

import numpy






class 							A_ :


     def __init__(							self			:		Any			,  UpperCAmelCase			:		numpy.ndarray			,  UpperCAmelCase			:		numpy.ndarray					)							->	str:
      __lowerCAmelCase:   Tuple   	=      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:   Union[str, Any]   	=      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:   List[Any]   	=      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:   str   	=      numpy.random.rand(3			,  1					)

      # Real output values provided.
      __lowerCAmelCase:   Dict   	=      output_array

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


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


     def   UpperCAmelCase  (							self			:		Union[str, Any]			,  UpperCAmelCase			:		numpy.ndarray					)							->	List[str]:
      __lowerCAmelCase:   Dict   	=      input_arr

      __lowerCAmelCase:   Optional[Any]   	=      sigmoid(
          numpy.dot(self.array			,  self.input_layer_and_first_hidden_layer_weights					)					)

      __lowerCAmelCase:   Optional[Any]   	=      sigmoid(
          numpy.dot(
              self.layer_between_input_and_first_hidden_layer			,  self.first_hidden_layer_and_second_hidden_layer_weights			,  )					)

      __lowerCAmelCase:   Tuple   	=      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 _a (       SCREAMING_SNAKE_CASE		:			Tuple		)    ->    numpy.ndarray:

 """simple docstring"""






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







def _a (       SCREAMING_SNAKE_CASE		:			List[str]		)    ->    numpy.ndarray:

 """simple docstring"""






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







def _a (       )    ->    int:

 """simple docstring"""






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

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

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


if __name__ == "__main__":
  example()













'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

