




'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Dict       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  float       ,		**__lowerCAmelCase     :  Optional[int]      ):
							__snake_case						 =   feature_size
							__snake_case						 =   sampling_rate
							__snake_case						 =   padding_value

							__snake_case						 =   kwargs.pop('padding_side'       ,		'right'      )
							__snake_case						 =   kwargs.pop('return_attention_mask'       ,		__lowerCAmelCase      )

							super().__init__(**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]       ,		__lowerCAmelCase     :  Union[
	        BatchFeature,
	        List[BatchFeature],
	        Dict[str, BatchFeature],
	        Dict[str, List[BatchFeature]],
	        List[Dict[str, BatchFeature]],
	    ]       ,		__lowerCAmelCase     :  Union[bool, str, PaddingStrategy] = True       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  Optional[bool] = None       ,		__lowerCAmelCase     :  Optional[Union[str, TensorType]] = None       ,		):
							# If we have a list of dicts, let's convert it in a dict of lists
							# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
							if isinstance(__lowerCAmelCase       ,		(list, tuple)      ) and isinstance(processed_features[0]       ,		(dict, BatchFeature)      ):
													__snake_case						 =   {
													    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
													}

							# The model's main input name, usually `input_values`, has be passed for padding
							if self.model_input_names[0] not in processed_features:
													raise ValueError(
													    'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
													    F' to this method that includes {self.model_input_names[0]}, but you provided'
													    F' {list(processed_features.keys()      )}'      )

							__snake_case						 =   processed_features[self.model_input_names[0]]
							__snake_case						 =   (
							    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
							)

							if len(__lowerCAmelCase      ) == 0:
													if return_attention_mask:
																			__snake_case						 =   []
													return processed_features

							# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
							# and rebuild them afterwards if no return_tensors is specified
							# Note that we lose the specific device the tensor may be on for PyTorch

							__snake_case						 =   required_input[0]
							if isinstance(__lowerCAmelCase       ,		(list, tuple)      ):
													# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
													__snake_case						 =   0
													while len(required_input[index]      ) == 0:
																			index += 1
													if index < len(__lowerCAmelCase      ):
																			__snake_case						 =   required_input[index][0]

							if return_tensors is None:
													if is_tf_tensor(__lowerCAmelCase      ):
																			__snake_case						 =   'tf'
													elif is_torch_tensor(__lowerCAmelCase      ):
																			__snake_case						 =   'pt'
													elif isinstance(__lowerCAmelCase       ,		(int, float, list, tuple, np.ndarray)      ):
																			__snake_case						 =   'np'
													else:
																			raise ValueError(
																			    F'type of {first_element} unknown: {type(__lowerCAmelCase      )}. '
																			    'Should be one of a python, numpy, pytorch or tensorflow object.'      )

							for key, value in processed_features.items():
													if isinstance(value[0]       ,		(int, float)      ):
																			__snake_case						 =   to_numpy(__lowerCAmelCase      )
													else:
																			__snake_case						 =   [to_numpy(__lowerCAmelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
							__snake_case						 =   self._get_padding_strategies(padding=__lowerCAmelCase       ,		max_length=__lowerCAmelCase      )

							__snake_case						 =   processed_features[self.model_input_names[0]]

							__snake_case						 =   len(__lowerCAmelCase      )
							if not all(len(__lowerCAmelCase      ) == batch_size for v in processed_features.values()      ):
													raise ValueError('Some items in the output dictionary have a different batch size than others.'      )

							__snake_case						 =   []
							for i in range(__lowerCAmelCase      ):
													__snake_case						 =   {k: v[i] for k, v in processed_features.items()}
													# truncation
													__snake_case						 =   self._truncate(
													    __lowerCAmelCase       ,		max_length=__lowerCAmelCase       ,		pad_to_multiple_of=__lowerCAmelCase       ,		truncation=__lowerCAmelCase       ,		)
													truncated_inputs.append(__lowerCAmelCase      )

							if padding_strategy == PaddingStrategy.LONGEST:
													# make sure that `max_length` cannot be longer than the longest truncated length
													__snake_case						 =   max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
													__snake_case						 =   PaddingStrategy.MAX_LENGTH

							__snake_case						 =   {}
							for i in range(__lowerCAmelCase      ):
													# padding
													__snake_case						 =   self._pad(
													    truncated_inputs[i]       ,		max_length=__lowerCAmelCase       ,		padding_strategy=__lowerCAmelCase       ,		pad_to_multiple_of=__lowerCAmelCase       ,		return_attention_mask=__lowerCAmelCase       ,		)

													for key, value in outputs.items():
																			if key not in batch_outputs:
																									__snake_case						 =   []
																			if value.dtype is np.dtype(np.floataa      ):
																									__snake_case						 =   value.astype(np.floataa      )
																			batch_outputs[key].append(__lowerCAmelCase      )

							return BatchFeature(__lowerCAmelCase       ,		tensor_type=__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  Union[Dict[str, np.ndarray], BatchFeature]       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  Optional[bool] = None       ,		):
							__snake_case						 =   processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
													__snake_case						 =   len(__lowerCAmelCase      )

							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													__snake_case						 =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							__snake_case						 =   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowerCAmelCase      ) < max_length

							if return_attention_mask and "attention_mask" not in processed_features:
													__snake_case						 =   np.ones(len(__lowerCAmelCase      )       ,		dtype=np.intaa      )

							if needs_to_be_padded:
													__snake_case						 =   max_length - len(__lowerCAmelCase      )
													if self.padding_side == "right":
																			if return_attention_mask:
																									__snake_case						 =   np.pad(
																									    processed_features['attention_mask']       ,		(0, difference)      )
																			__snake_case						 =   ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																			__snake_case						 =   np.pad(
																			    __lowerCAmelCase       ,		__lowerCAmelCase       ,		'constant'       ,		constant_values=self.padding_value      )
													elif self.padding_side == "left":
																			if return_attention_mask:
																									__snake_case						 =   np.pad(
																									    processed_features['attention_mask']       ,		(difference, 0)      )
																			__snake_case						 =   ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																			__snake_case						 =   np.pad(
																			    __lowerCAmelCase       ,		__lowerCAmelCase       ,		'constant'       ,		constant_values=self.padding_value      )
													else:
																			raise ValueError('Invalid padding strategy:' + str(self.padding_side      )      )

							return processed_features




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  Union[Dict[str, np.ndarray], BatchFeature]       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  Optional[bool] = None       ,		):
							if not truncation:
													return processed_features
							elif truncation and max_length is None:
													raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'      )

							__snake_case						 =   processed_features[self.model_input_names[0]]

							# find `max_length` that fits `pad_to_multiple_of`
							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
													__snake_case						 =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							__snake_case						 =   len(__lowerCAmelCase      ) > max_length

							if needs_to_be_truncated:
													__snake_case						 =   processed_features[self.model_input_names[0]][:max_length]
													if "attention_mask" in processed_features:
																			__snake_case						 =   processed_features['attention_mask'][:max_length]

							return processed_features




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[Any]=False       ,		__lowerCAmelCase     :  Optional[Any]=None      ):

							# Get padding strategy
							if padding is not False:
													if padding is True:
																			__snake_case						 =   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
													elif not isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ):
																			__snake_case						 =   PaddingStrategy(__lowerCAmelCase      )
													elif isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ):
																			__snake_case						 =   padding
							else:
													__snake_case						 =   PaddingStrategy.DO_NOT_PAD

							# Set max length if needed
							if max_length is None:
													if padding_strategy == PaddingStrategy.MAX_LENGTH:
																			raise ValueError(
																			    F'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'      )

        # Test if we have a padding value
							if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
													raise ValueError(
													    'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
													    ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'      )

							return padding_strategy







'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   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.
							__snake_case						 =   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.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

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

							# Real output values provided.
							__snake_case						 =   output_array

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




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   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.
							__snake_case						 =   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.
							__snake_case						 =   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     :  List[str]      ):
							__snake_case						 =   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      )       ,		)
							__snake_case						 =   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      )       ,		)
							__snake_case						 =   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     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

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

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

							__snake_case						 =   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 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   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.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   TwoHiddenLayerNeuralNetwork(
						    input_array=a    ,	output_array=a							)

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=a    ,	iterations=10    ,	give_loss=a							)

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


if __name__ == "__main__":
	example()

