from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			'roberta-prelayernorm'


   def __init__(self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[int]=5_0_2_6_5       ,  __UpperCAmelCase     :						List[str]=7_6_8       ,  __UpperCAmelCase     :						List[Any]=1_2       ,  __UpperCAmelCase     :						Any=1_2       ,  __UpperCAmelCase     :						Optional[int]=3_0_7_2       ,  __UpperCAmelCase     :						int="gelu"       ,  __UpperCAmelCase     :						int=0.1       ,  __UpperCAmelCase     :						Optional[Any]=0.1       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[Any]=2       ,  __UpperCAmelCase     :						Tuple=0.02       ,  __UpperCAmelCase     :						List[str]=1E-12       ,  __UpperCAmelCase     :						Any=1       ,  __UpperCAmelCase     :						Optional[int]=0       ,  __UpperCAmelCase     :						List[str]=2       ,  __UpperCAmelCase     :						Dict="absolute"       ,  __UpperCAmelCase     :						List[Any]=True       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Dict       ,  )						->				Optional[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      A							(				UpperCAmelCase_     ):


   @property
   def 			lowercase_      (self     :						int		)						->				Mapping[str, Mapping[int, str]]:
         """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),
             ]		)




import math
import random




def    lowerCAmelCase_ (     __A,  __A = False							)     ->				float:







      '''simple docstring'''


      if deriv:
            return value * (1 - value)
      return 1 / (1 + math.exp(-value							))


# Initial Value
UpperCamelCase__											=					0.0_2




def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


      UpperCAmelCase__								=  float(2 * (random.randint(1,  100							)) - 1							)

      for _ in range(__A							):
            # Forward propagation
            UpperCAmelCase__								=  sigmoid_function(INITIAL_VALUE * weight							)
            # How much did we miss?
            UpperCAmelCase__								=  (expected / 100) - layer_a
            # Error delta
            UpperCAmelCase__								=  layer_1_error * sigmoid_function(__A,  __A							)
            # Update weight
            weight += INITIAL_VALUE * layer_1_delta

      return layer_a * 100


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      UpperCamelCase__											=					int(input('Expected value: '))
      UpperCamelCase__											=					int(input('Number of propagations: '))
      print(forward_propagation(expected, number_propagations))



