



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__       =	get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

UpperCAmelCase__       =	25_0004
UpperCAmelCase__       =	25_0020







@require_sentencepiece
@require_tokenizers
class       __lowerCAmelCase   (      A    ,						unittest.TestCase						):
			UpperCamelCase        =					MBartaaTokenizer
			UpperCamelCase        =					MBartaaTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					True



			def    _lowerCamelCase	(						self       :  Any)       ->   Optional[Any]:



						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						_UpperCAmelCase			=       MBartaaTokenizer(A			,		src_lang='en_XX'			,		tgt_lang='ro_RO'			,		keep_accents=A)
						tokenizer.save_pretrained(self.tmpdirname)



			def    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       '<s>'
						_UpperCAmelCase			=       0

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



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]			,		'<s>')
						self.assertEqual(vocab_keys[1]			,		'<pad>')
						self.assertEqual(vocab_keys[-1]			,		'<mask>')
						self.assertEqual(len(A)			,		10_54)



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size			,		10_54)



			def    _lowerCamelCase	(						self       :  Any)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       MBartaaTokenizer(A			,		src_lang='en_XX'			,		tgt_lang='ro_RO'			,		keep_accents=A)

						_UpperCAmelCase			=       tokenizer.tokenize('This is a test')
						self.assertListEqual(A			,		['▁This', '▁is', '▁a', '▁t', 'est'])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(A)			,		[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]			,		)

						_UpperCAmelCase			=       tokenizer.tokenize('I was born in 92000, and this is falsé.')
						self.assertListEqual(
						    A			,		[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']			,		)
						_UpperCAmelCase			=       tokenizer.convert_tokens_to_ids(A)
						self.assertListEqual(
						    A			,		[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]			,		)

						_UpperCAmelCase			=       tokenizer.convert_ids_to_tokens(A)
						self.assertListEqual(
						    A			,		[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']			,		)



			@slow
			def    _lowerCamelCase	(						self       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       {'input_ids': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=A			,		model_name='facebook/mbart-large-50'			,		revision='d3913889c59cd5c9e456b269c376325eabad57e2'			,		)



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



						"""simple docstring"""






						if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

						_UpperCAmelCase			=       (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(A			,		**A)
												_UpperCAmelCase			=       self.tokenizer_class.from_pretrained(A			,		**A)

												_UpperCAmelCase			=       tempfile.mkdtemp()

												_UpperCAmelCase			=       tokenizer_r.save_pretrained(A)
												_UpperCAmelCase			=       tokenizer_p.save_pretrained(A)

												# Checks it save with the same files + the tokenizer.json file for the fast one
												self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
												_UpperCAmelCase			=       tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
												self.assertSequenceEqual(A			,		A)

												# Checks everything loads correctly in the same way
												_UpperCAmelCase			=       tokenizer_r.from_pretrained(A)
												_UpperCAmelCase			=       tokenizer_p.from_pretrained(A)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(A			,		A))
															# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
															# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

												shutil.rmtree(A)

												# Save tokenizer rust, legacy_format=True
												_UpperCAmelCase			=       tempfile.mkdtemp()

												_UpperCAmelCase			=       tokenizer_r.save_pretrained(A			,		legacy_format=A)
												_UpperCAmelCase			=       tokenizer_p.save_pretrained(A)

												# Checks it save with the same files
												self.assertSequenceEqual(A			,		A)

												# Checks everything loads correctly in the same way
												_UpperCAmelCase			=       tokenizer_r.from_pretrained(A)
												_UpperCAmelCase			=       tokenizer_p.from_pretrained(A)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(A			,		A))

												shutil.rmtree(A)

												# Save tokenizer rust, legacy_format=False
												_UpperCAmelCase			=       tempfile.mkdtemp()

												_UpperCAmelCase			=       tokenizer_r.save_pretrained(A			,		legacy_format=A)
												_UpperCAmelCase			=       tokenizer_p.save_pretrained(A)

												# Checks it saved the tokenizer.json file
												self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))

												# Checks everything loads correctly in the same way
												_UpperCAmelCase			=       tokenizer_r.from_pretrained(A)
												_UpperCAmelCase			=       tokenizer_p.from_pretrained(A)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
															self.assertTrue(hasattr(A			,		A))

												shutil.rmtree(A)







@require_torch
@require_sentencepiece
@require_tokenizers
class       __lowerCAmelCase   (      unittest.TestCase						):
			UpperCamelCase        =					'''facebook/mbart-large-50-one-to-many-mmt'''
			UpperCamelCase        =					[
			    ''' UN Chief Says There Is No Military Solution in Syria''',
			    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
			]
			UpperCamelCase        =					[
			    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
			    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
			    ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
			    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
			]
			UpperCamelCase        =					[EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       MBartaaTokenizer.from_pretrained(
						    cls.checkpoint_name			,		src_lang='en_XX'			,		tgt_lang='ro_RO')
						_UpperCAmelCase			=       1
						return cls



			def    _lowerCamelCase	(						self       :  int)       ->   str:



						"""simple docstring"""






						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']			,		25_00_01)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']			,		25_00_04)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']			,		25_00_20)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']			,		25_00_38)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
						self.assertListEqual(self.expected_src_tokens			,		A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						self.assertIn(A			,		self.tokenizer.all_special_ids)
						_UpperCAmelCase			=       [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
						_UpperCAmelCase			=       self.tokenizer.decode(A			,		skip_special_tokens=A)
						_UpperCAmelCase			=       self.tokenizer.decode(generated_ids[1:]			,		skip_special_tokens=A)
						self.assertEqual(A			,		A)
						self.assertNotIn(self.tokenizer.eos_token			,		A)



			def    _lowerCamelCase	(						self       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       ['this is gunna be a long sentence ' * 20]
						assert isinstance(src_text[0]			,		A)
						_UpperCAmelCase			=       10
						_UpperCAmelCase			=       self.tokenizer(A			,		max_length=A			,		truncation=A).input_ids[0]
						self.assertEqual(ids[0]			,		A)
						self.assertEqual(ids[-1]			,		2)
						self.assertEqual(len(A)			,		A)



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



						"""simple docstring"""






						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])			,		[25_00_53, 25_00_01])



			def    _lowerCamelCase	(						self       :  Any)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       tempfile.mkdtemp()
						_UpperCAmelCase			=       self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(A)
						_UpperCAmelCase			=       MBartaaTokenizer.from_pretrained(A)
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,		A)



			@require_torch
			def    _lowerCamelCase	(						self       :  Dict)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer(self.src_text			,		text_target=self.tgt_text			,		padding=A			,		return_tensors='pt')
						_UpperCAmelCase			=       shift_tokens_right(batch['labels']			,		self.tokenizer.pad_token_id)

						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == RO_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



			@require_torch
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer(
						    self.src_text			,		text_target=self.tgt_text			,		padding=A			,		truncation=A			,		max_length=len(self.expected_src_tokens)			,		return_tensors='pt'			,		)
						_UpperCAmelCase			=       shift_tokens_right(batch['labels']			,		self.tokenizer.pad_token_id)

						self.assertIsInstance(A			,		A)

						self.assertEqual((2, 14)			,		batch.input_ids.shape)
						self.assertEqual((2, 14)			,		batch.attention_mask.shape)
						_UpperCAmelCase			=       batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens			,		A)
						self.assertEqual(2			,		batch.decoder_input_ids[0, 0])  # decoder_start_token_id
						# Test that special tokens are reset
						self.assertEqual(self.tokenizer.prefix_tokens			,		[EN_CODE])
						self.assertEqual(self.tokenizer.suffix_tokens			,		[self.tokenizer.eos_token_id])



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer(self.src_text			,		padding=A			,		truncation=A			,		max_length=3			,		return_tensors='pt')
						_UpperCAmelCase			=       self.tokenizer(
						    text_target=self.tgt_text			,		padding=A			,		truncation=A			,		max_length=10			,		return_tensors='pt')
						_UpperCAmelCase			=       targets['input_ids']
						_UpperCAmelCase			=       shift_tokens_right(A			,		self.tokenizer.pad_token_id)

						self.assertEqual(batch.input_ids.shape[1]			,		3)
						self.assertEqual(batch.decoder_input_ids.shape[1]			,		10)



			@require_torch
			def    _lowerCamelCase	(						self       :  int)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.tokenizer._build_translation_inputs(
						    'A test'			,		return_tensors='pt'			,		src_lang='en_XX'			,		tgt_lang='ar_AR')

						self.assertEqual(
						    nested_simplify(A)			,		{
						        # en_XX, A, test, EOS
						        'input_ids': [[25_00_04, 62, 30_34, 2]],
						        'attention_mask': [[1, 1, 1, 1]],
						        # ar_AR
						        'forced_bos_token_id': 25_00_01,
						    }			,		)





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       :  str			,		A       :  str			,		A       :  Dict=13			,		A       :  int=7			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  Any=True			,		A       :  Dict=True			,		A       :  Dict=99			,		A       :  Tuple=32			,		A       :  Any=2			,		A       :  Any=4			,		A       :  Any=37			,		A       :  Optional[Any]="gelu"			,		A       :  List[Any]=0.1			,		A       :  Tuple=0.1			,		A       :  Optional[Any]=5_12			,		A       :  Tuple=16			,		A       :  int=2			,		A       :  List[str]=0.0_2			,		A       :  int=False			,		A       :  List[Any]=True			,		A       :  Optional[Any]="None"			,		A       :  Union[str, Any]=3			,		A       :  List[str]=4			,		A       :  List[Any]=None			,		)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       seq_length
						_UpperCAmelCase			=       is_training
						_UpperCAmelCase			=       use_input_mask
						_UpperCAmelCase			=       use_token_type_ids
						_UpperCAmelCase			=       use_labels
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       num_hidden_layers
						_UpperCAmelCase			=       num_attention_heads
						_UpperCAmelCase			=       intermediate_size
						_UpperCAmelCase			=       hidden_act
						_UpperCAmelCase			=       hidden_dropout_prob
						_UpperCAmelCase			=       attention_probs_dropout_prob
						_UpperCAmelCase			=       max_position_embeddings
						_UpperCAmelCase			=       type_vocab_size
						_UpperCAmelCase			=       type_sequence_label_size
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       num_labels
						_UpperCAmelCase			=       num_choices
						_UpperCAmelCase			=       relative_attention
						_UpperCAmelCase			=       position_biased_input
						_UpperCAmelCase			=       pos_att_type
						_UpperCAmelCase			=       scope



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



						"""simple docstring"""






						_UpperCAmelCase			=       ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

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

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

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

						_UpperCAmelCase			=       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=A			,		)

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



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  Optional[int]			,		A       :  Tuple			,		A       :  int			,		A       :  Any			,		A       :  List[str]			,		A       :  List[str]			,		A       :  int)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel(config=A)
						_UpperCAmelCase			=       {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

						_UpperCAmelCase			=       [input_ids, input_mask]
						_UpperCAmelCase			=       model(A)

						_UpperCAmelCase			=       model(A)

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



			def    _lowerCamelCase	(						self       :  str			,		A       :  Tuple			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  List[str]			,		A       :  Any			,		A       :  List[str]			,		A       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaForMaskedLM(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.vocab_size))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Tuple			,		A       :  Tuple			,		A       :  Optional[int]			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Any			,		A       :  Optional[int])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFDebertaVaForSequenceClassification(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels))



			def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  List[Any]			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Optional[Any]			,		A       :  int			,		A       :  Any			,		A       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.num_labels
						_UpperCAmelCase			=       TFDebertaVaForTokenClassification(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						_UpperCAmelCase			=       model(A)
						self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.seq_length, self.num_labels))



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  List[Any]			,		A       :  List[str]			,		A       :  Dict			,		A       :  Dict			,		A       :  Any			,		A       :  Tuple			,		A       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaForQuestionAnswering(config=A)
						_UpperCAmelCase			=       {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						_UpperCAmelCase			=       model(A)
						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    _lowerCamelCase	(						self       :  Union[str, Any])       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.prepare_config_and_inputs()
						(
						    (
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					(
						    _UpperCAmelCase
						)	,					
						)			=       config_and_inputs
						_UpperCAmelCase			=       {'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						):
			UpperCamelCase        =					(
			    (
			        TFDebertaVaModel,
			        TFDebertaVaForMaskedLM,
			        TFDebertaVaForQuestionAnswering,
			        TFDebertaVaForSequenceClassification,
			        TFDebertaVaForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase        =					(
			    {
			        '''feature-extraction''': TFDebertaVaModel,
			        '''fill-mask''': TFDebertaVaForMaskedLM,
			        '''question-answering''': TFDebertaVaForQuestionAnswering,
			        '''text-classification''': TFDebertaVaForSequenceClassification,
			        '''token-classification''': TFDebertaVaForTokenClassification,
			        '''zero-shot''': TFDebertaVaForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCamelCase        =					False
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  int)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModelTester(self)
						_UpperCAmelCase			=       ConfigTester(self			,		config_class=A			,		hidden_size=37)



			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						self.config_tester.run_common_tests()



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*A)



			def    _lowerCamelCase	(						self       :  int)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*A)



			@slow
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
						self.assertIsNotNone(A)







@require_tf
class       __lowerCAmelCase   (      unittest.TestCase						):



			@unittest.skip(reason='Model not available yet')
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Dict:



						"""simple docstring"""






						pass



			@slow
			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge')
						_UpperCAmelCase			=       tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]])
						_UpperCAmelCase			=       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
						_UpperCAmelCase			=       model(A			,		attention_mask=A)[0]

						_UpperCAmelCase			=       tf.constant(
						    [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]])
						tf.debugging.assert_near(output[:, 1:4, 1:4]			,		A			,		atol=1E-4)
