
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


UpperCAmelCase__	:  List[str]      =   get_tests_dir('fixtures/spiece.model')






@require_sentencepiece
@require_tokenizers
class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





   '''simple docstring'''
   __UpperCamelCase	:					List[str]				       =					ErnieMTokenizer
   __UpperCamelCase	:					Optional[Any]				       =					False
   __UpperCamelCase	:					Dict				       =					True
   __UpperCamelCase	:					List[str]				       =					False
   __UpperCamelCase	:					Any				       =					False


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      super().setUp()

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


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							List[str]  ):

      """simple docstring"""
      _A:						str	    = '''this is a test'''
      _A:						Union[str, Any]	    = '''this is a test'''
      return input_text, output_text


   def 				__magic_name__       (      self  :							List[str]  ):

      """simple docstring"""
      _A:						Union[str, Any]	    = '''<pad>'''
      _A:						Optional[int]	    = 0

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


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      _A:						int	    = 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_  )    ,    3_0_0_0_0  )


   def 				__magic_name__       (      self  :							str  ):

      """simple docstring"""
      self.assertEqual(self.get_tokenizer().vocab_size    ,    3_0_0_0_0  )


   def 				__magic_name__       (      self  :							Optional[int]  ):

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

      _A:						List[Any]	    = self.get_tokenizer()
      _A:						Optional[int]	    = self.get_rust_tokenizer()

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

      _A:						Tuple	    = tokenizer.tokenize(lowerCAmelCase_  )
      _A:						List[Any]	    = rust_tokenizer.tokenize(lowerCAmelCase_  )
      self.assertListEqual(lowerCAmelCase_    ,    lowerCAmelCase_  )

      _A:						Optional[int]	    = tokenizer.encode(lowerCAmelCase_    ,    add_special_tokens=lowerCAmelCase_  )
      _A:						Optional[int]	    = rust_tokenizer.encode(lowerCAmelCase_    ,    add_special_tokens=lowerCAmelCase_  )
      self.assertListEqual(lowerCAmelCase_    ,    lowerCAmelCase_  )

      _A:						Dict	    = self.get_rust_tokenizer()
      _A:						List[Any]	    = tokenizer.encode(lowerCAmelCase_  )
      _A:						Any	    = rust_tokenizer.encode(lowerCAmelCase_  )
      self.assertListEqual(lowerCAmelCase_    ,    lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Any  ):

      """simple docstring"""
      _A:						Optional[Any]	    = ErnieMTokenizer(lowerCAmelCase_    ,    do_lower_case=lowerCAmelCase_    ,    unk_token='''<unk>'''    ,    pad_token='''<pad>'''  )

      _A:						Union[str, Any]	    = tokenizer.tokenize('''This is a test'''  )
      self.assertListEqual(lowerCAmelCase_    ,    ['''▁this''', '''▁is''', '''▁a''', '''▁test''']  )

      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_  )    ,    [4_8, 2_5, 2_1, 1_2_8_9]  )

      _A:						Dict	    = 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:						str	    = tokenizer.convert_tokens_to_ids(lowerCAmelCase_  )
      self.assertListEqual(lowerCAmelCase_    ,    [3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]  )

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


   def 				__magic_name__       (      self  :							Optional[int]  ):

      """simple docstring"""
      _A:						Optional[int]	    = ErnieMTokenizer(lowerCAmelCase_    ,    unk_token='''<unk>'''    ,    pad_token='''<pad>'''  )

      _A:						Optional[int]	    = tokenizer.encode('''sequence builders'''  )
      _A:						List[str]	    = tokenizer.encode('''multi-sequence build'''  )

      _A:						str	    = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_  )
      _A:						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 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      # fmt: off
      _A:						Optional[int]	    = {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 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.''',
          ]    ,    )





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



