




'''simple docstring'''


import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


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




	def __init__(       self     :  str       ,		__lowerCAmelCase     :  str=-1      ):
							# in NER datasets, the last column is usually reserved for NER label
							__snake_case						 =   label_idx




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Union[Split, str]      ):
							if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ):
													__snake_case						 =   mode.value
							__snake_case						 =   os.path.join(__lowerCAmelCase       ,		F'{mode}.txt'      )
							__snake_case						 =   1
							__snake_case						 =   []
							with open(__lowerCAmelCase       ,		encoding='utf-8'      ) as f:
													__snake_case						 =   []
													__snake_case						 =   []
													for line in f:
																			if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
																									if words:
																															examples.append(InputExample(guid=F'{mode}-{guid_index}'       ,		words=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )      )
																															guid_index += 1
																															__snake_case						 =   []
																															__snake_case						 =   []
																			else:
																									__snake_case						 =   line.split(' '      )
																									words.append(splits[0]      )
																									if len(__lowerCAmelCase      ) > 1:
																															labels.append(splits[self.label_idx].replace('\n'       ,		''      )      )
																									else:
																															# Examples could have no label for mode = "test"
																															labels.append('O'      )
													if words:
																			examples.append(InputExample(guid=F'{mode}-{guid_index}'       ,		words=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )      )
							return examples




	def   lowercase__   (       self     :  str       ,		__lowerCAmelCase     :  TextIO       ,		__lowerCAmelCase     :  TextIO       ,		__lowerCAmelCase     :  List      ):
							__snake_case						 =   0
							for line in test_input_reader:
													if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
																			writer.write(__lowerCAmelCase      )
																			if not preds_list[example_id]:
																									example_id += 1
													elif preds_list[example_id]:
																			__snake_case						 =   line.split()[0] + ' ' + preds_list[example_id].pop(0      ) + '\n'
																			writer.write(__lowerCAmelCase      )
													else:
																			logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'       ,		line.split()[0]      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  str      ):
							if path:
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			__snake_case						 =   f.read().splitlines()
													if "O" not in labels:
																			__snake_case						 =   ['O'] + labels
													return labels
							else:
													return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]





class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  List[Any]      ):
							# in CONLL2003 dataset chunk column is second-to-last
							super().__init__(label_idx=-2      )




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  str      ):
							if path:
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			__snake_case						 =   f.read().splitlines()
													if "O" not in labels:
																			__snake_case						 =   ['O'] + labels
													return labels
							else:
													return [
													    "O",
													    "B-ADVP",
													    "B-INTJ",
													    "B-LST",
													    "B-PRT",
													    "B-NP",
													    "B-SBAR",
													    "B-VP",
													    "B-ADJP",
													    "B-CONJP",
													    "B-PP",
													    "I-ADVP",
													    "I-INTJ",
													    "I-LST",
													    "I-PRT",
													    "I-NP",
													    "I-SBAR",
													    "I-VP",
													    "I-ADJP",
													    "I-CONJP",
													    "I-PP",
													]





class 						a_				(			UpperCAmelCase__						):




	def   lowercase__   (       self     :  Any       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Union[Split, str]      ):
							if isinstance(__lowerCAmelCase       ,		__lowerCAmelCase      ):
													__snake_case						 =   mode.value
							__snake_case						 =   os.path.join(__lowerCAmelCase       ,		F'{mode}.txt'      )
							__snake_case						 =   1
							__snake_case						 =   []

							with open(__lowerCAmelCase       ,		encoding='utf-8'      ) as f:
													for sentence in parse_incr(__lowerCAmelCase      ):
																			__snake_case						 =   []
																			__snake_case						 =   []
																			for token in sentence:
																									words.append(token['form']      )
																									labels.append(token['upos']      )
																			assert len(__lowerCAmelCase      ) == len(__lowerCAmelCase      )
																			if words:
																									examples.append(InputExample(guid=F'{mode}-{guid_index}'       ,		words=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )      )
																									guid_index += 1
							return examples




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  TextIO       ,		__lowerCAmelCase     :  TextIO       ,		__lowerCAmelCase     :  List      ):
							__snake_case						 =   0
							for sentence in parse_incr(__lowerCAmelCase      ):
													__snake_case						 =   preds_list[example_id]
													__snake_case						 =   ''
													for token in sentence:
																			out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0      )}) '
													out += "\n"
													writer.write(__lowerCAmelCase      )
													example_id += 1




	def   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  str      ):
							if path:
													with open(__lowerCAmelCase       ,		'r'      ) as f:
																			return f.read().splitlines()
							else:
													return [
													    "ADJ",
													    "ADP",
													    "ADV",
													    "AUX",
													    "CCONJ",
													    "DET",
													    "INTJ",
													    "NOUN",
													    "NUM",
													    "PART",
													    "PRON",
													    "PROPN",
													    "PUNCT",
													    "SCONJ",
													    "SYM",
													    "VERB",
													    "X",
													]







'''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()

