
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


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







@require_sentencepiece
@require_tokenizers
class 	A__				(	UpperCAmelCase__		,  unittest.TestCase      ):
				lowercase            =    ErnieMTokenizer
				lowercase            =    False
				lowercase            =    True
				lowercase            =    False
				lowercase            =    False






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							super().setUp()

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






				def 				_lowerCamelCase						(		self   : List[Any]       ,	a   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Optional[Any]   =						"this is a test"
							lowerCAmelCase__ : str   =						"this is a test"
							return input_text, output_text






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						"<pad>"
							lowerCAmelCase__ : Tuple   =						0

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






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, 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(lowerCamelCase__  )       ,	30_000  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							self.assertEqual(self.get_tokenizer().vocab_size       ,	30_000  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							if not self.test_rust_tokenizer:
										return

							lowerCAmelCase__ : List[Any]   =						self.get_tokenizer()
							lowerCAmelCase__ : Tuple   =						self.get_rust_tokenizer()

							lowerCAmelCase__ : Any   =						"I was born in 92000, and this is falsé."

							lowerCAmelCase__ : Any   =						tokenizer.tokenize(lowerCamelCase__  )
							lowerCAmelCase__ : Dict   =						rust_tokenizer.tokenize(lowerCamelCase__  )
							self.assertListEqual(lowerCamelCase__       ,	lowerCamelCase__  )

							lowerCAmelCase__ : int   =						tokenizer.encode(lowerCamelCase__       ,	add_special_tokens=lowerCamelCase__  )
							lowerCAmelCase__ : List[Any]   =						rust_tokenizer.encode(lowerCamelCase__       ,	add_special_tokens=lowerCamelCase__  )
							self.assertListEqual(lowerCamelCase__       ,	lowerCamelCase__  )

							lowerCAmelCase__ : Optional[Any]   =						self.get_rust_tokenizer()
							lowerCAmelCase__ : Any   =						tokenizer.encode(lowerCamelCase__  )
							lowerCAmelCase__ : Optional[Any]   =						rust_tokenizer.encode(lowerCamelCase__  )
							self.assertListEqual(lowerCamelCase__       ,	lowerCamelCase__  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						ErnieMTokenizer(lowerCamelCase__       ,	do_lower_case=lowerCamelCase__       ,	unk_token='<unk>'       ,	pad_token='<pad>'  )

							lowerCAmelCase__ : 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, 1_289]  )

							lowerCAmelCase__ : Tuple   =						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', '.']  )
							lowerCAmelCase__ : Optional[Any]   =						tokenizer.convert_tokens_to_ids(lowerCamelCase__  )
							self.assertListEqual(lowerCamelCase__       ,	[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]  )

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






				def 				_lowerCamelCase						(		self   : Union[str, Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						ErnieMTokenizer(lowerCamelCase__       ,	unk_token='<unk>'       ,	pad_token='<pad>'  )

							lowerCAmelCase__ : Tuple   =						tokenizer.encode('sequence builders'  )
							lowerCAmelCase__ : Union[str, Any]   =						tokenizer.encode('multi-sequence build'  )

							lowerCAmelCase__ : Tuple   =						tokenizer.build_inputs_with_special_tokens(lowerCamelCase__  )
							lowerCAmelCase__ : List[str]   =						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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						{"input_ids": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 9, 304, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 5, 5, 5, 16, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 6_460, 1_328, 4_589, 42, 122_009, 115_774, 23, 3_559, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 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, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 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 torch
from torch import autograd






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float						,	lowerCamelCase__	:  bool			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.numel()
					_UpperCAmelCase		:							Any             =   int(0.0_0_5 * nb_elems			) + 1
					if sigmoid:
							_UpperCAmelCase		:							List[str]             =   (torch.sigmoid(lowerCamelCase__			) > threshold).type(inputs.type()			)
					else:
							_UpperCAmelCase		:							List[Any]             =   (inputs > threshold).type(inputs.type()			)
					if mask.sum() < nb_min:
							# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
							_UpperCAmelCase		:							str             =   inputs.flatten().kthvalue(max(nb_elems - nb_min						,	1			)			).values
							_UpperCAmelCase		:							str             =   (inputs > k_threshold).type(inputs.type()			)
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Optional[int]			) ->Dict:



					'''simple docstring'''







					return gradOutput, None, None






class 						lowerCAmelCase__      (      autograd.Function						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[int]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Tuple             =   inputs.flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							Optional[int]             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							str             =   mask.flatten()
					_UpperCAmelCase		:							Optional[int]             =   0
					_UpperCAmelCase		:							Optional[Any]             =   1
					return mask






			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Any			) ->Union[str, Any]:



					'''simple docstring'''







					return gradOutput, None






class 						lowerCAmelCase__      (      UpperCAmelCase__						):




			@staticmethod
			def 				lowerCAmelCase__     (		lowerCamelCase__	:  torch.tensor						,	lowerCamelCase__	:  float			) ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:							List[Any]             =   inputs.clone()
					_UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[Any]             =   inputs.abs().flatten().sort(descending=lowerCamelCase__			)
					_UpperCAmelCase		:							int             =   int(threshold * inputs.numel()			)

					# flat_out and mask access the same memory.
					_UpperCAmelCase		:							int             =   mask.flatten()
					_UpperCAmelCase		:							List[Any]             =   0
					_UpperCAmelCase		:							int             =   1
					return mask



