





import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__lowercase       :Any       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"linear"
							snake_case_							 =						"cosine"
							snake_case_							 =						"cosine_with_restarts"
							snake_case_							 =						"polynomial"
							snake_case_							 =						"constant"
							snake_case_							 =						"constant_with_warmup"
							snake_case_							 =						"piecewise_constant"







def       UpperCAmelCase							(     _lowerCamelCase      :  Optimizer			,   _lowerCamelCase      :  int = -1     ):





						'''simple docstring'''


						return LambdaLR(_lowerCamelCase			,   lambda _lowerCamelCase     : 1			,   last_epoch=_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Optimizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int = -1     ):





						'''simple docstring'''


						def lr_lambda(_lowerCamelCase      :  int     ):
												if current_step < num_warmup_steps:
																		return float(_lowerCamelCase     ) / float(max(1.0			,   _lowerCamelCase     )     )
												return 1.0

						return LambdaLR(_lowerCamelCase			,   _lowerCamelCase			,   last_epoch=_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Optimizer			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  int = -1     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					{}
						SCREAMING_SNAKE_CASE__     :				List[str]   =					step_rules.split(","     )
						for rule_str in rule_list[:-1]:
												SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				str   =					rule_str.split(":"     )
												SCREAMING_SNAKE_CASE__     :				Dict   =					int(_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					float(_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				List[Any]   =					value
						SCREAMING_SNAKE_CASE__     :				str   =					float(rule_list[-1]     )

						def create_rules_function(_lowerCamelCase      :  Any			,   _lowerCamelCase      :  List[Any]     ):
												def rule_func(_lowerCamelCase      :  int     ) -> float:
																		SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					sorted(rules_dict.keys()     )
																		for i, sorted_step in enumerate(_lowerCamelCase     ):
																								if steps < sorted_step:
																														return rules_dict[sorted_steps[i]]
																		return last_lr_multiple

												return rule_func

						SCREAMING_SNAKE_CASE__     :				List[Any]   =					create_rules_function(_lowerCamelCase			,   _lowerCamelCase     )

						return LambdaLR(_lowerCamelCase			,   _lowerCamelCase			,   last_epoch=_lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  int			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  Union[str, Any]			,   _lowerCamelCase      :  Union[str, Any]=-1     ):





						'''simple docstring'''


						def lr_lambda(_lowerCamelCase      :  int     ):
												if current_step < num_warmup_steps:
																		return float(_lowerCamelCase     ) / float(max(1			,   _lowerCamelCase     )     )
												return max(
												    0.0			,   float(num_training_steps - current_step     ) / float(max(1			,   num_training_steps - num_warmup_steps     )     )     )

						return LambdaLR(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Optimizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  float = 0.5			,   _lowerCamelCase      :  int = -1     ):





						'''simple docstring'''


						def lr_lambda(_lowerCamelCase      :  Union[str, Any]     ):
												if current_step < num_warmup_steps:
																		return float(_lowerCamelCase     ) / float(max(1			,   _lowerCamelCase     )     )
												SCREAMING_SNAKE_CASE__     :				str   =					float(current_step - num_warmup_steps     ) / float(max(1			,   num_training_steps - num_warmup_steps     )     )
												return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase     ) * 2.0 * progress     ))     )

						return LambdaLR(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  Optimizer			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int			,   _lowerCamelCase      :  int = 1			,   _lowerCamelCase      :  int = -1     ):





						'''simple docstring'''


						def lr_lambda(_lowerCamelCase      :  List[str]     ):
												if current_step < num_warmup_steps:
																		return float(_lowerCamelCase     ) / float(max(1			,   _lowerCamelCase     )     )
												SCREAMING_SNAKE_CASE__     :				Tuple   =					float(current_step - num_warmup_steps     ) / float(max(1			,   num_training_steps - num_warmup_steps     )     )
												if progress >= 1.0:
																		return 0.0
												return max(0.0			,   0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase     ) * progress) % 1.0)     ))     )

						return LambdaLR(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )







def       UpperCAmelCase							(     _lowerCamelCase      :  str			,   _lowerCamelCase      :  Any			,   _lowerCamelCase      :  Optional[int]			,   _lowerCamelCase      :  Any=1E-7			,   _lowerCamelCase      :  Any=1.0			,   _lowerCamelCase      :  int=-1     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					optimizer.defaults["lr"]
						if not (lr_init > lr_end):
												raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""     )

						def lr_lambda(_lowerCamelCase      :  int     ):
												if current_step < num_warmup_steps:
																		return float(_lowerCamelCase     ) / float(max(1			,   _lowerCamelCase     )     )
												elif current_step > num_training_steps:
																		return lr_end / lr_init  # as LambdaLR multiplies by lr_init
												else:
																		SCREAMING_SNAKE_CASE__     :				Any   =					lr_init - lr_end
																		SCREAMING_SNAKE_CASE__     :				Any   =					num_training_steps - num_warmup_steps
																		SCREAMING_SNAKE_CASE__     :				str   =					1 - (current_step - num_warmup_steps) / decay_steps
																		SCREAMING_SNAKE_CASE__     :				List[Any]   =					lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase     )


__lowercase       :Union[str, Any]       =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, SchedulerType]			,   _lowerCamelCase      :  Optimizer			,   _lowerCamelCase      :  Optional[str] = None			,   _lowerCamelCase      :  Optional[int] = None			,   _lowerCamelCase      :  Optional[int] = None			,   _lowerCamelCase      :  int = 1			,   _lowerCamelCase      :  float = 1.0			,   _lowerCamelCase      :  int = -1			,   ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[str]   =					SchedulerType(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					TYPE_TO_SCHEDULER_FUNCTION[name]
						if name == SchedulerType.CONSTANT:
												return schedule_func(_lowerCamelCase			,   last_epoch=_lowerCamelCase     )

						if name == SchedulerType.PIECEWISE_CONSTANT:
												return schedule_func(_lowerCamelCase			,   step_rules=_lowerCamelCase			,   last_epoch=_lowerCamelCase     )

						# All other schedulers require `num_warmup_steps`
						if num_warmup_steps is None:
												raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""     )

						if name == SchedulerType.CONSTANT_WITH_WARMUP:
												return schedule_func(_lowerCamelCase			,   num_warmup_steps=_lowerCamelCase			,   last_epoch=_lowerCamelCase     )

						# All other schedulers require `num_training_steps`
						if num_training_steps is None:
												raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""     )

						if name == SchedulerType.COSINE_WITH_RESTARTS:
												return schedule_func(
												    _lowerCamelCase			,   num_warmup_steps=_lowerCamelCase			,   num_training_steps=_lowerCamelCase			,   num_cycles=_lowerCamelCase			,   last_epoch=_lowerCamelCase			,   )

						if name == SchedulerType.POLYNOMIAL:
												return schedule_func(
												    _lowerCamelCase			,   num_warmup_steps=_lowerCamelCase			,   num_training_steps=_lowerCamelCase			,   power=_lowerCamelCase			,   last_epoch=_lowerCamelCase			,   )

						return schedule_func(
						    _lowerCamelCase			,   num_warmup_steps=_lowerCamelCase			,   num_training_steps=_lowerCamelCase			,   last_epoch=_lowerCamelCase     )






import numpy



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     numpy.ndarray							,	a				:     numpy.ndarray    )	->None:
													SCREAMING_SNAKE_CASE__     :				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.
													SCREAMING_SNAKE_CASE__     :				int   =					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.
													SCREAMING_SNAKE_CASE__     :				Dict   =					numpy.random.rand(
													    4							,	3    )

													# Random initial values for the second hidden layer.
													# Second hidden layer has 3 nodes.
													# Output layer has 1 node.
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					numpy.random.rand(3							,	1    )

													# Real output values provided.
													SCREAMING_SNAKE_CASE__     :				str   =					output_array

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

							def      A_  (      self				:     Union[str, Any]    )	->numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[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.
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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.
													SCREAMING_SNAKE_CASE__     :				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      A_  (      self				:     int    )	->None:
													SCREAMING_SNAKE_CASE__     :				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    )							,	)
													SCREAMING_SNAKE_CASE__     :				Union[str, 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    )							,	)
													SCREAMING_SNAKE_CASE__     :				int   =					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      A_  (      self				:     int							,	a				:     numpy.ndarray							,	a				:     int							,	a				:     bool    )	->None:
													for iteration in range(1							,	iterations + 1    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.feedforward()
																			self.back_propagation()
																			if give_loss:
																									SCREAMING_SNAKE_CASE__     :				int   =					numpy.mean(numpy.square(output - self.feedforward()    )    )
																									print(f"""Iteration {iteration} Loss: {loss}"""    )






							def      A_  (      self				:     Tuple							,	a				:     numpy.ndarray    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_arr

													SCREAMING_SNAKE_CASE__     :				Dict   =					sigmoid(
													    numpy.dot(self.array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													SCREAMING_SNAKE_CASE__     :				Any   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													SCREAMING_SNAKE_CASE__     :				Optional[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       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return 1 / (1 + numpy.exp(-value     ))







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


						return (value) * (1 - (value))







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					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.
						SCREAMING_SNAKE_CASE__     :				Any   =					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,   dtype=numpy.floataa     )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					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=10			,   give_loss=_lowerCamelCase     )

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


if __name__ == "__main__":
				example()