from ....configuration_utils import PretrainedConfig
from ....utils import logging


__lowerCamelCase    :				Tuple							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Dict							     =	{
    '''speechbrain/m-ctc-t-large''': '''https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json''',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"mctct"




   def __init__(					self    :       Dict ,			_lowercase    :       Union[str, Any]=80_65 ,			_lowercase    :       List[Any]=15_36 ,			_lowercase    :       Any=36 ,			_lowercase    :       List[str]=61_44 ,			_lowercase    :       Union[str, Any]=4 ,			_lowercase    :       List[str]=3_84 ,			_lowercase    :       Tuple=9_20 ,			_lowercase    :       Tuple=1E-5 ,			_lowercase    :       Dict=0.3 ,			_lowercase    :       Tuple="relu" ,			_lowercase    :       List[Any]=0.02 ,			_lowercase    :       Union[str, Any]=0.3 ,			_lowercase    :       Optional[Any]=0.3 ,			_lowercase    :       List[Any]=1 ,			_lowercase    :       Optional[int]=0 ,			_lowercase    :       Tuple=2 ,			_lowercase    :       Dict=1 ,			_lowercase    :       Dict=0.3 ,			_lowercase    :       str=1 ,			_lowercase    :       int=(7,) ,			_lowercase    :       int=(3,) ,			_lowercase    :       str=80 ,			_lowercase    :       int=1 ,			_lowercase    :       Tuple=None ,			_lowercase    :       Dict="sum" ,			_lowercase    :       Any=False ,			**_lowercase    :       Tuple ,			):



     """simple docstring"""






     super().__init__(**_lowercase ,			pad_token_id=_lowercase ,			bos_token_id=_lowercase ,			eos_token_id=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   vocab_size
     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   attention_head_dim
     SCREAMING_SNAKE_CASE__    							=   max_position_embeddings
     SCREAMING_SNAKE_CASE__    							=   layer_norm_eps
     SCREAMING_SNAKE_CASE__    							=   layerdrop
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   pad_token_id
     SCREAMING_SNAKE_CASE__    							=   bos_token_id
     SCREAMING_SNAKE_CASE__    							=   eos_token_id
     SCREAMING_SNAKE_CASE__    							=   conv_glu_dim
     SCREAMING_SNAKE_CASE__    							=   conv_dropout
     SCREAMING_SNAKE_CASE__    							=   num_conv_layers
     SCREAMING_SNAKE_CASE__    							=   input_feat_per_channel
     SCREAMING_SNAKE_CASE__    							=   input_channels
     SCREAMING_SNAKE_CASE__    							=   conv_channels
     SCREAMING_SNAKE_CASE__    							=   ctc_loss_reduction
     SCREAMING_SNAKE_CASE__    							=   ctc_zero_infinity

     # prevents config testing fail with exporting to json
     SCREAMING_SNAKE_CASE__    							=   list(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   list(_lowercase   )

     if len(self.conv_kernel   ) != self.num_conv_layers:
       raise ValueError(
           """Configuration for convolutional module is incorrect. """
           """It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
           f"""but is `len(config.conv_kernel) = {len(self.conv_kernel   )}`, """
           f"""`config.num_conv_layers = {self.num_conv_layers}`."""   )

# 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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple			)							->      int:







  """simple docstring"""





  return 1 / (1 + np.exp(-z			))



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Optional[Any]:







  """simple docstring"""





  return (-y * np.log(__UpperCamelCase			) - (1 - y) * np.log(1 - h			)).mean()



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
  return np.sum(y * scores - np.log(1 + np.exp(__UpperCamelCase			)			)			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Any=7_00_00			)							->      Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   np.zeros(x.shape[1]			)

  for iterations in range(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   np.dot(x.T				,  h - y			) / y.size
    SCREAMING_SNAKE_CASE__    							=   theta - alpha * gradient  # updating the weights
    SCREAMING_SNAKE_CASE__    							=   np.dot(__UpperCamelCase				,  __UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   sigmoid_function(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   cost_function(__UpperCamelCase				,  __UpperCamelCase			)
    if iterations % 1_00 == 0:
      print(f"""loss: {j} \t"""			)  # printing the loss after every 100 iterations
  return theta


# In[68]:

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

   __lowerCamelCase    :				List[str]							     =	0.1
   __lowerCamelCase    :				str							     =	logistic_reg(alpha, x, y, max_iterations=7_0000)
   print('''theta: ''', theta)  # printing the theta i.e our weights vector



   def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Tuple:







     """simple docstring"""





     return sigmoid_function(
         np.dot(__UpperCamelCase				,  __UpperCamelCase			)			)  # 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''')
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				int							     =	(x[:, 0].min(), x[:, 0].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	(x[:, 1].min(), x[:, 1].max())
   ((__lowerCamelCase)     ,		(__lowerCamelCase))    :				List[Any]							     =	np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
   __lowerCamelCase    :				Optional[int]							     =	np.c_[xxa.ravel(), xxa.ravel()]
   __lowerCamelCase    :				Optional[int]							     =	predict_prob(grid).reshape(xxa.shape)
   plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors='''black''')

   plt.legend()






   plt.show()
