
from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase_						     =					logging.get_logger(__name__)

lowerCamelCase_						     =					{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      """convbert"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_=3_05_22	,      SCREAMING_SNAKE_CASE_=7_68	,      SCREAMING_SNAKE_CASE_=12	,      SCREAMING_SNAKE_CASE_=12	,      SCREAMING_SNAKE_CASE_=30_72	,      SCREAMING_SNAKE_CASE_="gelu"	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=0.1	,      SCREAMING_SNAKE_CASE_=5_12	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=0.02	,      SCREAMING_SNAKE_CASE_=1E-12	,      SCREAMING_SNAKE_CASE_=1	,      SCREAMING_SNAKE_CASE_=0	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=7_68	,      SCREAMING_SNAKE_CASE_=2	,      SCREAMING_SNAKE_CASE_=9	,      SCREAMING_SNAKE_CASE_=1	,      SCREAMING_SNAKE_CASE_=None	,      **SCREAMING_SNAKE_CASE_	,      ):
       super().__init__(
           pad_token_id=SCREAMING_SNAKE_CASE_	,      bos_token_id=SCREAMING_SNAKE_CASE_	,      eos_token_id=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_	,      )

       UpperCamelCase__				=    vocab_size
       UpperCamelCase__				=    hidden_size
       UpperCamelCase__				=    num_hidden_layers
       UpperCamelCase__				=    num_attention_heads
       UpperCamelCase__				=    intermediate_size
       UpperCamelCase__				=    hidden_act
       UpperCamelCase__				=    hidden_dropout_prob
       UpperCamelCase__				=    attention_probs_dropout_prob
       UpperCamelCase__				=    max_position_embeddings
       UpperCamelCase__				=    type_vocab_size
       UpperCamelCase__				=    initializer_range
       UpperCamelCase__				=    layer_norm_eps
       UpperCamelCase__				=    embedding_size
       UpperCamelCase__				=    head_ratio
       UpperCamelCase__				=    conv_kernel_size
       UpperCamelCase__				=    num_groups
       UpperCamelCase__				=    classifier_dropout






class      __A(    __lowerCamelCase			):

  """simple docstring"""







  @property
  def 	UpperCAmelCase_ (self						):
       if self.task == "multiple-choice":
            UpperCamelCase__				=    {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
            UpperCamelCase__				=    {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]						)







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 						__magic_name__			(   __a      :	dict						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	np.ndarray	,		__a      :	np.ndarray	,		__a      :	np.ndarray						):



     '''simple docstring'''




     UpperCamelCase__				=    XGBRegressor(verbosity=0	,		random_state=42						)
     xgb.fit(__a	,		__a						)
     # Predict target for test data
     UpperCamelCase__				=    xgb.predict(__a						)
     UpperCamelCase__				=    predictions.reshape(len(__a						)	,		1						)
     return predictions






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    fetch_california_housing()
     UpperCamelCase__		,	UpperCamelCase__				=    data_handling(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    train_test_split(
         __a	,		__a	,		test_size=0.25	,		random_state=1						)
     UpperCamelCase__				=    xgboost(__a	,		__a	,		__a						)
     # Error printing
     print(f"Mean Absolute Error : {mean_absolute_error(__a	,		__a						)}"						)
     print(f"Mean Square Error  : {mean_squared_error(__a	,		__a						)}"						)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    main()





