


from collections import OrderedDict
from typing import Mapping

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


__a     =			logging.get_logger(__name__)

__a     =			{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class   __lowercase				(  __snake_case  ):
						UpperCamelCase								=       '''roberta'''





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






									"""simple docstring"""







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

									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   __lowercase				(  __snake_case  ):





						@property
						def        _lowercase				(		self						:					List[Any]       )    ->   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 numpy






class   __lowercase				:





      def __init__(		self						:					Union[str, Any]					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					numpy.ndarray       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     input_array

         # Random initial weights are assigned where first argument is the
         # number of nodes in previous layer and second argument is the
         # number of nodes in the next layer.

         # Random initial weights are assigned.
         # self.input_array.shape[1] is used to represent number of nodes in input layer.
         # First hidden layer consists of 4 nodes.
         UpperCAmelCase							=     numpy.random.rand(
             self.input_array.shape[1]					,       4       )

         # Random initial values for the first hidden layer.
         # First hidden layer has 4 nodes.
         # Second hidden layer has 3 nodes.
         UpperCAmelCase							=     numpy.random.rand(
             4					,       3       )

         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
         UpperCAmelCase							=     numpy.random.rand(3					,       1       )

         # Real output values provided.
         UpperCAmelCase							=     output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         UpperCAmelCase							=     numpy.zeros(output_array.shape       )





      def        _lowercase				(		self						:					List[str]       )    ->   numpy.ndarray:






         """simple docstring"""







         UpperCAmelCase							=     sigmoid(
             numpy.dot(self.input_array					,       self.input_layer_and_first_hidden_layer_weights       )       )

         # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
         # connecting the first hidden set of nodes with the second hidden set of nodes.
         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer					,       self.first_hidden_layer_and_second_hidden_layer_weights					,       )       )

         # layer_between_second_hidden_layer_and_output is the layer connecting
         # second hidden layer with the output node.
         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer					,       self.second_hidden_layer_and_output_layer_weights					,       )       )

         return self.layer_between_second_hidden_layer_and_output





      def        _lowercase				(		self						:					Optional[Any]       )    ->   None:






         """simple docstring"""







         UpperCAmelCase							=     numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer.T					,       2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output       )					,       )
         UpperCAmelCase							=     numpy.dot(
             self.layer_between_input_and_first_hidden_layer.T					,       numpy.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output       )					,       self.second_hidden_layer_and_output_layer_weights.T					,       )
             * sigmoid_derivative(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer       )					,       )
         UpperCAmelCase							=     numpy.dot(
             self.input_array.T					,       numpy.dot(
                 numpy.dot(
                     2
                     * (self.output_array - self.predicted_output)
                     * sigmoid_derivative(self.predicted_output       )					,       self.second_hidden_layer_and_output_layer_weights.T					,       )
                 * sigmoid_derivative(
                     self.layer_between_first_hidden_layer_and_second_hidden_layer       )					,       self.first_hidden_layer_and_second_hidden_layer_weights.T					,       )
             * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer       )					,       )

         self.input_layer_and_first_hidden_layer_weights += (
             updated_input_layer_and_first_hidden_layer_weights
         )
         self.first_hidden_layer_and_second_hidden_layer_weights += (
             updated_first_hidden_layer_and_second_hidden_layer_weights
         )
         self.second_hidden_layer_and_output_layer_weights += (
             updated_second_hidden_layer_and_output_layer_weights
         )





      def        _lowercase				(		self						:					Any					,       __lowerCamelCase						:					numpy.ndarray					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					bool       )    ->   None:






         """simple docstring"""







         for iteration in range(1					,       iterations + 1       ):
            UpperCAmelCase							=     self.feedforward()
            self.back_propagation()
            if give_loss:
               UpperCAmelCase							=     numpy.mean(numpy.square(output - self.feedforward()       )       )
               print(F"""Iteration {iteration} Loss: {loss}"""       )





      def        _lowercase				(		self						:					List[str]					,       __lowerCamelCase						:					numpy.ndarray       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     input_arr

         UpperCAmelCase							=     sigmoid(
             numpy.dot(self.array					,       self.input_layer_and_first_hidden_layer_weights       )       )

         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer					,       self.first_hidden_layer_and_second_hidden_layer_weights					,       )       )

         UpperCAmelCase							=     sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer					,       self.second_hidden_layer_and_output_layer_weights					,       )       )

         return int(self.layer_between_second_hidden_layer_and_output > 0.6       )

def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return 1 / (1 + numpy.exp(-value   ))
def   _UpperCamelCase				(						lowerCAmelCase_   )						->numpy.ndarray:
   return (value) * (1 - (value))
def   _UpperCamelCase				(						)						->int:
   UpperCAmelCase							=     numpy.array(
       (
           [0, 0, 0],
           [0, 0, 1],
           [0, 1, 0],
           [0, 1, 1],
           [1, 0, 0],
           [1, 0, 1],
           [1, 1, 0],
           [1, 1, 1],
       )  ,						dtype=numpy.floataa  ,						)

   # True output values for the given input values.
   UpperCAmelCase							=     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])  ,						dtype=numpy.floataa   )

   # Calling neural network class.
   UpperCAmelCase							=     TwoHiddenLayerNeuralNetwork(
       input_array=lowerCAmelCase_  ,						output_array=lowerCAmelCase_   )

   # Calling training function.
   # Set give_loss to True if you want to see loss in every iteration.
   neural_network.train(output=lowerCAmelCase_  ,						iterations=1_0  ,						give_loss=lowerCAmelCase_   )

   return neural_network.predict(numpy.array(([1, 1, 1])  ,						dtype=numpy.floataa   )   )


if __name__ == "__main__":
 example()



