



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
    from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_									=							get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase_									=							{"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase_									=							">>zh<<"
UpperCAmelCase_									=							"Helsinki-NLP/"

if is_torch_available():
    UpperCAmelCase_									=							"pt"
elif is_tf_available():
    UpperCAmelCase_									=							"tf"
else:
    UpperCAmelCase_									=							"jax"





@require_sentencepiece
class 		UpperCAmelCase						(						snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     MarianTokenizer
   SCREAMING_SNAKE_CASE__							=     False
   SCREAMING_SNAKE_CASE__							=     True





   def 	__lowerCAmelCase							(	self			):
         super().setUp()
         _lowerCAmelCase 			=  ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
         _lowerCAmelCase 			=  dict(zip(_lowerCAmelCase       ,     range(len(_lowerCAmelCase			)			)			)			)
         _lowerCAmelCase 			=  Path(self.tmpdirname			)
         save_json(_lowerCAmelCase       ,     save_dir / VOCAB_FILES_NAMES['''vocab''']			)
         save_json(_lowerCAmelCase       ,     save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']			)
         if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
               copyfile(_lowerCAmelCase       ,     save_dir / VOCAB_FILES_NAMES['''source_spm''']			)
               copyfile(_lowerCAmelCase       ,     save_dir / VOCAB_FILES_NAMES['''target_spm''']			)

         _lowerCAmelCase 			=  MarianTokenizer.from_pretrained(self.tmpdirname			)
         tokenizer.save_pretrained(self.tmpdirname			)





   def 	__lowerCAmelCase							(	self       ,     **_lowerCAmelCase			):
         return MarianTokenizer.from_pretrained(self.tmpdirname       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         return (
             "This is a test",
             "This is a test",
         )





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''</s>'''
         _lowerCAmelCase 			=  0

         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]       ,     '''<unk>'''			)
         self.assertEqual(vocab_keys[-1]       ,     '''<pad>'''			)
         self.assertEqual(len(_lowerCAmelCase			)       ,     9			)





   def 	__lowerCAmelCase							(	self			):
         self.assertEqual(self.get_tokenizer().vocab_size       ,     9			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''			)
         _lowerCAmelCase 			=  en_de_tokenizer(['''I am a small frog''']       ,     return_tensors=_lowerCAmelCase			)
         self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
         _lowerCAmelCase 			=  [38, 121, 14, 697, 38_848, 0]
         self.assertListEqual(_lowerCAmelCase       ,     batch.input_ids[0]			)

         _lowerCAmelCase 			=  tempfile.mkdtemp()
         en_de_tokenizer.save_pretrained(_lowerCAmelCase			)
         _lowerCAmelCase 			=  [x.name for x in Path(_lowerCAmelCase			).glob('''*'''			)]
         self.assertIn('''source.spm'''       ,     _lowerCAmelCase			)
         MarianTokenizer.from_pretrained(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.get_tokenizer()

         _lowerCAmelCase 			=  tok(
             ['''I am a small frog''' * 1_000, '''I am a small frog''']       ,     padding=_lowerCAmelCase       ,     truncation=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase			)
         self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
         self.assertEqual(batch.input_ids.shape       ,     (2, 512)			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.get_tokenizer()
         _lowerCAmelCase 			=  tok(['''I am a tiny frog''', '''I am a small frog''']       ,     padding=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase			)
         self.assertIsInstance(_lowerCAmelCase       ,     _lowerCAmelCase			)
         self.assertEqual(batch_smaller.input_ids.shape       ,     (2, 10)			)





   @slow
   def 	__lowerCAmelCase							(	self			):
         # fmt: off
         _lowerCAmelCase 			=  {'''input_ids''': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '''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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_lowerCAmelCase       ,     model_name='''Helsinki-NLP/opus-mt-en-de'''       ,     revision='''1a8c2263da11e68e50938f97e10cd57820bd504c'''       ,     decode_kwargs={'''use_source_tokenizer''': True}       ,     )





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''			)

         _lowerCAmelCase 			=  '''Tämä on testi'''
         _lowerCAmelCase 			=  '''This is a test'''

         _lowerCAmelCase 			=  [76, 7, 2_047, 2]
         _lowerCAmelCase 			=  [69, 12, 11, 940, 2]

         _lowerCAmelCase 			=  tokenizer(_lowerCAmelCase			).input_ids
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer(text_target=_lowerCAmelCase			).input_ids
         self.assertListEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         _lowerCAmelCase 			=  tokenizer.decode(_lowerCAmelCase       ,     skip_special_tokens=_lowerCAmelCase			)
         self.assertEqual(_lowerCAmelCase       ,     _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()