



'''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__  :					Optional[Any]				= get_tests_dir('''fixtures/spiece.model''')



@require_sentencepiece
@require_tokenizers
class       snake_case__		(  __UpperCAmelCase   ,			unittest.TestCase							):
 A__							=      ErnieMTokenizer
 A__							=      False
 A__							=      True
 A__							=      False
 A__							=      False
 def A_					(					self		:				Union[str, Any]   )	->					int:







       '''simple docstring'''
       super().setUp()

       # We have a SentencePiece fixture for testing
       __snake_case      :					Dict						=  ErnieMTokenizer(_lowerCamelCase	,			unk_token='<unk>'	,			pad_token='<pad>'   )
       tokenizer.save_pretrained(self.tmpdirname   )
 def A_					(					self		:				Tuple	,			__a		:				Optional[Any]   )	->					Any:







       '''simple docstring'''
       __snake_case      :					int						=  '''this is a test'''
       __snake_case      :					Dict						=  '''this is a test'''
       return input_text, output_text
 def A_					(					self		:				List[str]   )	->					Dict:







       '''simple docstring'''
       __snake_case      :					Optional[int]						=  '''<pad>'''
       __snake_case      :					Dict						=  0

       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		:				Optional[Any]   )	->					List[str]:







       '''simple docstring'''
       __snake_case      :					List[str]						=  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   )	,			30000   )
 def A_					(					self		:				int   )	->					Optional[int]:







       '''simple docstring'''
       self.assertEqual(self.get_tokenizer().vocab_size	,			30000   )
 def A_					(					self		:				str   )	->					Tuple:







       '''simple docstring'''
       if not self.test_rust_tokenizer:
             return

       __snake_case      :					List[str]						=  self.get_tokenizer()
       __snake_case      :					Dict						=  self.get_rust_tokenizer()

       __snake_case      :					Dict						=  '''I was born in 92000, and this is falsé.'''

       __snake_case      :					str						=  tokenizer.tokenize(_lowerCamelCase   )
       __snake_case      :					int						=  rust_tokenizer.tokenize(_lowerCamelCase   )
       self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

       __snake_case      :					List[str]						=  tokenizer.encode(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )
       __snake_case      :					Optional[int]						=  rust_tokenizer.encode(_lowerCamelCase	,			add_special_tokens=_lowerCamelCase   )
       self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )

       __snake_case      :					Dict						=  self.get_rust_tokenizer()
       __snake_case      :					List[str]						=  tokenizer.encode(_lowerCamelCase   )
       __snake_case      :					Optional[Any]						=  rust_tokenizer.encode(_lowerCamelCase   )
       self.assertListEqual(_lowerCamelCase	,			_lowerCamelCase   )
 def A_					(					self		:				Optional[Any]   )	->					Union[str, Any]:







       '''simple docstring'''
       __snake_case      :					Dict						=  ErnieMTokenizer(_lowerCamelCase	,			do_lower_case=_lowerCamelCase	,			unk_token='<unk>'	,			pad_token='<pad>'   )

       __snake_case      :					List[Any]						=  tokenizer.tokenize('This is a test'   )
       self.assertListEqual(_lowerCamelCase	,			['▁this', '▁is', '▁a', '▁test']   )

       self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase   )	,			[48, 25, 21, 1289]   )

       __snake_case      :					Union[str, Any]						=  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', '.']   )
       __snake_case      :					List[str]						=  tokenizer.convert_tokens_to_ids(_lowerCamelCase   )
       self.assertListEqual(_lowerCamelCase	,			[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]   )

       __snake_case      :					Dict						=  tokenizer.convert_ids_to_tokens(_lowerCamelCase   )
       self.assertListEqual(
           _lowerCamelCase	,			['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']	,			)
 def A_					(					self		:				Optional[Any]   )	->					Optional[Any]:







       '''simple docstring'''
       __snake_case      :					List[Any]						=  ErnieMTokenizer(_lowerCamelCase	,			unk_token='<unk>'	,			pad_token='<pad>'   )

       __snake_case      :					List[Any]						=  tokenizer.encode('sequence builders'   )
       __snake_case      :					Tuple						=  tokenizer.encode('multi-sequence build'   )

       __snake_case      :					Union[str, Any]						=  tokenizer.build_inputs_with_special_tokens(_lowerCamelCase   )
       __snake_case      :					Optional[Any]						=  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 A_					(					self		:				Optional[int]   )	->					Union[str, Any]:







       '''simple docstring'''
       # fmt: off
       __snake_case      :					List[Any]						=  {'''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, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 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, 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, 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.',
           ]	,			)










"""simple docstring"""





import numpy as np




def 		lowerCAmelCase_	(  lowercase_   :							str		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Dict												=       np.exp(lowercase_		)

 # Add up the all the exponentials
 __SCREAMING_SNAKE_CASE :			Tuple												=       np.sum(lowercase_		)

 # Divide every exponent by the sum of all exponents
 __SCREAMING_SNAKE_CASE :			List[Any]												=       exponent_vector / sum_of_exponents

 return softmax_vector


if __name__ == "__main__":
    print(softmax((0,)))

