



import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     BarthezTokenizer
   SCREAMING_SNAKE_CASE__							=     BarthezTokenizerFast
   SCREAMING_SNAKE_CASE__							=     True
   SCREAMING_SNAKE_CASE__							=     True





   def 	__lowerCAmelCase							(	self			):
         super().setUp()

         _lowerCAmelCase 			=  BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''			)
         tokenizer.save_pretrained(self.tmpdirname			)
         tokenizer.save_pretrained(self.tmpdirname       ,     legacy_format=_lowerCAmelCase			)
         _lowerCAmelCase 			=  tokenizer





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''<pad>'''
         _lowerCAmelCase 			=  1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCAmelCase			)       ,     _lowerCAmelCase			)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCAmelCase			)       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  list(self.get_tokenizer().get_vocab().keys()			)

         self.assertEqual(vocab_keys[0]       ,     '''<s>'''			)
         self.assertEqual(vocab_keys[1]       ,     '''<pad>'''			)
         self.assertEqual(vocab_keys[-1]       ,     '''<mask>'''			)
         self.assertEqual(len(_lowerCAmelCase			)       ,     101_122			)





   def 	__lowerCAmelCase							(	self			):
         self.assertEqual(self.get_tokenizer().vocab_size       ,     101_122			)





   @require_torch
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         _lowerCAmelCase 			=  [0, 57, 3_018, 70_307, 91, 2]

         _lowerCAmelCase 			=  self.tokenizer(
             _lowerCAmelCase       ,     max_length=len(_lowerCAmelCase			)       ,     padding=_lowerCAmelCase       ,     truncation=_lowerCAmelCase       ,     return_tensors='''pt'''			)
         self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)

         self.assertEqual((2, 6)       ,     batch.input_ids.shape			)
         self.assertEqual((2, 6)       ,     batch.attention_mask.shape			)
         _lowerCAmelCase 			=  batch.input_ids.tolist()[0]
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         if not self.test_rust_tokenizer:
               return

         _lowerCAmelCase 			=  self.get_tokenizer()
         _lowerCAmelCase 			=  self.get_rust_tokenizer()

         _lowerCAmelCase 			=  '''I was born in 92000, and this is falsé.'''

         _lowerCAmelCase 			=  tokenizer.tokenize(_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.tokenize(_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.encode(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  self.get_rust_tokenizer()
         _lowerCAmelCase 			=  tokenizer.encode(_lowerCAmelCase			)
         _lowerCAmelCase 			=  rust_tokenizer.encode(_lowerCAmelCase			)
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         # fmt: off
         _lowerCAmelCase 			=  {'''input_ids''': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
         # fmt: on

         # moussaKam/mbarthez is a french model. So we also use french texts.
         _lowerCAmelCase 			=  [
             '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
             '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
             '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
             '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
             '''telles que la traduction et la synthèse de texte.''',
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCAmelCase       ,     model_name='''moussaKam/mbarthez'''       ,     revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''       ,     sequences=_lowerCAmelCase       ,     )




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()