



'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase	:       int      	=	logging.get_logger(__name__)

_lowerCamelCase	:       List[Any]      	=	{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """roberta-prelayernorm"""






    def __init__(  self      :					Tuple  ,						UpperCamelCase__      :					Dict=5_0_2_6_5  ,						UpperCamelCase__      :					Optional[int]=7_6_8  ,						UpperCamelCase__      :					Any=1_2  ,						UpperCamelCase__      :					Optional[Any]=1_2  ,						UpperCamelCase__      :					Union[str, Any]=3_0_7_2  ,						UpperCamelCase__      :					List[Any]="gelu"  ,						UpperCamelCase__      :					List[Any]=0.1  ,						UpperCamelCase__      :					Dict=0.1  ,						UpperCamelCase__      :					Tuple=5_1_2  ,						UpperCamelCase__      :					Optional[int]=2  ,						UpperCamelCase__      :					List[str]=0.0_2  ,						UpperCamelCase__      :					List[str]=1E-1_2  ,						UpperCamelCase__      :					Optional[int]=1  ,						UpperCamelCase__      :					Union[str, Any]=0  ,						UpperCamelCase__      :					str=2  ,						UpperCamelCase__      :					Optional[int]="absolute"  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					Optional[int]=None  ,						**UpperCamelCase__      :					List[Any]  ,						):


       """simple docstring"""







       super().__init__(pad_token_id=UpperCamelCase__  ,						bos_token_id=UpperCamelCase__  ,						eos_token_id=UpperCamelCase__  ,						**UpperCamelCase__ )

       UpperCamelCase					=  vocab_size
       UpperCamelCase					=  hidden_size
       UpperCamelCase					=  num_hidden_layers
       UpperCamelCase					=  num_attention_heads
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  intermediate_size
       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					=  position_embedding_type
       UpperCamelCase					=  use_cache
       UpperCamelCase					=  classifier_dropout

class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    @property
    def  A       (  self      :					int ):


       """simple docstring"""







       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),
           ] )



"""simple docstring"""



# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str		)     ->					List[Any]:
  return 1 / (1 + np.exp(-z		))
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any] ,					__lowerCamelCase   :       List[str]		)     ->					Optional[Any]:
  return (-y * np.log(__lowerCamelCase		) - (1 - y) * np.log(1 - h		)).mean()
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict		)     ->					List[str]:
  _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
  return np.sum(y * scores - np.log(1 + np.exp(__lowerCamelCase		)		)		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       str=7_00_00		)     ->					Optional[Any]:
  _snake_case					     =       np.zeros(x.shape[1]		)

  for iterations in range(__lowerCamelCase		):
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       np.dot(x.T ,					h - y		) / y.size
    _snake_case					     =       theta - alpha * gradient  # updating the weights
    _snake_case					     =       np.dot(__lowerCamelCase ,					__lowerCamelCase		)
    _snake_case					     =       sigmoid_function(__lowerCamelCase		)
    _snake_case					     =       cost_function(__lowerCamelCase ,					__lowerCamelCase		)
    if iterations % 1_00 == 0:
      print(f'''loss: {j} \t'''		)  # printing the loss after every 100 iterations
  return theta


# In[68]:

if __name__ == "__main__":
 UpperCAmelCase__          = datasets.load_iris()
 UpperCAmelCase__          = iris.data[:, :2]
 UpperCAmelCase__          = (iris.target != 0) * 1

 UpperCAmelCase__          = 0.1
 UpperCAmelCase__          = logistic_reg(alpha, x, y, max_iterations=70000)
 print('theta: ', theta)  # printing the theta i.e our weights vector
 def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Union[str, Any]:
   return sigmoid_function(
       np.dot(__lowerCamelCase ,					__lowerCamelCase		)		)  # predicting the value of probability from the logistic regression algorithm

 plt.figure(figsize=(10, 6))
 plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color='b', label='0')
 plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color='r', label='1')
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 0].min(), x[:, 0].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = (x[:, 1].min(), x[:, 1].max())
 ((UpperCAmelCase__)      ,	(UpperCAmelCase__))          = np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
 UpperCAmelCase__          = np.c_[xxa.ravel(), xxa.ravel()]
 UpperCAmelCase__          = predict_prob(grid).reshape(xxa.shape)
 plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

 plt.legend()

 plt.show()



