



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

@require_sentencepiece
@require_tokenizers
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[Any]       =       ErnieMTokenizer
							UpperCamelCase__					:   List[Any]       =       False
							UpperCamelCase__					:   List[Any]       =       True
							UpperCamelCase__					:   int       =       False
							UpperCamelCase__					:   Union[str, Any]       =       False


							def _a   (				self		):
											super().setUp()

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


							def _a   (				self	,	a_		):
											a__					   =		"""this is a test"""
											a__					   =		"""this is a test"""
											return input_text, output_text


							def _a   (				self		):
											a__					   =		"""<pad>"""
											a__					   =		0

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


							def _a   (				self		):
											a__					   =		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_		)	,	30_000		)


							def _a   (				self		):
											self.assertEqual(self.get_tokenizer().vocab_size	,	30_000		)


							def _a   (				self		):
											if not self.test_rust_tokenizer:
															return

											a__					   =		self.get_tokenizer()
											a__					   =		self.get_rust_tokenizer()

											a__					   =		"""I was born in 92000, and this is falsé."""

											a__					   =		tokenizer.tokenize(a_		)
											a__					   =		rust_tokenizer.tokenize(a_		)
											self.assertListEqual(a_	,	a_		)

											a__					   =		tokenizer.encode(a_	,	add_special_tokens=a_		)
											a__					   =		rust_tokenizer.encode(a_	,	add_special_tokens=a_		)
											self.assertListEqual(a_	,	a_		)

											a__					   =		self.get_rust_tokenizer()
											a__					   =		tokenizer.encode(a_		)
											a__					   =		rust_tokenizer.encode(a_		)
											self.assertListEqual(a_	,	a_		)


							def _a   (				self		):
											a__					   =		ErnieMTokenizer(a_	,	do_lower_case=a_	,	unk_token="""<unk>"""	,	pad_token="""<pad>"""		)

											a__					   =		tokenizer.tokenize("""This is a test"""		)
											self.assertListEqual(a_	,	["""▁this""", """▁is""", """▁a""", """▁test"""]		)

											self.assertListEqual(tokenizer.convert_tokens_to_ids(a_		)	,	[48, 25, 21, 1_289]		)

											a__					   =		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""", """."""]		)
											a__					   =		tokenizer.convert_tokens_to_ids(a_		)
											self.assertListEqual(a_	,	[31, 23, 386, 19, 518, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9]		)

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


							def _a   (				self		):
											a__					   =		ErnieMTokenizer(a_	,	unk_token="""<unk>"""	,	pad_token="""<pad>"""		)

											a__					   =		tokenizer.encode("""sequence builders"""		)
											a__					   =		tokenizer.encode("""multi-sequence build"""		)

											a__					   =		tokenizer.build_inputs_with_special_tokens(a_		)
											a__					   =		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 _a   (				self		):
											# fmt: off
											a__					   =		{"""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=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.""",
											    ]	,	)



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCAmelCase   						=     logging.get_logger(__name__)

__UpperCAmelCase   						=     {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="deberta"



       def __init__(							self ,     _A=50265 ,     _A=768 ,     _A=12 ,     _A=12 ,     _A=3072 ,     _A="gelu" ,     _A=0.1 ,     _A=0.1 ,     _A=512 ,     _A=0 ,     _A=0.02 ,     _A=1E-7 ,     _A=False ,     _A=-1 ,     _A=0 ,     _A=True ,     _A=None ,     _A=0 ,     _A="gelu" ,     **_A ,     )				->				Optional[int]:
          super().__init__(**_A	)

          SCREAMING_SNAKE_CASE_	=      hidden_size
          SCREAMING_SNAKE_CASE_	=      num_hidden_layers
          SCREAMING_SNAKE_CASE_	=      num_attention_heads
          SCREAMING_SNAKE_CASE_	=      intermediate_size
          SCREAMING_SNAKE_CASE_	=      hidden_act
          SCREAMING_SNAKE_CASE_	=      hidden_dropout_prob
          SCREAMING_SNAKE_CASE_	=      attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_	=      max_position_embeddings
          SCREAMING_SNAKE_CASE_	=      type_vocab_size
          SCREAMING_SNAKE_CASE_	=      initializer_range
          SCREAMING_SNAKE_CASE_	=      relative_attention
          SCREAMING_SNAKE_CASE_	=      max_relative_positions
          SCREAMING_SNAKE_CASE_	=      pad_token_id
          SCREAMING_SNAKE_CASE_	=      position_biased_input

          # Backwards compatibility
          if type(_A	) == str:
             SCREAMING_SNAKE_CASE_	=      [x.strip() for x in pos_att_type.lower().split('''|'''	)]

          SCREAMING_SNAKE_CASE_	=      pos_att_type
          SCREAMING_SNAKE_CASE_	=      vocab_size
          SCREAMING_SNAKE_CASE_	=      layer_norm_eps

          SCREAMING_SNAKE_CASE_	=      kwargs.get('''pooler_hidden_size''' ,     _A	)
          SCREAMING_SNAKE_CASE_	=      pooler_dropout
          SCREAMING_SNAKE_CASE_	=      pooler_hidden_act







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							self	)				->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
             return OrderedDict(
                 [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]	)
          else:
             return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]	)



       @property
       def     _UpperCamelCase	(							self	)				->				int:
          return 12




       def     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     _A = 3 ,     _A = 40 ,     _A = 40 ,     _A = None ,     )				->				Mapping[str, Any]:
          SCREAMING_SNAKE_CASE_	=      super().generate_dummy_inputs(preprocessor=_A ,     framework=_A	)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
          return dummy_inputs


