




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


__SCREAMING_SNAKE_CASE			:      Optional[int]      			=get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class 	A	(						__lowercase	,  unittest.TestCase						):
     """simple docstring"""

     _snake_case:		Union[str, Any]   =ErnieMTokenizer
     _snake_case:		Dict   =False
     _snake_case:		str   =True
     _snake_case:		str   =False
     _snake_case:		Optional[int]   =False




     def        __snake_case      (			self     :      int	) ->				List[Any]:
           super().setUp()

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




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Optional[int]	) ->				Union[str, Any]:
           lowerCamelCase__					     = 'this is a test'
           lowerCamelCase__					     = 'this is a test'
           return input_text, output_text




     def        __snake_case      (			self     :      Optional[int]	) ->				Any:
           lowerCamelCase__					     = '<pad>'
           lowerCamelCase__					     = 0

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




     def        __snake_case      (			self     :      Any	) ->				int:
           lowerCamelCase__					     = 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(_UpperCamelCase	)		,     3_0_0_0_0	)




     def        __snake_case      (			self     :      List[Any]	) ->				str:
           self.assertEqual(self.get_tokenizer().vocab_size		,     3_0_0_0_0	)




     def        __snake_case      (			self     :      Tuple	) ->				str:
           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(_UpperCamelCase	)
           lowerCamelCase__					     = rust_tokenizer.tokenize(_UpperCamelCase	)
           self.assertListEqual(_UpperCamelCase		,     _UpperCamelCase	)

           lowerCamelCase__					     = tokenizer.encode(_UpperCamelCase		,     add_special_tokens=_UpperCamelCase	)
           lowerCamelCase__					     = rust_tokenizer.encode(_UpperCamelCase		,     add_special_tokens=_UpperCamelCase	)
           self.assertListEqual(_UpperCamelCase		,     _UpperCamelCase	)

           lowerCamelCase__					     = self.get_rust_tokenizer()
           lowerCamelCase__					     = tokenizer.encode(_UpperCamelCase	)
           lowerCamelCase__					     = rust_tokenizer.encode(_UpperCamelCase	)
           self.assertListEqual(_UpperCamelCase		,     _UpperCamelCase	)




     def        __snake_case      (			self     :      int	) ->				List[Any]:
           lowerCamelCase__					     = ErnieMTokenizer(_UpperCamelCase		,     do_lower_case=_UpperCamelCase		,     unk_token='<unk>'		,     pad_token='<pad>'	)

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

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

           lowerCamelCase__					     = 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(
               _UpperCamelCase		,     ['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']	)
           lowerCamelCase__					     = tokenizer.convert_tokens_to_ids(_UpperCamelCase	)
           self.assertListEqual(_UpperCamelCase		,     [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]	)

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




     def        __snake_case      (			self     :      List[Any]	) ->				int:
           lowerCamelCase__					     = ErnieMTokenizer(_UpperCamelCase		,     unk_token='<unk>'		,     pad_token='<pad>'	)

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

           lowerCamelCase__					     = tokenizer.build_inputs_with_special_tokens(_UpperCamelCase	)
           lowerCamelCase__					     = tokenizer.build_inputs_with_special_tokens(_UpperCamelCase		,     _UpperCamelCase	)

           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        __snake_case      (			self     :      Any	) ->				str:
           lowerCamelCase__					     = {'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=_UpperCamelCase		,     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 		_UpperCamelCase      (			UpperCamelCase_				:    float			,				UpperCamelCase_				:    float			,				UpperCamelCase_				:    float						)     ->      list[int]:

						"""simple docstring"""



						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'						)

						lowerCAmelCase__			  =  value * saturation
						lowerCAmelCase__			  =  hue / 60
						lowerCAmelCase__			  =  chroma * (1 - abs(hue_section % 2 - 1						))
						lowerCAmelCase__			  =  value - chroma

						if hue_section >= 0 and hue_section <= 1:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 1 and hue_section <= 2:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
						elif hue_section > 2 and hue_section <= 3:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
						elif hue_section > 3 and hue_section <= 4:
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						elif hue_section > 4 and hue_section <= 5:
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
						else:
												lowerCAmelCase__			  =  round(255 * (chroma + match_value)						)
												lowerCAmelCase__			  =  round(255 * (match_value)						)
												lowerCAmelCase__			  =  round(255 * (second_largest_component + match_value)						)

						return [red, green, blue]







def 		_UpperCamelCase      (			UpperCamelCase_				:    int			,				UpperCamelCase_				:    int			,				UpperCamelCase_				:    int						)     ->      list[float]:

						"""simple docstring"""



						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'						)

						lowerCAmelCase__			  =  red / 255
						lowerCAmelCase__			  =  green / 255
						lowerCAmelCase__			  =  blue / 255
						lowerCAmelCase__			  =  max(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  value - min(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  0 if value == 0 else chroma / value

						if chroma == 0:
												lowerCAmelCase__			  =  0.0
						elif value == float_red:
												lowerCAmelCase__			  =  60 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												lowerCAmelCase__			  =  60 * (2 + (float_blue - float_red) / chroma)
						else:
												lowerCAmelCase__			  =  60 * (4 + (float_red - float_green) / chroma)

						lowerCAmelCase__			  =  (hue + 360) % 360

						return [hue, saturation, value]







def 		_UpperCamelCase      (			UpperCamelCase_				:    list[float]			,				UpperCamelCase_				:    list[float]						)     ->      bool:

						"""simple docstring"""



						lowerCAmelCase__			  =  abs(hsv_a[0] - hsv_a[0]						) < 0.2
						lowerCAmelCase__			  =  abs(hsv_a[1] - hsv_a[1]						) < 0.002
						lowerCAmelCase__			  =  abs(hsv_a[2] - hsv_a[2]						) < 0.002

						return check_hue and check_saturation and check_value





