






from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class 							A_    (						UpperCamelCase_			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Any =					"xmod"


 def __init__(	self							,  _A=30522							,  _A=768							,  _A=12							,  _A=12							,  _A=3072							,  _A="gelu"							,  _A=0.1							,  _A=0.1							,  _A=512							,  _A=2							,  _A=0.02							,  _A=1e-12							,  _A=1							,  _A=0							,  _A=2							,  _A="absolute"							,  _A=True							,  _A=None							,  _A=False							,  _A=2							,  _A=False							,  _A=True							,  _A=True							,  _A=("en_XX",)							,  _A=None							,  **_A							,  ) ->		str:

     """simple docstring"""



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

     _UpperCAmelCase				:      Dict			  =				vocab_size
     _UpperCAmelCase				:      List[Any]			  =				hidden_size
     _UpperCAmelCase				:      str			  =				num_hidden_layers
     _UpperCAmelCase				:      Tuple			  =				num_attention_heads
     _UpperCAmelCase				:      int			  =				hidden_act
     _UpperCAmelCase				:      Any			  =				intermediate_size
     _UpperCAmelCase				:      Optional[Any]			  =				hidden_dropout_prob
     _UpperCAmelCase				:      str			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      Optional[int]			  =				max_position_embeddings
     _UpperCAmelCase				:      List[Any]			  =				type_vocab_size
     _UpperCAmelCase				:      Union[str, Any]			  =				initializer_range
     _UpperCAmelCase				:      List[Any]			  =				layer_norm_eps
     _UpperCAmelCase				:      Tuple			  =				position_embedding_type
     _UpperCAmelCase				:      List[str]			  =				use_cache
     _UpperCAmelCase				:      Optional[int]			  =				classifier_dropout
     _UpperCAmelCase				:      Tuple			  =				pre_norm
     _UpperCAmelCase				:      List[Any]			  =				adapter_reduction_factor
     _UpperCAmelCase				:      List[Any]			  =				adapter_layer_norm
     _UpperCAmelCase				:      Tuple			  =				adapter_reuse_layer_norm
     _UpperCAmelCase				:      Dict			  =				ln_before_adapter
     _UpperCAmelCase				:      Union[str, Any]			  =				list(UpperCamelCase__)
     _UpperCAmelCase				:      int			  =				default_language



class 							A_    (						UpperCamelCase_			):






 '''simple docstring'''




 @property
 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     if self.task == "multiple-choice":
         _UpperCAmelCase				:      List[Any]			  =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         _UpperCAmelCase				:      Any			  =				{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ])












# 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      _lowerCamelCase	(						__A    :							Optional[int] )      ->					str:
    return 1 / (1 + np.exp(-z ))

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any] )      ->					Optional[int]:
    return (-y * np.log(__A ) - (1 - y) * np.log(1 - h )).mean()

def      _lowerCamelCase	(						__A    :							Tuple		,		__A    :							List[Any]		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      int			  =				np.dot(__A		,		__A )
    return np.sum(y * scores - np.log(1 + np.exp(__A ) ) )

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							Union[str, Any]		,		__A    :							str		,		__A    :							Dict=70_000 )      ->					Optional[Any]:
    _UpperCAmelCase				:      Union[str, Any]			  =				np.zeros(x.shape[1] )

    for iterations in range(__A ):
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Optional[Any]			  =				np.dot(x.T		,		h - y ) / y.size
        _UpperCAmelCase				:      Optional[Any]			  =				theta - alpha * gradient  # updating the weights
        _UpperCAmelCase				:      List[Any]			  =				np.dot(__A		,		__A )
        _UpperCAmelCase				:      Any			  =				sigmoid_function(__A )
        _UpperCAmelCase				:      Union[str, Any]			  =				cost_function(__A		,		__A )
        if iterations % 100 == 0:
            print(f'''loss: {j} \t''' )  # printing the loss after every 100 iterations
    return theta


# In[68]:

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

  SCREAMING_SNAKE_CASE 							=						0.1
  SCREAMING_SNAKE_CASE 							=						logistic_reg(alpha, x, y, max_iterations=70000)
  print('theta: ', theta)  # printing the theta i.e our weights vector

  def      _lowerCamelCase	(						__A    :							Any )      ->					int:
      return sigmoid_function(
          np.dot(__A		,		__A ) )  # 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')
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 0].min(), x[:, 0].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						(x[:, 1].min(), x[:, 1].max())
  ((SCREAMING_SNAKE_CASE)							,			(SCREAMING_SNAKE_CASE)) 							=						np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
  SCREAMING_SNAKE_CASE 							=						np.c_[xxa.ravel(), xxa.ravel()]
  SCREAMING_SNAKE_CASE 							=						predict_prob(grid).reshape(xxa.shape)
  plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='black')

  plt.legend()


  plt.show()




