






"""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


__snake_case							 =  get_tests_dir("""fixtures/spiece.model""")







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



      """simple docstring"""
      UpperCamelCase_    : int									=ErnieMTokenizer
      UpperCamelCase_    : Dict									=False
      UpperCamelCase_    : Union[str, Any]									=True
      UpperCamelCase_    : int									=False
      UpperCamelCase_    : str									=False
      def    UpperCAmelCase						(    self			)    ->				Any:
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCamelCase	:Any     							=       ErnieMTokenizer(_A   ,    unk_token='''<unk>'''   ,    pad_token='''<pad>'''			)
          tokenizer.save_pretrained(self.tmpdirname			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:
          UpperCamelCase	:Optional[Any]     							=       '''this is a test'''
          UpperCamelCase	:Union[str, Any]     							=       '''this is a test'''
          return input_text, output_text
      def    UpperCAmelCase						(    self			)    ->				Tuple:
          UpperCamelCase	:str     							=       '''<pad>'''
          UpperCamelCase	:int     							=       0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_A			)   ,    _A			)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_A			)   ,    _A			)
      def    UpperCAmelCase						(    self			)    ->				Optional[Any]:
          UpperCamelCase	:List[Any]     							=       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(_A			)   ,    3_0000			)
      def    UpperCAmelCase						(    self			)    ->				Union[str, Any]:
          self.assertEqual(self.get_tokenizer().vocab_size   ,    3_0000			)
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:




          if not self.test_rust_tokenizer:
              return

          UpperCamelCase	:Optional[Any]     							=       self.get_tokenizer()
          UpperCamelCase	:Dict     							=       self.get_rust_tokenizer()

          UpperCamelCase	:Union[str, Any]     							=       '''I was born in 92000, and this is falsé.'''

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

          UpperCamelCase	:Tuple     							=       tokenizer.encode(_A   ,    add_special_tokens=_A			)
          UpperCamelCase	:int     							=       rust_tokenizer.encode(_A   ,    add_special_tokens=_A			)
          self.assertListEqual(_A   ,    _A			)

          UpperCamelCase	:Optional[Any]     							=       self.get_rust_tokenizer()
          UpperCamelCase	:Optional[int]     							=       tokenizer.encode(_A			)
          UpperCamelCase	:int     							=       rust_tokenizer.encode(_A			)
          self.assertListEqual(_A   ,    _A			)
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:Optional[int]     							=       ErnieMTokenizer(_A   ,    do_lower_case=_A   ,    unk_token='''<unk>'''   ,    pad_token='''<pad>'''			)

          UpperCamelCase	:Dict     							=       tokenizer.tokenize('''This is a test'''			)
          self.assertListEqual(_A   ,    ['''▁this''', '''▁is''', '''▁a''', '''▁test''']			)

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

          UpperCamelCase	:List[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(
              _A   ,    ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.''']			)
          UpperCamelCase	:Dict     							=       tokenizer.convert_tokens_to_ids(_A			)
          self.assertListEqual(_A   ,    [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]			)

          UpperCamelCase	:int     							=       tokenizer.convert_ids_to_tokens(_A			)
          self.assertListEqual(
              _A   ,    ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']   ,    )
      def    UpperCAmelCase						(    self			)    ->				Any:
          UpperCamelCase	:Dict     							=       ErnieMTokenizer(_A   ,    unk_token='''<unk>'''   ,    pad_token='''<pad>'''			)

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

          UpperCamelCase	:List[Any]     							=       tokenizer.build_inputs_with_special_tokens(_A			)
          UpperCamelCase	:Tuple     							=       tokenizer.build_inputs_with_special_tokens(_A   ,    _A			)

          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    UpperCAmelCase						(    self			)    ->				Dict:
          UpperCamelCase	:Tuple     							=       {'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 9, 304, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 5, 5, 5, 16, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 12_2009, 11_5774, 23, 3559, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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=_A   ,    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.''',
              ]   ,    )













def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     float	):
    if hue < 0 or hue > 360:
        raise Exception('''hue should be between 0 and 360'''	)

    if saturation < 0 or saturation > 1:
        raise Exception('''saturation should be between 0 and 1'''	)

    if value < 0 or value > 1:
        raise Exception('''value should be between 0 and 1'''	)

    UpperCamelCase	:Tuple     							=       value * saturation
    UpperCamelCase	:Any     							=       hue / 60
    UpperCamelCase	:Any     							=       chroma * (1 - abs(hue_section % 2 - 1	))
    UpperCamelCase	:Optional[int]     							=       value - chroma

    if hue_section >= 0 and hue_section <= 1:
        UpperCamelCase	:Any     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (match_value)	)
    elif hue_section > 1 and hue_section <= 2:
        UpperCamelCase	:Dict     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:Dict     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (match_value)	)
    elif hue_section > 2 and hue_section <= 3:
        UpperCamelCase	:Any     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[Any]     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (second_largest_component + match_value)	)
    elif hue_section > 3 and hue_section <= 4:
        UpperCamelCase	:Tuple     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[Any]     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (chroma + match_value)	)
    elif hue_section > 4 and hue_section <= 5:
        UpperCamelCase	:int     							=       round(255 * (second_largest_component + match_value)	)
        UpperCamelCase	:str     							=       round(255 * (match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (chroma + match_value)	)
    else:
        UpperCamelCase	:Union[str, Any]     							=       round(255 * (chroma + match_value)	)
        UpperCamelCase	:List[str]     							=       round(255 * (match_value)	)
        UpperCamelCase	:Optional[int]     							=       round(255 * (second_largest_component + match_value)	)

    return [red, green, blue]


def   _A       (    SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     int	):
    if red < 0 or red > 255:
        raise Exception('''red should be between 0 and 255'''	)

    if green < 0 or green > 255:
        raise Exception('''green should be between 0 and 255'''	)

    if blue < 0 or blue > 255:
        raise Exception('''blue should be between 0 and 255'''	)

    UpperCamelCase	:str     							=       red / 255
    UpperCamelCase	:List[str]     							=       green / 255
    UpperCamelCase	:int     							=       blue / 255
    UpperCamelCase	:Optional[int]     							=       max(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       value - min(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Dict     							=       0 if value == 0 else chroma / value

    if chroma == 0:
        UpperCamelCase	:Dict     							=       0.0
    elif value == float_red:
        UpperCamelCase	:Optional[Any]     							=       60 * (0 + (float_green - float_blue) / chroma)
    elif value == float_green:
        UpperCamelCase	:List[str]     							=       60 * (2 + (float_blue - float_red) / chroma)
    else:
        UpperCamelCase	:str     							=       60 * (4 + (float_red - float_green) / chroma)

    UpperCamelCase	:int     							=       (hue + 360) % 360

    return [hue, saturation, value]



def   _A       (    SCREAMING_SNAKE_CASE__  :     list[float]					,  SCREAMING_SNAKE_CASE__  :     list[float]	):
    UpperCamelCase	:str     							=       abs(hsv_a[0] - hsv_a[0]	) < 0.2
    UpperCamelCase	:List[str]     							=       abs(hsv_a[1] - hsv_a[1]	) < 0.0_02
    UpperCamelCase	:Any     							=       abs(hsv_a[2] - hsv_a[2]	) < 0.0_02

    return check_hue and check_saturation and check_value





