



import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class 		UpperCAmelCase						(						snake_case_				):





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  params

         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)
         _lowerCAmelCase 			=  np.array([len(_lowerCAmelCase			) for t in data]			)

         self.check()
         self.remove_long_sequences()
         self.remove_empty_sequences()
         self.remove_unknown_sequences()
         self.check()
         self.print_statistics()





   def __getitem__(	self       ,     _lowerCAmelCase			):
         return (self.token_ids[index], self.lengths[index])





   def __len__(	self			):
         return len(self.lengths			)





   def 	__lowerCAmelCase							(	self			):
         assert len(self.token_ids			) == len(self.lengths			)
         assert all(self.lengths[i] == len(self.token_ids[i]			) for i in range(len(self.lengths			)			)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.params.max_model_input_size
         _lowerCAmelCase 			=  self.lengths > max_len
         logger.info(F'''Splitting {sum(_lowerCAmelCase			)} too long sequences.'''			)

         def divide_chunks(_lowerCAmelCase       ,     _lowerCAmelCase			):
               return [l[i : i + n] for i in range(0       ,     len(_lowerCAmelCase			)       ,     _lowerCAmelCase			)]

         _lowerCAmelCase 			=  []
         _lowerCAmelCase 			=  []
         if self.params.mlm:
               _lowerCAmelCase			,   _lowerCAmelCase 			=  self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
         else:
               _lowerCAmelCase			,   _lowerCAmelCase 			=  self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

         for seq_, len_ in zip(self.token_ids       ,     self.lengths			):
               assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
               if len_ <= max_len:
                     new_tok_ids.append(seq_			)
                     new_lengths.append(len_			)
               else:
                     _lowerCAmelCase 			=  []
                     for sub_s in divide_chunks(seq_       ,     max_len - 2			):
                           if sub_s[0] != cls_id:
                                 _lowerCAmelCase 			=  np.insert(_lowerCAmelCase       ,     0       ,     _lowerCAmelCase			)
                           if sub_s[-1] != sep_id:
                                 _lowerCAmelCase 			=  np.insert(_lowerCAmelCase       ,     len(_lowerCAmelCase			)       ,     _lowerCAmelCase			)
                           assert len(_lowerCAmelCase			) <= max_len
                           assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                           sub_seqs.append(_lowerCAmelCase			)

                     new_tok_ids.extend(_lowerCAmelCase			)
                     new_lengths.extend([len(_lowerCAmelCase			) for l in sub_seqs]			)

         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)
         _lowerCAmelCase 			=  np.array(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  len(self			)
         _lowerCAmelCase 			=  self.lengths > 11
         _lowerCAmelCase 			=  self.token_ids[indices]
         _lowerCAmelCase 			=  self.lengths[indices]
         _lowerCAmelCase 			=  len(self			)
         logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''			)





   def 	__lowerCAmelCase							(	self			):
         if "unk_token" not in self.params.special_tok_ids:
               return
         else:
               _lowerCAmelCase 			=  self.params.special_tok_ids['''unk_token''']
         _lowerCAmelCase 			=  len(self			)
         _lowerCAmelCase 			=  np.array([np.count_nonzero(a == unk_token_id			) for a in self.token_ids]			)
         _lowerCAmelCase 			=  (unk_occs / self.lengths) < 0.5
         _lowerCAmelCase 			=  self.token_ids[indices]
         _lowerCAmelCase 			=  self.lengths[indices]
         _lowerCAmelCase 			=  len(self			)
         logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''			)





   def 	__lowerCAmelCase							(	self			):
         if not self.params.is_master:
               return
         logger.info(F'''{len(self			)} sequences'''			)
         # data_len = sum(self.lengths)
         # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
         # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

         # unk_idx = self.params.special_tok_ids['unk_token']
         # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
         # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  [t[0] for t in batch]
         _lowerCAmelCase 			=  [t[1] for t in batch]
         assert len(_lowerCAmelCase			) == len(_lowerCAmelCase			)

         # Max for paddings
         _lowerCAmelCase 			=  max(_lowerCAmelCase			)

         # Pad token ids
         if self.params.mlm:
               _lowerCAmelCase 			=  self.params.special_tok_ids['''pad_token''']
         else:
               _lowerCAmelCase 			=  self.params.special_tok_ids['''unk_token''']
         _lowerCAmelCase 			=  [list(t.astype(_lowerCAmelCase			)			) + [pad_idx] * (max_seq_len_ - len(_lowerCAmelCase			)) for t in token_ids]
         assert len(tk_			) == len(_lowerCAmelCase			)
         assert all(len(_lowerCAmelCase			) == max_seq_len_ for t in tk_			)

         _lowerCAmelCase 			=  torch.tensor(tk_			)  # (bs, max_seq_len_)
         _lowerCAmelCase 			=  torch.tensor(_lowerCAmelCase			)  # (bs)
         return tk_t, lg_t




import numpy





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  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.
         _lowerCAmelCase 			=  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.
         _lowerCAmelCase 			=  numpy.random.rand(
             4       ,     3			)

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

         # Real output values provided.
         _lowerCAmelCase 			=  output_array

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





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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  input_arr

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

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

         _lowerCAmelCase 			=  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__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return 1 / (1 + numpy.exp(-value				))
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return (value) * (1 - (value))
def 							UpperCAmelCase__		(				)->int:
      _lowerCAmelCase 			=  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.
      _lowerCAmelCase 			=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,				dtype=numpy.floataa				)

      # Calling neural network class.
      _lowerCAmelCase 			=  TwoHiddenLayerNeuralNetwork(
          input_array=_SCREAMING_SNAKE_CASE     ,				output_array=_SCREAMING_SNAKE_CASE				)

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

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


if __name__ == "__main__":
    example()