
'''simple docstring'''





import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a__		: List[Any]  							=							get_tests_dir("fixtures/spiece.model")


@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase__	(     lowerCamelCase_   ,  unittest.TestCase):
 UpperCAmelCase__     :     int    					=       ErnieMTokenizer
 UpperCAmelCase__     :     Optional[Any]    					=       False
 UpperCAmelCase__     :     Optional[int]    					=       True
 UpperCAmelCase__     :     Any    					=       False
 UpperCAmelCase__     :     str    					=       False






 def 						lowercase_    (      self	:Dict      )					->      Tuple:







       '''simple docstring'''



       super().setUp()

       # We have a SentencePiece fixture for testing
       __A													=      ErnieMTokenizer(lowerCAmelCase_						,					unk_token='<unk>'						,					pad_token='<pad>'      )
       tokenizer.save_pretrained(self.tmpdirname      )






 def 						lowercase_    (      self	:List[Any]						,					_A	:int      )					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      'this is a test'
       __A													=      'this is a test'
       return input_text, output_text






 def 						lowercase_    (      self	:int      )					->      str:







       '''simple docstring'''



       __A													=      '<pad>'
       __A													=      0

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






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      list(self.get_tokenizer().get_vocab().keys()      )

       self.assertEqual(vocab_keys[0]						,					'<pad>'      )
       self.assertEqual(vocab_keys[1]						,					'<unk>'      )
       self.assertEqual(vocab_keys[-1]						,					'▁eloquent'      )
       self.assertEqual(len(lowerCAmelCase_      )						,					30_000      )






 def 						lowercase_    (      self	:Any      )					->      Optional[int]:







       '''simple docstring'''



       self.assertEqual(self.get_tokenizer().vocab_size						,					30_000      )






 def 						lowercase_    (      self	:Union[str, Any]      )					->      Union[str, Any]:







       '''simple docstring'''



       if not self.test_rust_tokenizer:
             return

       __A													=      self.get_tokenizer()
       __A													=      self.get_rust_tokenizer()

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

       __A													=      tokenizer.tokenize(lowerCAmelCase_      )
       __A													=      rust_tokenizer.tokenize(lowerCAmelCase_      )
       self.assertListEqual(lowerCAmelCase_						,					lowerCAmelCase_      )

       __A													=      tokenizer.encode(lowerCAmelCase_						,					add_special_tokens=lowerCAmelCase_      )
       __A													=      rust_tokenizer.encode(lowerCAmelCase_						,					add_special_tokens=lowerCAmelCase_      )
       self.assertListEqual(lowerCAmelCase_						,					lowerCAmelCase_      )

       __A													=      self.get_rust_tokenizer()
       __A													=      tokenizer.encode(lowerCAmelCase_      )
       __A													=      rust_tokenizer.encode(lowerCAmelCase_      )
       self.assertListEqual(lowerCAmelCase_						,					lowerCAmelCase_      )






 def 						lowercase_    (      self	:Optional[Any]      )					->      Tuple:







       '''simple docstring'''



       __A													=      ErnieMTokenizer(lowerCAmelCase_						,					do_lower_case=lowerCAmelCase_						,					unk_token='<unk>'						,					pad_token='<pad>'      )

       __A													=      tokenizer.tokenize('This is a test'      )
       self.assertListEqual(lowerCAmelCase_						,					['▁this', '▁is', '▁a', '▁test']      )

       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )						,					[48, 25, 21, 1_289]      )

       __A													=      tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           lowerCAmelCase_						,					['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']      )
       __A													=      tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )
       self.assertListEqual(lowerCAmelCase_						,					[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]      )

       __A													=      tokenizer.convert_ids_to_tokens(lowerCAmelCase_      )
       self.assertListEqual(
           lowerCAmelCase_						,					['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']						,					)






 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       __A													=      ErnieMTokenizer(lowerCAmelCase_						,					unk_token='<unk>'						,					pad_token='<pad>'      )

       __A													=      tokenizer.encode('sequence builders'      )
       __A													=      tokenizer.encode('multi-sequence build'      )

       __A													=      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_      )
       __A													=      tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_						,					lowerCAmelCase_      )

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]






 @slow
 def 						lowercase_    (      self	:List[str]      )					->      Tuple:







       '''simple docstring'''



       __A													=      {'input_ids': [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCAmelCase_						,					model_name='susnato/ernie-m-base_pytorch'						,					sequences=[
               'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
               'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
               'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
               'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
               'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
               'conditioning on both left and right context in all layers.',
               'The quick brown fox jumps over the lazy dog.',
           ]						,					)







import numpy as np
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_=5       ,  lowerCAmelCase_=5       ):
		_lowercase   =depth
		_lowercase   =0
		_lowercase   =None
		_lowercase   =None
		_lowercase   =min_leaf_size
		_lowercase   =None


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if labels.ndim != 1:
			print("Error: Input labels must be one dimensional"       )

		return np.mean((labels - prediction) ** 2       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		if x.ndim != 1:
			print("Error: Input data set must be one dimensional"       )
			return
		if len(lowerCAmelCase_       ) != len(lowerCAmelCase_       ):
			print("Error: X and y have different lengths"       )
			return
		if y.ndim != 1:
			print("Error: Data set labels must be one dimensional"       )
			return

		if len(lowerCAmelCase_       ) < 2 * self.min_leaf_size:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		if self.depth == 1:
			_lowercase   =np.mean(lowerCAmelCase_       )
			return

		_lowercase   =0
		_lowercase   =self.mean_squared_error(lowerCAmelCase_       ,  np.mean(lowerCAmelCase_       )       ) * 2
		for i in range(len(lowerCAmelCase_       )       ):
			if len(x[:i]       ) < self.min_leaf_size:
				continue
			elif len(x[i:]       ) < self.min_leaf_size:
				continue
			else:
				_lowercase   =self.mean_squared_error(x[:i]       ,  np.mean(y[:i]       )       )
				_lowercase   =self.mean_squared_error(x[i:]       ,  np.mean(y[i:]       )       )
				_lowercase   =error_left + error_right
				if error < min_error:
					_lowercase   =i
					_lowercase   =error

		if best_split != 0:
			_lowercase   =x[:best_split]
			_lowercase   =y[:best_split]
			_lowercase   =x[best_split:]
			_lowercase   =y[best_split:]

			_lowercase   =x[best_split]
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			_lowercase   =DecisionTree(
			    depth=self.depth - 1       ,  min_leaf_size=self.min_leaf_size       )
			self.left.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
			self.right.train(lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =np.mean(lowerCAmelCase_       )

		return


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		if self.prediction is not None:
			return self.prediction
		elif self.left or self.right is not None:
			if x >= self.decision_boundary:
				return self.right.predict(lowerCAmelCase_       )
			else:
				return self.left.predict(lowerCAmelCase_       )
		else:
			print("Error: Decision tree not yet trained"       )
			return None

class 	_a  :



	"""simple docstring"""



	@staticmethod
	def __lowerCAmelCase      (   lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =float(0       )
		for label in labels:
			squared_error_sum += (label - prediction) ** 2

		return float(squared_error_sum / labels.size       )

def 	__lowerCamelCase							(				)      ->       Union[str, Any]:
	_lowercase   =np.arange(-1.0      ,							1.0      ,							0.005		)
	_lowercase   =np.sin(__a		)

	_lowercase   =DecisionTree(depth=10      ,							min_leaf_size=10		)
	tree.train(__a      ,							__a		)

	_lowercase   =(np.random.rand(10		) * 2) - 1
	_lowercase   =np.array([tree.predict(__a		) for x in test_cases]		)
	_lowercase   =np.mean((predictions - test_cases) ** 2		)

	print("Test values: " + str(__a		)		)
	print("Predictions: " + str(__a		)		)
	print("Average error: " + str(__a		)		)


if __name__ == "__main__":
						main()
						import doctest

						doctest.testmod(name="mean_squarred_error", verbose=True)




