_id=A__     ,   bos_token_id=A__     ,   eos_token_id=A__     ,   **A__						)

        _snake_case		=				vocab_size
        _snake_case		=				hidden_size
        _snake_case		=				num_hidden_layers
        _snake_case		=				activation
        _snake_case		=				attention_activation
        _snake_case		=				intermediate_size
        _snake_case		=				ema_projection_size
        _snake_case		=				bidirectional
        _snake_case		=				shared_representation_size
        _snake_case		=				use_chunking
        _snake_case		=				chunk_size
        _snake_case		=				truncation
        _snake_case		=				normalize_before_mega
        _snake_case		=				normalization_type
        _snake_case		=				norm_affine
        _snake_case		=				dropout_prob
        _snake_case		=				hidden_dropout_prob
        _snake_case		=				attention_probs_dropout_prob
        _snake_case		=				use_feature_dropout
        _snake_case		=				use_normalized_ffn
        _snake_case		=				nffn_hidden_size
        _snake_case		=				normalize_before_ffn
        _snake_case		=				nffn_activation_dropout_prob
        _snake_case		=				max_positions
        _snake_case		=				add_token_type_embeddings
        _snake_case		=				type_vocab_size
        _snake_case		=				initializer_range
        _snake_case		=				ema_delta_alpha_range
        _snake_case		=				ema_beta_range
        _snake_case		=				ema_gamma_omega_range
        _snake_case		=				relative_positional_bias
        _snake_case		=				use_cache
        _snake_case		=				classifier_dropout
        _snake_case		=				add_lm_hidden_dense_layer
        _snake_case		=				1  # not used but required by Hugging Face



class     lowercase_       (    __lowercase	):



      @property
      def 		UpperCamelCase_						(     self :							Optional[int]						)       ->	Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          _snake_case		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
          _snake_case		=				{0: '''batch''', 1: '''sequence'''}
        return OrderedDict(
            [
                ('''input_ids''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ]						)




import numpy as np



class     lowercase_       :



      def __init__(     self :							Any     ,   A__ :							Optional[int]=5     ,   A__ :							Any=5						)       ->	Any:
        _snake_case		=				depth
        _snake_case		=				0
        _snake_case		=				None
        _snake_case		=				None
        _snake_case		=				min_leaf_size
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Tuple     ,   A__ :							Union[str, Any]						)       ->	int:
        if labels.ndim != 1:
          print('''Error: Input labels must be one dimensional'''						)

        return np.mean((labels - prediction) ** 2						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							str     ,   A__ :							int						)       ->	Optional[Any]:
        if x.ndim != 1:
          print('''Error: Input data set must be one dimensional'''						)
          return
        if len(A__						) != len(A__						):
          print('''Error: X and y have different lengths'''						)
          return
        if y.ndim != 1:
          print('''Error: Data set labels must be one dimensional'''						)
          return

        if len(A__						) < 2 * self.min_leaf_size:
          _snake_case		=				np.mean(A__						)
          return

        if self.depth == 1:
          _snake_case		=				np.mean(A__						)
          return

        _snake_case		=				0
        _snake_case		=				self.mean_squared_error(A__     ,   np.mean(A__						)						) * 2
        for i in range(len(A__						)						):
          if len(x[:i]						) < self.min_leaf_size:
            continue
          elif len(x[i:]						) < self.min_leaf_size:
            continue
          else:
            _snake_case		=				self.mean_squared_error(x[:i]     ,   np.mean(y[:i]						)						)
            _snake_case		=				self.mean_squared_error(x[i:]     ,   np.mean(y[i:]						)						)
            _snake_case		=				error_left + error_right
            if error < min_error:
              _snake_case		=				i
              _snake_case		=				error

        if best_split != 0:
          _snake_case		=				x[:best_split]
          _snake_case		=				y[:best_split]
          _snake_case		=				x[best_split:]
          _snake_case		=				y[best_split:]

          _snake_case		=				x[best_split]
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          self.left.train(A__     ,   A__						)
          self.right.train(A__     ,   A__						)
        else:
          _snake_case		=				np.mean(A__						)

        return







      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Tuple						)       ->	Any:
        if self.prediction is not None:
          return self.prediction
        elif self.left or self.right is not None:
          if x >= self.decision_boundary:
            return self.right.predict(A__						)
          else:
            return self.left.predict(A__						)
        else:
          print('''Error: Decision tree not yet trained'''						)
          return None



class     lowercase_       :



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        _snake_case		=				float(0						)
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size						)



def  snake_case_()				->      Any:



  """simple docstring"""






  _snake_case		=				np.arange(-1.0					,			1.0					,			0.005	)
  _snake_case		=				np.sin(_UpperCamelCase	)

  _snake_case		=				DecisionTree(depth=10					,			min_leaf_size=10	)
  tree.train(_UpperCamelCase					,			_UpperCamelCase	)

  _snake_case		=				(np.random.rand(10	) * 2) - 1
  _snake_case		=				np.array([tree.predict(_UpperCamelCase	) for x in test_cases]	)
  _snake_case		=				np.mean((predictions - test_cases) ** 2	)

  print('''Test values: ''' + str(_UpperCamelCase	)	)
  print('''Predictions: ''' + str(_UpperCamelCase	)	)
  print('''Average error: ''' + str(_UpperCamelCase	)	)


if __name__ == "__main__":
 main()
 import doctest

 doctest.testmod(name='''mean_squarred_error''', verbose=True)

