

'''simple docstring'''



import numpy







class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       str					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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     =      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     =      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     =      numpy.random.rand(3					,							1			)

      # Real output values provided.
      __SCREAMING_SNAKE_CASE     =      output_array

      # Predicted output values by the neural network.
      # Predicted_output array initially consists of zeroes.
      __SCREAMING_SNAKE_CASE     =      numpy.zeros(output_array.shape			)
 def       UpperCAmelCase__		(		self					:       int			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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     =      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     =      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					:       Dict			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      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     =      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     =      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					:       Optional[Any]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       List[str]					,							__SCREAMING_SNAKE_CASE					:       str			)  -> List[str]:


      """simple docstring"""




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


 def       UpperCAmelCase__		(		self					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       int			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      input_arr

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

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

      __SCREAMING_SNAKE_CASE     =      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__       (  a__						):




     """simple docstring"""


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







def    a__       (  a__						):




     """simple docstring"""


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







def    a__       (  ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      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     =      numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])      ,    dtype=numpy.floataa						)

     # Calling neural network class.
     __SCREAMING_SNAKE_CASE     =      TwoHiddenLayerNeuralNetwork(
         input_array=_A      ,    output_array=_A						)

     # Calling training function.
     # Set give_loss to True if you want to see loss in every iteration.
     neural_network.train(output=_A      ,    iterations=10      ,    give_loss=_A						)

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


if __name__ == "__main__":
  example()







'''simple docstring'''




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowercase          =  get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Tuple      =       XLMRobertaTokenizer
			UpperCAmelCase_			:     int      =       XLMRobertaTokenizerFast
			UpperCAmelCase_			:     List[str]      =       True
			UpperCAmelCase_			:     Optional[int]      =       True



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()

					# We have a SentencePiece fixture for testing
					lowerCAmelCase   					=  XLMRobertaTokenizer(__lowerCAmelCase       ,    keep_accents=__lowerCAmelCase)
					tokenizer.save_pretrained(self.tmpdirname)



			def 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


					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)       ,    1002)



			def 							a_       (   self):




					"""simple docstring"""


					self.assertEqual(self.get_tokenizer().vocab_size       ,    1002)



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  XLMRobertaTokenizer(__lowerCAmelCase       ,    keep_accents=__lowerCAmelCase)

					lowerCAmelCase   					=  tokenizer.tokenize("""This is a test""")
					self.assertListEqual(__lowerCAmelCase       ,    ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(__lowerCAmelCase)       ,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]       ,    )

					lowerCAmelCase   					=  tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
					self.assertListEqual(
					    __lowerCAmelCase       ,    [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """9""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """é""",
					        """.""",
					    ]       ,    )
					lowerCAmelCase   					=  tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
					self.assertListEqual(
					    __lowerCAmelCase       ,    [
					        value + tokenizer.fairseq_offset
					        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
					        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
					    ]       ,    )

					lowerCAmelCase   					=  tokenizer.convert_ids_to_tokens(__lowerCAmelCase)
					self.assertListEqual(
					    __lowerCAmelCase       ,    [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """<unk>""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """<unk>""",
					        """.""",
					    ]       ,    )



			def 							a_       (   self):




					"""simple docstring"""


					if not self.test_slow_tokenizer:
							# as we don't have a slow version, we can't compare the outputs between slow and fast versions
							return

					lowerCAmelCase   					=  (self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase   					=  self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)

									lowerCAmelCase   					=  tempfile.mkdtemp()

									lowerCAmelCase   					=  tokenizer_r.save_pretrained(__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.save_pretrained(__lowerCAmelCase)

									# Checks it save with the same files + the tokenizer.json file for the fast one
									self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files))
									lowerCAmelCase   					=  tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f)
									self.assertSequenceEqual(__lowerCAmelCase       ,    __lowerCAmelCase)

									# Checks everything loads correctly in the same way
									lowerCAmelCase   					=  tokenizer_r.from_pretrained(__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.from_pretrained(__lowerCAmelCase)

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(__lowerCAmelCase       ,    __lowerCAmelCase))
											# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
											# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

									shutil.rmtree(__lowerCAmelCase)

									# Save tokenizer rust, legacy_format=True
									lowerCAmelCase   					=  tempfile.mkdtemp()

									lowerCAmelCase   					=  tokenizer_r.save_pretrained(__lowerCAmelCase       ,    legacy_format=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.save_pretrained(__lowerCAmelCase)

									# Checks it save with the same files
									self.assertSequenceEqual(__lowerCAmelCase       ,    __lowerCAmelCase)

									# Checks everything loads correctly in the same way
									lowerCAmelCase   					=  tokenizer_r.from_pretrained(__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.from_pretrained(__lowerCAmelCase)

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(__lowerCAmelCase       ,    __lowerCAmelCase))

									shutil.rmtree(__lowerCAmelCase)

									# Save tokenizer rust, legacy_format=False
									lowerCAmelCase   					=  tempfile.mkdtemp()

									lowerCAmelCase   					=  tokenizer_r.save_pretrained(__lowerCAmelCase       ,    legacy_format=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.save_pretrained(__lowerCAmelCase)

									# Checks it saved the tokenizer.json file
									self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files))

									# Checks everything loads correctly in the same way
									lowerCAmelCase   					=  tokenizer_r.from_pretrained(__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.from_pretrained(__lowerCAmelCase)

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
											self.assertTrue(hasattr(__lowerCAmelCase       ,    __lowerCAmelCase))

									shutil.rmtree(__lowerCAmelCase)



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base""")



			def 							a_       (   self):




					"""simple docstring"""


					with tempfile.NamedTemporaryFile() as f:
							shutil.copyfile(__lowerCAmelCase       ,    f.name)
							lowerCAmelCase   					=  XLMRobertaTokenizer(f.name       ,    keep_accents=__lowerCAmelCase)
							lowerCAmelCase   					=  pickle.dumps(__lowerCAmelCase)
					pickle.loads(__lowerCAmelCase)



			def 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  """Hello World!"""
					lowerCAmelCase   					=  [0, 35378, 6661, 38, 2]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

					self.assertListEqual(__lowerCAmelCase       ,    self.big_tokenizer.encode(__lowerCAmelCase))



			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  (
					    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
					    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
					)
					lowerCAmelCase   					=  [
					    0,
					    3293,
					    83,
					    10,
					    4552,
					    4989,
					    7986,
					    678,
					    10,
					    5915,
					    111,
					    179459,
					    124850,
					    4,
					    6044,
					    237,
					    12,
					    6,
					    5,
					    6,
					    4,
					    6780,
					    705,
					    15,
					    1388,
					    44,
					    378,
					    10114,
					    711,
					    152,
					    20,
					    6,
					    5,
					    22376,
					    642,
					    1221,
					    15190,
					    34153,
					    450,
					    5608,
					    959,
					    1119,
					    57702,
					    136,
					    186,
					    47,
					    1098,
					    29367,
					    47,
					    # 4426, # What fairseq tokenizes from "<unk>": "_<"
					    # 3678, # What fairseq tokenizes from "<unk>": "unk"
					    # 2740, # What fairseq tokenizes from "<unk>": ">"
					    3,  # What we tokenize from "<unk>": "<unk>"
					    6,  # Residue from the tokenization: an extra sentencepiece underline
					    4,
					    6044,
					    237,
					    6284,
					    50901,
					    528,
					    31,
					    90,
					    34,
					    927,
					    2,
					]
					# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
					# xlmr.eval()
					# xlmr.encode(symbols)

					self.assertListEqual(__lowerCAmelCase       ,    self.big_tokenizer.encode(__lowerCAmelCase))







			@slow
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  {"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 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, 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, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 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, 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]], """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, 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], [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, 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="""xlm-roberta-base"""       ,    revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""       ,    )




