





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 							snake_case_		(      _A    ,       unittest.TestCase):
		lowerCamelCase     :Dict							=  ErnieMTokenizer
		lowerCamelCase     :int							=  False
		lowerCamelCase     :Tuple							=  True
		lowerCamelCase     :Optional[Any]							=  False
		lowerCamelCase     :Any							=  False







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

				# We have a SentencePiece fixture for testing
				lowerCamelCase :			Optional[Any]    		=ErnieMTokenizer(__lowercase      ,  unk_token='''<unk>'''      ,  pad_token='''<pad>''' )
				tokenizer.save_pretrained(self.tmpdirname )







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







		def       __lowercase (		self )					-> Dict:
				lowerCamelCase :			Optional[int]    		='''<pad>'''
				lowerCamelCase :			Optional[int]    		=0

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







		def       __lowercase (		self )					-> int:
				lowerCamelCase :			Dict    		=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(__lowercase )      ,  3_0_0_0_0 )







		def       __lowercase (		self )					-> str:
				self.assertEqual(self.get_tokenizer().vocab_size      ,  3_0_0_0_0 )







		def       __lowercase (		self )					-> List[Any]:
				if not self.test_rust_tokenizer:
						return

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

				lowerCamelCase :			Tuple    		='''I was born in 92000, and this is falsé.'''

				lowerCamelCase :			Tuple    		=tokenizer.tokenize(__lowercase )
				lowerCamelCase :			str    		=rust_tokenizer.tokenize(__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )

				lowerCamelCase :			List[str]    		=tokenizer.encode(__lowercase      ,  add_special_tokens=__lowercase )
				lowerCamelCase :			Union[str, Any]    		=rust_tokenizer.encode(__lowercase      ,  add_special_tokens=__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )

				lowerCamelCase :			List[str]    		=self.get_rust_tokenizer()
				lowerCamelCase :			Optional[int]    		=tokenizer.encode(__lowercase )
				lowerCamelCase :			Tuple    		=rust_tokenizer.encode(__lowercase )
				self.assertListEqual(__lowercase      ,  __lowercase )







		def       __lowercase (		self )					-> Any:
				lowerCamelCase :			Union[str, Any]    		=ErnieMTokenizer(__lowercase      ,  do_lower_case=__lowercase      ,  unk_token='''<unk>'''      ,  pad_token='''<pad>''' )

				lowerCamelCase :			Tuple    		=tokenizer.tokenize('''This is a test''' )
				self.assertListEqual(__lowercase      ,  ['''▁this''', '''▁is''', '''▁a''', '''▁test'''] )

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

				lowerCamelCase :			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(
				    __lowercase      ,  ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''] )
				lowerCamelCase :			Optional[Any]    		=tokenizer.convert_tokens_to_ids(__lowercase )
				self.assertListEqual(__lowercase      ,  [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 :			Union[str, Any]    		=tokenizer.convert_ids_to_tokens(__lowercase )
				self.assertListEqual(
				    __lowercase      ,  ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']      ,  )







		def       __lowercase (		self )					-> int:
				lowerCamelCase :			Optional[int]    		=ErnieMTokenizer(__lowercase      ,  unk_token='''<unk>'''      ,  pad_token='''<pad>''' )

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

				lowerCamelCase :			Any    		=tokenizer.build_inputs_with_special_tokens(__lowercase )
				lowerCamelCase :			Any    		=tokenizer.build_inputs_with_special_tokens(__lowercase      ,  __lowercase )

				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 )					-> Any:
				# fmt: off
				lowerCamelCase :			str    		={'''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=__lowercase      ,  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_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		lowerCamelCase :			int    		=[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		lowerCamelCase :			List[Any]    		=1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(SCREAMING_SNAKE_CASE_				,							n + 1		):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

