



'''simple docstring'''

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 	__magic_name__						:







							def __init__(							self					:       List[str]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any=13	,_UpperCAmelCase					:       Optional[Any]=7	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Dict=True	,_UpperCAmelCase					:       List[str]=True	,_UpperCAmelCase					:       int=99	,_UpperCAmelCase					:       int=32	,_UpperCAmelCase					:       str=2	,_UpperCAmelCase					:       str=4	,_UpperCAmelCase					:       Optional[int]=37	,_UpperCAmelCase					:       Union[str, Any]="gelu"	,_UpperCAmelCase					:       str=0.1	,_UpperCAmelCase					:       Optional[Any]=0.1	,_UpperCAmelCase					:       Optional[Any]=512	,_UpperCAmelCase					:       Any=16	,_UpperCAmelCase					:       int=2	,_UpperCAmelCase					:       Dict=0.02	,_UpperCAmelCase					:       int=False	,_UpperCAmelCase					:       Optional[int]=True	,_UpperCAmelCase					:       Tuple="None"	,_UpperCAmelCase					:       Optional[Any]=3	,_UpperCAmelCase					:       Dict=4	,_UpperCAmelCase					:       Dict=None	,):
									_a     :      str        =							parent
									_a     :      str        =							batch_size
									_a     :      int        =							seq_length
									_a     :      Tuple        =							is_training
									_a     :      Dict        =							use_input_mask
									_a     :      Optional[int]        =							use_token_type_ids
									_a     :      str        =							use_labels
									_a     :      Optional[Any]        =							vocab_size
									_a     :      Any        =							hidden_size
									_a     :      Union[str, Any]        =							num_hidden_layers
									_a     :      Optional[int]        =							num_attention_heads
									_a     :      int        =							intermediate_size
									_a     :      Union[str, Any]        =							hidden_act
									_a     :      int        =							hidden_dropout_prob
									_a     :      Dict        =							attention_probs_dropout_prob
									_a     :      Optional[int]        =							max_position_embeddings
									_a     :      Tuple        =							type_vocab_size
									_a     :      List[str]        =							type_sequence_label_size
									_a     :      Tuple        =							initializer_range
									_a     :      int        =							num_labels
									_a     :      Tuple        =							num_choices
									_a     :      List[Any]        =							relative_attention
									_a     :      Optional[int]        =							position_biased_input
									_a     :      Union[str, Any]        =							pos_att_type
									_a     :      List[Any]        =							scope







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Any        =							ids_tensor([self.batch_size, self.seq_length]	,self.vocab_size		)

									_a     :      Optional[int]        =							None
									if self.use_input_mask:
											_a     :      Optional[int]        =							random_attention_mask([self.batch_size, self.seq_length]		)

									_a     :      Tuple        =							None
									if self.use_token_type_ids:
											_a     :      List[str]        =							ids_tensor([self.batch_size, self.seq_length]	,self.type_vocab_size		)

									_a     :      List[Any]        =							None
									_a     :      Optional[Any]        =							None
									_a     :      Optional[int]        =							None
									if self.use_labels:
											_a     :      List[Any]        =							ids_tensor([self.batch_size]	,self.type_sequence_label_size		)
											_a     :      List[str]        =							ids_tensor([self.batch_size, self.seq_length]	,self.num_labels		)

									_a     :      Dict        =							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 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       str		):
									_a     :      List[Any]        =							TFDebertaVaModel(config=_UpperCAmelCase		)
									_a     :      Optional[int]        =							{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									_a     :      Optional[Any]        =							[input_ids, input_mask]
									_a     :      str        =							model(_UpperCAmelCase		)

									_a     :      Any        =							model(_UpperCAmelCase		)

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







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       Optional[Any]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Any		):
									_a     :      Optional[Any]        =							TFDebertaVaForMaskedLM(config=_UpperCAmelCase		)
									_a     :      Optional[int]        =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_a     :      Optional[Any]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.vocab_size)		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       List[str]		):
									_a     :      Any        =							self.num_labels
									_a     :      Union[str, Any]        =							TFDebertaVaForSequenceClassification(config=_UpperCAmelCase		)
									_a     :      List[Any]        =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_a     :      Dict        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       Any		):
									_a     :      Tuple        =							self.num_labels
									_a     :      Dict        =							TFDebertaVaForTokenClassification(config=_UpperCAmelCase		)
									_a     :      Dict        =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									_a     :      Union[str, Any]        =							model(_UpperCAmelCase		)
									self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length, self.num_labels)		)







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Any	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       int		):
									_a     :      List[str]        =							TFDebertaVaForQuestionAnswering(config=_UpperCAmelCase		)
									_a     :      List[str]        =							{
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									_a     :      str        =							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 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Dict        =							self.prepare_config_and_inputs()
									(
									    (
									    _a
									)       ,					(
									    _a
									)       ,					(
									    _a
									)       ,					(
									    _a
									)       ,					(
									    _a
									)       ,					(
									    _a
									)       ,					(
									    _a
									)       ,					
									)     :      Tuple        =							config_and_inputs
									_a     :      Union[str, Any]        =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict

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

							lowerCAmelCase       :    str   				=					False
							lowerCAmelCase       :    int   				=					False







							def 							__lowercase			(							self					:       Dict		):
									_a     :      str        =							TFDebertaVaModelTester(self		)
									_a     :      Tuple        =							ConfigTester(self	,config_class=_UpperCAmelCase	,hidden_size=37		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									self.config_tester.run_common_tests()







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Dict        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Optional[int]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Optional[Any]        =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Any        =							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'		)
									self.assertIsNotNone(_UpperCAmelCase		)

@require_tf
class 	__magic_name__						(    unittest.TestCase   ):







							@unittest.skip(reason='Model not available yet'		)
							def 							__lowercase			(							self					:       List[str]		):
									pass







							@slow
							def 							__lowercase			(							self					:       int		):
									_a     :      str        =							TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'		)
									_a     :      List[Any]        =							tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
									_a     :      Union[str, Any]        =							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
									_a     :      Tuple        =							model(_UpperCAmelCase	,attention_mask=_UpperCAmelCase		)[0]

									_a     :      Tuple        =							tf.constant(
									    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]		)
									tf.debugging.assert_near(output[:, 1:4, 1:4]	,_UpperCAmelCase	,atol=1E-4		)









'''simple docstring'''

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Optional[int]   				=					BarthezTokenizer
							lowerCAmelCase       :    int   				=					BarthezTokenizerFast
							lowerCAmelCase       :    Dict   				=					True
							lowerCAmelCase       :    str   				=					True







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

									_a     :      List[Any]        =							BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez'		)
									tokenizer.save_pretrained(self.tmpdirname		)
									tokenizer.save_pretrained(self.tmpdirname	,legacy_format=_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							tokenizer







							def 							__lowercase			(							self					:       Tuple		):
									_a     :      Optional[Any]        =							'<pad>'
									_a     :      List[Any]        =							1

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







							def 							__lowercase			(							self					:       str		):
									_a     :      Any        =							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(_UpperCAmelCase		)	,101122		)







							def 							__lowercase			(							self					:       Dict		):
									self.assertEqual(self.get_tokenizer().vocab_size	,101122		)







							@require_torch
							def 							__lowercase			(							self					:       Dict		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Dict        =							[0, 57, 3018, 70307, 91, 2]

									_a     :      Dict        =							self.tokenizer(
									    _UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
									self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

									self.assertEqual((2, 6)	,batch.input_ids.shape		)
									self.assertEqual((2, 6)	,batch.attention_mask.shape		)
									_a     :      Tuple        =							batch.input_ids.tolist()[0]
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									if not self.test_rust_tokenizer:
											return

									_a     :      str        =							self.get_tokenizer()
									_a     :      List[str]        =							self.get_rust_tokenizer()

									_a     :      Dict        =							'I was born in 92000, and this is falsé.'

									_a     :      List[Any]        =							tokenizer.tokenize(_UpperCAmelCase		)
									_a     :      Tuple        =							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Optional[Any]        =							tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							self.get_rust_tokenizer()
									_a     :      Any        =							tokenizer.encode(_UpperCAmelCase		)
									_a     :      Optional[int]        =							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@slow
							def 							__lowercase			(							self					:       Optional[int]		):
									# fmt: off
									_a     :      Optional[int]        =							{'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 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], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
									# fmt: on

									# moussaKam/mbarthez is a french model. So we also use french texts.
									_a     :      Optional[Any]        =							[
									    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
									    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
									    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
									    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
									    'telles que la traduction et la synthèse de texte.',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase	,model_name='moussaKam/mbarthez'	,revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'	,sequences=_UpperCAmelCase	,)




