





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_a     :    Any         =						_symbol_database.Default()


_a     :    List[str]         =						_descriptor_pool.Default().AddSerializedFile(
    B'\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)

_a     :    Dict         =						globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
			_a     :    Optional[int]         =						None
			_a     :    Dict         =						B'H\003'
			# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
			# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
			# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
			# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
			# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
			_a     :    List[str]         =						45
			_a     :    Optional[Any]         =						1_581
			_a     :    int         =						1_517
			_a     :    Optional[Any]         =						1_570
			_a     :    Any         =						1_584
			_a     :    Tuple         =						1_793
			_a     :    Tuple         =						1_795
			_a     :    Optional[int]         =						1_916
			_a     :    Tuple         =						1_864
			_a     :    List[str]         =						1_905
			_a     :    List[Any]         =						1_919
			_a     :    Dict         =						2_429
			_a     :    Union[str, Any]         =						2_208
			_a     :    Tuple         =						2_418
			_a     :    Dict         =						2_323
			_a     :    Tuple         =						2_407
# @@protoc_insertion_point(module_scope)


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFDebertaVaForMaskedLM,
			    TFDebertaVaForQuestionAnswering,
			    TFDebertaVaForSequenceClassification,
			    TFDebertaVaForTokenClassification,
			    TFDebertaVaModel,
			)





class       _lowerCAmelCase   :




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase=13							,							_UpperCamelCase=7							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=99							,							_UpperCamelCase=32							,							_UpperCamelCase=2							,							_UpperCamelCase=4							,							_UpperCamelCase=37							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=16							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=False							,							_UpperCamelCase=True							,							_UpperCamelCase="None"							,							_UpperCamelCase=3							,							_UpperCamelCase=4							,							_UpperCamelCase=None							,							)							-> Tuple:
							lowerCAmelCase_         =      parent
							lowerCAmelCase_         =      batch_size
							lowerCAmelCase_         =      seq_length
							lowerCAmelCase_         =      is_training
							lowerCAmelCase_         =      use_input_mask
							lowerCAmelCase_         =      use_token_type_ids
							lowerCAmelCase_         =      use_labels
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      type_sequence_label_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      num_labels
							lowerCAmelCase_         =      num_choices
							lowerCAmelCase_         =      relative_attention
							lowerCAmelCase_         =      position_biased_input
							lowerCAmelCase_         =      pos_att_type
							lowerCAmelCase_         =      scope




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size	)

							lowerCAmelCase_         =      None
							if self.use_input_mask:
										lowerCAmelCase_         =      random_attention_mask([self.batch_size, self.seq_length]	)

							lowerCAmelCase_         =      None
							if self.use_token_type_ids:
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.type_vocab_size	)

							lowerCAmelCase_         =      None
							lowerCAmelCase_         =      None
							lowerCAmelCase_         =      None
							if self.use_labels:
										lowerCAmelCase_         =      ids_tensor([self.batch_size]							,							self.type_sequence_label_size	)
										lowerCAmelCase_         =      ids_tensor([self.batch_size, self.seq_length]							,							self.num_labels	)

							lowerCAmelCase_         =      DebertaVaConfig(
							    vocab_size=self.vocab_size							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							max_position_embeddings=self.max_position_embeddings							,							type_vocab_size=self.type_vocab_size							,							relative_attention=self.relative_attention							,							position_biased_input=self.position_biased_input							,							initializer_range=self.initializer_range							,							return_dict=_UpperCamelCase							,							)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

							lowerCAmelCase_         =      [input_ids, input_mask]
							lowerCAmelCase_         =      model(_UpperCamelCase	)

							lowerCAmelCase_         =      model(_UpperCamelCase	)

							self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Any:
							lowerCAmelCase_         =      TFDebertaVaForMaskedLM(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.vocab_size)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> str:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForSequenceClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> int:
							lowerCAmelCase_         =      self.num_labels
							lowerCAmelCase_         =      TFDebertaVaForTokenClassification(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}
							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length, self.num_labels)	)




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaForQuestionAnswering(config=_UpperCamelCase	)
							lowerCAmelCase_         =      {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							    "token_type_ids": token_type_ids,
							}

							lowerCAmelCase_         =      model(_UpperCamelCase	)
							self.parent.assertEqual(result.start_logits.shape							,							(self.batch_size, self.seq_length)	)
							self.parent.assertEqual(result.end_logits.shape							,							(self.batch_size, self.seq_length)	)




				def 	__a		(   self	)							-> Tuple:
							lowerCAmelCase_         =      self.prepare_config_and_inputs()
							(
							    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    (
							    lowerCAmelCase_
							)      ,    
							)         =      config_and_inputs
							lowerCAmelCase_         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict





@require_tf
class       _lowerCAmelCase   (      __a   , __a   , unittest.TestCase			):
				_lowercase     						=(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_lowercase     						=(
				    {
				        '''feature-extraction''': TFDebertaVaModel,
				        '''fill-mask''': TFDebertaVaForMaskedLM,
				        '''question-answering''': TFDebertaVaForQuestionAnswering,
				        '''text-classification''': TFDebertaVaForSequenceClassification,
				        '''token-classification''': TFDebertaVaForTokenClassification,
				        '''zero-shot''': TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_lowercase     						=False
				_lowercase     						=False




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      TFDebertaVaModelTester(self	)
							lowerCAmelCase_         =      ConfigTester(self							,							config_class=_UpperCamelCase							,							hidden_size=37	)




				def 	__a		(   self	)							-> str:
							self.config_tester.run_common_tests()




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase	)




				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase	)




				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> Union[str, Any]:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							self.assertIsNotNone(_UpperCamelCase	)





@require_tf
class       _lowerCAmelCase   (      unittest.TestCase			):




				@unittest.skip(reason="Model not available yet"	)
				def 	__a		(   self	)							-> List[str]:
							pass




				@slow
				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"	)
							lowerCAmelCase_         =      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]	)
							lowerCAmelCase_         =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
							lowerCAmelCase_         =      model(_UpperCamelCase							,							attention_mask=_UpperCamelCase	)[0]

							lowerCAmelCase_         =      tf.constant(
							    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]	)
							tf.debugging.assert_near(output[:, 1:4, 1:4]							,							_UpperCamelCase							,							atol=1e-4	)



