

'''simple docstring'''


from ...configuration_utils import PretrainedConfig


lowerCAmelCase__										= {
    "google/tapas-base-finetuned-sqa": (
        "https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-wtq": (
        "https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-wikisql-supervised": (
        "https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"
    ),
    "google/tapas-base-finetuned-tabfact": (
        "https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"
    ),
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "tapas"


							def __init__(			self    ,			_snake_case=3_0522    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=12    ,			_snake_case=3072    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=1024    ,			_snake_case=[3, 256, 256, 2, 256, 256, 10]    ,			_snake_case=0.02    ,			_snake_case=1e-12    ,			_snake_case=0    ,			_snake_case=10.0    ,			_snake_case=0    ,			_snake_case=1.0    ,			_snake_case=None    ,			_snake_case=1.0    ,			_snake_case=False    ,			_snake_case=None    ,			_snake_case=1.0    ,			_snake_case=1.0    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case="ratio"    ,			_snake_case=None    ,			_snake_case=None    ,			_snake_case=64    ,			_snake_case=32    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=False    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Union[str, Any]:
								super().__init__(pad_token_id=_snake_case    ,			**_snake_case)

								# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
								UpperCAmelCase_		:      Optional[Any]       	=					vocab_size
								UpperCAmelCase_		:      Tuple       	=					hidden_size
								UpperCAmelCase_		:      List[str]       	=					num_hidden_layers
								UpperCAmelCase_		:      List[str]       	=					num_attention_heads
								UpperCAmelCase_		:      Dict       	=					hidden_act
								UpperCAmelCase_		:      Optional[Any]       	=					intermediate_size
								UpperCAmelCase_		:      int       	=					hidden_dropout_prob
								UpperCAmelCase_		:      str       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Dict       	=					type_vocab_sizes
								UpperCAmelCase_		:      Optional[Any]       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps

								# Fine-tuning task hyperparameters
								UpperCAmelCase_		:      Dict       	=					positive_label_weight
								UpperCAmelCase_		:      List[str]       	=					num_aggregation_labels
								UpperCAmelCase_		:      Tuple       	=					aggregation_loss_weight
								UpperCAmelCase_		:      Tuple       	=					use_answer_as_supervision
								UpperCAmelCase_		:      List[Any]       	=					answer_loss_importance
								UpperCAmelCase_		:      int       	=					use_normalized_answer_loss
								UpperCAmelCase_		:      str       	=					huber_loss_delta
								UpperCAmelCase_		:      Optional[int]       	=					temperature
								UpperCAmelCase_		:      Optional[int]       	=					aggregation_temperature
								UpperCAmelCase_		:      str       	=					use_gumbel_for_cells
								UpperCAmelCase_		:      Any       	=					use_gumbel_for_aggregation
								UpperCAmelCase_		:      Any       	=					average_approximation_function
								UpperCAmelCase_		:      str       	=					cell_selection_preference
								UpperCAmelCase_		:      str       	=					answer_loss_cutoff
								UpperCAmelCase_		:      Optional[int]       	=					max_num_rows
								UpperCAmelCase_		:      Optional[Any]       	=					max_num_columns
								UpperCAmelCase_		:      Tuple       	=					average_logits_per_cell
								UpperCAmelCase_		:      int       	=					select_one_column
								UpperCAmelCase_		:      str       	=					allow_empty_column_selection
								UpperCAmelCase_		:      Tuple       	=					init_cell_selection_weights_to_zero
								UpperCAmelCase_		:      int       	=					reset_position_index_per_cell
								UpperCAmelCase_		:      Optional[Any]       	=					disable_per_token_loss

								# Aggregation hyperparameters
								UpperCAmelCase_		:      List[Any]       	=					aggregation_labels
								UpperCAmelCase_		:      Optional[int]       	=					no_aggregation_label_index

								if isinstance(self.aggregation_labels    ,			_snake_case):
									UpperCAmelCase_		:      Any       	=					{int(_snake_case): v for k, v in aggregation_labels.items()}









'''simple docstring'''


import numpy



class 					lowercase						:


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Optional[Any]       	=					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_		:      Tuple       	=					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_		:      List[str]       	=					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_		:      Dict       	=					numpy.random.rand(3    ,			1)

								# Real output values provided.
								UpperCAmelCase_		:      str       	=					output_array

								# Predicted output values by the neural network.
								# Predicted_output array initially consists of zeroes.
								UpperCAmelCase_		:      Union[str, Any]       	=					numpy.zeros(output_array.shape)


							def 						_snake_case							(			self)							->				numpy.ndarray:
								UpperCAmelCase_		:      Any       	=					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_		:      Tuple       	=					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_		:      int       	=					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 						_snake_case							(			self)							->				None:
								UpperCAmelCase_		:      Optional[int]       	=					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_		:      Any       	=					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_		:      Tuple       	=					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 						_snake_case							(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case)							->				None:
								for iteration in range(1    ,			iterations + 1):
									UpperCAmelCase_		:      int       	=					self.feedforward()
									self.back_propagation()
									if give_loss:
										UpperCAmelCase_		:      List[Any]       	=					numpy.mean(numpy.square(output - self.feedforward()))
										print(F"""Iteration {iteration} Loss: {loss}""")





							def 						_snake_case							(			self    ,			_snake_case)							->				int:
								UpperCAmelCase_		:      Optional[int]       	=					input_arr

								UpperCAmelCase_		:      Tuple       	=					sigmoid(
								    numpy.dot(self.array    ,			self.input_layer_and_first_hidden_layer_weights))

								UpperCAmelCase_		:      Optional[int]       	=					sigmoid(
								    numpy.dot(
								        self.layer_between_input_and_first_hidden_layer    ,			self.first_hidden_layer_and_second_hidden_layer_weights    ,			))

								UpperCAmelCase_		:      int       	=					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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return 1 / (1 + numpy.exp(-value  ))







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    numpy.ndarray:
	return (value) * (1 - (value))







def 						SCREAMING_SNAKE_CASE(  )    ->    int:
	UpperCAmelCase_		:      Optional[int]       	=					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_		:      Dict       	=					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])   ,dtype=numpy.floataa  )

	# Calling neural network class.
	UpperCAmelCase_		:      List[str]       	=					TwoHiddenLayerNeuralNetwork(
	    input_array=UpperCamelCase   ,output_array=UpperCamelCase  )

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

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


if __name__ == "__main__":
						example()






